From d8d70bcc79f63761b392598ac88463f297305a62 Mon Sep 17 00:00:00 2001 From: James Kent Date: Sat, 18 Jun 2022 10:06:02 +0100 Subject: [PATCH 1/6] wip: intro --- content/intro.md | 25 +++++++++++++++++++------ 1 file changed, 19 insertions(+), 6 deletions(-) diff --git a/content/intro.md b/content/intro.md index f8cdc73..80635a4 100644 --- a/content/intro.md +++ b/content/intro.md @@ -1,11 +1,24 @@ -# Welcome to your Jupyter Book +# Meta-Analyses in Python -This is a small sample book to give you a feel for how book content is -structured. -It shows off a few of the major file types, as well as some sample content. -It does not go in-depth into any particular topic - check out [the Jupyter Book documentation](https://jupyterbook.org) for more information. +## What is meta-analysis -Check out the content pages bundled with this sample book to see more. +Meta-analysis is the statistical aggregration of group level results. +Meta-analysis does not require the +- datasets: mega, meta + - group: GLM, mega + - subject: GLM, mega + - session: GLM, mega + - run: GLM, mega + +## How is meta-analysis in neuroimaging different from your standard, effect-size meta-analysis? + +standard meta-analysis is aggregating single variables, whereas neuroimaging meta-analyses are +- unique because: + - coordinates require special processing + - there is a spatial extent + +## Why would you want to do a meta-analysis? +get consensus on a scientific question ```{tableofcontents} ``` From 530f36d09c70436c9be0c62086d8364b19f2fb25 Mon Sep 17 00:00:00 2001 From: James Kent Date: Sat, 18 Jun 2022 10:06:56 +0100 Subject: [PATCH 2/6] combine content from other sources --- content/nimare-paper/00_abstract.md | 25 + content/nimare-paper/01_introduction.md | 13 + content/nimare-paper/02_overview.md | 84 + content/nimare-paper/03_download_data.md | 37 + content/nimare-paper/04_resources.md | 221 +++ content/nimare-paper/05_cbma.md | 454 ++++++ content/nimare-paper/06_ibma.md | 244 +++ 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a/content/nimare-paper/00_abstract.md b/content/nimare-paper/00_abstract.md new file mode 100644 index 0000000..e6f1e03 --- /dev/null +++ b/content/nimare-paper/00_abstract.md @@ -0,0 +1,25 @@ +# Abstract + +We present NiMARE (Neuroimaging Meta-Analysis Research Environment; RRID:SCR_017398; {cite:t}`salo_taylor_2022_6091632`), a Python library for neuroimaging meta-analyses and meta-analysis-related analyses. +NiMARE is an open source, collaboratively-developed package that implements a range of meta-analytic algorithms, including coordinate- and image-based meta-analyses, automated annotation, functional decoding, and meta-analytic coactivation modeling. +By consolidating meta-analytic methods under a common library and syntax, NiMARE makes it straightforward for users to employ the appropriate approach for a given analysis. +In this paper, we describe NiMARE's architecture and the methods implemented in the library. +Additionally, we provide example code and results for each of the available tools in the library. + +```{figure} images/figure_00.svg +--- +name: top_level_fig +align: center +--- +**A graphical representation of tools and methods implemented in NiMARE.** +This diagram outlines six of the most common use-cases for NiMARE. +**(A)** [](05_cbma) (CBMA) is performed by creating a NiMARE `Dataset` with coordinate information stored in the `Dataset.coordinates` attribute, which is then used in a CBMA `Estimator`. This produces a `MetaResult` object with statistical maps, which can then be used in a `Corrector` object for multiple comparisons correction. Once the `Corrector` has been fitted, it will produce a corrected version of the `MetaResult` object, containing updated statistical maps. +**(B)** [](06_ibma) (IBMA) operates similarly to CBMA, except that IBMA `Estimator`s use statistical maps stored in the `Dataset.images` attribute. +**(C)** [](10_macm) (MACM) uses a region of interest to select coordinate-based studies within a `Dataset`, after which the standard CBMA workflow is performed. +**(D)** [](11_annotation) infers labels from textual (and sometimes other) data associated with the `Dataset`, as stored in the `Dataset.texts` attribute. The annotation functions produce labels which may be integrated into the `Dataset` as the `Dataset.annotations` attribute. +**(E)** [Functional decoding of continuous statistical maps](content:decoding:continuous) operates similarly to discrete decoding, in that the input `Dataset` must have both `coordinates` and `annotations` attributes. The `Dataset`, along with an unthresholded statistical map to decode, is provided to the `Decoder` object, which then outputs measures of similarity or associativeness with each label. +**(F)** [Functional decoding of discrete inputs](content:decoding:discrete) applies a selection criterion to a `Dataset` with both `coordinates` and `annotations` attributes, using a `Decoder` object. The decoding algorithm will output measures of similarity or associativeness with each label in the annotations. +``` + +```{tableofcontents} +``` diff --git a/content/nimare-paper/01_introduction.md b/content/nimare-paper/01_introduction.md new file mode 100644 index 0000000..5d55bc2 --- /dev/null +++ b/content/nimare-paper/01_introduction.md @@ -0,0 +1,13 @@ +# Introduction + +We introduce **NiMARE** (Neuroimaging Meta-Analysis Research Environment), a Python package for analyzing meta-analytic neuroimaging data. +NiMARE is a new library developed as a component in a burgeoning open-source meta-analytic ecosystem for neuroimaging data, which currently includes Neurosynth, NeuroVault, NeuroQuery, and PyMARE. + +While several libraries already exist for neuroimaging meta-analysis, these libraries are generally algorithm-specific, and are provided in a range of very different user interfaces, languages, and licenses. +This variability may prevent meta-analysts from using the most appropriate algorithm for a given analysis. +Further, having multiple meta-analysis algorithms available in one library facilitates direct comparisons of methods. +With NiMARE, we consolidate meta-analytic algorithms from a range of libraries and publications, and provide a common Python syntax and well documented application program interfaces. +Additionally, NiMARE is a collaboratively-developed open source package, enabling researchers to contribute new methods not included in the current version. + +In this paper, we describe NiMARE's aims, architecture and the functionality it supports—including tools for database extraction, automated annotation, meta-analysis, meta-analytic coactivation modeling, and functional decoding. +The text is accompanied by extensive code samples and results (also available online in the form of Python scripts; https://github.com/NBCLab/nimare-paper with additional documentation in https://github.com/neurodatascience/meta_analysis_notebook), ensuring that users can follow along interactively. diff --git a/content/nimare-paper/02_overview.md b/content/nimare-paper/02_overview.md new file mode 100644 index 0000000..9eae63f --- /dev/null +++ b/content/nimare-paper/02_overview.md @@ -0,0 +1,84 @@ +# NiMARE Overview + +NiMARE is designed to be modular and object-oriented, with an interface that mimics popular Python libraries, including scikit-learn and nilearn. +This standardized interface allows users to employ a wide range of meta-analytic algorithms without having to familiarize themselves with the idiosyncrasies of algorithm-specific tools. +This lets users use whatever method is most appropriate for a given research question with minimal mental overhead from switching methods. +Additionally, NiMARE emphasizes citability, with references in the documentation and citable boilerplate text that can be copied directly into manuscripts, in order to ensure that the original algorithm developers are appropriately recognized. + +NiMARE works with Python versions 3.6 and higher, and can easily be installed with `pip`. +Its source code is housed and version controlled in a GitHub repository at https://github.com/neurostuff/NiMARE. + +NiMARE is under continued active development, and we anticipate that the user-facing API (application programming interface) may change over time. +Our emphasis in this paper is thus primarily on reviewing the functionality implemented in the package and illustrating the general interface, and not on providing a detailed and static user guide that will be found within the package documentation. + +Tools in NiMARE are organized into several modules, including {py:mod}`nimare.meta`, {py:mod}`nimare.correct`, {py:mod}`nimare.annotate`, {py:mod}`nimare.decode`, and {py:mod}`nimare.workflows`. +In addition to these primary modules, there are several secondary modules for data wrangling and internal helper functions, including {py:mod}`nimare.io`, {py:mod}`nimare.dataset`, {py:mod}`nimare.extract`, {py:mod}`nimare.stats`, {py:mod}`nimare.utils`, and {py:mod}`nimare.base`. +These modules are summarized in [](overview:api), as well as in {numref}`table_modules`. + +(overview:api)= +## Application Programming Interface + +One of the principal goals of NiMARE is to implement a range of methods with a set of shared interfaces, to enable users to employ the most appropriate algorithm for a given question without introducing a steep learning curve. +This approach is modeled on the widely-used `scikit-learn` package {cite:p}`scikit-learn,sklearn_api`, which implements a large number of machine learning algorithms - all with simple, consistent interfaces. +Regardless of the algorithm employed, data should be in the same format and the same class methods should be called to fit and/or generate predictions from the model. + +To this end, we have adopted an object-oriented approach to NiMARE’s core API that organizes tools based on the type of inputs and outputs they operate over. +The key data structure is the {py:class}`~nimare.dataset.Dataset` class, which stores a range of neuroimaging data amenable to various forms of meta-analysis. +There are two main types of tools that operate on a `Dataset` class. +{py:class}`~nimare.base.Transformer` classes, as their name suggests, perform some transformation on a `Dataset`- i.e., they take a `Dataset` instance as input, and return a modified version of that `Dataset` instance as output (for example, with newly generated maps stored within the object). +{py:class}`~nimare.base.Estimator` classes apply a meta-analytic algorithm to a `Dataset` and return a set of statistical images stored in a MetaResult container class. +The key methods supported by each of these base classes, as well as the main arguments to those methods, are consistent throughout the hierarchy (e.g., all `Transformer` classes must implement a `transform()` method), minimizing the learning curve and ensuring a high degree of predictability for users. + +```{figure} images/figure_01.svg +:name: figure_schematic +:align: center +:width: 400px + +A schematic figure of `Datasets`, `Estimators`, `Transformers`, and `MetaResults` in NiMARE. +``` + +## Package Organization + +At present, the package is organized into 14 distinct modules. +{py:mod}`nimare.dataset` defines the `Dataset` class. +{py:mod}`nimare.meta` includes `Estimators` for coordinate- and image-based meta-analysis methods. +{py:mod}`nimare.results` defines the `MetaResult` class, which stores statistical maps produced by meta-analyses. +{py:mod}`nimare.correct` implements `Corrector` classes for family-wise error (FWE) and false discovery rate (FDR) multiple comparisons correction. +{py:mod}`nimare.annotate` implements a range of automated annotation methods, including latent Dirichlet allocation (LDA) and generalized correspondence latent Dirichlet allocation (GCLDA). +{py:mod}`nimare.decode` implements a number of meta-analytic functional decoding and encoding algorithms. +{py:mod}`nimare.io` provides functions for converting alternative meta-analytic dataset structure, such as Sleuth text files or Neurosynth datasets, to NiMARE format. +{py:mod}`nimare.transforms` implements a range of spatial and data type transformations, including a function to generate new images in the `Dataset` from existing image types. +{py:mod}`nimare.extract` provides methods for fetching datasets and models across the internet. +{py:mod}`nimare.generate` includes functions for generating data for internal testing and validation. +{py:mod}`nimare.base` defines a number of base classes used throughout the rest of the package. +Finally, {py:mod}`nimare.stats` and {py:mod}`nimare.utils` are modules for statistical and generic utility functions, respectively. +These modules are summarized in {numref}`table_modules`. + ++++ + +```{table} Summaries of modules in NiMARE. +:name: table_modules + +| Module | Description | +|:-------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| `dataset` | This module stores the `Dataset` class, which contains NiMARE datasets. | +| `meta` | This module contains `Estimators` for image- and coordinate-based meta-analysis algorithms, as well as `KernelTransformers`, which are used in conjunction with coordinate-based methods. | +| `results` | This module stores the `MetaResult` class, which in turn is used to manage statistical maps produced by meta-analytic algorithms. | +| `correct` | This module contains classes for multiple comparisons correction, including `FWECorrector` (family-wise error rate correction) and `FDRCorrector` (false discovery rate correction). | +| `annotate` | This module includes a range of tools for automated annotation of studies. Methods in this module include: topic models, such as latent Dirichlet allocation and generalized correspondence latent Dirichlet allocation; ontology-based annotation, such as Cognitive Atlas term extract from text; and general text-based feature extraction, such as count or tf-idf extraction from text. | +| `decode` | This module includes a number of methods for functional characterization analysis, also known as functional decoding. Methods in this module are divided into three groups: discrete, for decoding regions of interest or subsets of the Dataset; continuous, for decoding unthresholded statistical maps; and encoding, for simulating statistical maps from labels. | +| `io` | This module contains functions for converting common file types, such as Neurosynth- or Sleuth-format files, into NiMARE-compatible formats, such as `Dataset` objects. | +| `transforms` | This module contains classes and functions for converting between common data types. Two important classes in this module are the `ImageTransformer`, which uses available images and metadata to produce new images in a Dataset, and the `ImagesToCoordinates`, which extracts peak coordinates from images in the Dataset, so that image-based studies can be used for coordinate-based meta-analyses. | +| `extract` | This module contains functions for downloading external resources, such as the Neurosynth dataset and the Cognitive Atlas ontology. | +| `stats` | This module contains miscellaneous statistical methods used throughout the rest of the library. | +| `generate` | This module contains functions for generating useful data for internal testing and validation. | +| `utils` | This module contains miscellaneous utility functions used throughout the rest of the library. | +| `workflows` | This module contains a number of common workflows that can be run from the command line, such as an ALE meta-analysis or a contrast-permutation image-based meta-analysis. All of the workflow functions additionally generate boilerplate text that can be included in manuscript methods sections. | +| `base` | This module defines a number of base classes used throughout the rest of the library. | +``` + +## Dependencies + +NiMARE depends on the standard SciPy stack, as well as a small number of widely-used packages. +Dependencies from the SciPy stack include `scipy` {cite:p}`Virtanen2020-mb`, `numpy` {cite:p}`Walt2011-dh,Harris2020-bo`, `pandas` {cite:p}`McKinney2010-gz`, and `scikit-learn` {cite:p}`scikit-learn, sklearn_api`. +Additional requirements include `fuzzywuzzy`, `nibabel` {cite:p}`brett_matthew_2020_3924343`, `nilearn` {cite:p}`Abraham2014-wt`, `statsmodels` {cite:p}`Seabold2010-ip`, and `tqdm` {cite:p}`casper_da_costa_luis_2020_4026750`. diff --git a/content/nimare-paper/03_download_data.md b/content/nimare-paper/03_download_data.md new file mode 100644 index 0000000..ef45ed4 --- /dev/null +++ b/content/nimare-paper/03_download_data.md @@ -0,0 +1,37 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.10.3 +kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + +# Download the Data + ++++ + +```{code-cell} ipython3 +# First, import the necessary modules and functions +import os + +from repo2data.repo2data import Repo2Data + +# Install the data if running locally, or points to cached data if running on neurolibre +DATA_REQ_FILE = os.path.abspath("../binder/data_requirement.json") +repo2data = Repo2Data(DATA_REQ_FILE) +data_path = repo2data.install() +data_path = os.path.join(data_path[0], "data") +print(f"Data are located at {data_path}") +``` + +We will also create a directory in which to save files that are generated within the book. + +```{code-cell} ipython3 +os.makedirs("../outputs/", exist_ok=True) +print(f"Files generated by the book will be saved to {os.path.abspath('../outputs/')}") +``` diff --git a/content/nimare-paper/04_resources.md b/content/nimare-paper/04_resources.md new file mode 100644 index 0000000..da9a45b --- /dev/null +++ b/content/nimare-paper/04_resources.md @@ -0,0 +1,221 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.10.3 +kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + +# External Meta-Analytic Resources + ++++ + +```{code-cell} ipython3 +:tags: [hide-cell] +# First, import the necessary modules and functions +import os +from pprint import pprint + +from repo2data.repo2data import Repo2Data + +# Install the data if running locally, or points to cached data if running on neurolibre +DATA_REQ_FILE = os.path.abspath("../binder/data_requirement.json") +repo2data = Repo2Data(DATA_REQ_FILE) +data_path = repo2data.install() +data_path = os.path.join(data_path[0], "data") + +# Set an output directory for any files generated during the book building process +out_dir = os.path.abspath("../outputs/") +os.makedirs(out_dir, exist_ok=True) +``` + ++++ + +Large-scale meta-analytic databases have made systematic meta-analyses of the neuroimaging literature possible. +These databases combine results from neuroimaging studies, whether represented as coordinates of peak activations or unthresholded statistical images, with important study metadata, such as information about the samples acquired, stimuli used, analyses performed, and mental constructs putatively manipulated. +The two most popular coordinate-based meta-analytic databases are [BrainMap](http://www.brainmap.org) and [Neurosynth](http://neurosynth.org), while the most popular image-based database is [NeuroVault](https://neurovault.org). + +The studies archived in these databases may be either manually or automatically annotated—often with reference to a formal ontology or controlled vocabulary. +Ontologies for cognitive neuroscience define what mental states or processes are postulated to be manipulated or measured in experiments, and may also include details of said experiments (e.g.,the cognitive tasks employed), relationships between concepts (e.g., verbal working memory is a kind of working memory), and various other metadata that can be standardized and represented in a machine-readable form {cite:p}`Poldrack2016-ym,Poldrack2010-jz,Turner2012-ai`. +Some of these ontologies are very well-defined, such as expert-generated taxonomies designed specifically to describe only certain aspects of experiments and the relationships between elements within the taxonomy, while others are more loosely defined, in some cases simply building a vocabulary based on which terms are commonly used in cognitive neuroscience articles. + ++++ + +(content:resources:brainmap)= +## BrainMap + +**BrainMap** {cite:p}`Fox2005-rt,Fox2002-nv,Laird2005-al` relies on expert annotators to label individual comparisons within studies according to its internally developed ontology, the BrainMap Taxonomy {cite:p}`Fox2005-rt`. +While this approach is likely to be less noisy than an automated annotation method using article text or imaging results to predict content, it is also subject to a number of limitations. +First, there are simply not enough annotators to keep up with the ever-expanding literature. +Second, any development of the underlying ontology has the potential to leave the database outdated. +For example, if a new label is added to the BrainMap Taxonomy, then each study in the full BrainMap database needs to be evaluated for that label before that label can be properly integrated into the database. +Finally, a manually annotated database like BrainMap will be biased by which subdomains within the literature are annotated. +While outside contributors can add and annotate studies to the database, the main source of annotations has been researchers associated with the BrainMap project. + +While BrainMap is a semi-closed resource (i.e., a collaboration agreement is required to access the full database), registered users may search the database using the Sleuth search tool, in order to collect samples for meta-analyses. +Sleuth can export these study collections as text files with coordinates. +NiMARE provides a function to import data from Sleuth text files into the NiMARE Dataset format. + +The function {py:func}`~nimare.io.convert_sleuth_to_dataset` can be used to convert text files exported from Sleuth into NiMARE `Dataset`s. +Here, we convert two files from a previous publication by NiMARE contributors {cite:p}`yanes2018` into two separate `Dataset`s. + +```{code-cell} ipython3 +from nimare import io + +sleuth_dset1 = io.convert_sleuth_to_dataset( + os.path.join(data_path, "contrast-CannabisMinusControl_space-talairach_sleuth.txt") +) +sleuth_dset2 = io.convert_sleuth_to_dataset( + os.path.join(data_path, "contrast-ControlMinusCannabis_space-talairach_sleuth.txt") +) +print(sleuth_dset1) +print(sleuth_dset2) + +# Save the Datasets to files for future use +sleuth_dset1.save(os.path.join(out_dir, "sleuth_dset1.pkl.gz")) +sleuth_dset2.save(os.path.join(out_dir, "sleuth_dset2.pkl.gz")) +``` + ++++ + +## Neurosynth + +**Neurosynth** {cite:p}`Yarkoni2011-dk` uses a combination of web scraping and text mining to automatically harvest neuroimaging studies from the literature and to annotate them based on term frequency within article abstracts. +As a consequence of its relatively crude automated approach, Neurosynth has its own set of limitations. +First, Neurosynth is unable to delineate individual comparisons within studies, and consequently uses the entire paper as its unit of measurement, unlike BrainMap. +This risks conflating directly contrasted comparisons (e.g., A>B and B>A), as well as comparisons which have no relation to one another. +Second, coordinate extraction and annotation are noisy. +Third, annotations automatically performed by Neurosynth are also subject to error, although the reasons behind this are more nuanced and will be discussed later in this paper. +Given Neurosynth's limitations, we recommend that it be used for casual, exploratory meta-analyses rather than for publication-quality analyses. +Nevertheless, while individual meta-analyses should not be published from Neurosynth, many derivative analyses have been performed and published (e.g., {cite:p}`Chang2013-si,De_la_Vega2016-wg,De_la_Vega2018-jc,Poldrack2012-it`). +As evidence of its utility, Neurosynth has been used to define _a priori_ regions of interest (e.g., {cite:p}`Josipovic2014-hx,Zeidman2012-fj,Wager2013-ab`) or perform meta-analytic functional decoding (e.g., {cite:p}`Chen2018-of,Pantelis2015-bq,Tambini2017-iu`) in many first-order (rather than meta-analytic) fMRI studies. + +Here, we show code that would download the Neurosynth database from where it is stored (https://github.com/neurosynth/neurosynth-data) and convert it to a NiMARE `Dataset` using {py:func}`~nimare.extract.fetch_neurosynth`, for the first step, and {py:func}`~nimare.io.convert_neurosynth_to_dataset`, for the second. + +```{code-cell} ipython3 +from nimare import extract + +# Download the desired version of Neurosynth from GitHub. +files = extract.fetch_neurosynth( + data_dir=data_path, + version="7", + source="abstract", + vocab="terms", + overwrite=False, +) +pprint(files) +neurosynth_db = files[0] +``` + +```{note} +Converting the large Neurosynth and NeuroQuery datasets to NiMARE {py:class}`~nimare.dataset.Dataset` objects can be a very memory-intensive process. +For the sake of this book, we show how to perform the conversions below, but actually load and use pre-converted `Dataset`s. +``` + +```python +# Convert the files to a Dataset. +# This may take a while (~10 minutes) +neurosynth_dset = io.convert_neurosynth_to_dataset( + coordinates_file=neurosynth_db["coordinates"], + metadata_file=neurosynth_db["metadata"], + annotations_files=neurosynth_db["features"], +) +print(neurosynth_dset) + +# Save the Dataset for later use. +neurosynth_dset.save(os.path.join(out_dir, "neurosynth_dataset.pkl.gz")) +``` + +Here, we load a pre-generated version of the Neurosynth `Dataset`. + +```{code-cell} ipython3 +from nimare import dataset + +neurosynth_dset = dataset.Dataset.load(os.path.join(data_path, "neurosynth_dataset.pkl.gz")) +print(neurosynth_dset) +``` + +```{note} +Many of the methods in NiMARE can be very time-consuming or memory-intensive. +Therefore, for the sake of ensuring that the analyses in this article may be reproduced by as many people as possible, we will use a reduced version of the Neurosynth `Dataset`, only containing the first 500 studies, for those methods which may not run easily on the full database. +``` + +```{code-cell} ipython3 +neurosynth_dset_first_500 = neurosynth_dset.slice(neurosynth_dset.ids[:500]) +print(neurosynth_dset) + +# Save this Dataset for later use. +neurosynth_dset_first_500.save(os.path.join(out_dir, "neurosynth_dataset_first500.pkl.gz")) +``` + ++++ + +In addition to a large corpus of coordinates, Neurosynth provides term frequencies derived from article abstracts that can be used as annotations. + ++++ + +One additional benefit to Neurosynth is that it has made available the coordinates for a large number of studies for which the study abstracts are also readily available. +This has made the Neurosynth database a common resource upon which to build other automated ontologies. +Data-driven ontologies which have been developed using the Neurosynth database include the generalized correspondence latent Dirichlet allocation (GCLDA) {cite:p}`Rubin2017-rd` topic model and Deep Boltzmann machines {cite:p}`Monti2016-aq`. + ++++ + +## NeuroQuery + +A related resource is **NeuroQuery** {cite:p}`Dockes2020-uv`. +NeuroQuery is an online service for large-scale predictive meta-analysis. +Unlike Neurosynth, which performs statistical inference and produces statistical maps, NeuroQuery is a supervised learning model and produces a prediction of the brain areas most likely to contain activations. +These maps predict locations where studies investigating a given area (determined by the text prompt) are likely to produce activations, but they cannot be used in the same manner as statistical maps from a standard coordinate-based meta-analysis. +In addition to this predictive meta-analytic tool, NeuroQuery also provides a new database of coordinates, text annotations, and metadata via an automated extraction approach that improves on Neurosynth's original methods. + +While NiMARE does not currently include an interface to NeuroQuery's predictive meta-analytic method, there are functions for downloading the NeuroQuery database and converting it to NiMARE format, much like Neurosynth. +The functions for downloading the NeuroQuery database and converting it to a `Dataset` are {py:func}`~nimare.extract.fetch_neuroquery` and {py:func}`~nimare.io.convert_neurosynth_to_dataset`, respectively. +We are able to use the same function for converting the database to a `Dataset` for NeuroQuery as Neurosynth because both databases store their data in the same structure. + +```{code-cell} ipython3 +# Download the desired version of NeuroQuery from GitHub. +files = extract.fetch_neuroquery( + data_dir=data_path, + version="1", + source="combined", + vocab="neuroquery6308", + type="tfidf", + overwrite=False, +) +pprint(files) +neuroquery_db = files[0] +``` + +```python +# Convert the files to a Dataset. +# This may take a while (~10 minutes) +neuroquery_dset = io.convert_neurosynth_to_dataset( + coordinates_file=neuroquery_db["coordinates"], + metadata_file=neuroquery_db["metadata"], + annotations_files=neuroquery_db["features"], +) +print(neuroquery_dset) + +# Save the Dataset for later use. +neuroquery_dset.save(os.path.join(out_dir, "neuroquery_dataset.pkl.gz")) +``` + +Here, we load a pre-generated version of the NeuroQuery `Dataset`. + +```{code-cell} ipython3 +neuroquery_dset = dataset.Dataset.load(os.path.join(data_path, "neuroquery_dataset.pkl.gz")) +print(neuroquery_dset) +``` + ++++ + +## NeuroVault + +**NeuroVault** {cite:p}`Gorgolewski2015-sd` is a public repository of user-uploaded, whole-brain, unthresholded brain maps. +Users may associate their image collections with publications, and can annotate individual maps with labels from the Cognitive Atlas, which is the ontology of choice for NeuroVault. +NiMARE includes a function, {py:func}`~nimare.io.convert_neurovault_to_dataset`, with which users can search for images in NeuroVault, download those images, and convert them into a `Dataset` object. diff --git a/content/nimare-paper/05_cbma.md b/content/nimare-paper/05_cbma.md new file mode 100644 index 0000000..fbdec20 --- /dev/null +++ b/content/nimare-paper/05_cbma.md @@ -0,0 +1,454 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.10.3 +kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + +# Coordinate-Based Meta-Analysis + ++++ + +```{code-cell} ipython3 +:tags: [hide-cell] +# First, import the necessary modules and functions +import os +from pprint import pprint + +import matplotlib.pyplot as plt +import numpy as np +from myst_nb import glue +from nilearn import plotting +from repo2data.repo2data import Repo2Data + +from nimare import dataset + +# Install the data if running locally, or points to cached data if running on neurolibre +DATA_REQ_FILE = os.path.abspath("../binder/data_requirement.json") +repo2data = Repo2Data(DATA_REQ_FILE) +data_path = repo2data.install() +data_path = os.path.join(data_path[0], "data") + +# Set an output directory for any files generated during the book building process +out_dir = os.path.abspath("../outputs/") +os.makedirs(out_dir, exist_ok=True) + +# Now, load the Datasets we will use in this chapter +sleuth_dset1 = dataset.Dataset.load(os.path.join(data_path, "sleuth_dset1.pkl.gz")) +sleuth_dset2 = dataset.Dataset.load(os.path.join(data_path, "sleuth_dset2.pkl.gz")) +neurosynth_dset = dataset.Dataset.load(os.path.join(data_path, "neurosynth_dataset.pkl.gz")) +``` + ++++ + +Coordinate-based meta-analysis (CBMA) is currently the most popular method for neuroimaging meta-analysis, given that the majority of fMRI papers currently report their findings as peaks of statistically significant clusters in standard space and do not release unthresholded statistical maps. +These peaks indicate where significant results were found in the brain, and thus do not reflect an effect size estimate for each hypothesis test (i.e., each voxel) as one would expect for a typical meta-analysis. +As such, standard methods for effect size-based meta-analysis cannot be applied. +Over the past two decades, a number of algorithms have been developed to determine whether peaks converge across experiments in order to identify locations of consistent or specific activation associated with a given hypothesis {cite:p}`Samartsidis2017-ej,Muller2018-mt`. + +Kernel-based methods evaluate convergence of coordinates across studies by first convolving foci with a spatial kernel to produce study-specific modeled activation maps, then combining those modeled activation maps into a sample-wise map, which is compared to a null distribution to evaluate voxel-wise statistical significance. +Additionally, for each of the following approaches, except for SCALE, voxel- or cluster-level multiple comparisons correction may be performed using Monte Carlo simulations or false discovery rate (FDR) {cite:p}`Laird2005-qh` correction. Basic multiple-comparisons correction methods (e.g., Bonferroni correction) are also supported. + ++++ + +```{figure} images/figure_02.svg +:name: meta_workflow_fig +:align: center +:width: 300px + +A flowchart of the typical workflow for coordinate-based meta-analyses in NiMARE. +``` + +## CBMA kernels + +CBMA kernels are available as {py:class}`~nimare.meta.kernel.KernelTransformer`s in the {py:mod}`nimare.meta.kernel` module. +There are three standard kernels that are currently available: {py:class}`~nimare.meta.kernel.MKDAKernel`, {py:class}`~nimare.meta.kernel.KDAKernel`, and {py:class}`~nimare.meta.kernel.ALEKernel`. +Each class may be configured with certain parameters when a new object is initialized. +For example, `MKDAKernel` accepts an `r` parameter, which determines the radius of the spheres that will be created around each peak coordinate. +`ALEKernel` automatically uses the sample size associated with each experiment in the `Dataset` to determine the appropriate full-width-at-half-maximum of its Gaussian distribution, as described in {cite:t}`EICKHOFF20122349`; however, users may provide a constant `sample_size` or `fwhm` parameter when sample size information is not available within the `Dataset` metadata. + +Here we show how these three kernels can be applied to the same `Dataset`. + +```{code-cell} ipython3 +from nimare.meta import kernel + +mkda_kernel = kernel.MKDAKernel(r=10) +mkda_ma_maps = mkda_kernel.transform(sleuth_dset1) +kda_kernel = kernel.KDAKernel(r=10) +kda_ma_maps = kda_kernel.transform(sleuth_dset1) +ale_kernel = kernel.ALEKernel(sample_size=20) +ale_ma_maps = ale_kernel.transform(sleuth_dset1) +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +# Here we delete the recent variables for the sake of reducing memory usage +del mkda_kernel, kda_kernel, ale_kernel +``` + ++++ + +```{code-cell} ipython3 +:tags: [hide-cell] +# Generate figure +study_idx = 10 # a study with overlapping kernels +max_value = np.max(kda_ma_maps[study_idx].get_fdata()) + 1 + +ma_maps = { + "MKDA Kernel": mkda_ma_maps[study_idx], + "KDA Kernel": kda_ma_maps[study_idx], + "ALE Kernel": ale_ma_maps[study_idx], +} + +fig, axes = plt.subplots(nrows=3, figsize=(6, 6)) + +for i_meta, (name, img) in enumerate(ma_maps.items()): + if "ALE" in name: + vmax = None + else: + vmax = max_value + + display = plotting.plot_stat_map( + img, + annotate=False, + axes=axes[i_meta], + cmap="Reds", + cut_coords=[5, 0, 29], + draw_cross=False, + figure=fig, + vmax=vmax, + ) + axes[i_meta].set_title(name) + + colorbar = display._cbar + colorbar_ticks = colorbar.get_ticks() + if colorbar_ticks[0] < 0: + new_ticks = [colorbar_ticks[0], 0, colorbar_ticks[-1]] + else: + new_ticks = [colorbar_ticks[0], colorbar_ticks[-1]] + colorbar.set_ticks(new_ticks, update_ticks=True) + +glue("figure_ma_maps", fig, display=False) +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +# Here we delete the recent variables for the sake of reducing memory usage +del mkda_ma_maps, kda_ma_maps, ale_ma_maps +``` + +```{glue:figure} figure_ma_maps +:name: "figure_ma_maps" +:align: center + +Modeled activation maps produced by NiMARE's `KernelTransformer` classes. +``` + ++++ + +```{code-cell} ipython3 +from nimare import dataset, meta + +neurosynth_dset_first500 = dataset.Dataset.load( + os.path.join(data_path, "neurosynth_dataset_first500.pkl.gz") +) + +# Specify where images for this Dataset should be located +target_folder = os.path.join(out_dir, "neurosynth_dataset_maps") +os.makedirs(target_folder, exist_ok=True) +neurosynth_dset_first500.update_path(target_folder) + +# Initialize a kernel transformer to use +kern = meta.kernel.MKDAKernel(memory_limit="500mb") + +# Run the kernel transformer with return_type set to "dataset" to return an updated Dataset +# with the MA maps stored as files within its "images" attribute. +neurosynth_dset_first500 = kern.transform(neurosynth_dset_first500, return_type="dataset") +neurosynth_dset_first500.save( + os.path.join(out_dir, "neurosynth_dataset_first500_with_mkda_ma.pkl.gz"), +) +``` + ++++ + +(content:cbma:mkdad)= +## Multilevel kernel density analysis + +**Multilevel kernel density analysis** (MKDA) {cite:p}`Wager2007-jc` is a kernel-based method that convolves each peak from each study with a binary sphere of a set radius. +These peak-specific binary maps are then combined into study-specific maps by taking the maximum value for each voxel. +Study-specific maps are then averaged across the meta-analytic sample. +This averaging is generally weighted by studies’ sample sizes, although other covariates may be included, such as weights based on the type of inference (random or fixed effects) employed in the study’s analysis. +An arbitrary threshold is generally employed to zero-out voxels with very low values, and then a Monte Carlo procedure is used to assess statistical significance, either at the voxel or cluster level. + +In NiMARE, the MKDA meta-analyses can be performed with the {py:class}`~nimare.meta.cbma.mkda.MKDADensity` class. +This class, like most other CBMA classes in NiMARE, accepts a `null_method` parameter, which determines how voxel-wise (uncorrected) statistical significance is calculated. + +```{admonition} On CBMA "null methods" +:class: tip +The `null_method` parameter allows two options: "approximate" or "montecarlo." +The "approximate" option builds a histogram-based null distribution of summary-statistic values, which can then be used to determine the associated p-value for _observed_ summary-statistic values (i.e., the values in the meta-analytic map). +The "montecarlo" option builds a null distribution of summary-statistic values by randomly shuffling the coordinates the `Dataset` many times, and computing the summary-statistic values for each permutation. +In general, the "montecarlo" method is slightly more accurate when there are enough permutations, while the "approximate" method is much faster. +``` + +```{warning} +Fitting the CBMA `Estimator` to a `Dataset` will produce p-value, z-statistic, and summary-statistic maps, but these are not corrected for multiple comparisons. + +When performing a meta-analysis with the goal of statistical inference, you will want to perform multiple comparisons correction with NiMARE's `Corrector` +classes. +Please see the multiple comparisons correction chapter for more information. +``` + +Here we perform an MKDADensity meta-analysis on one of the Sleuth-based Datasets. +We will use the "approximate" null method for speed. + +```{code-cell} ipython3 +from nimare.meta.cbma import mkda + +mkdad_meta = mkda.MKDADensity(null_method="approximate") +mkdad_results = mkdad_meta.fit(sleuth_dset1) +``` + +(content:cbma:metaresult)= +### The `MetaResult` class + +Fitting an `Estimator` to a `Dataset` produces a {py:class}`~nimare.results.MetaResult` object. +The `MetaResult` class is a light container holding the different statistical maps produced by the `Estimator`. + +```{code-cell} ipython3 +print(mkdad_results) +``` + +This result is also retained as an attribute in the `Estimator`. + +```{code-cell} ipython3 +print(mkdad_meta.results) +``` + +The `maps` attribute is a dictionary containing statistical map names and associated numpy arrays. + +```{code-cell} ipython3 +pprint(mkdad_results.maps) +``` + +These arrays can be transformed into image-like objects using the `masker` attribute. +We can also use the `get_map` method to get that image object. + +```{code-cell} ipython3 +mkdad_img = mkdad_results.get_map("z", return_type="image") +print(mkdad_img) +``` + +We can save the statistical maps to an output directory as gzipped nifti files, with a prefix. +Here, we will save all of the statistical maps with the MKDADensity prefix. + +```{code-cell} ipython3 +mkdad_results.save_maps(output_dir=out_dir, prefix="MKDADensity") +``` + +We will also save the `Estimator` itself, which we will reuse when we get to multiple comparisons correction. + +```{code-cell} ipython3 +mkdad_meta.save(os.path.join(out_dir, "MKDADensity.pkl.gz")) +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +# Here we delete the recent variables for the sake of reducing memory usage +del mkdad_meta, mkdad_results +``` + ++++ + +Since this is a kernel-based algorithm, the kernel transformer is an optional input to the meta-analytic estimator, and can be controlled in a more fine-grained manner. + +```{code-cell} ipython3 +# These two approaches (initializing the kernel ahead of time or +# providing the arguments with the kernel__ prefix) are equivalent. +mkda_kernel = kernel.MKDAKernel(r=2) +mkdad_meta = mkda.MKDADensity(kernel_transformer=mkda_kernel) +mkdad_meta = mkda.MKDADensity(kernel_transformer=kernel.MKDAKernel, kernel__r=2) + +# A completely different kernel could even be provided, although this is not +# recommended and should only be used for testing algorithms. +mkdad_meta = mkda.MKDADensity(kernel_transformer=kernel.KDAKernel) +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +# Here we delete the recent variables for the sake of reducing memory usage +del mkda_kernel, mkdad_meta +``` + ++++ + +## Kernel density analysis + +**Kernel density analysis** (KDA) {cite:p}`Wager2003-no,Wager2004-ak` is a precursor algorithm that has been replaced in the field by MKDA. +For the sake of completeness, NiMARE also includes a KDA estimator that implements the older KDA algorithm for comparison purposes. +The interface is virtually identical, but since there are few if any legitimate uses of KDA (which models studies as fixed rather than random effects), we do not discuss the algorithm further here. + +```{code-cell} ipython3 +kda_meta = mkda.KDA(null_method="approximate") +kda_results = kda_meta.fit(sleuth_dset1) + +# Retain the z-statistic map for later use +kda_img = kda_results.get_map("z", return_type="image") +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +# Here we delete the recent variables for the sake of reducing memory usage +del kda_meta, kda_results +``` + ++++ + +## Activation likelihood estimation + +**Activation likelihood estimation** (ALE) {cite:p}`Eickhoff2012-hk,Turkeltaub2012-no,Turkeltaub2002-dn` assesses convergence of peaks across studies by first generating a modeled activation map for each study, in which each of the experiment’s peaks is convolved with a 3D Gaussian distribution determined by the experiment’s sample size, and then by combining these modeled activation maps across studies into an ALE map, which is compared to an empirical null distribution to assess voxel-wise statistical significance. + +```{code-cell} ipython3 +from nimare.meta.cbma import ale + +ale_meta = ale.ALE() +ale_results = ale_meta.fit(sleuth_dset1) + +# Retain the z-statistic map for later use +ale_img = ale_results.get_map("z", return_type="image") +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +# Here we delete the recent variables for the sake of reducing memory usage +del ale_meta, ale_results +``` + ++++ + +## Specific coactivation likelihood estimation + +**Specific coactivation likelihood estimation** (SCALE) {cite:p}`Langner2014-ei` is an extension of the ALE algorithm developed for meta-analytic coactivation modeling (MACM) analyses. +Rather than comparing convergence of foci within the sample to a null distribution derived under the assumption of spatial randomness within the brain, SCALE assesses whether the convergence at each voxel is greater than in the general literature. +Each voxel in the brain is assigned a null distribution determined based on the base rate of activation for that voxel across an existing coordinate-based meta-analytic database. +This approach allows for the generation of a statistical map for the sample, but no methods for multiple comparisons correction have yet been developed. +While this method was developed to support analysis of joint activation or "coactivation" patterns, it is generic and can be applied to any CBMA; see [](08_about_derivative_analyses.md) + +```{code-cell} ipython3 +# Here we use the coordinates from Neurosynth as our measure of coordinate +# base-rates, because we do not have access to the full BrainMap database. +# However, one assumption of SCALE is that the Dataset being analyzed comes +# from the same source as the database you use for calculating base-rates. +xyz = neurosynth_dset.coordinates[["x", "y", "z"]].values +# Typically, you would have >=2500 iterations, but we're using 500 here. +scale_meta = ale.SCALE(n_iters=500, xyz=xyz, memory_limit="100mb", n_cores=1) +scale_results = scale_meta.fit(sleuth_dset1) + +# Retain the z-statistic map for later use +scale_img = scale_results.get_map("z", return_type="image") +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +# Here we delete the recent variables for the sake of reducing memory usage +del xyz, scale_meta, scale_results +``` + ++++ + +## MKDA Chi-Squared Analysis + +An alternative to the density-based approaches (i.e., MKDA, KDA, ALE, and SCALE) is the **MKDA Chi-squared** extension {cite:p}`Wager2007-jc`. +Though still a kernel-based method in which foci are convolved with a binary sphere and combined within studies, this approach uses voxel-wise chi-squared tests to assess both consistency (i.e., higher convergence of foci within the meta-analytic sample than expected by chance) and specificity (i.e., higher convergence of foci within the meta-analytic sample than detected in an unrelated dataset) of activation. +Such an analysis also requires access to a reference meta-analytic sample or database of studies. +For example, to perform a chi-squared analysis of working memory studies, the researcher will also need a comprehensive set of studies which did not manipulate working memory—ideally one that is matched with the working memory study set on all relevant attributes _except_ the involvement of working memory. + +```{code-cell} ipython3 +mkdac_meta = mkda.MKDAChi2() +mkdac_results = mkdac_meta.fit(sleuth_dset1, sleuth_dset2) + +# Retain the specificity analysis's z-statistic map for later use +mkdac_img = mkdac_results.get_map("z_desc-specificity", return_type="image") +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +# Here we delete the recent variables for the sake of reducing memory usage +del mkdac_meta, mkdac_results +``` + ++++ + +## Comparing algorithms + +Here we load the z-statistic map from each of the CBMA Estimators we've used throughout this chapter and plot them all side by side. + +```{code-cell} ipython3 +:tags: [hide-output] + +meta_results = { + "MKDA Density": mkdad_img, + "MKDA Chi-Squared": mkdac_img, + "KDA": kda_img, + "ALE": ale_img, + "SCALE": scale_img, +} +order = [ + ["MKDA Density", "ALE"], + ["MKDA Chi-Squared", "SCALE"], + ["KDA", None] +] + +fig, axes = plt.subplots(figsize=(12, 6), nrows=3, ncols=2) + +for i_row, row_names in enumerate(order): + for j_col, name in enumerate(row_names): + if not name: + axes[i_row, j_col].axis("off") + continue + + img = meta_results[name] + if name == "MKDA Chi-Squared": + cmap = "RdBu_r" + else: + cmap = "Reds" + + display = plotting.plot_stat_map( + img, + annotate=False, + axes=axes[i_row, j_col], + cmap=cmap, + cut_coords=[5, -15, 10], + draw_cross=False, + figure=fig, + ) + axes[i_row, j_col].set_title(name) + + colorbar = display._cbar + colorbar_ticks = colorbar.get_ticks() + if colorbar_ticks[0] < 0: + new_ticks = [colorbar_ticks[0], 0, colorbar_ticks[-1]] + else: + new_ticks = [colorbar_ticks[0], colorbar_ticks[-1]] + colorbar.set_ticks(new_ticks, update_ticks=True) + +glue("figure_cbma_uncorr", fig, display=False) +``` + +```{glue:figure} figure_cbma_uncorr +:name: "figure_cbma_uncorr" +:align: center + +Thresholded results from MKDA Density, KDA, ALE, and SCALE meta-analyses. +``` + ++++ + +A number of other coordinate-based meta-analysis algorithms exist which are not yet implemented in NiMARE. +We describe these algorithms briefly in [](13_future_directions.md). diff --git a/content/nimare-paper/06_ibma.md b/content/nimare-paper/06_ibma.md new file mode 100644 index 0000000..0c8694e --- /dev/null +++ b/content/nimare-paper/06_ibma.md @@ -0,0 +1,244 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.10.3 +kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + +# Image-Based Meta-Analysis + ++++ + +```{code-cell} ipython3 +:tags: [hide-cell] +# First, import the necessary modules and functions +import os + +import matplotlib.pyplot as plt +from myst_nb import glue +from nilearn import plotting +from repo2data.repo2data import Repo2Data + +# Install the data if running locally, or points to cached data if running on neurolibre +DATA_REQ_FILE = os.path.abspath("../binder/data_requirement.json") +repo2data = Repo2Data(DATA_REQ_FILE) +data_path = repo2data.install() +data_path = os.path.join(data_path[0], "data") +``` + ++++ + +Image-based meta-analysis (IBMA) methods perform a meta-analysis directly on brain images (either whole-brain or partial) rather than on extracted peaks. +On paper, IBMA is superior to CBMA in virtually all respects, as the availability of analysis-level parameter and variance estimates at all analyzed voxels allows researchers to use the full complement of standard meta-analysis techniques, instead of having to resort to kernel-based or other methods that require additional spatial assumptions. +In principle, given a set of maps that contains no missing values (i.e., where there are _k_ valid pairs of parameter and variance estimates at each voxel), one can simply conduct a voxel-wise version of any standard meta-analysis or meta-regression method commonly used in other biomedical or social science fields. + +In practice, the utility of IBMA methods has historically been quite limited, as unthresholded statistical maps have been unavailable for the vast majority of neuroimaging studies. +However, the introduction and rapid adoption of NeuroVault {cite:p}`Gorgolewski2015-sd`, a database for unthresholded statistical images, has made image-based meta-analysis increasingly viable. +Although coverage of the literature remains limited, and IBMAs of maps drawn from the NeuroVault database are likely to omit at least some (and in some cases most) relevant studies due to limited metadata, we believe the time is ripe for researchers to start including both CBMAs and IBMAs in published meta-analyses, with the aspirational goal of eventually transitioning exclusively to the latter. +To this end, NiMARE supports a range of different IBMA methods, including a number of estimators of the gold standard mixed-effects meta-regression model, as well as several alternative estimators suitable for use when some of the traditional inputs are unavailable. + +```{note} +NiMARE's IBMA `Estimator`s are light wrappers around classes from [PyMARE](https://pymare.readthedocs.io), a library for standard (i.e., non-neuroimaging) meta-analyses developed by the same team as NiMARE. +``` + +In the optimal situation, meta-analysts have access to both contrast (i.e., parameter estimate) maps and their associated standard error maps for a number of studies. +With these data, researchers can fit the traditional random-effects meta-regression model using one of several methods that vary in the way they estimate the between-study variance ($\tau^{2}$). +Currently supported estimators include the **DerSimonian-Laird** method {cite:p}`DerSimonian1986-hu`, the **Hedges** method {cite:p}`Hedges1985-ka`, and **maximum-likelihood** (ML) and **restricted maximum-likelihood** (REML) approaches. +NiMARE can also perform fixed-effects meta-regression via weighted least-squares, though there are few IBMA scenarios where a fixed-effects analysis would be indicated. +It is worth noting that the non-likelihood-based estimators (i.e., DerSimonian-Laird and Hedges) have a closed-form solution, and are implemented in an extremely efficient way in NiMARE (i.e., computation is performed on all voxels in parallel). +However, these estimators also produce more biased estimates under typical conditions (e.g., when sample sizes are very small), implying a tradeoff from the user's perspective. + +Alternatively, when users only have access to contrast maps and associated sample sizes, they can use the supported **Sample Size-Based Likelihood** estimator, which assumes that within-study variance is constant across studies, and uses maximum-likelihood or restricted maximum-likelihood to estimate between-study variance, as described in {cite:t}`Sangnawakij2019-mq`. +When users have access only to contrast maps, they can use the **Permuted OLS** estimator, which uses ordinary least squares and employs a max-type permutation scheme for family-wise error correction {cite:p}`Freedman1983-ld,Anderson2001-uc` that has been validated on neuroimaging data {cite:p}`Winkler2014-wh` and relies on the nilearn library. + +Finally, when users only have access to z-score maps, they can use either the **Fisher's** {cite:p}`Fisher1925-zh` or the **Stouffer's** {cite:p}`Riley1949-uz` estimators. +When sample size information is available, users may incorporate that information into the Stouffer's method, via the method described in {cite:t}`Zaykin2011-fs`. + ++++ + +Given the paucity of image-based meta-analytic datasets, we have included the tools to build a Dataset from a NeuroVault collection of 21 pain studies, originally described in {cite:t}`Maumet2016-rr`. + +```{code-cell} ipython3 +from nimare import dataset, extract, utils + +dset_dir = extract.download_nidm_pain(data_dir=data_path, overwrite=False) +dset_file = os.path.join(utils.get_resource_path(), "nidm_pain_dset.json") +img_dset = dataset.Dataset(dset_file) + +# Point the Dataset toward the images we've downloaded +img_dset.update_path(dset_dir) +``` + +## Transforming images + +Researchers may share their statistical maps in many forms, some of which are direct transformations of one another. +For example, researchers may share test statistic maps with z-statistics or t-statistics, and, as long as we know the degrees of freedom associated with the t-test, we can convert between the two easily. To that end, NiMARE includes a class, {py:class}`~nimare.transforms.ImageTransformer`, which will calculate target image types from available ones, as long as the available images are compatible with said transformation. + +Here, we use `ImageTransformer` to calculate z-statistic and variance maps for all studies with compatible images. +This allows us to apply more image-based meta-analysis algorithms to the `Dataset`. + +```{code-cell} ipython3 +from nimare import transforms + +img_transformer = transforms.ImageTransformer(target=["z", "varcope"], overwrite=False) +img_dset = img_transformer.transform(img_dset) +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +# Here we delete the recent variables for the sake of reducing memory usage +del img_transformer +``` + +Now that we have filled in as many gaps in the `Dataset` as possible, we can start running meta-analyses. +We will start with a DerSimonian-Laird meta-analysis ({py:class}`~nimare.meta.ibma.DerSimonianLaird`). + +```{code-cell} ipython3 +from nimare import meta + +dsl_meta = meta.ibma.DerSimonianLaird(resample=True) +dsl_results = dsl_meta.fit(img_dset) + +# Retain the z-statistic map for later use +dsl_img = dsl_results.get_map("z", return_type="image") +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +# Here we delete the recent variables for the sake of reducing memory usage +del dsl_meta, dsl_results +``` + ++++ + +Now we will apply other available IBMA `Estimator`s to the same `Dataset`, and save their results to files for comparison. + +```{code-cell} ipython3 +# Stouffer's +stouffers_meta = meta.ibma.Stouffers(use_sample_size=False, resample=True) +stouffers_results = stouffers_meta.fit(img_dset) +stouffers_img = stouffers_results.get_map("z", return_type="image") +del stouffers_meta, stouffers_results + +# Stouffer's with weighting based on sample size +wstouffers_meta = meta.ibma.Stouffers(use_sample_size=True, resample=True) +wstouffers_results = wstouffers_meta.fit(img_dset) +wstouffers_img = wstouffers_results.get_map("z", return_type="image") +del wstouffers_meta, wstouffers_results + +# Fisher's +fishers_meta = meta.ibma.Fishers(resample=True) +fishers_results = fishers_meta.fit(img_dset) +fishers_img = fishers_results.get_map("z", return_type="image") +del fishers_meta, fishers_results + +# Permuted Ordinary Least Squares +ols_meta = meta.ibma.PermutedOLS(resample=True) +ols_results = ols_meta.fit(img_dset) +ols_img = ols_results.get_map("z", return_type="image") +del ols_meta, ols_results + +# Weighted Least Squares +wls_meta = meta.ibma.WeightedLeastSquares(resample=True) +wls_results = wls_meta.fit(img_dset) +wls_img = wls_results.get_map("z", return_type="image") +del wls_meta, wls_results + +# Hedges' +hedges_meta = meta.ibma.Hedges(resample=True) +hedges_results = hedges_meta.fit(img_dset) +hedges_img = hedges_results.get_map("z", return_type="image") +del hedges_meta, hedges_results + +# Use atlas for likelihood-based estimators +from nilearn import datasets, image, input_data + +atlas = datasets.fetch_atlas_harvard_oxford("cort-maxprob-thr25-2mm") + +# nilearn's NiftiLabelsMasker cannot handle NaNs at the moment, +# and some of the NIDM-Results packs' beta images have NaNs at the edge of the brain. +# So, we will create a reduced version of the atlas for this analysis. +nan_mask = image.math_img("~np.any(np.isnan(img), axis=3)", img=img_dset.images["beta"].tolist()) +atlas = image.resample_to_img(atlas["maps"], nan_mask) +nanmasked_atlas = image.math_img("mask * atlas", mask=nan_mask, atlas=atlas) +masker = input_data.NiftiLabelsMasker(nanmasked_atlas) +del atlas, nan_mask, nanmasked_atlas + +# Variance-Based Likelihood +vbl_meta = meta.ibma.VarianceBasedLikelihood(method="reml", mask=masker, resample=True) +vbl_results = vbl_meta.fit(img_dset) +vbl_img = vbl_results.get_map("z", return_type="image") +del vbl_meta, vbl_results + +# Sample Size-Based Likelihood +ssbl_meta = meta.ibma.SampleSizeBasedLikelihood(method="reml", mask=masker, resample=True) +ssbl_results = ssbl_meta.fit(img_dset) +ssbl_img = ssbl_results.get_map("z", return_type="image") +del ssbl_meta, ssbl_results, masker +``` + ++++ + +## Comparing algorithms + +Here we load the z-statistic map from each of the IBMA Estimators we've used throughout this chapter and plot them all side by side. + +```{code-cell} ipython3 +:tags: [hide-output] +meta_results = { + "DerSimonian-Laird": dsl_img, + "Stouffer's": stouffers_img, + "Weighted Stouffer's": wstouffers_img, + "Fisher's": fishers_img, + "Ordinary Least Squares": ols_img, + "Weighted Least Squares": wls_img, + "Hedges'": hedges_img, + "Variance-Based Likelihood": vbl_img, + "Sample Size-Based Likelihood": ssbl_img, +} +order = [ + ["Fisher's", "Stouffer's", "Weighted Stouffer's"], + ["DerSimonian-Laird", "Hedges'", "Weighted Least Squares"], + ["Ordinary Least Squares", "Variance-Based Likelihood", "Sample Size-Based Likelihood"], +] + +fig, axes = plt.subplots(figsize=(18, 6), nrows=3, ncols=3) + +for i_row, row_names in enumerate(order): + for j_col, name in enumerate(row_names): + file_ = meta_results[name] + display = plotting.plot_stat_map( + file_, + annotate=False, + axes=axes[i_row, j_col], + cmap="RdBu_r", + cut_coords=[5, -15, 10], + draw_cross=False, + figure=fig, + ) + axes[i_row, j_col].set_title(name) + + colorbar = display._cbar + colorbar_ticks = colorbar.get_ticks() + if colorbar_ticks[0] < 0: + new_ticks = [colorbar_ticks[0], 0, colorbar_ticks[-1]] + else: + new_ticks = [colorbar_ticks[0], colorbar_ticks[-1]] + colorbar.set_ticks(new_ticks, update_ticks=True) + +glue("figure_uncorr_ibma", fig, display=False) +``` + +```{glue:figure} figure_uncorr_ibma +:name: figure_uncorr_ibma +:align: center + +An array of plots of the statistical maps produced by the image-based meta-analysis methods. +The likelihood-based meta-analyses are run on atlases instead of voxelwise. +``` diff --git a/content/nimare-paper/07_correction.md b/content/nimare-paper/07_correction.md new file mode 100644 index 0000000..77aebb4 --- /dev/null +++ b/content/nimare-paper/07_correction.md @@ -0,0 +1,136 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.10.3 +kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + +# Multiple Comparisons Correction + ++++ + +```{code-cell} ipython3 +:tags: [hide-cell] +# First, import the necessary modules and functions +import os + +import matplotlib.pyplot as plt +from myst_nb import glue +from nilearn import plotting +from repo2data.repo2data import Repo2Data + +# Install the data if running locally, or points to cached data if running on neurolibre +DATA_REQ_FILE = os.path.abspath("../binder/data_requirement.json") +repo2data = Repo2Data(DATA_REQ_FILE) +data_path = repo2data.install() +data_path = os.path.join(data_path[0], "data") + +# Set an output directory for any files generated during the book building process +out_dir = os.path.abspath("../outputs/") +os.makedirs(out_dir, exist_ok=True) +``` + ++++ + +In NiMARE, multiple comparisons correction is separated from each CBMA and IBMA `Estimator`, so that any number of relevant correction methods can be applied after the `Estimator` has been fit to the `Dataset`. +Some correction options, such as the `montecarlo` option for FWE correction, are designed to work specifically with a given `Estimator` (and are indeed implemented within the `Estimator` class, and only called by the `Corrector`). + +`Corrector`s are divided into two subclasses: `FWECorrector`s, which correct based on family-wise error rate, and `FDRCorrector`s, which correct based on false discovery rate. + +All `Corrector`s are initialized with a number of parameters, including the correction method that will be used. +After that, you can use the `transform` method on a `MetaResult` object produced by a CBMA or IBMA `Estimator` to apply the correction method. +This will return an updated `MetaResult` object, with both the statistical maps from the original `MetaResult`, as well as new, corrected maps. + +Here we will apply both FWE and FDR correction to results from a MKDADensity meta-analysis, performed back in [](content:cbma:mkdad). + +```{warning} +In the following example, we use 5000 iterations for Monte Carlo FWE correction. +Normally, one would use at least 10000 iterations, but we reduced this for the sake of speed. +``` + +```{code-cell} ipython3 +from nimare import meta, correct + +mkdad_meta = meta.cbma.mkda.MKDADensity.load(os.path.join(data_path, "MKDADensity.pkl.gz")) + +mc_corrector = correct.FWECorrector(method="montecarlo", n_iters=5000, n_cores=4) +mc_results = mc_corrector.transform(mkdad_meta.results) +mc_results.save_maps(output_dir=out_dir, prefix="MKDADensity_FWE") + +fdr_corrector = correct.FDRCorrector(method="indep") +fdr_results = fdr_corrector.transform(mkdad_meta.results) +``` + +Statistical maps saved by NiMARE `MetaResult`s automatically follow a naming convention based loosely on the Brain Imaging Data Standard (BIDS). + +Let's take a look at the files created by the `FWECorrector`. + +```{code-cell} ipython3 +from glob import glob + +fwe_maps = sorted(glob(os.path.join(out_dir, "MKDADensity_FWE*.nii.gz"))) +fwe_maps = [os.path.basename(fwe_map) for fwe_map in fwe_maps] +print("\n".join(fwe_maps)) +``` + +If you ignore the prefix, which was specified in the call to `MetaResult.save_maps`, the maps all have a common naming convention. +The maps from the original meta-analysis (before multiple comparisons correction) are simply named according to the values contained in the map (e.g., `z`, `stat`, `p`). + +Maps generated by the correction method, however, use a series of key-value pairs to indicate how they were generated. +The `corr` key indicates whether FWE or FDR correction was applied. +The `method` key reflects the correction method employed, which was defined by the `method` parameter used to create the `Corrector`. +The `level` key simply indicates if the map was corrected at the voxel or cluster level. +Finally, the `desc` key reflects any necessary description that goes beyond what is already covered by the other entities. + ++++ + +```{code-cell} ipython3 +:tags: [hide-cell] +meta_results = { + "Cluster-level Monte Carlo": mc_results.get_map( + "z_desc-size_level-cluster_corr-FWE_method-montecarlo", + return_type="image", + ), + "Independent FDR": fdr_results.get_map( + "z_corr-FDR_method-indep", + return_type="image", + ), +} + +fig, axes = plt.subplots(figsize=(6, 4), nrows=2) + +for i_meta, (name, file_) in enumerate(meta_results.items()): + display = plotting.plot_stat_map( + file_, + annotate=False, + axes=axes[i_meta], + draw_cross=False, + cmap="Reds", + cut_coords=[0, 0, 0], + figure=fig, + ) + axes[i_meta].set_title(name) + + colorbar = display._cbar + colorbar_ticks = colorbar.get_ticks() + if colorbar_ticks[0] < 0: + new_ticks = [colorbar_ticks[0], 0, colorbar_ticks[-1]] + else: + new_ticks = [colorbar_ticks[0], colorbar_ticks[-1]] + colorbar.set_ticks(new_ticks, update_ticks=True) + +glue("figure_corr_cbma", fig, display=False) +``` + +```{glue:figure} figure_corr_cbma +:name: figure_corr_cbma +:align: center + +An array of plots of the corrected statistical maps produced by the different multiple comparisons correction methods. +``` diff --git a/content/nimare-paper/08_about_derivative_analyses.md b/content/nimare-paper/08_about_derivative_analyses.md new file mode 100644 index 0000000..4407815 --- /dev/null +++ b/content/nimare-paper/08_about_derivative_analyses.md @@ -0,0 +1,4 @@ +# Derivative Analyses + +Meta-analytic databases and algorithms may be employed for derivative analyses, including subtraction analysis, meta-analytic coactivation modeling (MACM), meta-analytic clustering, coactivation-based parcellation (CBP), meta-analytic independent component analysis (meta-ICA), semantic model development, and meta-analytic functional decoding. +In this part, we describe the derivative analyses implemented in NiMARE and include examples of use cases. diff --git a/content/nimare-paper/09_subtraction.md b/content/nimare-paper/09_subtraction.md new file mode 100644 index 0000000..f1b42d5 --- /dev/null +++ b/content/nimare-paper/09_subtraction.md @@ -0,0 +1,105 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.10.3 +kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + +# Meta-Analytic Subtraction Analysis + ++++ + +```{code-cell} ipython3 +:tags: [hide-cell] +# First, import the necessary modules and functions +import os + +import matplotlib.pyplot as plt +from myst_nb import glue +from nilearn import image, plotting +from repo2data.repo2data import Repo2Data + +from nimare import dataset + +# Install the data if running locally, or points to cached data if running on neurolibre +DATA_REQ_FILE = os.path.abspath("../binder/data_requirement.json") +repo2data = Repo2Data(DATA_REQ_FILE) +data_path = repo2data.install() +data_path = os.path.join(data_path[0], "data") + +# Now, load the Datasets we will use in this chapter +sleuth_dset1 = dataset.Dataset.load(os.path.join(data_path, "sleuth_dset1.pkl.gz")) +sleuth_dset2 = dataset.Dataset.load(os.path.join(data_path, "sleuth_dset2.pkl.gz")) +``` + ++++ + +Subtraction analysis refers to the voxel-wise comparison of two meta-analytic samples. +In image-based meta-analysis, comparisons between groups of maps can generally be accomplished within the standard meta-regression framework (i.e., by adding a covariate that codes for group membership). +However, coordinate-based subtraction analysis requires special extensions for CBMA algorithms. + +Subtraction analysis to compare the results of two ALE meta-analyses was originally implemented by {cite:t}`Laird2005-qh` and later extended by {cite:t}`Eickhoff2012-hk`. +In this approach, two groups of experiments (A and B) are compared using a group assignment randomization procedure in which voxel-wise null distributions are generated by randomly reassigning experiments between the two groups and calculating ALE-difference scores for each permutation. +Real ALE-difference scores (i.e., the ALE values for one group minus the ALE values for the other) are compared against these null distributions to determine voxel-wise significance. +In the original implementation of the algorithm, this procedure is performed separately for a group A > B contrast and a group B > A contrast, where each contrast is limited to voxels that were significant in the first group's original meta-analysis. + +```{important} +In NiMARE, we use an adapted version of the subtraction analysis method in {py:class}`~nimare.meta.cbma.ale.ALESubtraction`. +The NiMARE implementation analyzes all voxels, rather than only those that show a significant effect of A alone or B alone as in the original implementation. +``` + +```{important} +Running a subtraction analysis with the standard number of iterations (10000) may require more than 4 GB of RAM, which is NeuroLibre's limit. +We will instead use only 1000 iterations, so that the analysis will run successfully on NeuroLibre's server. +For publication-quality subtraction analyses, we recommend using the standard 10000 iterations. +``` + +```{code-cell} ipython3 +from nimare import meta + +kern = meta.kernel.ALEKernel() +sub_meta = meta.cbma.ale.ALESubtraction(kernel_transformer=kern, n_iters=1000) +sub_results = sub_meta.fit(sleuth_dset1, sleuth_dset2) +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +fig, ax = plt.subplots(figsize=(6, 2)) +display = plotting.plot_stat_map( + sub_results.get_map("z_desc-group1MinusGroup2", return_type="image"), + annotate=False, + axes=ax, + cmap="RdBu_r", + cut_coords=[0, 0, 0], + draw_cross=False, + figure=fig, +) +ax.set_title("ALE Subtraction") + +colorbar = display._cbar +colorbar_ticks = colorbar.get_ticks() +if colorbar_ticks[0] < 0: + new_ticks = [colorbar_ticks[0], 0, colorbar_ticks[-1]] +else: + new_ticks = [colorbar_ticks[0], colorbar_ticks[-1]] +colorbar.set_ticks(new_ticks, update_ticks=True) +glue("figure_subtraction", fig, display=False) +``` + +```{glue:figure} figure_subtraction +:name: figure_subtraction +:align: center + +Unthresholded z-statistic map for the subtraction analysis of the two example Sleuth-based `Datasets`. +``` + +Alternatively, MKDA Chi-squared analysis is inherently a subtraction analysis method, in that it compares foci from two groups of studies. +Generally, one of these groups is a sample of interest, while the other is a meta-analytic database (minus the studies in the sample). +With this setup, meta-analysts can infer whether there is greater convergence of foci in a voxel as compared to the baseline across the field (as estimated with the meta-analytic database), much like SCALE. +However, if the database is replaced with a second sample of interest, the analysis ends up comparing convergence between the two groups. diff --git a/content/nimare-paper/10_macm.md b/content/nimare-paper/10_macm.md new file mode 100644 index 0000000..f0f1061 --- /dev/null +++ b/content/nimare-paper/10_macm.md @@ -0,0 +1,171 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.10.3 +kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + +# Meta-Analytic Coactivation Modeling + ++++ + +```{code-cell} ipython3 +:tags: [hide-cell] +# First, import the necessary modules and functions +import os + +import matplotlib.pyplot as plt +from myst_nb import glue +from repo2data.repo2data import Repo2Data + +from nimare import dataset + +# Install the data if running locally, or points to cached data if running on neurolibre +DATA_REQ_FILE = os.path.abspath("../binder/data_requirement.json") +repo2data = Repo2Data(DATA_REQ_FILE) +data_path = repo2data.install() +data_path = os.path.join(data_path[0], "data") + +neurosynth_dset = dataset.Dataset.load(os.path.join(data_path, "neurosynth_dataset.pkl.gz")) +``` + ++++ + +Meta-analytic coactivation modeling (MACM) {cite:p}`Laird2009-gc,Robinson2010-iv,Eickhoff2010-vx`, also known as meta-analytic connectivity modeling, uses meta-analytic data to measure co-occurrence of activations between brain regions providing evidence of functional connectivity of brain regions across tasks. +In coordinate-based MACM, whole-brain studies within the database are selected based on whether or not they report at least one peak in a region of interest specified for the analysis. +These studies are then subjected to a meta-analysis, often comparing the selected studies to those remaining in the database. +In this way, the significance of each voxel in the analysis corresponds to whether there is greater convergence of foci at the voxel among studies which also report foci in the region of interest than those which do not. + + + +MACM results have historically been accorded a similar interpretation to task-related functional connectivity (e.g., {cite:p}`Hok2015-lt,Kellermann2013-en`), although this approach is quite removed from functional connectivity analyses of task fMRI data (e.g., beta-series correlations, psychophysiological interactions, or even seed-to-voxel functional connectivity analyses on task data). +Nevertheless, MACM analyses do show high correspondence with resting-state functional connectivity {cite:p}`Reid2017-ez`. +MACM has been used to characterize the task-based functional coactivation of the cerebellum {cite:p}`Riedel2015-tx`, lateral prefrontal cortex {cite:p}`Reid2016-ba`, fusiform gyrus {cite:p}`Caspers2014-ja`, and several other brain regions. + +Within NiMARE, MACMs can be performed by selecting studies in a Dataset based on the presence of activation within a target mask or coordinate-centered sphere. +While some algorithms, such as SCALE, may have been designed with MACMs in mind, in practice MACMs may be performed with any valid Estimator. + +In this section, we will perform two MACMs- one with a target mask and one with a coordinate-centered sphere. +For the former, we use {py:meth}`~nimare.dataset.Dataset.get_studies_by_mask`. +For the latter, we use {py:meth}`~nimare.dataset.Dataset.get_studies_by_coordinate`. + +```{code-cell} ipython3 +# Create Dataset only containing studies with peaks within the amygdala mask +amygdala_mask = os.path.join(data_path, "amygdala_roi.nii.gz") +amygdala_ids = neurosynth_dset.get_studies_by_mask(amygdala_mask) +dset_amygdala = neurosynth_dset.slice(amygdala_ids) + +# Create Dataset only containing studies with peaks within the sphere ROI +sphere_ids = neurosynth_dset.get_studies_by_coordinate([[24, -2, -20]], r=6) +dset_sphere = neurosynth_dset.slice(sphere_ids) +``` + +```{important} +The amygdala dataset includes more than 1300 studies. +Running a meta-analysis on such a large dataset may require more than 4 GB of RAM, which is NeuroLibre's limit. +Therefore, we will further reduce the Dataset to its first 500 studies, in order to run the meta-analysis successfully on NeuroLibre's server. +For publication-quality analyses, we would recommend using the entire Dataset. +``` + +```{code-cell} ipython3 +print(dset_amygdala) +dset_amygdala = dset_amygdala.slice(dset_amygdala.ids[:500]) +print(dset_amygdala) +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +import numpy as np +from nilearn import input_data, plotting + +# In order to plot a sphere with a precise radius around a coordinate with +# nilearn, we need to use a NiftiSpheresMasker +mask_img = neurosynth_dset.masker.mask_img +sphere_masker = input_data.NiftiSpheresMasker([[24, -2, -20]], radius=6, mask_img=mask_img) +sphere_masker.fit(mask_img) +sphere_img = sphere_masker.inverse_transform(np.array([[1]])) + +fig, axes = plt.subplots(figsize=(6, 4), nrows=2) +display = plotting.plot_roi( + amygdala_mask, + annotate=False, + draw_cross=False, + axes=axes[0], + figure=fig, +) +axes[0].set_title("Amygdala ROI") +display = plotting.plot_roi( + sphere_img, + annotate=False, + draw_cross=False, + axes=axes[1], + figure=fig, +) +axes[1].set_title("Spherical ROI") +glue("figure_macm_rois", fig, display=False) +``` + +```{glue:figure} figure_macm_rois +:name: figure_macm_rois +:align: center + +Region of interest masks for (1) a target mask-based MACM and (2) a coordinate-based MACM. +``` + ++++ + +Once the `Dataset` has been reduced to studies with coordinates within the mask or sphere requested, any of the supported CBMA Estimators can be run. + +```{code-cell} ipython3 +from nimare import meta + +meta_amyg = meta.cbma.ale.ALE(kernel__sample_size=20) +results_amyg = meta_amyg.fit(dset_amygdala) + +meta_sphere = meta.cbma.ale.ALE(kernel__sample_size=20) +results_sphere = meta_sphere.fit(dset_sphere) +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +meta_results = { + "Amygdala ALE MACM": results_amyg.get_map("z", return_type="image"), + "Sphere ALE MACM": results_sphere.get_map("z", return_type="image"), +} + +fig, axes = plt.subplots(figsize=(6, 4), nrows=2) +for i_meta, (name, file_) in enumerate(meta_results.items()): + display = plotting.plot_stat_map( + file_, + annotate=False, + axes=axes[i_meta], + cmap="Reds", + cut_coords=[24, -2, -20], + draw_cross=False, + figure=fig, + ) + axes[i_meta].set_title(name) + + colorbar = display._cbar + colorbar_ticks = colorbar.get_ticks() + if colorbar_ticks[0] < 0: + new_ticks = [colorbar_ticks[0], 0, colorbar_ticks[-1]] + else: + new_ticks = [colorbar_ticks[0], colorbar_ticks[-1]] + colorbar.set_ticks(new_ticks, update_ticks=True) + +glue("figure_macm", fig, display=False) +``` + +```{glue:figure} figure_macm +:name: figure_macm +:align: center + +Unthresholded z-statistic maps for (1) the target mask-based MACM and (2) the coordinate-based MACM. +``` diff --git a/content/nimare-paper/11_annotation.md b/content/nimare-paper/11_annotation.md new file mode 100644 index 0000000..7ddd902 --- /dev/null +++ b/content/nimare-paper/11_annotation.md @@ -0,0 +1,386 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.10.3 +kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + +# Automated Annotation + ++++ + +```{code-cell} ipython3 +:tags: [hide-cell] +# First, import the necessary modules and functions +import os + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from myst_nb import glue +from nilearn import image, plotting +from repo2data.repo2data import Repo2Data + +from nimare import dataset, extract + +# Install the data if running locally, or points to cached data if running on neurolibre +DATA_REQ_FILE = os.path.abspath("../binder/data_requirement.json") +repo2data = Repo2Data(DATA_REQ_FILE) +data_path = repo2data.install() +data_path = os.path.join(data_path[0], "data") +``` + ++++ + +As mentioned in the discussion of BrainMap ([](content:resources:brainmap)), manually annotating studies in a meta-analytic database can be a time-consuming and labor-intensive process. +To facilitate more efficient (albeit lower-quality) annotation, NiMARE supports a number of automated annotation approaches. +These include [](content:annotation:ngram), [](content:annotation:cogat), [](content:annotation:lda), and [](content:annotation:gclda). + +NiMARE users may download abstracts from PubMed as long as study identifiers in the `Dataset` correspond to PubMed IDs (as in Neurosynth and NeuroQuery). +Abstracts are much more easily accessible than full article text, so most annotation methods in NiMARE rely on them. + +Below, we use the function {py:func}`~nimare.extract.download_abstracts` to download abstracts for the Neurosynth `Dataset`. +This will attempt to extract metadata about each study in the `Dataset` from PubMed, and then add the abstract available on Pubmed to the `Dataset`'s `texts` attribute, under a new column names "abstract". + +```{important} +{py:func}`~nimare.extract.download_abstracts` only works when there is internet access. +Since this book will often be built on nodes without internet access, we will share the code +used to download abstracts, but will actually load and use a pre-generated version of the Dataset. +``` + +```python +# First, load a Dataset without abstracts +neurosynth_dset_first_500 = dataset.Dataset.load( + os.path.join(data_path, "neurosynth_dataset_first500.pkl.gz") +) + +# Now, download the abstracts using your email address +neurosynth_dset_first_500 = extract.download_abstracts( + neurosynth_dset_first_500, + email="example@email.com", +) + +# Finally, save the Dataset with abstracts to a pkl.gz file +neurosynth_dset_first_500.save( + os.path.join(data_path, "neurosynth_dataset_first500_with_abstracts.pkl.gz"), +) +``` + +```{code-cell} ipython3 +neurosynth_dset_first_500 = dataset.Dataset.load( + os.path.join(data_path, "neurosynth_dataset_first500_with_abstracts.pkl.gz"), +) +``` + ++++ + +(content:annotation:ngram)= +## N-gram term extraction + +**N-gram term extraction** refers to the vectorization of text into contiguous sets of words that can be counted as individual tokens. +The upper limit on the number of words in these tokens is set by the user. + +NiMARE has the function {py:func}`~nimare.annotate.text.generate_counts` to extract n-grams from text. +This method produces either term counts or term frequency- inverse document frequency (tf-idf) values for each of the studies in a `Dataset`. + +```{code-cell} ipython3 +from nimare import annotate + +counts_df = annotate.text.generate_counts( + neurosynth_dset_first_500.texts, + text_column="abstract", + tfidf=False, + min_df=10, + max_df=0.95, +) +``` + +This term count `DataFrame` will be used later, to train a GCLDA model. + ++++ + +(content:annotation:cogat)= +## Cognitive Atlas term extraction and hierarchical expansion + +**Cognitive Atlas term extraction** leverages the structured nature of the Cognitive Atlas in order to extract counts for individual terms and their synonyms in the ontology, as well as to apply hierarchical expansion to these counts based on the relationships specified between terms. +This method produces both basic term counts and expanded term counts based on the weights applied to different relationship types present in the ontology. + +First, we must use {py:func}`~nimare.extract.download_cognitive_atlas` to download the current version of the Cognitive Atlas ontology. +This includes both information about individual terms in the ontology and asserted relationships between those terms. + +NiMARE will automatically attempt to extrapolate likely alternate forms of each term in the ontology, in order to make extraction easier. +For an example, see {numref}`tbl:table_cogat_forms`. + +```{code-cell} ipython3 +cogatlas = extract.download_cognitive_atlas(data_dir=data_path, overwrite=False) +id_df = pd.read_csv(cogatlas["ids"]) +rel_df = pd.read_csv(cogatlas["relationships"]) + +cogat_counts_df, rep_text_df = annotate.cogat.extract_cogat( + neurosynth_dset_first_500.texts, id_df, text_column="abstract" +) +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +example_forms = id_df.loc[id_df["name"] == "dot motion task"][["id", "name", "alias"]] +glue("table_cogat_forms", example_forms) +``` + +```{glue:figure} table_cogat_forms +:name: "tbl:table_cogat_forms" +:align: center + +An example of alternate forms characterized by the Cognitive Atlas and extrapolated by NiMARE. +Certain alternate forms (i.e., synonyms) are specified within the Cognitive Atlas, while others are inferred automatically by NiMARE according to certain rules (e.g., removing parentheses). +``` + +```{code-cell} ipython3 +# Define a weighting scheme. +# In this scheme, observed terms will also count toward any hypernyms (isKindOf), +# holonyms (isPartOf), and parent categories (inCategory) as well. +weights = {"isKindOf": 1, "isPartOf": 1, "inCategory": 1} +expanded_df = annotate.cogat.expand_counts(cogat_counts_df, rel_df, weights) + +# Sort by total count and reduce for better visualization +series = expanded_df.sum(axis=0) +series = series.sort_values(ascending=False) +series = series[series > 0] +columns = series.index.tolist() +``` + +```{code-cell} ipython3 +:tags: [hide-output] +# Raw counts +fig, axes = plt.subplots(figsize=(16, 16), nrows=2, sharex=True) +pos = axes[0].imshow( + cogat_counts_df[columns].values, + aspect="auto", + vmin=0, + vmax=10, +) +fig.colorbar(pos, ax=axes[0]) +axes[0].set_title("Counts Before Expansion", fontsize=20) +axes[0].yaxis.set_visible(False) +axes[0].xaxis.set_visible(False) +axes[0].set_ylabel("Study", fontsize=16) +axes[0].set_xlabel("Cognitive Atlas Term", fontsize=16) + +# Expanded counts +pos = axes[1].imshow( + expanded_df[columns].values, + aspect="auto", + vmin=0, + vmax=10, +) +fig.colorbar(pos, ax=axes[1]) +axes[1].set_title("Counts After Expansion", fontsize=20) +axes[1].yaxis.set_visible(False) +axes[1].xaxis.set_visible(False) +axes[1].set_ylabel("Study", fontsize=16) +axes[1].set_xlabel("Cognitive Atlas Term", fontsize=16) + +fig.tight_layout() +glue("figure_cogat_expansion", fig, display=False) +``` + +```{glue:figure} figure_cogat_expansion +:name: "figure_cogat_expansion" +:align: center + +The effect of hierarchical expansion on Cognitive Atlas term counts from abstracts in Neurosynth's first 500 papers. There are too many terms and studies to show individual labels. +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +# Here we delete the recent variables for the sake of reducing memory usage +del cogatlas, id_df, rel_df, cogat_counts_df, rep_text_df +del weights, expanded_df, series, columns +``` + ++++ + +(content:annotation:lda)= +## Latent Dirichlet allocation + +**Latent Dirichlet allocation** (LDA) {cite:p}`Blei2003-lh` was originally combined with meta-analytic neuroimaging data in {cite:t}`Poldrack2012-it`. +LDA is a generative topic model which, for a text corpus, builds probability distributions across documents and words. +In LDA, each document is considered a mixture of topics. +This works under the assumption that each document was constructed by first randomly selecting a topic based on the document's probability distribution across topics, and then randomly selecting a word from that topic based on the topic's probability distribution across words. +While this is not a useful generative model for producing documents, LDA is able to discern cohesive topics of related words. +{cite:t}`Poldrack2012-it` were able to apply LDA to full texts from neuroimaging articles in order to develop cognitive neuroscience-related topics and to run topic-wise meta-analyses. +This method produces two sets of probability distributions: (1) the probability of a word given topic and (2) the probability of a topic given article. + +NiMARE's {py:class}`~nimare.annotate.lda.LDAModel` is a light wrapper around scikit-learn's LDA implementation. + +Here, we train an LDA model ({py:class}`~nimare.annotate.lda.LDAModel`) on the first 500 studies of the Neurosynth `Dataset`, with 50 topics in the model. + +```{code-cell} ipython3 +:tags: [hide-output] +from nimare import annotate + +lda_model = annotate.lda.LDAModel(n_topics=50, max_iter=1000, text_column="abstract") + +# Fit the model +lda_model.fit(neurosynth_dset_first_500) +``` + +The most important products of training the `LDAModel` object is its `distributions_` attribute. +`LDAModel.distributions_` is a dictionary containing arrays and DataFrames created from training the model. +We are particularly interested in the `p_topic_g_word_df` distribution, which is a `pandas` `DataFrame` in which each row corresponds to a topic and each column corresponds to a term (n-gram) extracted from the `Dataset`'s texts. +The cells contain weights indicating the probability distribution across terms for each topic. + +Additionally, the `LDAModel` updates the `Dataset`'s {py:attr}`~nimare.dataset.Dataset.annotations` attribute, by adding columns corresponding to each of the topics in the model. +Each study in the `Dataset` thus receives a weight for each topic, which can be used to select studies for topic-based meta-analyses or functional decoding. + +Let's take a look at the results of the model training. +First, we will reorganize the DataFrame a bit to show the top ten terms for each of the first ten topics. + +```{code-cell} ipython3 +:tags: [hide-output] + +lda_df = lda_model.distributions_["p_topic_g_word_df"].T +column_names = {c: f"Topic {c}" for c in lda_df.columns} +lda_df = lda_df.rename(columns=column_names) +temp_df = lda_df.copy() +lda_df = pd.DataFrame(columns=lda_df.columns, index=np.arange(10)) +lda_df.index.name = "Term" +for col in lda_df.columns: + top_ten_terms = temp_df.sort_values(by=col, ascending=False).index.tolist()[:10] + lda_df.loc[:, col] = top_ten_terms + +lda_df = lda_df[lda_df.columns[:10]] +glue("table_lda", lda_df) +``` + +```{glue:figure} table_lda +:name: "tbl:table_lda" +:align: center + +The top ten terms for each of the first ten topics in the trained LDA model. +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +# Here we delete the recent variables for the sake of reducing memory usage +del lda_model, lda_df, temp_df +``` + ++++ + +(content:annotation:gclda)= +## Generalized correspondence latent Dirichlet allocation + +**Generalized correspondence latent Dirichlet allocation** (GCLDA) is a recently-developed algorithm that trains topics on both article abstracts and coordinates {cite:p}`Rubin2017-rd`. +GCLDA assumes that topics within the fMRI literature can also be localized to brain regions, in this case modeled as three-dimensional Gaussian distributions. +These spatial distributions can also be restricted to pairs of Gaussians that are symmetric across brain hemispheres. +This method produces two sets of probability distributions: the probability of a word given topic (`GCLDAModel.p_word_g_topic_`) and the probability of a voxel given topic (`GCLDAModel.p_voxel_g_topic_`). + +Here we train a GCLDA model ({py:class}`~nimare.annotate.gclda.GCLDAModel`) on the first 500 studies of the Neurosynth Dataset. +The model will include 50 topics, in which the spatial distribution for each topic will be defined as having two Gaussian distributions that are symmetrically localized across the longitudinal fissure. + +```{important} +{py:class}`~nimare.annotate.gclda.GCLDAModel` generally takes a very long time to train. + +Below, we show how one would train a GCLDA model. +However, we will load a pretrained model instead of actually training the model. +``` + +```python +gclda_model = annotate.gclda.GCLDAModel( + counts_df, + neurosynth_dset_first_500.coordinates, + n_regions=2, + n_topics=50, + symmetric=True, + mask=neurosynth_dset_first_500.masker.mask_img, +) +gclda_model.fit(n_iters=2500, loglikely_freq=500) +``` + +```{code-cell} ipython3 +gclda_model = annotate.gclda.GCLDAModel.load(os.path.join(data_path, "gclda_model.pkl.gz")) +``` + +The `GCLDAModel` retains the relevant probability distributions in the form of `numpy` arrays, rather than `pandas` DataFrames. +However, for the topic-term weights (`p_word_g_topic_`), the data are more interpretable as a DataFrame, so we will create one. +We will also reorganize the raw DataFrame to show the top ten terms for each of the first ten topics. + +```{code-cell} ipython3 +:tags: [hide-output] + +gclda_arr = gclda_model.p_word_g_topic_ +gclda_vocab = gclda_model.vocabulary +topic_names = [f"Topic {str(i).zfill(3)}" for i in range(gclda_arr.shape[1])] +gclda_df = pd.DataFrame(index=gclda_vocab, columns=topic_names, data=gclda_arr) + +temp_df = gclda_df.copy() +gclda_df = pd.DataFrame(columns=gclda_df.columns, index=np.arange(10)) +gclda_df.index.name = "Term" +for col in temp_df.columns: + top_ten_terms = temp_df.sort_values(by=col, ascending=False).index.tolist()[:10] + gclda_df.loc[:, col] = top_ten_terms + +gclda_df = gclda_df[gclda_df.columns[:10]] +glue("table_gclda", gclda_df) +``` + +```{glue:figure} table_gclda +:name: "tbl:table_gclda" +:align: center + +The top ten terms for each of the first ten topics in the trained GCLDA model. +``` + +We also want to see how the topic-voxel weights render on the brain, so we will simply unmask the `p_voxel_g_topic_` array with the `Dataset`'s masker. + +```{code-cell} ipython3 +:tags: [hide-output] +fig, axes = plt.subplots(nrows=5, ncols=2, figsize=(12, 10)) + +topic_img_4d = neurosynth_dset_first_500.masker.inverse_transform(gclda_model.p_voxel_g_topic_.T) +# Plot first ten topics +topic_counter = 0 +for i_row in range(5): + for j_col in range(2): + topic_img = image.index_img(topic_img_4d, index=topic_counter) + display = plotting.plot_stat_map( + topic_img, + annotate=False, + cmap="Reds", + draw_cross=False, + figure=fig, + axes=axes[i_row, j_col], + ) + axes[i_row, j_col].set_title(f"Topic {str(topic_counter).zfill(3)}") + topic_counter += 1 + + colorbar = display._cbar + colorbar_ticks = colorbar.get_ticks() + if colorbar_ticks[0] < 0: + new_ticks = [colorbar_ticks[0], 0, colorbar_ticks[-1]] + else: + new_ticks = [colorbar_ticks[0], colorbar_ticks[-1]] + colorbar.set_ticks(new_ticks, update_ticks=True) + +glue("figure_gclda_topics", fig, display=False) +``` + +```{glue:figure} figure_gclda_topics +:name: figure_gclda_topics +:align: center + +Topic weight maps for the first ten topics in the GCLDA model. +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +# Here we delete the recent variables for the sake of reducing memory usage +del gclda_model, temp_df, gclda_df, counts_df +``` diff --git a/content/nimare-paper/12_decoding.md b/content/nimare-paper/12_decoding.md new file mode 100644 index 0000000..ae00486 --- /dev/null +++ b/content/nimare-paper/12_decoding.md @@ -0,0 +1,337 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.10.3 +kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + +# Meta-Analytic Functional Decoding + ++++ + +```{code-cell} ipython3 +:tags: [hide-cell] +# First, import the necessary modules and functions +import os + +import matplotlib.pyplot as plt +from myst_nb import glue +from nilearn import plotting +from repo2data.repo2data import Repo2Data + +# Install the data if running locally, or points to cached data if running on neurolibre +DATA_REQ_FILE = os.path.abspath("../binder/data_requirement.json") +repo2data = Repo2Data(DATA_REQ_FILE) +data_path = repo2data.install() +data_path = os.path.join(data_path[0], "data") +``` + +```{code-cell} ipython3 +:tags: [hide-cell] + +from nimare import dataset, meta + +neurosynth_dset = dataset.Dataset.load(os.path.join(data_path, "neurosynth_dataset.pkl.gz")) + +kern = meta.kernel.MKDAKernel(memory_limit="500mb") +neurosynth_dset_first500 = dataset.Dataset.load( + os.path.join(data_path, "neurosynth_dataset_first500_with_mkda_ma.pkl.gz"), +) + +# Collect features for decoding +# We use any features that appear in >10% of studies and <90%. +id_cols = ["id", "study_id", "contrast_id"] +frequency_threshold = 0.001 +cols = neurosynth_dset_first500.annotations.columns +cols = [c for c in cols if c not in id_cols] +df = neurosynth_dset_first500.annotations.copy()[cols] +n_studies = df.shape[0] +feature_counts = (df >= frequency_threshold).sum(axis=0) +target_features = feature_counts.between(n_studies * 0.1, n_studies * 0.9) +target_features = target_features[target_features] +target_features = target_features.index.values +print(f"{len(target_features)} features selected.", flush=True) + +continuous_map = os.path.join(data_path, "map_to_decode.nii.gz") +amygdala_roi = os.path.join(data_path, "amygdala_roi.nii.gz") +amygdala_ids = neurosynth_dset_first500.get_studies_by_mask(amygdala_roi) +``` + ++++ + +Functional decoding performed with meta-analytic data, refers to methods which attempt to predict mental states from neuroimaging data using a large-scale meta-analytic database {cite:p}`Smith2009-wk`. +Such analyses may also be referred to as "informal reverse inference" {cite:p}`Poldrack2011-zp`, "functional characterization analysis" {cite:p}`Bzdok2013-gc,Cieslik2013-kz,Rottschy2013-cd`, "open-ended decoding" {cite:p}`Rubin2017-rd`, or simply "functional decoding" {cite:p}`Amft2015-kw,Bzdok2013-jv,Nickl-Jockschat2015-rg`. +While the terminology is far from standardized, we will refer to this method as **meta-analytic functional decoding** in order to distinguish it from alternative methods like multivariate decoding and model-based decoding {cite:p}`Poldrack2011-zp`. +Meta-analytic functional decoding is often used in conjunction with MACM, meta-analytic clustering, meta-analytic parcellation, and meta-ICA, in order to characterize resulting brain regions, clusters, or components. +Meta-analytic functional decoding models have also been extended for the purpose of **meta-analytic functional encoding**, wherein text is used to generate statistical images {cite:p}`Dockes2018-ug,Nunes2018-du,Rubin2017-rd`. + +Four common approaches are correlation-based decoding, dot-product decoding, weight-sum decoding, and chi-square decoding. +We will first discuss continuous decoding methods (i.e., correlation and dot-product), followed by discrete decoding methods (weight-sum and chi-square). + ++++ + +(content:decoding:continuous)= +## Decoding continuous inputs + +When decoding unthresholded statistical maps (such as {numref}`figure_map_to_decode`), the most common approaches are to simply correlate the input map with maps from the database, or to compute the dot product between the two maps. +In Neurosynth, meta-analyses are performed for each label (i.e., term or topic) in the database and then the input image is correlated with the resulting unthresholded statistical map from each meta-analysis. +Performing statistical inference on the resulting correlations is not straightforward, however, as voxels display strong spatial correlations, and the true degrees of freedom are consequently unknown (and likely far smaller than the nominal number of voxels). +In order to interpret the results of this decoding approach, users typically select some arbitrary number of top correlation coefficients ahead of time, and use the associated labels to describe the input map. +However, such results should be interpreted with great caution. + +```{code-cell} ipython3 +:tags: [hide-cell] +fig, ax = plt.subplots(figsize=(10, 6)) +plotting.plot_stat_map(continuous_map, axes=ax, figure=fig, annotate=False, draw_cross=False) +glue("figure_map_to_decode", fig, display=False) +``` + +```{glue:figure} figure_map_to_decode +:name: figure_map_to_decode +:align: center + +The unthresholded statistical map that will be used for continuous decoding. +``` + +This approach can also be applied to an image-based database like NeuroVault, either by correlating input data with meta-analyzed statistical maps, or by deriving distributions of correlation coefficients by grouping statistical maps in the database according to label. +Using these distributions, it is possible to statistically compare labels in order to assess label significance. +NiMARE includes methods for both correlation-based decoding and correlation distribution-based decoding, although the correlation-based decoding is better established and should be preferred over the correlation distribution-based decoding. +As such, we will only show the {py:class}`~nimare.decode.continuous.CorrelationDecoder` here. + +```{important} +{py:class}`~nimare.decode.continuous.CorrelationDecoder` currently runs _very_ slowly. +We strongly recommend running it on a subset of labels within the `Dataset`. +It is also quite memory-intensive. + +In this example, we have only run the decoder using features appearing in >10% and <90% of the first 500 studies in the `Dataset`. +Additionally, we have pre-generated the results and will simply show the code that _would_ generate +those results, as the decoder requires too much memory for NeuroLibre's servers. +``` + +```python +from nimare import decode, meta + +corr_decoder = decode.continuous.CorrelationDecoder( + frequency_threshold=0.001, + meta_estimator=meta.MKDADensity(kernel_transformer=kern, memory_limit=None), + target_image="z", + features=target_features, + memory_limit="500mb", +) +corr_decoder.fit(neurosynth_dset_first500) +corr_df = corr_decoder.transform(continuous_map) +``` + +```{code-cell} ipython3 +import pandas as pd + +corr_df = pd.read_table( + os.path.join(data_path, "correlation_decoder_results.tsv"), + index_col="feature", +) +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +corr_df = corr_df.reindex(corr_df["r"].abs().sort_values(ascending=False).index) +corr_df = corr_df.iloc[:10] +glue("table_corr", corr_df) +``` + +```{glue:figure} table_corr +:name: "tbl:table_corr" +:align: center + +The top ten terms, sorted by absolute correlation coefficient, from the correlation decoding method. +``` + ++++ + +(content:decoding:discrete)= +## Decoding discrete inputs + +Decoding regions of interest (ROIs) requires a different approach than decoding unthresholded statistical maps. +One simple approach, used by GCLDA and implemented in the function {py:func}`~nimare.decode.discrete.gclda_decode_roi`, simply sums the `P(topic|voxel)` distribution across all voxels in the ROI in order to produce a value associated with each topic for the ROI. +These **weight sum** values are arbitrarily scaled and cannot be compared across ROIs. +We will not show this method because of its simplicity and the fact that it can only currently be applied to a GCLDA model. + +Before we dig into the other decoding methods are are available, let's take a look at the ROI we want to decode. + +```{code-cell} ipython3 +:tags: [hide-cell] +fig, ax = plt.subplots(figsize=(10, 6)) +plotting.plot_roi(amygdala_roi, axes=ax, figure=fig, annotate=False, draw_cross=False) +glue("figure_roi_to_decode", fig, display=False) +``` + +```{glue:figure} figure_roi_to_decode +:name: figure_roi_to_decode +:align: center + +The amygdala region of interest mask that will be used for discrete decoding. +``` + +One method which relies on correlations, much like the continuous correlation decoder, is the **ROI association** decoding method ({py:class}`~nimare.decode.discrete.ROIAssociationDecoder`), originally implemented in the Neurosynth Python library. +In this method, each study with coordinates in the dataset is convolved with a kernel transformer to produce a modeled activation map. +The resulting modeled activation maps are then masked with a region of interest (i.e., the target of the decoding), and the values are averaged within the ROI. +These averaged modeled activation values are then correlated with the term weights for all labels in the dataset. +This decoding method produces a single correlation coefficient for each of the dataset's labels. + +```{important} +Because the `ROIAssociationDecoder` generates modeled activation maps for all of the experiments in the `Dataset`, we will only fit this decoder to the first 500 studies. +``` + +```{code-cell} ipython3 +from nimare import decode + +assoc_decoder = decode.discrete.ROIAssociationDecoder( + amygdala_roi, + kernel_transformer=kern, + u=0.05, + correction="fdr_bh", +) +assoc_decoder.fit(neurosynth_dset_first500) +assoc_df = assoc_decoder.transform() +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +assoc_df = assoc_df.reindex(assoc_df["r"].abs().sort_values(ascending=False).index) +assoc_df = assoc_df.iloc[:10] +glue("table_assoc", assoc_df) +``` + +```{glue:figure} table_assoc +:name: "tbl:table_assoc" +:align: center + +The top ten terms, sorted by absolute correlation coefficient, from the ROI association decoding method. +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +# Here we delete the recent variables for the sake of reducing memory usage +del assoc_decoder, assoc_df +``` + +A more theoretically driven approach to ROI decoding is to use **chi-square-based** methods. +The two methods which use chi-squared tests are the BrainMap decoding method and an adaptation of Neurosynth's meta-analysis method. + +In both chi-square-based methods, studies are first selected from a coordinate-based database according to some criterion. +For example, if decoding a region of interest, users might select studies reporting at least one coordinate within 5 mm of the ROI. +Metadata (such as ontological labels) for this subset of studies are then compared to those of the remaining, unselected portion of the database in a confusion matrix. +For each label in the ontology, studies are divided into four groups: selected and label-positive (SS+L+), selected and label-negative (SS+L-), unselected and label-positive (SS-L+), and unselected and label-negative (SS-L-). +Each method then compares these groups in order to evaluate both consistency and specificity of the relationship between the selection criteria and each label, which are evaluated in terms of both statistical significance and effect size. + ++++ + +### BrainMap method + +The BrainMap discrete decoding method, implemented in {py:class}`~nimare.decode.discrete.BrainMapDecoder`, compares the distributions of studies with each label within the sample against those in a larger database while accounting for the number of foci from each study. +Broadly speaking, this method assumes that the selection criterion is associated with one peak per study, which means that it is likely only appropriate for selection criteria based around foci, such as regions of interest. +One common analysis, meta-analytic clustering, involves dividing studies within a database into meta-analytic groupings based on the spatial similarity of their modeled activation maps (i.e., study-wise pseudo-statistical maps produced by convolving coordinates with a kernel). +The resulting sets of studies are often functionally decoded in order to build a functional profile associated with each meta-analytic grouping. +While these groupings are defined as subsets of the database, they are not selected based on the location of an individual peak, and so weighting based on the number of foci would be inappropriate. + +This decoding method produces four outputs for each label. +First, the distribution of studies in the sample with the label are compared to the distributions of other labels within the sample. +This consistency analysis produces both a measure of statistical significance (i.e., a p-value) and a measure of effect size (i.e., the likelihood of being selected given the presence of the label). +Next, the studies in the sample are compared to the studies in the rest of the database. +This specificity analysis produces a p-value and an effect size measure of the posterior probability of having the label given selection into the sample. +A detailed algorithm description is presented in [](appendices/brainmap_decoding). + +```{code-cell} ipython3 +brainmap_decoder = decode.discrete.BrainMapDecoder( + frequency_threshold=0.001, + u=0.05, + correction="fdr_bh", +) +brainmap_decoder.fit(neurosynth_dset) +brainmap_df = brainmap_decoder.transform(amygdala_ids) +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +brainmap_df = brainmap_df.reindex( + brainmap_df["probReverse"].abs().sort_values(ascending=False).index +) +brainmap_df = brainmap_df.iloc[:10] +glue("table_brainmap", brainmap_df) +``` + +```{glue:figure} table_brainmap +:name: "tbl:table_brainmap" +:align: center + +The top ten terms, sorted by reverse-inference posterior probability, from the BrainMap chi-squared decoding method. +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +# Here we delete the recent variables for the sake of reducing memory usage +del brainmap_decoder, brainmap_df +``` + ++++ + +### Neurosynth method + +The implementation of the MKDA Chi-squared meta-analysis method used by Neurosynth is quite similar to BrainMap's method for decoding, if applied to annotations instead of modeled activation values. +This method, implemented in {py:class}`~nimare.decode.discrete.NeurosynthDecoder`, compares the distributions of studies with each label within the sample against those in a larger database, but, unlike the BrainMap method, does not take foci into account. +For this reason, the Neurosynth method would likely be more appropriate for selection criteria not based on regions of interest (e.g., for characterizing meta-analytic groupings from a meta-analytic clustering analysis). +However, the Neurosynth method requires user-provided information that BrainMap does not. +Namely, in order to estimate probabilities for the consistency and specificity analyses with Bayes' Theorem, the Neurosynth method requires a prior probability of a given label. +Typically, a value of 0.5 is used (i.e., the estimated probability that an individual is undergoing a given mental process described by a label, barring any evidence from neuroimaging data, is predicted to be 50%). +This is, admittedly, a poor prediction, which means that probabilities estimated based on this prior are not likely to be accurate, though they may still serve as useful estimates of effect size for the analysis. + +Like the BrainMap method, this method produces four outputs for each label. +For the consistency analysis, this method produces both a p-value and a conditional probability of selection given the presence of the label and the prior probability of having the label. +For the specificity analysis, the Neurosynth method produces both a p-value and a posterior probability of presence of the label given selection and the prior probability of having the label. +A detailed algorithm description is presented in [](appendices/neurosynth_decoding). + +```{code-cell} ipython3 +neurosynth_decoder = decode.discrete.NeurosynthDecoder( + frequency_threshold=0.001, + u=0.05, + correction="fdr_bh", +) +neurosynth_decoder.fit(neurosynth_dset) +neurosynth_df = neurosynth_decoder.transform(amygdala_ids) +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +neurosynth_df = neurosynth_df.reindex( + neurosynth_df["probReverse"].abs().sort_values(ascending=False).index +) +neurosynth_df = neurosynth_df.iloc[:10] +glue("table_neurosynth", neurosynth_df) +``` + +```{glue:figure} table_neurosynth +:name: "tbl:table_neurosynth" +:align: center + +The top ten terms, sorted by reverse-inference posterior probability, from the Neurosynth chi-squared decoding method. +``` + +```{code-cell} ipython3 +:tags: [hide-cell] +# Here we delete the recent variables for the sake of reducing memory usage +del neurosynth_decoder, neurosynth_df +``` + ++++ + +In both methods, the database acts as an estimate of the underlying distribution of labels in the real world, such that the probability of having a peak in an ROI given the presence of the label might be interpreted as the probability of a brain activating a specific brain region given that the individual is experiencing a given mental state. +This is a very poor interpretation, given that any database of neuroimaging results will be skewed more toward the interests of the field than the distribution of mental states or processes experienced by humans, which is why decoding must be interpreted with extreme caution. +It is important not to place too much emphasis on the results of functional decoding analyses, although they are very useful in that they can provide a quantitative estimate behind the kinds of interpretations generally included in discussion sections that are normally only backed by informal literature searches or prior knowledge. + +The meta-analytic functional decoding methods in NiMARE provide a very rudimentary approach for open-ended decoding (i.e., decoding across a very large range of mental states) that can be used with resources like NeuroVault. +However, standard classification methods have also been applied to datasets from NeuroVault (e.g., {cite:t}`Varoquaux2018-lo`), although these methods do not fall under NiMARE's scope. diff --git a/content/nimare-paper/13_future_directions.md b/content/nimare-paper/13_future_directions.md new file mode 100644 index 0000000..ebdea8e --- /dev/null +++ b/content/nimare-paper/13_future_directions.md @@ -0,0 +1,48 @@ +# Future Directions + +NiMARE's mission statement encompasses a range of tools that have not yet been implemented in the package. +In the future, we plan to incorporate a number of additional methods. Here we briefly describe several of these tools. + +## Integration with external databases + +A resource which may ultimately be integrated with Neurosynth is [Brainspell](https://github.com/OpenNeuroLab/brainspell-neo). +Brainspell is a port of the Neurosynth database in which users may manually annotate the automatically extracted study information. +The goal of Brainspell is to crowdsource annotation through both expert and nonexpert annotators, which would address the primary weaknesses of BrainMap (i.e., slow growth) and Neurosynth (i.e., noise in data extraction and annotation). +Annotations in Brainspell may use labels from the [Cognitive Paradigm Ontology](http://www.cogpo.org) (CogPO) {cite:p}`Turner2012-ai`, an ontology adapted from the BrainMap Taxonomy, or from the [Cognitive Atlas](https://www.cognitiveatlas.org) {cite:p}`Poldrack2011-yh`, a collaboratively generated ontology built by contributions from experts across the field of cognitive science. +Users may also correct the coordinates extracted by Neurosynth, which may suffer from extraction errors, and may add important metadata like the number of subjects associated with each comparison in each study. + +Brainspell has suffered from low growth, which is why its annotations have not been integrated back into Neurosynth, but a new frontend tool for Brainspell, geared toward meta-analysts, has been developed called [metaCurious](https://metacurious.org). +MetaCurious facilitates neuroimaging meta-analyses by allowing users to iteratively perform literature searches and to annotate rejected articles with reasons for exclusion. +In addition to these features, metaCurious users can annotate studies with the same labels and metadata as Brainspell, but with the features geared toward meta-analysts site usage is expected to exceed that of Brainspell proper. + +While NiMARE does not natively include tools for interacting with Brainspell or metaCurious, there are plans to support NiMARE-format exports in both services. + +## Seed-based D-Mapping + +[Seed-based d-mapping](https://www.sdmproject.com) (SDM) {cite:p}`Radua2012-dy`, previously known as signed differential mapping, is a relatively recently-developed approach designed to incorporate both peak-specific effect size estimates and unthresholded images, when available. +In SDM, foci are convolved with an anisotropic kernel which, unlike the Gaussian and spherical kernels employed in ALE and MKDA, respectively, accounts for tissue type to provide more empirically realistic spatial models of the clusters from the original studies. +The SDM algorithm is not yet supported in NiMARE, given the difficulty in implementing an algorithm without access to code. + +## Model-based CBMA + +Model-based algorithms, a recent alternative to kernel-based approaches, model foci from studies as the products of stochastic models sampling some underlying distribution. +Some of these methods include the Bayesian hierarchical independent cluster process model (BHICP) {cite:p}`Kang2011-rl`, the Bayesian spatially adaptive binary regression model (SBR) {cite:p}`Yue2012-pd`, the hierarchical Poisson/Gamma random field model (HPGRF/BHPGM) {cite:p}`Kang2014-tp`, the spatial Bayesian latent factor regression model (SBLFRM) {cite:p}`Montagna2018-rq`, and the random effects log Gaussian Cox process model (RFX-LGCP) {cite:p}`Samartsidis2019-cl`. + +Although these methods are much more computationally intensive than kernel-based algorithms, they provide information that kernel-based methods cannot, such as spatial confidence intervals, effect size estimate confidence intervals, and the facilitation of reverse inference. +A more thorough description of the relative strengths of model-based algorithms is presented in {cite:t}`Samartsidis2017-ej`, but these benefits, at the cost of computational efficiency, have led the authors to recommend kernel-based methods for exploratory analysis and model-based methods for confirmatory analysis. + +NiMARE does not currently implement any model-based CBMA algorithms, although there are plans to include at least one in the future. + +## Additional automated annotation methods + +Several papers have used article text to automatically annotate meta-analytic databases with a range of methods. +{cite:t}`Alhazmi2018-nj` used a combination of correspondence analysis and clustering to identify subdomains in the cognitive neuroscience literature from Neurosynth text. +{cite:t}`Monti2016-aq` generated word and document embeddings in vector space from Neurosynth abstracts using deep Boltzmann machines, which allowed them to cluster words based on semantic similarity or to describe Neurosynth articles in terms of these word clusters. +{cite:t}`Nunes2018-du` used article abstracts from Neurosynth to represent documents as dense vectors as well. +These document vectors were then used in conjunction with corresponding coordinates to cluster words into categories, essentially annotating Neurosynth articles according to a new "ontology" based on both abstract text and coordinates. + +Meta-analytic databases may also be used in conjunction with existing ontologies in order to redefine mental states or to refine the ontology. +For example, {cite:t}`Yeo2016-vu` used the Author-Topic model to identify connections between Paradigm Classes (i.e., tasks) and Behavioral Domains (i.e., mental states) from the BrainMap Taxonomy using the BrainMap database. +Other examples include using meta-analytic clustering, combined with functional decoding, to identify groups of terms/labels that co-occur in neuroimaging data, in order to determine if the divisions currently employed in existing ontologies accurately reflect how mental states are separated in the mind (e.g., {cite:p}`Laird2015-sr,Riedel2018-je,Bottenhorn2019-bm`). + + diff --git a/content/nimare-paper/14_summary.md b/content/nimare-paper/14_summary.md new file mode 100644 index 0000000..e8feb24 --- /dev/null +++ b/content/nimare-paper/14_summary.md @@ -0,0 +1,8 @@ +# Summary + +The advent of open, large-scale databases of neuroimaging results, whether full, unthresholded statistical maps or simple coordinates, has allowed for the development of a wide variety of methods for performing fMRI meta-analyses and related analyses. +These methods are often (but not always) released as tools for the community to use, written in a range of languages and with highly variable interfaces. +As a consequence, it is difficult for meta-analysts to keep abreast of the current literature and to employ whatever method is most appropriate to address a given question. +NiMARE provides a centralized repository for these tools, which will make it easier for researchers to keep track of new methods, and also provides said tools with extensive documentation and a standardized programmatic interface, which will allow researchers to use whatever tool is most appropriate for their research, without unnecessarily steep learning curves. + +Given that NiMARE is open source and collaboratively developed on GitHub, methodologists may contribute their own meta-analytic algorithms directly, or interested third parties may implement these algorithms using papers or external tools as a basis for understanding the methods. diff --git a/content/nimare-paper/15_acknowledgements.md b/content/nimare-paper/15_acknowledgements.md new file mode 100644 index 0000000..9337e15 --- /dev/null +++ b/content/nimare-paper/15_acknowledgements.md @@ -0,0 +1,5 @@ +# Acknowledgements + +We would like to thank Yifan Yu and Jérôme Dockès, who provided feedback on the manuscript. + +This work was partially funded by the National Institutes of Health (NIH) NIH-NIBIB P41 EB019936 (ReproNim), NIH-NIMH R01 MH083320 (CANDIShare), and NIH RF1 MH120021 (NIDM), the National Institute Of Mental Health under Award Number R01MH096906 (Neurosynth), as well as the Canada First Research Excellence Fund, awarded to McGill University for the Healthy Brains for Healthy Lives initiative and the Brain Canada Foundation with support from Health Canada. diff --git a/content/nimare-paper/16_bibliography.md b/content/nimare-paper/16_bibliography.md new file mode 100644 index 0000000..fceb358 --- /dev/null +++ b/content/nimare-paper/16_bibliography.md @@ -0,0 +1,4 @@ +# References + +```{bibliography} references.bib +``` diff --git a/content/nimare-paper/17_build_information.md b/content/nimare-paper/17_build_information.md new file mode 100644 index 0000000..ac5bf6e --- /dev/null +++ b/content/nimare-paper/17_build_information.md @@ -0,0 +1,4 @@ +# Build Information + +```{nb-exec-table} +``` diff --git a/content/nimare-paper/_static/myfile.css b/content/nimare-paper/_static/myfile.css new file mode 100644 index 0000000..a79653d --- /dev/null +++ b/content/nimare-paper/_static/myfile.css @@ -0,0 +1,15 @@ +p { + text-align: justify; +} +p.caption { + text-align: left; +} +p.prevnext-title { + text-align: left; +} 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+############################################################################### +# Start with the necessary imports +# ----------------------------------------------------------------------------- +import os + +from nimare.dataset import Dataset +from nimare.extract import download_nidm_pain +from nimare.transforms import ImageTransformer +from nimare.utils import get_resource_path + +############################################################################### +# Datasets are stored as json or pkl[.gz] files +# ----------------------------------------------------------------------------- +# Json files are used to create Datasets, while generated Datasets are saved +# to, and loaded from, pkl[.gz] files. +# We use jsons because they are easy to edit, and thus build by hand, if +# necessary. +# We then store the generated Datasets as pkl.gz files because an initialized +# Dataset is no longer a dictionary. + +# Let's start by downloading a dataset +dset_dir = download_nidm_pain() + +# Now we can load and save the Dataset object +dset_file = os.path.join(get_resource_path(), "nidm_pain_dset.json") +dset = Dataset(dset_file, target="mni152_2mm", mask=None) +dset.save("pain_dset.pkl") +dset = Dataset.load("pain_dset.pkl") +os.remove("pain_dset.pkl") # cleanup + +############################################################################### +# Much of the data in Datasets is stored as DataFrames +# ----------------------------------------------------------------------------- +# The five DataFrames in Dataset are "coordinates" (reported peaks), +# "images" (statistical maps), "metadata", "texts", and "annotations" (labels). + +############################################################################### +# ``Dataset.annotations`` contains labels describing studies +# ````````````````````````````````````````````````````````````````````````````` +# Columns include the standard identifiers and any labels. +# The labels may be grouped together based on label source, in which case they +# should be prefixed with some string followed by two underscores. +dset.annotations.head() + +############################################################################### +# ``Dataset.coordinates`` contains reported peaks +# ````````````````````````````````````````````````````````````````````````````` +# Columns include the standard identifiers, as well as mm coordinates (x, y, z) +# and voxel indices (i, j, k) specific to the Dataset's masker's space. +dset.coordinates.head() + +############################################################################### +# ``Dataset.images`` contains images from studies +# ````````````````````````````````````````````````````````````````````````````` +# Columns include the standard identifiers, as well as paths to images grouped +# by image type (e.g., z, beta, t). + +# Here we'll only show a subset of these image types to fit in the window. +columns_to_show = ["id", "study_id", "contrast_id", "beta__relative", "z__relative"] +dset.images[columns_to_show].head() + +############################################################################### +# ``Dataset.metadata`` contains metadata describing studies +# ````````````````````````````````````````````````````````````````````````````` +# Columns include the standard identifiers, as well as one column for each +# metadata field. +dset.metadata.head() + +############################################################################### +# ``Dataset.texts`` contains texts associated with studies +# ````````````````````````````````````````````````````````````````````````````` +# Columns include the standard identifiers, as well as one for each text type. +dset.texts.head() + +############################################################################### +# There are a handful of other important Dataset attributes +# ----------------------------------------------------------------------------- + +############################################################################### +# ``Dataset.ids`` contains study identifiers +dset.ids + +############################################################################### +# ``Dataset.masker`` is a nilearn Masker object +dset.masker + +############################################################################### +# ``Dataset.space`` is a string +print(f"Template space: {dset.space}") + +############################################################################### +# Statistical images are not stored internally +# ----------------------------------------------------------------------------- +# Images are not stored within the Dataset. +# Instead, relative paths to image files are retained in the Dataset.images +# attribute. +# When loading a Dataset, you will likely need to specify the path to the images. +# To do this, you can use :func:`~nimare.dataset.Dataset.update_path`. +dset.update_path(dset_dir) +columns_to_show = ["id", "study_id", "contrast_id", "beta", "beta__relative"] +dset.images[columns_to_show].head() + +############################################################################### +# Images can also be calculated based on available files +# ````````````````````````````````````````````````````````````````````````````` +# When some images are available, but others are not, sometimes required images +# can be calculated from the available ones. +# +# For example, ``varcope = t / beta``, so if you have t-statistic images and +# beta images, you can also calculate varcope (variance) images. +# +# We use the :mod:`~nimare.transforms` module to perform these transformations +# (especially :class:`~nimare.transforms.ImageTransformer`) +varcope_transformer = ImageTransformer(target="varcope") +dset = varcope_transformer.transform(dset) +dset.images[["id", "varcope"]].head() + +############################################################################### +# Datasets support many search methods +# ----------------------------------------------------------------------------- +# There are ``get_[X]`` and ``get_studies_by_[X]`` methods for a range of +# possible search criteria. +# The ``get_[X]`` methods allow you to search for specific metadata, while the +# ``get_studies_by_[X]`` methods let you search for study identifiers within +# the Dataset matching criteria. +# +# Note that the ``get_[X]`` methods return a value for every study in the Dataset +# by default, and for every requested study if the ``ids`` argument is provided. +# If a study does not have the data requested, the returned list will have +# ``None`` for that study. +z_images = dset.get_images(imtype="z") +z_images = [str(z) for z in z_images] +print("\n".join(z_images)) + +############################################################################### +# Let's try to fill in missing z images +# ````````````````````````````````````````````````````````````````````````````` +z_transformer = ImageTransformer(target="z") +dset = z_transformer.transform(dset) +z_images = dset.get_images(imtype="z") +z_images = [str(z) for z in z_images] +print("\n".join(z_images)) + +############################################################################### +# Datasets can also search for studies matching criteria +# ----------------------------------------------------------------------------- +# ``get_studies_by_[X]`` methods return a list of study identifiers matching +# the criteria, such as reporting a peak coordinate near a search coordinate. +sel_studies = dset.get_studies_by_coordinate(xyz=[[0, 0, 0]], r=20) +print("\n".join(sel_studies)) + +############################################################################### +# Datasets are meant to be mostly immutable +# ----------------------------------------------------------------------------- +# While some elements of Datasets are designed to be changeable, like the paths +# to image files, most elements are not. +# NiMARE Estimators operate on Datasets and return *new*, updated Datasets. +# If you want to reduce a Dataset based on a subset of the studies in the +# Dataset, you need to use :meth:`~nimare.dataset.Dataset.slice`. +sub_dset = dset.slice(ids=sel_studies) +print("\n".join(sub_dset.ids)) diff --git a/content/nimare-repo/01_datasets/02_download_neurosynth.py b/content/nimare-repo/01_datasets/02_download_neurosynth.py new file mode 100644 index 0000000..447b4a5 --- /dev/null +++ b/content/nimare-repo/01_datasets/02_download_neurosynth.py @@ -0,0 +1,108 @@ +""" + +.. _datasets_databases: + +========================= +Neurosynth and NeuroQuery +========================= + +Neurosynth and NeuroQuery are the two largest publicly-available coordinate-based databases. +NiMARE includes functions for downloading releases of each database and converting the databases +to NiMARE Datasets. + +In this example, we download and convert the Neurosynth and NeuroQuery databases for analysis with +NiMARE. + +.. warning:: + In August 2021, the Neurosynth database was reorganized according to a new file format. + As such, the ``fetch_neurosynth`` function for NiMARE versions before 0.0.10 will not work + with its default parameters. + In order to download the Neurosynth database in its older format using NiMARE <= 0.0.9, + do the following:: + + nimare.extract.fetch_neurosynth( + url=( + "https://github.com/neurosynth/neurosynth-data/blob/" + "e8f27c4a9a44dbfbc0750366166ad2ba34ac72d6/current_data.tar.gz?raw=true" + ), + ) + +For information about where these files will be downloaded to on your machine, +see :doc:`../../fetching`. +""" +############################################################################### +# Start with the necessary imports +# ----------------------------------------------------------------------------- +import os +from pprint import pprint + +from nimare.extract import download_abstracts, fetch_neuroquery, fetch_neurosynth +from nimare.io import convert_neurosynth_to_dataset + +############################################################################### +# Download Neurosynth +# ----------------------------------------------------------------------------- +# Neurosynth's data files are stored at https://github.com/neurosynth/neurosynth-data. +out_dir = os.path.abspath("../example_data/") +os.makedirs(out_dir, exist_ok=True) + +files = fetch_neurosynth( + data_dir=out_dir, + version="7", + overwrite=False, + source="abstract", + vocab="terms", +) +# Note that the files are saved to a new folder within "out_dir" named "neurosynth". +pprint(files) +neurosynth_db = files[0] + +############################################################################### +# Convert Neurosynth database to NiMARE dataset file +# ----------------------------------------------------------------------------- +neurosynth_dset = convert_neurosynth_to_dataset( + coordinates_file=neurosynth_db["coordinates"], + metadata_file=neurosynth_db["metadata"], + annotations_files=neurosynth_db["features"], +) +neurosynth_dset.save(os.path.join(out_dir, "neurosynth_dataset.pkl.gz")) +print(neurosynth_dset) + +############################################################################### +# Add article abstracts to dataset +# ----------------------------------------------------------------------------- +# This is only possible because Neurosynth uses PMIDs as study IDs. +# +# Make sure you replace the example email address with your own. +neurosynth_dset = download_abstracts(neurosynth_dset, "example@example.edu") +neurosynth_dset.save(os.path.join(out_dir, "neurosynth_dataset_with_abstracts.pkl.gz")) + +############################################################################### +# Do the same with NeuroQuery +# ----------------------------------------------------------------------------- +# NeuroQuery's data files are stored at https://github.com/neuroquery/neuroquery_data. +files = fetch_neuroquery( + data_dir=out_dir, + version="1", + overwrite=False, + source="combined", + vocab="neuroquery7547", + type="tfidf", +) +# Note that the files are saved to a new folder within "out_dir" named "neuroquery". +pprint(files) +neuroquery_db = files[0] + +# Note that the conversion function says "neurosynth". +# This is just for backwards compatibility. +neuroquery_dset = convert_neurosynth_to_dataset( + coordinates_file=neuroquery_db["coordinates"], + metadata_file=neuroquery_db["metadata"], + annotations_files=neuroquery_db["features"], +) +neuroquery_dset.save(os.path.join(out_dir, "neuroquery_dataset.pkl.gz")) +print(neuroquery_dset) + +# NeuroQuery also uses PMIDs as study IDs. +neuroquery_dset = download_abstracts(neuroquery_dset, "example@example.edu") +neuroquery_dset.save(os.path.join(out_dir, "neuroquery_dataset_with_abstracts.pkl.gz")) diff --git a/content/nimare-repo/01_datasets/03_plot_neurovault_io.py b/content/nimare-repo/01_datasets/03_plot_neurovault_io.py new file mode 100644 index 0000000..40560d5 --- /dev/null +++ b/content/nimare-repo/01_datasets/03_plot_neurovault_io.py @@ -0,0 +1,103 @@ +""" + +.. _datasets_neurovault: + +========================================= +Use NeuroVault statistical maps in NiMARE +========================================= + +Download statistical maps from NeuroVault, then use them in a meta-analysis, +with NiMARE. +""" +import matplotlib.pyplot as plt +from nilearn.plotting import plot_stat_map + +############################################################################### +# Neurovault + NiMARE: Load freely shared statistical maps for Meta-Analysis +# ----------------------------------------------------------------------------- +# `Neurovault `_ is an online platform that hosts +# unthresholded statistical maps, including group statistical maps. +# NiMARE can read these statistical maps when given a list of collection_ids. +# I search "working memory" on neurovault, and find these relevant collections: +# +# * `2884 `_ +# * `2621 `_ +# * `3085 `_ +# * `5623 `_ +# * `3264 `_ +# * `3192 `_ +# * `457 `_ +# +# I can load specific statistical maps from these collections +# into a NiMARE dataset: +from nimare.io import convert_neurovault_to_dataset + +# The specific collections I would like to download group level +# statistical maps from +collection_ids = (2884, 2621, 3085, 5623, 3264, 3192, 457) + +# A mapping between what I want the contrast(s) to be +# named in the dataset and what their respective group +# statistical maps are named on neurovault +contrasts = { + "working_memory": ( + "Working memory load of 2 faces versus 1 face - NT2_Tstat|" + "t-value contrast 2-back minus 0-back|" + "Searchlight multivariate Decoding 2: visual working memory|" + "Context-dependent group-specific WM information|" + "WM working memory zstat1|" + "WM task over CRT task map|" + "tfMRI WM 2BK PLACE zstat1" + ) +} + +# Convert how the statistical maps on neurovault are represented +# in a NiMARE dataset. +map_type_conversion = {"Z map": "z", "T map": "t"} + +dset = convert_neurovault_to_dataset( + collection_ids, + contrasts, + img_dir=None, + map_type_conversion=map_type_conversion, +) + +############################################################################### +# Conversion of Statistical Maps +# ----------------------------------------------------------------------------- +# Some of the statistical maps are T statistics and others are Z statistics. +# To perform a Fisher's meta analysis, we need all Z maps. +# Thoughtfully, NiMARE has a class named ``ImageTransformer`` that will +# help us. +from nimare.transforms import ImageTransformer + +# Not all studies have Z maps! +dset.images[["z"]] + +############################################################################### +z_transformer = ImageTransformer(target="z") +dset = z_transformer.transform(dset) + +############################################################################### +# All studies now have Z maps! +dset.images[["z"]] + +############################################################################### +# Run a Meta-Analysis +# ----------------------------------------------------------------------------- +# With the missing Z maps filled in, we can run a Meta-Analysis +# and plot our results +from nimare.meta.ibma import Fishers + +# The default template has a slightly different, but completely compatible, +# affine than the NeuroVault images, so we allow the Estimator to resample +# images during the fitting process. +meta = Fishers(resample=True) + +meta_res = meta.fit(dset) + +fig, ax = plt.subplots() +display = plot_stat_map(meta_res.get_map("z"), threshold=3.3, axes=ax, figure=fig) +fig.show() +# The result may look questionable, but this code provides +# a template on how to use neurovault in your meta analysis. diff --git a/content/nimare-repo/01_datasets/04_transform_images_to_coordinates.py b/content/nimare-repo/01_datasets/04_transform_images_to_coordinates.py new file mode 100644 index 0000000..67d93cb --- /dev/null +++ b/content/nimare-repo/01_datasets/04_transform_images_to_coordinates.py @@ -0,0 +1,197 @@ +""" + +.. _datasets_imgs_to_coords: + +================================= +Transform images into coordinates +================================= + +Create a dataset with coordinates derived from peak statistic identification in images. + +Why would you want to do this? + +* Compare CBMA and IBMA +* Add more studies to your existing CBMA dataset +* Normalize how coordinates were derived (provided the image data is available) +""" +import os + +import matplotlib.pyplot as plt +from nilearn.plotting import plot_stat_map + +from nimare.dataset import Dataset +from nimare.extract import download_nidm_pain +from nimare.meta.cbma import ALE +from nimare.transforms import ImagesToCoordinates, ImageTransformer +from nimare.utils import get_resource_path + +############################################################################### +# Download data +# ----------------------------------------------------------------------------- +dset_dir = download_nidm_pain() + +############################################################################### +# Load Dataset +# ----------------------------------------------------------------------------- +dset_file = os.path.join(get_resource_path(), "nidm_pain_dset.json") +dset = Dataset(dset_file) +dset.update_path(dset_dir) + +# ImagesToCoordinates uses z or p statistical maps +z_transformer = ImageTransformer(target="z") +dset = z_transformer.transform(dset) + +study_no_images = "pain_02.nidm-1" +# delete images for study +dset.images = dset.images.query(f"id != '{study_no_images}'") + +study_no_coordinates = "pain_03.nidm-1" + +# delete coordinates for study +dset.coordinates = dset.coordinates.query(f"id != '{study_no_coordinates}'") + + +############################################################################### +# Inspect Dataset +# ----------------------------------------------------------------------------- + +# There is only one study contrast with coordinates, but no images +print(f"studies with only coordinates: {set(dset.coordinates['id']) - set(dset.images['id'])}\n") + +print(f"studies with only images: {set(dset.images['id']) - set(dset.coordinates['id'])}\n") + +# the images dataframe has z maps as one of the columns +print(f"columns in images dataframe: {dset.images.columns}\n") + +# there is no z_stat column in the coordinates dataframe +print(f"columns in coordinates dataframe: {dset.coordinates.columns}\n") + + +############################################################################### +# Use different strategies to overwrite existing coordinate data +# ----------------------------------------------------------------------------- +# There are three choices for how to treat existing coordinate +# data in the dataset which are named: 'fill', 'replace', and 'demolish'. +# +# * 'fill' will only create coordinates for study contrasts with images, but +# no coordinates. With 'fill' you trust and want to keep all +# existing coordinate data and the transformer will help "fill" in +# the blanks for study contrasts with no coordinates +# * 'replace' will create coordinates for study contrasts with images. +# In addition to filling in the blanks, 'replace' will overwrite existing +# coordinate data if images are available. +# However, if image data is not available and only coordinates exist +# for a particular study contrast, those coordinates will be retained +# in the resulting dataset. +# With 'replace', you prefer to have coordinates generated consistently +# by NiMARE, but you will keep other coordinate data if that particular +# study contrast does not have images. +# * 'demolish' will create coordinates for study contrasts with images +# and remove any coordinates from the dataset it cannot overwrite. +# With 'demolish', you only trust coordinates generated by NiMARE. + +# create coordinates from statistical maps +coord_fill = ImagesToCoordinates(merge_strategy="fill") +coord_replace = ImagesToCoordinates(merge_strategy="replace") +coord_demolish = ImagesToCoordinates(merge_strategy="demolish") + +dset_fill = coord_fill.transform(dset) +dset_replace = coord_replace.transform(dset) +dset_demolish = coord_demolish.transform(dset) + +############################################################################### +# Inspect generated datasets +# ----------------------------------------------------------------------------- + +example_study = "pain_01.nidm-1" + +print(f"no coordinate data for {study_no_coordinates}") +assert study_no_coordinates not in dset.coordinates["id"] + +# 'fill' will add coordinates for study without coordinates +print(f"'fill' strategy for study {study_no_coordinates}") +print(dset_fill.coordinates.query(f"id == '{study_no_coordinates}'")) +print("\n\n") + + +# 'replace' will change the data for studies with images +print(f"original data for study {example_study}") +print(dset.coordinates.query(f"id == '{example_study}'")) +print(f"'replace' strategy for study {example_study}") +print(dset_replace.coordinates.query(f"id == '{example_study}'")) + +# 'demolish' will remove studies that do not have images +print(f"'demolish' strategy for study {study_no_images}") +assert study_no_images not in dset.coordinates["id"] + +# while studies with only coordinates (no images) are in 'replace', +# they are removed from 'demolish'. +print( + "studies in 'replace', but not 'demolish': " + f"{set(dset_replace.coordinates['id']) - set(dset_demolish.coordinates['id'])}" +) + +############################################################################### +# ALE (CBMA) +# ----------------------------------------------------------------------------- +# Run a meta analysis using each of the strategies. +# The biggest difference is between 'fill' and the other two strategies. +# The difference is because in 'fill' most of the original coordinates +# in the dataset are used, whereas with 'replace' and 'demolish' the +# majority/all of the coordinates are generated by NiMARE. + +ale = ALE() +res_fill = ale.fit(dset_fill) +res_replace = ale.fit(dset_replace) +res_demolish = ale.fit(dset_demolish) +fig, axs = plt.subplots(3, figsize=(6, 8)) +for ax, strat, res in zip( + axs, ["fill", "replace", "demolist"], [res_fill, res_replace, res_demolish] +): + plot_stat_map( + res.get_map("z"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + axes=ax, + title=f"'{strat}' strategy", + ) + +fig.show() + + +############################################################################### +# Tracking positive and negative z-scores +# ----------------------------------------------------------------------------- +# There is a new column in the transformed coordinates, ``z_stat``. +# This column contains the z-score of the individual peak. +# Currently, no CBMA algorithm implemented in NiMARE takes advantage +# of z-scores, but we can still take advantage of whether the peak was positive +# or negative by running a CBMA on positive and negative z-scores separately, +# testing the convergence of positive and negative z-scores separately. + +coord_two_sided = ImagesToCoordinates(merge_strategy="demolish", two_sided=True) + +dset_two_sided = coord_two_sided.transform(dset) + +dset_positive = dset_two_sided.copy() +dset_negative = dset_two_sided.copy() +dset_positive.coordinates = dset_two_sided.coordinates.query("z_stat >= 0.0") +dset_negative.coordinates = dset_two_sided.coordinates.query("z_stat < 0.0") + +# plot the results +ale = ALE() +res_positive = ale.fit(dset_positive) +res_negative = ale.fit(dset_negative) +fig, axs = plt.subplots(2, figsize=(6, 6)) +for ax, sign, res in zip(axs, ["positive", "negative"], [res_positive, res_negative]): + plot_stat_map( + res.get_map("z"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + axes=ax, + title=f"'{sign}' z-scores", + ) + +fig.show() diff --git a/content/nimare-repo/01_datasets/README.txt b/content/nimare-repo/01_datasets/README.txt new file mode 100644 index 0000000..737dc13 --- /dev/null +++ b/content/nimare-repo/01_datasets/README.txt @@ -0,0 +1,12 @@ +.. _examples-datasets: + +Working with datasets +--------------------- + +NiMARE stores meta-analytic data in its :class:`~nimare.dataset.Dataset` class. +Dataset objects may contain a range of elements, including coordinates (for coordinate-based meta-analysis), +links to statistical maps (for image-based meta-analysis), article text, label weights, and other metadata. + +Additionally, NiMARE contains fetching and conversion tools for a number of meta-analytic resources, +including Neurosynth, NeuroQuery, NeuroVault, and, to a limited extent, BrainMap. +In the examples below, we show what a Dataset can do and exhibit tools for working with data from external meta-analytic resources. diff --git a/content/nimare-repo/02_meta-analyses/01_plot_cbma.py b/content/nimare-repo/02_meta-analyses/01_plot_cbma.py new file mode 100644 index 0000000..db40d13 --- /dev/null +++ b/content/nimare-repo/02_meta-analyses/01_plot_cbma.py @@ -0,0 +1,183 @@ +""" + +.. _metas_cbma: + +========================================= +Coordinate-based meta-analysis algorithms +========================================= + +A tour of CBMA algorithms in NiMARE. + +This tutorial is intended to provide a brief description and example of each of +the CBMA algorithms implemented in NiMARE. +For a more detailed introduction to the elements of a coordinate-based +meta-analysis, see other stuff. +""" +############################################################################### +# Load Dataset +# ----------------------------------------------------------------------------- +# .. note:: +# The data used in this example come from a collection of NIDM-Results packs +# downloaded from Neurovault collection 1425, uploaded by Dr. Camille Maumet. +# +# Creation of the Dataset from the NIDM-Results packs was done with custom +# code. The Results packs for collection 1425 are not completely +# NIDM-Results-compliant, so the nidmresults library could not be used to +# facilitate data extraction. +import os + +from nilearn.plotting import plot_stat_map + +from nimare.correct import FWECorrector +from nimare.dataset import Dataset +from nimare.utils import get_resource_path + +dset_file = os.path.join(get_resource_path(), "nidm_pain_dset.json") +dset = Dataset(dset_file) + +# Some of the CBMA algorithms compare two Datasets, +# so we'll split this example Dataset in half. +dset1 = dset.slice(dset.ids[:10]) +dset2 = dset.slice(dset.ids[10:]) + +############################################################################### +# Multilevel Kernel Density Analysis +# ----------------------------------------------------------------------------- +from nimare.meta.cbma.mkda import MKDADensity + +meta = MKDADensity() +results = meta.fit(dset) + +corr = FWECorrector(method="montecarlo", n_iters=10, n_cores=1) +cres = corr.transform(results) + +plot_stat_map( + results.get_map("z"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + threshold=0.1, +) +plot_stat_map( + cres.get_map("z_level-voxel_corr-FWE_method-montecarlo"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + threshold=0.1, +) + +############################################################################### +# MKDA Chi-Squared +# ----------------------------------------------------------------------------- +from nimare.meta.cbma.mkda import MKDAChi2 + +meta = MKDAChi2(kernel__r=10) +results = meta.fit(dset1, dset2) + +corr = FWECorrector(method="montecarlo", n_iters=10, n_cores=1) +cres = corr.transform(results) + +plot_stat_map( + results.get_map("z_desc-consistency"), + draw_cross=False, + cmap="RdBu_r", + threshold=0.1, +) +plot_stat_map( + cres.get_map("z_desc-consistencySize_level-cluster_corr-FWE_method-montecarlo"), + draw_cross=False, + cmap="RdBu_r", + threshold=0.1, +) + +############################################################################### +# Kernel Density Analysis +# ----------------------------------------------------------------------------- +from nimare.meta.cbma.mkda import KDA + +meta = KDA() +results = meta.fit(dset) + +corr = FWECorrector(method="montecarlo", n_iters=10, n_cores=1) +cres = corr.transform(results) + +plot_stat_map( + results.get_map("z"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + threshold=0.1, +) +plot_stat_map( + cres.get_map("z_desc-size_level-cluster_corr-FWE_method-montecarlo"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + threshold=0.1, +) + +############################################################################### +# Activation Likelihood Estimation +# ----------------------------------------------------------------------------- +from nimare.meta.cbma.ale import ALE + +meta = ALE() +results = meta.fit(dset) + +corr = FWECorrector(method="montecarlo", n_iters=10, n_cores=1) +cres = corr.transform(results) + +plot_stat_map( + results.get_map("z"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + threshold=0.1, +) +plot_stat_map( + cres.get_map("z_desc-size_level-cluster_corr-FWE_method-montecarlo"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + threshold=0.1, +) + +############################################################################### +# Specific Co-Activation Likelihood Estimation +# ----------------------------------------------------------------------------- +# +# .. important:: +# +# The SCALE algorithm is very memory intensive, so we don't run it within the +# documentation. +# +# .. code-block:: python +# +# import numpy as np +# +# from nimare.meta.cbma.ale import SCALE +# from nimare.utils import vox2mm +# +# xyz = vox2mm( +# np.vstack(np.where(dset.masker.mask_img.get_fdata())).T, +# dset.masker.mask_img.affine, +# ) +# +# meta = SCALE(xyz=xyz, n_iters=10) +# results = meta.fit(dset) + +############################################################################### +# ALE-Based Subtraction Analysis +# ----------------------------------------------------------------------------- +from nimare.meta.cbma.ale import ALESubtraction + +meta = ALESubtraction(n_iters=10, n_cores=1) +results = meta.fit(dset1, dset2) + +plot_stat_map( + results.get_map("z_desc-group1MinusGroup2"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + threshold=0.1, +) diff --git a/content/nimare-repo/02_meta-analyses/02_plot_ibma.py b/content/nimare-repo/02_meta-analyses/02_plot_ibma.py new file mode 100644 index 0000000..c984deb --- /dev/null +++ b/content/nimare-repo/02_meta-analyses/02_plot_ibma.py @@ -0,0 +1,157 @@ +""" + +.. _metas_ibma: + +==================================== +Image-based meta-analysis algorithms +==================================== + +A tour of IBMA algorithms in NiMARE. + +This tutorial is intended to provide a brief description and example of each of +the IBMA algorithms implemented in NiMARE. +For a more detailed introduction to the elements of an image-based +meta-analysis, see other stuff. +""" +from nilearn.plotting import plot_stat_map + +############################################################################### +# Download data +# ----------------------------------------------------------------------------- +# .. note:: +# The data used in this example come from a collection of NIDM-Results packs +# downloaded from Neurovault collection 1425, uploaded by Dr. Camille Maumet. +from nimare.extract import download_nidm_pain + +dset_dir = download_nidm_pain() + +############################################################################### +# Load Dataset +# ----------------------------------------------------------------------------- +import os + +from nimare.dataset import Dataset +from nimare.transforms import ImageTransformer +from nimare.utils import get_resource_path + +dset_file = os.path.join(get_resource_path(), "nidm_pain_dset.json") +dset = Dataset(dset_file) +dset.update_path(dset_dir) + +# Calculate missing images +xformer = ImageTransformer(target=["varcope", "z"]) +dset = xformer.transform(dset) + +############################################################################### +# Stouffer's +# ----------------------------------------------------------------------------- +from nimare.meta.ibma import Stouffers + +meta = Stouffers(use_sample_size=False, resample=True) +results = meta.fit(dset) + +plot_stat_map( + results.get_map("z"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", +) + +############################################################################### +# Stouffer's with weighting by sample size +# ----------------------------------------------------------------------------- +meta = Stouffers(use_sample_size=True, resample=True) +results = meta.fit(dset) + +plot_stat_map( + results.get_map("z"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", +) + +############################################################################### +# Fisher's +# ----------------------------------------------------------------------------- +from nimare.meta.ibma import Fishers + +meta = Fishers(resample=True) +results = meta.fit(dset) + +plot_stat_map( + results.get_map("z"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", +) + +############################################################################### +# Permuted OLS +# ----------------------------------------------------------------------------- +from nimare.correct import FWECorrector +from nimare.meta.ibma import PermutedOLS + +meta = PermutedOLS(two_sided=True, resample=True) +results = meta.fit(dset) + +plot_stat_map( + results.get_map("z"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", +) + +corrector = FWECorrector(method="montecarlo", n_iters=100, n_cores=1) +cresult = corrector.transform(results) + +plot_stat_map( + cresult.get_map("z_level-voxel_corr-FWE_method-montecarlo"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", +) + +############################################################################### +# Weighted Least Squares +# ----------------------------------------------------------------------------- +from nimare.meta.ibma import WeightedLeastSquares + +meta = WeightedLeastSquares(tau2=0, resample=True) +results = meta.fit(dset) + +plot_stat_map( + results.get_map("z"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", +) + +############################################################################### +# DerSimonian-Laird +# ----------------------------------------------------------------------------- +from nimare.meta.ibma import DerSimonianLaird + +meta = DerSimonianLaird(resample=True) +results = meta.fit(dset) + +plot_stat_map( + results.get_map("z"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", +) + +############################################################################### +# Hedges +# ----------------------------------------------------------------------------- +from nimare.meta.ibma import Hedges + +meta = Hedges(resample=True) +results = meta.fit(dset) + +plot_stat_map( + results.get_map("z"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", +) diff --git a/content/nimare-repo/02_meta-analyses/03_plot_kernel_transformers.py b/content/nimare-repo/02_meta-analyses/03_plot_kernel_transformers.py new file mode 100644 index 0000000..50a162f --- /dev/null +++ b/content/nimare-repo/02_meta-analyses/03_plot_kernel_transformers.py @@ -0,0 +1,153 @@ +""" + +.. _metas_kernels: + +=========================== +KernelTransformers and CBMA +=========================== + +``KernelTransformer`` classes are tools for converting individual studies' +coordinates into images. + +For coordinate-based meta-analyses, individual studies' statistical maps are +mimicked by generating "modeled activation" (MA) maps from the coordinates. +These MA maps are used in the CBMA algorithms, although the specific method +used to generate the MA maps differs by algorithm. + +This example provides an introduction to the ``KernelTransformer`` class and +a tour of available types. +""" +# sphinx_gallery_thumbnail_number = 2 +import os + +import matplotlib.pyplot as plt +from nilearn.plotting import plot_stat_map + +############################################################################### +# Load Dataset +# ----------------------------------------------------------------------------- +from nimare.dataset import Dataset +from nimare.utils import get_resource_path + +dset_file = os.path.join(get_resource_path(), "nidm_pain_dset.json") +dset = Dataset(dset_file) + +# First, let us reduce this Dataset to only two studies +dset = dset.slice(dset.ids[2:4]) + +############################################################################### +# Kernels ingest Datasets and can produce a few types of outputs +# ----------------------------------------------------------------------------- +from nimare.meta.kernel import MKDAKernel + +# First, the kernel should be initialized with any parameters. +kernel = MKDAKernel() + +# Then, the ``transform`` method takes in the Dataset and produces the MA maps. +output = kernel.transform(dset) + +############################################################################### +# ``return_type="image"`` returns a list of 3D niimg objects. +# +# This is the default option. +image_output = kernel.transform(dset, return_type="image") +print(type(image_output)) +print(type(image_output[0])) +print(image_output[0].shape) + +############################################################################### +# ``return_type="array"`` returns a 2D numpy array +array_output = kernel.transform(dset, return_type="array") +print(type(array_output)) +print(array_output.shape) + +############################################################################### +# There is also an option to return an updated Dataset +# (``return_type="dataset"``), with the MA maps saved as nifti files and +# references in the Dataset's images attribute. +# However, this will only work if the Dataset has a location set for its +# images. +try: + dataset_output = kernel.transform(dset, return_type="dataset") +except ValueError as error: + print(error) + +############################################################################### +# Each kernel can accept certain parameters that control behavior +# ----------------------------------------------------------------------------- +# You can see what options are available via the API documentation or through +# the help string. +help(MKDAKernel) + +############################################################################### +# For example, :class:`~nimare.meta.kernel.MKDAKernel` kernel accepts an ``r`` +# argument to control the radius of the kernel. +RADIUS_VALUES = [4, 8, 12] +fig, axes = plt.subplots(ncols=3, figsize=(20, 10)) + +for i, radius in enumerate(RADIUS_VALUES): + kernel = MKDAKernel(r=radius) + ma_maps = kernel.transform(dset, return_type="image") + + plot_stat_map( + ma_maps[0], + display_mode="z", + cut_coords=[-2], + title=f"r={radius}mm", + axes=axes[i], + draw_cross=False, + annotate=False, + colorbar=False, + cmap="RdBu_r", + ) + +############################################################################### +# There are several kernels available +# ----------------------------------------------------------------------------- +# :class:`~nimare.meta.kernel.MKDAKernel` convolves coordinates with a +# sphere and takes the union across voxels. +kernel = MKDAKernel(r=10) +ma_maps = kernel.transform(dset, return_type="image") + +plot_stat_map( + ma_maps[0], + cut_coords=[-2, -10, -4], + title="MKDA", + draw_cross=False, + cmap="RdBu_r", +) + +############################################################################### +# :class:`~nimare.meta.kernel.KDAKernel` convolves coordinates with a +# sphere as well, but takes the *sum* across voxels. +from nimare.meta.kernel import KDAKernel + +kernel = KDAKernel(r=10) +ma_maps = kernel.transform(dset, return_type="image") + +plot_stat_map( + ma_maps[0], + cut_coords=[-2, -10, -4], + title="KDA", + draw_cross=False, + cmap="RdBu_r", +) + +############################################################################### +# :class:`~nimare.meta.kernel.ALEKernel` convolves coordinates with a 3D +# Gaussian, for which the FWHM is determined by the sample size of each study. +# This sample size will be inferred automatically, if that information is +# available in the Dataset, or it can be set as a constant value across all +# studies in the Dataset with the ``sample_size`` argument. +from nimare.meta.kernel import ALEKernel + +kernel = ALEKernel(sample_size=20) +ma_maps = kernel.transform(dset, return_type="image") + +plot_stat_map( + ma_maps[0], + cut_coords=[-2, -10, -4], + title="ALE", + draw_cross=False, + cmap="RdBu_r", +) diff --git a/content/nimare-repo/02_meta-analyses/04_plot_estimators.py b/content/nimare-repo/02_meta-analyses/04_plot_estimators.py new file mode 100644 index 0000000..8e6edb5 --- /dev/null +++ b/content/nimare-repo/02_meta-analyses/04_plot_estimators.py @@ -0,0 +1,159 @@ +""" + +.. _metas_estimators: + +=================== +The Estimator class +=================== + +An introduction to the Estimator class. + +The Estimator class is the base for all meta-analyses in NiMARE. +A general rule of thumb for Estimators is that they ingest Datasets and output +MetaResult objects. +""" +############################################################################### +# Start with the necessary imports +# ----------------------------------------------------------------------------- +import os + +############################################################################### +# Load Dataset +# ----------------------------------------------------------------------------- +from nimare.dataset import Dataset +from nimare.utils import get_resource_path + +dset_file = os.path.join(get_resource_path(), "nidm_pain_dset.json") +dset = Dataset(dset_file) + +# We will reduce the Dataset to the first 10 studies +dset = dset.slice(dset.ids[:10]) + +############################################################################### +# The Estimator +# ----------------------------------------------------------------------------- +from nimare.meta.cbma.ale import ALE + +# First, the Estimator should be initialized with any parameters. +meta = ALE() + +# Then, the ``fit`` method takes in the Dataset and produces a MetaResult. +results = meta.fit(dset) + +############################################################################### +# Coordinate-based Estimators allow you to provide a specific KernelTransformer +# ----------------------------------------------------------------------------- +# Each CBMA Estimator's default KernelTransformer will always be the most +# appropriate type for that algorithm, but you can swap out the kernel as you +# wish. +# +# For example, an ALE Estimator could be initialized with an MKDAKernel, +# though there is no guarantee that the results would make sense. +from nimare.meta.kernel import MKDAKernel + +meta = ALE(kernel_transformer=MKDAKernel) +results = meta.fit(dset) + +############################################################################### +from nilearn.plotting import plot_stat_map + +plot_stat_map(results.get_map("z"), draw_cross=False, cmap="RdBu_r") + +############################################################################### +# CBMA Estimators can accept KernelTransformers a few different ways +# ----------------------------------------------------------------------------- +from nimare.meta.kernel import ALEKernel + +############################################################################### +# Initializing the Estimator with a KernelTransformer class alone will use +# its default settings. +meta = ALE(kernel_transformer=ALEKernel) +print(meta.kernel_transformer) + +############################################################################### +# You can also initialize the Estimator with an initialized KernelTransformer +# object. +kernel = ALEKernel() +meta = ALE(kernel_transformer=kernel) +print(meta.kernel_transformer) + +############################################################################### +# This is especially useful if you want to initialize the KernelTransformer +# with parameters with non-default values. +kernel = ALEKernel(sample_size=20) +meta = ALE(kernel_transformer=kernel) +print(meta.kernel_transformer) + +############################################################################### +# You can also provide specific initialization values to the KernelTransformer +# via the Estimator, by including keyword arguments starting with ``kernel__``. +meta = ALE(kernel__sample_size=20) +print(meta.kernel_transformer) + +###################################################################################### +# .. _null-method-example: +# +# Most CBMA Estimators have multiple ways to test uncorrected statistical significance +# ------------------------------------------------------------------------------------ +# For most Estimators, the two options, defined with the ``null_method`` +# parameter, are ``"approximate"`` and ``"montecarlo"``. +# For more information about these options, see :ref:`null methods`. +meta = ALE(null_method="approximate") +results = meta.fit(dset) + +###################################################################################### +# Note that, to measure significance appropriately with the montecarlo method, +# you need a lot more than 10 iterations. +# We recommend 10000 (the default value). +mc_meta = ALE(null_method="montecarlo", n_iters=10, n_cores=1) +mc_results = mc_meta.fit(dset) + +############################################################################### +# The null distributions are stored within the Estimators +# ````````````````````````````````````````````````````````````````````````````` +from pprint import pprint + +pprint(meta.null_distributions_) + +############################################################################### +# As well as the MetaResult, which stores a copy of the Estimator +pprint(results.estimator.null_distributions_) + +############################################################################### +# The null distributions also differ based on the null method. +# For example, the ``"montecarlo"`` option creates a +# ``histweights_corr-none_method-montecarlo`` distribution, instead of the +# ``histweights_corr-none_method-approximate`` produced by the +# ``"approximate"`` method. +pprint(mc_meta.null_distributions_) + +############################################################################### +import matplotlib.pyplot as plt +import seaborn as sns + +with sns.axes_style("whitegrid"): + fig, axes = plt.subplots(figsize=(8, 8), sharex=True, nrows=3) + sns.histplot( + x=meta.null_distributions_["histogram_bins"], + weights=meta.null_distributions_["histweights_corr-none_method-approximate"], + bins=100, + ax=axes[0], + ) + axes[0].set_xlim(0, None) + axes[0].set_title("Approximate Null Distribution") + sns.histplot( + x=mc_meta.null_distributions_["histogram_bins"], + weights=mc_meta.null_distributions_["histweights_corr-none_method-montecarlo"], + bins=100, + ax=axes[1], + ) + axes[1].set_title("Monte Carlo Null Distribution") + sns.histplot( + x=mc_meta.null_distributions_["histogram_bins"], + weights=mc_meta.null_distributions_["histweights_level-voxel_corr-fwe_method-montecarlo"], + bins=100, + ax=axes[2], + ) + axes[2].set_title("Monte Carlo Voxel-Level FWE Null Distribution") + axes[2].set_xlabel("ALE Value") + fig.tight_layout() diff --git a/content/nimare-repo/02_meta-analyses/05_plot_correctors.py b/content/nimare-repo/02_meta-analyses/05_plot_correctors.py new file mode 100644 index 0000000..1cdd0d8 --- /dev/null +++ b/content/nimare-repo/02_meta-analyses/05_plot_correctors.py @@ -0,0 +1,167 @@ +""" + +.. _metas_correctors: + +=================== +The Corrector class +=================== + +Here we take a look at multiple comparisons correction in meta-analyses. +""" + +import matplotlib.pyplot as plt +import seaborn as sns +from nilearn.plotting import plot_stat_map + +############################################################################### +# Download data +# ----------------------------------------------------------------------------- +from nimare.extract import download_nidm_pain + +dset_dir = download_nidm_pain() + +############################################################################### +# Load Dataset +# ----------------------------------------------------------------------------- +import os + +from nimare.dataset import Dataset +from nimare.utils import get_resource_path + +dset_file = os.path.join(get_resource_path(), "nidm_pain_dset.json") +dset = Dataset(dset_file) +dset.update_path(dset_dir) + +mask_img = dset.masker.mask_img + +############################################################################### +# .. _corrector-cbma-example: +# +# Multiple comparisons correction in coordinate-based meta-analyses +# ----------------------------------------------------------------------------- +# .. tip:: +# For more information multiple comparisons correction and CBMA in NiMARE, +# see :ref:`multiple comparisons correction`. +from nimare.meta.cbma.ale import ALE + +# First, we need to fit the Estimator to the Dataset. +meta = ALE(null_method="approximate") +results = meta.fit(dset) + +# We can check which FWE correction methods are available for the ALE Estimator +# with the ``inspect`` class method. +from nimare.correct import FWECorrector + +print(FWECorrector.inspect(results)) + +############################################################################### +# Apply the Corrector to the MetaResult +# ============================================================================= +# Now that we know what FWE correction methods are available, we can use one. +# +# The "montecarlo" method is a special one that is implemented within the +# Estimator, rather than in the Corrector. +corr = FWECorrector(method="montecarlo", n_iters=50, n_cores=2) +cres = corr.transform(results) + +DISTS_TO_PLOT = [ + "values_desc-size_level-cluster_corr-fwe_method-montecarlo", + "values_desc-mass_level-cluster_corr-fwe_method-montecarlo", + "values_level-voxel_corr-fwe_method-montecarlo", +] +XLABELS = [ + "Maximum Cluster Size (Voxels)", + "Maximum Cluster Mass", + "Maximum Summary Statistic (ALE Value)", +] + +fig, axes = plt.subplots(figsize=(8, 8), nrows=3) +null_dists = cres.estimator.null_distributions_ + +for i_ax, dist_name in enumerate(DISTS_TO_PLOT): + xlabel = XLABELS[i_ax] + sns.histplot(x=null_dists[dist_name], bins=40, ax=axes[i_ax]) + axes[i_ax].set_title(dist_name) + axes[i_ax].set_xlabel(xlabel) + axes[i_ax].set_xlim(0, None) + +fig.tight_layout() + +############################################################################### +# Show corrected results +# ============================================================================= +MAPS_TO_PLOT = [ + "z", + "z_desc-size_level-cluster_corr-FWE_method-montecarlo", + "z_desc-mass_level-cluster_corr-FWE_method-montecarlo", + "z_level-voxel_corr-FWE_method-montecarlo", +] +TITLES = [ + "Uncorrected z-statistics", + "Cluster-size FWE-corrected z-statistics", + "Cluster-mass FWE-corrected z-statistics", + "Voxel-level FWE-corrected z-statistics", +] + +fig, axes = plt.subplots(figsize=(8, 10), nrows=4) + +for i_ax, map_name in enumerate(MAPS_TO_PLOT): + title = TITLES[i_ax] + plot_stat_map( + cres.get_map(map_name), + draw_cross=False, + cmap="RdBu_r", + threshold=0.5, + cut_coords=[0, 0, -8], + figure=fig, + axes=axes[i_ax], + ) + axes[i_ax].set_title(title) + +############################################################################### +# Multiple comparisons correction in image-based meta-analyses +# ----------------------------------------------------------------------------- +from nimare.correct import FDRCorrector +from nimare.meta.ibma import Stouffers + +meta = Stouffers(resample=True) +results = meta.fit(dset) +print(f"FWECorrector options: {FWECorrector.inspect(results)}") +print(f"FDRCorrector options: {FDRCorrector.inspect(results)}") + +############################################################################### +# Note that the FWECorrector does not support a "montecarlo" method for the +# Stouffers Estimator. +# This is because NiMARE does not have a Monte Carlo-based method implemented +# for most IBMA algorithms. + +############################################################################### +# Apply the Corrector to the MetaResult +# ============================================================================= +corr = FDRCorrector(method="indep", alpha=0.05) +cres = corr.transform(results) + +############################################################################### +# Show corrected results +# ============================================================================= +fig, axes = plt.subplots(figsize=(8, 6), nrows=2) +plot_stat_map( + cres.get_map("z"), + draw_cross=False, + cmap="RdBu_r", + threshold=0.5, + cut_coords=[0, 0, -8], + figure=fig, + axes=axes[0], +) +axes[0].set_title("Uncorrected z-statistics") +plot_stat_map( + cres.get_map("z_corr-FDR_method-indep"), + draw_cross=False, + cmap="RdBu_r", + threshold=0.5, + cut_coords=[0, 0, -8], + figure=fig, + axes=axes[1], +) +axes[1].set_title("FDR-corrected z-statistics") diff --git a/content/nimare-repo/02_meta-analyses/06_plot_compare_ibma_and_cbma.py b/content/nimare-repo/02_meta-analyses/06_plot_compare_ibma_and_cbma.py new file mode 100644 index 0000000..3e184ca --- /dev/null +++ b/content/nimare-repo/02_meta-analyses/06_plot_compare_ibma_and_cbma.py @@ -0,0 +1,80 @@ +""" + +.. _metas_cbma_vs_ibma: + +================================================ +Compare image and coordinate based meta-analyses +================================================ + +Run IBMAs and CBMAs on a toy dataset, then compare the results qualitatively. + +Collection of NIDM-Results packs downloaded from Neurovault collection 1425, +uploaded by Dr. Camille Maumet. +""" +import os + +import pandas as pd +from nilearn.plotting import plot_stat_map + +from nimare.dataset import Dataset +from nimare.extract import download_nidm_pain +from nimare.meta.cbma import ALE +from nimare.meta.ibma import DerSimonianLaird +from nimare.transforms import ImagesToCoordinates, ImageTransformer +from nimare.utils import get_resource_path + +############################################################################### +# Download data +# ----------------------------------------------------------------------------- +dset_dir = download_nidm_pain() + +############################################################################### +# Load Dataset +# ----------------------------------------------------------------------------- +dset_file = os.path.join(get_resource_path(), "nidm_pain_dset.json") +dset = Dataset(dset_file) +dset.update_path(dset_dir) + +# Calculate missing statistical images from the available stats. +xformer = ImageTransformer(target=["varcope"]) +dset = xformer.transform(dset) + +# create coordinates from statistical maps +coord_gen = ImagesToCoordinates(merge_strategy="fill") +dset = coord_gen.transform(dset) + +############################################################################### +# ALE (CBMA) +# ----------------------------------------------------------------------------- +meta_cbma = ALE() +cbma_results = meta_cbma.fit(dset) +plot_stat_map( + cbma_results.get_map("z"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", +) + +############################################################################### +# DerSimonian-Laird (IBMA) +# ----------------------------------------------------------------------------- +# We must resample the image data to the same MNI template as the Dataset. +meta_ibma = DerSimonianLaird(resample=True) +ibma_results = meta_ibma.fit(dset) +plot_stat_map( + ibma_results.get_map("z"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", +) + +############################################################################### +# Compare CBMA and IBMA Z-maps +# ----------------------------------------------------------------------------- +stat_df = pd.DataFrame( + { + "CBMA": cbma_results.get_map("z", return_type="array"), + "IBMA": ibma_results.get_map("z", return_type="array"), + } +) +print(stat_df.corr()) diff --git a/content/nimare-repo/02_meta-analyses/07_macm.py b/content/nimare-repo/02_meta-analyses/07_macm.py new file mode 100644 index 0000000..ee331d3 --- /dev/null +++ b/content/nimare-repo/02_meta-analyses/07_macm.py @@ -0,0 +1,88 @@ +""" + +.. _metas_macm: + +============================================ +Meta-analytic coactivation modeling analysis +============================================ + +Perform a MACM analysis with Neurosynth data. + +Meta-analytic coactivation modeling (MACM) is a common coordinate-based +analysis in which task-independent "connectivity" is assessed by selecting +studies within a larger database based on locations of report coordinates. +""" +import nibabel as nib +import numpy as np +from nilearn import datasets, image, plotting + +from nimare.correct import FWECorrector +from nimare.dataset import Dataset +from nimare.meta.cbma.ale import SCALE +from nimare.meta.cbma.mkda import MKDAChi2 + +############################################################################### +# Load Dataset +# ----------------------------------------------------------------------------- +# We will assume that the Neurosynth database has already been downloaded and +# converted to a NiMARE dataset. +dset_file = "neurosynth_nimare_with_abstracts.pkl.gz" +dset = Dataset.load(dset_file) + +############################################################################### +# Define a region of interest +# ----------------------------------------------------------------------------- +# We'll use the right amygdala from the Harvard-Oxford atlas +atlas = datasets.fetch_atlas_harvard_oxford("sub-maxprob-thr50-2mm") +img = nib.load(atlas["maps"]) + +roi_idx = atlas["labels"].index("Right Amygdala") +img_vals = np.unique(img.get_fdata()) +roi_val = img_vals[roi_idx] +roi_img = image.math_img(f"img1 == {roi_val}", img1=img) + +############################################################################### +# Select studies with a reported coordinate in the ROI +# ----------------------------------------------------------------------------- +roi_ids = dset.get_studies_by_mask(roi_img) +dset_sel = dset.slice(roi_ids) +print(f"{len(roi_ids)}/{len(dset.ids)} studies report at least one coordinate in the ROI") + +############################################################################### +# Select studies with *no* reported coordinates in the ROI +# ----------------------------------------------------------------------------- +no_roi_ids = list(set(dset.ids).difference(roi_ids)) +dset_unsel = dset.slice(no_roi_ids) +print(f"{len(no_roi_ids)}/{len(dset.ids)} studies report zero coordinates in the ROI") + + +############################################################################### +# MKDA Chi2 with FWE correction +# ----------------------------------------------------------------------------- +mkda = MKDAChi2(kernel__r=10) +results = mkda.fit(dset_sel, dset_unsel) + +corr = FWECorrector(method="montecarlo", n_iters=10000) +cres = corr.transform(results) + +# We want the "specificity" map (2-way chi-square between sel and unsel) +plotting.plot_stat_map( + cres.get_map("z_desc-consistency_level-voxel_corr-FWE_method-montecarlo"), + threshold=3.09, + draw_cross=False, + cmap="RdBu_r", +) + +############################################################################### +# SCALE +# ----------------------------------------------------------------------------- +# Another good option for a MACM analysis is the SCALE algorithm, which was +# designed specifically for MACM. Unfortunately, SCALE does not support +# multiple-comparisons correction. + +# First, we must define our null model of reported coordinates in the literature. +# We will use the coordinates in Neurosynth +xyz = dset.coordinates[["x", "y", "z"]].values +scale = SCALE(xyz=xyz, n_iters=10000, n_cores=1, kernel__n=20) +results = scale.fit(dset_sel) +plotting.plot_stat_map(results.get_map("z"), draw_cross=False, cmap="RdBu_r") diff --git a/content/nimare-repo/02_meta-analyses/08_plot_cbma_subtraction_conjunction.py b/content/nimare-repo/02_meta-analyses/08_plot_cbma_subtraction_conjunction.py new file mode 100644 index 0000000..483f765 --- /dev/null +++ b/content/nimare-repo/02_meta-analyses/08_plot_cbma_subtraction_conjunction.py @@ -0,0 +1,168 @@ +""" + +.. _metas_subtraction: + +============================ +Two-sample ALE meta-analysis +============================ + +Meta-analytic projects often involve a number of common steps comparing two or more samples. + +In this example, we replicate the ALE-based analyses from :footcite:t:`enge2021meta`. + +A common project workflow with two meta-analytic samples involves the following: + +1. Run a within-sample meta-analysis of the first sample. +2. Characterize/summarize the results of the first meta-analysis. +3. Run a within-sample meta-analysis of the second sample. +4. Characterize/summarize the results of the second meta-analysis. +5. Compare the two samples with a subtraction analysis. +6. Compare the two within-sample meta-analyses with a conjunction analysis. +""" +import os + +import matplotlib.pyplot as plt +from nilearn.plotting import plot_stat_map + +############################################################################### +# Load Sleuth text files into Datasets +# ----------------------------------------------------------------------------- +# The data for this example are a subset of studies from a meta-analysis on +# semantic cognition in children :footcite:p:`enge2021meta`. +# A first group of studies probed children's semantic world knowledge +# (e.g., correctly naming an object after hearing its auditory description) +# while a second group of studies asked children to decide if two (or more) +# words were semantically related to one another or not. +from nimare.io import convert_sleuth_to_dataset +from nimare.utils import get_resource_path + +knowledge_file = os.path.join(get_resource_path(), "semantic_knowledge_children.txt") +related_file = os.path.join(get_resource_path(), "semantic_relatedness_children.txt") + +knowledge_dset = convert_sleuth_to_dataset(knowledge_file) +related_dset = convert_sleuth_to_dataset(related_file) + +############################################################################### +# Individual group ALEs +# ----------------------------------------------------------------------------- +# Computing separate ALE analyses for each group is not strictly necessary for +# performing the subtraction analysis but will help the experimenter to appreciate the +# similarities and differences between the groups. +from nimare.correct import FWECorrector +from nimare.meta.cbma import ALE + +ale = ALE(null_method="approximate") +knowledge_results = ale.fit(knowledge_dset) +related_results = ale.fit(related_dset) + +corr = FWECorrector(method="montecarlo", voxel_thresh=0.001, n_iters=100, n_cores=2) +knowledge_corrected_results = corr.transform(knowledge_results) +related_corrected_results = corr.transform(related_results) + +fig, axes = plt.subplots(figsize=(12, 10), nrows=2) +knowledge_img = knowledge_corrected_results.get_map( + "z_desc-size_level-cluster_corr-FWE_method-montecarlo" +) +plot_stat_map( + knowledge_img, + cut_coords=4, + display_mode="z", + title="Semantic knowledge", + threshold=2.326, # cluster-level p < .01, one-tailed + cmap="RdBu_r", + vmax=4, + axes=axes[0], + figure=fig, +) + +related_img = related_corrected_results.get_map( + "z_desc-size_level-cluster_corr-FWE_method-montecarlo" +) +plot_stat_map( + related_img, + cut_coords=4, + display_mode="z", + title="Semantic relatedness", + threshold=2.326, # cluster-level p < .01, one-tailed + cmap="RdBu_r", + vmax=4, + axes=axes[1], + figure=fig, +) +fig.show() + +############################################################################### +# Characterize the relative contributions of experiments in the ALE results +# ----------------------------------------------------------------------------- +# NiMARE contains two methods for this: :class:`~nimare.diagnostics.Jackknife` +# and :class:`~nimare.diagnostics.FocusCounter`. +# We will show both below, but for the sake of speed we will only apply one to +# each subgroup meta-analysis. +from nimare.diagnostics import FocusCounter + +counter = FocusCounter( + target_image="z_desc-size_level-cluster_corr-FWE_method-montecarlo", + voxel_thresh=None, +) +knowledge_count_table, _ = counter.transform(knowledge_corrected_results) +knowledge_count_table.head(10) + +############################################################################### +from nimare.diagnostics import Jackknife + +jackknife = Jackknife( + target_image="z_desc-size_level-cluster_corr-FWE_method-montecarlo", + voxel_thresh=None, +) +related_jackknife_table, _ = jackknife.transform(related_corrected_results) +related_jackknife_table.head(10) + +############################################################################### +# Subtraction analysis +# ----------------------------------------------------------------------------- +# Typically, one would use at least 10000 iterations for a subtraction analysis. +# However, we have reduced this to 100 iterations for this example. +from nimare.meta.cbma import ALESubtraction + +sub = ALESubtraction(n_iters=100, n_cores=1) +res_sub = sub.fit(knowledge_dset, related_dset) +img_sub = res_sub.get_map("z_desc-group1MinusGroup2") + +plot_stat_map( + img_sub, + cut_coords=4, + display_mode="z", + title="Subtraction", + cmap="RdBu_r", + vmax=4, +) + +############################################################################### +# Conjunction analysis +# ----------------------------------------------------------------------------- +# To determine the overlap of the meta-analytic results, a conjunction image +# can be computed by (a) identifying voxels that were statistically significant +# in *both* individual group maps and (b) selecting, for each of these voxels, +# the smaller of the two group-specific *z* values :footcite:t:`nichols2005valid`. +# Since this is simple arithmetic on images, conjunction is not implemented as +# a separate method in :code:`NiMARE` but can easily be achieved with +# :func:`nilearn.image.math_img`. +from nilearn.image import math_img + +formula = "np.where(img1 * img2 > 0, np.minimum(img1, img2), 0)" +img_conj = math_img(formula, img1=knowledge_img, img2=related_img) + +plot_stat_map( + img_conj, + cut_coords=4, + display_mode="z", + title="Conjunction", + threshold=2.326, # cluster-level p < .01, one-tailed + cmap="RdBu_r", + vmax=4, +) + +############################################################################### +# References +# ----------------------------------------------------------------------------- +# .. footbibliography:: diff --git a/content/nimare-repo/02_meta-analyses/09_plot_simulated_data.py b/content/nimare-repo/02_meta-analyses/09_plot_simulated_data.py new file mode 100644 index 0000000..87d8185 --- /dev/null +++ b/content/nimare-repo/02_meta-analyses/09_plot_simulated_data.py @@ -0,0 +1,162 @@ +""" + +.. _metas_simulation: + +================================================ +Simulate data for coordinate based meta-analysis +================================================ + +Simulating data before you run your meta-analysis is a great way to test your assumptions and see +how the meta-analysis would perform with simplified data +""" +import matplotlib.pyplot as plt +from nilearn.plotting import plot_stat_map + +from nimare.correct import FDRCorrector +from nimare.generate import create_coordinate_dataset +from nimare.meta import ALE + +############################################################################### +# Create function to perform a meta-analysis and plot results +# ----------------------------------------------------------------------------- + + +def analyze_and_plot(dset, ground_truth_foci=None, correct=True, return_cres=False): + meta = ALE(kernel__fwhm=10) + results = meta.fit(dset) + if correct: + corr = FDRCorrector() + cres = corr.transform(results) + else: + cres = results + + # get the z coordinates + if ground_truth_foci: + stat_map_kwargs = {"cut_coords": [c[2] for c in ground_truth_foci]} + else: + stat_map_kwargs = {} + + fig, ax = plt.subplots() + display = plot_stat_map( + cres.get_map("z"), + display_mode="z", + draw_cross=False, + cmap="Purples", + threshold=2.3, + symmetric_cbar=False, + figure=fig, + axes=ax, + **stat_map_kwargs, + ) + + if ground_truth_foci: + # place red dots indicating the ground truth foci + display.add_markers(ground_truth_foci) + + if return_cres: + return fig, cres + + return fig + + +############################################################################### +# Create Dataset +# ----------------------------------------------------------------------------- +# In this example, each of the 30 generated fake studies +# select 4 coordinates from a probability map representing the probability +# that particular coordinate will be chosen. +# There are 4 "hot" spots centered on 3D gaussian distributions, +# meaning each study will likely select 4 foci that are close +# to those hot spots, but there is still random jittering. +# Each study has a ``sample_size`` sampled from a uniform distribution from 20 to 40. +# so some studies may have fewer than 30 participants and some +# more. + +ground_truth_foci, dset = create_coordinate_dataset(foci=4, sample_size=(20, 40), n_studies=30) + +############################################################################### +# Analyze and plot simple dataset +# ----------------------------------------------------------------------------- +# The red dots in this plot and subsequent plots represent the +# simulated ground truth foci, and the clouds represent the statistical +# maps of the simulated data. + +analyze_and_plot(dset, ground_truth_foci) + +############################################################################### +# Fine-tune dataset creation +# ----------------------------------------------------------------------------- +# Perhaps you want more control over the studies being generated. +# you can set: +# +# - the specific peak coordinates (i.e., ``foci``) +# - the percentage of studies that contain the foci of interest (``foci_percentage``) +# - how tightly the study specific foci are selected around the ground truth (i.e., ``fwhm``) +# - the sample size for each study (i.e., ``sample_size``) +# - the number of noise foci in each study (i.e., ``n_noise_foci``) +# - the number of studies (i.e., ``n_studies``) + +foci = [(0, 0, 0)] +foci_percentage = 1.0 +fwhm = 10.0 +n_studies = 30 +sample_sizes = [30] * n_studies +sample_sizes[0] = 300 +n_noise_foci = 10 + +_, manual_dset = create_coordinate_dataset( + foci=foci, fwhm=fwhm, sample_size=sample_sizes, n_studies=n_studies, n_noise_foci=n_noise_foci +) + +############################################################################### +# Analyze and plot manual dataset +# ----------------------------------------------------------------------------- + +fig = analyze_and_plot(manual_dset, ground_truth_foci) +fig.show() + +############################################################################### +# Control percentage of studies with the foci of interest +# ----------------------------------------------------------------------------- +# Often times a converging peak is not found in all studies within +# the meta-analysis, but only a portion. +# We can select a percentage of studies where a coordinate +# is selected around the ground truth foci. + +_, perc_foci_dset = create_coordinate_dataset( + foci=ground_truth_foci[0:2], foci_percentage="50%", fwhm=10.0, sample_size=30, n_studies=30 +) + +############################################################################### +# Analyze and plot the 50% foci dataset +# ----------------------------------------------------------------------------- + +fig = analyze_and_plot(perc_foci_dset, ground_truth_foci[0:2]) +fig.show() + +############################################################################### +# Create a null dataset +# ----------------------------------------------------------------------------- +# Perhaps you are interested in the number of false positives your favorite +# meta-analysis algorithm typically gives. +# At an alpha of 0.05 we would expect no more than 5% of results to be false positives. +# To test this, we can create a dataset with no foci that converge, but have many +# distributed foci. + +_, no_foci_dset = create_coordinate_dataset( + foci=0, sample_size=(20, 30), n_studies=30, n_noise_foci=100 +) + +############################################################################### +# Analyze and plot no foci dataset +# ----------------------------------------------------------------------------- +# When not performing a multiple comparisons correction, +# there is a false positive rate of approximately 5%. + +fig, cres = analyze_and_plot(no_foci_dset, correct=False, return_cres=True) +fig.show() + +p_values = cres.get_map("p", return_type="array") +# what percentage of voxels are not significant? +non_significant_percent = ((p_values > 0.05).sum() / p_values.size) * 100 +print(f"{non_significant_percent}% of voxels are not significant") diff --git a/content/nimare-repo/02_meta-analyses/10_peaks2maps.py b/content/nimare-repo/02_meta-analyses/10_peaks2maps.py new file mode 100644 index 0000000..916bebc --- /dev/null +++ b/content/nimare-repo/02_meta-analyses/10_peaks2maps.py @@ -0,0 +1,41 @@ +""" + +.. _metas_peaks2maps: + +================================ +Generate MA maps with peaks2maps +================================ + +.. warning:: + peaks2maps has been deprecated within NiMARE and will be removed in version 0.0.13. +""" +############################################################################### +# Start with the necessary imports +# ----------------------------------------------------------------------------- +import os + +from nilearn.plotting import plot_glass_brain + +from nimare.dataset import Dataset +from nimare.meta.kernel import Peaks2MapsKernel +from nimare.utils import get_resource_path + +############################################################################### +# Load Dataset +# ----------------------------------------------------------------------------- +dset_file = os.path.join(get_resource_path(), "nidm_pain_dset.json") +dset = Dataset(dset_file) + +############################################################################### +# Run peaks2maps +# ----------------------------------------------------------------------------- +k = Peaks2MapsKernel() +imgs = k.transform(dset, return_type="image") + +############################################################################### +# Plot modeled activation maps +# ----------------------------------------------------------------------------- +for img in imgs: + display = plot_glass_brain( + img, display_mode="lyrz", plot_abs=False, colorbar=True, vmax=1, threshold=0 + ) diff --git a/content/nimare-repo/02_meta-analyses/README.txt b/content/nimare-repo/02_meta-analyses/README.txt new file mode 100644 index 0000000..f4da7b1 --- /dev/null +++ b/content/nimare-repo/02_meta-analyses/README.txt @@ -0,0 +1,10 @@ +.. _examples-metas: + +Performing meta-analyses +------------------------ + +NiMARE implements a number of coordinate- and image-based meta-analysis algorithms in its :mod:`~nimare.meta` module. +In the examples below, we exhibit a range of meta-analyses that can be done with coordinates and/or images in NiMARE. + +For more information about the components that go into coordinate-based meta-analyses in NiMARE, see :doc:`../cbma`, +as well as :doc:`../outputs`. diff --git a/content/nimare-repo/03_annotation/01_plot_tfidf.py b/content/nimare-repo/03_annotation/01_plot_tfidf.py new file mode 100644 index 0000000..94c35bb --- /dev/null +++ b/content/nimare-repo/03_annotation/01_plot_tfidf.py @@ -0,0 +1,52 @@ +""" + +.. _annotations_tfidf: + +=========================== +Simple annotation from text +=========================== + +Perform simple term count or tf-idf value extraction from texts stored in a Dataset. +""" +import os + +from nimare import annotate, dataset, utils + +############################################################################### +# Load dataset with abstracts +# ----------------------------------------------------------------------------- +# We'll load a small dataset composed only of studies in Neurosynth with +# Angela Laird as a coauthor, for the sake of speed. +dset = dataset.Dataset(os.path.join(utils.get_resource_path(), "neurosynth_laird_studies.json")) +dset.texts.head(2) + +############################################################################### +# Generate term counts +# ----------------------------------------------------------------------------- +# Let's start by extracting terms and their associated counts from article +# abstracts. +counts_df = annotate.text.generate_counts( + dset.texts, + text_column="abstract", + tfidf=False, + max_df=0.99, + min_df=0.01, +) +counts_df.head(5) + +############################################################################### +# Generate term counts +# ----------------------------------------------------------------------------- +# We can also extract term frequency-inverse document frequency (tf-idf) +# values from text using the same function. +# While the terms and values will differ based on the dataset provided and the +# settings used, this is the same general approach used to generate Neurosynth's +# standard features. +tfidf_df = annotate.text.generate_counts( + dset.texts, + text_column="abstract", + tfidf=True, + max_df=0.99, + min_df=0.01, +) +tfidf_df.head(5) diff --git a/content/nimare-repo/03_annotation/02_plot_cognitive_atlas.py b/content/nimare-repo/03_annotation/02_plot_cognitive_atlas.py new file mode 100644 index 0000000..fbd9c34 --- /dev/null +++ b/content/nimare-repo/03_annotation/02_plot_cognitive_atlas.py @@ -0,0 +1,91 @@ +""" + +.. _annotations_cogat: + +=================== +The Cognitive Atlas +=================== + +Download the Cognitive Atlas and extract CogAt terms from text. +""" +import os + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +from nimare import annotate, extract +from nimare.dataset import Dataset +from nimare.utils import get_resource_path + +############################################################################### +# Load dataset with abstracts +# ----------------------------------------------------------------------------- +dset = Dataset(os.path.join(get_resource_path(), "neurosynth_laird_studies.json")) + +############################################################################### +# Download Cognitive Atlas +# ----------------------------------------------------------------------------- +cogatlas = extract.download_cognitive_atlas(data_dir=get_resource_path(), overwrite=False) +id_df = pd.read_csv(cogatlas["ids"]) +rel_df = pd.read_csv(cogatlas["relationships"]) + +############################################################################### +# ID DataFrame +id_df.head() + +############################################################################### +# Relationships DataFrame +rel_df.head() + +############################################################################### +# Extract Cognitive Atlas terms from text +# ----------------------------------------------------------------------------- +counts_df, rep_text_df = annotate.cogat.extract_cogat(dset.texts, id_df, text_column="abstract") + +############################################################################### +# Expand counts +# ------------- +weights = {"isKindOf": 1, "isPartOf": 1, "inCategory": 1} +expanded_df = annotate.cogat.expand_counts(counts_df, rel_df, weights) +# Sort by total count and reduce for better visualization +series = expanded_df.sum(axis=0) +series = series.sort_values(ascending=False) +series = series[series > 0] +columns = series.index.tolist() + +############################################################################### +# Make some plots +# ----------------------------------------------------------------------------- +# We will reduce the dataframes to only columns with at least one count to make +# visualization easier. + +# Raw counts +fig1, ax1 = plt.subplots(figsize=(16, 8)) +pos = ax1.imshow(counts_df[columns].values, aspect="auto", vmin=0, vmax=np.max(expanded_df.values)) +fig1.colorbar(pos, ax=ax1) +ax1.set_title("Counts Before Expansion", fontsize=20) +ax1.set_yticks(range(counts_df.shape[0])) +ax1.set_yticklabels(counts_df.index) +ax1.set_ylabel("Study", fontsize=16) +ax1.set_xticks(range(len(columns))) +ax1.set_xticklabels(columns, rotation=90) +ax1.set_xlabel("Cognitive Atlas Term", fontsize=16) +fig1.tight_layout() +fig1.show() + +# Expanded counts +fig2, ax2 = plt.subplots(figsize=(16, 8)) +pos = ax2.imshow( + expanded_df[columns].values, aspect="auto", vmin=0, vmax=np.max(expanded_df.values) +) +fig2.colorbar(pos, ax=ax2) +ax2.set_title("Counts After Expansion", fontsize=20) +ax2.set_yticks(range(counts_df.shape[0])) +ax2.set_yticklabels(counts_df.index) +ax2.set_ylabel("Study", fontsize=16) +ax2.set_xticks(range(len(columns))) +ax2.set_xticklabels(columns, rotation=90) +ax2.set_xlabel("Cognitive Atlas Term", fontsize=16) +fig2.tight_layout() +fig2.show() diff --git a/content/nimare-repo/03_annotation/03_plot_lda.py b/content/nimare-repo/03_annotation/03_plot_lda.py new file mode 100644 index 0000000..1296da6 --- /dev/null +++ b/content/nimare-repo/03_annotation/03_plot_lda.py @@ -0,0 +1,54 @@ +""" + +.. _annotations_lda: + +================== +LDA topic modeling +================== + +Trains a latent Dirichlet allocation model with scikit-learn using abstracts from Neurosynth. +""" +import os + +import pandas as pd + +from nimare import annotate +from nimare.dataset import Dataset +from nimare.utils import get_resource_path + +############################################################################### +# Load dataset with abstracts +# ----------------------------------------------------------------------------- +dset = Dataset(os.path.join(get_resource_path(), "neurosynth_laird_studies.json")) + +############################################################################### +# Initialize LDA model +# ----------------------------------------------------------------------------- +model = annotate.lda.LDAModel(n_topics=5, max_iter=1000, text_column="abstract") + +############################################################################### +# Run model +# ----------------------------------------------------------------------------- +new_dset = model.fit(dset) + +############################################################################### +# View results +# ----------------------------------------------------------------------------- +# This DataFrame is very large, so we will only show a slice of it. +new_dset.annotations[new_dset.annotations.columns[:10]].head(10) + +############################################################################### +# Given that this DataFrame is very wide (many terms), we will transpose it before presenting it. +model.distributions_["p_topic_g_word_df"].T.head(10) + +############################################################################### +n_top_terms = 10 +top_term_df = model.distributions_["p_topic_g_word_df"].T +temp_df = top_term_df.copy() +top_term_df = pd.DataFrame(columns=top_term_df.columns, index=range(n_top_terms)) +top_term_df.index.name = "Token" +for col in top_term_df.columns: + top_tokens = temp_df.sort_values(by=col, ascending=False).index.tolist()[:n_top_terms] + top_term_df.loc[:, col] = top_tokens + +top_term_df diff --git a/content/nimare-repo/03_annotation/04_plot_gclda.py b/content/nimare-repo/03_annotation/04_plot_gclda.py new file mode 100644 index 0000000..6b5098d --- /dev/null +++ b/content/nimare-repo/03_annotation/04_plot_gclda.py @@ -0,0 +1,124 @@ +""" + +.. _annotations_gclda: + +==================== +GCLDA topic modeling +==================== + +Train a generalized correspondence latent Dirichlet allocation model using abstracts. + +.. warning:: + The model in this example is trained using (1) a very small, + nonrepresentative dataset and (2) very few iterations. As such, it will not + provide useful results. + If you are interested in using GCLDA, we recommend using a large dataset + like Neurosynth, and training with at least 10k iterations. +""" +import os + +import nibabel as nib +import numpy as np +from nilearn import image, masking, plotting + +from nimare import annotate, decode +from nimare.dataset import Dataset +from nimare.utils import get_resource_path + +############################################################################### +# Load dataset with abstracts +# ----------------------------------------------------------------------------- +# We'll load a small dataset composed only of studies in Neurosynth with +# Angela Laird as a coauthor, for the sake of speed. +dset = Dataset(os.path.join(get_resource_path(), "neurosynth_laird_studies.json")) +dset.texts.head(2) + +############################################################################### +# Generate term counts +# ----------------------------------------------------------------------------- +# GCLDA uses raw word counts instead of the tf-idf values generated by +# Neurosynth. +counts_df = annotate.text.generate_counts( + dset.texts, + text_column="abstract", + tfidf=False, + max_df=0.99, + min_df=0.01, +) +counts_df.head(5) + +############################################################################### +# Run model +# ----------------------------------------------------------------------------- +# Five iterations will take ~10 minutes with the full Neurosynth dataset. +# It's much faster with this reduced example dataset. +# Note that we're using only 10 topics here. This is because there are only +# 13 studies in the dataset. +# If the number of topics is higher than the number of studies in the dataset, +# errors can occur during training. +model = annotate.gclda.GCLDAModel( + counts_df, + dset.coordinates, + mask=dset.masker.mask_img, + n_topics=10, + n_regions=4, + symmetric=True, +) +model.fit(n_iters=100, loglikely_freq=20) +model.save("gclda_model.pkl.gz") + +# Let's remove the model now that you know how to generate it. +os.remove("gclda_model.pkl.gz") + +############################################################################### +# Look at topics +# ----------------------------------------------------------------------------- +topic_img_4d = masking.unmask(model.p_voxel_g_topic_.T, model.mask) +for i_topic in range(5): + topic_img_3d = image.index_img(topic_img_4d, i_topic) + plotting.plot_stat_map( + topic_img_3d, + draw_cross=False, + colorbar=False, + annotate=False, + title=f"Topic {i_topic + 1}", + ) + +############################################################################### +# Generate a pseudo-statistic image from text +# ----------------------------------------------------------------------------- +text = "dorsal anterior cingulate cortex" +encoded_img, _ = decode.encode.gclda_encode(model, text) +plotting.plot_stat_map(encoded_img, draw_cross=False) + +############################################################################### +# +# .. _gclda-decode-map-example: +# +# Decode an unthresholded statistical map +# ----------------------------------------------------------------------------- +# For the sake of simplicity, we will use the pseudo-statistic map generated +# in the previous step. + +# Run the decoder +decoded_df, _ = decode.continuous.gclda_decode_map(model, encoded_img) +decoded_df.sort_values(by="Weight", ascending=False).head(10) + +############################################################################### +# +# .. _gclda-decode-roi-example: +# +# Decode an ROI image +# ----------------------------------------------------------------------------- + +############################################################################### +# First we'll make an ROI +arr = np.zeros(dset.masker.mask_img.shape, int) +arr[65:75, 50:60, 50:60] = 1 +mask_img = nib.Nifti1Image(arr, dset.masker.mask_img.affine) +plotting.plot_roi(mask_img, draw_cross=False) + +############################################################################### +# Run the decoder +decoded_df, _ = decode.discrete.gclda_decode_roi(model, mask_img) +decoded_df.sort_values(by="Weight", ascending=False).head(10) diff --git a/content/nimare-repo/03_annotation/README.txt b/content/nimare-repo/03_annotation/README.txt new file mode 100644 index 0000000..fa37fa8 --- /dev/null +++ b/content/nimare-repo/03_annotation/README.txt @@ -0,0 +1,7 @@ +.. _examples-annotations: + +Annotating Datasets +------------------- + +Annotation tools within NiMARE (:mod:`~nimare.annotate`) refer to methods which +assign labels to studies in a Dataset, generally based on study text. diff --git a/content/nimare-repo/04_decoding/01_plot_discrete_decoders.py b/content/nimare-repo/04_decoding/01_plot_discrete_decoders.py new file mode 100644 index 0000000..123eec0 --- /dev/null +++ b/content/nimare-repo/04_decoding/01_plot_discrete_decoders.py @@ -0,0 +1,86 @@ +""" + +.. _decode_discrete: + +============================ +Discrete functional decoding +============================ + +Perform meta-analytic functional decoding on regions of interest. + +We can use the methods in ``nimare.decode.discrete`` to apply functional +characterization analysis to regions of interest or subsets of the Dataset. +""" +import os + +import nibabel as nib +import numpy as np +from nilearn.plotting import plot_roi + +from nimare.dataset import Dataset +from nimare.decode import discrete +from nimare.utils import get_resource_path + +############################################################################### +# Load dataset with abstracts +# ----------------------------------------------------------------------------- +# We'll load a small dataset composed only of studies in Neurosynth with +# Angela Laird as a coauthor, for the sake of speed. +dset = Dataset(os.path.join(get_resource_path(), "neurosynth_laird_studies.json")) +dset.annotations.head(5) + +############################################################################### +# Create a region of interest +# ----------------------------------------------------------------------------- + +# First we'll make an ROI +arr = np.zeros(dset.masker.mask_img.shape, int) +arr[65:75, 50:60, 50:60] = 1 +mask_img = nib.Nifti1Image(arr, dset.masker.mask_img.affine) +plot_roi(mask_img, draw_cross=False) + +# Get studies with voxels in the mask +ids = dset.get_studies_by_mask(mask_img) + +############################################################################### +# +# .. _brain-map-decoder-example: +# +# Decode an ROI image using the BrainMap method +# ----------------------------------------------------------------------------- + +# Run the decoder +decoder = discrete.BrainMapDecoder(correction=None) +decoder.fit(dset) +decoded_df = decoder.transform(ids=ids) +decoded_df.sort_values(by="probReverse", ascending=False).head() + +############################################################################### +# +# .. _neurosynth-chi2-decoder-example: +# +# Decode an ROI image using the Neurosynth chi-square method +# ----------------------------------------------------------------------------- + +# Run the decoder +decoder = discrete.NeurosynthDecoder(correction=None) +decoder.fit(dset) +decoded_df = decoder.transform(ids=ids) +decoded_df.sort_values(by="probReverse", ascending=False).head() + +############################################################################### +# +# .. _neurosynth-roi-decoder-example: +# +# Decode an ROI image using the Neurosynth ROI association method +# ----------------------------------------------------------------------------- + +# This method decodes the ROI image directly, rather than comparing subsets of the Dataset like the +# other two. +decoder = discrete.ROIAssociationDecoder(mask_img) +decoder.fit(dset) + +# The `transform` method doesn't take any parameters. +decoded_df = decoder.transform() + +decoded_df.sort_values(by="r", ascending=False).head() diff --git a/content/nimare-repo/04_decoding/README.txt b/content/nimare-repo/04_decoding/README.txt new file mode 100644 index 0000000..65fa403 --- /dev/null +++ b/content/nimare-repo/04_decoding/README.txt @@ -0,0 +1,8 @@ +.. _examples-decode: + +Decoding ROIs and images +------------------------ + +Functional characterization analysis refers to methods which use meta-analytic databases to characterize, or "decode", +brain regions or statistical maps in terms of tasks and/or mental processes. +For more information about functional characterization analysis, see :doc:`../decoding`. diff --git a/content/nimare-repo/README.txt b/content/nimare-repo/README.txt new file mode 100644 index 0000000..8fdb9f0 --- /dev/null +++ b/content/nimare-repo/README.txt @@ -0,0 +1,4 @@ +.. _examples-index: + +Examples +=================== diff --git a/content/nimare-repo/misc-notebooks/nidm_pain_meta-analyses.ipynb b/content/nimare-repo/misc-notebooks/nidm_pain_meta-analyses.ipynb new file mode 100755 index 0000000..afe48fd --- /dev/null +++ b/content/nimare-repo/misc-notebooks/nidm_pain_meta-analyses.ipynb @@ -0,0 +1,1425 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Run meta-analyses on 21 pain studies\n", + "In this notebook, we will run a series of image- and coordinate-based meta-analyses on a dataset containing statistical maps from 21 studies of pain.\n", + "\n", + "Collection of NIDM-Results packs downloaded from Neurovault collection [1425](https://www.neurovault.org/collections/1425/), uploaded by Dr. Camille Maumet." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/tsalo/.local/lib/python3.6/site-packages/scikit_learn-0.21.2-py3.6-macosx-10.7-x86_64.egg/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n", + " warnings.warn(msg, category=DeprecationWarning)\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import os\n", + "import urllib.request\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import nibabel as nib\n", + "from nilearn.masking import apply_mask, unmask\n", + "from nilearn.plotting import plot_stat_map\n", + "\n", + "import nimare\n", + "from nimare.meta.esma import fishers\n", + "from nimare.meta.ibma import (Fishers, Stouffers, WeightedStouffers,\n", + " RFX_GLM, FFX_GLM, ffx_glm)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "url = \"https://raw.githubusercontent.com/tsalo/NiMARE/coco2019/download_test_data.py\"\n", + "u = urllib.request.urlopen(url)\n", + "data = u.read()\n", + "u.close()\n", + "\n", + "# write python to file\n", + "with open(\"download_test_data.py\", \"wb\") as f:\n", + " f.write(data)\n", + "\n", + "# download the requisite data\n", + "from download_test_data import download_dataset\n", + "dset_dir = download_dataset()\n", + "os.remove(\"download_test_data.py\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "dset_file = os.path.join(os.path.dirname(nimare.__file__),\n", + " 'tests', 'data', 'nidm_pain_dset.json')\n", + "dset = nimare.dataset.Dataset(dset_file)\n", + "dset.update_path(dset_dir)\n", + "\n", + "mask_img = dset.masker.mask_img\n", + "\n", + "logp_thresh = -np.log(.05)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Image-based meta-analyses\n", + "IBMA algorithms can be performed using functions operating on arrays or Estimators operating on datasets. If you use a function, you will need to wrangle and mask your data ahead of time. If you use an Estimator, it will internally find all matching data within the dataset and analyze that subset specifically.\n", + "\n", + "For the first meta-analysis, we will show both the function-based and Estimator-based approaches. After that, we will switch to just using the Estimator-based approach for subsequent meta-analyses." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fisher's" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11 studies found.\n", + "CPU times: user 2.32 s, sys: 364 ms, total: 2.68 s\n", + "Wall time: 3.32 s\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "

    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "# Get images for analysis\n", + "files = dset.get_images(imtype='z')\n", + "files = [f for f in files if f]\n", + "z_imgs = [nib.load(f) for f in files]\n", + "z_data = apply_mask(z_imgs, mask_img)\n", + "print('{0} studies found.'.format(z_data.shape[0]))\n", + "\n", + "result = fishers(z_data, mask_img)\n", + "fishers_result = unmask(result['z'], mask_img)\n", + "plot_stat_map(fishers_result, cut_coords=[0, 0, -8], \n", + " draw_cross=False, cmap='RdBu_r')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.dataset:Retaining 11/21 studies\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 2.07 s, sys: 321 ms, total: 2.39 s\n", + "Wall time: 2.57 s\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "# Here is the object-oriented approach\n", + "meta = Fishers()\n", + "meta.fit(dset)\n", + "plot_stat_map(meta.results.get_map('z'), cut_coords=[0, 0, -8], \n", + " draw_cross=False, cmap='RdBu_r')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stouffer's" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fixed-effects inference" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.dataset:Retaining 11/21 studies\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1.88 s, sys: 252 ms, total: 2.13 s\n", + "Wall time: 2.12 s\n" + ] + }, + { + "data": { + "image/png": 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Dg8NGwQMiUW/Bf0vFKZ1GmRsz5gIJyw/WObv1yvPnm6tkDCIhxXWd4UCVs/rCsM2KS1Oz/L5FiTVVk/D90qQtD//SKgEevacfAHD/+LE6+uMhzQuVPWLby7TZcXjK07MvgGrF63GLCpTPvemmmwAAH/zgB5e0veXghd54SDg4atemkYXm6aKp8etR5oRTRrWWamQuSHVU+FrVJqpCUWRarQVD0EQochADcux3tvSrC5S7ptpeY3JmcJpLe186oXEyEWLXOp8mqzdi32+/wMZufpfpglLbL7Dvde+01y2Wkx+eZQ8O7oi0KJ+9xxwl4xyr739xHxyWjt+7wqpORsloHJ4ic0RGQ9UbccWHMgb1mQn7nhiCKfUg4bMkxt8n+MwJKxO0HqXmNT7ljyFNhs9kyFcjZ+xvjJqLBLVGkQVy/GImqmTuSiGG5ijHkw9qPOr8XTyq+8s+rkdsP6px+14sySqtORvXYqOlNfF7pdB3JNlkz9hki2mZ9IyPNrXMu/8vRHgeEElEF//iInDMhsO8UOOh7du3b/SuODg4ODhsFLyFWY3TltkYVfTG/GKlZkxAjHnx5pTl3xJV5uVYky2vgTJ/L4W+xyisWtI8335XmrFlcWQisP25MZstF4ZM6T/ytDErv/i55cmfnArNyhfBNKfnZUabhTF2UiRjotl4hL4blbRFERNz9rtXvfEGAMBXvvIV+33Bfv/+979/Wfvx23jDG94Az/MwPT2Nq6666gXZeAgADo2FGA1VfxTpX+F3lbRznuS1mYGxa9McU1L3a6lctSLHJCO2XMoiszQjCDEgk8VgFUKxYp9nxeJFghGHF4oop/j7w1xO8N4Z4/oSD5gOSNqO6UOm4WjaZ7qj/GXGgOTPYVdMsm3Veor7w/2kPinJSPprDxtToiqD15/bDYfnozRq57+Y3wwAGKI2ZoLnMx6V5odxIS93tS6fjBqXXuj7thQRFiYaIgj/kVBvlSDnIaYinmFXVmoeNO7iSY5HVlFlqHtrYc+TDungYsFxqqoYMXoaJ1omQ0yfmBc9K6v+bgaZv3SMxx8PMhrVabuffW+lBrLI7LWiZ6zY5EjeGKbiD/8ZAJB6zXvwQoXnAdH48ZiNpVXwOGbDIYBbb70VU1NTuPPOO/HUU09heHh4o3fJwcHBwWED4UW9Bf8tFacEs1FkxC5GQ/lITcLDh9tBZXQ9YofnMb9Yq84/A5MPRnHK1hsN5acauo1piKZsPdNHjPE4/KBFJctlNIRSKG8v7YiqUSDldCh6Vd41y8qEN73lrQCA5JhV2VQet06GsXNfuaL9AoKNh2699dYVr+dUwbODxlzkab3YwDGQnjYPlUiBLBfr+j2fNWPOOGGREaIWyUm7oeqA2gJWjmGthpiIAh1EQfX+BN9vVfdMVggl2PUy3WqRZ67bGBYxFWnqgbQfBd4DGnu7qe3YTO+XIiuz5sbt/Ujc7qGmbnPPbeg6x9ZHh0i0WIR4SL4MoXIIRdpyUA07pV7R98LMjX/hC18AALzxbe8AABycsGfIkSk7jzqNfqTPYSKGQ8xXPBpkBHxmw9dq2O/0zJBPi6qa1EuFZK7PKIiROMJliUxLeU69Rlgdwuqmpg4b/9vabdnFcdvKahRpNZrJ4ImJEOMSZjZUPaPeLYqe6/X5/7hpPJeqtp2GJLvk0m9Dz/56aBkJdY+VP4fvIk2NSukX3wKwdv2yTiV4nve8v4kBVKsLf/ZbWPfJxlIbDzmcfHihNh5ycHBwcCC856dnA1jaXGP9JxvXXntt4PXf/M3f4O///u+P+xtVmdRZZTJETYZq/qXwl6uiZvMKHutRm02rekOdAtWDRNUecovTrHeIyu/nnjVNRmKPVaFs5ew+22H7M8e86koZDaGd+dB01mbTqkZJMUpVjxRIu8GOhlkyNhkpv2esQiJStOi7SqdUzcZvvs86Gn7oQx9a3v79VuOhb3/728v67cmOp48akxEL+Vg08I6IjpsOJzJh7FWV3ir1InswMAJSRBShhiLLFTQl7fM8r9EYK6DKaRuk8tlQ7loOogWydmI4ooxkO+SPQYfIPEPdeNaYgXjHJgBArsfGgqpM0k025nN0oByyzfp6oQP0NdDrlqds7PT0mzZFNGm6+2cAgKTZQKCVXTmbc7ZMRe1eO0BfCGkOilVpCoIMzmjRtvvv1D3leb5eKEyHfDVGC3RsHTf2VudNDIOvxfAdZfXMY5dTv3rElmIwNK7l95Lif/R5OqLusPZ+KmYrUC8eMRP7uSzQA6is6hg9e6nZ6Gm27/WQ6fJ9Y7g+MX2qUslEg8en4xUrH3ZKzdSC2o6wyZSOvxoRlWjn8ZhjKHV+RVWjkPlp4XmkRsMTo8G/IZEMPYYqQV+QFxQWYzbKC3/021jXycZSGg85nDxwjYccHBwcHH4bnodlaTMWwkmVz6j0P2n/yVnPkEKdtdihnJBmyco75jjbbWC05/tsMJqK5NgPosP8KdLtFr0lG425SDczb1e0aHCAUaXygCnOvvOMOhQF7ptd4pQuhCwvXB9ryPObLb+YP8P2M9dj+ylGxqOLHlhVEykY8+KxI2J9xJT/FbpBKgqX1uPPLrT1FW+3qpXU1X+yov0+lXGAkX4TNRlbY/a6xu6S6q0QmbZzG501hqB0yHqPVIeMHSrP2DmPUsugvjkJ5nY15prz5sA5TdZqmhoMaRbyZCh8PwPlnBmJieGQdmOMzIQi384q/S7Y/TLaapFyqtWqSJKsDogmghHjTEhTMalqktCYHuX2c2Q6GvueAgC0kGWLd28FAMQ22bKry3wiyr4/CP1E5N+gSJv7MTwbrrKx74tx2tl5eltFn3PFlQCAp0eMKXtmxFjLVCyovRAU0adYhSImQNU/tH1BOhZkMMQAxIPEEjxWVSWSFrnHQgxDe86YOvU0Gaf2osLrJV8NvwolFPmKmZMTbiZUzSB2ukoNRtHvfiw/DTqRSrsR0nboeGL+efICxx+ZIctbCnoe1aSF4u3qP1vFvHnBE1WPG1OD5En1p/LEYjFmY4l4AZ9BBwcHBwcHh+PBw8ImbcvBhk425ID5nje/DgAQnbOoxlO0FrdZt5/X82vIqXWIK99OZTQ/90pkHNidFUnLJ8a37ABwLDqrzFD5TU+E9KBFGelQVCFPAkV/E+UlKmIWwFbm3zvOsqi6dacxD63nWpSYYj7XV0Rzdh8RY8O8Y2002Ptl5oBF3wX6gyj6buwzBieSsPBn/It/AwBItJveIPP65Wk5TiU81G/HLkYjNWvsj7qleq3mb6AcrbpISveibpHyXJkbD/ZcaJTbK/U10V6LBKNxY0ja0sZOzVUsAlQPihH6boSZjSEuo37OnY6PZPPKoWoSMRuJNvOxSNAJUVUkQrjyaTGI4dh1wI5/4jmrypHOKXPY2LM0u8emz7OxuW3TuQCAfNLOx3BBzIUt1VNF7KR2S9UKirAP0+dEe93TfHr157lrn42PZwaNYXjqsC3P3sTrSYbD733CE+VrdqiJOKbBEMvL81imtkjVU7XgM8ursOeJr9mwCF5VLOHqKS+kHUlQq9HSYOM9T5Y26XdzlaaEzAWfnbq+6tFSLQfHu44zx2e8/sZJ86MKvFZWQyVrdhxiaqJjdt9W9lm/KvXD0n1bKbJKK94UOD6/8i9UbVbjOK4nTq/xtyx4z6/QXAkcs+Hg4ODg4OAwP7zl+WkshA2ZbEh0+O4//VMAQGToWQDHlP6RJPOJjNoaqFQXgyGEGQ/NStU3wncQ5aw00m5RLLN0aKWrXHHUorf0I9RmMC9YIDOyl0XoS/NJWxyNnP237TRtStsFZ9r2N/fafjL/X5+jJmOW0bTc8BR1T1nUXhqzKGlir0WfU/32OpVnJ0TlR4vBrrKxrH2/afSjtl9/8ndrdIQnD7LULHSzK2v0CPvppNhPh9/zyhYhSQ9TmbLl3IBVo0zy3GqsqI+OGI8mutMq/onapUQuZ+e+K0dtCGy7YijUZVMRrLpk5hnhNTGSzYVy36ou8KrzV0SJgaiH24AuE2I48tRuyF03123nIcfjFpKMENvJGLU0mXZlZC4aOA5FvIqgm+kf4u8/l/JjENOx6TRhOMRo7KHPySi7qz7Fz7tZRaSIX5G9X83haxfknmyvY6PmtaPKNXkNheHxGemV2XW4yaqZ5OMSdvKM83WNjEqM11E+Ghqnrdmgr4bG9QzHuTQksyF2OBlitgR9X5oQMRqZEfubUTtgOr/SPluOsevx5D5WkZG1josJabLxIzfpBJlL6dwiHL/S+XkxOpHGXrjVKJ5jNhwcHBwcHBzWE57nLWJXvjSc0MnGF7/4RQDAn9xwPYBjnfl8BXCONfZkKKLTll/PShEsxkKzctVSa/auvDvzkceqUmKB73n0v5c3QSxjXVvVN0LVKJOVteIyghjmLD9G349UpzEc0mj4/S5qwe2LydAsXK+Vj5w5Yq8Hf2PnrcQOirnOo1wvowT6eagXS2OvaT22zPwlAKD1g/9ttYe44RigA+aWDHPCT90JAKiyS2q00yJvVfR4zHGLXavNWOQ5zkhp+EljNiqsChGtWOZrsUfqIuk7EXbb2G6k50s5bWzTmfQjUM+UUWoZMnGOzagiVltPByPIdubGG8jYeDN2PDUel3LPxTE7jgIdQZer2RDkJZOm50wfmQ2dB0F9hhrmbHuxDmM0EtusSqWt/Sx7P50K/K5QlhOm/d7P5deCEa6YkKMTdpyd+eyKjudkwe4BdpbmM6ZE35FRfi4NRBhiMqTZUKQfH9kHAKjt+w0AoDxt18sjgyc/GHVB9asw+CxVz59cwhxou6jFOKPNzvMIfTZGuB+1BboW50NdX6XVCPvHTHDcqyprM/08wpoUVed0ZOx1dsi4n5k7/jcA4OAdDwEADvzcKvKeG7H7WdVUeWo8NnF97ZvtGdt2tjGSDYPGYOZ67BmZare/QaruinVusddkOtDWixccTsfSVwcHBwcHB4eTCJ73PMH5SnBCJhtf/epXAQA3/IFVnUQ4Cxe8BbQWitK8CvtSMD+tPHW9YNFBhLP3WrqZ37c8pKo3aowq/Twlq1OidI1TfnumGvQcWC9okhhjrxU/2mA0rF4u6kio99XBsF5S/woyGuzUOcboU/0uxNDk6OqYk1aEFQaqCCjsttn9NPtoXNx+IwAg+5b/svqDPcFQt9YW5rAjD/w7AGD03nsAAJl2i1CSZI9ibcFupDUxG7N2juQ4KO1DlbnmejEY2UXIKk0doCMmIyGNsQj1Ry2Ntr1w7wtFdIoEFdnJN6CRHjINUUaiM3bNxf5VlXum2t7fLzIGq5Ru4MFxG3OqxNrB12E9UGmKLrcjtj8Zsm+JHfZ+c/dOe00WU7051ANDfgvqgyTMUmckp1cxHKq+yGbSqzvAE4xjvip2nCle/ySZATlwyu+ih34XZ7Xas64vb8+ExMATAIC5B60f0tD9j9n6eT2kUcjSuyfVaz1uxOxF8saqik1OJ4IMhapKxFyEGQ1pjDq5nxrHkVB1iTQaewbZOZvMQy8ZPml59HtpPnLcn5aSjXMxGg/dfDsA4KePGst7dG7+CsHDtNkQQ7eJyx18Bnb2WqPJlu2m8WjaboyGnHjT1MeJGYpuvWTe7ZzO8Lxjz5HVwDEbDg4ODg4ODvPD8xBJnOTMxuc//3kAwLt/92J7Y8K0AZCrmxqySXNRC+WBxWAUQ/n0Mt+ftTyjnDYjaqtQsuiuWmTnysZgvkm+FcqrKwqYKK8voyG0cRbv92oRk0GvBuVTlW+VT4gYDmk5xGxM7LfZ99BBm60fYv5/jMfju0EyWhDjkWD0oWULmZGDP/oVAGBr+/+wzV91w+oO+ARglOrythjZsEd+DAA4/N3vAQDGnjXGoeNCi+xae/oAANVkUENQ59iSbibRaNcg2UgHRebWS9QsyKFTELOgngzHVkxWjTqlRnaJlU+CIjjl5PU6VqOz6RzH+rhda1UbVIZNS1Jjjl5MQ4JVAXE6mEapz1ktnqNmQ/fK5U9YZFhWlcqE7j36iJCRaCR7p6qKHKtVauzOWVSXThbXqA/SLMeuHEmnY/JZYGQvp0x2hk6lTw2G441pq6b4FrYDOObUqaoLaRnE5HRTQyHtQnzQvHXEaOz537Y8+Et67f9h5OMAACAASURBVOh60B257Wxj2LoutYi99SV89rSYbk16N22vmVoQMRaqOplgdZKYmTT/CIWrpaqhKqM5+cOQydB1baImKUcm5Vj1je2Wqmzqv/45AODRL9lxfu2+I1gJDvPZp2fgdi7PprapusDfgFNjVK0XPF+TtRqsfg0ODg4ODg4OG45qtYqLLroIr3vd65732d13342LL74YsVgM3/rWt5a8Ts/zEEnEF/y3VKwrs7Ft2zYAQJ2eBaq2ADsW1kvKLwejQWkS5CuhaNPvuEnI3VFaBvUC0fcF/c5nELh9RYOrVewvF23skJhoJJORDCrExWBEyGjU1MWW56c8peoTi3JHn7VoRXnL6ZDmRK/CWhT1aJGviPLxY3vsenU8bS58yatWcpQnFlmOoei+BwEAw3f9FAAw8uThwPcicepiOFb8McHujhorytE276DDKCOzWNrOtTr/JsgcZLtML5RqNTW/tBpipfyKKTIc8obR8lgFlR2HN25jOzJluWqN1SrvpQrvEb8KhdB+eqEca7hL5mqhiqr7R41RuJDaC1VACVX2opA+KUsPmTjPfyRtjEYmbectmTNtQalKnwZuZ6IozYb9Lh3Tkl1zq2p/avquVC6/2kNcV5RKdp62ddj52NZu40/H10jNxlZWaWzJs0qjQNfgR60L76Gf/hoAsPcO89f42ZCNX93rjbw+F7NKQ+Mj02U0cMNWc3yVTk6MQgOZij5uf7qX45rMg6pTpC0JPzobQ71E9DpHJkS9grrInGgpZkVOockpYzCO3G73888fOoq1gJ6Ez0yL1bZxdvG9QXZdDJ2QWZOtrx8++9nPYteuXZicnHzeZ1u2bMFXv/pVfPrTn17eSj0g4pgNBwcHBwcHh/7+fnz/+9/Hu9/97nk/7+vrwwUXXLD8iQM1Gwv9WyrWhdn45je/CQB4fRujrxmbD6rqwmcgxGSwm2md3gaK4oQwU+Glg4xAmAHRa/97aUatcoNjdFlnVCjPgDUoJT4u1D22sZfR3CaLnmPt5kmg81MrBr0Tjvlu8DjVIZFeEhNHLZqRNmOplQfqAJqO1gO/SzHPW2RFQeU3P7H9PP9VS1vxBiBGF9rxu34IABh53Hp2zA5bRJfr5hiQsybHnBgNv5KJY0QWNknemK1cpjvse+oeqZtNkbuYjYgqneQRE1V3ySDD4TuXFi0ij8wac1EbN5V9+aj5fMg1tjBgGglpIQSNCTmaju6mjmfYxoZy1GsNsWn3MoLezjEYVq8nm21/Uu2m+teY9vVWjWQf6ffQ02asaDxi502ajTIH6bOj9v0ZRsTSGORztt3ygI2HeNf2VR/jesAj0/SOi823oTtnDJn8VTJy7uS4iA2aF1DxcdNTHfqRVVft+ZEd5y+Gg4yGoNd38vM0tQ75rdYbKHsJO0SzS7EkCzLybKHmoqfB7ouZNuq/UtLMBJ1CJ+bKgddTvE/EfBzTJNn1khZF2gwxfX731qO2vyNPmBZlpZ22F4PG8d08Ty+/17ZXIvMhhq51Xba+Nvjwhz+MT33qU5iamlrT9XqA02w4ODg4ODi80PG9730PHR0duOSSdSjNjZyEzMZnP/tZAMAHrr4AAFDeYzl/zWikrRCBoEi+zkje7/kxacuwZ0A0lQysz9c4hF3xiEjGNCJSvIP5eTCq1fbmJm07hdWaESyCdnZKzHYyb73ZXBXrndS2sBeLesM8D4wGS6y8mD5q5+3onP1OWo3l1tQo/97D6LAwwuqfKrvjHrIoCHkyMVvOX+YW1g+loQMAgHqoN8LEfosIpccRWyPU6XTp63nU1VEMRCOPlayYKoUSm+3a+MwIx5SvMyIidKmtJajZiCW5DDo3elVWz9DJtDZqkX/lsDEzxcMWYanyaHaAXS2ZS5bZjnLLk/123EdYJfIYx/Z6e8do/b9hj4/sQbvLY8zpZzuNJRMzI3V/VKwmx1okbWM6Rgaoo7UPAFCu2fkbYKQ5OGrHtZdOqb2Ndn2b+owpaeZ1LB+2qo34ph1rcJRrhzSrZi6I0N33AMcxx5GeTeUhu/6HH7LjGHqMEf79FvHfQy1GcYl6MzFQ3U8bo7GZ25HXkRgNPaP9ailqLlpCPWxKZNTEZAzOkC1VLxXul3w38hyvbayWauMzR464UVZfRad4/Hvtb8gQx/N6Q2yvGI5dD9j9uKXf7vuzTsheLB/33HMPvvvd7+Lf/u3fUCwWMTk5ieuvvx633HLLmqx/LXw2HLPh4ODg4OBwCuPGG29Ef38/9u3bh69//eu46qqr1myi4dFBdKF/S8WaMhubN5tyXz4Q0kwofy1H0GqBmgwyHbVJU/ir50dhiN1MJ6lZiAZ9KVIttp5okymqY3RrVDQphkPRpBiNOhXXEW5XDIGi30J1faO/cLWLzkuFCny/S+1MMNpQJUM0b/n8KC+wXC3jzP+qtn21+1eaZrTN8y4/Ev98nkTw+i3ymWblTGXGrqV6lvjdWXmN58YsQlHkGBmxyEUMRi1lmot6UtUM1HLwfY0d3+2W18grGvMg59F6ltqOpBgOutzyEi2oDwq5xFZm5RZr+12atDErv4+aHDjJYMhFVir7sRPkHSOUeXzafgvZt9xRdnJusOoaaWfS1FnFpEviMyJKtjIatbHX1mjahtkyGY+sjcUnpqf4vjQD7ImRYfUENTGlYeudkdjg3hZf/vKXAQBxVkUNfN4qA6SPUkfm2WE7b+N77XruI1O32uuq343s5jOX4z9BrVCc93hOTB94PslsyDdDvXxUnSItTZkMlXw42ny/ELu+vexELYffBhqrRMhmS7NUOWAalcGf3wsAOHA0WHW13hDDcT+dcg/yeAdadwEAbhh58oTuz0rxsY99DJdeeimuu+463Hffffj93/99jI2N4bbbbsPf/u3f4vHHH198JZ73vOq2lcA5iDo4ODg4OJwmuPLKK3HllVcCAD7xiU/471922WXo7+9f9vqs6+vS/TQWwppMNnytxh9cDQCoD+6zD0IuiupSWj5o+ccCO+4paivPBHt+VEI1zmI2sqwRz3RxVkwvg0gzGQLlx0PdXv1KAM/ylqKAwi6Q6wV5HURCYW2VinsxC+ksmaBQr5hoo72viohMmx13J/OeUmrPVOfvE7AQ4twdOZtKU5JmH5EI9QqI2nZmZu38bWQ/irlxRsiMzDR25PMgR8twrlFjSmMs3ksmgTn+Gv0e6inT+0jGE8mQ4agHI0pVk6gfj/wyqnQILTNCLDKiVPfSWEQ+G2SN5PvBcy1PmkicOX0eR5RVL2IGamRwxGxMMCe/XtUnS4XU/c+oCoKReYJVFxKW6XiizdS88PhrdAf2yBAlI3Yh5MMgbYEi6/10SPV7c7CzcU+zsa1RsoVzY3Y+k82da3GYy4aqUMplG5+PfsV8MtQNVbor6bAOFtbnOt7db0xK3/futiXPe3LnRbafTcYAJTju9aciFrHzOl0KPmPEXEyQcZNjqLQaYjQ2N9DRlj19olN2ParSKPFvw5FfWo+XfT+xapvh0vKeaWsNjeehORtnJ7+n8trCMRsODg4ODg4O64eTqetrV5d1ylMkXp8L9mHQ68qIKajHnzHl9dGH9gEAimNiGqKB39WYh1WNbzJfCrxWdUra2l34vURqjCqh2uBQ7xWvZFGtoltBTpoz1fWJJvKMyjId1AOw8+Ucw+dJzt4LjAai0mowisurfwS1KUkq8NUHozC4srzmhXlbz5kvsmiv82I7oaneLQB+yzOC13eaUXp2RVtbHSamLVJuGLYKmTlWlagao0xmI0qWRktVRYQhbYQYizq1FTpGdSVVMUcN9lrymGTUxmCKVSd8eUz/MheswOLu+CyXX41SDbrn+tUZYt98Fs6WpSnboUoxqE0RE6N+N0utUlgvqNtmG5mNJHuAJBrsPKfpS+J748hNl/dyPRqkb1Ud0SBmhMep7qkPHLLtqHpCPhCtGRvDOj9T1IO1NZ6YUXzzzTcDAFpa7J7PZm27T/DZt5dMnHizcz77F/afD31mXfZH2o0Hb9sN4Ng4636xOe6mdl4IAIj1WP1Fa956qKQabL8rNWPshmfpwKmKOD3D+OyezdgyJWaOjEbk8FMAgLlnHwUAjD9p9/PwY+aEOvCwMR7PsBO1+j1tNHR9/ipuFYSfKu/ZuJ05QfA8x2w4ODg4ODg4rCdOJmajuZn5bHVp9bu6WlSiapPZfcZojD9ns+fhpyyPOkA/f0Xw6k6qfF+aHgmeOjwyqkm1Mq/daNFCtcEi81oov+5XoczN30dCEPNwOGiZsGbYTgah45KdAIAKPQTGCjb7H5HCm1FvmvsjN71GVtVIo6LZpjp9rtQnpJsdHdvOMYaq+SzmuVvJWNFfw9fAbECw/NyQ5fDPmLVIYu7J+21f5EIrFXzJzoW6nYrR0BiS06c/U5ebrSqBeIwlRmhiOIqVOpfsQspz3cAIOhm172kMKzevzajXQ4rXMkZ2LUItQX3EhFvVkVAXV1Yn6BrLyVCut566ZMqvgMfbQgplvf01lgpVUXSSfWs5K3hcfn8jPjM8euSEfUl0/nrIkMyWLcJ+ZsTWW+B1e2SAHZAn7Wa+oNPYwK6cnZ/yOnvqhNHRYffQ49f/NYBj3UcXwhPrxGiE8TP6dET/P2MaRp425qH9PKsG6fodq75IXXAFACC32Tx2GlmtRUmO7wwa9/VoNv7EaLSm6Ri71xiM2QfZ2+XuRwAAhx+wcX/okN0XGi8brdVYCCfLfXVC4HnHXKxXAcdsODg4ODg4OCwAzw8CVoNVTTY+97nPAQCuedMfAQBqJXO3i3Varl+KcrkhqmJAngejnFWrikJ5bkVlYjh6ojbLzXYEo5F4llUoVO7Lr2KwYN+r0bMswox5Q9KqK7I5W1/jdtMmtGw3BfQO+kuIIVgrH/5OOoe27jQGJnnu7wAAxmt2+sepNzhA90VFCYriVNu+ifnlGjUw5RlWIDAvvty5diPXn2pmx0VWucTaurjsBgBUWKFRztr5LTKqfnbQosftHY3L3PLy0cOItPobi4Smn7UIya+YYWlWLB0c0qo00vsxVjRJMyBvFnmIyL5AOppxRqDDHAtzoYhGr3PJYDfSDEt8FNk1qIpCKvxRczot77d6/dIhy1VLRySPmXCFVpGOmbVQZJ5iJ+HGXtufM5lDl/fKelU1LBVS80+yV418JBr75JrLe1muv9RdqZ+RKrLCzpaqgpB2Y5BMxv5h2147dU01noeXbLbx0pi09cmBNtG+ZQ2O8vn46le/CgCY/tCNABZnNDYK6p2y+T57lux6ytjosWfNufNMehI1vtS+v+nsKwEAs2W7bw5PscePp94ndn77OC6Tw1ZVMveY+WYcvOMhAMCe2/cBAHbzGbZX1WQbKzVaFKrg++fmswEA7xl7agP3Zp3h/Zbn0irgmA0HBwcHBweH+eF5x1p9rAKrmmzkcpYvVc17LatOlxZNRBqYD2Qknmy2aG6hygBBDIeisgqVzVUtmbdWnr4ecnWcKSu/Ti8CrqdQsdnZlkbTdsS3nQsA6LjwmcD26w+adwOoJVkpw5Fl9KVqD2kivFarYZ8o2n4fmWL/imKwP0GcUbHyy7V2uVoGEXYmXSpUsSBIBKROnNU0l2SMRmbZyXGO+oQTYE8i9mRLlA6dtWAOVy6wNfoWSJtRa2SdP3cy1WIRs6offB+LBjp90ldjltTGNLUfujZjhXLgc+Wmy77uh11fY8EIgOQRvKIdR2zC9ErFJ8xfYfiB3wA41tVSrJ/GuiC3WFWfSJOi6g7dU/JeaWGEeOYRnieOETEMGwXl4vPPWuTc0GualSZ1bJb7rxxXk8ZwlOp2Iivq/srjUTfYcd6jI1z/AFlCvd+UsfN1kH4ku3iepDOrPH4HACB27ivX5DiF1lZ7Jj48XVrkmycHxIDJp+XCX9t4VbVTHx1s2/nH54xtLwn8Xs9cMU7bm+x7lbt+aev/kXWtFaPxEMe7tneqKCFyvo7u9O/44cFbk66vjtlwcHBwcHBwmB8nA7Mhf3+m6Xy3xJh2jPlW+TTEM6wIYP5ckbUEzOmoOgMGZ43pJvtdhq6AchINd30NQ/0StJzi7DwZs+1s6jVmo+Viy//LtyNFt7vYLy3ajNJt77mZ5TEcW+l/sePqPgBAzzWvAACU27cDAPYeNE3Lc8zDKypL8rjz1GpIw1FgRUTGr0YJnr/lQl1eM612XhONtow2m3K+SgZoYMaijqPTweNXVLmeaOE+RoctRK8W2CG4FKrSYNVGvCF4LDFdU1YupTaZX0B801bbQLO9LlDPc2jMIt/nRu2ayD9AY0jVDmnqiqSrEQsnskjVJw11i9xi41ZtMve05aoH7r4PALD3J+ZzoLEglbuuqe4R9b/R61ZG6s3b2AMkEWRUsh1ZnidbX5/OE0mwjVL5i2GZHrTzO9VvLsLqbhvfRu8bVpbJ2bXCsS+WssL1iGkanaZ+iUxGhcc3yG66+6kRSfB69TbYepv5zPIqa+vjcNNNN9n2PmrLjfI7Ebs6s8zqG2km1BukSLZXzFq6w+6XHO+fhrSxtVvy9myS02tsyLQa409bdUv/vXYf3E+n241m2laKM/lsbyTDeMdFlwMAXvnQrzZsn9YTjtlwcHBwcHBwWD94kY1nNqJUqCptrXxdLraAWyMjYeWfFb1p0q2oZzQUdTUXpdGgBkPeA0W6P6qvRMWYgTQZjwZWe6hiQHl3MQU55lObd1g/gAZ6G5RnbNbdPmHR0kXc0RI7MB4qHD+/mOL6z2f1Sd9rrfoketlrAQBPj9l+7hsPRl3TihoYpfqVDnztn1/6imRYidHJXiadZEYWixY2Mb/fSY8I9UJJttl65asxWbfzKKX5gQnb35RfcbF++crDYxbhttXYRZVdIcVmyWtFzIYYjDj7teR62ux76gzcapU10fYe+13Orn25xSqSBiZtbOzlNdlDZmM2NBbVzVLLFrJPylHnWZXSDHrH9FuPB2k0Dt/5AADg2R+YTuiOg3Z8S63b19jq4ljZwf1rPYOaFKr/c+pvQw1Lcr+dt/x+2556b8i58kT5Boi91L0ujYo6PWfZjddrNa8XkXZijlQlNFZQlZCNeWkzZnhcNa5fXj36XMzUUR53V6cxQ+kF2NGVIp+39Y6ucyfphaA7U+yqHDhX2i1Wv9/abwzcxHPGRDUW2fMma9dLrLHuAwxaJ+/ZobHA+tK+4OvUYjZUWah+VLq/0q0b1ydq3eGqURwcHBwcHBzWFd4G+mx8+tOfBgBs22b+8GnOZlVjjTpnq1R6g4yGqh2kNYgxGlS0o46HWorxaJMPBzUDtXC0IHcz9j5JRW22WfA7RbIjoe/fb8sROnc2dO4AACR2WHTVVOJ2pAPgLPYK7ufE0WCVimb92t+XU1uy7TWmCUm/5FoAwIGIRdOPHrVo4MkjFmUeGZ/fsjQRk16gFlhG1K/gPNMdjO8174ZdzIMXqra+cLTaTCZiM48ny0qGdCu1Noz6qw3GbIyxWkYujCM83o4so5ec/f7efVZZcEVfy7zHsRqoi2rYKTTTRWaCLFSFfhTSZkiT4bugttixVXLGeJST9r2BKVuvWKa9ZDSeGZgK7Id0NPJzqNZ061hEkydb1EanxNgBYy5mfv1TAMDhn1vVyd479gEA7jkU1GgsFcr9H+C1UKfeZo4N6aGyXVZlE00zt7zFjrvzfGOMNh+x7W/bbRHn4wNB58b1gtxx5WUzO0IGiR2gq2M2lhNF279qY7BHjaqExFKqUiu5QFVAmVqbIVahiKkSa6j1pamDWquqlMZGG18trESbKAefbevNJO2kJkX3+gTPw0qZDTFR6k47O2D3fIVaqmzXBYHv5eljUh0zZqM4Ys86aYhWC/lcyJNpvbUf0g/2pINVYE1bjV1u3r4xXYRPDJyDqIODg4ODg8N6wvPgbRSzkUrZbP3yV74aANCSYEjvMU9asKgkMmvRSo15WOVlpw5bFDXO6ESz/UJIMa/XigqUp5Z3QiTBE5DM8GhsNh/j79Oc/lZqQY2BtA9yhWxgx87WHVYzrvxte5Pl21Ot1o/D79ZKbD9ixzPBjpaqRe+5wtwIO1/zGgBAqfs8AED/EdNmHCZTMMhoSx0rpdXQUlG0qj766bLX0WVMTPpFtr+bGDWUmYdOP2IdE+VW6DNEXG9nr7lmpljl41f3pOTaaOexarvnd3JU3lwdNeWauZZC+6eP2rG0Z9iHpaxrzcqmtO2jvEBiZDyqHGORrB2bKqA8+miI0ZhLWMQ5SJbsOXbdDOtnDlEtr2ugKgZdG70Wm6f+NdEpO/dzT1nvFjkl7r/bPGZ+dtjujZVGmILv7sH9U4TVdp7lzvNbTaMSa2mb9/eVUXOGHGfuvftZqzY4+157/egqPWbCUCSqe3qyYm8001F0hvdSsd8cPcVIRViVkogH7z2NRaX+86zOaWTkOaGuu0X6o1DbMcDrPEV2sygdWMrW78VWp934/Oc/DwB43YBpcw6dY+f/JaxoOzxg4+tJ+beschyEIX7nrBY6dzbavdzCZ8FKnWT9KiIxQkftONTDR46u0sNF+ezXfenr7arBZ/1KIS2KmJsedbFdgCVeKaRvO5NVJ+1krVvPsueKGI32y4zFrjzyIwBA7MJXr+l+bCy8Y9mDVcAxGw4ODg4ODg7zwvM8ePENZjZy6vcwx4oBaTao1YiU2cODs1vlZecU0ddUlcLqkFDeVYplaTqUJ1O+3q8sSFk0W5OvB2vyFfVJIZ2MBas6pN0YLbLTJyscurYbY/DPP7E8+5+/4W0AgCb2eIGiaeYjS2N2XGJcMpdeacdLd70D48Hqk93UAyiPrCi5g8fX22Kz5zS1JspHS3n/3Jjt566dLwcANNN7ojAYVHw3ssOmtC7qE6JoJ5GzAeRHHYxSIsyXp+M2e1c1zDijxGlpX5gnXUu/DbFP2arte7hTrzxVvKQt63Se9OfdrAfX9+TTINZrjsc6ykhP/WieHTS2rZ+MxjR9G1TNIH1RtRbnMqgVUK+N6CGLzId/Y70S1Nl4PyOutYpkfVU8I1hFWC27+my/zrEKKDTZ+3LKBM9Xkk6sqV22f22HzGumafsTAICep805cuBhG+N7huw6PMIKreVC93i40myyZOetwKqU6UOm2Uhtou9Gj43pdKvGIj1oUnYdpCPK8bUYDlWdzKkzMq+X2MRBOqxKs9PVaYyQetesFC0tpltqTBmzpJ42uufk9BolG/rY5NoyHOfTI6jz/PbA+71kkOTUOcR7d6lble+Gvq8qjEjOGLUiP9B1Vi8bQb2I6mKcVkiHip09m9UfbTvtb0EXz+ODZOZWix3shK3tNJINbttpTFXTDhsvjWebZ1LqXPPZqGbWXre24fDgmA0HBwcHBweH9cQGajYSCfaBKFsE7JUtv+uxN4lH1z5ficw87MRevqZLo5iHLs72j1WhBJmJJuZhU5xNZ+heF2Vevh5hlYv2j0xIIhrUPlRrtt+Knuf83in2uwKn74embfZ/5513BpbCN7/wT7Z+MgCyO3njf/4b+893fsN3/jsA4DXUbuSveB0AoIfMhaKwNBmVHmoouljlEQ9FZYqyVVUzMke/jN6dAIBcD7uyUsMhROJ2PeoLuAjKfVPamgT7d3RtsvN7VqvpJMQKaD9Gi6oIWH1UJq3GlkZ2bx2wMYMSe4XMkD0rBXOy9UqwesJnPnhzaGwI2tUJRniDzJ2L0Rjn61poLFb52gv5NmgMJXmtKkMWWU3uNZX+DCuEpDtSvLfYGZOfxhaOEeWO86wykI9G70us6qb9IlZUbbeqANCnQqzfgmi3irJEj/2+57wXAwA6Dlo1TdfDDwMA+p4xzcl5vzFNyjgj81+xc/NCjqTSaqR9t9v5lxqbGrvHHEXtmRFvMqfKdCx4DwulBao7EslQF2BdP+rFjqpzcsnuuRZWtFUOPg4AiG0+d971LoRk0tYTjdkzKt3OqqCU2ETuP3P/jb+x4/sFu66uluE4s8sqy9ovDHaxVZfgi+kEquoNjW9VNy20fVWyiVlINAY7bvsu0GSRvWljdGb3WzfjyQOmEdJ1DrPYuj8W8y4S49BOLUzrTmPuChw38V/ZuFlp19iL+Qw+7wJ7lrafZwxVstnOa9OZxqYnuu3+inX3AQAqzex0zvtt9X+aTyJ4TrPh4ODg4ODgsK7wgMjqpworWkM8JBYRkyGGozZiUV1p35MAgOFHLR888rRFzjPMr0mTkaAfhmavyudp9hvhrFrVHtOHbJYca7L1JjLB6C0hRTmjlDg7RybIGDSWVZVi25GCWpPtxfKJN/3LtwAA7/jT99p+LfL9t1x/Q+D1izeb8n2uEvzdMUaG0QHfDwdtqrJpmrZZ/Nyv/g0AMPBr0wmo2qfiO6/adpQvLYcU6bPsrDpzgL1gmi2qjSXsvG1usui3XCPDwR2Sx8HcKjwDnhsydqgpRX+KUYuEasO2L36FTNE0A2I2wgyHtBqokNFQrwuOAYgB8SwyEmsUDXe+5SDw/FbGCLyuh661xg6lBP5+xVjho1x9msxJO7UWhVCEp6UYjLYOi3ybt1mEnO2wyEo9KRr7LOLKnn+Jba/DelOoqqKWacZ8UOQ1W7X9ln4p2mjfT7ewS277mQCAFvbJyW1+1F7vskh8gsxNwz37AADP7JG7Lj1xeJ50j+u4wz1emhlJSj8kbxvfUZS+G14ne5uQOZCLrapKpovzV81EQhG0rvcgK8K6Gmx9uhe98hyXs/OubyF87nOfAwD8pz80FhP9dJTl52Lgqor4B+34pKO64j47n9LEqJJsueh6kUX6reefFXhfuqwK7/043ZCPTh5fg9NHZk3LRnbLLU3S3+aoMZAJMmkJstsYNH2bnEal/ZFWRtc/PP4XcmfeTkZj+3nGaHRdaoye2FyPzFue2qrl9v5Rtcmus43N7f0Pdv7aLjoHwLHqqHivaTTUEbvG5VjU7qvSnI2jZs/OTy5zGjiLes9niFcCa5n4SQAAIABJREFUx2w4ODg4ODg4LADvWLfVVWBFkw31RKnHmR8vMZI+aB0sS/ttOfqURakDD9rs9uABi6CPRT3BKEfLYrimm0r1iZ/Z7HXoCWM22s8x/4uuS62zYMsVpgiOSlmeDM4qG+L2Opu2eKOeJCPiR8WMfhfo7SKI2ZmYkxvh8ZmN7KPGPCjqbWC06OXy8/+As0hVUtSjtr1owaIRdQ7d/+93AQD2/MjO94GjwcoNRQfKm0e9+c93jo6oin7UUTXPKpf8i2z757RZH5EZ5lcLIRfGlUAEQj7KSI4RUX2OzIW0Fzx3tbkCD47bDHUjjPB9xTXRiNgxu/YNTZZjblPXVC5bGTmpikFdQ6s8J9FocDsNZCDkTqsKLDntpelkqu6rfdRubMsFGxqpOiHDiLH1LFOz53oswmqkX8a7/t26ZmLExsC3PvDXdl64PT+O88cy+wYx0qyRpapSNVKokCXktdOzRC61yagxKS27rgQAJLot0ksO7bPjZ0TbvMP2q/ku0ynp3iyFOiQnGDmqO63YNVU1eDy/GoO+MyxZ0vicPWNi6dbAetVPSFUmhZAnjK5jna9HNeZ53qXdkd9GNcteOstkNhoaWBHH8xzpMR1VsoUOtmLk+IzJkHlTd+Jsh53H1N12Xn+wh91wl1i1IU3Fphfvsu2ed4V9wPuhjdVHpcngcZWftOslZ8x8PKhVEqOhLsa6rkepNel63LyIGskue/TBKdNBd+BBe2b/4qBpfEqhahQ5gLbLu4cMh6pl9PnZrAbputie7c2sBpFmJEkmTAzacpmNi86wZ7Gvgbr8YgBA4pzLAAC1rI27UqPdjywwxDDH8ZEp+o7wvG2mc+w5pwGzUQdQjzpmw8HBwcHBwWG94HnPK2VeCVY02YgweorOmAZDjMb0Y48A+G2NhuVb9z5jPvrqu6CIWrNWzaJVA76oIpvK7c59Npv9HeY7d7GzYOcV59v+qVqF3gLqGKqKBblMhqNjRaf/6/OfAQC86b1/Efi8o8OYiQpn55UFoo9rr7WeKPv/9ZsAgLlxm/3KiTTO2nMhnrVZsKId1aYrj33kl6aQf+q7FgXdzfMws0CVyWLw+wvQa2IH1zfJzo49A3Z9O2eNkUrRuyHRbrP/PGf7Q97ylcpDk3Yu5JsQG+8HAJQP7wNw7NphMQ8PRog1sjD1oh1DhD1UdC2jGfZMYZfKtrRFSn1Nds5VpaKcvu/TwMofaTbydC5sb6TXjKodyEZpTOWpWpfPQvM2i5jLrH5QhJ9ld9p0q42J7LY+AMDbv2I9OvBMsLJIqNH1Vp2OIzwujxGsesiAryM8XokIMnF7XebYFaMhHVPR17LQ16LJIsm4XHrp35HvMW1HuseqRdoeNZ2WIujiqO2/qjGqRdvf2eFghC1Go8ScvsfqgrkBq55IjNs9nshbZJli1UMbmaKmTND/BJQiqHKqxEi5wPVP8zr6DAh/J7bW92dZBDfddBMA4HVv+2MAwDifJfUUNTY5Oy9J2Dj1ymScqva60ax4kGyi8y0r0373e8YM3NY///UPQ8xEptfOT72pO/B5fKtV1bTxvlMHbum6EmRFS+qOG3qm7KZ7sXqsJAbtvGZvM6fU7fyenl2DD9nfhMcetb8B4d4levaIydis7qnNQQdX3Sdt1FK0XWDjTb2PhGSTnW91sn5yank9fnqvMM1T11UvtfVfeCUAoNxs75f4p3Jk1s5XP5m0I6xeU4WgmM62jI2ffSP2LO1rXaQq7KTGBgpEHRwcHBwcHF4YqG8Us6H8p8f8+uzTFnEPPmAR98DDVosvjYZc8tTpUFUniiqW62YnaLas2X/se1aNoShKSmXBC+Xd1YVWzIFeJ9qNuUgwKn7Zy14GAGhrsyj08quNsairBwvP4tdv+X9tPQ//OwBg7//4OgDgwL3UmrA3irQqaSrxI3ytvH2qmdoSViDMUTn/9E/3AQB+MrS8fPJCUC26zuNR6iGy4+azcSW9FBSlbuH5SHIpPUAitfyOh/KlUACt7q5yBlXu1/fNYFWKNBnSbigHrmvoiaKITAY+1/qivGmaW60KYSf9DuQuq5yrfDQm1OmW7q5dZEI2MScrfwFpIWIdfQCAOH1BOqlv8rUn1KL4vV1aLQKVG+4f/vV/m+90+fj2Z8zLpX7U2ENpWcrUNogJ8s+bWLwGVsnwmmXVcyRjEeNgnW66zEFXa+p1YauN80Ab+Ls6tQke2a1k1o6nnXopMUzy2qmTYVE1xhirB9Q9VEzHDCNsvY5nTfeVOcu+H+mwyLYxYdsVM3WIDq3qSip90iwjzgoj8rD2RpGo2Fb5CchxdTG/DT0TJufsvO4ZDVZJiVlpJfOQjqW5XTt/rdtME5CifquH2x/fY+elkd14F+sSq66kGlf1lDF5or/1fqJF/h+2zHXTK4nXV/2Vqqxoe45M0HMz81f7PPYAmaecaXbETBz8pen0FupVomePGIhmXocdrL5qOcvOq9jdbLddb/lbiEGsk9HUM7+R2iAMz7vZ5+Fy7u/mV5veL3bpNQCAmaxpbWaoaSqxqm0/q4WeHuY45vNB404MW54Miyr2+oJSo1MLzmfDwcHBwcHBYd2xUcxGsUhXR7kl7rOoSozG46y5f2yBGm4prItzy1MML4aDjIZ6DliesFYK1qqHmQ0/TzwVZAryZ1pU2sgo8cPXXGTr6zJFfj1B/wr6eXhF+37kaev0OXLPzwAAQ09YVPfcQYsenqVmRYxOC8+PlNht7F+Q78wE9k+9ZFZae79cSAPy/SM2e3/VbZY/9vOi/F68z44rf04wP3w8lA/buqLUAEQZUYOdgn3UgpoDaS/qfrUF3UsnmNunNiJKdsqbmp/9iXN9UV67rta+4Gallue5lx9DBzUaLcqNMxL7v/7i3QCAW/7n18AdBXAsklSuXPB9OHrMu6RKdXuVOdH/9blPAQDe9P6/AgB8+0bTC6l7ZnmPqf9rU+OB9RWHRgPbUaQXz1uEGyvxGnE90bwtk/RH6A6dB1UcST4lLYdH/jGXyASOt0rGA9vNryPCSDAq3wr1ZuF1jjVZrn+MGg8vYvfK7HCwN0l5Juir4oUeemKk1KNGTqLS2hRDXWvV40bde8Vk+TIx+UTIDbl6/Nx/Pm+MxFNDNv52s2twPjROBrk9MR3K6XdSO7J1kzm/JrZbSN52jjEFPb80LdPkMjUIui71UETqUXMTZcfsOGlZefHIy0jajEOLdInVM34rq2dmyQCO0ll2qXiEurHO/WQELzStRMMWG1fSBEUaTfvkpTj+aqv7G7KTPWRSl14FABhL2/ZGqc0Qe63O12I0xKRJIzTBvlVayr8lGVv9H+mNh+eqURwcHBwcHBzWER42jtmYmLBc/uSw5Y0HH7F86mKMxnrDr27hLHRmwGbbqumXNiLG2bzc9KYYwet1ccyi4rlxi8LamPdPakONlk+s05k0MmGMTuFJYzbG6C8yxvMhRiOcd51RfpyzZzEeSfaOKbWy0qK6uk6Jq4U0IvnvWxRaYUXBppfb/sXPuNC+mO5dfGWcIUcZIaura22WzIaqS6h58KhxUEdavV+ZtDGonH+JKnup+QWxQ1qmyAjEmeuN00+hu8n2Pddp7I00Ck+RdUqpMy8j0Zv/+r2B7Vz/dusM/O1//CgAoMrtCOrVIobGv3lDN7GqSb79f/+tHecB0yFV2JXV77nCSiFVd+iaiNnJdFsEmGm386dq/yg9XqojlmuPqlqH+9eVN+3IWJHVONVgtYaIAjl4xhgpexUyGGQEVM1Ro5dNnUyIqjGk+2kKOcHOsPuuxnyKfiWKZOU5o3tFTJRcbMVsLOSPkhTjwGdFnEyZfCQqnqqXgg6s4Yz1l7/8ZQBA+uJXAwAe6bfrvZsai27qscSgdFNbomqnSbKUc/Q7kWvwFvpzdL3MfB4uuc98N568px/Hg38+Zuw+isonRJ491NDIiVf6NGk1fPZ0wD6Xjmsxnw99PsrfZfjs2jc7v8ZjIYhN3U92umOvMVy53qDuzncG5lJVVxUyYDPL3G7HhTbexTBqvJe4lJRHfiy6bqPsCq3rqXGn19JqqFP3QxwfF/U2LWv/Tg6sTTXK6cDxODg4ODg4OCwBP/zhD7Fz505s374d//AP/7Ck39S9yIL/looVTVc++MEPAgC+9KUvAQA6Hv4xgI1jNIQWusypNltud1K41xjtJBidzo7YLFpVInIs3cR8oxiPGPPfLayIiHVZh79Ii+X3pF1Rz5Yx5i+fClXhLIRRMjFSxEs5r6gjnbMoSX0zFlKGrzfuoa9J4l6LtlTt0/2WJTAahCJer8ROwdS7SFUuTxR/Kd8IMh9im6YOkE1ijwn5BniMVCvslSGWS91E4+xZkiUjklNvhz5zXmzqMJfUXW3mHNDq+zfY/n/0/X+8pOOUZkOddGvTjChzrBYo2NiKxMb4A96KI8YSlrlfE49a7v7wL4zhkEPnoUFqA6gJUM8KOZI2U2tSD3XkTYeqVeQxE8sYoxMhE5GlDwdX9zzX2JkyxybddhNk+aKsKoqwI3JN91qdrGPEGIqGrrMBAMmz7TiauT/qQKwKKI0xMTKVpO1naZa+IDw+5cblDDpNBkPMhnqkNHN/9D39Tqn1+JyNE2lQxMSEIa1GlVUvcjJVDn+EbKacZhXxqsNzIeRw6fszdJg2oeFCq4Db9hpjtDazW+vBBTQUYgYKh6ySLDlqTIi8hUp8Rs2SESuOGDNYVbUFnyly3hyaW54+TF1box6daVfYL2kvq182U/+X7aT/CJmYtJ4HYsRq6pdlTMhSGRVVobTssvu9TodhMRJ5OpGGPZSyvH5irHTd5bsjxqzZ1+zYwErGVm/3vRaoVqv4wAc+gB//+Mfo7e3FZZddhuuuuw7nnHPOwj9ao2oUx2w4ODg4ODi8APDrX/8a27dvx7Zt25BIJPDWt74V3/nOdxb5FR1EF/q3RKwqEaMeKfePLk95vNZQxN+wyaIeMRsVVnfMcqlus8pvyqk03KlyjMzCZs72m86wqEDKaNV4e7PML1NPoGhhgtUnS2UglPfUfrRx/1v4e9Wud/H4sEyl91pBedwD++w4Nx206GPptSjHEKH7rEdnyOoCXV11TlWVoJ4ZYjSmjgSrWKSqVw5abFaS/XWSVMurekVMSSOrWlK9FkE2n82qi06LwN/6trct7wCZU1YViapHPGolIo02hmKb+oI/67dI9m1fuj3w/ktvtz5AC42pFHPdW8jEnM0xUlVVCc+fjjfdYZqElNyA6ffh5W39yRRZNj+wC/qiKIKfYiScpBangV1n9Qiqx+18q4+QerGUUvZ+e+c2bs+YkTb+rkjn0GQbqw/yxnAU6P8hhmW2HGQIxCSUKozok/a9fCao1UhQc6LfF3ig9WwusL5kU1AzIKTTFgnvZu4+3PlZOfxZnp88P4/5DEfQPXmYEfkotTJpaojkRHsBNSAHyYiFIXZUXVbTm61/kqpPCgf22efU/EzsNYZMz8ZyqNJNLOtS3YnFiMh3plBdGbMh59Ih+lk077b9jGeCPXTkM1qdtvE8sdeqmRarnpHH0zZWTfnjq2znoY3XP8YquapHZsqz6y0mrIPOtWN8ZkszFPHkT0PvpLgYksWO/MTg0KFD2Lx5s/+6t7cXv/rVrxb9nev66uDg4ODg4LBuqMNDDRvU9fWf/umfAAA7d5py+t419stYLi5i5C/HTUH5evVb0Ozb7ybLpaICudqJ8RDTcQbd9JRHTsnzgdF36Yjl2ScP2Cx8YAHXvMWg7YcV4EnmA9t20jXxOYuSl6v4Xiuox83FkxYNFG//CgAgdfWfLPpbj907I5MWiZSP7ANwTLOhHieKxFV1MTfGiqFZe18Ok0UyFrrWVUaqpWlWyoR8BGqKZKnXmeK1lZq9mRGZqh+iKcsZ/6t8NAj5g7z1HX8UeP8P/4+/AwB886+ut/0Y4nHS90O556R6t7CDsBiQIz+ziPSDuy3n/pPdpi15bpF+QRozujYa032MELd02vpbz7KIrmWHOSSqJ4fGsrQW0pCoQ0icYz5DbYV6Rch/QJHbrBwyM/J54PlXyxLde9R8NDZa5B6nViRFtjTWbpoVOb+qWkAMyaSqZUJVAGmfuYgE3hfEQBTowXOY/g4DPG95Otjmosc/3+r8rPVpu9KC6H1tP7yUw6QiX0XCqoKoZ6iBobOs7v02MlbhrqbyxRh71sZbuv2ZwOfSNEwesPEk5k+6sFyHjfc+doCWy/E0v1dehOAQEyLn1uW2a5IDahfPn7Y/Q23S6DPGdEnTkx6krwcZTmmZFqueuYx/K3KdHFd0tE0OGqOYzgR9XqIcl50NxnAl+Ps893OQ40ZVJ3N8fhzguGouUUNFDcdGo6enBwcPHvRf9/f3o6enZ5Ff1X0GbjVwmg0HBwcHB4cXAC677DLs3r0be/fuRalUwte//nVcd911x/1NHUCtvvC/pWJFzEYiYfFOJpNZ5JsnBpr1qyZf+XhFuVLSj4aYDeUVF5q163uqWpllV9ms+j2M2PpGHrMeMaOMQsMdDpcLzSIVrcs7oqHX8osXb6by+umRVW1npRDjo6hI+oSlQD4SYd8M+QCI0ZB/hBiN2UH6azC3XCCjodfyUtE5q0kdn6dvAiNPVatobIyz/4uqVwqsWukggyIX2RSrVWr0X1BVzTf+xVidt/xRkNV586duOe55+H9ebHnTGLenTskPfMf6C0mbEe6APPD6FwEAur7z8HHXr8jX918weY3P8ETkpEntRmPmWQBAQr1U6AAqXwvl/uv000jwPChHfaxaxbZT4TOCzU0xRSahHPIxGC3Yfjaxl4dHrYKXsbEuX5bJmEWY+4Ysst9HL5z9Y/Z6iNU36mmjKoEwo7BQh2ZpN8Qw+E6iiyC8/nCVSfj91hyZBDIb3TnTrsjHxa9a4LhTFU7XxTZeXvysPYN+Sq2SNBWqSpI/SYHPKrGxk+wfpfGucaB+TA3ddn5VqZc6EtSGPDJx/EpDPQpm+ExdjGEII8+qjc1kIjM8P0X6doyU7HjEyKRb6aHE+0T3v/R76iCu9UrL1Ma+UzpPqmqLxO8GcKw3U0SVh73WiydWYtUUx2c0G/zbp/tM4yjN65EK9YDaaMRiMdx000245pprUK1W8a53vQvnnjt/3x+hjudrkla07VWvwcHBwcHBweGUwLXXXotrr712Wb9ZCzvJVXV9FaTwXe5sdrXYQUWwqlDkoeAzG6HoThG5XhcWSSyK2dDsWl4NxcOm+Fb0XWYUPDcZzJevFPq91qftqDdJ29lsIbhBzEYPow/V6KsD61Lg0dmwRmfQ6jhzyLxmYjYE9a1R7lYRid+dUir4RDAjKCYjRRV/irlWaTgUCc0wR60KIkX8kagxDdP9xiLlt1v3z8xWi3TiO6xfToX1+d/84k0AgDe/+4NLOQ34s19a3vRD+6064H5pMxapYFqM0QhDzEiakV4vIy+56RaY+46lTCOhiDrW3QcAiLLyCoz06nGL6GrUshSrdt4PT6nSy77exkhSj4QJMkdFOXyqIkyVYFV62aSN0VA3Wn1/YMb2/xAZjN1DNh76R+mVw4i3GKpOiYSYBWkqWvnsUJfOeMhptOQxwsf8ONRsejXpwXIpdnXluBOjISfRJvZCac/Ks8e+v6mBLDEjcDmJ1vlojjRZfU7bBTbu2h8zH43LeD3FYJ3ZGmQoBLGycjNW/yhha5pak25WYaSDfxJ6fd8NWy7Un2lghX2b9Lejk0xGY15VJ/b+BCsdp3meW6iHa+y217q/W8+ycfMKMjWV0P6IydF6p8ncHPyZaVuOPrQPAJBppQ/MFmOUmlglF202zUi8h9VrXXb9y7wv1M1XVVLh6hT5dpySqDtmw8HBwcHBwWEdUQewFpW7K5psvO997wMA3HHHHQCAV7bbbPBezkLDeeb1QjOjgRijBCmV5yaDTMNMqLeIGI3F9lJajnFGX8r3J1roAsnovGGz+W80b7PodAuj8JU6qsqNr8hosch8abLJjieRtePNSrG9XOn3CqEo5EVkVja92HQMmdd/aOkroTOoqi/EaEgPo6qQGFkq9asph+rn5Z8hx8xELs7XHAuMyBo3W6SXpp5H3VCn2Bk4xshudtj2qxDyMFG1yjh7NbSdZ/st94XYucHup8vFz56y9a1XR1+p/M/kmJG+qXmnOVWqGkV6p2irVakod42kRWxiNKo5i7THK1Tjz9jY7J8MMlJiLORLoEg+mbLX0m6IuXiO2oupUjKwHuXAtT4txWgcZOReZOTrcYxWy/Ozi1OMoMVwbG2ziH6ajpnSnmxqzs3z62MQEyI/Dfl3nNFmka4Yk15G6ury2kWNRkNS54XVSTWeP94fGk+1HKsgzrCO092XG6sqBqKP1RBNZ9gzSb4cgu6vZCO7vfJ86ZER4XGICRTjVSRTqM8Ti4gOVvvM1691X4OXT89qPYvF+ma5bOozX5fwcVdL1GANGaMzvseezWJIhw8ZQ1E4YGy1jq+9265f+4zYZLs/MtQsVanXi+TtPmls4n0TUVWUbV+nS1VayehJItpYIVZomxKAYzYcHBwcHBwc5kW9/nzpxEqwqslGoWDRSN9/MKV0/D7LP+9nXm0v88+L9QZZKTQ7VyWBNBVSXg+wW6mcOVWLvljNeBia9Wa7LSpMbL8AABDJ2Kw2vsny+6UpmzWfx7zo3kdM6bxc5kHaF0W7LcwvSm+gPgbr1QVWDIa0GWcyz7zpfIuy+n73fABA/mVXLXvd0Z0vBQB4+62DrJiGaoHVJ+NBFbyOVTnXupwYFRHSEVQuq2lGqjVWPyQaLSIXU6LtaL1ajyI7OSrKp8PvT8P1Z9osQioftQgz2mrXXn1yvvV3/xkA8MaP/vfjngdpLw4f91vPR5wBUpohlO6tVCjybGeOuI8R9nmvtB4Q7RduBQDkd/bZ+nrO5HGYj0UtZ2O8nrZIucyeJ3JSHJy18/r4oEWGY7z3RnmPNSSC/huK6AV9LkZjmtdJTqTlapGv7f1j3Wbt80GyfdJoFPi6UlZvHNtOgRF/mX4cNT4sc8zxi5Eo14LPpqXeqvLJCGs0xGg0clx1N7CvEbUZbaq2YNVJpGDVIV7Jxr1XJhtaCzJdkaw9a1rON4ZDzyKNc+m5oo1kXak10DOpQd1UB9nRWpVuZIfDfjWq9poL6d7WGsfYZo4HXjdVo8hvIxcLMofN2+w4Oy4x7UR2u50X9fxRT6XKgGmR4pndAIC5X1jloJ6t0qL462efLFXlZDrsPpdzaYbdkROd/JuXtesQ9VJcsotuJdiNeK56Cms2sHzflPngmA0HBwcHBweHeWE+GxvMbMzM2Cxw2+suBwA09Nrssaffor9dVEAfYm23NAxrpeko14N9MLSU8vo55t00e13uVi9mFLTrdy0abLv61QCAwo4rAQCzPI6WdosOO+gZoWjjVcz//+wwo8BlHrd8/s84orwr+3os0dVvqVBVz9lUtDeye27bTsvPN+2wWXzj2dYJNXWuXe+qvBBWAHU/lf4FsOoM6VOUu1WkpRy1cspxRh5S30tzoIqkCit4FJFE6dwp9ksOpL5Ph7p2ii1T1QYj1ojvaGjXYoZdY6NNFinFqN+R4+XX/vRqAM/vcXLeD6yL6/DCp2ZeNIf8ArI8LkWc8llIMtLOn2HnQyr9npdfCABIv+gltsIOG9OzeetgrN4gWl9ZjMKU1PV2XQ5M2Jge5L2lh5AiOhEsRa5PzMXE3Pyaljh1UDVG8kcK9FXg+udCrOjAeLA6oUhfjRI1F2K+ZicYwU+P8n3m1KM2lsWMDHIppmIhH44w1BtD40NaEh2vMFchM8DDOPbQDvp5hBmNCHt1+JVejKjjm3fYy3b2EqI/je4naW5qZDbaE8GeIqq2UjVGPBt0tlR1jZjE9WI0BDEXYT1dS7M963Ks9si0kaGU1ubaS+3zy41djfC+87tF0/vH43lr5vNg4EF7zoitDjMm/n6Q2Rl71rRaen7o+RIbtzs4Qk1NrkF1S+qJYuuN8/hiJ4vRxgrhmA0HBwcHBweHdcVazDlXNdkYHzcGIXWtdcTcfJ7lr6sjpt2Yfc6Yji3P2Gxy0532+Vop8FW1oYoBKdD3MO+3VCdP5bvlyy/l/s6r+gAAZ/3pWwAAk+cYs/HrgxYdlxmu7Gq3WXXfi98AANjUYFGGdAINPzZ9wt5nLMp6kJqWxXxJ5PMxqe6vVFJr1r1SaC5/RYsxGee+xJTcfdeYd0Rmq3XiTGwzZ7l61twi5a0wlbEadEW/6tS5vJ2goycjL52rNB3+FGGpl4k0E77zJb+X7rBznaETpq8BYSQjhkR+Enrf75tDbYZm7r57K19rbEi7oUqgukJVuacygopk7BzFt1gE+j//mNUTP7oHAPA02aM7eS0X0/PoWu1qsEhv8xZjLLId2cD3VG2Q7WZVwlbTYKRa7PvJneY8Wj3LmI2Rsp1HOXL6vR1CTIK0Eur5MMl7dopLVZs0U5uhMSFHzBw1GtJ0+D0jQr0itP4pMhT7h+38jPBeljZCfYekyVAVijQb5SIruCYt8izPGKsaz+YD2ytwf/aQqZLvRnmpzAbHQRcZNvVsOciqHPmKiP2c4PmKhwiehhirb8RwcFknUyTmAmSAIny2eHQWRdy2L2fbMiNtr2z7kWg1x8tOfj/bZcza6JP77TyMBD2JNM6rjcHXO8xmwmcewr1ZlgsxdS3SvPB4xTjofs+fYdetsc8Ym5Zdffa9V7zZjrfZmKqIei4VpYGxcS2lRIrnMZm/D8AxXw8gyGgKYjylZZkZsOeHHHcT/BuXoA+NegDlqOEo8Pjku5FPzM/snQqo19emN4pjNhwcHBwcHBwWxFrUIqxqsuFxNnokZbPnhp2W/02DyvTzrBoj++wDAIBUq/nPx77/KADgaTIc96+wS2o6VLssR8vlVmnkQl4EfZdaVCgtCi66BgDw0IBFW08NBSsmtLl4l83Ce3dZ9NgRUrrVebChAAAgAElEQVTXquZCeZQdChdzixTzMcboslF5REZ1K8VLGV3v+B3zWtj2ussAAA2vfTsAoNJmzMYoiScF8Wkq6JXfb1jFbD1xxe8DAGo/+iKAY8yE6uKrY0G/C+lVkk3M4XaIPbJjUTfVGJkNvVa3VeWs5dch/4B0yFlUY0iRjXLEfjVKh11jMSoRRjaKOGuNFkHWE5ZjTuYt0uzlccirpeFeU8k/S33RYV8zYue2XY6K6oZJL5s0r122014rh5xosNepVtu/zCbbj2izbT/aYXswTEbj4ISNoSPT1GJQC3GEy2NdVINdTLWcZuSu7qp5MhVS4asrpnqNKBLeR5+Hg/LD4PrU20QaitGJIIMRU6TI17NclujeWy7KgdZYR2k1aszhJxrYy4X7K61FA/czywh7qQFcO8fLNJmLREW5eluPWM94SFszxfEV47Mry/5SkQT9TLQBPjsiFd6EMXbPjbHaIhpkNKbjxmAN0Wk16tn6WltMT5Y9zxi3Ro6HZNOvAQATz1m1harA/v/2vjzarrrMct/53jcPmV5mEuYgCWEuBUGFogoZLLQEpQAhIGF2WECD3QbLpgB7UaIQrCIqdiECAWWoprCKJdh2tVpUSSihAQUNkHl683Sn0398e5/HOXkvecnLnG+vlXVzpzPdc877ffu3v72VpSJIKxGe/38w5mCZfsf+0bHTulMcwq6cWWSS1JWk80P39DyZy9pJpjlSF07hqA8DANbVGBu7fK2dr80Fuz+Mr7V7Wm2tnUcpMg4ppho3HWA87ARqtjK8/rQdteomom6ticxK7SQ7zrq/CAHTm0NmigxLC7N+wkiZiu7ZhWGOzp6NADtGu+PMhsPhcDgcjhGx2zUbra1W1a3qtlFb5wardobc8myUOf4IG+WNZ/WnnvCan74MAOj9uVV5r3dvW8Wueb7Q/U5Jj1LG83Mj9YDo/SNYNc84yuYFp37oUABA4cQzAADvVW103zloXSWqxlT9aV57E0f54+lZUKCHQc0EmyeVzkCdA0kyQFvrUQkzWrj8rWW6bA2z55knxKRjrCOhMMMeKw22/x38GTQPzcOJmoyN+ptyPL69zGapUd2+/VBFrspfkEZDkG+G5k7FYFRLSn0tR54PtPM3o1ZDjIXSLtXVog6fcqxSUxdDw1RqJaawMpxov7E0GglqUHRVSt9S5mNu3skAgCns/6/jctqW29xv14pOPnZxP6LdOEM+HzXcL2oD2Okl5mfcEdRKsKIP01qzds2J9ZOPRVyjUYw9Tykll8dBGSO6BpQJogyIroEoW6drYyXZy/98l46OZDLKygHi7zJILUVV57y0O2UxGZ2R5VdL9vmymI3+nsjzdN72O1dnFXKKTEOa9ww5iWr7D5nYgNFAl6CYHKXYqvtEjIbIV2lBQt+Fsr0vZqReWosKzz92pSTYBaTKOaDGI2Dl3J+1xzXUHr26ltoF3psObjUmcDKX3zCNjrvsnmqiU2z/KnPGVBaRtE/qwlA6aqHVujNyzGVqoRPnSKmw8oU5Ywp9Qg4kQ0BmqHetscWgDi7N6z1FJivcDmpO1AEnH5e1dLDVY1+jXdfS1LS2zrTvK6OETqNxBkdZS8qIaZxh26kut82ddu36DwrR8yXBLqK0NBxkOBPV0Sdj72kIgiHH37HAmQ2Hw+FwOBzDYrf6bNxzzz0AgNM+fQkAYCWrQrkADpRt1KxqodBoo7+6KZal0XCijdKns/qcy26S13+zZpu2Q1WDesVVtWreT+/HJRyqnT/QaFXCrPlW0U/5E/ORaD7OerjLLTMBAO29tl+aj20lc6PWac1La/+7yAjka20ULiZH8+xTOF+ppMTRduXIQ2H8drZsz+X+zviIdZko2VC9+UFFven2eR0/DWqLmoeWMp7uhwM9HPXXRRX/o0H+9AUAgL4n/geAIS2GNBYFZqXEu040d5rK2z4Vu6xCko+GGAk9qitFFVNVmoRW+23yzcz+GIiya+r8UeZKMgw/4Bw/u1Hka5BklkiCx6ZKdXq52fRMuSNOBAA0k+WrbbNzpI6VI2AdWxuoZ1rPilH+EdoPVWLtdMltmWQV7F+nmOnxurGGj1zFzAhWynk5IfJcnkBmRxqD1AT7/gvf+GLkOHx6kaXadrFbpJ3+FmI4xGgosySeEvkHdt+sW2W/TzdzlIp9ZCLYPVDut89V+LxCRqNMxkLOkOmCbafmzMPPF4dPIBbzlKO2pIX7fVib3Zs+NmvbPGPUVaK57A7uv+4BqqTltxAyHjzvxHQoiyWVZbYPmahsgucv2dQEzzM5u1YLdq310CF1Hc8HaXDUxTOUPsprlBqjmnFWmVc22j23MIEdb7y+EvS3ULdYgRqguqn2+fqpxsjV/tYeU8vs/H23L1rB654z+3TTgdXwfNd1KlY6TLguxRxMqSVRDpUYHnVBiUFSGvBg6Axr12GlasdzIq/DQpuxuoWVxtA0kDmTZqswXtelfT5dE/MpoZOr0pArOaUh87rn+anrLVemZoy/I6gd2psQINjMaXd74MyGw+FwOByO4RHsxiA2hbKoeGlhtaDRvKBRpnqN6xhcoHnu2plW7U0+znw4ZrKaW943uvmtMB2Vo2AlPtbGkgqlH86GmR+2vQdNse2YON+24weZmQCAm6i1KGVsVKuW7KkNnDcfQXGuKkbzsC3NpmWQ58KEozoi23vEv1lVoF5zaTMqMVfGZq5PDE5dA+dhWfWOlhmZPcmqwZDR4DyoqpiAiuksNRk6v1SFdbCK6qN16UQmgKqnH9h2ZkOoOe/Lts7+2+z5JGY/kP2SpkOMgnwHVLGW++zckbpeCcBiQJQELPYrTw1EKhu9BKQBETRnrOWIWaj22G8ZUOUe9FnlogosVJoorIMVmXwR0hPtnAuoOciTwZGPh/wk9Nvq3O1dboyJ3GiVgvnDWdHUyxCsSBL8bSexm0d6nEoQ3d82nnsH3fF3AIC/v/nzAIADmqgV4f7LMVOpr12xc1CV9SZWjhtX2/HpZBdA10rz4OnbaL9XmczQ1pCkn0mK3RvScpT6uob9vJiQoUfbP/lq6Bobrb+GsHTpUgDA8X9q3jqa09ZxKPBeoApc94x63kzycsKVj0vIknIF1G5Ia4OEHbcq91u6ra5B+z1U2b9B5ugdHudO+qfIIXVSLSt4OW3quPO46npK1tq9UflP6Ul2vsrB9PKf/8C+18hK/cPDV+yv6j+Ud51wkH3/6oOMgdr0hvl96LyXdipMc6ZGS5CGpZYHSkydjr/Shwdjfx2byGAW6B00jtdtyJSSwdB1HjIZcibl+SNHUjm7JvKD/Dz/yvB6l3ZD7G919R/s/QkzsbchwOYZQtsDZzYcDofD4XAMi90qEL3hhhsAAC+vsOouy1FgHZMmQ9e8Qat+GgdtNeMKUddDjRI1T3YkvQSWvxNVnI8EaTNUrSjBsJW90vNZLah6CRMEJ9h6JrErY9yRptW45YJbIstXddpYsnnDKc02On51NV0Jw24U29+jptp+LF68GABw1VVX2QJONofV8XwcaDedQpU/4HgyHaqalUugZNJ4R8K4Q63qP2qFVYurVkd9P0bC5KNNmyFGQx4MYVJi1n4fKezVZaSOAlVnM5tsHrY9YcdD1fKOQN2FXx3V5/p+8rcA3ldp8NjJibS80Y5NH50o41kQtROGP/Wlrwm1HuzaEMMhzUaxgzk4pU1cLisjVTxkMDbLemRFGVaS7AYQg1MNMynsYzp3xXaJ0dD78WyHOFJMp0wdcHTk9XjXRSph+9MpTQaZi6/e+2B083lcwuyHWPeQulnW0yejY70d/02r7Dj1t9vc/rYyGoIqzIw0G3xeYpdKEKvAsjHnUAnd5EiqrpyDJ4yuC0Xo7LT1qQtFviK69wipRPQxx/+I8SikoyysGKgEGZtATB79NcJulZStR/4Qa6nVWE1GrEO+KdwuXcPhnwxpi3Ttx34HdTEleY9APVlMVu5P/O1/BQCc94W/xmjwsY9ZVtDCM0/kftj5lWkz7qPxXcsY6mJ3ljQd8e60BPWAOo76m6PfVT4xYtbkYDuD2pECM4HEdqfGUycYcwLe7LnWr+NFZiTRbrnNKb4e+qFkyMjQ72VvRoBg9wexORwOh8Ph2Hdh0yi7ebDx3m/MEbRw6AkAhkaXm8IERCZnsjqbVEfNQI1VG1L2ZqlBaGNXSPNWUlJrOao9YqYtp/UQujimlNBJ332O6usn23oztRrdGgPRMNMU2ZqHHAliNIQj2my969kBEa8SQ0ZjBIz/kB2vtDoputnJkFGyKb0N5BXBUX5cKR1mpDz1uy2uTzkE9dPJZLCaTlIZXa231/vSnM+nC+Ef2Fkgpf14KvjzMVfEHclsjBY1n/gCAKD30b8BMDTXqjne3tXGukmjIUZD7JAYED2qglJXgCAlgipmfa5M58r+jVbhZuvpbMrPp3hsVQlBmgF1Fajrgt0AvattUruXXRuqhFXxao5eOiXpfKayYvtq0iq626jCf+zmiyPbsTW0FGx5YjbEYinjRJ1X8odIJqJdAeo+UWXdy7nzDmaP9G20ClA+GaEvyTZCv4P8NMRcNEw5JLLcpJgldVPU23HJ8Z7Q1kTPm9z23QKvvfZaAMB/rrL9qWeFPb1xeIdI3RuzytiJPaaqZDTIXKjyD50pyXjIZ0PptGKgVNF3dEe7l+QjIu8jqeqkBVEnWoIapDCriJoiOeLKqTSuQVq6+C57utxcoXU+S1v18Bpb78V/aq7KpSbzXkJKzImtp37aWwCATK39Tdn0+nJ+jAxgigxGwbajg5oj7b9SgnX+yQOprdH2p3vQrs8m7ke6ifdc6dV0XZLhqfL61HUbdp+R0SivWh75vI6bjqechdFoz7NzTsVei2Dz7rLtgTMbDofD4XA4hsUewWz09Fh1sZajanWf9LA66qASWuKSiXU2Kj+Y2RsZjo4b1Uvfa8v52Fs2z/UsM0SUjDmZo/Sj6aI45VhT4MsVTvPmrXPo88H5Ork1SquQrKOXAyt8vb6tGN+wfRV9dtYRAIAmdiJo9Kx5/vg8oeYPk03cD46aD2mx/fgLdvH8mK5+cfwJj5eOg0bzaLYqo9JoDE9HP90eqQhfx3ng16lwVxX7RoP97tOZGvvBA1q3us87GmI0Kn1U6ZMFKrOrI+zbJ7um7pOG6Tb3XD/d9Do6R6TNEMr020jRx0GOgyUuP3Su1HN+X2mw6QZzZEyw8+ov7zD1/mNfZv7MetMsdC+3ir/9LflsGOR/UYnl4IjxmMhKeuoJdu7XTzH/gh+e8AH7nNIoYx1iI0Hn8mvrdS3bfh82yVi719fYOXDCzKgfxeJfLgcwlNK6nrlB8tEY6DI/A2WXpKgFSNXbcuSXoQwTMRPyy4h3maR57ub4/Xyj/Z45erzIgVIFeIFpuQV2n9TxHqRK7ZJjpo90SEYF6bbiU9oZZXzE8pvqslGtRjpgVs8gK2Rma6jrIlGKOl0G2rG8nc/S0MSdX+WQKgZnAve/PiFNCO+RPE/Ce2DBztcKfSnEpCA5wp8KMYJ01ExQC6LXL5ljlX2FjIIyXURaJ8lUZCcau5w91I5DM687XYe6ZytzSFoZpemq66ZHGUP8fcV4KM8JBd5j5cgaYzQqnXYPrbSvi+4nj/PgJmOAhu4Hdp7mm+241fMeHrLG2b0vCyWO3Wrq5XA4HA6HY99HEAS736580yZjIF57px3AELOhUaVyFJTkKCRn2qhv5gTLIMlz1Kz6eDarxXNrzU2xm50EuQYbNU4+2kbRkz9kVVzuAJuvlQZkaEWscqT8b+AaqlJ0Mzci2L4e4t+ts6prW5XsVXZ9ZGba/ou5COf3s7no9lGJXqGvRSnHvIdaO45zLjAld//9/woA+A3nLTWvP/2D5vdRe6AlJ6boI1Lm8gYD+1wvq/XlZKrWcd59E6vrHs2P8vnhk+q3ab93Bi56ynJnHviwqcxVCRVZ0WTJEDTOsN++4QA7d9R1EmaqsBILnUd7ByLLk36m1Eeny9j7upD0ft8KU9Uv+If/E9leOY0OrrPKabDD9Ekdf2TaLa+ZOHSGFuSs2mTnhBiNCcfYuSTWTBWqzrWtKSR0LkvDcNTscZH3xXDEcdWJM4d9/cCFPwYwdDzFaISaCjllqhKM+TzIP6NC35K4BiNL3Veev2+G2y2H0LyyMVjZP3n5CcNu51jxy2cfBwCc+6nzAQBd0qlRHaFmIenWaviCNBpiNJI9xgAFm4zpqvbaeSE/l0TeKvr0dP7+dcbM6W+A7rlhqi0fxT625Hnc+6LsZ4I+GjpPlLkih1IxKUkyLkk5mpJ5ShTt9SoZKGmRErWxe4OYlLLd27K6t3H5QcZeT7bavSo9ybQfwcrlkeWnmcdU12z3PrGtyuhpZDeQmJ50Ut0/0Tynyhrz96i0k3lbZ8e9Z6Wx6QMbox2R0mrpPhB2pYnhYKZM/WHmkq2/OSnlR2Em9mY4s+FwOBwOh2OnwXw2drOp1zXXXAMAeOqppwAAi9dZNSQlsNBJR9CarI0WpzChUvOWU1roTjfDRq/jjrT5bM2P5//I0exEG31POsFc4GrmnwQACMbPBDA0GhdCRXfRRp3h2ExMRiq6+yVWE6r0c0xl1Tzj+j729LOK0GDvLVWFrB4aB2y0nBygqyT98xMlzsOKWaltimyPGAwlhmp/iglb/ybOR5a7bbum0aG0eZ4dj8M+YUxT62+jGTPSKag6krK8REV6D5Xdq6lkfy/Wq95LDU4/NRyd/N0+e9TY015Hi4Hnvx95rjlcoRLTTKQ4dy//DXX6ZOuZwqhKOZUa9vtxKDNlIEyTtXNKmQoZOhBe96r66ofvr692W6Xaw2yGvjXRzyVSw2ss5DagzqLWg+w3rGFnUvbAI237p9m5UOG5tDXNhs5dnctTG6wiU5fFkZO3zxX2rfv/YtjXJ19gzqRiJtJZOrOqMue9I0NNSmi8ymurwoq1sZWVPtk7aTVeuOHk7dre7cWll14KABjssN+zpmDbpYo91CbwZ0hTg5EoktEgs4EO0+yom6Paa79LeZNV2nFn22y9MRsHNNl1cGhbzDeFK5zG7hh1FYGOo4E0GDrAYTqwfb4YqOvEHnJiOGLMgLRH0jSEGg7q0JLN7HbhPS8gYxWKauS8qe+r+yYbzSQRA5Hmcapnmuth4+3eqs5HMR3q1jlonFJvqYN705xrS++8Yd/jdSjn4U6x9GTj5Xmk61JMqTRggrRflW77fsjYxfxt9kYEGGLQxgJnNhwOh8PhcAyLPSqIbe1aG20ODjKRMKz8mfHB0WkfR58ahWr0KaWzeqnVJSKI0aiZRAX6ZOZANNv8e7nZXBKVSRLWcpyozhZY/SWiPesJVRVcv0b7OY6ukwNW4TM4EFkyIfKX0Hxg2DOv8oWj9nJLVOkuV0D55mu0X80ZkxFQi7GJGSTdvVRAc3t1/GpZzYkJyUw2vcKUU6xqaphp84/962yU3XSwHZ/MVHNKLVJp3s716PE9uj6uj2k1BqnVKJIBKY4yi2VHIv+xzwEASi89DeB9ffCE5liVgZKtk7Moz0UxHPLRkDOhOoF4jqZYaSXIqgVhyupgZHnSVqjS+cLbo3NxrXQZsyGtiCD3Wy23TMfHRmVAyHGSFVUdK9lCqz2K6aloLlwpp+xgimM5HVZVCUrdr2unmUyCulDkQHn0tLGlVq760ee3+P5hNxhLKsYiQWYmxe3M0y+ilv4Jz1/zoTFtz46CKv5EjzERZfpJ5HTNK1F5wI6ntA8B/ULU/RA+stOvnx47Ycpxs30vy/VNbDXW8hBW8HIy1b3pAGpW5LgZsqu97ZHtV2qpGAbZoIQdj2JfWbkP/t60Uu2/t1wrdWMpw6h+FjUa1NEl2e0iljhg7pQq5qQYDWo7Qh0bGYLqgL0u5ifdsgIAMLnVOhsPH08dm9Jguf8Ht7J7qd2YmNIfXgMAdPzuXVsNGcue1XTQpXt170a7RyuvSlqpPJnMphncr4yYUbs+Ot9cbus71JLDt+zvu5fAfTYcDofD4XDsTFSDzVurtwc7ZLBxxRVXAAAeu9eU98pzyHKUKYXwYZNtNNhMxbhG35ofTOdtVKyeb3UOaL5cHQRKIKzWWdUmRqOTFbqqITErhQwflcBIRqCcos8GyzmNsjNMMVWPdOjql4hWDfqeqk7Nk8Z708Pe+FgiYIXzrv1V+14HnTt/t5GJjWQQkjENzOzmaBJikl026baZAIAmutg1cN5XboDlJtNY9MOOfw+rESUlylcjTOyUfwozWsRQ/e8vn4LdhcQk66RJbVoZeT302RhQ2ms68rx7pWkjGg+0ylAaiyT9CrLN0Yo9FZsjVwWkLpRK1parc3LJkfZbLnjh7WG3e+mtNrdfWmWdQ5naaB9/tSRnzOhvPmG6XTM56pykgpcLbqqGGgE6G6a6rAsmdHxElNno7++PPM/EzmXVL0rVbKR2or+8fY6f24rXv3lO5PlxX/tnAJt3m8wav+tda7eE7z7zIgBgwUnWGZdRhS5NhDQLPUwB5e8lDU95IzNB1hozMthhzMZgu52vcrrNNdl5E/Ta92pb7Zo9nLlSB7BLSXlV42uoaenk9dJl368ypVg+MKGvBx/TMT2bNCbaXjEaK35hGohK0fazlix0/bvGdk8mQ5E78oO2IN6zEzwum0mUdI+kvkz5TUl2yoXbS+1Lho8T6A6dowuyWOapdTxvXzOX5WK7MTOlWDeZEsOlxdB1KJfmbDKIfE6vi2nM08lX2pryarvO0x/APgFnNhwOh8PhcOw0BEGw5zAbguZPFy5dBmCI0ahnD/SMJvV823NVVdmAo8teqz41etY8peYDNa+eoItglcmHGnRJ+b25mMWeS1sRcGY6o55zjqYzXJ7mE/W6mIlsEHUFFJOhwXlYHdLlToxFP+e7G1idlQo2z5rkZipltb8shsb2V/PkdfyeGCP1jIdVE5Xbmh8N5EAqRTjTdiucl9UoNWAdq6yTSXRb7J5g1fpqMizVnK3nl7d+DLsdYomo1xFj0P/qS7GPsRuAGSaqZKRjCTi3nJNWg2xQQF8H1fH5Vvpr9EYZgZS6XuhIqMrzkWusMj//3qcin0/SYTEd+kZYFoS6ZZTdorlfdblIE5KihqG+jU6F04xJkVNo6NHCcyIzcRaGQy8vEl17Sm8VeSbtRi5Wck5s3D1Mwr/9t9N3y3q3FVdeeSUAoPK6ZXsEnfTNiDEY8cwN+WhIc6TuCDEb0gyFzBoftdxkn53P46j7Qo73SDIRqbV2j6u2m9ZBXR1yKc6QRa7quiIDkyC7K6Yj2d/J9dNps5/OnO1Rx151cRTJjta2GaMybpqtN9Fserswa4h6tFATIs2UUmnlnkyGRhoOaV0S1L60sstMrLNY7FTHe9zvKJNYmGDMn457odnWV+U9t0Q2N9tp+yOmJPTX6Cnxe7z3ktHQ36z86QuwryDAjmE29gn9isPhcDgc+zteeuklpNNpPP7448O+f+utt2LatGmoq6sb9v3hEFCzMdK/0WKnTKMcvOIFAEDyOKvy6pVRwgo9bARgZa0uDbnEadQvTwRlnqRiB0ij2gZ+Tl4BnYPRrpciV6iqToO0RnZlSKux2fwhR9t5uuHlsW1Ycu+9AICPX2AJnKoaxWBodXIX1OhZmhYpyzPcfimtNY+uqkZK96Ck40ilO6vosAoasFF8XT01K1x+gluSDKtdW36R+RhKUtwTkJ5qDn2VP/6HvUCWKzPB1P9NB9NBlM5+SiaIOwKqYgwdRNevjL1fibyv9ElVLuH2MLk3U2trytCdVXjib/8rgKHfSA6iYkrK/VGNSc04VnT8zdOFdOR9pdqGrN8gO6b4m4MpnSMpLHQOxpOMHTsGytpQ10SYtcEupJCxUHptr5g3Zsmw+0Q+LmK00nm6M5PZqJKhyDCDQ90w4D1AWR9lrj9kVniPTcTY0GQLHVwr1JtJO6HOPab1qktEjJ5QUiYJ77k6j0Popp8cob6Vvo2eRGLshv5YkPng+a57XbKO20UdXh11f6GfibyW2F6j61SOn4lYwra6S3S91SnDRpk7YrXZOZknIyJmQxqu3YFKpYKbbroJp58+Mht41lln4ZprrsFBBx006uUGCFyz4XA4HA6HA/j2t7+N8847Dy+99NKInznhhG237Q+CoSiSsWCnDDa+8IUvAAAWL14MAMiccBaAIf8NOVaqgq6qy6SJqayNdMCM5SWEo3BqEORGl2KF3ySnTnaNSKMhE0WZQ2qUpp73FDUWZR4OSSLyNWObp5bD6gAV6EVuVxdd/AoZMhmaZ03Yfs6golzz6nETyHp5EHRzPpjzwIGqW1Yfgt7PdHPekt01TfQnke99gKiWZnWLHZdvnHXE6Hd6FyFFZ77ir34CAEjWW3dGbpx5scSZC6W8iiWTVkKPGLC52RSZitDzRZkpZDriDqNi39RBFdSben7p391tz5khUd1gzEmVlaaYknA5TOXU8qXuV6WoSkvb07eeuiZWaLo2Mk3RTBNB5+Dk5u1zBHWMDukPfBQAMPDQbQCAXjrEilkTMyFUY+epfGLk55LWPYwVuCrogOxved0KLojLIaMhB1LdC8Ruaju0PL2eVufaNEtfrYqYEKMgjUnM3yaIWUuGTrC8txfG23WpFNSwMy9kOvgniPekIPQ8inJzYjTC7Rngec/OvioZmBSkBYneA5M1ygqKaiykB5TmRBCDIV8NXYfSckhjVUt9Wy2dfOsvvg27AytXrsRPfvITvPDCC1scbGwPAvfZcDgcDofDccMNN+DOO+9EcqRpqjEgQIDi7s5G2RquuuoqAMCjjz4KACjNsdwCOXCWQg0DnUYbTamcOVA+/uyG0Dwj/furmeh8oHqukxw1NzJTRAp7aSGKHJ01VPl5uujpMGa4/Fz92FwS48jX2fYkWNU25qx6lpdBoc8YhwLnHRtrydAoRyB2AiWYuSKltjQuofJd85rFqNairFF9m9wCWU1Qs9FFn7AvP2YAABpWSURBVJJ1TEy9/oPDdzTsScie8AkAwODP/ieA97FgrFzCTJQmq/w1FxweI7FBYtHU1aGOpi5mWGSiTITmePtZKdZyDjkxgr+C5u7V518mkyJGY4Cq90rRztFq6FzKOXKyWQOb7FpQl02eDqL5iXYOyWl1MyS9rtiVEFPRTb+JPmbQlHhthY61vAeE2gGlxrKyljYnZNCofxNjoe4SdbUUO7oiy5N/jDr4goqdf9I0iUmrqRkhuZp+G7r3Jqlfy9QUuD3Ud/Heqo45dVPJ7TlRb4xjNRXVeghh1wuZQGlP4k6iYZI3XaYDdhAi1k2jlNnwelT3S1Z+JcZI6PfRcVA3jbRUgtJ8U3ShTg3K18e2U+7WuxL33XcfHnjgAQBAZ2cnzj/fkoc3bNiAZ599Ful0Gueee+6Y1+PMhsPhcDgc+ymuvvpqXH311Zu9fskll+DjH//4DhloANb6qmaBsWCXDDY6O636m6p8g3S0UleKapFK4kyG8+as4FNZjt6z1FAooTBeralHnCmvDWIEOCgrcH4PnN+Tn0Y4v1fVAd2xzIbwwD/8CABw0ecuAwDk+2y/pDkJQffHkNmIp9SG/h9Rnw8hyR51MTaJQlThLefSTUX7prJRNvQxg6U09hNrVyOIsTuCKkPN2cqZMEkmI0jHKi0th5VQboJpMVQpVulk2LNyQ/R7rLg0Jx3oHC6zcmVFpW4DzRX3bbBzVX4FqqiCWCWhz6nykqOoKt5qXx+2hLHqjxzbhobP/TUAYPUvLf1245vGbPVtsHuQ5vzVtRH+nnw910BnSjJX6qJQJZ1iRokq/3i3Vb7JKu3Qe4eaoxSvh/JAXKNAjUTM7TjUWIgVJQMiB9uWA+1e1bU6mg2k/ZGWSUnWymAJ791iItT1Uoqex7pu1OWTkIcQYoyGumd4zwwTt8n4SMsiRkS6Px0PaWTiviEDvB43KNeLQsnZ9L+RBqxpwX/Hnop58+Zh2TLzvrrxxhvx8MMPo6+vD1OnTsWCBQuwaNGiLS8g8G4Uh8PhcDgc78ODDz4Yea6BBgDcdddduOuuu7ZpedU9uRslDmWnPPLIIwCA4/7culM6WFFnksNX6OrxDqTZiGWNJMJ5cT4qrVUJjBo183Gg7UgA71MskwEZKRlzR0PdKcJAgu6PcuuLpcEOMS7cD3WNiNGQv0Y56rOhaialKp7PUaAim1VGZxc1GnT7k5bms0dNHcNe7h5Iq9Cx5FYAQJmaBs2dJ7NW4YSJwpr7zUS1GmJGgmqU3ck02jHMsvujnpWg5mrVDRMixj6pAi12W4UlhkNdJ1L1q6KqlJQqq/RadS3Yo7pThN2lgndsGXVTrDso/3u7J616z373IrUbE+lPUWiOdjko00faIKX7iqEQQuZO2qQGq9iVAiwmD+zekHZJ10XYtcVuFDmNJsQYxJiIJLNKcm12jyiMe9P2j+7D6UKK20FdmLq5+P0qO//k9pwFGY2YxgnUaCR0Hca6U7Rc6fdC7Qahvx1iNMJ0ZzE0vF4LSqmlM680NboOe2J/ZCfSK2rWfHMc/tlcc1U+GPsudpSDqDMbDofD4XA4hsUemY2yNUgt+/DDDwMATjzdGI4B7kiF2SM5prFmqS1IkblI0u8fSihUN0XM7S5gV4dG02JM0oPsPU/K7W3XMBojQfPojzz9GwDAJ/9kjr2hlFkpqTk6V7WdiPXOl9fRw0FJjlJua35TegLNkwbRrJgMH8+Z07aD9mz3QXOnq2834VRSKvMOOzZ5qvKlqkc8R0eMR8h88FiSHaqVXkgeL3JwFLMR8w0Q+5Qab6p8ZZp0vxvNahDEaKgroUK/gmyd/ZZJdi9oTlwZD449E1O++vcAgL51f2mPzBtas8o0Du09UW1A40R2nWTUVSXnS55vA1GHXKUXZ1voZ1ET9SISqqFPRdQvJuyqWm1dGar8M+rqauQ9kvcMZZaEzAB9NHINzAhiF4q2SwjZW96zM2JKmEKcKKo7rCeyHUM7IC0V74G8DnV9KQtGmpCQWYwxQVquunNqZloidiOdXMU4lWLOvq3sCqtrs+9NZbrvcczE2dfhzIbD4XA4HI6dhiAAynsbsyF85jOfAQDcc889AIArL70IwPuU/EmOWosxnwlpGarRHmhkmI0S606Rz36FXSwaBWebJ+6Q/dhREOPzne98BwCw4M8/aG9oP1Vl8/PVolXp8m6ohoprVg9pY3BCJbq0HPIjoVK7MWfzlbPHD+86uTej7Zb7AABr7rw28royI5SMq06dsFKSlws/H/p2sJIL58Kl3t/Mn4PnaGx7xEKpq6WGFeEA1e+prL1eO6GGrxt7pznwxhn2WyllsmGmzRlP/PI9WzgKjj0FB933GACg71NnABjS6KxdyxRV6qXK/XQGHShHHtW9FHfGDZOwVbHLf0KOn+xaUTaKslnE9AlyNs010eGWn0/lowyJ6lsxJy2HzYx8v0hNUw0dNRMxhkXdIolk1A8j7CrRetRVFu8yk3YjGctuIXMC+XgoRVYarFLUUVTHScxkjllBSlmuGWfXodJgxXiMP9y6ayZ/5X7sLwiCIWfYscCZDYfD4XA4HCMgGIp1GAN262Dj+uuv3/IHCpx/UwIgsWTJEgDAJWcYA6AqNeytjmk2cnsYkzESrozN/1Xe/FcAQ9V4ooHVgqpkMR6sauo//V9GtR7puvc9PmNzTLrp2wCATfffBACoveArW/x86T/+FwCEtr/SbITZJ1S9B6qg1CFFXw1pNQR1GIldUveAUjOlvdAccY5z9PWTTVMy/8l/Hs1uOvYSzF36HADgl6d+GADQTC1AQh151E+Fmp1i1PclZDLCzJ5oVkgQz5GixkJOufKFiGf8hMvPRH1npE2Ka5ukhQjvPcwekt+H/F9CB1Dp7JjUjbiTaIyt1nrD5Go5/XK/Q1tuLj/ZSAfWPNncTDSzKNwOaU7kqFpjy1emUi27h6RlSbHLpzxg3z/k73+M/Q7ObDgcDofD4diZCDBkejYW7JWDjQULFuzuTdglSB3ywS2+v1f+eLsJLQvvHNXnlC6pCi/ZYhVbpZaZKgWmV6ajzMYgL8ZcgtkW8nqRWj4XnVNXtkTNhGgKq7ImDvv+06PbMcdeiZaD7HzS7y1nWGkD4hDzJW1ElQyZNBLiCUKnXD4myQrLbyPFCj/Vbt1QpU7Td4npCH05iCDGoob+M1yOUmBL7OYQIyCfm8pG8+1Ik0lISUsRss9kZ8vD+9voOgzENGo5sfyqqjoP4w6kzGRJiRHJxXK1eD2m2S1WN8W0KuFx5v7kmnZ8wNleA2c2HA6Hw+Fw7EwECFDdW7tRHI49FvJ0YddJlXk9VTIbvTCNRbGkxGJWnBz5F+iD0UKPGKSsGyCVN6ZDPgi1bS2Rx97Vm+z9rF+S+wM09//qZ88EMKTNUPdJHHpf/hrSbMgxdEQkY90q6raSn0Z9NfI5MQYJ+tCEjIa8fcj8hSnGZFZKvdQmkSEp99MJl90wST2SuQg75eiwW+2y87/K5ca7T6TdSNeLWbTrMHQmpSuytCDVWjmLkqHs1XLZkSe35pjuLTfJurwK3K8y90t+KfslAqAaOLPhcDgcDodjJ2JLzEa8zX8k+GDD4Xgfsh8yp8fyitcBDDEag0mmtqrCJJPRy5AHNhOE2SVCIWsMSd24mQCAjFTx9OnQHHm6wea2a8778g7cG8eejlyTMRMS4KkrRM6hdRPt/XTeuqDS7CZRN1OaTp2pOqaq8vySVkL+MKrgq8yZCrs7iNAnRq7DYjKS1FhQmxF+nx1yof9HVZk99Kth14w0IckaS7gWYyHfDyH0DOqyx8qAaVhSNXZ96DpRhkvI1JCBlE9Hld5KxQTTbXlca3gdJ0Nn0VJ0v7k98g+pmzHZ3o+nQu+HCIIAlS20vo52EOGDDYfD4XA4HCMiGLtkwwcbDsdw2FBnmQldZDIG+2wOuhxTZWsuM5dmnz8vyn4yHKIYazmnnOBcdfqw4wEMudymOafs2L8gZ9G3rjMX4bhmI99q5438MTLUIuh5mPLK80oZKCEDIcdOZfqQWZAfhpDMGFORJ7MRevtI4yFmg27Fpe4ebi/9MHjil7h8OZUq5VgaCDExYkDEiEiLouepfJRRUDdJqOng9qWnzLbtLJi2SumxsuGgbQmKATUu/FyYEM5HeQ/p+KTYxZM/ff/ofNwSggCobGEaJTPiO1H4YMPhcDgcDsfwCIBgb2l9HRwcxMKFC/H444+jpqYGN954I774xS/uilU79hHs6nNoEufSJ/H5/1tjleAHJjdFPvfHDTb3XSCzUcNulCIrvfqAlVL7KgBAQpkVOVPTy/U2Nfv4Hb4Pjr0HB37rEQDAH790IQCgb31U0xBqIeQXQWfLUGNBSJtR6WOXyIZNke8JYhJKff3R18lA5JrqIusNu0zIZMR9KOSzUdYjHTfTeVveYLsxHdkGajBiTqhanrqxck31kddziEIuykMZKqy86eCbTtCxN5aVkpDDr5LCNT9A/45020wAQOqwk+EwBNiyZmO02CWDjUWLFuH3v/893nnnHaxZswannnoqDj/8cJxxxhm7YvWOfQB+DjkcDsduwK5iNr7xjW/gV7/6FZ544onwteuuuw6JRCJMbd0afvCDH+DBBx9Ec3Mzmpubcfnll+PBBx/0PxT7Cd5++20ce+yxeP755zF//nysWrUKc+fOxdKlS3HKKaeMahm7+xw6fFLjsK835ZlFweckNpCjI6L6+5PF3sj3kgOcMy8O7tgNdezVUGWvTI4SmQY9zzVT+xMyA9H0YTCzRE6eAxtNu1Ep0jk0a58rdtn5KKZCjET/OmNUxGxIG5LK5yLrVRfKYHs312OP/UwxLivrp9G+l2uIdq1sls3C51pvMUmtR6tdd2FXirpQ5I8hHw4xF3IgFYMh5iKe7aKMIzEcTI9FNs6hOADsEGZjqx6sF154IZ577jl0dNhJWC6X8cgjj+Ciiy7CVVddhaampmH/HXnkkQCA9vZ2rF69GnPnzg2XOXfuXLz22mtj3njH3oHZs2fjzjvvxIUXXoi+vj587nOfw8UXX4xTTjnFzyGHw+HYgxEEAYLqyP9Gi60yG21tbTj55JOxdOlSXH755Xjuuecwbtw4HH300Tj66KOxePHiLX6/p8fm6hobhyrDxsZGdHd3j/QVxz6Iyy+/HM888wyOP/54JBIJPP20ZX8sXrx4rz6HMjTYSFP1nuw3xiJRtG1W2qUqqRBMufS5Ycf7MeNvHgQA/G7hJwEMMQglZo3IX0MaiVzo7EkNA7s0xIiIwZAWQ1oPaS3EKPS3m3ZD3TDpDfY9ZbfkGuiwSeZBDIcYFDmclpjtMtgVzXhJifIDt5P+F/qe1pPm8vSHSb4icQdUkMGoMOMlzfcDdocphVkOowleb3oeajj4erVjAwAgc/SZcGyOSnl4Z9ttwajSZS6++GI89NBDAICHHnoIf/VXfzXqFdTV2cnZ1dUVvtbV1YX6+vpt2U7HPoDLL78cr776Kq699lrkcqOnK/0ccjgcjt2EIEBQrYz4b7QYlUD03HPPxcKFC/Hqq6/iH//xH3HXXXcBAK688spwEBLHjBkz8Nprr6G5uRltbW145ZVXcNpppwEAXnnlFcyZM2fUG+nY+9HT04MbbrgBl112GRYtWoTzzjsPLS0te/05NMD+czEcOfpmpJQ+qUqKzxOsvJIHnrALt9Kxt+Hg+x8HALx5xV8M+766NIodNgDPlOy5GA89isHQ5+NdKdJsKHVWzIYYCn1+gFqMfJc9Zgp2XouZEJMhJ9RkJr4ebkcpqtnQ5+Wqodel7RBDA5B5qCezEvqB2PtVOqKmlBJbJrOhbhRqM5Cmv0afZbVUN6625R57NhzDIwgCVEvDpxFvC0bFbOTzeXzyk5/EZz7zGRx33HGYPt0Mj77zne+gp6dn2H/vn0+/6KKL8PWvfx3t7e1444038MADD+CSSy4Z88Y79h5cf/31OOaYY7BkyRKceeaZuPLKKwH4OeRwOBx7NnYhswHYVMqSJUvwve99b5s39bbbbsPChQsxY8YMFAoF3HTTTd6Jsh/hqaeewnPPPYff/va3AIC7774b8+bNww9/+EN89rOfHdUy9tRzqL7Kyktibc4FV3Oc4mFqLGot3VXMhicuOEYDpcNKw6HukgydOIVyX/T5SIh3g0g7kUxF604xDkPdLdE/KuUBdc3Y99JkOqpcbpLWndJiSOOh9ep5OjP85wRpTirFKEMTdsnwj52yU0ZKuUWO3SzUUpV+9zIAIH/GFXBsBUGAannszMaoBxvTp09HoVDAeeedt80ryeVy+N73vrddAxXH3o9zzjkH55xzTvi8rq4Ob7311jYtw88hh8Ph2PUIyGyMFaMabFSrVdx99904//zz0dDQMOaVOhz7HDQnrMfY3LiYj2zLJDgc2wppON647Fx7gYW8/DP0mKYfhrpOpLnQo7pb1EVS7LXvVTfzvTBmI1Nr2ggxHwkyFmEXCZkNPdZPruNzMh90IBUjoa4XtUyKASnRlyMjLUps/8VkDG0ffTm4X0q7lZ9GmC7LLBW9XqZGo+ac6+EYJYIAlV3BbPT29mLixImYMWMGnnvuuTGv0OFwOBwOx16CYBcxG7W1taHPgcPhiCLXNH53b4JjP8Kh330SwFCXSjwltcTnlX4yBOzmUFaJoO6Tvg3GNBR7ld5qjMOQv4YxJWIs5BA62DkYWY4cQxtnmEZp3BHG4OXpeCrtRTJrWS39G5QaS58LrrdntWlS0vl0ZDuEMBOGSJEJSXWb6aS0GvLhKK9ebtvZYd0qLQvvhGPbsKO6UTz11eFwOBwOx4jYZZoNh8PhcOwcPPbYY/jqV7+KFStWYNq0abj99ttx7rnnbvE78S4V+Wr00fmzfyPTVmNOnoXWAoAh34whfwz6xbCrJBnrMhFzUtMqDYhpLXrX2fryzcYoNB1gTF/dFHvMNpnGr2YyuFzTgIQ+Ib3SnNgfswE+lzZEzIoYDmk35B8y2NETeZ7tMoYjyQyYh7psOz6/8CtwbCeC6q7tRnE4HA7HjsXKlStx4YUX4qmnnsIZZ5yBZ599Fp/61KewfPlyTJgwYXdvnsOBAEPTdWOBDzYcDodjlHj00Udx2WWXhc9LpRJOPPFEvPjii9u1vBUrVqCpqQl/9md/BgA488wzUVtbi7fffntUgw11qQi/PPXDAICetcZsiDGo0rlTjEXIKNA/Q0xFyCAUoj4agrJRhGxdJvL6hBM+YN+fMtuWmzMmJRg0bUgD6XhpKIo9VjH39as7xp6r+0VaEG3XuCObAQDpmM9Iimm56rKpv/g2AMDn4RgzdpDPxqgcRB0Oh8MBfPrTnw4dbletWoVZs2bhggsuwB133DFienFTU9OIyzvmmGNw2GGH4emnn0alUsGTTz6JXC4XJh47HKNBZ2cnzjrrLMydOxdz5szB97///c0+09fXhzPPPBOHHnoo5syZg5tvvnl0Cw8CVKuVEf+NFolAMXkOh8PhGBWq1SrOPvtsTJs2Dffff/+YlvXd734X119/PQYGBpDNZrF06VKceebY0kd/c+7pkefdq4xJUPeHulDEIOQaTJMR10o0z7KBUv10Y1my9Zb9E/pcNBujkZ9sooz0+Cm2woMt++f/vmrmfS0t5qCbzdp6pr/5UwBA7x+WAwA63loJYEh7Ueq1LhcxM0qdrZ0yzpbTwAwidqf8uHAIAOCKK/ZPR9Dbb78dnZ2duPPOO7F+/XoccsghWLNmTXi8ARts/PrXv8app56KYrGIj370o7jllltCVm0kJGsnIHfEp0Z8f07l1/j3f//3rW6jMxsOh8Oxjbj11lvR3d2Nb33rW6P+zrvvvou6urrwHwA8//zzuPHGG/Hiiy+iWCzi5z//ORYsWIBly5btrE137INIJBLo7u5GEATo6elBS0sL0umoSqKmpgannnoqABv0zZ8/HytWrBjF0ndxNorD4XA4gEceeQQ/+tGP8NJLLyHD7orbb78dt99++4jf6enpwfTp0zfzLFq2bBlOPvlkHHPMMQCAY489Fscffzyef/55zJs3b7u3cf6T/xx5rq4V+VtUDzRCW06eB33ixGGXk2owZiPVHPWTkWNnssZ8NNITptpzphnfc889AIBZs2YBAAoF024kEsacvHeo5Rod0PoKAKDuwHW2XUxvrQ5Yt4w0GGJgUhykPdHfBsACGgFg/+QzhnDNNdfg7LPPxuTJk9Hd3Y1HH30UybiL8fvQ0dGBZ555Btdfv3Un1dNPOhobNozMXIwbN25U2+iDDYfD4RglXn75ZVx77bX4l3/5F4wfP/QH+JZbbsEtt9yyzcs79thjcccdd2DZsmWYN28eXn75ZfziF7/AVVddtSM327GP46c//SnmzZuHn/3sZ3j77bdx2mmn4aSTTho2XqRcLuOCCy7AddddFw4Gt4Qd5Rzumg2Hw+EYJRYtWoSvf/3ryOeHuiFOOukk/NM//dN2L/Pee+/FN7/5Taxduxbjx4/H1VdfjS996Us7YnNHROWP/wEACLqYHZI2hiZonQYASPa12+tJq0eDVDSnuJozzUR2wsxRrW/JkiUAhqrg+npjRKQpSKWY+krmY4DMRnu7bcf69esBAJ//vPeXCPfddx8eeOABAEBzczO+9rWv4aSTTgIAfOQjH8Edd9yB4447brPvXXrppairq9umKcAdAR9sOBwOx34GH2zsW1i4cCEmTpyIRYsWYe3atZg/fz5eeeWVzaY4vvKVr+D111/H0qVLtzjNsjPggw2Hw+FwOPZirFq1CpdccglWr16NIAhw880348ILLwQAzJs3D8uWLQsdag899FDkctZtdM0112DBggW7ZBt9sOFwOBwOh2OnwltfHQ6Hw+Fw7FT4YMPhcDgcDsdOhQ82HA6Hw+Fw7FT4YMPhcDgcDsdOhQ82HA6Hw+Fw7FT4YMPhcDgcDsdOhQ82HA6Hw+Fw7FT4YMPhcDgcDsdOhQ82HA6Hw+Fw7FT4YMPhcDgcDsdOhQ82HA6Hw+Fw7FT8f2v60bIfAS+5AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "meta = Stouffers(inference='ffx', null='theoretical', n_iters=None)\n", + "meta.fit(dset)\n", + "plot_stat_map(meta.results.get_map('z'), cut_coords=[0, 0, -8], \n", + " draw_cross=False, cmap='RdBu_r')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random-effects inference with theoretical null distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.dataset:Retaining 11/21 studies\n", + "/Users/tsalo/anaconda/envs/python3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:901: RuntimeWarning: invalid value encountered in greater\n", + " return (a < x) & (x < b)\n", + "/Users/tsalo/anaconda/envs/python3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:901: RuntimeWarning: invalid value encountered in less\n", + " return (a < x) & (x < b)\n", + "/Users/tsalo/anaconda/envs/python3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1892: RuntimeWarning: invalid value encountered in less_equal\n", + " cond2 = cond0 & (x <= _a)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 2.07 s, sys: 277 ms, total: 2.34 s\n", + "Wall time: 2.56 s\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "meta = Stouffers(inference='rfx', null='theoretical', n_iters=None)\n", + "meta.fit(dset)\n", + "plot_stat_map(meta.results.get_map('z'), cut_coords=[0, 0, -8], \n", + " draw_cross=False, cmap='RdBu_r')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random-effects inference with empirical null distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.dataset:Retaining 11/21 studies\n", + "/Users/tsalo/anaconda/envs/python3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:901: RuntimeWarning: invalid value encountered in greater\n", + " return (a < x) & (x < b)\n", + "/Users/tsalo/anaconda/envs/python3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:901: RuntimeWarning: invalid value encountered in less\n", + " return (a < x) & (x < b)\n", + "/Users/tsalo/anaconda/envs/python3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1892: RuntimeWarning: invalid value encountered in less_equal\n", + " cond2 = cond0 & (x <= _a)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1min 28s, sys: 9.61 s, total: 1min 37s\n", + "Wall time: 1min 45s\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "meta = Stouffers(inference='rfx', null='empirical', n_iters=1000)\n", + "meta.fit(dset)\n", + "plot_stat_map(meta.results.get_map('z'), cut_coords=[0, 0, -8], \n", + " draw_cross=False, cmap='RdBu_r')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Weighted Stouffer's" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.dataset:Retaining 11/21 studies\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1.99 s, sys: 262 ms, total: 2.26 s\n", + "Wall time: 2.45 s\n" + ] + }, + { + "data": { + "image/png": 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\n", 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "meta = WeightedStouffers()\n", + "meta.fit(dset)\n", + "plot_stat_map(meta.results.get_map('z'), cut_coords=[0, 0, -8], \n", + " draw_cross=False, cmap='RdBu_r')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## FFX GLM" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "191120-09:27:19,226 nipype.interface INFO:\n", + "\t stdout 2019-11-20T09:27:19.226047:Log directory is: stats\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nipype.interface:stdout 2019-11-20T09:27:19.226047:Log directory is: stats\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "191120-09:27:19,230 nipype.interface INFO:\n", + "\t stdout 2019-11-20T09:27:19.230378:Setting up:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nipype.interface:stdout 2019-11-20T09:27:19.230378:Setting up:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "191120-09:27:20,78 nipype.interface INFO:\n", + "\t stdout 2019-11-20T09:27:20.078715:ntptsing=21.000000 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nipype.interface:stdout 2019-11-20T09:27:20.078715:ntptsing=21.000000 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "191120-09:27:20,82 nipype.interface INFO:\n", + "\t stdout 2019-11-20T09:27:20.078715:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nipype.interface:stdout 2019-11-20T09:27:20.078715:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "191120-09:27:20,91 nipype.interface INFO:\n", + "\t stdout 2019-11-20T09:27:20.078715:evs_group=1.000000 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nipype.interface:stdout 2019-11-20T09:27:20.078715:evs_group=1.000000 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "191120-09:27:20,98 nipype.interface INFO:\n", + "\t stdout 2019-11-20T09:27:20.078715:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nipype.interface:stdout 2019-11-20T09:27:20.078715:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "191120-09:27:20,108 nipype.interface INFO:\n", + "\t stdout 2019-11-20T09:27:20.108292:No f contrasts\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nipype.interface:stdout 2019-11-20T09:27:20.108292:No f contrasts\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "191120-09:27:20,263 nipype.interface INFO:\n", + "\t stdout 2019-11-20T09:27:20.263371:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nipype.interface:stdout 2019-11-20T09:27:20.263371:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "191120-09:27:20,265 nipype.interface INFO:\n", + "\t stdout 2019-11-20T09:27:20.263371:WARNING: The passed in varcope file, /Users/tsalo/Documents/tsalo/NiMARE/examples/ffx_glm/varcope.nii.gz, contains voxels inside the mask with zero (or negative) values. These voxels will be excluded from the analysis.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nipype.interface:stdout 2019-11-20T09:27:20.263371:WARNING: The passed in varcope file, /Users/tsalo/Documents/tsalo/NiMARE/examples/ffx_glm/varcope.nii.gz, contains voxels inside the mask with zero (or negative) values. 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2019-11-20T09:27:27.887708:1\t524\t0.0107\t1.97\t4.68\t-2\t-54\t10\t0.442\t-55.1\t11.5\t0.517\t-2\t-54\t10\t0.257\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nipype.interface:stdout 2019-11-20T09:27:27.887708:1\t524\t0.0107\t1.97\t4.68\t-2\t-54\t10\t0.442\t-55.1\t11.5\t0.517\t-2\t-54\t10\t0.257\n", + "INFO:nimare.meta.ibma:Cleaning up...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 8.03 s, sys: 1.34 s, total: 9.36 s\n", + "Wall time: 17.4 s\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "meta = FFX_GLM()\n", + "meta.fit(dset)\n", + "plot_stat_map(meta.results.get_map('z'), cut_coords=[0, 0, -8], \n", + " draw_cross=False, cmap='RdBu_r')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RFX GLM" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Theoretical null distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/tsalo/anaconda/envs/python3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:901: RuntimeWarning: invalid value encountered in greater\n", + " return (a < x) & (x < b)\n", + "/Users/tsalo/anaconda/envs/python3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:901: RuntimeWarning: invalid value encountered in less\n", + " return (a < x) & (x < b)\n", + "/Users/tsalo/anaconda/envs/python3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1892: RuntimeWarning: invalid value encountered in less_equal\n", + " cond2 = cond0 & (x <= _a)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 2.42 s, sys: 350 ms, total: 2.77 s\n", + "Wall time: 2.71 s\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "meta = RFX_GLM(null='theoretical', n_iters=None)\n", + "meta.fit(dset)\n", + "plot_stat_map(meta.results.get_map('z'), cut_coords=[0, 0, -8], \n", + " draw_cross=False, cmap='RdBu_r')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Empirical null distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/tsalo/anaconda/envs/python3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:901: RuntimeWarning: invalid value encountered in greater\n", + " return (a < x) & (x < b)\n", + "/Users/tsalo/anaconda/envs/python3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:901: RuntimeWarning: invalid value encountered in less\n", + " return (a < x) & (x < b)\n", + "/Users/tsalo/anaconda/envs/python3/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1892: RuntimeWarning: invalid value encountered in less_equal\n", + " cond2 = cond0 & (x <= _a)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1min 54s, sys: 14.8 s, total: 2min 8s\n", + "Wall time: 2min 18s\n" + ] + }, + { + "data": { + "image/png": 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    Failed to display Jupyter Widget of type HBox.

    \n", + "

    \n", + " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", + " that the widgets JavaScript is still loading. If this message persists, it\n", + " likely means that the widgets JavaScript library is either not installed or\n", + " not enabled. See the Jupyter\n", + " Widgets Documentation for setup instructions.\n", + "

    \n", + "

    \n", + " If you're reading this message in another frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

    \n" + ], + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=10), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/tsalo/Documents/tsalo/NiMARE/nimare/meta/cbma/mkda.py:178: RuntimeWarning: divide by zero encountered in log\n", + " vfwe_map[i_vox] = -np.log(null_to_p(val, perm_max_values, 'upper'))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 8.28 s, sys: 415 ms, total: 8.7 s\n", + "Wall time: 10.6 s\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "mkda = nimare.meta.MKDADensity(kernel__r=10)\n", + "mkda.fit(dset)\n", + "corr = nimare.correct.FWECorrector(method='permutation', n_iters=10, n_cores=1)\n", + "cres = corr.transform(mkda.results)\n", + "plot_stat_map(cres.get_map('logp_level-voxel_corr-FWE_method-permutation'),\n", + " cut_coords=[0, 0, -8], draw_cross=False, cmap='RdBu_r')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Chi2 with FDR correction" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/tsalo/Documents/tsalo/NiMARE/nimare/meta/cbma/mkda.py:277: RuntimeWarning: invalid value encountered in true_divide\n", + " pFgA = pAgF * pF / pA\n", + "/Users/tsalo/Documents/tsalo/NiMARE/nimare/meta/cbma/mkda.py:281: RuntimeWarning: invalid value encountered in true_divide\n", + " pFgA_prior = pAgF * self.prior / pAgF_prior\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 3.21 s, sys: 308 ms, total: 3.52 s\n", + "Wall time: 5.54 s\n" + ] + }, + { + "data": { + "image/png": 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\n", 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    Failed to display Jupyter Widget of type HBox.

    \n", + "

    \n", + " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", + " that the widgets JavaScript is still loading. If this message persists, it\n", + " likely means that the widgets JavaScript library is either not installed or\n", + " not enabled. See the Jupyter\n", + " Widgets Documentation for setup instructions.\n", + "

    \n", + "

    \n", + " If you're reading this message in another frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

    \n" + ], + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=10), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "CPU times: user 11.6 s, sys: 880 ms, total: 12.5 s\n", + "Wall time: 15.8 s\n" + ] + }, + { + "data": { + "image/png": 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\n", 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    Failed to display Jupyter Widget of type HBox.

    \n", + "

    \n", + " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", + " that the widgets JavaScript is still loading. If this message persists, it\n", + " likely means that the widgets JavaScript library is either not installed or\n", + " not enabled. See the Jupyter\n", + " Widgets Documentation for setup instructions.\n", + "

    \n", + "

    \n", + " If you're reading this message in another frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

    \n" + ], + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=10), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "CPU times: user 6.66 s, sys: 309 ms, total: 6.97 s\n", + "Wall time: 7.4 s\n" + ] + }, + { + "data": { + "image/png": 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\n", 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    \n", + "

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    \n", + " If you're reading this message in another frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

    \n" + ], + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=10), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "CPU times: user 9.78 s, sys: 1.28 s, total: 11.1 s\n", + "Wall time: 13.9 s\n" + ] + }, + { + "data": { + "image/png": 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\n", 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    \n", + " If you're reading this message in another frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

    \n" + ], + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=10), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "CPU times: user 37.4 s, sys: 6.27 s, total: 43.7 s\n", + "Wall time: 45.1 s\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "ijk = np.vstack(np.where(mask_img.get_fdata())).T\n", + "scale = nimare.meta.SCALE(ijk=ijk, n_iters=10, n_cores=1)\n", + "scale.fit(dset)\n", + "plot_stat_map(scale.results.get_map('z_vthresh'), cut_coords=[0, 0, -8],\n", + " draw_cross=False, cmap='RdBu_r')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:python3]", + "language": "python", + "name": "conda-env-python3-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/content/nimare-repo/misc-notebooks/save_nidm_to_dset.ipynb b/content/nimare-repo/misc-notebooks/save_nidm_to_dset.ipynb new file mode 100755 index 0000000..db729d9 --- /dev/null +++ b/content/nimare-repo/misc-notebooks/save_nidm_to_dset.ipynb @@ -0,0 +1,315 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Save NIDM-Results packs to NiMARE dataset\n", + "NOTE: This will ultimately be replaced with a simple `nidm2nimare` function in `nimare.io`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/james/.conda/envs/nimare_dev/lib/python3.7/site-packages/duecredit-0.6.4-py3.7.egg/duecredit/utils.py:32: DeprecationWarning: dist() and linux_distribution() functions are deprecated in Python 3.5\n", + " and platform.linux_distribution()[0] == 'debian' \\\n", + "/home/james/.conda/envs/nimare_dev/lib/python3.7/site-packages/duecredit-0.6.4-py3.7.egg/duecredit/io.py:18: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n", + " from collections import defaultdict, Iterator\n", + "/home/james/.conda/envs/nimare_dev/lib/python3.7/site-packages/nipype-1.1.7-py3.7.egg/nipype/interfaces/base/traits_extension.py:28: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n", + " from collections import Sequence\n", + "/home/james/.conda/envs/nimare_dev/lib/python3.7/site-packages/nltk/decorators.py:68: DeprecationWarning: `formatargspec` is deprecated since Python 3.5. Use `signature` and the `Signature` object directly\n", + " regargs, varargs, varkwargs, defaults, formatvalue=lambda value: \"\"\n", + "/home/james/.conda/envs/nimare_dev/lib/python3.7/importlib/_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n", + " return f(*args, **kwds)\n", + "/home/james/.conda/envs/nimare_dev/lib/python3.7/site-packages/fuzzywuzzy/fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning\n", + " warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')\n" + ] + } + ], + "source": [ + "import json\n", + "from glob import glob\n", + "import nibabel as nib\n", + "import pandas as pd\n", + "import numpy as np\n", + "from scipy import ndimage\n", + "from os.path import basename, join, isfile, dirname\n", + "\n", + "import nimare" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def _local_max(data, affine, min_distance):\n", + " \"\"\"Find all local maxima of the array, separated by at least min_distance.\n", + " Adapted from https://stackoverflow.com/a/22631583/2589328\n", + " Parameters\n", + " ----------\n", + " data : array_like\n", + " 3D array of with masked values for cluster.\n", + " min_distance : :obj:`int`\n", + " Minimum distance between local maxima in ``data``, in terms of mm.\n", + " Returns\n", + " -------\n", + " ijk : :obj:`numpy.ndarray`\n", + " (n_foci, 3) array of local maxima indices for cluster.\n", + " vals : :obj:`numpy.ndarray`\n", + " (n_foci,) array of values from data at ijk.\n", + " \"\"\"\n", + " # Initial identification of subpeaks with minimal minimum distance\n", + " data_max = ndimage.filters.maximum_filter(data, 3)\n", + " maxima = (data == data_max)\n", + " data_min = ndimage.filters.minimum_filter(data, 3)\n", + " diff = ((data_max - data_min) > 0)\n", + " maxima[diff == 0] = 0\n", + "\n", + " labeled, n_subpeaks = ndimage.label(maxima)\n", + " ijk = np.array(ndimage.center_of_mass(data, labeled,\n", + " range(1, n_subpeaks + 1)))\n", + " ijk = np.round(ijk).astype(int)\n", + "\n", + " vals = np.apply_along_axis(arr=ijk, axis=1, func1d=_get_val,\n", + " input_arr=data)\n", + "\n", + " # Sort subpeaks in cluster in descending order of stat value\n", + " order = (-vals).argsort()\n", + " vals = vals[order]\n", + " ijk = ijk[order, :]\n", + " xyz = nib.affines.apply_affine(affine, ijk) # Convert to xyz in mm\n", + "\n", + " # Reduce list of subpeaks based on distance\n", + " keep_idx = np.ones(xyz.shape[0]).astype(bool)\n", + " for i in range(xyz.shape[0]):\n", + " for j in range(i + 1, xyz.shape[0]):\n", + " if keep_idx[i] == 1:\n", + " dist = np.linalg.norm(xyz[i, :] - xyz[j, :])\n", + " keep_idx[j] = dist > min_distance\n", + " ijk = ijk[keep_idx, :]\n", + " vals = vals[keep_idx]\n", + " return ijk, vals\n", + "\n", + "\n", + "def _get_val(row, input_arr):\n", + " \"\"\"Small function for extracting values from array based on index.\n", + " \"\"\"\n", + " i, j, k = row\n", + " return input_arr[i, j, k]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "f1 = join(dirname(nimare.__file__), 'tests', 'data', 'nidm_pain_dset.json')\n", + "f2 = 'nidm_pain_dset_with_subpeaks.json'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "ddict = {}\n", + "folders = sorted(glob('/Users/tsalo/Downloads/nidm-pain-results/pain_*.nidm'))\n", + "for folder in folders:\n", + " name = basename(folder)\n", + " ddict[name] = {}\n", + " ddict[name]['contrasts'] = {}\n", + " ddict[name]['contrasts']['1'] = {}\n", + " ddict[name]['contrasts']['1']['coords'] = {}\n", + " ddict[name]['contrasts']['1']['coords']['space'] = 'MNI'\n", + " ddict[name]['contrasts']['1']['images'] = {}\n", + " ddict[name]['contrasts']['1']['images']['space'] = 'MNI_2mm'\n", + " # beta file\n", + " files = glob(join(folder, 'Contrast*.nii.gz'))\n", + " files = [f for f in files if 'StandardError' not in basename(f)]\n", + " if files:\n", + " f = sorted(files)[0]\n", + " else:\n", + " f = None\n", + " ddict[name]['contrasts']['1']['images']['beta'] = f\n", + " # se file\n", + " files = glob(join(folder, 'ContrastStandardError*.nii.gz'))\n", + " if files:\n", + " f = sorted(files)[0]\n", + " else:\n", + " f = None\n", + " ddict[name]['contrasts']['1']['images']['se'] = f\n", + " # z file\n", + " files = glob(join(folder, 'ZStatistic*.nii.gz'))\n", + " if files:\n", + " f = sorted(files)[0]\n", + " else:\n", + " f = None\n", + " ddict[name]['contrasts']['1']['images']['z'] = f\n", + " # t file\n", + " # z file\n", + " files = glob(join(folder, 'TStatistic*.nii.gz'))\n", + " if files:\n", + " f = sorted(files)[0]\n", + " else:\n", + " f = None\n", + " ddict[name]['contrasts']['1']['images']['t'] = f\n", + " # sample size\n", + " f = join(folder, 'DesignMatrix.csv')\n", + " if isfile(f):\n", + " df = pd.read_csv(f, header=None)\n", + " n = [df.shape[0]]\n", + " else:\n", + " n = None\n", + " ddict[name]['contrasts']['1']['sample_sizes'] = n\n", + " # foci\n", + " files = glob(join(folder, 'ExcursionSet*.nii.gz'))\n", + " f = sorted(files)[0]\n", + " img = nib.load(f)\n", + " data = np.nan_to_num(img.get_fdata())\n", + " # positive clusters\n", + " binarized = np.copy(data)\n", + " binarized[binarized>0] = 1\n", + " binarized[binarized<0] = 0\n", + " binarized = binarized.astype(int)\n", + " labeled = ndimage.measurements.label(binarized, np.ones((3, 3, 3)))[0]\n", + " clust_ids = sorted(list(np.unique(labeled)[1:]))\n", + " ijk = np.hstack([np.where(data * (labeled == c) == np.max(data * (labeled == c))) for c in clust_ids])\n", + " ijk = ijk.T\n", + " xyz = nib.affines.apply_affine(img.affine, ijk)\n", + " ddict[name]['contrasts']['1']['coords']['x'] = list(xyz[:, 0])\n", + " ddict[name]['contrasts']['1']['coords']['y'] = list(xyz[:, 1])\n", + " ddict[name]['contrasts']['1']['coords']['z'] = list(xyz[:, 2])\n", + "\n", + "with open(f1, 'w') as fo:\n", + " json.dump(ddict, fo, sort_keys=True, indent=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "ddict = {}\n", + "folders = sorted(glob('/Users/tsalo/Downloads/nidm-pain-results/pain_*.nidm'))\n", + "for folder in folders:\n", + " name = basename(folder)\n", + " ddict[name] = {}\n", + " ddict[name]['contrasts'] = {}\n", + " ddict[name]['contrasts']['1'] = {}\n", + " ddict[name]['contrasts']['1']['coords'] = {}\n", + " ddict[name]['contrasts']['1']['coords']['space'] = 'MNI'\n", + " ddict[name]['contrasts']['1']['images'] = {}\n", + " ddict[name]['contrasts']['1']['images']['space'] = 'MNI_2mm'\n", + " # beta file\n", + " files = glob(join(folder, 'Contrast*.nii.gz'))\n", + " files = [f for f in files if 'StandardError' not in basename(f)]\n", + " if files:\n", + " f = sorted(files)[0]\n", + " else:\n", + " f = None\n", + " ddict[name]['contrasts']['1']['images']['beta'] = f\n", + " # se file\n", + " files = glob(join(folder, 'ContrastStandardError*.nii.gz'))\n", + " if files:\n", + " f = sorted(files)[0]\n", + " else:\n", + " f = None\n", + " ddict[name]['contrasts']['1']['images']['se'] = f\n", + " # z file\n", + " files = glob(join(folder, 'ZStatistic*.nii.gz'))\n", + " if files:\n", + " f = sorted(files)[0]\n", + " else:\n", + " f = None\n", + " ddict[name]['contrasts']['1']['images']['z'] = f\n", + " # t file\n", + " # z file\n", + " files = glob(join(folder, 'TStatistic*.nii.gz'))\n", + " if files:\n", + " f = sorted(files)[0]\n", + " else:\n", + " f = None\n", + " ddict[name]['contrasts']['1']['images']['t'] = f\n", + " # sample size\n", + " f = join(folder, 'DesignMatrix.csv')\n", + " if isfile(f):\n", + " df = pd.read_csv(f, header=None)\n", + " n = [df.shape[0]]\n", + " else:\n", + " n = None\n", + " ddict[name]['contrasts']['1']['sample_sizes'] = n\n", + " # foci\n", + " files = glob(join(folder, 'ExcursionSet*.nii.gz'))\n", + " f = sorted(files)[0]\n", + " img = nib.load(f)\n", + " data = np.nan_to_num(img.get_fdata())\n", + " # positive clusters\n", + " binarized = np.copy(data)\n", + " binarized[binarized>0] = 1\n", + " binarized[binarized<0] = 0\n", + " binarized = binarized.astype(int)\n", + " labeled = ndimage.measurements.label(binarized, np.ones((3, 3, 3)))[0]\n", + " clust_ids = sorted(list(np.unique(labeled)[1:]))\n", + " \n", + " peak_vals = np.array([np.max(data * (labeled == c)) for c in clust_ids])\n", + " clust_ids = [clust_ids[c] for c in (-peak_vals).argsort()] # Sort by descending max value\n", + "\n", + " ijk = []\n", + " for c_id, c_val in enumerate(clust_ids):\n", + " cluster_mask = labeled == c_val\n", + " masked_data = data * cluster_mask\n", + "\n", + " # Get peaks, subpeaks and associated statistics\n", + " subpeak_ijk, subpeak_vals = _local_max(masked_data, img.affine,\n", + " min_distance=8)\n", + "\n", + " # Only report peak and, at most, top 3 subpeaks.\n", + " n_subpeaks = np.min((len(subpeak_vals), 4))\n", + " #n_subpeaks = len(subpeak_vals)\n", + " subpeak_ijk = subpeak_ijk[:n_subpeaks, :]\n", + " ijk.append(subpeak_ijk)\n", + " ijk = np.vstack(ijk)\n", + " xyz = nib.affines.apply_affine(img.affine, ijk)\n", + " ddict[name]['contrasts']['1']['coords']['x'] = list(xyz[:, 0])\n", + " ddict[name]['contrasts']['1']['coords']['y'] = list(xyz[:, 1])\n", + " ddict[name]['contrasts']['1']['coords']['z'] = list(xyz[:, 2])\n", + "\n", + "\n", + "with open(f2, 'w') as fo:\n", + " json.dump(ddict, fo, sort_keys=True, indent=4)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:python3]", + "language": "python", + "name": "conda-env-python3-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/content/ohbm2021-tutorial.md b/content/ohbm2021-tutorial.md new file mode 100644 index 0000000..488b8d3 --- /dev/null +++ b/content/ohbm2021-tutorial.md @@ -0,0 +1,567 @@ +--- +jupyter: + jupytext: + formats: ipynb,md + text_representation: + extension: .md + format_name: markdown + format_version: '1.3' + jupytext_version: 1.11.2 + kernelspec: + display_name: Python 3 + language: python + name: python3 +--- + + +# OHBM 2021 NiMARE tutorial + +## What is NiMARE? + +![NiMARE banner](images/nimare_banner.png) + +[NiMARE](https://nimare.readthedocs.io/en/latest/) is a Python library for performing neuroimaging meta-analyses and related analyses, like automated annotation and functional decoding. The goal of NiMARE is to centralize and standardize implementations of common meta-analytic tools, so that researchers can use whatever tool is most appropriate for a given research question. + +There are already a number of tools for neuroimaging meta-analysis: + +|

    Tool

    |

    Scope

    | +| :------------ | :------------- | +| BrainMap | BrainMap includes a suite of applications for (1) searching its manually-annotated coordinate-based database, (2) adding studies to the database, and (3) running ALE meta-analyses. While search results can be extracted using its Sleuth app, access to the full database requires a collaborative use agreement. | +| Neurosynth | Neurosynth provides (1) a large, automatically-extracted coordinate-based database, (2) a website for performing large-scale automated meta-analyses, and (3) a Python library for performing meta-analyses and functional decoding, mostly relying on a version of the MKDA algorithm. The Python library has been deprecated in favor of `NiMARE`. | +| Neurovault | Neurovault is a repository for sharing unthresholded statistical images, which can be used to search for images to use in image-based meta-analyses. Neurovault provides a tool for basic meta-analyses and an integration with Neurosynth's database for online functional decoding. | +| SDM | The Seed-based _d_ Mapping (SDM) app provides a graphical user interface and SPM toolbox for performing meta-analyses with the SDM algorithm, which supports a mix of coordinates and images. | +| MKDA | The MATLAB-based MKDA toolbox includes functions for performing coordinate-based meta-analyses with the MKDA algorithm. | + +The majority of the above tools are (1) closed source, (2) based on graphical user interfaces, and/or (3) written in a programming language that is rarely used by neuroimagers, such as Java. + +In addition to these established tools, there are always interesting new methods that are described in journal articles, but which are never translated to a well-documented and supported implementation. + +NiMARE attempts to consolidate the different algorithms that are currently spread out across a range of tools (or which never make the jump from paper to tool), while still ensuring that the original tools and papers can be cited appropriately. + +## NiMARE's design philosophy + +NiMARE's API is designed to be similar to that of [`scikit-learn`](https://scikit-learn.org/stable/), in that most tools are custom classes. These classes follow the following basic structure: + +1. Initialize the class with general parameters +```python +cls = Class(param1, param2) +``` + +2. For Estimator classes, apply a `fit` method to a `Dataset` object to generate a `MetaResult` object +```python +result = cls.fit(dataset) +``` + +3. For Transformer classes, apply a `transform` method to an object to return a transformed version of that object + + - An example transformer that accepts a `Dataset`: +```python +dataset = cls.transform(dataset) +``` + - A transformer that accepts a `MetaResult`: +```python +result = cls.transform(result) +``` + +## Stability and consistency + +NiMARE is currently in alpha development, so we appreciate any feedback or bug reports users can provide. Given its status, NiMARE's API may change in the future. + +Usage questions can be submitted to [Neurostars with the 'nimare' tag](https://neurostars.org/tag/nimare), while bug reports and feature requests can be submitted to [NiMARE's issue tracker](https://github.com/neurostuff/NiMARE/issues). + + +# Goals for this tutorial + +1. Working with NiMARE meta-analytic datasets +1. Searching large datasets +1. Performing coordinate-based meta-analyses +1. Performing image-based meta-analyses +1. Performing functional decoding using Neurosynth + + +# Before we start, let's download the necessary data **only** if running locally + +The code in the following cell checks whether you have the [data](https://osf.io/u9sqa/), and if you don't, it starts downloading it. + +If you're running this notebook locally or using mybinder, then you will need to download the data. You can copy the code below into a new cell, with the Jupyter magic command `%%bash` at the top of the cell. + +If you're running it using binder hosted on neurolibre, then you already have access to the data on neurolibre, and you don't need to run this code snippet. + +```bash +DIR=$"../data/nimare_tutorial/" +if [ -d "$DIR" ]; then + echo "$DIR exists." +else + mkdir -p $DIR; + pip install osfclient + osf -p u9sqa clone $DIR; + echo "Created $DIR and downloaded the data"; +fi +``` + + +```python +# Import the packages we'll need for this tutorial +%matplotlib inline +import json +import os.path as op +from pprint import pprint + +import matplotlib.pyplot as plt +from nilearn import plotting, reporting + +import nimare +``` + +```python +DATA_DIR = op.abspath("../data/nimare_tutorial/osfstorage/") +``` + +# Basics of NiMARE datasets +NiMARE relies on a specification for meta-analytic datasets named [NIMADS](https://github.com/neurostuff/NIMADS). Under NIMADS, meta-analytic datasets are stored as JSON files, with information about peak coordinates, _relative_ links to any unthresholded statistical images, metadata, annotations, and raw text. + +**NOTE**: NiMARE users generally do not need to create JSONs manually, so we won't go into that structure in this tutorial. Instead, users will typically have access to datasets stored in more established formats, like [Neurosynth](https://github.com/neurosynth/neurosynth-data) and [Sleuth](http://brainmap.org/sleuth/) files. + + +We will start by loading a dataset in NIMADS format, because this particular dataset contains both coordinates and images. This dataset is created from [Collection 1425 on NeuroVault](https://identifiers.org/neurovault.collection:1425), which contains [NIDM-Results packs](http://nidm.nidash.org/specs/nidm-results_130.html) for 21 pain studies. + +```python +pain_dset = nimare.dataset.Dataset(op.join(DATA_DIR, "nidm_pain_dset.json")) + +# In addition to loading the NIMADS-format JSON file, +# we need to download the associated statistical images from NeuroVault, +# for which NiMARE has a useful function. +dset_dir = nimare.extract.download_nidm_pain(data_dir=DATA_DIR) + +# We then notify the Dataset about the location of the images, +# so that the *relative paths* in the Dataset can be used to determine *absolute paths*. +pain_dset.update_path(dset_dir) +``` + +In NiMARE, datasets are stored in a special `Dataset` class. The `Dataset` class stores most relevant information as properties. + +The full list of identifiers in the Dataset is located in `Dataset.ids`. Identifiers are composed of two parts- a study ID and a contrast ID. Within the Dataset, those two parts are separated with a `-`. + +```python +print(pain_dset.ids) +``` + +Most other information is stored in `pandas` DataFrames. The five DataFrame-based attributes are `Dataset.metadata`, `Dataset.coordinates`, `Dataset.images`, `Dataset.annotations`, and `Dataset.texts`. + +Each DataFrame contains at least three columns: `study_id`, `contrast_id`, and `id`, which is the combined `study_id` and `contrast_id`. + +```python +pain_dset.coordinates.head() +``` + +```python +pain_dset.metadata.head() +``` + +```python +pain_dset.images.head() +``` + +```python +pain_dset.annotations.head() +``` + +```python +pain_dset.texts.head() +``` + +Other relevant attributes are `Dataset.masker` and `Dataset.space`. + +`Dataset.masker` is a [nilearn Masker object](https://nilearn.github.io/manipulating_images/masker_objects.html#), which specifies the manner in which voxel-wise information like peak coordinates and statistical images are mapped into usable arrays. Most meta-analytic tools within NiMARE accept a `masker` argument, so the Dataset's masker can be overridden in most cases. + +`Dataset.space` is just a string describing the standard space and resolution in which data within the Dataset are stored. + +```python +pain_dset.masker +``` + +```python +pain_dset.space +``` + + +Datasets can also be saved to, and loaded from, binarized (pickled) files. + +We cannot save files on Binder, so here is the code we would use to save the pain Dataset: + +```python +pain_dset.save("pain_dataset.pkl.gz") +``` + + +Now for a more common situation, where users want to use NiMARE on data from Neurosynth or a Sleuth file. + + +Downloading and converting the Neurosynth dataset takes a long time, so we will use a pregenerated version of the dataset. However, here is the code we would use to download and convert the dataset from scratch: + +```python +nimare.extract.fetch_neurosynth("data/", unpack=True) +ns_dset = nimare.io.convert_neurosynth_to_dataset( + "data/database.txt", + "data/features.txt", +) +``` + + +```python +ns_dset = nimare.dataset.Dataset.load(op.join(DATA_DIR, "neurosynth_dataset.pkl.gz")) +print(f"There are {len(ns_dset.ids)} studies in the Neurosynth database.") +``` + +```python +sleuth_dset = nimare.io.convert_sleuth_to_dataset(op.join(DATA_DIR, "sleuth_dataset.txt")) +print(f"There are {len(sleuth_dset.ids)} studies in this dataset.") +``` + +## Searching large datasets + +The `Dataset` class contains multiple methods for selecting subsets of studies within the dataset. + +One common approach is to search by "labels" or "terms" that apply to studies. In Neurosynth, labels are derived from term frequency within abstracts. + +The `slice` method creates a reduced `Dataset` from a list of IDs. + +```python +pain_ids = ns_dset.get_studies_by_label("Neurosynth_TFIDF__pain", label_threshold=0.001) +ns_pain_dset = ns_dset.slice(pain_ids) +print(f"There are {len(pain_ids)} studies labeled with 'pain'.") +``` + +A MACM (meta-analytic coactivation modeling) analysis is generally performed by running a meta-analysis on studies with a peak in a region of interest, so Dataset includes two methods for searching based on the locations of coordinates: `Dataset.get_studies_by_coordinate` and `Dataset.get_studies_by_mask`. + +```python +sphere_ids = ns_dset.get_studies_by_coordinate([[24, -2, -20]], r=6) +sphere_dset = ns_dset.slice(sphere_ids) +print(f"There are {len(sphere_ids)} studies with at least one peak within 6mm of [24, -2, -20].") +``` + +# Running meta-analyses + +## Coordinate-based meta-analysis + +Most coordinate-based meta-analysis algorithms are kernel-based, in that they convolve peaks reported in papers with a "kernel". Kernels are generally either binary spheres, as in multi-level kernel density analysis (MKDA), or 3D Gaussian distributions, as in activation likelihood estimation (ALE). + +NiMARE includes classes for different kernel transformers, which accept Datasets and generate the images resulting from convolving each study's peaks with the associated kernel. + +```python +# Create a figure +fig, axes = plt.subplots(ncols=3, figsize=(20, 5)) + +# Apply different kernel transformers to the same Dataset +kernels = [ + nimare.meta.kernel.MKDAKernel(r=10), + nimare.meta.kernel.KDAKernel(r=10), + nimare.meta.kernel.ALEKernel(sample_size=20), +] + +for i_kernel, kernel in enumerate(kernels): + ma_maps = kernel.transform(pain_dset, return_type="image") + + # Plot the kernel + plotting.plot_stat_map( + ma_maps[0], + annotate=False, + axes=axes[i_kernel], + cmap="Reds", + cut_coords=[0, 0, -24], + draw_cross=False, + figure=fig, + title=type(kernel), + ) + +# Show the overall figure +fig.show() +``` + +Meta-analytic Estimators are initialized with parameters which determine how the Estimator will be run. For example, ALE accepts a kernel transformer (which defaults to the standard `ALEKernel`), a null method, the number of iterations used to define the null distribution, and the number of cores to be used during fitting. + +The Estimators also have a `fit` method, which accepts a `Dataset` object and returns a `MetaResult` object. [`MetaResult`s](https://nimare.readthedocs.io/en/latest/generated/nimare.results.MetaResult.html#nimare.results.MetaResult) link statistical image names to numpy arrays, and can be used to produce nibabel images from those arrays, as well as save the images to files. + +```python +meta = nimare.meta.cbma.ale.ALE(null_method="approximate") +meta_results = meta.fit(pain_dset) +``` + +```python +print(type(meta_results)) +``` + +```python +print(type(meta_results.maps)) +print("Available maps:") +print("\t- " + "\n\t- ".join(meta_results.maps.keys())) +``` + +```python +z_img = meta_results.get_map("z") +print(type(z_img)) +``` + +```python +plotting.plot_stat_map( + z_img, + draw_cross=False, + cut_coords=[0, 0, 0], +) +``` + +## Multiple comparisons correction + +Most of the time, you will want to follow up your meta-analysis with some form of multiple comparisons correction. For this, NiMARE provides Corrector classes in the `correct` module. Specifically, there are two Correctors: [`FWECorrector`](https://nimare.readthedocs.io/en/latest/generated/nimare.correct.FWECorrector.html) and [`FDRCorrector`](https://nimare.readthedocs.io/en/latest/generated/nimare.correct.FDRCorrector.html). In both cases, the Corrector supports a range of naive correction options relying on [`statsmodels`' methods](https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html). + +In addition to generic multiple comparisons correction, the Correctors also reference algorithm-specific correction methods, such as the `montecarlo` method supported by most coordinate-based meta-analysis algorithms. + +Correctors are initialized with parameters, and they have a `transform` method that accepts a `MetaResult` object and returns an updated one with the corrected maps. + +```python +mc_corrector = nimare.correct.FWECorrector( + method="montecarlo", + n_iters=100, + n_cores=1, +) +mc_results = mc_corrector.transform(meta_results) + +# Let's store the CBMA result for later +cbma_z_img = mc_results.get_map("z_level-cluster_corr-FWE_method-montecarlo") +``` + +```python +print(type(mc_results.maps)) +print("Available maps:") +print("\t- " + "\n\t- ".join(mc_results.maps.keys())) +``` + +```python +plotting.plot_stat_map( + mc_results.get_map("z_level-cluster_corr-FWE_method-montecarlo"), + draw_cross=False, + cut_coords=[0, 0, 0], + vmax=3, +) +``` + +```python +# Report a standard cluster table for the meta-analytic map using a threshold of p<0.05 +reporting.get_clusters_table(cbma_z_img, stat_threshold=1.65) +``` + +## Image-based meta-analysis + +```python +pain_dset.images +``` + +Note that "z" images are missing for some, but not all, of the studies. + +NiMARE's `transforms` module contains a class, `ImageTransformer`, which can generate images from other images- as long as the right images and metadata are available. In this case, it can generate z-statistic images from t-statistic maps, combined with sample size information in the metadata. It can also generate "varcope" (contrast variance) images from the contrast standard error images. + +```python +# Calculate missing images +z_transformer = nimare.transforms.ImageTransformer(target="z", overwrite=False) +pain_dset = z_transformer.transform(pain_dset) + +varcope_transformer = nimare.transforms.ImageTransformer(target="varcope", overwrite=False) +pain_dset = varcope_transformer.transform(pain_dset) +``` + +```python +pain_dset.images.head() +``` + +Now that we have all of the image types we will need for our meta-analyses, we can run a couple of image-based meta-analysis types. + +The `DerSimonianLaird` method uses "beta" and "varcope" images, and estimates between-study variance (a.k.a. $\tau^2$). + +```python +meta = nimare.meta.ibma.DerSimonianLaird() +meta_results = meta.fit(pain_dset) +``` + +```python +plotting.plot_stat_map( + meta_results.get_map("z"), + draw_cross=False, + cut_coords=[0, 0, 0], +) +``` + +The `PermutedOLS` method uses z-statistic images, and relies on [nilearn's `permuted_ols`](https://nilearn.github.io/modules/generated/nilearn.mass_univariate.permuted_ols.html) tool. + +```python +meta = nimare.meta.ibma.PermutedOLS() +meta_results = meta.fit(pain_dset) +``` + +```python +plotting.plot_stat_map( + meta_results.get_map("z"), + draw_cross=False, + cut_coords=[0, 0, 0], +) +``` + +```python +mc_corrector = nimare.correct.FWECorrector(method="montecarlo", n_iters=100) +mc_results = mc_corrector.transform(meta_results) +``` + +```python +print(type(mc_results.maps)) +print("Available maps:") +print("\t- " + "\n\t- ".join(mc_results.maps.keys())) +``` + +```python +plotting.plot_stat_map( + mc_results.get_map("z_level-voxel_corr-FWE_method-montecarlo"), + draw_cross=False, + cut_coords=[0, 0, 0], + vmax=3, +) +``` + +```python +# Report a standard cluster table for the meta-analytic map using a threshold of p<0.05 +reporting.get_clusters_table( + mc_results.get_map("z_level-voxel_corr-FWE_method-montecarlo"), + stat_threshold=1.65, + cluster_threshold=10, +) +``` + +## Compare to results from the SPM IBMA extension + +![IBMA comparison](images/ibma_comparison.png) + +Adapted from [Maumet & Nichols (2014)](https://www.frontiersin.org/10.3389/conf.fninf.2014.18.00025/event_abstract). + +```python +plotting.plot_stat_map( + mc_results.get_map("z_level-voxel_corr-FWE_method-montecarlo"), + threshold=1.65, + vmax=3, + draw_cross=False, + cut_coords=[0, 0, 0], +) +``` + +```python +plotting.plot_stat_map( + cbma_z_img, + threshold=1.65, + vmax=3, + draw_cross=False, + cut_coords=[0, 0, 0], +) +``` + +# Meta-Analytic Functional Decoding + +Functional decoding refers to approaches which attempt to infer mental processes, tasks, etc. from imaging data. There are many approaches to functional decoding, but one set of approaches uses meta-analytic databases like Neurosynth or BrainMap, which we call "meta-analytic functional decoding." For more information on functional decoding in general, read [Poldrack (2011)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3240863/). + +In NiMARE, we group decoding methods into three general types: discrete decoding, continuous decoding, and encoding. + +- **Discrete decoding methods** use a meta-analytic database and annotations of studies in that database to describe something discrete (like a region of interest) in terms of those annotations. + +- **Continuous decoding methods** use the same type of database to describe an unthresholded brain map in terms of the database's annotations. One example of this kind of method is the Neurosynth-based decoding available on Neurovault. In that method, the map you want to decode is correlated with Neurosynth term-specific meta-analysis maps. You end up with one correlation coefficient for each term in Neurosynth. Users generally report the top ten or so terms. + +- **Encoding methods** do the opposite- they take in annotations or raw text and produce a synthesized brain map. One example of a meta-analytic encoding tool is [NeuroQuery](https://neuroquery.org/). + + +Most of the continuous decoding methods available in NiMARE are too computationally intensive and time-consuming for Binder, so we will focus on discrete decoding methods. +The two most useful discrete decoders in NiMARE are the [`BrainMapDecoder`](https://nimare.readthedocs.io/en/latest/generated/nimare.decode.discrete.BrainMapDecoder.html#nimare.decode.discrete.BrainMapDecoder) and the [`NeurosynthDecoder`](https://nimare.readthedocs.io/en/latest/generated/nimare.decode.discrete.NeurosynthDecoder.html#nimare.decode.discrete.NeurosynthDecoder). Detailed descriptions of the two approaches are available in [NiMARE's documentation](https://nimare.readthedocs.io/en/latest/methods/decoding.html#discrete-decoding), but here's the basic idea: + +0. A NiMARE `Dataset` must contain both annotations/labels and coordinates. +1. A subset of studies in the `Dataset` must be selected according to some criterion, such as having at least one peak in a region of interest or having a specific label. +2. The algorithm then compares the frequency of each label within the selected subset of studies against the frequency of other labels in that subset to calculate "forward-inference" posterior probability, p-value, and z-statistic. +3. The algorithm also compares the frequency of each label within the subset of studies against the the frequency of that label in the *unselected* studies from the `Dataset` to calculate "reverse-inference" posterior probability, p-value, and z-statistic. + +```python +# Given the sheer size of Neurosynth, we will only use the first 500 studies in this example +ns_dset = ns_dset.slice(ns_dset.ids[:500]) + +label_ids = ns_dset.get_studies_by_label("Neurosynth_TFIDF__amygdala", label_threshold=0.001) +print(f"There are {len(label_ids)} studies in the Dataset with the 'Neurosynth_TFIDF__amygdala' label.") +``` + +```python +decoder = nimare.decode.discrete.BrainMapDecoder(correction=None) +decoder.fit(ns_dset) +decoded_df = decoder.transform(ids=label_ids) +decoded_df.sort_values(by="probReverse", ascending=False).head(10) +``` + +```python +decoder = nimare.decode.discrete.NeurosynthDecoder(correction=None) +decoder.fit(ns_dset) +decoded_df = decoder.transform(ids=label_ids) +decoded_df.sort_values(by="probReverse", ascending=False).head(10) +``` + +# Exercise: Run a MACM and Decode an ROI + +Remember that a MACM is a meta-analysis performed on studies which report at least one peak within a region of interest. This type of analysis is generally interpreted as a meta-analytic version of functional connectivity analysis. + +We will use an amygdala mask as our ROI, which we will use to (1) run a MACM using the (reduced) Neurosynth dataset and (2) decode the ROI using labels from Neurosynth. + + +First, we have to prepare some things for the exercise. You just need to run these cells without editing anything. + +```python +ROI_FILE = op.join(DATA_DIR, "amygdala_roi.nii.gz") + +plotting.plot_roi( + ROI_FILE, + title="Right Amygdala", + draw_cross=False, +) +``` + +Below, try to write code in each cell based on its comment. + +```python +# First, use the Dataset class's get_studies_by_mask method +# to identify studies with at least one coordinate in the ROI. +``` + +```python +# Now, create a reduced version of the Dataset including only +# studies identified above. +``` + +```python +# Next, run a meta-analysis on the reduced ROI dataset. +# This is a MACM. +# Use the nimare.meta.cbma.MKDADensity meta-analytic estimator. +# Do not perform multiple comparisons correction. +``` + +```python +# Initialize, fit, and transform a Neurosynth Decoder. +``` + +## After the exercise + +Your MACM results should look something like this: + +![MACM Results](images/macm_result.png) + +And your decoding results should look something like this, after sorting by probReverse: + +| Term | pForward | zForward | probForward | pReverse | zReverse | probReverse | +|:--------------------------------|-------------:|-----------:|--------------:|------------:|-----------:|--------------:| +| Neurosynth_TFIDF__amygdala | 4.14379e-113 | 22.602 | 0.2455 | 1.17242e-30 | 11.5102 | 0.964733 | +| Neurosynth_TFIDF__reinforcement | 7.71236e-05 | 3.95317 | 0.522177 | 7.35753e-15 | 7.77818 | 0.957529 | +| Neurosynth_TFIDF__olfactory | 0.0147123 | 2.43938 | 0.523139 | 5.84089e-11 | 6.54775 | 0.955769 | +| Neurosynth_TFIDF__fear | 1.52214e-11 | 6.74577 | 0.448855 | 6.41482e-19 | 8.88461 | 0.95481 | +| Neurosynth_TFIDF__age sex | 0.503406 | 0.669141 | 0.524096 | 3.8618e-07 | 5.07565 | 0.954023 | +| Neurosynth_TFIDF__appraisal | 0.503406 | 0.669141 | 0.524096 | 3.8618e-07 | 5.07565 | 0.954023 | +| Neurosynth_TFIDF__apart | 0.503406 | 0.669141 | 0.524096 | 3.8618e-07 | 5.07565 | 0.954023 | +| Neurosynth_TFIDF__naturalistic | 0.555471 | 0.589582 | 0.52505 | 0.00122738 | 3.23244 | 0.95229 | +| Neurosynth_TFIDF__controls hc | 0.555471 | 0.589582 | 0.52505 | 0.00122738 | 3.23244 | 0.95229 | +| Neurosynth_TFIDF__morphology | 0.555471 | 0.589582 | 0.52505 | 0.00122738 | 3.23244 | 0.95229 | From b866af5d292afbaaf68f9ecce4051c5dbbb8d18d Mon Sep 17 00:00:00 2001 From: "Katherine L. Bottenhorn" Date: Mon, 8 Aug 2022 11:40:00 -0700 Subject: [PATCH 3/6] Add text about meta-analysis --- content/intro.md | 29 +++++++++++++++-------------- 1 file changed, 15 insertions(+), 14 deletions(-) diff --git a/content/intro.md b/content/intro.md index 80635a4..21b7a23 100644 --- a/content/intro.md +++ b/content/intro.md @@ -1,24 +1,25 @@ # Meta-Analyses in Python -## What is meta-analysis +## What is meta-analysis? -Meta-analysis is the statistical aggregration of group level results. -Meta-analysis does not require the -- datasets: mega, meta - - group: GLM, mega - - subject: GLM, mega - - session: GLM, mega - - run: GLM, mega +Meta-analysis is the re-use, and often aggregation, of previously computed statistical results. Analyzing previously computed results, often across several published studies, in this way is used to find consensus and/or parse differences across studies of a similar topic or paradigm. -## How is meta-analysis in neuroimaging different from your standard, effect-size meta-analysis? +Meta-analysis leans on relatively light-weight data representations (e.g., published tabular data) and, thus, does not require the original source of data. This allows wide reuse without the privacy concerns or computational resources necessary for re-analysis of primary datasets. Further, published results are often more accessible than original data sources, making it possible to synthesize many independent sources of information. -standard meta-analysis is aggregating single variables, whereas neuroimaging meta-analyses are -- unique because: - - coordinates require special processing - - there is a spatial extent ## Why would you want to do a meta-analysis? -get consensus on a scientific question + +In the face of replication crises–such as those plaguing neuroimaging, psychology, and cancer research–being able to aggregate results in this way is particularly valuable.Researchers can use meta-analysis to identify consistency across studies and papers. Neuroimaging meta-analyses, specifically, can allow researchers to find consistency across relatively homogeneous or related groups of studies, regardless of individual study sample sizes, pipeline differences, and intrinsic differences in functional neuroanatomy between study samples. + +Further, neuroimaging meta-analyses can be used to generate hypotheses for future primary data analysis, as well. Meta-analytically generated brain regions of interest (ROIs) that are significantly activated across a cognitive or behavioral paradigm can be used in subsequent analyses if primary neuroimaging data (e.g., for limited field-of-view, functional connectivity, or psychophysiological interaction studies). Data-driven classification of a larger set of studies into distinct categories based on similarity of brain activations can be used to investigate or propose underlying cognitive/neurobiological models of complex processing (such as those by Bottenhorn et al., 2018; Flannery et al., 2020; Laird et al., 2016; Pintos Lobo et al., 2022; Riedel et al., 2018). Further, meta-analyses can be useful in dentifying common neural phenomena shared by multiple, seemingly distinct psychiatric diagnoses (Dugré et al., 2022; Janiri et al., 2020; Opel et al., 2020). + +## How is meta-analysis in neuroimaging different from your standard, effect-size meta-analysis? + +A standard meta-analysis aggregates results across a single measure. Neuroimaging meta-analyses are a special case because: +- data from a single statistical analysis of neuroimaging often include more than one effect size (e.g., multiple significant voxels, represented by stereotaxic coordinates) +- stereotaxic coordinates include spatial information, in addition to effect sizes and significance, and thus require additional processing + +There are two over-arching categories of neuroimaging meta-analyses, differentiated by the input data source. *Coordinate-based* meta-analyses are run on stereotaxic x-, y-, z-coordinate data, such as those published in neuroimaging papers, while *image-based* meta-analyses run on 3D statistical image data, such as those shared on various online repositories (e.g., NeuroVault, the Open Science Framework). There are several algorithms for both categories, but they are both distinguished from standard meta-analyses by both the volume of data per study/case (i.e., coordinates, voxels), the spatial information that is inherent to that data, and the assumptions of independence that those two features violate. ```{tableofcontents} ``` From 853db000b287a15e891d4dab0ea14d0bfdfca72f Mon Sep 17 00:00:00 2001 From: "Katherine L. Bottenhorn" Date: Mon, 8 Aug 2022 12:11:10 -0700 Subject: [PATCH 4/6] add references --- content/references.bib | 178 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 178 insertions(+) diff --git a/content/references.bib b/content/references.bib index 783ec6a..ee57a61 100644 --- a/content/references.bib +++ b/content/references.bib @@ -54,3 +54,181 @@ @book{ruby year = {2008}, publisher = {O'Reilly Media} } + + +@article{bartley_meta-analytic_2018, + title = {Meta-analytic evidence for a core problem solving network across multiple representational domains}, + volume = {92}, + issn = {0149-7634}, + url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6425494/}, + doi = {10.1016/j.neubiorev.2018.06.009}, + abstract = {Problem solving is a complex skill engaging multi-stepped reasoning processes to find unknown solutions. The breadth of real-world contexts requiring problem solving is mirrored by a similarly broad, yet unfocused neuroimaging literature, and the domain-general or context-specific brain networks associated with problem solving are not well understood. To more fully characterize those brain networks, we performed activation likelihood estimation meta-analysis on 280 neuroimaging problem solving experiments reporting 3166 foci from 1919 individuals across 131 papers. The general map of problem solving revealed broad fronto-cingulo-parietal convergence, regions similarly identified when considering separate mathematical, verbal, and visuospatial problem solving domain-specific analyses. Conjunction analysis revealed a common network supporting problem solving across diverse contexts, and difference maps distinguished functionally-selective sub-networks specific to task type. Our results suggest cooperation between representationally specialized sub-network and whole-brain systems provide a neural basis for problem solving, with the core network contributing general purpose resources to perform cognitive operations and manage problem demand. Further characterization of cross-network dynamics could inform neuroeducational studies on problem solving skill development.}, + urldate = {2020-10-01}, + journal = {Neuroscience and biobehavioral reviews}, + author = {Bartley, Jessica E. and Boeving, Emily R. and Riedel, Michael C. and Bottenhorn, Katherine L. and Salo, Taylor and Eickhoff, Simon B. and Brewe, Eric and Sutherland, Matthew T. and Laird, Angela R.}, + month = sep, + year = {2018}, + pmid = {29944961}, + pmcid = {PMC6425494}, + pages = {318--337}, + file = {PubMed Central Full Text PDF:/Users/katherine.b/Zotero/storage/7WG2J7G3/Bartley et al. - 2018 - Meta-analytic evidence for a core problem solving .pdf:application/pdf}, +} + + +@article{riedel_dissociable_2018, + title = {Dissociable meta-analytic brain networks contribute to coordinated emotional processing}, + volume = {39}, + issn = {1097-0193}, + url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.24018}, + doi = {10.1002/hbm.24018}, + abstract = {Meta-analytic techniques for mining the neuroimaging literature continue to exert an impact on our conceptualization of functional brain networks contributing to human emotion and cognition. Traditional theories regarding the neurobiological substrates contributing to affective processing are shifting from regional- towards more network-based heuristic frameworks. To elucidate differential brain network involvement linked to distinct aspects of emotion processing, we applied an emergent meta-analytic clustering approach to the extensive body of affective neuroimaging results archived in the BrainMap database. Specifically, we performed hierarchical clustering on the modeled activation maps from 1,747 experiments in the affective processing domain, resulting in five meta-analytic groupings of experiments demonstrating whole-brain recruitment. Behavioral inference analyses conducted for each of these groupings suggested dissociable networks supporting: (1) visual perception within primary and associative visual cortices, (2) auditory perception within primary auditory cortices, (3) attention to emotionally salient information within insular, anterior cingulate, and subcortical regions, (4) appraisal and prediction of emotional events within medial prefrontal and posterior cingulate cortices, and (5) induction of emotional responses within amygdala and fusiform gyri. These meta-analytic outcomes are consistent with a contemporary psychological model of affective processing in which emotionally salient information from perceived stimuli are integrated with previous experiences to engender a subjective affective response. This study highlights the utility of using emergent meta-analytic methods to inform and extend psychological theories and suggests that emotions are manifest as the eventual consequence of interactions between large-scale brain networks.}, + language = {en}, + number = {6}, + urldate = {2022-08-08}, + journal = {Human Brain Mapping}, + author = {Riedel, Michael C. and Yanes, Julio A. and Ray, Kimberly L. and Eickhoff, Simon B. and Fox, Peter T. and Sutherland, Matthew T. and Laird, Angela R.}, + year = {2018}, + note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/hbm.24018}, + keywords = {affective processing, BrainMap, co-activations, data mining, emotion, functional connectivity, functional magnetic resonance imaging, meta-analysis, neuroinformatics}, + pages = {2514--2531}, + file = {Full Text PDF:/Users/katherine.b/Zotero/storage/W3RGZZU4/Riedel et al. - 2018 - Dissociable meta-analytic brain networks contribut.pdf:application/pdf;Snapshot:/Users/katherine.b/Zotero/storage/BNSH2ZAA/hbm.html:text/html}, +} + +@misc{pintos_lobo_neural_2022, + title = {Neural systems underlying {RDoC} social constructs: {An} activation likelihood estimation meta-analysis}, + copyright = {© 2022, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/}, + shorttitle = {Neural systems underlying {RDoC} social constructs}, + url = {https://www.biorxiv.org/content/10.1101/2022.04.04.487016v2}, + doi = {10.1101/2022.04.04.487016}, + abstract = {Neuroscientists have sought to identify the underlying neural systems supporting social processing that allows interaction and communication, forming social relationships, and navigating the social world. Through the use of NIMH’s Research Domain Criteria (RDoC) framework, we evaluated consensus among studies that examined brain activity during social tasks to elucidate regions comprising the “social brain”. We examined convergence across tasks corresponding to the four RDoC social constructs, including Affiliation and Attachment, Social Communication, Perception and Understanding of Self, and Perception and Understanding of Others. We performed a series of coordinate-based meta-analyses using the activation likelihood estimate (ALE) method. Meta-analysis was performed on whole-brain coordinates reported from 864 fMRI contrasts using the NiMARE Python package, revealing convergence in medial prefrontal cortex, anterior cingulate cortex, posterior cingulate cortex, temporoparietal junction, bilateral insula, amygdala, fusiform gyrus, precuneus, and thalamus. Additionally, four separate RDoC-based meta-analyses revealed differential convergence associated with the four social constructs. These outcomes highlight the neural support underlying these social constructs and inform future research on alterations among neurotypical and atypical populations.}, + language = {en}, + urldate = {2022-08-08}, + publisher = {bioRxiv}, + author = {Pintos Lobo, Rosario and Bottenhorn, Katherine L. and Riedel, Michael C. and Toma, Afra I. and Hare, Megan M. and Smith, Donisha D. and Moor, Alexandra C. and Cowan, Isis K. and Valdes, Javier A. and Bartley, Jessica E. and Salo, Taylor and Boeving, Emily R. and Pankey, Brianna and Sutherland, Matthew T. and Musser, Erica D. and Laird, Angela R.}, + month = jul, + year = {2022}, + note = {Pages: 2022.04.04.487016 +Section: New Results}, + file = {Full Text PDF:/Users/katherine.b/Zotero/storage/8I8JQGTT/Lobo et al. - 2022 - Neural systems underlying RDoC social constructs .pdf:application/pdf;Snapshot:/Users/katherine.b/Zotero/storage/MBZD6236/2022.04.04.html:text/html}, +} + +@article{laird_neural_2015, + title = {Neural architecture underlying classification of face perception paradigms.}, + volume = {119}, + issn = {1095-9572}, + doi = {10.1016/j.neuroimage.2015.06.044}, + abstract = {We present a novel strategy for deriving a classification system of functional neuroimaging paradigms that relies on hierarchical clustering of experiments archived in the BrainMap database. The goal of our proof-of-concept application was to examine the underlying neural architecture of the face perception literature from a meta-analytic perspective, as these studies include a wide range of tasks. Task-based results exhibiting similar activation patterns were grouped as similar, while tasks activating different brain networks were classified as functionally distinct. We identified four sub-classes of face tasks: (1) Visuospatial Attention and Visuomotor Coordination to Faces, (2) Perception and Recognition of Faces, (3) Social Processing and Episodic Recall of Faces, and (4) Face Naming and Lexical Retrieval. Interpretation of these sub-classes supports an extension of a well-known model of face perception to include a core system for visual analysis and extended systems for personal information, emotion, and salience processing. Overall, these results demonstrate that a large-scale data mining approach can inform the evolution of theoretical cognitive models by probing the range of behavioral manipulations across experimental tasks.}, + journal = {NeuroImage}, + author = {Laird, Angela R and Riedel, Michael C and Sutherland, Matthew T and Eickhoff, Simon B and Ray, Kimberly L and Uecker, Angela M and Fox, P Mickle and Turner, Jessica A and Fox, Peter T}, + year = {2015}, + pages = {70--80}, +} + + +@article{flannery_meta-analytic_2020, + title = {Meta-analytic clustering dissociates brain activity and behavior profiles across reward processing paradigms}, + volume = {20}, + issn = {1530-7026}, + url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7117996/}, + doi = {10.3758/s13415-019-00763-7}, + abstract = {Reward learning is a ubiquitous cognitive mechanism guiding adaptive choices and behaviors, and when impaired, can lead to considerable mental health consequences. Reward-related functional neuroimaging studies have begun to implicate networks of brain regions essential for processing various peripheral influences (e.g., risk, subjective preference, delay, social context) involved in the multifaceted reward processing construct. To provide a more complete neurocognitive perspective on reward processing that synthesizes findings across the literature while also appreciating these peripheral influences, we used emerging meta-analytic techniques to elucidate brain regions, and in turn networks, consistently engaged in distinct aspects of reward processing. Using a data-driven, meta-analytic, k-means clustering approach, we dissociated seven meta-analytic groupings (MAGs) of neuroimaging results (i.e., brain activity maps) from 749 experimental contrasts across 176 reward processing studies involving 13,358 healthy participants. We then performed an exploratory functional decoding approach to gain insight into the putative functions associated with each MAG. We identified a seven-MAG clustering solution that represented dissociable patterns of convergent brain activity across reward processing tasks. Additionally, our functional decoding analyses revealed that each of these MAGs mapped onto discrete behavior profiles that suggested specialized roles in predicting value (MAG-1 \& MAG-2) and processing a variety of emotional (MAG-3), external (MAG-4 \& MAG-5), and internal (MAG-6 \& MAG-7) influences across reward processing paradigms. These findings support and extend aspects of well-accepted reward learning theories and highlight large-scale brain network activity associated with distinct aspects of reward processing.}, + number = {2}, + urldate = {2022-08-08}, + journal = {Cognitive, affective \& behavioral neuroscience}, + author = {Flannery, Jessica S. and Riedel, Michael C. and Bottenhorn, Katherine L. and Poudel, Ranjita and Salo, Taylor and Hill-Bowen, Lauren D. and Laird, Angela R. and Sutherland, Matthew T.}, + month = apr, + year = {2020}, + pmid = {31872334}, + pmcid = {PMC7117996}, + pages = {215--235}, + file = {Full Text:/Users/katherine.b/Zotero/storage/QKW5JYK8/Flannery et al. - 2020 - Meta-analytic clustering dissociates brain activit.pdf:application/pdf}, +} + +@article{dugre_meta-analytical_2022, + title = {Meta-analytical transdiagnostic neural correlates in common pediatric psychiatric disorders}, + volume = {12}, + copyright = {2022 The Author(s)}, + issn = {2045-2322}, + url = {https://www.nature.com/articles/s41598-022-08909-3}, + doi = {10.1038/s41598-022-08909-3}, + abstract = {In the last decades, neuroimaging studies have attempted to unveil the neurobiological markers underlying pediatric psychiatric disorders. Yet, the vast majority of neuroimaging studies still focus on a single nosological category, which limit our understanding of the shared/specific neural correlates between these disorders. Therefore, we aimed to investigate the transdiagnostic neural correlates through a novel and data-driven meta-analytical method. A data-driven meta-analysis was carried out which grouped similar experiments’ topographic map together, irrespectively of nosological categories and task-characteristics. Then, activation likelihood estimation meta-analysis was performed on each group of experiments to extract spatially convergent brain regions. One hundred forty-seven experiments were retrieved (3124 cases compared to 3100 controls): 79 attention-deficit/hyperactivity disorder, 32 conduct/oppositional defiant disorder, 14 anxiety disorders, 22 major depressive disorders. Four significant groups of experiments were observed. Functional characterization suggested that these groups of aberrant brain regions may be implicated internally/externally directed processes, attentional control of affect, somato-motor and visual processes. Furthermore, despite that some differences in rates of studies involving major depressive disorders were noticed, nosological categories were evenly distributed between these four sets of regions. Our results may reflect transdiagnostic neural correlates of pediatric psychiatric disorders, but also underscore the importance of studying pediatric psychiatric disorders simultaneously rather than independently to examine differences between disorders.}, + language = {en}, + number = {1}, + urldate = {2022-08-08}, + journal = {Scientific Reports}, + author = {Dugré, Jules R. and Eickhoff, Simon B. and Potvin, Stéphane}, + month = mar, + year = {2022}, + note = {Number: 1 +Publisher: Nature Publishing Group}, + keywords = {Human behaviour, Social neuroscience}, + pages = {4909}, + file = {Full Text PDF:/Users/katherine.b/Zotero/storage/GWUM4EAX/Dugré et al. - 2022 - Meta-analytical transdiagnostic neural correlates .pdf:application/pdf;Snapshot:/Users/katherine.b/Zotero/storage/FF3MI2LD/s41598-022-08909-3.html:text/html}, +} + +@article{opel_cross-disorder_2020, + series = {New {Mechanisms} of {Psychosis}: {Clinical} {Implications}}, + title = {Cross-{Disorder} {Analysis} of {Brain} {Structural} {Abnormalities} in {Six} {Major} {Psychiatric} {Disorders}: {A} {Secondary} {Analysis} of {Mega}- and {Meta}-analytical {Findings} {From} the {ENIGMA} {Consortium}}, + volume = {88}, + issn = {0006-3223}, + shorttitle = {Cross-{Disorder} {Analysis} of {Brain} {Structural} {Abnormalities} in {Six} {Major} {Psychiatric} {Disorders}}, + url = {https://www.sciencedirect.com/science/article/pii/S0006322320315857}, + doi = {10.1016/j.biopsych.2020.04.027}, + abstract = {Background +Neuroimaging studies have consistently reported similar brain structural abnormalities across different psychiatric disorders. Yet, the extent and regional distribution of shared morphometric abnormalities between disorders remains unknown. +Methods +Here, we conducted a cross-disorder analysis of brain structural abnormalities in 6 psychiatric disorders based on effect size estimates for cortical thickness and subcortical volume differences between healthy control subjects and psychiatric patients from 11 mega- and meta-analyses from the ENIGMA (Enhancing Neuro Imaging Genetics Through Meta Analysis) consortium. Correlational and exploratory factor analyses were used to quantify the relative overlap in brain structural effect sizes between disorders and to identify brain regions with disorder-specific abnormalities. +Results +Brain structural abnormalities in major depressive disorder, bipolar disorder, schizophrenia, and obsessive-compulsive disorder were highly correlated (r = .443 to r = .782), and one shared latent underlying factor explained between 42.3\% and 88.7\% of the brain structural variance of each disorder. The observed shared morphometric signature of these disorders showed little similarity with brain structural patterns related to physiological aging. In contrast, patterns of brain structural abnormalities independent of all other disorders were observed in both attention-deficit/hyperactivity disorder and autism spectrum disorder. Brain regions showing high proportions of independent variance were identified for each disorder to locate disorder-specific morphometric abnormalities. +Conclusions +Taken together, these results offer novel insights into transdiagnostic as well as disorder-specific brain structural abnormalities across 6 major psychiatric disorders. Limitations comprise the uncertain contribution of risk factors, comorbidities, and medication effects to the observed pattern of results that should be clarified by future research.}, + language = {en}, + number = {9}, + urldate = {2022-08-08}, + journal = {Biological Psychiatry}, + author = {Opel, Nils and Goltermann, Janik and Hermesdorf, Marco and Berger, Klaus and Baune, Bernhard T. and Dannlowski, Udo}, + month = nov, + year = {2020}, + keywords = {Cross-disorder, ENIGMA, Neuroimaging, Psychiatric disorders, Structural MRI, Transdiagnostic}, + pages = {678--686}, + file = {ScienceDirect Snapshot:/Users/katherine.b/Zotero/storage/FMEKC5W7/S0006322320315857.html:text/html}, +} + +@article{janiri_shared_2020, + title = {Shared {Neural} {Phenotypes} for {Mood} and {Anxiety} {Disorders}: {A} {Meta}-analysis of 226 {Task}-{Related} {Functional} {Imaging} {Studies}}, + volume = {77}, + issn = {2168-622X}, + shorttitle = {Shared {Neural} {Phenotypes} for {Mood} and {Anxiety} {Disorders}}, + url = {https://doi.org/10.1001/jamapsychiatry.2019.3351}, + doi = {10.1001/jamapsychiatry.2019.3351}, + abstract = {Major depressive disorder, bipolar disorder, posttraumatic stress disorder, and anxiety disorders are highly comorbid and have shared clinical features. It is not yet known whether their clinical overlap is reflected at the neurobiological level.To detect transdiagnostic convergence in abnormalities in task-related brain activation.Task-related functional magnetic resonance imaging articles published in PubMed, Web of Science, and Google Scholar during the last decade comparing control individuals with patients with mood, posttraumatic stress, and anxiety disorders were examined.Following Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guidelines, articles were selected if they reported stereotactic coordinates of whole-brain–based activation differences between adult patients and control individuals.Coordinates of case-control differences coded by diagnosis and by cognitive domain based on the research domain criteria were analyzed using activation likelihood estimation.Identification of transdiagnostic clusters of aberrant activation and quantification of the contribution of diagnosis and cognitive domain to each cluster.A total of 367 experiments (major depressive disorder, 149; bipolar disorder, 103; posttraumatic stress disorder, 55; and anxiety disorders, 60) were included comprising observations from 4507 patients and 4755 control individuals. Three right-sided clusters of hypoactivation were identified centered in the inferior prefrontal cortex/insula (volume, 2120 mm3), the inferior parietal lobule (volume, 1224 mm3), and the putamen (volume, 888 mm3); diagnostic differences were noted only in the putamen (χ23 = 8.66; P = .03), where hypoactivation was more likely in bipolar disorder (percentage contribution = 72.17\%). Tasks associated with cognitive systems made the largest contribution to each cluster (percentage contributions \>29\%). Clusters of hyperactivation could only be detected using a less stringent threshold. These were centered in the perigenual/dorsal anterior cingulate cortex (volume, 2208 mm3), the left amygdala/parahippocampal gyrus (volume, 2008 mm3), and the left thalamus (volume, 1904 mm3). No diagnostic differences were observed (χ23 \< 3.06; P \> .38), while tasks associated with negative valence systems made the largest contribution to each cluster (percentage contributions \>49\%). All findings were robust to the moderator effects of age, sex, and magnetic field strength of the scanner and medication.In mood disorders, posttraumatic stress disorder, and anxiety disorders, the most consistent transdiagnostic abnormalities in task-related brain activity converge in regions that are primarily associated with inhibitory control and salience processing. Targeting these shared neural phenotypes could potentially mitigate the risk of affective morbidity in the general population and improve outcomes in clinical populations.}, + number = {2}, + urldate = {2022-08-08}, + journal = {JAMA Psychiatry}, + author = {Janiri, Delfina and Moser, Dominik A. and Doucet, Gaelle E. and Luber, Maxwell J. and Rasgon, Alexander and Lee, Won Hee and Murrough, James W. and Sani, Gabriele and Eickhoff, Simon B. and Frangou, Sophia}, + month = feb, + year = {2020}, + pages = {172--179}, + file = {Full Text:/Users/katherine.b/Zotero/storage/76QG447G/Janiri et al. - 2020 - Shared Neural Phenotypes for Mood and Anxiety Diso.pdf:application/pdf;Snapshot:/Users/katherine.b/Zotero/storage/UNZZABNU/2753513.html:text/html}, +} + + +@article{bottenhorn_cooperating_2018, + title = {Cooperating yet distinct brain networks engaged during naturalistic paradigms: {A} meta-analysis of functional {MRI} results}, + volume = {3}, + issn = {2472-1751}, + shorttitle = {Cooperating yet distinct brain networks engaged during naturalistic paradigms}, + url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6326731/}, + doi = {10.1162/netn_a_00050}, + abstract = {Cognitive processes do not occur by pure insertion and instead depend on the full complement of co-occurring mental processes, including perceptual and motor functions. As such, there is limited ecological validity to human neuroimaging experiments that use highly controlled tasks to isolate mental processes of interest. However, a growing literature shows how dynamic, interactive tasks have allowed researchers to study cognition as it more naturally occurs. Collective analysis across such neuroimaging experiments may answer broader questions regarding how naturalistic cognition is biologically distributed throughout the brain. We applied an unbiased, data-driven, meta-analytic approach that uses k-means clustering to identify core brain networks engaged across the naturalistic functional neuroimaging literature. Functional decoding allowed us to, then, delineate how information is distributed between these networks throughout the execution of dynamical cognition in realistic settings. This analysis revealed six recurrent patterns of brain activation, representing sensory, domain-specific, and attentional neural networks that support the cognitive demands of naturalistic paradigms. Although gaps in the literature remain, these results suggest that naturalistic fMRI paradigms recruit a common set of networks that allow both separate processing of different streams of information and integration of relevant information to enable flexible cognition and complex behavior., Naturalistic fMRI paradigms offer increased ecological validity over traditional paradigms, addressing the gap left by studying highly interactive cognitive processes as isolated neural phenomena. This study identifies the connectional architecture supporting dynamic cognition in naturalistic fMRI paradigms, the first meta-analysis of a wide range of more realistic neuroimaging experiments. Here we identify and characterize six core patterns of neural activity that support functional segregation and integration in large-scale brain networks. This study provides a unique investigation of the cooperating neural systems that enable complex behavior.}, + number = {1}, + urldate = {2020-10-01}, + journal = {Network Neuroscience}, + author = {Bottenhorn, Katherine L. and Flannery, Jessica S. and Boeving, Emily R. and Riedel, Michael C. and Eickhoff, Simon B. and Sutherland, Matthew T. and Laird, Angela R.}, + month = oct, + year = {2018}, + pmid = {30793072}, + pmcid = {PMC6326731}, + pages = {27--48}, + file = {PubMed Central Full Text PDF:/Users/katherine.b/Zotero/storage/GDS86K7E/Bottenhorn et al. - 2018 - Cooperating yet distinct brain networks engaged du.pdf:application/pdf}, +} From c6ca14228b5f9baa9ad0563e394faef2eb95f79d Mon Sep 17 00:00:00 2001 From: "Katherine L. Bottenhorn" Date: Mon, 8 Aug 2022 12:11:20 -0700 Subject: [PATCH 5/6] update formatting --- content/intro.md | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/content/intro.md b/content/intro.md index 21b7a23..ec6b2b4 100644 --- a/content/intro.md +++ b/content/intro.md @@ -1,5 +1,8 @@ # Meta-Analyses in Python +```{tableofcontents} +``` + ## What is meta-analysis? Meta-analysis is the re-use, and often aggregation, of previously computed statistical results. Analyzing previously computed results, often across several published studies, in this way is used to find consensus and/or parse differences across studies of a similar topic or paradigm. @@ -9,9 +12,9 @@ Meta-analysis leans on relatively light-weight data representations (e.g., publi ## Why would you want to do a meta-analysis? -In the face of replication crises–such as those plaguing neuroimaging, psychology, and cancer research–being able to aggregate results in this way is particularly valuable.Researchers can use meta-analysis to identify consistency across studies and papers. Neuroimaging meta-analyses, specifically, can allow researchers to find consistency across relatively homogeneous or related groups of studies, regardless of individual study sample sizes, pipeline differences, and intrinsic differences in functional neuroanatomy between study samples. +In the face of replication crises–such as those plaguing neuroimaging, psychology, and cancer research–being able to aggregate results in this way is particularly valuable. Researchers can use meta-analysis to identify consistency across studies and papers. Neuroimaging meta-analyses, specifically, can allow researchers to find consistency across relatively homogeneous or related groups of studies, regardless of individual study sample sizes, pipeline differences, and intrinsic differences in functional neuroanatomy between study samples (i.e., {cite}`pintos_lobo_neural_2022`). On the other hand, meta-analysis can help identify a common effect across related, but distinct, studies {cite}`bartley_meta-analytic_2018`. -Further, neuroimaging meta-analyses can be used to generate hypotheses for future primary data analysis, as well. Meta-analytically generated brain regions of interest (ROIs) that are significantly activated across a cognitive or behavioral paradigm can be used in subsequent analyses if primary neuroimaging data (e.g., for limited field-of-view, functional connectivity, or psychophysiological interaction studies). Data-driven classification of a larger set of studies into distinct categories based on similarity of brain activations can be used to investigate or propose underlying cognitive/neurobiological models of complex processing (such as those by Bottenhorn et al., 2018; Flannery et al., 2020; Laird et al., 2016; Pintos Lobo et al., 2022; Riedel et al., 2018). Further, meta-analyses can be useful in dentifying common neural phenomena shared by multiple, seemingly distinct psychiatric diagnoses (Dugré et al., 2022; Janiri et al., 2020; Opel et al., 2020). +Further, neuroimaging meta-analyses can be used to generate hypotheses for future primary data analysis, as well. Meta-analytically generated brain networks or regions of interest (ROIs) that are significantly activated across a cognitive or behavioral paradigm can be used in subsequent analyses if primary neuroimaging data (e.g., for limited field-of-view, functional connectivity, or psychophysiological interaction studies) . Data-driven classification of a larger set of studies into distinct categories based on similarity of brain activations can be used to investigate or propose underlying cognitive/neurobiological models of complex processing {cite}`bottenhorn_cooperating_2018, flannery_meta-analytic_2020, laird_neural_2015, riedel_dissociable_2018`. Further, meta-analyses can be useful in dentifying common neural phenomena shared by multiple, seemingly distinct psychiatric diagnoses {cite}`dugre_meta-analytical_2022, janiri_shared_2020, opel_cross-disorder_2020`. ## How is meta-analysis in neuroimaging different from your standard, effect-size meta-analysis? @@ -21,5 +24,6 @@ A standard meta-analysis aggregates results across a single measure. Neuroimagin There are two over-arching categories of neuroimaging meta-analyses, differentiated by the input data source. *Coordinate-based* meta-analyses are run on stereotaxic x-, y-, z-coordinate data, such as those published in neuroimaging papers, while *image-based* meta-analyses run on 3D statistical image data, such as those shared on various online repositories (e.g., NeuroVault, the Open Science Framework). There are several algorithms for both categories, but they are both distinguished from standard meta-analyses by both the volume of data per study/case (i.e., coordinates, voxels), the spatial information that is inherent to that data, and the assumptions of independence that those two features violate. -```{tableofcontents} -``` +```{bibliography} +:filter: docname in docnames +``` \ No newline at end of file From cadb03d2946c09ee6c4026a66c32efdddae23ec4 Mon Sep 17 00:00:00 2001 From: Katie Bottenhorn Date: Thu, 18 Aug 2022 15:22:29 -0700 Subject: [PATCH 6/6] Update content/intro.md Co-authored-by: James Kent --- content/intro.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/intro.md b/content/intro.md index ec6b2b4..f6fea22 100644 --- a/content/intro.md +++ b/content/intro.md @@ -14,7 +14,7 @@ Meta-analysis leans on relatively light-weight data representations (e.g., publi In the face of replication crises–such as those plaguing neuroimaging, psychology, and cancer research–being able to aggregate results in this way is particularly valuable. Researchers can use meta-analysis to identify consistency across studies and papers. Neuroimaging meta-analyses, specifically, can allow researchers to find consistency across relatively homogeneous or related groups of studies, regardless of individual study sample sizes, pipeline differences, and intrinsic differences in functional neuroanatomy between study samples (i.e., {cite}`pintos_lobo_neural_2022`). On the other hand, meta-analysis can help identify a common effect across related, but distinct, studies {cite}`bartley_meta-analytic_2018`. -Further, neuroimaging meta-analyses can be used to generate hypotheses for future primary data analysis, as well. Meta-analytically generated brain networks or regions of interest (ROIs) that are significantly activated across a cognitive or behavioral paradigm can be used in subsequent analyses if primary neuroimaging data (e.g., for limited field-of-view, functional connectivity, or psychophysiological interaction studies) . Data-driven classification of a larger set of studies into distinct categories based on similarity of brain activations can be used to investigate or propose underlying cognitive/neurobiological models of complex processing {cite}`bottenhorn_cooperating_2018, flannery_meta-analytic_2020, laird_neural_2015, riedel_dissociable_2018`. Further, meta-analyses can be useful in dentifying common neural phenomena shared by multiple, seemingly distinct psychiatric diagnoses {cite}`dugre_meta-analytical_2022, janiri_shared_2020, opel_cross-disorder_2020`. +Further, neuroimaging meta-analyses can be used to generate hypotheses for future primary data analysis, as well. Meta-analytically generated brain networks or regions of interest (ROIs) that are significantly activated across a cognitive or behavioral paradigm can be used in subsequent analyses (e.g., for limited field-of-view, functional connectivity, or psychophysiological interaction studies). Data-driven classification of a larger set of studies into distinct categories based on similarity of brain activations can be used to investigate or propose underlying cognitive/neurobiological models of complex processing {cite}`bottenhorn_cooperating_2018, flannery_meta-analytic_2020, laird_neural_2015, riedel_dissociable_2018`. Further, meta-analyses can be useful in identifying common neural phenomena shared by multiple, seemingly distinct psychiatric diagnoses {cite}`dugre_meta-analytical_2022, janiri_shared_2020, opel_cross-disorder_2020`. ## How is meta-analysis in neuroimaging different from your standard, effect-size meta-analysis?

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