diff --git a/content/intro.md b/content/00_intro.md similarity index 98% rename from content/intro.md rename to content/00_intro.md index f6fea22..ad1d0a0 100644 --- a/content/intro.md +++ b/content/00_intro.md @@ -1,4 +1,4 @@ -# Meta-Analyses in Python +# Neuroimaging Meta-Analyses in Python ```{tableofcontents} ``` @@ -26,4 +26,4 @@ There are two over-arching categories of neuroimaging meta-analyses, differentia ```{bibliography} :filter: docname in docnames -``` \ No newline at end of file +``` diff --git a/content/01_overview.md b/content/01_overview.md new file mode 100644 index 0000000..c45e501 --- /dev/null +++ b/content/01_overview.md @@ -0,0 +1,41 @@ +# Overview + +This book will walk you through conceptualizing your own meta-analysis to interpreting the results +using existing standards and reproducible tools. + +## Conceptualizing your meta-analysis + +This is the step that will require the most thought and planning. You will need to immerse +yourself in the literature to identify the topic of your meta-analysis, what question does the +meta-analysis answer. You will need to decide whether you are using a coordinate-based or +image-based meta-analysis, and what method you will use to run your meta-analysis. + +## Finding and preparing your data + +Once you have a topic for your meta-analysis, you will need to define search criteria to find +studies that are relevant to your meta-analysis. + +## Selecting your meta-analysis method + +There are several methods for running either coordinate-based or image-based meta-analyses. +Which method you can use is dependent on the assumptions you would like to make about your data +and the availablity of certain data. + + +## Running your meta-analysis + + + +## Interpreting your meta-analysis + + +## Decision tree +Do I have coordinates or images? + +### Coordinate-based meta-analysis +There is not a lot of comparison between ALE and MKDA, +so choosing either or both for your meta-analysis may be a good idea +to test if there is consensus between the two methods. + + +### Image-based meta-analysis diff --git a/content/02_planning_and_preparation.md b/content/02_planning_and_preparation.md new file mode 100644 index 0000000..3eb6ebf --- /dev/null +++ b/content/02_planning_and_preparation.md @@ -0,0 +1,56 @@ +# PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) + +To begin our journey into the world of neuroimaging meta-analyses, we will first discuss the importance of reporting and reproducibility in scientific research. We will then introduce the PRISMA guidelines, which are a set of guidelines for reporting systematic reviews and meta-analyses. + + + +## What is PRISMA? + +PRISMA is a set of guidelines for reporting systematic reviews and meta-analyses. The guidelines are intended to help authors and readers of systematic reviews and meta-analyses to understand the methods used in a given study, and to facilitate the interpretation of the results. The PRISMA guidelines are intended to be a minimum standard for reporting systematic reviews and meta-analyses. The PRISMA statement is a 27-item checklist that authors can use to ensure that their manuscript includes all of the necessary information for a reader to understand the methods used in a given study. The PRISMA statement is available at . + + +## Create your question + +Sadly, we are not able to automate this stage of the process yet (but the advent of large language models and projects +like NeuroLang may make this process more machine friendly in the future). However, we can provide some guidance on how to formulate your question. +It is critical your question is well defined and specific. If you are interested in the broad topic of emotion for example, there are several important questions to ask yourself: + +1. What is the specific emotion or set of emotions you are interested in? +2. How do you define those specific emotions? +3. How are those emotions elicited in different psychological paradigms? +4. Are you interested in a particular population or age group? +5. Could you define sub-analyses based on the above questions? (for example, you may be interested in the effects of emotion on a specific cognitive task in a specific age group) + +The following paper "Ten Simple rules for neuroimaging meta-analyses" provides a good overview of the process of formulating a question and the pitfalls to avoid. The paper is available at . + + + +## Determine your eligibility criteria + +Once you've constructed a specific and focused research question, +you will already have begun thinking about inclusion and exclusion criteria for your meta-analysis. +Here is a non-exhaustive list of criteria that you may want to consider: +1. neuroimaging modalities (e.g. fMRI, PET, structural etc.) +2. populations (e.g., children, patients with or without medications or comordities, older adults, etc.) +3. task paradigms (e.g., stroop, flanker, Go/No-Go, etc.) +4. between or within subject designs + + +## Cultivate your search strategy + +With a precise question and a set of inclusion and exclusion criteria, you are now ready to begin constructing your search strategy. +If you +There are several popular databases that you can use to search for relevant studies such as PubMed, Web of Science, and Google Scholar. +Additionally, our own NeuroStore database is a great resource for finding relevant studies. +NeuroStore combines studies extracted from NeuroSynth, NeuroQuery, and NeuroVault with tags to get a unqiue search experience for neuroimaging meta-analyses. +A nice feature of studies searched on NeuroStore +is that the coordinates and images are pre-extracted and ready to use in your meta-analysis. +There will be some error in the extraction process, so it is important to check the coordinates, but editing is much easier than +inserting all of the coordinates manually. + + +## Search for studies +- pubmed +- neurostore + + diff --git a/content/03_curation.md b/content/03_curation.md new file mode 100644 index 0000000..711e632 --- /dev/null +++ b/content/03_curation.md @@ -0,0 +1,22 @@ +# Curation + +While creating your precise question, thinking about your inclusion and exclusion criteria, and deriving your search strategy is intellectually challenging, the curation stage of the process is the most time consuming. +Here is where you will need to be patient and diligent while reading through the studies you've found and identifying which studies are eligible for inclusion in your meta-analysis. +Using a common PRISMA workflow, NeuroSynth Compose provides a simple interface for curating studies with four columns: +- Identification +- Screening +- Eligibility +- Included + +This is how we will categorize the studies we've found in our search, but you can use any categorization scheme that is more appropriate for your research question. +This is an opinionated guide, not a manual, so feel free to adapt the process to your needs. + + +With the studies you've aggregated from your search, you are ready to begin curating the studies to determine which studies are eligible for inclusion in your meta-analysis. + +If you've performed searches on multiple databases +(and sometimes even on a single database), you will likely find duplicate studies. +Don't fret, this is a common problem. +We will need to mark duplicate studies, however. +Within NeuroSynth Compose you can mark duplicate studies by clicking the "Mark as duplicate" button when you are uploading studies. + diff --git a/content/04_extraction.md b/content/04_extraction.md new file mode 100644 index 0000000..247bef7 --- /dev/null +++ b/content/04_extraction.md @@ -0,0 +1,15 @@ +# Data extraction/collection from studies + +Now that you have a curated list of studies you want to analyze, you will need to extract the coordinates and other pertinant information from those studies. +This is another time consuming process. +Neurosynth-Compose contains coordinates from studies that were automatically extracted, but this process +is not perfect and you should check the coordinates for accuracy. +In addition to coordinates, you may want to keep track of other information about the study such as +sample size, the specific task used, the type of stimuli, etc. +We differentiate between facts about the study and more opinionated information about the study. +The factual information is stored as meta-data about the study and you will need to own the study to +change this information. +The opinionated information is stored as annotations and you can add annotations to any study. +Annotations could represent inclusion crtieria, quality ratings, or any specific categorization you +want to make about the study that is specific to your meta-analysis. + diff --git a/content/05_specification.md b/content/05_specification.md new file mode 100644 index 0000000..c3a2c94 --- /dev/null +++ b/content/05_specification.md @@ -0,0 +1,13 @@ +# Methods to choose from +When coming up with your search process, you should have decided whether you were going to use a coordinate-based meta-analysis (CBMA) or an image-based meta-analysis (IBMA). +While in previous meta-analyses you may been constrained by the tool you were using to perform the meta-analysis, Neurosynth-Compose allows you to use a multitude of methods. + +## CBMA + +While sharing of statistical maps is becoming more common, it is still not the norm, and aggregation of statistical maps is still a difficult process. +Coordinates are much more commonly shared. + +## IBMA + +Image based meta-analysis is relatively new in the field of neuroimaging, although the methods have been around for a while since we can perform a more standard meta-analysis on the statistical maps where each voxel looked at for consensus whereas coordinates have to be transformed by some method to guess what the underlying statistical map looks like. + diff --git a/content/06_execution.md b/content/06_execution.md new file mode 100644 index 0000000..070170e --- /dev/null +++ b/content/06_execution.md @@ -0,0 +1,6 @@ +# Execution + +Execution of the meta-analysis is designed to be as simple as possible, +if the meta-analysis is small enough it can be run within google collab. +If the meta-analysis is too large to run in google collab, it can be run on a local machine or on a cloud computing service. + diff --git a/content/07_interpretation.md b/content/07_interpretation.md new file mode 100644 index 0000000..ec4cadd --- /dev/null +++ b/content/07_interpretation.md @@ -0,0 +1,4 @@ +# interpretation of results + +So you have your meta-analytic result, and you want to publish it. +There are several considerations and follow up analyses you can do to make sure your meta-analysis is as robust as possible. diff --git a/content/_config.yml b/content/_config.yml index 8189f24..2246bf4 100644 --- a/content/_config.yml +++ b/content/_config.yml @@ -12,7 +12,7 @@ logo : "" # A path to the book logo # Patterns to skip when building the book. Can be glob-style (e.g. "*skip.ipynb") exclude_patterns : [_build, Thumbs.db, .DS_Store, "**.ipynb_checkpoints"] # Auto-exclude files not in the toc -only_build_toc_files : false +only_build_toc_files : true ####################################################################################### # Execution settings diff --git a/content/_toc.yml b/content/_toc.yml index 2ce5471..4e4515a 100644 --- a/content/_toc.yml +++ b/content/_toc.yml @@ -2,9 +2,12 @@ # Learn more at https://jupyterbook.org/customize/toc.html format: jb-book -root: intro +root: 00_intro chapters: -- file: download_data -- file: markdown -- file: notebooks -- file: markdown-notebooks +- file: 01_overview +- file: 02_planning_and_preparation +- file: 03_curation +- file: 04_extraction +- file: 05_specification +- file: 06_execution +- file: 07_interpretation diff --git a/content/download_data.md b/content/inspiration/download_data.md similarity index 100% rename from content/download_data.md rename to 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content/inspiration/nimare-repo/misc-notebooks/save_nidm_to_dset.ipynb diff --git a/content/ohbm2021-tutorial.md b/content/inspiration/ohbm2021-tutorial.md similarity index 100% rename from content/ohbm2021-tutorial.md rename to content/inspiration/ohbm2021-tutorial.md diff --git a/content/markdown-notebooks.md b/content/markdown-notebooks.md deleted file mode 100644 index 40e4892..0000000 --- a/content/markdown-notebooks.md +++ /dev/null @@ -1,54 +0,0 @@ ---- -jupytext: - cell_metadata_filter: -all - formats: md:myst - text_representation: - extension: .md - format_name: myst - format_version: 0.13 - jupytext_version: 1.11.5 -kernelspec: - display_name: Python 3 - language: python - name: python3 ---- - -# Notebooks with MyST Markdown - -Jupyter Book also lets you write text-based notebooks using MyST Markdown. -See [the Notebooks with MyST Markdown documentation](https://jupyterbook.org/file-types/myst-notebooks.html) for more detailed instructions. -This page shows off a notebook written in MyST Markdown. - -## An example cell - -With MyST Markdown, you can define code cells with a directive like so: - -```{code-cell} -print(2 + 2) -``` - -When your book is built, the contents of any `{code-cell}` blocks will be -executed with your default Jupyter kernel, and their outputs will be displayed -in-line with the rest of your content. - -```{seealso} -Jupyter Book uses [Jupytext](https://jupytext.readthedocs.io/en/latest/) to convert text-based files to notebooks, and can support [many other text-based notebook files](https://jupyterbook.org/file-types/jupytext.html). -``` - -## Create a notebook with MyST Markdown - -MyST Markdown notebooks are defined by two things: - -1. YAML metadata that is needed to understand if / how it should convert text files to notebooks (including information about the kernel needed). - See the YAML at the top of this page for example. -2. The presence of `{code-cell}` directives, which will be executed with your book. - -That's all that is needed to get started! - -## Quickly add YAML metadata for MyST Notebooks - -If you have a markdown file and you'd like to quickly add YAML metadata to it, so that Jupyter Book will treat it as a MyST Markdown Notebook, run the following command: - -``` -jupyter-book myst init path/to/markdownfile.md -``` diff --git a/content/markdown.md b/content/markdown.md deleted file mode 100644 index 0ddaab3..0000000 --- a/content/markdown.md +++ /dev/null @@ -1,55 +0,0 @@ -# Markdown Files - -Whether you write your book's content in Jupyter Notebooks (`.ipynb`) or -in regular markdown files (`.md`), you'll write in the same flavor of markdown -called **MyST Markdown**. -This is a simple file to help you get started and show off some syntax. - -## What is MyST? - -MyST stands for "Markedly Structured Text". It -is a slight variation on a flavor of markdown called "CommonMark" markdown, -with small syntax extensions to allow you to write **roles** and **directives** -in the Sphinx ecosystem. - -For more about MyST, see [the MyST Markdown Overview](https://jupyterbook.org/content/myst.html). - -## Sample Roles and Directives - -Roles and directives are two of the most powerful tools in Jupyter Book. They -are kind of like functions, but written in a markup language. They both -serve a similar purpose, but **roles are written in one line**, whereas -**directives span many lines**. They both accept different kinds of inputs, -and what they do with those inputs depends on the specific role or directive -that is being called. - -Here is a "note" directive: - -```{note} -Here is a note -``` - -It will be rendered in a special box when you build your book. - -Here is an inline directive to refer to a document: {doc}`markdown-notebooks`. - - -## Citations - -You can also cite references that are stored in a `bibtex` file. For example, -the following syntax: `` {cite}`holdgraf_evidence_2014` `` will render like -this: {cite}`holdgraf_evidence_2014`. - -Moreover, you can insert a bibliography into your page with this syntax: -The `{bibliography}` directive must be used for all the `{cite}` roles to -render properly. -For example, if the references for your book are stored in `references.bib`, -then the bibliography is inserted with: - -```{bibliography} -``` - -## Learn more - -This is just a simple starter to get you started. -You can learn a lot more at [jupyterbook.org](https://jupyterbook.org). diff --git a/content/notebooks.ipynb b/content/notebooks.ipynb deleted file mode 100644 index fdb7176..0000000 --- a/content/notebooks.ipynb +++ /dev/null @@ -1,122 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Content with notebooks\n", - "\n", - "You can also create content with Jupyter Notebooks. This means that you can include\n", - "code blocks and their outputs in your book.\n", - "\n", - "## Markdown + notebooks\n", - "\n", - "As it is markdown, you can embed images, HTML, etc into your posts!\n", - "\n", - "![](https://myst-parser.readthedocs.io/en/latest/_static/logo-wide.svg)\n", - "\n", - "You can also $add_{math}$ and\n", - "\n", - "$$\n", - "math^{blocks}\n", - "$$\n", - "\n", - "or\n", - "\n", - "$$\n", - "\\begin{aligned}\n", - "\\mbox{mean} la_{tex} \\\\ \\\\\n", - "math blocks\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "But make sure you \\$Escape \\$your \\$dollar signs \\$you want to keep!\n", - "\n", - "## MyST markdown\n", - "\n", - "MyST markdown works in Jupyter Notebooks as well. For more information about MyST markdown, check\n", - "out [the MyST guide in Jupyter Book](https://jupyterbook.org/content/myst.html),\n", - "or see [the MyST markdown documentation](https://myst-parser.readthedocs.io/en/latest/).\n", - "\n", - "## Code blocks and outputs\n", - "\n", - "Jupyter Book will also embed your code blocks and output in your book.\n", - "For example, here's some sample Matplotlib code:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from matplotlib import rcParams, cycler\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "plt.ion()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Fixing random state for reproducibility\n", - "np.random.seed(19680801)\n", - "\n", - "N = 10\n", - "data = [np.logspace(0, 1, 100) + np.random.randn(100) + ii for ii in range(N)]\n", - "data = np.array(data).T\n", - "cmap = plt.cm.coolwarm\n", - "rcParams['axes.prop_cycle'] = cycler(color=cmap(np.linspace(0, 1, N)))\n", - "\n", - "\n", - "from matplotlib.lines import Line2D\n", - "custom_lines = [Line2D([0], [0], color=cmap(0.), lw=4),\n", - " Line2D([0], [0], color=cmap(.5), lw=4),\n", - " Line2D([0], [0], color=cmap(1.), lw=4)]\n", - "\n", - "fig, ax = plt.subplots(figsize=(10, 5))\n", - "lines = ax.plot(data)\n", - "ax.legend(custom_lines, ['Cold', 'Medium', 'Hot']);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There is a lot more that you can do with outputs (such as including interactive outputs)\n", - "with your book. For more information about this, see [the Jupyter Book documentation](https://jupyterbook.org)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.0" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..9b6efc2 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,2 @@ +jupyter-book +nimare