Merge pull request #6 from neurostuff/enh/add_material

[ENH] add material

content/intro.md → 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
-```
+```

content/01_overview.md

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+# 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

content/02_planning_and_preparation.md

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+# 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 <https://www.prisma-statement.org/PRISMAStatement/Default.aspx>.
+
+
+## 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 <https://pubmed.ncbi.nlm.nih.gov/29180258/>.
+
+
+
+## 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
+
+

content/03_curation.md

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+# 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.
+

content/04_extraction.md

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+# 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.
+

content/05_specification.md

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+# 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.
+

content/06_execution.md

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+# 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.
+

content/07_interpretation.md

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+# 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.

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

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