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fMRI Imaging Analysis

Home Page: https://carpentries-incubator.github.io/SDC-BIDS-fMRI/

License: Other

Shell 0.01% Python 0.04% Jupyter Notebook 99.95%

carpentries-incubator python neuroimaging programming english pre-alpha lesson binder fmri fmri-analysis

sdc-bids-fmri's Introduction

Introduction to fMRI Analysis in Python

Background

This is one sub-module within the Neuroimaging cirriculum. Visit the link to view all the modules associated with the Neuroimaging Carpentries program.

fMRI Analysis in Python is a programme developed to facilitate reproducibility in functional neuroimaging analyses. Python is emerging as a standard language of data analysis, visualization, and workflow building. More recently, it has rapidly been adopted by the neuroimaging community as a means of developing powerful open-source tools in favour of historically used opaque software such as AFNI, FSL and SPM. In addition, the barrier to entry to Python is low - meaning that you as the user can easily develop your own packages and contribute to the open-source codebase of neuroimaging!

The fMRI Analysis in Python is a workshop series started up via a collaboration between researchers and staff at the Centre for Addiction and Mental Health (Toronto, ON), the University of Western Ontario (London, Ontario), and McGill University (Montreal, Quebec).

About the lesson

This lesson covers fMRI imaging analysis from the basic steps of preprocessing and data cleaning, to running an analysis, to exploring connectivity patterns in the brain.

Episodes

Time	Episode	Question(s) Answered
	Setup	Download files required for the lesson
00:00	1. Course Overview and Introduction	What steps do I need to take before beginning to work with fMRI data?
00:25	2. Exploring Preprocessed fMRI Data from fMRIPREP	How does fMRIPrep store preprocessed neuroimaging data? How do I access preprocessed neuroimaging data?
00:50	3. Introduction to Image Manipulation using Nilearn	How can I perform arithmetic operations on MR images?
01:35	4. Integrating Functional Data	How is fMRI data represented? How can I access fMRI data along spatial and temporal dimensions? How can I integrate fMRI and structural MRI together?
02:20	6. Cleaning Confounders in your Data with Nilearn	How can I clean the data so that it more closely reflects BOLD instead of artifacts?
02:50	7. Applying Parcellations to Resting State Data	How can I reduce amount of noise-related variance in my data? How can I frame my data as a set of meaningful features?
03:30	8. Functional Connectivity Analysis	How can we estimate brain functional connectivity patterns from resting state data?
04:15	Finish

Contributing

We welcome all contributions to improve the lesson! Maintainers will do their best to help you if you have any questions, concerns, or experience any difficulties along the way.

We'd like to ask you to familiarize yourself with our Contribution Guide and have a look at the more detailed guidelines on proper formatting, ways to render the lesson locally, and even how to write new episodes.

Please see the current list of issues for ideas for contributing to this repository. For making your contribution, we use the GitHub flow, which is nicely explained in the chapter Contributing to a Project in Pro Git by Scott Chacon. Look for the tag . This indicates that the mantainers will welcome a pull request fixing this issue.

Maintainer(s)

Authors

A list of contributors to the lesson can be found in AUTHORS

Citation

To cite this lesson, please consult with CITATION

sdc-bids-fmri's People

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sdc-bids-fmri's Issues

[ENH]: Update source dataset to fMRIPrep 20.2.0 LTS on open OSF

The current dataset used in the lessons is running a (very) legacy version of fMRIPrep (0.4.0). Since then, fMRIPrep has changed its output structure to follow the BIDS derivatives convention more closely and as a result the outputs learners are dealing with during these lessons do not map to what they would see when running their own analyses.

Ideally we'd use pre-existing fMRIPrep 20.2.0 LTS outputs, however with the requirement that the dataset must have a case-control comparison with resting state data and available task data we've decided to run the subset of individuals we use in this lesson through LTS and host the data on OSF.

Instead of using the AWS S3 cli to access data, we will switch over to using Datalad to follow more closely to SDC-BIDS-IntroMRI; the pre-requisite to this course.

TODO:

Set up fMRIPrep 20.2.3 LTS run
Run data through fMRIPrep 20.2.3 LTS
Host data on OSF
#31
Deprecate setup scripts that rely on AWS CLI
Update Data Carpentry markdown to reflect changes in Jupyter

importing plotting from nilearn is not consistent across the lesson

For example, sometimes (ep 6) it's imported as:

from nilearn import plotting

where others (ep 3, ep 4, ep 7) do:

from nilearn import plotting as plot

In one case (ep 5), it's aliased as plt but then matplotlib.pyplot uses plt too.

update branding to Incubator

Currently this lesson is rendering with a Data Carpentry logo. @fmichonneau - could you please pull in the remote theme and update to Incubator branding?

lifecycle (pre-alpha --> alpha)

This lesson is currently rendering with a "pre-alpha" banner. From quickly skimming through the materials, however, it looks pretty complete. I would recommend changing the lifecycle tag in _config.yml to "alpha".

Access to dataset

Could you provide me access to some part of the entire dataset that you have? I wish to perform some experiments and want to be able to generate correlation maps between various parcels/voxels. Having access to a few subjects would be splendid!

Update Jupyter Notebooks to use Datalad to access data

[RFC] Single source material for Jupyter Notebooks/Data Carpentry Markdown

With the requirement of Carpentries to use their markdown format and our preferred live teaching medium being Juypter Notebooks we've run into an issue of having to update the Carpentries MD and Jupyter Notebooks independently anytime an update is made to the lessons.

Tools like https://github.com/mwouts/jupytext enable automated Markdown/Notebook/Script generation from a single source file. However, an issue that we're running into is with the generation of data carpentry markdown tags.

This issue is to discuss ways in which we can integrate the data carpentry markdown tags into our future source format (possibly Jupytext MyST?)

[RFC] Task fMRI analysis modules

With the continuing development of nilearn statistics (formerly nistats) it is a good time to consider building out a task GLM module. Some work towards task fMRI has already been done here #9, but it needs to be updated to reflect the merging of nilearn and nistats.

Introduce the changes in PR #39 into the notebooks

Introduce the changes in #39 into the notebooks.

[ENH] Instructor Guide development

This is an on-going issue for completing of the beta phase of this workshop. Part of the requirement is the development of an instructor guide which orients new carpentries instructors to the material flow, teaching medium, as well as common technical issues that pop up during teaching.

This documentation should be placed under _extras/guide.md.