Open kel-github opened 1 year ago
Hi @brainhackorg/project-monitors my project is ready!
Got Bayes on the brain? Put Bayes on the brain! We're applying multilevel modelling to the analysis of fMRI data. Join us :)
LGTM @kel-github
Will tell our bot minion to put that on the website
Title
Bayes on the Brain in Python
Leaders
Kelly Garner mastodon: garner_theory@fediscience.org github: kel-github mattermost: @kels
Gang Chen twitter: @gangchen6 github: afni-gangc mattermost: @gangchen
Collaborators
Christopher Nolan mastodon: @cnolan@fediscience.org github: crnolan
Brainhack Global 2022 Event
Brainhack Australasia
Project Description
Human brain imaging data is massively multidimensional, yet current approaches to modeling functional brain responses apply univariate tests to each voxel separately. This leads to controlling for a vast number of statistical inferences, and to an implicit but unrealistic assumption of a uniform distribution over voxels – no information is shared across the brain.
A more reasoned approach to assessing regional activity focuses on estimating the strength of an effect; this can be achieved readily under a Bayesian multilevel modeling framework. A further advantage to this approach is that you can build in better assumptions about the data (e.g. normality across space, see Chen et al, 2019, Neuroinformatics and eradicate the need for adjusting for masses of simultaneous statistical inferences.
Applying such a Bayesian multilevel modeling framework to the analysis of fMRI data is in its infancy. The methodology has been implemented at the region level into the AFNI programme (see Chen et al, 2022, Aperture Neuro, using Stan through the R package BRMS (Burkner et al, 2017, Journal of Statistical Software). At OHBM Brainhack 2022, we also implemented this methodology in Python using the PyMC framework (Salvatier et al, 2016, PeerJ Computer Science) and the Bambi interface (Capretto et al, 2022, Journal of Statistical Software).
At Brainhack Global 2022, we will be expanding the capability of the Python implementation. We will:
Our long term goal is to build a python interface and this is the first step!
To get started, take a look at Chen (2022, see above) for more details on the method. Also check out our implementation in Python
Link to project repository/sources
Repo:
https://github.com/crnolan/pyrba
Resources
https://bambinos.github.io/bambi/main/index.html
https://www.pymc.io/projects/docs/en/stable/learn.html
https://nilab-uva.github.io/AOMIC.github.io/
{Chen et al, 2022, Aperture Neuro](http://dx.doi.org/10.52294/2e179dbf-5e37-4338-a639-9ceb92b055ea)
Goals for Brainhack Global
Test for computational limitations of applying a Bayesian multilevel framework to fMRI data analysis
Build a jupyter notebook tutorial workflow that includes model definition, fitting, quality checks, and results interpretation
Start translating the notebook into a Python interface for the people!
Good first issues
Communication channels
mattermost channel: bayes-on-the-brain
Skills
Onboarding documentation
See the link to the project repository and resources.
What will participants learn?
Data to use
https://nilab-uva.github.io/AOMIC.github.io/
Number of collaborators
more
Credit to collaborators
Project contributors will be listed on the project README and included as authors on any further outputs.
Image
Leave this text if you don't have an image yet.![project-image](https://user-images.githubusercontent.com/7220723/201803083-16858c76-bec4-45bd-8bb6-86baff84fcac.png)
Type
coding_methods, method_development, pipeline_development
Development status
1_basic structure
Topic
bayesian_approaches, MR_methodologies, reproducible_scientific_methods, statistical_modelling
Tools
AFNI, BIDS, fMRIPrep, Jupyter, other
Programming language
documentation, Python,
R
Modalities
fMRI
Git skills
0_no_git_skills, 1_commit_push, 2_branches_PRs, 3_continuous_integration
Anything else?
No response
Things to do after the project is submitted and ready to review.
Hi @brainhackorg/project-monitors my project is ready!