Automated linear models for functional neuroimaging data

# GLM-Express

This is a package for modeling functional neuroimaging tasks. As the name implies, it's optimized to be simple and straightforward! The task_information.json file stores all of the regressors and modeling specifications for each task; modifying this file allows you to test a range of analytical outcomes.

## Included

This package comes equipped with the following modeling objects:

• Subject is a first-level modeler for subject-specific functional neuroimaging data
• GroupLevel is a second-level modeler that is optimized to aggregate contrast maps derived by the Subject object
• Aggregator applies first-level models to all subjects in your BIDS project (not efficient for larger datasets)
• RestingState for analyses of subject-level resting state functional connectivity

## Assumptions

• Your data is in valid BIDS format
• Your data has been preprocessed via fmriprep
• Your preprocessed data are stored in a derivatives folder nested in your BIDS project
• Any parametric modulators are stored within each event file
• Otherwise, you can build custom design matrices and feed them into the modeling function

## About task_information.json

Glossary of keys in the task_information file; manipulating these allow you to quickly and effectively customize your modeling parameters without editing any source code

• tr: Repetition time (defined here, but can be overriden for any one subject)
• excludes: Subjects in your project you need to exclude for a given task
• condition_identifier: Column in your events file that denotes trial type; NOTE this will be changed to trial_type in the script
• confound_regressors: Regressors to include from fmriprep output
• modulators: Parametric modulators to weight trial type (these should be in your events file)
• block_identifier: Column in your events file that denotes block type; defaults to null
• design_contrasts: Your defined contrasts! Include as few or as many as you see fit

## Project details

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