semvec/pypi-packages huggingface dataset:
glm-express 2.0.3
Automated linear models for functional neuroimaging data
License: MIT

# GLM-Express

<img src="./img/GLM_Express_cardinal.png" width=65%>

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

We assume the following about your data:

* Your data is in valid `BIDS` format     <br> 
* Your data has been preprocessed via `fmriprep` 
* Your preprocessed data are stored in a `derivatives` folder nested in your `BIDS` project <br>
* You have adequate events TSV files for all of your functional tasks <br>
* Any parametric modulators are stored within each event file <br>
  * Otherwise, you can build custom design matrices and feed them into the modeling function

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## 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