Automated linear models for functional neuroimaging data
Project description
GLM-Express
This is a package for modeling functional neuroimaging tasks. As the name implies, it's intended 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 dataGroupLevel
is a second-level modeler that is optimized to aggregate contrast maps derived by theSubject
objectAggregator
applies first-level models to all subjects in your BIDS project (not efficient for larger datasets)RestingState
object is currently in development, for analyses of subject-level resting state functional connectivity
Assumptions
We assume the following about your data:
- 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 yourBIDS
project - You have adequate events TSV files for all of your functional tasks
- 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 taskcondition_identifier
: Column in your events file that denotes trial type; NOTE this will be changed totrial_type
in the scriptconfound_regressors
: Regressors to include fromfmriprep
outputmodulators
: 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 tonull
design_contrasts
: Your defined contrasts! Include as few or as many as you see fit
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for glm_express-2.0.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0538f02272a333b088922fa0801d1b48c152b46f810eaaca37440cb8f478f860 |
|
MD5 | 9a98cd0901dd227ca7014cbace73675a |
|
BLAKE2b-256 | c41610970fd3f9b3d66bc8067cd46fb0ecc9650c0e508220f7af3a8721a45495 |