Skip to main content

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 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)

In Development

  • 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 your BIDS 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 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


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

glm_express-1.0.3.tar.gz (18.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

glm_express-1.0.3-py3-none-any.whl (21.5 kB view details)

Uploaded Python 3

File details

Details for the file glm_express-1.0.3.tar.gz.

File metadata

  • Download URL: glm_express-1.0.3.tar.gz
  • Upload date:
  • Size: 18.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.7.0

File hashes

Hashes for glm_express-1.0.3.tar.gz
Algorithm Hash digest
SHA256 8e1e8d4d654c9d7ca4f45d1a4a648673f33c494a2b6a2c2ac8b2ece60fab5d12
MD5 0ab9ca2dc1ac6e8f228cfca72141a52e
BLAKE2b-256 cd3b1c5700e2403bb3851d2287314d4c90e42c0d3e88127ad4f203abdba1ae42

See more details on using hashes here.

File details

Details for the file glm_express-1.0.3-py3-none-any.whl.

File metadata

  • Download URL: glm_express-1.0.3-py3-none-any.whl
  • Upload date:
  • Size: 21.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.7.0

File hashes

Hashes for glm_express-1.0.3-py3-none-any.whl
Algorithm Hash digest
SHA256 dbb6d5cdc4eeffaa8429de83066863631c1ded340d1181f0e24f8080b87e048e
MD5 9ed58a13f1cc8281ebe818f5f914faec
BLAKE2b-256 a9e0f46f3cdff8571a7209c481a38f87f1492ab21d957b4acbbe973b519caa4c

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page