BIDS App for resting state signal extraction using nilearn.
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The Resting-state signal estraction App

This is a BIDS-App to extract signal from a parcellation with nilearn, typically useful in a context of resting-state data processing.


Nilearn is a Python tools for general multivariate manipulation of series of neuroimaging volumes. It may be used for many purposes by writing simple Python scripts, as described in the documentation The strength of nilearn are multivariate statistics and predictive models, in partical with appications to decoding or resting-state analysis.

Here, we use the nilearn NiftiLabelsMasker to extract time-series on a parcellation, or "max-prob" atlas:


The nilearn documentation can be found on:

How to report errors

If there are bugs or incomprehensible errors with nilearn, please report them on the nilearn github issue page:

Please ask questions on how to use nilearn, on neurostars, with the nilearn tag:


If you use nilearn, please cite the corresponding paper: Abraham 2014, Front. Neuroinform., Machine learning for neuroimaging with scikit-learn

We acknowledge all the nilearn developers ( as well as the BIDS-Apps team


This App has the following command line arguments:

  usage: [-h]
                [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
                bids_dir output_dir {participant,group}

  BIDS App entrypoint script to extract time-series from resting-state.

  positional arguments:
    bids_dir              The directory with the input dataset formatted
                          according to the BIDS standard.
    output_dir            The directory where the output files should be stored.
                          If you are running group level analysis this folder
                          should be prepopulated with the results of
                          theparticipant level analysis.
    {participant,group}   Level of the analysis that will be performed. Multiple
                          participant level analyses can be run independently
                          (in parallel) using the same output_dir.

  optional arguments:
    -h, --help            show this help message and exit
    --participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]
                          The label(s) of the participant(s) that should be
                          analyzed. The label corresponds to
                          sub-<participant_label> from the BIDS spec (so it does
                          not include "sub-"). If this parameter is not provided
                          all subjects should be analyzed. Multiple participants
                          can be specified with a space separated list.

Special considerations

None foreseen