This BIDS app is not longer maintained.

The Resting-state signal extraction App

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

Description

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 http://nilearn.github.io. 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: http://nilearn.github.io/connectivity/functional_connectomes.html#time-series-from-a-brain-parcellation-or-maxprob-atlas

Documentation

The nilearn documentation can be found on: http://nilearn.github.io

How to report errors

If there are bugs or incomprehensible errors with nilearn, please report them on the nilearn github issue page: https://github.com/nilearn/nilearn/issues

Please ask questions on how to use nilearn, on neurostars, with the nilearn tag: http://neurostars.org/t/nilearn/

Acknowledgements

If you use nilearn, please cite the corresponding paper: Abraham 2014, Front. Neuroinform., Machine learning for neuroimaging with scikit-learn http://dx.doi.org/10.3389/fninf.2014.00014

We acknowledge all the nilearn developers (https://github.com/nilearn/nilearn/graphs/contributors) as well as the BIDS-Apps team https://github.com/orgs/BIDS-Apps/people

Usage

This App has the following command line arguments:

```

usage: run.py [-h] [--participantlabel PARTICIPANTLABEL [PARTICIPANTLABEL ...]] bidsdir output_dir {participant,group}

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

positional arguments: bidsdir The directory with the input dataset formatted according to the BIDS standard. outputdir 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 --participantlabel PARTICIPANTLABEL [PARTICIPANTLABEL ...] The label(s) of the participant(s) that should be analyzed. The label corresponds to sub-<participantlabel> 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