https://github.com/bids-apps/rs_signal_extract
BIDS App for resting state signal extraction using nilearn.
https://github.com/bids-apps/rs_signal_extract
bids bidsapp resting-state-fmri
Last synced: 19 days ago
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BIDS App for resting state signal extraction using nilearn.
- Host: GitHub
- URL: https://github.com/bids-apps/rs_signal_extract
- Owner: bids-apps
- License: apache-2.0
- Created: 2016-08-02T16:17:49.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2026-03-16T09:31:07.000Z (3 months ago)
- Last Synced: 2026-03-16T22:19:54.536Z (3 months ago)
- Topics: bids, bidsapp, resting-state-fmri
- Language: Python
- Homepage:
- Size: 47.9 KB
- Stars: 6
- Watchers: 3
- Forks: 6
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
[](http://unmaintained.tech/)
# 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]
[--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- 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