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 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
The nilearn documentation can be found on: http://nilearn.github.io
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/
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
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-<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.
None foreseen