The Configurable Pipeline for the Analysis of Connectomes C-PAC is a software for performing high-throughput preprocessing and analysis of connectomes data using high-performance computers. This docker container, when built, is an application for performing participant and group level analyses.
Extensive information can be found in the C-PAC User Guide.
Please report errors on the C-PAC github page issue tracker. Please use the C-PAC google group for help using C-PAC and this application.
We currently have a publication in preparation, in the meantime please cite our poster from INCF:
Craddock C, Sikka S, Cheung B, Khanuja R, Ghosh SS, Yan C, Li Q, Lurie D, Vogelstein J, Burns R, Colcombe S,
Mennes M, Kelly C, Di Martino A, Castellanos FX and Milham M (2013). Towards Automated Analysis of Connectomes:
The Configurable Pipeline for the Analysis of Connectomes (C-PAC). Front. Neuroinform. Conference Abstract:
Neuroinformatics 2013. doi:10.3389/conf.fninf.2013.09.00042
@ARTICLE{cpac2013,
AUTHOR={Craddock, Cameron and Sikka, Sharad and Cheung, Brian and Khanuja, Ranjeet and Ghosh, Satrajit S
and Yan, Chaogan and Li, Qingyang and Lurie, Daniel and Vogelstein, Joshua and Burns, Randal and
Colcombe, Stanley and Mennes, Maarten and Kelly, Clare and Di Martino, Adriana and Castellanos,
Francisco Xavier and Milham, Michael},
TITLE={Towards Automated Analysis of Connectomes: The Configurable Pipeline for the Analysis of Connectomes (C-PAC)},
JOURNAL={Frontiers in Neuroinformatics},
YEAR={2013},
NUMBER={42},
URL={http://www.frontiersin.org/neuroinformatics/10.3389/conf.fninf.2013.09.00042/full},
DOI={10.3389/conf.fninf.2013.09.00042},
ISSN={1662-5196}
}
This App has the following command line arguments:
usage: run.py [-h]
[--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
[--n_cpus #]
[--mem #]
[--save_working_directory]
bids_dir output_dir {participant,group}
Example BIDS App entrypoint script.
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).
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.
--pipeline_file Name for the pipeline configuration file to use, the path
must be accessible from inside the container.
default="/cpac_resources/default_pipeline.yaml"
--n_cpus Number of execution resources available for the pipeline
default="1"
--mem Amount of RAM available to the pipeline in GB
default="6"
--save_working_dir Indicates that the working directory, which contains
intermediary files, should be saved. If specified, the
working directory will be saved in the output directory.
To run it in participant level mode (for one participant):
docker run -i --rm \
-v /Users/filo/data/ds005:/bids_dataset \
-v /Users/filo/outputs:/outputs \
bids/example \
/bids_dataset /outputs participant --participant_label 01
After doing this for all subjects (potentially in parallel) the group level analysis can be run:
docker run -i --rm \
-v /Users/filo/data/ds005:/bids_dataset \
-v /Users/filo/outputs:/outputs \
bids-apps/cpac:v1.0.0 \
/bids_dataset /outputs group