{"id":50316666,"url":"https://github.com/bids-apps/rs_signal_extract","last_synced_at":"2026-05-29T00:03:54.193Z","repository":{"id":65631306,"uuid":"64772766","full_name":"bids-apps/rs_signal_extract","owner":"bids-apps","description":"BIDS App for resting state signal extraction using nilearn.","archived":false,"fork":false,"pushed_at":"2026-03-16T09:31:07.000Z","size":49,"stargazers_count":6,"open_issues_count":2,"forks_count":6,"subscribers_count":3,"default_branch":"master","last_synced_at":"2026-03-16T22:19:54.536Z","etag":null,"topics":["bids","bidsapp","resting-state-fmri"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/bids-apps.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2016-08-02T16:17:49.000Z","updated_at":"2026-03-16T09:31:11.000Z","dependencies_parsed_at":"2024-03-28T22:36:35.644Z","dependency_job_id":"2098bda2-ff2b-4069-932a-2ec226dc3095","html_url":"https://github.com/bids-apps/rs_signal_extract","commit_stats":null,"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"purl":"pkg:github/bids-apps/rs_signal_extract","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bids-apps%2Frs_signal_extract","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bids-apps%2Frs_signal_extract/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bids-apps%2Frs_signal_extract/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bids-apps%2Frs_signal_extract/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/bids-apps","download_url":"https://codeload.github.com/bids-apps/rs_signal_extract/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bids-apps%2Frs_signal_extract/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33631002,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-05-28T02:00:06.440Z","response_time":99,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bids","bidsapp","resting-state-fmri"],"created_at":"2026-05-29T00:03:54.106Z","updated_at":"2026-05-29T00:03:54.177Z","avatar_url":"https://github.com/bids-apps.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![No Maintenance Intended](http://unmaintained.tech/badge.svg)](http://unmaintained.tech/)\n\n# This BIDS app is not longer maintained.\n\n# The Resting-state signal extraction App\n\nThis is a BIDS-App to extract signal from a parcellation with nilearn,\ntypically useful in a context of resting-state data processing.\n\n## Description\n\nNilearn is a Python tools for general multivariate manipulation of series\nof neuroimaging volumes. It may be used for many purposes by writing\nsimple Python scripts, as described in the documentation\nhttp://nilearn.github.io. The strength of nilearn are multivariate\nstatistics and predictive models, in partical with appications to\ndecoding or resting-state analysis.\n\nHere, we use the nilearn NiftiLabelsMasker to extract time-series on a\nparcellation, or \"max-prob\" atlas:\nhttp://nilearn.github.io/connectivity/functional_connectomes.html#time-series-from-a-brain-parcellation-or-maxprob-atlas\n\n## Documentation\n\nThe nilearn documentation can be found on:\nhttp://nilearn.github.io\n\n## How to report errors\n\nIf there are bugs or incomprehensible errors with nilearn, please report\nthem on the nilearn github issue page:\nhttps://github.com/nilearn/nilearn/issues\n\nPlease ask questions on how to use nilearn, on neurostars, with the\nnilearn tag:\nhttp://neurostars.org/t/nilearn/\n\n## Acknowledgements\n\nIf you use nilearn, please cite the corresponding paper: Abraham 2014,\nFront. Neuroinform., Machine learning for neuroimaging with scikit-learn\nhttp://dx.doi.org/10.3389/fninf.2014.00014\n\nWe acknowledge all the nilearn developers\n(https://github.com/nilearn/nilearn/graphs/contributors)\nas well as the BIDS-Apps team\nhttps://github.com/orgs/BIDS-Apps/people\n\n\n## Usage\n\nThis App has the following command line arguments:\n\n```\n\n  usage: run.py [-h]\n                [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]\n                bids_dir output_dir {participant,group}\n\n  BIDS App entrypoint script to extract time-series from resting-state.\n\n  positional arguments:\n    bids_dir              The directory with the input dataset formatted\n                          according to the BIDS standard.\n    output_dir            The directory where the output files should be stored.\n                          If you are running group level analysis this folder\n                          should be prepopulated with the results of\n                          theparticipant level analysis.\n    {participant,group}   Level of the analysis that will be performed. Multiple\n                          participant level analyses can be run independently\n                          (in parallel) using the same output_dir.\n\n  optional arguments:\n    -h, --help            show this help message and exit\n    --participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]\n                          The label(s) of the participant(s) that should be\n                          analyzed. The label corresponds to\n                          sub-\u003cparticipant_label\u003e from the BIDS spec (so it does\n                          not include \"sub-\"). If this parameter is not provided\n                          all subjects should be analyzed. Multiple participants\n                          can be specified with a space separated list.\n\n```\n\n\n### Special considerations\nNone foreseen\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbids-apps%2Frs_signal_extract","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbids-apps%2Frs_signal_extract","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbids-apps%2Frs_signal_extract/lists"}