{"id":50315671,"url":"https://github.com/bids-apps/rshrf","last_synced_at":"2026-05-29T00:01:17.527Z","repository":{"id":54410213,"uuid":"102780962","full_name":"bids-apps/rsHRF","owner":"bids-apps","description":"Resting state HRF estimation from BOLD-fMRI signal","archived":false,"fork":false,"pushed_at":"2026-05-27T22:39:19.000Z","size":216330,"stargazers_count":41,"open_issues_count":7,"forks_count":14,"subscribers_count":14,"default_branch":"master","last_synced_at":"2026-05-28T00:20:06.047Z","etag":null,"topics":["bids","bidsapp","fmri","fmri-data-analysis","timeseries"],"latest_commit_sha":null,"homepage":"http://bids-apps.neuroimaging.io/rsHRF/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/bids-apps.png","metadata":{"files":{"readme":"README.md","changelog":"NEWS.md","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":"2017-09-07T20:08:52.000Z","updated_at":"2026-05-12T13:08:20.000Z","dependencies_parsed_at":"2025-09-09T14:37:14.966Z","dependency_job_id":"aa88805a-bf0d-4d79-8b74-9cdec76c2ac6","html_url":"https://github.com/bids-apps/rsHRF","commit_stats":null,"previous_names":[],"tags_count":22,"template":false,"template_full_name":null,"purl":"pkg:github/bids-apps/rsHRF","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bids-apps%2FrsHRF","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bids-apps%2FrsHRF/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bids-apps%2FrsHRF/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bids-apps%2FrsHRF/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/bids-apps","download_url":"https://codeload.github.com/bids-apps/rsHRF/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bids-apps%2FrsHRF/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33630999,"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","fmri","fmri-data-analysis","timeseries"],"created_at":"2026-05-29T00:00:50.049Z","updated_at":"2026-05-29T00:01:17.511Z","avatar_url":"https://github.com/bids-apps.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Resting state HRF estimation and deconvolution\n\n[![PyPI version](https://badge.fury.io/py/rshrf.svg)](https://badge.fury.io/py/rshrf)\n\nPlease refer to https://github.com/compneuro-da/rsHRF for MATLAB version\n\n![BOLD HRF](https://github.com/guorongwu/rsHRF/raw/master/docs/BOLD_HRF.png)\n\n## The basic idea\n\nThis toolbox is aimed to retrieve the onsets of pseudo-events triggering an hemodynamic response from resting state fMRI BOLD voxel-wise signal. It is based on point process theory, and fits a model to retrieve the optimal lag between the events and the HRF onset, as well as the HRF shape, using a choice of basis functions (the canonical shape with two derivatives, (smoothed) Finite Impulse Response, mixture of gammas).\n\n![BOLD HRF](https://users.ugent.be/~dmarinaz/example_hrf.png)\n\nOnce that the HRF has been retrieved for each voxel, it can be deconvolved from the time series (for example to improve lag-based connectivity estimates), or one can map the shape parameters everywhere in the brain (including white matter), and use the shape as a pathophysiological indicator.\n\n![HRF map](https://users.ugent.be/~dmarinaz/FIR_Height_full_layout.png)\n\n## How to use the toolbox\n\nThe input is voxelwise BOLD signal, already preprocessed according to your favorite recipe. Important thing are:\n\n- bandpass filter in the 0.01-0.08 Hz interval (or something like that)\n- z-score the voxel BOLD time series\n\nTo be on the safe side, these steps are performed again in the code.\n\nThe input can be images (3D or 4D), or directly matrices of [observation x voxels].\n\nIt is possible to use a temporal mask to exclude some time points (for example after scrubbing).\n\nThe demos allow you to run the analyses on several formats of input data.\n\n## Python Package and BIDS-app\n\nA BIDS-App has been made for easy and reproducible analysis. Its documentation can be accessed at:\n\nhttps://bids-apps.neuroimaging.io/rsHRF/\n\n## Collaborators\n\n- Guorong Wu\n- Nigel Colenbier\n- Sofie Van Den Bossche\n- Daniele Marinazzo\n\n- Madhur Tandon (Python - BIDS)\n- Asier Erramuzpe (Python - BIDS)\n- Amogh Johri   (Python - BIDS)\n\n## Docker Usage\n\nYou can run rsHRF as a containerized BIDS App using Docker. This avoids the need to install Python dependencies locally and ensures reproducibility.\n\n### 1. Pull the current development image:\n\n```bash\ndocker pull bids/rshrf:unstable\n```\n\n### 2. Run the analysis:\n\nTo run the analysis on a BIDS derivative dataset, such as an fMRIPrep derivatives directory, use the BIDS Apps-style command structure:\n\n```bash\ndocker run -ti --rm \\\n  -v /path/to/your/bids_derivative_dataset:/data:ro \\\n  -v /path/to/your/output_dir:/out \\\n  bids/rshrf:unstable \\\n  /data \\\n  /out \\\n  participant \\\n  --participant-label 01 \\\n  -m BIDS \\\n  --estimation canon2dd\n```\n\nHere, `/data` should point to the BIDS derivative dataset containing files such as `dataset_description.json`, preprocessed BOLD images, and brain masks. The participant label should be provided without the `sub-` prefix.\n\n**References**\n--------\n1. Wu, G. R., Colenbier, N., Van Den Bossche, S., Clauw, K., Johri, A., Tandon, M., \u0026 Marinazzo, D. (2021). rsHRF: A toolbox for resting-state HRF estimation and deconvolution. Neuroimage, 244, 118591. [open access journal page](https://www.sciencedirect.com/science/article/pii/S1053811921008648)\n\n2. Guo-Rong Wu, Wei Liao, Sebastiano Stramaglia, Ju-Rong Ding, Huafu Chen, Daniele Marinazzo*. \"A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data.\" Medical Image Analysis, 2013, 17:365-374. [Open access institutional repo](https://biblio.ugent.be/publication/3118166)\n\n3. Guo-Rong Wu, Daniele Marinazzo. \"Sensitivity of the resting state hemodynamic response function estimation to autonomic nervous system fluctuations.\" Philosophical Transactions of the Royal Society A, 2016, 374: 20150190. [Open access institutional repo](https://biblio.ugent.be/publication/7174286)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbids-apps%2Frshrf","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbids-apps%2Frshrf","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbids-apps%2Frshrf/lists"}