https://github.com/bids-apps/rshrf
Resting state HRF estimation from BOLD-fMRI signal
Science Score: 20.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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○DOI references
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✓Academic publication links
Links to: sciencedirect.com -
✓Committers with academic emails
1 of 10 committers (10.0%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (15.9%) to scientific vocabulary
Keywords
Repository
Resting state HRF estimation from BOLD-fMRI signal
Basic Info
- Host: GitHub
- Owner: bids-apps
- License: mit
- Language: Python
- Default Branch: master
- Homepage: http://bids-apps.neuroimaging.io/rsHRF/
- Size: 206 MB
Statistics
- Stars: 37
- Watchers: 14
- Forks: 12
- Open Issues: 6
- Releases: 0
Topics
Metadata Files
README.md
Critical Bug Fixes - Ready for Upstream Integration
This branch contains essential fixes for the rsHRF package to ensure compatibility with modern Python environments.
Issues Addressed:
The rsHRF package currently has a critical dependency issue with pyyawt. This package is outdated, no longer maintained, has compatibility issues with modern Python versions, and suffers from wheel installation problems. This prevents the core iterative Wiener deconvolution algorithm from running, making the software unusable in current Python environments.
Fixes Applied:
- Modernized wavelet dependency: Replaced unmaintained
pyyawtwith actively maintainedPyWavelets - Maintained scientific accuracy: Implemented proper noise estimation using MAD (Median Absolute Deviation) method
- Enhanced stability: Added bounds checking to prevent runtime errors
- Updated package configuration: Fixed requirements.txt and setup.py with current, supported dependencies
- Modernized file I/O: Updated deprecated GIFTI handling to use current nibabel functions
Testing:
All existing unit tests have been updated and pass with the new dependencies. The mathematical accuracy of the HRF estimation remains unchanged - only the implementation details have been modernized.
Compatibility:
These fixes ensure rsHRF works with: - Python 3.6+ - Modern NumPy/SciPy versions - Current neuroimaging software stacks - Standard pip installation workflows
Resting state HRF estimation and deconvolution.
Please refer to https://github.com/compneuro-da/rsHRF for MATLAB version
The basic idea
This 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).
Once 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.
How to use the toolbox
The input is voxelwise BOLD signal, already preprocessed according to your favorite recipe. Important thing are:
- bandpass filter in the 0.01-0.08 Hz interval (or something like that)
- z-score the voxel BOLD time series
To be on the safe side, these steps are performed again in the code.
The input can be images (3D or 4D), or directly matrices of [observation x voxels].
It is possible to use a temporal mask to exclude some time points (for example after scrubbing).
The demos allow you to run the analyses on several formats of input data.
Python Package and BIDS-app
A BIDS-App has been made for easy and reproducible analysis. Its documentation can be accessed at:
http://bids-apps.neuroimaging.io/rsHRF/
Collaborators
- Guorong Wu
- Nigel Colenbier
- Sofie Van Den Bossche
Daniele Marinazzo
Madhur Tandon (Python - BIDS)
Asier Erramuzpe (Python - BIDS)
Amogh Johri (Python - BIDS)
References
Wu, G. R., Colenbier, N., Van Den Bossche, S., Clauw, K., Johri, A., Tandon, M., & Marinazzo, D. (2021). rsHRF: A toolbox for resting-state HRF estimation and deconvolution. Neuroimage, 244, 118591. open access journal page
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
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
Owner
- Name: BIDS Apps
- Login: bids-apps
- Kind: organization
- Website: http://bids-apps.neuroimaging.io
- Twitter: BIDSStandard
- Repositories: 42
- Profile: https://github.com/bids-apps
A collection of containerized neuroimaging workflows and pipelines that accept datasets organized according to the Brain Imaging Data Structure (BIDS).
GitHub Events
Total
- Watch event: 6
- Pull request review event: 1
- Pull request event: 1
- Fork event: 4
Last Year
- Watch event: 6
- Pull request review event: 1
- Pull request event: 1
- Fork event: 4
Committers
Last synced: almost 3 years ago
All Time
- Total Commits: 257
- Total Committers: 10
- Avg Commits per committer: 25.7
- Development Distribution Score (DDS): 0.665
Top Committers
| Name | Commits | |
|---|---|---|
| Madhur Tandon | m****3@i****n | 86 |
| Daniele Marinazzo | d****o@g****m | 85 |
| amogh | a****i@i****g | 39 |
| GuorongWu | g****u@g****m | 30 |
| NigelCol | n****r@g****m | 7 |
| Remi Gau | r****u@h****m | 4 |
| Sofie Van Den Bossche | s****e@u****e | 3 |
| “Guo-Rong Wu” | “****u@g****” | 1 |
| Amogh Johri | 3****i@u****m | 1 |
| wgrmath | w****h@1****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 13
- Total pull requests: 3
- Average time to close issues: about 1 month
- Average time to close pull requests: 6 minutes
- Total issue authors: 8
- Total pull request authors: 3
- Average comments per issue: 2.08
- Average comments per pull request: 0.33
- Merged pull requests: 2
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- aweinstein (3)
- soichih (3)
- danielemarinazzo (2)
- effigies (1)
- alecrimi (1)
- bjsmith (1)
- NigelCol (1)
- Remi-Gau (1)
Pull Request Authors
- danielemarinazzo (1)
- aavelozb (1)
- NigelCol (1)
- PeerHerholz (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 195 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 20
- Total maintainers: 2
pypi.org: rshrf
BIDs App to retrieve the haemodynamic response function from resting state fMRI data
- Homepage: https://github.com/BIDS-Apps/rsHRF
- Documentation: https://rshrf.readthedocs.io/
- License: MIT
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Latest release: 1.5.8
published over 4 years ago
Rankings
Dependencies
- joblib *
- matplotlib *
- mpld3 *
- nibabel *
- numpy *
- pandas *
- patsy *
- pybids ==0.11.1
- pytest *
- pyyawt *
- scipy *
- numpy *
- alpine 3.11 build