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        "author": "Madhur Tandon",
        "author_email": "madhurtandon23@gmail.com",
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        "description": "Resting state HRF estimation and deconvolution.\n========\n[![PyPI version](https://badge.fury.io/py/rsHRF.png)](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\nThe basic idea \n-------------\n\nThis toolbox is aimed to retrieve the onsets of pseudo-events triggering an hemodynamic response from resting state fMRI BOLD voxel-wise signal.\nIt 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 either the canonical shape with two derivatives, or a (smoothed) Finite Impulse Response.\n\n![BOLD HRF](http://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](http://users.ugent.be/~dmarinaz/FIR_Height_full_layout.png)\n\nHow to use the toolbox \n-------------\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\nPython Package and BIDS-app \n-------------\nA BIDS-App has been made for easy and reproducible analysis. Its documentation can be accessed at:\n\nhttp://bids-apps.neuroimaging.io/rsHRF/\n\nCollaborators \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\n\n**References**\n--------\n\n1. 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. [PDF](https://github.com/guorongwu/rsHRF/raw/master/docs/2013_MIA.pdf)\n\n2. 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. [PDF](https://github.com/guorongwu/rsHRF/raw/master/docs/2016_PTA.pdf)\n\n3. Guo-Rong Wu, Daniele Marinazzo. \"Retrieving the Hemodynamic Response Function in resting state fMRI: methodology and applications.\" PeerJ PrePrints, 2015. [PDF](https://github.com/guorongwu/rsHRF/raw/master/docs/poster_OHBM2016_HRF.pdf)\n\n\n",
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