compneuro-da / rsHRF

rsHRF: A Toolbox for Resting State HRF Deconvolution and Connectivity Analysis (MATLAB)

rsHRF: A Toolbox for Resting State HRF Deconvolution and Connectivity Analysis (MATLAB)

MATLAB

The current GitHub repository contains the MATLAB code for resting-state HRF estimation, deconvolution, visualization, and connectivity analysis, both for 1) the MATLAB Standalone as well as 2) the SPM plugin

Python

For information concerning 1) the Python Standalone, along with 2) the BIDS-App through Dockers/brainlife, head over to http://bids-apps.neuroimaging.io/rsHRF

Getting Started

• Overview and Usage
  • Step-by-step instructions for resting-state HRF analysis are available in the rsHRF Matlab Manual.
• Demos & Tutorials
• Demo videos: install, 3D volume analysis, 2D surface analysis, HRF Visualization.
• 中文介绍

Collaborators

• Guo-Rong Wu
• Nigel Colenbier
• Sofie Van Den Bossche
• Daniele Marinazzo
• Kenzo Clauw (BIDS)
• Madhur Tandon (Python - BIDS)
• Asier Erramuzpe (Python - BIDS)

References:

Guo-Rong Wu, Nigel Colenbier, Sofie Van Den Bossche, Kenzo Clauw, Amogh Johri, Madhur Tandon, Daniele Marinazzo. “rsHRF: A Toolbox for Resting-State HRF Estimation and Deconvolution.” Neuroimage, 2021, 244: 118591. DOI:10.1016/j.neuroimage.2021.118591

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. DOI:[10.1016/j.media.2013.01.003

            1 comment on PubPeer (by: Daniele Marinazzo)
          
        

      

        
        

          
            1 comment on PubPeer (by: Daniele Marinazzo)

](https://doi.org/10.1016/j.media.2013.01.003)

If you think rsHRF is useful for your work, citing it in your paper would be greatly appreciated.