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Related Experiment Video

Updated: Oct 19, 2025

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rsHRF: A toolbox for resting-state HRF estimation and deconvolution.

Guo-Rong Wu1, Nigel Colenbier2, Sofie Van Den Bossche3

  • 1Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing 400715, China; Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium.

Neuroimage
|September 24, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces rsHRF, a new toolbox for estimating the hemodynamic response function (HRF) from resting-state fMRI data. This tool aids in analyzing brain connectivity and BOLD signal variability.

Keywords:
BIDSHRFMATLABPythonbrain connectivitydeconvolutionresting-state fMRI

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biophysics

Background:

  • The hemodynamic response function (HRF) significantly impacts brain activation and connectivity variability in fMRI.
  • Accurate HRF estimation is crucial for interpreting neuroimaging studies, especially temporal precedence in connectivity analyses.
  • Existing HRF estimation algorithms are often limited to task-based fMRI, with few applicable to resting-state protocols.

Purpose of the Study:

  • To introduce rsHRF, a novel toolbox for estimating and deconvoluting the HRF from resting-state BOLD signals.
  • To provide researchers with a tool for analyzing resting-state fMRI data, addressing limitations of current methods.
  • To facilitate a deeper understanding of BOLD signal components and variability in resting-state conditions.

Main Methods:

  • Development of rsHRF, a toolbox available in Matlab and Python, for HRF estimation and deconvolution.
  • Implementation of the core algorithm for processing resting-state BOLD signals.
  • Validation of the rsHRF toolbox using a publicly available resting-state fMRI dataset.

Main Results:

  • Demonstration of the feasibility and utility of the rsHRF toolbox through validation experiments.
  • Successful estimation and deconvolution of HRF from resting-state fMRI data.
  • Provision of integrated tools for statistical analysis and visualization of results.

Conclusions:

  • The rsHRF toolbox offers a valuable resource for researchers analyzing resting-state fMRI data.
  • This tool can improve the accuracy of brain connectivity analyses and the interpretation of BOLD signal variability.
  • rsHRF is expected to significantly advance the understanding of resting-state brain activity.