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

The hemodynamic response function (HRF) greatly influences the intra- and inter-subject variability of brain activation and connectivity, and might confound the estimation of temporal precedence in connectivity analyses, making its estimation necessary for a correct interpretation of neuroimaging st...

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Bibliographic Details
Published inNeuroImage (Orlando, Fla.) Vol. 244; p. 118591
Main Authors Wu, Guo-Rong, Colenbier, Nigel, Van Den Bossche, Sofie, Clauw, Kenzo, Johri, Amogh, Tandon, Madhur, Marinazzo, Daniele
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 01.12.2021
Elsevier Limited
Elsevier
Subjects
Adult
Algorithms
BIDS
Blood
Brain - diagnostic imaging
brain connectivity
Brain mapping
deconvolution
Female
Fourier transforms
Functional magnetic resonance imaging
Hemodynamics - physiology
HRF
Humans
Magnetic Resonance Imaging - methods
Male
MATLAB
Middle Aged
Neural networks
Neuroimaging
Python
Research Design
resting-state fMRI
Statistical analysis
Young Adult
brain connectivity
deconvolution
BIDS
resting-state fMRI
HRF
MATLAB
Python
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Summary:The hemodynamic response function (HRF) greatly influences the intra- and inter-subject variability of brain activation and connectivity, and might confound the estimation of temporal precedence in connectivity analyses, making its estimation necessary for a correct interpretation of neuroimaging studies. Additionally, the HRF shape itself is a useful local measure. However, most algorithms for HRF estimation are specific for task-related fMRI data, and only a few can be directly applied to resting-state protocols. Here we introduce rsHRF, a Matlab and Python toolbox that implements HRF estimation and deconvolution from the resting-state BOLD signal. We first provide an overview of the main algorithm, practical implementations, and then demonstrate the feasibility and usefulness of rsHRF by validation experiments with a publicly available resting-state fMRI dataset. We also provide tools for statistical analyses and visualization. We believe that this toolbox may significantly contribute to a better analysis and understanding of the components and variability of BOLD signals.
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ISSN:1053-8119
1095-9572
1095-9572
DOI:10.1016/j.neuroimage.2021.118591