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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Published in	NeuroImage (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
Language	English
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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Abstract	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.
AbstractList	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. 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.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.
ArticleNumber	118591
Author	Colenbier, Nigel Van Den Bossche, Sofie Johri, Amogh Clauw, Kenzo Marinazzo, Daniele Tandon, Madhur Wu, Guo-Rong
Author_xml	– sequence: 1 givenname: Guo-Rong surname: Wu fullname: Wu, Guo-Rong email: guorongwu@swu.edu.cn organization: Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing 400715, China – sequence: 2 givenname: Nigel surname: Colenbier fullname: Colenbier, Nigel organization: Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium – sequence: 3 givenname: Sofie surname: Van Den Bossche fullname: Van Den Bossche, Sofie organization: Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium – sequence: 4 givenname: Kenzo surname: Clauw fullname: Clauw, Kenzo organization: Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium – sequence: 5 givenname: Amogh surname: Johri fullname: Johri, Amogh organization: International Institute of Information Technology, Bangalore 560100, India – sequence: 6 givenname: Madhur surname: Tandon fullname: Tandon, Madhur organization: Indraprastha Institute of Information Technology, Delhi 110020, India – sequence: 7 givenname: Daniele surname: Marinazzo fullname: Marinazzo, Daniele organization: Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium
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Keywords	brain connectivity deconvolution BIDS resting-state fMRI HRF MATLAB Python
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SubjectTerms	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
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Title	rsHRF: A toolbox for resting-state HRF estimation and deconvolution
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