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Estimating High-Order Brain Functional Networks in Bayesian View for Autism Spectrum Disorder Identification

Brain functional network (BFN) has become an increasingly important tool to understand the inherent organization of the brain and explore informative biomarkers of neurological disorders. Pearson’s correlation (PC) is the most widely accepted method for constructing BFNs and provides a basis for des...

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Autores principales: Jiang, Xiao, Zhou, Yueying, Zhang, Yining, Zhang, Limei, Qiao, Lishan, De Leone, Renato
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9094041/
https://www.ncbi.nlm.nih.gov/pubmed/35573311
http://dx.doi.org/10.3389/fnins.2022.872848
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author Jiang, Xiao
Zhou, Yueying
Zhang, Yining
Zhang, Limei
Qiao, Lishan
De Leone, Renato
author_facet Jiang, Xiao
Zhou, Yueying
Zhang, Yining
Zhang, Limei
Qiao, Lishan
De Leone, Renato
author_sort Jiang, Xiao
collection PubMed
description Brain functional network (BFN) has become an increasingly important tool to understand the inherent organization of the brain and explore informative biomarkers of neurological disorders. Pearson’s correlation (PC) is the most widely accepted method for constructing BFNs and provides a basis for designing new BFN estimation schemes. Particularly, a recent study proposes to use two sequential PC operations, namely, correlation’s correlation (CC), for constructing the high-order BFN. Despite its empirical effectiveness in identifying neurological disorders and detecting subtle changes of connections in different subject groups, CC is defined intuitively without a solid and sustainable theoretical foundation. For understanding CC more rigorously and providing a systematic BFN learning framework, in this paper, we reformulate it in the Bayesian view with a prior of matrix-variate normal distribution. As a result, we obtain a probabilistic explanation of CC. In addition, we develop a Bayesian high-order method (BHM) to automatically and simultaneously estimate the high- and low-order BFN based on the probabilistic framework. An efficient optimization algorithm is also proposed. Finally, we evaluate BHM in identifying subjects with autism spectrum disorder (ASD) from typical controls based on the estimated BFNs. Experimental results suggest that the automatically learned high- and low-order BFNs yield a superior performance over the artificially defined BFNs via conventional CC and PC.
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spelling pubmed-90940412022-05-12 Estimating High-Order Brain Functional Networks in Bayesian View for Autism Spectrum Disorder Identification Jiang, Xiao Zhou, Yueying Zhang, Yining Zhang, Limei Qiao, Lishan De Leone, Renato Front Neurosci Neuroscience Brain functional network (BFN) has become an increasingly important tool to understand the inherent organization of the brain and explore informative biomarkers of neurological disorders. Pearson’s correlation (PC) is the most widely accepted method for constructing BFNs and provides a basis for designing new BFN estimation schemes. Particularly, a recent study proposes to use two sequential PC operations, namely, correlation’s correlation (CC), for constructing the high-order BFN. Despite its empirical effectiveness in identifying neurological disorders and detecting subtle changes of connections in different subject groups, CC is defined intuitively without a solid and sustainable theoretical foundation. For understanding CC more rigorously and providing a systematic BFN learning framework, in this paper, we reformulate it in the Bayesian view with a prior of matrix-variate normal distribution. As a result, we obtain a probabilistic explanation of CC. In addition, we develop a Bayesian high-order method (BHM) to automatically and simultaneously estimate the high- and low-order BFN based on the probabilistic framework. An efficient optimization algorithm is also proposed. Finally, we evaluate BHM in identifying subjects with autism spectrum disorder (ASD) from typical controls based on the estimated BFNs. Experimental results suggest that the automatically learned high- and low-order BFNs yield a superior performance over the artificially defined BFNs via conventional CC and PC. Frontiers Media S.A. 2022-04-27 /pmc/articles/PMC9094041/ /pubmed/35573311 http://dx.doi.org/10.3389/fnins.2022.872848 Text en Copyright © 2022 Jiang, Zhou, Zhang, Zhang, Qiao and De Leone. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Jiang, Xiao
Zhou, Yueying
Zhang, Yining
Zhang, Limei
Qiao, Lishan
De Leone, Renato
Estimating High-Order Brain Functional Networks in Bayesian View for Autism Spectrum Disorder Identification
title Estimating High-Order Brain Functional Networks in Bayesian View for Autism Spectrum Disorder Identification
title_full Estimating High-Order Brain Functional Networks in Bayesian View for Autism Spectrum Disorder Identification
title_fullStr Estimating High-Order Brain Functional Networks in Bayesian View for Autism Spectrum Disorder Identification
title_full_unstemmed Estimating High-Order Brain Functional Networks in Bayesian View for Autism Spectrum Disorder Identification
title_short Estimating High-Order Brain Functional Networks in Bayesian View for Autism Spectrum Disorder Identification
title_sort estimating high-order brain functional networks in bayesian view for autism spectrum disorder identification
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9094041/
https://www.ncbi.nlm.nih.gov/pubmed/35573311
http://dx.doi.org/10.3389/fnins.2022.872848
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