Understanding the Relationship Between Human Brain Structure and Function by Predicting the Structural Connectivity From Functional Connectivity

Over the past decade, a growing number of studies have investigated the relationship between the structure and function of human brain by predicting the resting-state functional connectivity (rsFC) from structural connectivity (SC). Yet how the whole-brain patterns of FC emerge from SC still remains...

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Autores principales:	Yanjiang Wang, Xue Chen, Baodi Liu, Weifeng Liu, Richard Martin Shiffrin
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	Human brain mapping brain connectivity structure-function relation network deconvolution Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/6d78a90ce623437388df6eb5f2687974
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spelling	oai:doaj.org-article:6d78a90ce623437388df6eb5f26879742021-11-19T00:06:25ZUnderstanding the Relationship Between Human Brain Structure and Function by Predicting the Structural Connectivity From Functional Connectivity2169-353610.1109/ACCESS.2020.3039837https://doaj.org/article/6d78a90ce623437388df6eb5f26879742020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9266034/https://doaj.org/toc/2169-3536Over the past decade, a growing number of studies have investigated the relationship between the structure and function of human brain by predicting the resting-state functional connectivity (rsFC) from structural connectivity (SC). Yet how the whole-brain patterns of FC emerge from SC still remains incompletely understood. Unlike previous studies, here we propose an alternative approach for addressing this issue by predicting SC from rsFC. We first hypothesize that the functional couplings among brain areas at rest are shaped at least in three phases temporally: the initial direct interplay between brain areas, the communications within and between network modules, and followed by the indirect interactions ascribed to indirect structural pathways. We then introduce a network deconvolution (ND) algorithm inspired from the mechanism of cell differentiation, named CDA, to distinguish the direct dependencies from the functional network followed by a weight trimming algorithm based on Euclidean distance kernel function for shrinking the modular effects. Finally, we keep those region pairs with shorter shortest path length (SPL) together with shorter Euclidean distance as the structural connections. We apply the model and the algorithms to three intensively studied group averaged empirical connectome datasets with different parcellation resolutions and the results demonstrate that the predicted intrahemispheric structural connections and the weights distribution are highly consistent with the empirical SC derived from diffusion magnetic resonance imaging (dMRI) and probabilistic tractography, thus strongly supporting the model and algorithms proposed.Yanjiang WangXue ChenBaodi LiuWeifeng LiuRichard Martin ShiffrinIEEEarticleHuman brain mappingbrain connectivitystructure-function relationnetwork deconvolutionElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 209926-209938 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Human brain mapping brain connectivity structure-function relation network deconvolution Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Human brain mapping brain connectivity structure-function relation network deconvolution Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yanjiang Wang Xue Chen Baodi Liu Weifeng Liu Richard Martin Shiffrin Understanding the Relationship Between Human Brain Structure and Function by Predicting the Structural Connectivity From Functional Connectivity
description	Over the past decade, a growing number of studies have investigated the relationship between the structure and function of human brain by predicting the resting-state functional connectivity (rsFC) from structural connectivity (SC). Yet how the whole-brain patterns of FC emerge from SC still remains incompletely understood. Unlike previous studies, here we propose an alternative approach for addressing this issue by predicting SC from rsFC. We first hypothesize that the functional couplings among brain areas at rest are shaped at least in three phases temporally: the initial direct interplay between brain areas, the communications within and between network modules, and followed by the indirect interactions ascribed to indirect structural pathways. We then introduce a network deconvolution (ND) algorithm inspired from the mechanism of cell differentiation, named CDA, to distinguish the direct dependencies from the functional network followed by a weight trimming algorithm based on Euclidean distance kernel function for shrinking the modular effects. Finally, we keep those region pairs with shorter shortest path length (SPL) together with shorter Euclidean distance as the structural connections. We apply the model and the algorithms to three intensively studied group averaged empirical connectome datasets with different parcellation resolutions and the results demonstrate that the predicted intrahemispheric structural connections and the weights distribution are highly consistent with the empirical SC derived from diffusion magnetic resonance imaging (dMRI) and probabilistic tractography, thus strongly supporting the model and algorithms proposed.
format	article
author	Yanjiang Wang Xue Chen Baodi Liu Weifeng Liu Richard Martin Shiffrin
author_facet	Yanjiang Wang Xue Chen Baodi Liu Weifeng Liu Richard Martin Shiffrin
author_sort	Yanjiang Wang
title	Understanding the Relationship Between Human Brain Structure and Function by Predicting the Structural Connectivity From Functional Connectivity
title_short	Understanding the Relationship Between Human Brain Structure and Function by Predicting the Structural Connectivity From Functional Connectivity
title_full	Understanding the Relationship Between Human Brain Structure and Function by Predicting the Structural Connectivity From Functional Connectivity
title_fullStr	Understanding the Relationship Between Human Brain Structure and Function by Predicting the Structural Connectivity From Functional Connectivity
title_full_unstemmed	Understanding the Relationship Between Human Brain Structure and Function by Predicting the Structural Connectivity From Functional Connectivity
title_sort	understanding the relationship between human brain structure and function by predicting the structural connectivity from functional connectivity
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/6d78a90ce623437388df6eb5f2687974
work_keys_str_mv	AT yanjiangwang understandingtherelationshipbetweenhumanbrainstructureandfunctionbypredictingthestructuralconnectivityfromfunctionalconnectivity AT xuechen understandingtherelationshipbetweenhumanbrainstructureandfunctionbypredictingthestructuralconnectivityfromfunctionalconnectivity AT baodiliu understandingtherelationshipbetweenhumanbrainstructureandfunctionbypredictingthestructuralconnectivityfromfunctionalconnectivity AT weifengliu understandingtherelationshipbetweenhumanbrainstructureandfunctionbypredictingthestructuralconnectivityfromfunctionalconnectivity AT richardmartinshiffrin understandingtherelationshipbetweenhumanbrainstructureandfunctionbypredictingthestructuralconnectivityfromfunctionalconnectivity
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Understanding the Relationship Between Human Brain Structure and Function by Predicting the Structural Connectivity From Functional Connectivity

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