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A Riemannian approach to predicting brain function from the structural connectome

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dc.contributor.authorBenkarim, Oualid-
dc.contributor.authorPaquola, Casey-
dc.contributor.authorBo-yong Park-
dc.contributor.authorRoyer, Jessica-
dc.contributor.authorRodríguez-Cruces, Raúl-
dc.contributor.authorVos de Wael, Reinder-
dc.contributor.authorMisic, Bratislav-
dc.contributor.authorPiella, Gemma-
dc.contributor.authorBernhardt, Boris C.-
dc.date.accessioned2022-07-29T07:41:41Z-
dc.date.available2022-07-29T07:41:41Z-
dc.date.created2022-07-18-
dc.date.issued2022-08-
dc.identifier.issn1053-8119-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/11998-
dc.description.abstract© 2022Ongoing brain function is largely determined by the underlying wiring of the brain, but the specific rules governing this relationship remain unknown. Emerging literature has suggested that functional interactions between brain regions emerge from the structural connections through mono- as well as polysynaptic mechanisms. Here, we propose a novel approach based on diffusion maps and Riemannian optimization to emulate this dynamic mechanism in the form of random walks on the structural connectome and predict functional interactions as a weighted combination of these random walks. Our proposed approach was evaluated in two different cohorts of healthy adults (Human Connectome Project, HCP; Microstructure-Informed Connectomics, MICs). Our approach outperformed existing approaches and showed that performance plateaus approximately around the third random walk. At macroscale, we found that the largest number of walks was required in nodes of the default mode and frontoparietal networks, underscoring an increasing relevance of polysynaptic communication mechanisms in transmodal cortical networks compared to primary and unimodal systems.-
dc.language영어-
dc.publisherAcademic Press Inc.-
dc.titleA Riemannian approach to predicting brain function from the structural connectome-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.wosid000817013400002-
dc.identifier.scopusid2-s2.0-85131448820-
dc.identifier.rimsid78485-
dc.contributor.affiliatedAuthorBo-yong Park-
dc.identifier.doi10.1016/j.neuroimage.2022.119299-
dc.identifier.bibliographicCitationNeuroImage, v.257-
dc.relation.isPartOfNeuroImage-
dc.citation.titleNeuroImage-
dc.citation.volume257-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaNeurosciences & Neurology-
dc.relation.journalResearchAreaRadiology, Nuclear Medicine & Medical Imaging-
dc.relation.journalWebOfScienceCategoryNeurosciences-
dc.relation.journalWebOfScienceCategoryNeuroimaging-
dc.relation.journalWebOfScienceCategoryRadiology, Nuclear Medicine & Medical Imaging-
dc.subject.keywordPlusHUMAN CEREBRAL-CORTEX-
dc.subject.keywordPlusSURFACE-BASED ANALYSIS-
dc.subject.keywordPlusRESTING-STATE-
dc.subject.keywordPlusCONNECTIVITY-
dc.subject.keywordPlusNETWORK-
dc.subject.keywordPlusTRACTOGRAPHY-
dc.subject.keywordPlusCOST-
dc.subject.keywordPlusMODELS-
dc.subject.keywordPlusSYSTEM-
dc.subject.keywordAuthorDiffusion maps-
dc.subject.keywordAuthorFunctional connectivity-
dc.subject.keywordAuthorManifold optimization-
dc.subject.keywordAuthorStructural connectome-
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > 1. Journal Papers (저널논문)
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