A Riemannian approach to predicting brain function from the structural connectome

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• dc.contributor.author Benkarim, Oualid
• dc.contributor.author Paquola, Casey
• dc.contributor.author Park, Bo-yong
• dc.contributor.author Royer, Jessica
• dc.contributor.author Rodríguez-Cruces, Raúl
• dc.contributor.author Wael, Reinder Vos de
• dc.contributor.author Misic, Bratislav
• dc.contributor.author Piella Fenoy, Gemma
• dc.contributor.author Bernhardt, Boris C.
• dc.date.accessioned 2023-03-06T07:30:16Z
• dc.date.available 2023-03-06T07:30:16Z
• dc.date.issued 2022
• dc.description.abstract Ongoing 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.format.mimetype application/pdf
• dc.identifier.citation Benkarim O, Paquola C, Park BY, Royer J, Rodríguez-Cruces R, Vos de Wael R, Misic B, Piella G, Bernhardt BC. A Riemannian approach to predicting brain function from the structural connectome. Neuroimage. 2022;257:119299. DOI: 10.1016/j.neuroimage.2022.119299
• dc.identifier.doi http://dx.doi.org/10.1016/j.neuroimage.2022.119299
• dc.identifier.issn 1053-8119
• dc.identifier.uri http://hdl.handle.net/10230/56047
• dc.language.iso eng
• dc.publisher Elsevier
• dc.relation.ispartof NeuroImage. 2022;257:119299.
• dc.relation.isreferencedby https://github.com/MICA-MNI/micaopen/tree/master/sf_prediction
• dc.relation.isreferencedby https://portal.conp.ca/dataset?id=projects/mica-mics
• dc.relation.isreferencedby https://ars.els-cdn.com/content/image/1-s2.0-S1053811922004189-mmc1.docx
• dc.rights © 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
• dc.subject.keyword Functional connectivity
• dc.subject.keyword Structural connectome
• dc.subject.keyword Diffusion maps
• dc.subject.keyword Manifold optimization
• dc.title A Riemannian approach to predicting brain function from the structural connectome
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion