A Riemannian approach to predicting brain function from the structural connectome

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...

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Autores: Benkarim, Oualid, Paquola, Casey, Park, Bo-yong, Royer, Jessica, Rodríguez-Cruces, Raúl, Wael, Reinder Vos de, Misic, Bratislav, Piella Fenoy, Gemma, Bernhardt, Boris C.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/56047
Acceso en línea:http://hdl.handle.net/10230/56047
http://dx.doi.org/10.1016/j.neuroimage.2022.119299
Access Level:acceso abierto
Palabra clave:Functional connectivity
Structural connectome
Diffusion maps
Manifold optimization
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spelling A Riemannian approach to predicting brain function from the structural connectomeBenkarim, OualidPaquola, CaseyPark, Bo-yongRoyer, JessicaRodríguez-Cruces, RaúlWael, Reinder Vos deMisic, BratislavPiella Fenoy, GemmaBernhardt, Boris C.Functional connectivityStructural connectomeDiffusion mapsManifold optimizationOngoing 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.Elsevier202320232022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/56047http://dx.doi.org/10.1016/j.neuroimage.2022.119299reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésNeuroImage. 2022;257:119299.https://github.com/MICA-MNI/micaopen/tree/master/sf_predictionhttps://portal.conp.ca/dataset?id=projects/mica-micshttps://ars.els-cdn.com/content/image/1-s2.0-S1053811922004189-mmc1.docx© 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/)http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/560472026-06-12T07:21:37Z
dc.title.none.fl_str_mv A Riemannian approach to predicting brain function from the structural connectome
title A Riemannian approach to predicting brain function from the structural connectome
spellingShingle A Riemannian approach to predicting brain function from the structural connectome
Benkarim, Oualid
Functional connectivity
Structural connectome
Diffusion maps
Manifold optimization
title_short A Riemannian approach to predicting brain function from the structural connectome
title_full A Riemannian approach to predicting brain function from the structural connectome
title_fullStr A Riemannian approach to predicting brain function from the structural connectome
title_full_unstemmed A Riemannian approach to predicting brain function from the structural connectome
title_sort A Riemannian approach to predicting brain function from the structural connectome
dc.creator.none.fl_str_mv Benkarim, Oualid
Paquola, Casey
Park, Bo-yong
Royer, Jessica
Rodríguez-Cruces, Raúl
Wael, Reinder Vos de
Misic, Bratislav
Piella Fenoy, Gemma
Bernhardt, Boris C.
author Benkarim, Oualid
author_facet Benkarim, Oualid
Paquola, Casey
Park, Bo-yong
Royer, Jessica
Rodríguez-Cruces, Raúl
Wael, Reinder Vos de
Misic, Bratislav
Piella Fenoy, Gemma
Bernhardt, Boris C.
author_role author
author2 Paquola, Casey
Park, Bo-yong
Royer, Jessica
Rodríguez-Cruces, Raúl
Wael, Reinder Vos de
Misic, Bratislav
Piella Fenoy, Gemma
Bernhardt, Boris C.
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Functional connectivity
Structural connectome
Diffusion maps
Manifold optimization
topic Functional connectivity
Structural connectome
Diffusion maps
Manifold optimization
description 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.
publishDate 2022
dc.date.none.fl_str_mv 2022
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/56047
http://dx.doi.org/10.1016/j.neuroimage.2022.119299
url http://hdl.handle.net/10230/56047
http://dx.doi.org/10.1016/j.neuroimage.2022.119299
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv NeuroImage. 2022;257:119299.
https://github.com/MICA-MNI/micaopen/tree/master/sf_prediction
https://portal.conp.ca/dataset?id=projects/mica-mics
https://ars.els-cdn.com/content/image/1-s2.0-S1053811922004189-mmc1.docx
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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