Cargando…

A Riemannian Revisiting of Structure–Function Mapping Based on Eigenmodes

Understanding the link between brain structure and function may not only improve our knowledge of brain organization, but also lead to better quantification of pathology. To quantify this link, recent studies have attempted to predict the brain's functional connectivity from its structural conn...

Descripción completa

Detalles Bibliográficos
Autores principales: Deslauriers-Gauthier, Samuel, Zucchelli, Mauro, Laghrissi, Hiba, Deriche, Rachid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406294/
https://www.ncbi.nlm.nih.gov/pubmed/37555180
http://dx.doi.org/10.3389/fnimg.2022.850266
_version_ 1785085721012338688
author Deslauriers-Gauthier, Samuel
Zucchelli, Mauro
Laghrissi, Hiba
Deriche, Rachid
author_facet Deslauriers-Gauthier, Samuel
Zucchelli, Mauro
Laghrissi, Hiba
Deriche, Rachid
author_sort Deslauriers-Gauthier, Samuel
collection PubMed
description Understanding the link between brain structure and function may not only improve our knowledge of brain organization, but also lead to better quantification of pathology. To quantify this link, recent studies have attempted to predict the brain's functional connectivity from its structural connectivity. However, functional connectivity matrices live in the Riemannian manifold of the symmetric positive definite space and a specific attention must be paid to operate on this appropriate space. In this work we investigated the implications of using a distance based on an affine invariant Riemannian metric in the context of structure–function mapping. Specifically, we revisit previously proposed structure–function mappings based on eigendecomposition and test them on 100 healthy subjects from the Human Connectome Project using this adapted notion of distance. First, we show that using this Riemannian distance significantly alters the notion of similarity between subjects from a functional point of view. We also show that using this distance improves the correlation between the structural and functional similarity of different subjects. Finally, by using a distance appropriate to this manifold, we demonstrate the importance of mapping function from structure under the Riemannian manifold and show in particular that it is possible to outperform the group average and the so–called glass ceiling on the performance of mappings based on eigenmodes.
format Online
Article
Text
id pubmed-10406294
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-104062942023-08-08 A Riemannian Revisiting of Structure–Function Mapping Based on Eigenmodes Deslauriers-Gauthier, Samuel Zucchelli, Mauro Laghrissi, Hiba Deriche, Rachid Front Neuroimaging Neuroimaging Understanding the link between brain structure and function may not only improve our knowledge of brain organization, but also lead to better quantification of pathology. To quantify this link, recent studies have attempted to predict the brain's functional connectivity from its structural connectivity. However, functional connectivity matrices live in the Riemannian manifold of the symmetric positive definite space and a specific attention must be paid to operate on this appropriate space. In this work we investigated the implications of using a distance based on an affine invariant Riemannian metric in the context of structure–function mapping. Specifically, we revisit previously proposed structure–function mappings based on eigendecomposition and test them on 100 healthy subjects from the Human Connectome Project using this adapted notion of distance. First, we show that using this Riemannian distance significantly alters the notion of similarity between subjects from a functional point of view. We also show that using this distance improves the correlation between the structural and functional similarity of different subjects. Finally, by using a distance appropriate to this manifold, we demonstrate the importance of mapping function from structure under the Riemannian manifold and show in particular that it is possible to outperform the group average and the so–called glass ceiling on the performance of mappings based on eigenmodes. Frontiers Media S.A. 2022-05-25 /pmc/articles/PMC10406294/ /pubmed/37555180 http://dx.doi.org/10.3389/fnimg.2022.850266 Text en Copyright © 2022 Deslauriers-Gauthier, Zucchelli, Laghrissi and Deriche. 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 Neuroimaging
Deslauriers-Gauthier, Samuel
Zucchelli, Mauro
Laghrissi, Hiba
Deriche, Rachid
A Riemannian Revisiting of Structure–Function Mapping Based on Eigenmodes
title A Riemannian Revisiting of Structure–Function Mapping Based on Eigenmodes
title_full A Riemannian Revisiting of Structure–Function Mapping Based on Eigenmodes
title_fullStr A Riemannian Revisiting of Structure–Function Mapping Based on Eigenmodes
title_full_unstemmed A Riemannian Revisiting of Structure–Function Mapping Based on Eigenmodes
title_short A Riemannian Revisiting of Structure–Function Mapping Based on Eigenmodes
title_sort riemannian revisiting of structure–function mapping based on eigenmodes
topic Neuroimaging
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406294/
https://www.ncbi.nlm.nih.gov/pubmed/37555180
http://dx.doi.org/10.3389/fnimg.2022.850266
work_keys_str_mv AT deslauriersgauthiersamuel ariemannianrevisitingofstructurefunctionmappingbasedoneigenmodes
AT zucchellimauro ariemannianrevisitingofstructurefunctionmappingbasedoneigenmodes
AT laghrissihiba ariemannianrevisitingofstructurefunctionmappingbasedoneigenmodes
AT dericherachid ariemannianrevisitingofstructurefunctionmappingbasedoneigenmodes
AT deslauriersgauthiersamuel riemannianrevisitingofstructurefunctionmappingbasedoneigenmodes
AT zucchellimauro riemannianrevisitingofstructurefunctionmappingbasedoneigenmodes
AT laghrissihiba riemannianrevisitingofstructurefunctionmappingbasedoneigenmodes
AT dericherachid riemannianrevisitingofstructurefunctionmappingbasedoneigenmodes