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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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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 |
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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 |
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