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Mapping individual differences across brain network structure to function and behavior with connectome embedding

The connectome, a comprehensive map of the brain’s anatomical connections, is often summarized as a matrix comprising all dyadic connections among pairs of brain regions. This representation cannot capture higher-order relations within the brain graph. Connectome embedding (CE) addresses this limita...

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Autores principales: Levakov, Gidon, Faskowitz, Joshua, Avidan, Galia, Sporns, Olaf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8464439/
https://www.ncbi.nlm.nih.gov/pubmed/34390875
http://dx.doi.org/10.1016/j.neuroimage.2021.118469
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author Levakov, Gidon
Faskowitz, Joshua
Avidan, Galia
Sporns, Olaf
author_facet Levakov, Gidon
Faskowitz, Joshua
Avidan, Galia
Sporns, Olaf
author_sort Levakov, Gidon
collection PubMed
description The connectome, a comprehensive map of the brain’s anatomical connections, is often summarized as a matrix comprising all dyadic connections among pairs of brain regions. This representation cannot capture higher-order relations within the brain graph. Connectome embedding (CE) addresses this limitation by creating compact vectorized representations of brain nodes capturing their context in the global network topology. Here, nodes “context” is defined as random walks on the brain graph and as such, represents a generative model of diffusive communication around nodes. Applied to group-averaged structural connectivity, CE was previously shown to capture relations between inter-hemispheric homologous brain regions and uncover putative missing edges from the network reconstruction. Here we extend this framework to explore individual differences with a novel embedding alignment approach. We test this approach in two lifespan datasets (NKI: n = 542; Cam-CAN: n = 601) that include diffusion-weighted imaging, resting-state fMRI, demographics and behavioral measures. We demonstrate that modeling functional connectivity with CE substantially improves structural to functional connectivity mapping both at the group and subject level. Furthermore, age-related differences in this structure-function mapping, are preserved and enhanced. Importantly, CE captures individual differences by out-of-sample prediction of age and intelligence. The resulting predictive accuracy was higher compared to using structural connectivity and functional connectivity. We attribute these findings to the capacity of the CE to incorporate aspects of both anatomy (the structural graph) and function (diffusive communication). Our novel approach allows mapping individual differences in the connectome through structure to function and behavior.
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spelling pubmed-84644392021-11-15 Mapping individual differences across brain network structure to function and behavior with connectome embedding Levakov, Gidon Faskowitz, Joshua Avidan, Galia Sporns, Olaf Neuroimage Article The connectome, a comprehensive map of the brain’s anatomical connections, is often summarized as a matrix comprising all dyadic connections among pairs of brain regions. This representation cannot capture higher-order relations within the brain graph. Connectome embedding (CE) addresses this limitation by creating compact vectorized representations of brain nodes capturing their context in the global network topology. Here, nodes “context” is defined as random walks on the brain graph and as such, represents a generative model of diffusive communication around nodes. Applied to group-averaged structural connectivity, CE was previously shown to capture relations between inter-hemispheric homologous brain regions and uncover putative missing edges from the network reconstruction. Here we extend this framework to explore individual differences with a novel embedding alignment approach. We test this approach in two lifespan datasets (NKI: n = 542; Cam-CAN: n = 601) that include diffusion-weighted imaging, resting-state fMRI, demographics and behavioral measures. We demonstrate that modeling functional connectivity with CE substantially improves structural to functional connectivity mapping both at the group and subject level. Furthermore, age-related differences in this structure-function mapping, are preserved and enhanced. Importantly, CE captures individual differences by out-of-sample prediction of age and intelligence. The resulting predictive accuracy was higher compared to using structural connectivity and functional connectivity. We attribute these findings to the capacity of the CE to incorporate aspects of both anatomy (the structural graph) and function (diffusive communication). Our novel approach allows mapping individual differences in the connectome through structure to function and behavior. 2021-08-11 2021-11-15 /pmc/articles/PMC8464439/ /pubmed/34390875 http://dx.doi.org/10.1016/j.neuroimage.2021.118469 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) )
spellingShingle Article
Levakov, Gidon
Faskowitz, Joshua
Avidan, Galia
Sporns, Olaf
Mapping individual differences across brain network structure to function and behavior with connectome embedding
title Mapping individual differences across brain network structure to function and behavior with connectome embedding
title_full Mapping individual differences across brain network structure to function and behavior with connectome embedding
title_fullStr Mapping individual differences across brain network structure to function and behavior with connectome embedding
title_full_unstemmed Mapping individual differences across brain network structure to function and behavior with connectome embedding
title_short Mapping individual differences across brain network structure to function and behavior with connectome embedding
title_sort mapping individual differences across brain network structure to function and behavior with connectome embedding
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8464439/
https://www.ncbi.nlm.nih.gov/pubmed/34390875
http://dx.doi.org/10.1016/j.neuroimage.2021.118469
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