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Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks
The human brain displays rich communication dynamics that are thought to be particularly well-reflected in its marked community structure. Yet, the precise relationship between community structure in structural brain networks and the communication dynamics that can emerge therefrom is not well under...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MIT Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7655114/ https://www.ncbi.nlm.nih.gov/pubmed/33195950 http://dx.doi.org/10.1162/netn_a_00157 |
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author | Patankar, Shubhankar P. Kim, Jason Z. Pasqualetti, Fabio Bassett, Danielle S. |
author_facet | Patankar, Shubhankar P. Kim, Jason Z. Pasqualetti, Fabio Bassett, Danielle S. |
author_sort | Patankar, Shubhankar P. |
collection | PubMed |
description | The human brain displays rich communication dynamics that are thought to be particularly well-reflected in its marked community structure. Yet, the precise relationship between community structure in structural brain networks and the communication dynamics that can emerge therefrom is not well understood. In addition to offering insight into the structure-function relationship of networked systems, such an understanding is a critical step toward the ability to manipulate the brain’s large-scale dynamical activity in a targeted manner. We investigate the role of community structure in the controllability of structural brain networks. At the region level, we find that certain network measures of community structure are sometimes statistically correlated with measures of linear controllability. However, we then demonstrate that this relationship depends on the distribution of network edge weights. We highlight the complexity of the relationship between community structure and controllability by performing numerical simulations using canonical graph models with varying mesoscale architectures and edge weight distributions. Finally, we demonstrate that weighted subgraph centrality, a measure rooted in the graph spectrum, and which captures higher order graph architecture, is a stronger and more consistent predictor of controllability. Our study contributes to an understanding of how the brain’s diverse mesoscale structure supports transient communication dynamics. |
format | Online Article Text |
id | pubmed-7655114 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MIT Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-76551142020-11-13 Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks Patankar, Shubhankar P. Kim, Jason Z. Pasqualetti, Fabio Bassett, Danielle S. Netw Neurosci Focus Feature: Network Communication in the Brain The human brain displays rich communication dynamics that are thought to be particularly well-reflected in its marked community structure. Yet, the precise relationship between community structure in structural brain networks and the communication dynamics that can emerge therefrom is not well understood. In addition to offering insight into the structure-function relationship of networked systems, such an understanding is a critical step toward the ability to manipulate the brain’s large-scale dynamical activity in a targeted manner. We investigate the role of community structure in the controllability of structural brain networks. At the region level, we find that certain network measures of community structure are sometimes statistically correlated with measures of linear controllability. However, we then demonstrate that this relationship depends on the distribution of network edge weights. We highlight the complexity of the relationship between community structure and controllability by performing numerical simulations using canonical graph models with varying mesoscale architectures and edge weight distributions. Finally, we demonstrate that weighted subgraph centrality, a measure rooted in the graph spectrum, and which captures higher order graph architecture, is a stronger and more consistent predictor of controllability. Our study contributes to an understanding of how the brain’s diverse mesoscale structure supports transient communication dynamics. MIT Press 2020-11-01 /pmc/articles/PMC7655114/ /pubmed/33195950 http://dx.doi.org/10.1162/netn_a_00157 Text en © 2020 Massachusetts Institute of Technology This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode. |
spellingShingle | Focus Feature: Network Communication in the Brain Patankar, Shubhankar P. Kim, Jason Z. Pasqualetti, Fabio Bassett, Danielle S. Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks |
title | Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks |
title_full | Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks |
title_fullStr | Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks |
title_full_unstemmed | Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks |
title_short | Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks |
title_sort | path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks |
topic | Focus Feature: Network Communication in the Brain |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7655114/ https://www.ncbi.nlm.nih.gov/pubmed/33195950 http://dx.doi.org/10.1162/netn_a_00157 |
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