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The maturation and cognitive relevance of structural brain network organization from early infancy to childhood
The interactions of brain regions with other regions at the network level likely provide the infrastructure necessary for cognitive processes to develop. Specifically, it has been theorized that in infancy brain networks become more modular, or segregated, to support early cognitive specialization,...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8372198/ https://www.ncbi.nlm.nih.gov/pubmed/34091033 http://dx.doi.org/10.1016/j.neuroimage.2021.118232 |
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author | Woodburn, Mackenzie Bricken, Cheyenne L. Wu, Zhengwang Li, Gang Wang, Li Lin, Weili Sheridan, Margaret A. Cohen, Jessica R. |
author_facet | Woodburn, Mackenzie Bricken, Cheyenne L. Wu, Zhengwang Li, Gang Wang, Li Lin, Weili Sheridan, Margaret A. Cohen, Jessica R. |
author_sort | Woodburn, Mackenzie |
collection | PubMed |
description | The interactions of brain regions with other regions at the network level likely provide the infrastructure necessary for cognitive processes to develop. Specifically, it has been theorized that in infancy brain networks become more modular, or segregated, to support early cognitive specialization, before integration across networks increases to support the emergence of higher-order cognition. The present study examined the maturation of structural covariance networks (SCNs) derived from longitudinal cortical thickness data collected between infancy and childhood (0–6 years). We assessed modularity as a measure of network segregation and global efficiency as a measure of network integration. At the group level, we observed trajectories of increasing modularity and decreasing global efficiency between early infancy and six years. We further examined subject-based maturational coupling networks (sbMCNs) in a subset of this cohort with cognitive outcome data at 8–10 years, which allowed us to relate the network organization of longitudinal cortical thickness maturation to cognitive outcomes in middle childhood. We found that lower global efficiency of sbMCNs throughout early development (across the first year) related to greater motor learning at 8–10 years. Together, these results provide novel evidence characterizing the maturation of brain network segregation and integration across the first six years of life, and suggest that specific trajectories of brain network maturation contribute to later cognitive outcomes. |
format | Online Article Text |
id | pubmed-8372198 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
record_format | MEDLINE/PubMed |
spelling | pubmed-83721982021-09-01 The maturation and cognitive relevance of structural brain network organization from early infancy to childhood Woodburn, Mackenzie Bricken, Cheyenne L. Wu, Zhengwang Li, Gang Wang, Li Lin, Weili Sheridan, Margaret A. Cohen, Jessica R. Neuroimage Article The interactions of brain regions with other regions at the network level likely provide the infrastructure necessary for cognitive processes to develop. Specifically, it has been theorized that in infancy brain networks become more modular, or segregated, to support early cognitive specialization, before integration across networks increases to support the emergence of higher-order cognition. The present study examined the maturation of structural covariance networks (SCNs) derived from longitudinal cortical thickness data collected between infancy and childhood (0–6 years). We assessed modularity as a measure of network segregation and global efficiency as a measure of network integration. At the group level, we observed trajectories of increasing modularity and decreasing global efficiency between early infancy and six years. We further examined subject-based maturational coupling networks (sbMCNs) in a subset of this cohort with cognitive outcome data at 8–10 years, which allowed us to relate the network organization of longitudinal cortical thickness maturation to cognitive outcomes in middle childhood. We found that lower global efficiency of sbMCNs throughout early development (across the first year) related to greater motor learning at 8–10 years. Together, these results provide novel evidence characterizing the maturation of brain network segregation and integration across the first six years of life, and suggest that specific trajectories of brain network maturation contribute to later cognitive outcomes. 2021-06-04 2021-09 /pmc/articles/PMC8372198/ /pubmed/34091033 http://dx.doi.org/10.1016/j.neuroimage.2021.118232 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 Woodburn, Mackenzie Bricken, Cheyenne L. Wu, Zhengwang Li, Gang Wang, Li Lin, Weili Sheridan, Margaret A. Cohen, Jessica R. The maturation and cognitive relevance of structural brain network organization from early infancy to childhood |
title | The maturation and cognitive relevance of structural brain network organization from early infancy to childhood |
title_full | The maturation and cognitive relevance of structural brain network organization from early infancy to childhood |
title_fullStr | The maturation and cognitive relevance of structural brain network organization from early infancy to childhood |
title_full_unstemmed | The maturation and cognitive relevance of structural brain network organization from early infancy to childhood |
title_short | The maturation and cognitive relevance of structural brain network organization from early infancy to childhood |
title_sort | maturation and cognitive relevance of structural brain network organization from early infancy to childhood |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8372198/ https://www.ncbi.nlm.nih.gov/pubmed/34091033 http://dx.doi.org/10.1016/j.neuroimage.2021.118232 |
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