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Evolution of Cortical Functional Networks in Healthy Infants

During normal childhood development, functional brain networks evolve over time in parallel with changes in neuronal oscillations. Previous studies have demonstrated differences in network topology with age, particularly in neonates and in cohorts spanning from birth to early adulthood. Here, we eva...

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Autores principales: Hu, Derek K., Goetz, Parker W., To, Phuc D., Garner, Cristal, Magers, Amber L., Skora, Clare, Tran, Nhi, Yuen, Tammy, Hussain, Shaun A., Shrey, Daniel W., Lopour, Beth A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10013075/
https://www.ncbi.nlm.nih.gov/pubmed/36926103
http://dx.doi.org/10.3389/fnetp.2022.893826
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author Hu, Derek K.
Goetz, Parker W.
To, Phuc D.
Garner, Cristal
Magers, Amber L.
Skora, Clare
Tran, Nhi
Yuen, Tammy
Hussain, Shaun A.
Shrey, Daniel W.
Lopour, Beth A.
author_facet Hu, Derek K.
Goetz, Parker W.
To, Phuc D.
Garner, Cristal
Magers, Amber L.
Skora, Clare
Tran, Nhi
Yuen, Tammy
Hussain, Shaun A.
Shrey, Daniel W.
Lopour, Beth A.
author_sort Hu, Derek K.
collection PubMed
description During normal childhood development, functional brain networks evolve over time in parallel with changes in neuronal oscillations. Previous studies have demonstrated differences in network topology with age, particularly in neonates and in cohorts spanning from birth to early adulthood. Here, we evaluate the developmental changes in EEG functional connectivity with a specific focus on the first 2 years of life. Functional connectivity networks (FCNs) were calculated from the EEGs of 240 healthy infants aged 0–2 years during wakefulness and sleep using a cross-correlation-based measure and the weighted phase lag index. Topological features were assessed via network strength, global clustering coefficient, characteristic path length, and small world measures. We found that cross-correlation FCNs maintained a consistent small-world structure, and the connection strengths increased after the first 3 months of infancy. The strongest connections in these networks were consistently located in the frontal and occipital regions across age groups. In the delta and theta bands, weighted phase lag index networks decreased in strength after the first 3 months in both wakefulness and sleep, and a similar result was found in the alpha and beta bands during wakefulness. However, in the alpha band during sleep, FCNs exhibited a significant increase in strength with age, particularly in the 21–24 months age group. During this period, a majority of the strongest connections in the networks were located in frontocentral regions, and a qualitatively similar distribution was seen in the beta band during sleep for subjects older than 3 months. Graph theory analysis suggested a small world structure for weighted phase lag index networks, but to a lesser degree than those calculated using cross-correlation. In general, graph theory metrics showed little change over time, with no significant differences between age groups for the clustering coefficient (wakefulness and sleep), characteristics path length (sleep), and small world measure (sleep). These results suggest that infant FCNs evolve during the first 2 years with more significant changes to network strength than features of the network structure. This study quantifies normal brain networks during infant development and can serve as a baseline for future investigations in health and neurological disease.
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spelling pubmed-100130752023-03-15 Evolution of Cortical Functional Networks in Healthy Infants Hu, Derek K. Goetz, Parker W. To, Phuc D. Garner, Cristal Magers, Amber L. Skora, Clare Tran, Nhi Yuen, Tammy Hussain, Shaun A. Shrey, Daniel W. Lopour, Beth A. Front Netw Physiol Network Physiology During normal childhood development, functional brain networks evolve over time in parallel with changes in neuronal oscillations. Previous studies have demonstrated differences in network topology with age, particularly in neonates and in cohorts spanning from birth to early adulthood. Here, we evaluate the developmental changes in EEG functional connectivity with a specific focus on the first 2 years of life. Functional connectivity networks (FCNs) were calculated from the EEGs of 240 healthy infants aged 0–2 years during wakefulness and sleep using a cross-correlation-based measure and the weighted phase lag index. Topological features were assessed via network strength, global clustering coefficient, characteristic path length, and small world measures. We found that cross-correlation FCNs maintained a consistent small-world structure, and the connection strengths increased after the first 3 months of infancy. The strongest connections in these networks were consistently located in the frontal and occipital regions across age groups. In the delta and theta bands, weighted phase lag index networks decreased in strength after the first 3 months in both wakefulness and sleep, and a similar result was found in the alpha and beta bands during wakefulness. However, in the alpha band during sleep, FCNs exhibited a significant increase in strength with age, particularly in the 21–24 months age group. During this period, a majority of the strongest connections in the networks were located in frontocentral regions, and a qualitatively similar distribution was seen in the beta band during sleep for subjects older than 3 months. Graph theory analysis suggested a small world structure for weighted phase lag index networks, but to a lesser degree than those calculated using cross-correlation. In general, graph theory metrics showed little change over time, with no significant differences between age groups for the clustering coefficient (wakefulness and sleep), characteristics path length (sleep), and small world measure (sleep). These results suggest that infant FCNs evolve during the first 2 years with more significant changes to network strength than features of the network structure. This study quantifies normal brain networks during infant development and can serve as a baseline for future investigations in health and neurological disease. Frontiers Media S.A. 2022-06-15 /pmc/articles/PMC10013075/ /pubmed/36926103 http://dx.doi.org/10.3389/fnetp.2022.893826 Text en Copyright © 2022 Hu, Goetz, To, Garner, Magers, Skora, Tran, Yuen, Hussain, Shrey and Lopour. 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 Network Physiology
Hu, Derek K.
Goetz, Parker W.
To, Phuc D.
Garner, Cristal
Magers, Amber L.
Skora, Clare
Tran, Nhi
Yuen, Tammy
Hussain, Shaun A.
Shrey, Daniel W.
Lopour, Beth A.
Evolution of Cortical Functional Networks in Healthy Infants
title Evolution of Cortical Functional Networks in Healthy Infants
title_full Evolution of Cortical Functional Networks in Healthy Infants
title_fullStr Evolution of Cortical Functional Networks in Healthy Infants
title_full_unstemmed Evolution of Cortical Functional Networks in Healthy Infants
title_short Evolution of Cortical Functional Networks in Healthy Infants
title_sort evolution of cortical functional networks in healthy infants
topic Network Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10013075/
https://www.ncbi.nlm.nih.gov/pubmed/36926103
http://dx.doi.org/10.3389/fnetp.2022.893826
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