Predicting individual brain maturity using dynamic functional connectivity

Neuroimaging-based functional connectivity (FC) analyses have revealed significant developmental trends in specific intrinsic connectivity networks linked to cognitive and behavioral maturation. However, knowledge of how brain functional maturation is associated with FC dynamics at rest is limited....

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Main Authors:	Qin, Jian, Chen, Shan-Guang, Hu, Dewen, Zeng, Ling-Li, Fan, Yi-Ming, Chen, Xiao-Ping, Shen, Hui
Format:	Online
Language:	English
Published:	Frontiers Media S.A. 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4503925/

id	pubmed-4503925
recordtype	oai_dc
spelling	pubmed-45039252015-07-31 Predicting individual brain maturity using dynamic functional connectivity Qin, Jian Chen, Shan-Guang Hu, Dewen Zeng, Ling-Li Fan, Yi-Ming Chen, Xiao-Ping Shen, Hui Neuroscience Neuroimaging-based functional connectivity (FC) analyses have revealed significant developmental trends in specific intrinsic connectivity networks linked to cognitive and behavioral maturation. However, knowledge of how brain functional maturation is associated with FC dynamics at rest is limited. Here, we examined age-related differences in the temporal variability of FC dynamics with data publicly released by the Nathan Kline Institute (NKI; n = 183, ages 7–30) and showed that dynamic inter-region interactions can be used to accurately predict individual brain maturity across development. Furthermore, we identified a significant age-dependent trend underlying dynamic inter-network FC, including increasing variability of the connections between the visual network, default mode network (DMN) and cerebellum as well as within the cerebellum and DMN and decreasing variability within the cerebellum and between the cerebellum and DMN as well as the cingulo-opercular network. Overall, the results suggested significant developmental changes in dynamic inter-network interaction, which may shed new light on the functional organization of typical developmental brains. Frontiers Media S.A. 2015-07-16 /pmc/articles/PMC4503925/ /pubmed/26236224 http://dx.doi.org/10.3389/fnhum.2015.00418 Text en Copyright © 2015 Qin, Chen, Hu, Zeng, Fan, Chen and Shen. http://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) or licensor 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.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Qin, Jian Chen, Shan-Guang Hu, Dewen Zeng, Ling-Li Fan, Yi-Ming Chen, Xiao-Ping Shen, Hui
spellingShingle	Qin, Jian Chen, Shan-Guang Hu, Dewen Zeng, Ling-Li Fan, Yi-Ming Chen, Xiao-Ping Shen, Hui Predicting individual brain maturity using dynamic functional connectivity
author_facet	Qin, Jian Chen, Shan-Guang Hu, Dewen Zeng, Ling-Li Fan, Yi-Ming Chen, Xiao-Ping Shen, Hui
author_sort	Qin, Jian
title	Predicting individual brain maturity using dynamic functional connectivity
title_short	Predicting individual brain maturity using dynamic functional connectivity
title_full	Predicting individual brain maturity using dynamic functional connectivity
title_fullStr	Predicting individual brain maturity using dynamic functional connectivity
title_full_unstemmed	Predicting individual brain maturity using dynamic functional connectivity
title_sort	predicting individual brain maturity using dynamic functional connectivity
description	Neuroimaging-based functional connectivity (FC) analyses have revealed significant developmental trends in specific intrinsic connectivity networks linked to cognitive and behavioral maturation. However, knowledge of how brain functional maturation is associated with FC dynamics at rest is limited. Here, we examined age-related differences in the temporal variability of FC dynamics with data publicly released by the Nathan Kline Institute (NKI; n = 183, ages 7–30) and showed that dynamic inter-region interactions can be used to accurately predict individual brain maturity across development. Furthermore, we identified a significant age-dependent trend underlying dynamic inter-network FC, including increasing variability of the connections between the visual network, default mode network (DMN) and cerebellum as well as within the cerebellum and DMN and decreasing variability within the cerebellum and between the cerebellum and DMN as well as the cingulo-opercular network. Overall, the results suggested significant developmental changes in dynamic inter-network interaction, which may shed new light on the functional organization of typical developmental brains.
publisher	Frontiers Media S.A.
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4503925/
_version_	1613248434746163200

Predicting individual brain maturity using dynamic functional connectivity

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