Decoding Lifespan Changes of the Human Brain Using Resting-State Functional Connectivity MRI

The development of large-scale functional brain networks is a complex, lifelong process that can be investigated using resting-state functional connectivity MRI (rs-fcMRI). In this study, we aimed to decode the developmental dynamics of the whole-brain functional network in seven decades (8–79 years...

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Main Authors:	Wang, Lubin, Su, Longfei, Shen, Hui, Hu, Dewen
Format:	Online
Language:	English
Published:	Public Library of Science 2012
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3431403/

id	pubmed-3431403
recordtype	oai_dc
spelling	pubmed-34314032012-09-05 Decoding Lifespan Changes of the Human Brain Using Resting-State Functional Connectivity MRI Wang, Lubin Su, Longfei Shen, Hui Hu, Dewen Research Article The development of large-scale functional brain networks is a complex, lifelong process that can be investigated using resting-state functional connectivity MRI (rs-fcMRI). In this study, we aimed to decode the developmental dynamics of the whole-brain functional network in seven decades (8–79 years) of the human lifespan. We first used parametric curve fitting to examine linear and nonlinear age effect on the resting human brain, and then combined manifold learning and support vector machine methods to predict individuals' “brain ages” from rs-fcMRI data. We found that age-related changes in interregional functional connectivity exhibited spatially and temporally specific patterns. During brain development from childhood to senescence, functional connections tended to linearly increase in the emotion system and decrease in the sensorimotor system; while quadratic trajectories were observed in functional connections related to higher-order cognitive functions. The complex patterns of age effect on the whole-brain functional network could be effectively represented by a low-dimensional, nonlinear manifold embedded in the functional connectivity space, which uncovered the inherent structure of brain maturation and aging. Regression of manifold coordinates with age further showed that the manifold representation extracted sufficient information from rs-fcMRI data to make prediction about individual brains' functional development levels. Our study not only gives insights into the neural substrates that underlie behavioral and cognitive changes over age, but also provides a possible way to quantitatively describe the typical and atypical developmental progression of human brain function using rs-fcMRI. Public Library of Science 2012-08-30 /pmc/articles/PMC3431403/ /pubmed/22952990 http://dx.doi.org/10.1371/journal.pone.0044530 Text en © 2012 Wang et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
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	Wang, Lubin Su, Longfei Shen, Hui Hu, Dewen
spellingShingle	Wang, Lubin Su, Longfei Shen, Hui Hu, Dewen Decoding Lifespan Changes of the Human Brain Using Resting-State Functional Connectivity MRI
author_facet	Wang, Lubin Su, Longfei Shen, Hui Hu, Dewen
author_sort	Wang, Lubin
title	Decoding Lifespan Changes of the Human Brain Using Resting-State Functional Connectivity MRI
title_short	Decoding Lifespan Changes of the Human Brain Using Resting-State Functional Connectivity MRI
title_full	Decoding Lifespan Changes of the Human Brain Using Resting-State Functional Connectivity MRI
title_fullStr	Decoding Lifespan Changes of the Human Brain Using Resting-State Functional Connectivity MRI
title_full_unstemmed	Decoding Lifespan Changes of the Human Brain Using Resting-State Functional Connectivity MRI
title_sort	decoding lifespan changes of the human brain using resting-state functional connectivity mri
description	The development of large-scale functional brain networks is a complex, lifelong process that can be investigated using resting-state functional connectivity MRI (rs-fcMRI). In this study, we aimed to decode the developmental dynamics of the whole-brain functional network in seven decades (8–79 years) of the human lifespan. We first used parametric curve fitting to examine linear and nonlinear age effect on the resting human brain, and then combined manifold learning and support vector machine methods to predict individuals' “brain ages” from rs-fcMRI data. We found that age-related changes in interregional functional connectivity exhibited spatially and temporally specific patterns. During brain development from childhood to senescence, functional connections tended to linearly increase in the emotion system and decrease in the sensorimotor system; while quadratic trajectories were observed in functional connections related to higher-order cognitive functions. The complex patterns of age effect on the whole-brain functional network could be effectively represented by a low-dimensional, nonlinear manifold embedded in the functional connectivity space, which uncovered the inherent structure of brain maturation and aging. Regression of manifold coordinates with age further showed that the manifold representation extracted sufficient information from rs-fcMRI data to make prediction about individual brains' functional development levels. Our study not only gives insights into the neural substrates that underlie behavioral and cognitive changes over age, but also provides a possible way to quantitatively describe the typical and atypical developmental progression of human brain function using rs-fcMRI.
publisher	Public Library of Science
publishDate	2012
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3431403/
_version_	1611553460742782976

Decoding Lifespan Changes of the Human Brain Using Resting-State Functional Connectivity MRI

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