Aging influence on grey matter structural associations within the default mode network utilizing Bayesian network modeling
Recent neuroimaging studies have revealed normal aging-related alterations in functional and structural brain networks such as the default mode network (DMN). However, less is understood about specific brain structural dependencies or interactions between brain regions within the DMN in the normal a...
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doaj-3883461cda6e418bb8f05c03d6e1d2b02020-11-25T01:01:37ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652014-05-01610.3389/fnagi.2014.0010569407Aging influence on grey matter structural associations within the default mode network utilizing Bayesian network modelingYan eWang0Kewei eChen1Jiacai eZhang2Li eYao3Li eYao4Ke eLi5Zhen eJin6Qing eYe7Xiaojuan eGuo8College of Information Science and Technology, Beijing Normal UniversityBanner Alzheimer’s Institute and Banner Good Samaritan PET CenterCollege of Information Science and Technology, Beijing Normal UniversityCollege of Information Science and Technology, Beijing Normal UniversityState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal UniversityLaboratory of Magnetic Resonance Imaging, Beijing 306 HospitalLaboratory of Magnetic Resonance Imaging, Beijing 306 HospitalCollege of Information Science and Technology, Beijing Normal UniversityCollege of Information Science and Technology, Beijing Normal UniversityRecent neuroimaging studies have revealed normal aging-related alterations in functional and structural brain networks such as the default mode network (DMN). However, less is understood about specific brain structural dependencies or interactions between brain regions within the DMN in the normal aging process. In this study, using Bayesian network (BN) modeling, we analyzed grey matter volume data from 109 young and 82 old subjects to characterize the influence of aging on associations between core brain regions within the DMN. Furthermore, we investigated the discriminability of the aging-associated BN models for the young and old groups. Compared to their young counterparts, the old subjects showed significant reductions in connections from right inferior temporal cortex (ITC) to medial prefrontal cortex (mPFC), right hippocampus (HP) to right ITC, and mPFC to posterior cingulate cortex (PCC) and increases in connections from left HP to mPFC and right inferior parietal cortex (IPC) to right ITC. Moreover, the classification results showed that the aging-related BN models could predict group membership with 88.48% accuracy, 88.07% sensitivity and 89.02% specificity. Our findings suggest that structural associations within the DMN may be affected by normal aging and provide crucial information about aging effects on brain structural networks.http://journal.frontiersin.org/Journal/10.3389/fnagi.2014.00105/fullDefault Mode NetworkGrey Matternormal agingBayesian network modelingStructural associations |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yan eWang Kewei eChen Jiacai eZhang Li eYao Li eYao Ke eLi Zhen eJin Qing eYe Xiaojuan eGuo |
spellingShingle |
Yan eWang Kewei eChen Jiacai eZhang Li eYao Li eYao Ke eLi Zhen eJin Qing eYe Xiaojuan eGuo Aging influence on grey matter structural associations within the default mode network utilizing Bayesian network modeling Frontiers in Aging Neuroscience Default Mode Network Grey Matter normal aging Bayesian network modeling Structural associations |
author_facet |
Yan eWang Kewei eChen Jiacai eZhang Li eYao Li eYao Ke eLi Zhen eJin Qing eYe Xiaojuan eGuo |
author_sort |
Yan eWang |
title |
Aging influence on grey matter structural associations within the default mode network utilizing Bayesian network modeling |
title_short |
Aging influence on grey matter structural associations within the default mode network utilizing Bayesian network modeling |
title_full |
Aging influence on grey matter structural associations within the default mode network utilizing Bayesian network modeling |
title_fullStr |
Aging influence on grey matter structural associations within the default mode network utilizing Bayesian network modeling |
title_full_unstemmed |
Aging influence on grey matter structural associations within the default mode network utilizing Bayesian network modeling |
title_sort |
aging influence on grey matter structural associations within the default mode network utilizing bayesian network modeling |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Aging Neuroscience |
issn |
1663-4365 |
publishDate |
2014-05-01 |
description |
Recent neuroimaging studies have revealed normal aging-related alterations in functional and structural brain networks such as the default mode network (DMN). However, less is understood about specific brain structural dependencies or interactions between brain regions within the DMN in the normal aging process. In this study, using Bayesian network (BN) modeling, we analyzed grey matter volume data from 109 young and 82 old subjects to characterize the influence of aging on associations between core brain regions within the DMN. Furthermore, we investigated the discriminability of the aging-associated BN models for the young and old groups. Compared to their young counterparts, the old subjects showed significant reductions in connections from right inferior temporal cortex (ITC) to medial prefrontal cortex (mPFC), right hippocampus (HP) to right ITC, and mPFC to posterior cingulate cortex (PCC) and increases in connections from left HP to mPFC and right inferior parietal cortex (IPC) to right ITC. Moreover, the classification results showed that the aging-related BN models could predict group membership with 88.48% accuracy, 88.07% sensitivity and 89.02% specificity. Our findings suggest that structural associations within the DMN may be affected by normal aging and provide crucial information about aging effects on brain structural networks. |
topic |
Default Mode Network Grey Matter normal aging Bayesian network modeling Structural associations |
url |
http://journal.frontiersin.org/Journal/10.3389/fnagi.2014.00105/full |
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