title: Aging Influence on Gray Matter Structural Associations within the Default Mode Network Utilizing Bayesian Network Modeling.
abstract: 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 gray 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 and increases in connections from left HP to mPFC and right inferior parietal cortex 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.
authors.affiliation: College of Information Science and Technology, Beijing Normal University , Beijing , China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Banner Alzheimer's Institute and Banner Good Samaritan PET Center , Phoenix, AZ , USA.
authors.forename: Kewei
authors.initials: K
authors.lastname: Chen
authors.affiliation: College of Information Science and Technology, Beijing Normal University , Beijing , China.
authors.forename: Jiacai
authors.initials: J
authors.lastname: Zhang
authors.affiliation: College of Information Science and Technology, Beijing Normal University , Beijing , China ; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University , Beijing , China.
authors.forename: Li
authors.initials: L
authors.lastname: Yao
authors.affiliation: Laboratory of Magnetic Resonance Imaging, The 306th Hospital of People's Liberation Army , Beijing , China.
authors.forename: Ke
authors.initials: K
authors.lastname: Li
authors.affiliation: Laboratory of Magnetic Resonance Imaging, The 306th Hospital of People's Liberation Army , Beijing , China.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Jin
authors.affiliation: College of Information Science and Technology, Beijing Normal University , Beijing , China.
authors.forename: Qing
authors.initials: Q
authors.lastname: Ye
authors.affiliation: College of Information Science and Technology, Beijing Normal University , Beijing , China ; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University , Beijing , China.
authors.forename: Xiaojuan
authors.initials: X
authors.lastname: Guo
keywords: Bayesian network modeling; default mode network; gray matter; normal aging; structural associations