title: Characterizing structural association alterations within brain networks in normal aging using Gaussian Bayesian networks.
abstract: Recent multivariate neuroimaging studies have revealed aging-related alterations in brain structural networks. However, the sensory/motor networks such as the auditory, visual and motor networks, have obtained much less attention in normal aging research. In this study, we used Gaussian Bayesian networks (BN), an approach investigating possible inter-regional directed relationship, to characterize aging effects on structural associations between core brain regions within each of these structural sensory/motor networks using volumetric MRI data. We then further examined the discriminability of BN models for the young (N = 109; mean age =22.73 years, range 20-28) and old (N = 82; mean age =74.37 years, range 60-90) groups. The results of the BN modeling demonstrated that structural associations exist between two homotopic brain regions from the left and right hemispheres in each of the three networks. In particular, compared with the young group, the old group had significant connection reductions in each of the three networks and lesser connection numbers in the visual network. Moreover, it was found that the aging-related BN models could distinguish the young and old individuals with 90.05, 73.82, and 88.48% accuracy for the auditory, visual, and motor networks, respectively. Our findings suggest that BN models can be used to investigate the normal aging process with reliable statistical power. Moreover, these differences in structural inter-regional interactions may help elucidate the neuronal mechanism of anatomical changes in normal aging.
authors.affiliation: Information Processing Lab, 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
authors.affiliation: Information Processing Lab, College of Information Science and Technology, Beijing Normal University Beijing, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Computational Image Analysis Lab, Banner Alzheimer's Institute, Banner Health Phoenix, AZ, USA.
authors.forename: Kewei
authors.initials: K
authors.lastname: Chen
authors.affiliation: Information Processing Lab, 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: Xia
authors.initials: X
authors.lastname: Wu
authors.affiliation: Information Processing Lab, College of Information Science and Technology, Beijing Normal University Beijing, China.
authors.forename: Jiacai
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Laboratory of Magnetic Resonance Imaging, Beijing 306 Hospital Beijing, China.
authors.forename: Ke
authors.initials: K
authors.lastname: Li
authors.affiliation: Laboratory of Magnetic Resonance Imaging, Beijing 306 Hospital Beijing, China.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Jin
authors.affiliation: Information Processing Lab, 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
keywords: Bayesian networks; aging; gray matter volume; structural association; structural networks