%0 Journal Article
%T Identifying Brain Connectivity Using Network
%A Jae-Hong Lee
%A Ji Eun Kim
%A Joon-Kyung Seong
%A Minsuk Choi
%A Sung-Woo Kim
%J American Journal of Alzheimer's Disease & Other Dementias£¿
%@ 1938-2731
%D 2019
%R 10.1177/1533317518813556
%X The aim of this study was to identify white matter structural networks of amnestic mild cognitive impairment (aMCI) dichotomized by ¦Â amyloid (A¦Â) status and compare them using network-based statistics (NBS). Patients underwent whole-brain diffusion-weighted magnetic resonance imaging, detailed neuropsychological test and [18F]-Florbetaben amyloid positron emission tomography. We performed the NBS analysis to compare the whole-brain white matter structural networks extracted from diffusion tensor images. One hundred sixteen participants (A¦Â£¿ cognitively normal [CN], n = 35; A¦Â£¿ aMCI, n = 42; A¦Â+ aMCI, n = 39) were included. There was no subnetwork showing significant difference between A¦Â+ aMCI and A¦Â£¿ aMCI. However, by comparing each aMCI group with control group, we found that supplementary motor areas were common hub regions. Intriguingly, A¦Â+ aMCI showed reduced connectivity mainly in the medial frontal regions, while A¦Â£¿ aMCI showed somewhat uniform disruption when compared to CN. Structural network analysis using network-based approach in aMCI may shed light on further understanding of white matter disruption in the prodromal stage of Alzheimer¡¯s disease
%K Alzheimer¡¯s disease
%K mild cognitive impairment
%K ¦Â amyloid peptide
%K neural network
%U https://journals.sagepub.com/doi/full/10.1177/1533317518813556