title: Independent component analysis-based identification of covariance patterns of microstructural white matter damage in Alzheimer's disease.
abstract: The existing DTI studies have suggested that white matter damage constitutes an important part of the neurodegenerative changes in Alzheimer's disease (AD). The present study aimed to identify the regional covariance patterns of microstructural white matter changes associated with AD. In this study, we applied a multivariate analysis approach, independent component analysis (ICA), to identify covariance patterns of microstructural white matter damage based on fractional anisotropy (FA) skeletonised images from DTI data in 39 AD patients and 41 healthy controls (HCs) from the Alzheimer's Disease Neuroimaging Initiative database. The multivariate ICA decomposed the subject-dimension concatenated FA data into a mixing coefficient matrix and a source matrix. Twenty-eight independent components (ICs) were extracted, and a two sample t-test on each column of the corresponding mixing coefficient matrix revealed significant AD/HC differences in ICA weights for 7 ICs. The covariant FA changes primarily involved the bilateral corona radiata, the superior longitudinal fasciculus, the cingulum, the hippocampal commissure, and the corpus callosum in AD patients compared to HCs. Our findings identified covariant white matter damage associated with AD based on DTI in combination with multivariate ICA, potentially expanding our understanding of the neuropathological mechanisms of AD.
authors.affiliation: College of Information Science and Technology, Beijing Normal University, Beijing, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Ouyang
authors.affiliation: Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, Arizona, United States of America.
authors.forename: Kewei
authors.initials: K
authors.lastname: Chen
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: 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: 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: 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
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000473:pathology*; D016880:Anisotropy; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D066127:White Matter / Q000473:pathology*