Xiaojuan Guo1, Kewei Chen, Yumei Zhang, Yan Wang, Li Yao. 1. College of Information Science and Technology, Beijing Normal University, Beijing, China; National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Abstract
PURPOSE: To identify regional network covariance patterns of gray matter associated with Alzheimer's disease (AD) and to further evaluate its replicability and stability. MATERIALS AND METHODS: This study applied a multivariate analytic approach based on scaled subprofile modeling (SSM) to structural magnetic resonance imaging (MRI) data from 19 patients with AD and 19 healthy controls (HC). We further applied the derived covariance patterns to examine the replicability and stability of AD-associated covariance patterns in an independent dataset (13 AD and 14 HC) acquired with a different scanner. RESULTS: The AD-associated covariance patterns identified from SSM combined principal components mainly involved the temporal lobe and parietal lobe. The expression of covariance patterns was significantly higher in AD patients than HC (t(36) = 5.84, P = 5.75E-7) and predicted the AD/HC group membership (84% sensitivity and 90% specificity). In replicability evaluation, the expression of the forward applied covariance patterns was still statistically significant and had acceptable discriminability (69% sensitivity and 71% specificity). CONCLUSION: AD patients showed regional gray matter alterations in a reliable covariance manner. The results suggest that SSM has utility for characterizing covariant features, and therefore can assist with further understanding covariance patterns of gray matter in AD based on the view of the network.
PURPOSE: To identify regional network covariance patterns of gray matter associated with Alzheimer's disease (AD) and to further evaluate its replicability and stability. MATERIALS AND METHODS: This study applied a multivariate analytic approach based on scaled subprofile modeling (SSM) to structural magnetic resonance imaging (MRI) data from 19 patients with AD and 19 healthy controls (HC). We further applied the derived covariance patterns to examine the replicability and stability of AD-associated covariance patterns in an independent dataset (13 AD and 14 HC) acquired with a different scanner. RESULTS: The AD-associated covariance patterns identified from SSM combined principal components mainly involved the temporal lobe and parietal lobe. The expression of covariance patterns was significantly higher in ADpatients than HC (t(36) = 5.84, P = 5.75E-7) and predicted the AD/HC group membership (84% sensitivity and 90% specificity). In replicability evaluation, the expression of the forward applied covariance patterns was still statistically significant and had acceptable discriminability (69% sensitivity and 71% specificity). CONCLUSION:ADpatients showed regional gray matter alterations in a reliable covariance manner. The results suggest that SSM has utility for characterizing covariant features, and therefore can assist with further understanding covariance patterns of gray matter in AD based on the view of the network.
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