PurposeTo identify regional network covariance patterns of gray matter associated with Alzheimer's disease (AD) and to further evaluate its replicability and stability. Materials and MethodsThis 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. ResultsThe 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). ConclusionAD 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. J. Magn. Reson. Imaging 2014;39:143-149. (c) 2013 Wiley Periodicals, Inc.