Applying spatial covariance modeling on cortical thickness measurement

In neuroimaging studies of human cognitive abilities, in vivo MRI-derived measurements of human cerebral cortex thickness are of particular interest, but those data typically use univariate analyses that do not explicitly test the interrelationship among brain regions. Among the existing spatial covariance models, Scaled Subprofile Model (SSM) has been highly successful, particularly in capturing sources of between and within group variance, identifying group differences in regional network. Current article describes feasibility of applying spatial covariance models on cortical thickness map, followed by an application to discriminate normal control group and Alzheimer disease group. The results showed that SSM can not only capture patterns of difference between groups but also summarize the degree to which individual subjects express anatomical topography.