Application of the scaled subprofile model to functional imaging in neuropsychiatric disorders: A principal component approach to modeling brain function in disease

Recent advances in functional neuroimaging have presented a challenge to traditional statistical methods in characterizing the effects of neuropsychiatric illness on brain function. The most common approach for analyzing regional group differences has relied on t‐tests with significance thresholds selected to reduce the potential effect of multiple statistical tests. Regional covariance analysis offers an alternative to this threshold‐based, group difference approach by identifying the functional interactions among brain regions that can be spatially distributed throughout the brain. The Scaled Subprofile Model (SSM) is one form of regional covariance analysis that has been applied to the study of patient groups. Based on a modified principal component analysis, the SSM offers a method for modeling regionally specific patterns of brain function whose expression can be evaluated between groups and validated against clinical measures of patient disease severity and neuropsychological test scores. We review the application of the SSM, to date, in studies of the effects of neurological and psychiatric illness on brain function, including a discussion of SSM methodology and its application to the study of resting state functional neuroimaging in patient groups. SSM analyses applied to studies of Alzheimer's disease, Parkinson's disease, major depressive disorder, AIDS dementia complex, and neoplastic disease each identified functionally specific topographic effects that were associated with clinical disease severity. The results of the SSM analyses suggest that neuropsychiatric disorders may alter functional networks or systems of neural activity in ways that can be expressed as regional covariance patterns in resting functional imaging data. © 1994 Wiley‐Liss, Inc.