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Bayesian Analysis of Morphological Changes Associated with Mild Cognitive Impairment

By Hanchuan Peng, Resnick Susan, Dinggang Shen, Christos Davatzikos and Edward Herskovits


A new method, referred to as Bayesian Morphometry Algorithm (BMA), is presented, for morphology-function analysis of population-based medical imaging studies. In this paper, we apply BMA to a set of cross-sectional magnetic resonance images of subjects in the Baltimore Longitudinal Study of Aging, some of whom have very mild cognitive impairment, to demonstrate the algorithm's utility for determining morphological changes associated with clinical variables

Year: 2002
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