  In this paper, we studied the association between the change of structural
brain volumes to the potential development of Alzheimer's disease (AD). Using a
simple abstraction technique, we converted regional cortical and subcortical
volume differences over two time points for each study subject into a graph. We
then obtained substructures of interest using a graph decomposition algorithm
in order to extract pivotal nodes via multi-view feature selection. Intensive
experiments using robust classification frameworks were conducted to evaluate
the performance of using the brain substructures obtained under different
thresholds. The results indicated that compact substructures acquired by
examining the differences between patient groups were sufficient to
discriminate between AD and healthy controls with an area under the receiver
operating curve of 0.72.
