Combining regional metrics for disease-related brain population analysis

In this paper, we present a new metric combining regional measurements to improve image based population studies that use manifold learning techniques. These studies currently rely on a single score over the whole brain image domain. Thus, they require large amount of training data to uncover spatially complex variation in the whole brain impacted by diseases. We reduce the impact of this issue by first computing pairwise measurements in local regions separately and then combining regional measurements into a single pairwise metric. We apply the new metric to learn the manifold of ADNI data and evaluate the resulting morphological representation by fitting multiple linear regression models to the mini-mental state examination (MMSE) score. The regression models show that the morphological representations from the proposed metric achieves higher estimation accuracy of MMSE score compared to those from the conventional global scores.