BRAIN PATTERN ANALYSIS OF CORTICAL VALUED DISTRIBUTIONS

Abstract

We introduce a new representation of cortical regions via distribution functions of their features. The distribution functions are estimated non-parametrically from the data and are observed to be non Gaussian. Cortical pattern matching is enabled by using the information-based Jensen-Shannon divergence as a measure between features. Our approach explicitly avoids pairwise registrations between brains, but instead focuses on modeling and discriminating between the cortical structural patterns. We demonstrate our approach on 120 subject brains from an Alzheimer's dataset, and present applications to clustering, classification, and dimension reduction.

Fig. 1

Distribution of features on a parcellated cortex. The distribution of the cortical measure (thickness in this case) is shown by histograms. (Best viewed in color).

Fig. 2

Distribution functions of cortical thickness for 34 regions.

Fig. 3

Dendrogram of the pairwise Jensen-Shannon divergence between thickness patterns for subjects of the ADNI dataset.

Fig. 4

Dendrogram of the pairwise Euclidean distances between thickness patterns of subjects of the ADNI dataset.

Fig. 5

Scatter plot of multidimensional scaling applied to the pairwise dissimilarity matrix. (Best viewed in color).