Figure 4. Discriminating power of different feature sets.

Sensitivity (blue) and specificity (yellow) for gray matter density and white matter streamlines (left), network statistics (middle), and majority vote (right). (A) Asterisk denotes that local efficiency and the majority vote perform significantly better than gray matter and white matter streamline classifiers. Observe that both sensitivity and specificity demonstrate approximately chance performance when only regional gray matter density is considered. (B) The spatial distribution of weights for the local efficiency classifier. In this classifier, the left middle temporal gyrus, bilateral insula, and right lateral temporal-parietal-occipital regions contribute the highest weight. Importantly, this weight is assigned in the context of the entire support vector machine (SVM) classifier; thus, the weights are only meaningful in the context of all regions contributing to the classifier. Increasing red represents increasing absolute values of normalized weight in the classifier on a (0:1) interval.