Figure 1.

ANOVA Feature Selection and SVM Classification

(A) Individuals within the training set (twin A for twin groups or scan 1 for repeat-scan individuals) provided 5-min resting state time courses, extracted from each of the 255 ROIs.

(B) Time courses were then split into 20-s segments, and a correlation matrix was created for each segment. All correlation matrices were then converted into vectors of unique, off-diagonal correlation values.

(C) These vectors were then used for ANOVA feature selection to create the feature mask used by the classifier.

(D) The resulting feature mask is then applied to each full 5-min resting state time course, and the SVM classifier is trained on the same training set used for feature selection and tested on the held-out testing set (either the individual's second scan or co-twin data).