Figure 4. Machine-learning algorithm.

Initially, the connectivity matrices of all 70 PD subjects were vectorized, and entered into the 70 leave-one-out cross-validation (LOOCV) iterations. For each iteration, one subject was defined as the test set, whereas the remaining 69 subjects made up the training set. Each training set was then fed into the 10 repeats of the feature selection procedure, randomized logistic regression (RLR). In each repeat, the features selected were used in a nested LOOCV loop (69 iterations) to tune the support vector machine (SVM) C parameter, and then to train a linear SVM on the training set. The resulting classifier model was then used to classify the corresponding test subject.