Confusion matrices for each diagnostic decision (classifiers I–IV in panels A-D respectively).

<p>Numbers in each cell describe the total number of predictions.</p

Sensitivity (Sens) and predictive value (PV) for each class within each diagnostic classifier based on the subcortical motor network features (classifiers I–IV in panels A-D respectively).

<p>Bars denote the chance levels determined by the proportion of samples in the training set. * = p < 0.01, # = p < 0.05 + = p < 0.1.</p

Example confusion matrix for an m-class classification problem.

<p>C_i,j denotes the number of predictions in row i, column j. The sensitivity and predictive value measure the performance of each class. The accuracy and overall predictive value are constructed by averaging the sensitivity and predictive value over all classes. Note that the accuracy and overall predictive value are balanced in that they avoid potential bias arising from variable numbers of samples in each class.</p

Sensitivity (Sens) and predictive value (PV) for each region in the subcortical motor network for the four-class classifier contrasting PSP, IPD, HC and MSA (Classifier II).

<p>A: cerebellum; B: brainstem; C: caudate; D: putamen; E: pallidum; F: accumbens. Bars denote the chance levels determined by the proportion of samples in the training set. * = p < 0.01, # = p < 0.05 + = p < 0.1.</p