Within-group decoding accuracy in Healthy Controls (HC) and Depressed Patients (DP).

*<p>Overall Accuracy is the mean between emotional accuracy (emotional correctly classified as emotional) and neutral accuracy (neutral correctly classified as neutral).</p

Demographic Features and Severity of Depression.

*<p>HRSD-25∶25-item Hamilton Rating Scale for Depression.</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

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

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

Illustration of a pattern regression model with hypothetical 2D data.

<p>The numbers in the squares correspond to activation levels in different brain voxels. Each brain scan corresponds to a point in a 2D voxel space. The goal is to “train” a predictive function that given a new brain scan can accurately decode the correspondent clinical score.</p

Maps for decoding PGBI-10M at screen based on patterns of activation to win blocks using a leave-one-out cross-validation framework.

<p>A: Voxel-based predictive pattern. The colour bar indicates the weight of the voxels for decoding the clinical score. B: Region-based pattern localization map computed from the voxel based predictive pattern displayed in Fig 2A. The colour bar indicates the percentage of the total normalized weights that each anatomically labelled region explains.</p