Demographic features.

<p>Remission was defined as a final HRSD≤7 after 8 weeks of treatment with fluoxetine or 16 weeks of treatment with CBT, and Non-remission was a final HRSD>7; HRSD: Hamilton Rating Scale for Depression; CBT: cognitive behavioural therapy.</p

Sagittal cross-sectional view of regions pertinent for diagnosis and prediction of treatment response in depression.

<p>In the top panel, sagittal views are presented which show medial regions of decreased grey matter density which contributed to the diagnosis of depression (coloured in green) in the right subgenual anterior cingulate (BA 25) and precuneus (BA 7). No regions of increased grey matter in patients with depression relative to healthy individuals contributed to the diagnosis. In the lower panel, increased grey matter density in the anterior and posterior cingulate cortices (red) increased the probability of clinical remission to treatment with the antidepressant medication fluoxetine. Greater density in the orbitofrontal cortex (blue) increased the odds of residual symptoms of depression following antidepressant medication. Regions depicted were selected as relevant to the classification of patients as achieving remission or non-remission clinical status following fluoxetine treatment by every cross-validated support vector machine classification model. Sagittal views are presented in MNI space at z = −4, 10, 12 and 14.</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

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