Classification subject age and gender breakdown.

Classification subject age and gender breakdown.</p

MRI and FDG PET fusion classification accuracies (%).

MRI and FDG PET fusion classification accuracies (%).</p

Convolutional neural network for one modality.

A single MRI or PET volume is taken as input, and the output is a binary diagnosis label of either “Healthy” or “AD”.</p

Pre-processing pipeline for a single subject.

A subject has N MRI scanning sessions and M PET scanning sessions; therefore, the pipeline yields N MRI images and M PET images. The pipeline is repeated for each subject in the dataset.</p

Convolutional neural network for fusing MRI and PET modalities.

An MRI and PET scan from a single patient is taken as input, and the output is again a binary diagnosis label.</p

MRI and amyloid PET fusion classification accuracies (%).

MRI and amyloid PET fusion classification accuracies (%).</p

Heatmap assessment of slope (<i>β</i>) differences between CTRL, MDD, and CD.

Brain volumes are labeled on the y-axis and keypress metrics on the x-axis. (A) 29 brain volumes and 15 keypress metrics were regressed without the inclusion of covariates to obtain β term values [range -1 (red) to +1 (blue)] for CTRL, MDD, and CD. (B) The difference between absolute β values are displayed on a scale of -1 (orange) to +1 (purple). (C) The absolute differences between β terms are given on a scale of 0 (orange) to 2 (purple). (D) Orange cells indicate that β term directionality differed between any two groups and purple indicates that β terms were in the same direction.</p

Structure-behavior regression results without covariate inclusion.

Structure-behavior regression results without covariate inclusion.</p

Assessment of brain volume differences between CTRL, MDD, and CD participants.

Assessment of brain volume differences between CTRL, MDD, and CD participants.</p

kNN results.

(A) Classification results for CTRL and MDD. (B) Classification results for CTRL and CD. (C) Classification results for MDD and CD. RPT refers to relative preference theory, the analytic framework used to derive the keypress metrics. *Refer to Table 3 for brain volume inclusion. #classified/n = the number of correctly classified participants out of the total number participants in a respective group; %classified = the percentage of correctly classified participants in a given group; #misclassified/n = the number of incorrectly classified participants out of the total number participants in a respective group; %classified = the percentage of incorrectly classified participants in a given group; N = the total sample size for a given kNN model.</p