Publication Date
1-1-2024
Journal
IEEE Transactions on Affective Computing
DOI
10.1109/taffc.2024.3395117
PMID
39687201
PMCID
PMC11649003
PubMedCentral® Posted Date
12-16-2024
PubMedCentral® Full Text Version
Author MSS
Published Open-Access
yes
Keywords
Obsessive-compulsive disorder (OCD), depression, deep brain stimulation (DBS), mixed-effects, multimodal machine learning, shapley feature reduction
Abstract
To develop reliable, valid, and efficient measures of obsessive-compulsive disorder (OCD) severity, comorbid depression severity, and total electrical energy delivered (TEED) by deep brain stimulation (DBS), we trained and compared random forests regression models in a clinical trial of participants receiving DBS for refractory OCD. Six participants were recorded during open-ended interviews at pre- and post-surgery baselines and then at 3-month intervals following DBS activation. Ground-truth severity was assessed by clinical interview and self-report. Visual and auditory modalities included facial action units, head and facial landmarks, speech behavior and content, and voice acoustics. Mixed-effects random forest regression with Shapley feature reduction strongly predicted severity of OCD, comorbid depression, and total electrical energy delivered by the DBS electrodes (intraclass correlation, ICC, = 0.83, 0.87, and 0.81, respectively. When random effects were omitted from the regression, predictive power decreased to moderate for severity of OCD and comorbid depression and remained comparable for total electrical energy delivered (ICC = 0.60, 0.68, and 0.83, respectively). Multimodal measures of behavior outperformed ones from single modalities. Feature selection achieved large decreases in features and corresponding increases in prediction. The approach could contribute to closed-loop DBS that would automatically titrate DBS based on affect measures.