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Can an Unobtrusive, Multimodal Mixed-Effects Regressor Based on Open-Ended Interviews Predict OCD Severity?
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  • Saurabh Hinduja,
  • Ali Darzi,
  • Itir Onal Ertugrul,
  • Nicole Provenza,
  • Ron Gadot,
  • Eric Storch,
  • Sameer Sheth,
  • Wayne Goodman,
  • Jeffrey Cohn
Saurabh Hinduja
University of Pittsburgh
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Ali Darzi
University of Pittsburgh
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Itir Onal Ertugrul
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Nicole Provenza
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Eric Storch
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Sameer Sheth
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Wayne Goodman
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Jeffrey Cohn
University of Pittsburgh

Corresponding Author:[email protected]

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Abstract

Reliable, valid, efficient measurement of symptom severity in internalizing disorders is critical to gauge treatment response. Self-report and clinical interview are subjective and difficult to standardize, impose patient burden, and lack granularity. We tested the hypothesis that comprehensive sampling of audio and visual modalities during open-ended interviews can reveal severity of obsessive-compulsive disorder (OCD) and comorbid depression. Participants were six patients with chronic, refractory OCD that were treated with deep brain stimulation (DBS). They were recorded during open-ended interviews at pre- and post-surgery baselines and at 3-month intervals following activation of the DBS. 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. Using mixed-effects random forest regression with Shapley feature reduction strongly predicted severity of OCD, severity of comorbid depression, and total electrical energy delivered by the DBS electrodes (ICC = 0.83, 0.87, and 0.81, respectively). Multimodal measures of behavior outperformed ones from single modalities. The approach could contribute to closed-loop DBS that would automatically titrate DBS based on affect measures.
23 Jan 2024Submitted to TechRxiv
26 Jan 2024Published in TechRxiv