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Multimodal Prediction of Obsessive-Compulsive Disorder and Comorbid Depression Severity and Energy Delivered by Deep Brain Electrodes

  • 0Department of Psychology, University of Pittsburgh, Pittsburgh, PA 15213 USA.
IEEE Transactions on Affective Computing +

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December 17, 2024

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December 17, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Researchers developed a novel method using random forests to accurately predict obsessive-compulsive disorder (OCD) and depression severity, as well as deep brain stimulation (DBS) energy delivery, from patient interviews.

Area Of Science

  • Neuroscience
  • Computational Psychiatry
  • Medical Technology

Background

  • Developing objective measures for obsessive-compulsive disorder (OCD) severity and comorbid depression is crucial for treatment.
  • Deep brain stimulation (DBS) is an emerging treatment for refractory OCD, requiring precise energy delivery.
  • Current assessment methods for OCD and depression severity can be subjective and time-consuming.

Purpose Of The Study

  • To create reliable, valid, and efficient measures for OCD severity, comorbid depression severity, and total electrical energy delivered (TEED) by DBS.
  • To evaluate the efficacy of random forests regression models in predicting these outcomes.
  • To explore the potential for automated, closed-loop DBS titration based on behavioral measures.

Main Methods

  • Trained and compared random forests regression models on data from six participants undergoing DBS for refractory OCD.
  • Collected multimodal behavioral data (visual, auditory) during open-ended interviews at various time points.
  • Utilized mixed-effects random forest regression with Shapley feature reduction for prediction and feature selection.

Main Results

  • Mixed-effects random forest regression strongly predicted OCD severity (ICC=0.83), comorbid depression (ICC=0.87), and TEED (ICC=0.81).
  • Multimodal behavioral measures significantly outperformed single-modality measures.
  • Feature selection substantially reduced the number of features while improving prediction accuracy.

Conclusions

  • Random forests regression models can reliably and validly predict OCD severity, depression severity, and DBS energy delivery from behavioral interview data.
  • This multimodal approach offers a promising avenue for developing automated, closed-loop DBS systems.
  • The findings support the integration of computational methods for objective clinical assessment and treatment optimization in neuropsychiatry.

Keywords:

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