Automated Detection of Optimal DBS Device Settings

Ding, Yaohan; Ertuğrul, Itır Önal; Darzi, Ali; Provenza, Nicole R.; Jeni, László A.; Borton, David A.; Goodman, Wayne K.; Cohn, Jeffrey F.

doi:10.1145/3395035.3425354

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…Preliminary evidence suggests that facial AUs and head dynamics may differentiate between different levels of DBS stimulation [45] and predict OCD severity [46].…”

Section: Visual Featuresmentioning

confidence: 99%

Multimodal prediction of obsessive-compulsive disorder, comorbid depression, and energy of deep brain stimulation

Hinduja,

Darzi,

Ertugrul

et al. 2024

Preprint

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To develop reliable, valid, and efficient measures of obsessive-compulsive disorder (OCD) severity, comorbid depressionseverity, and total electrical energy delivered (TEED) by deep brain stimulation (DBS), we trained and compared random forestsregression models in a clinical trial of participants receiving DBS for refractory OCD. Six participants were recorded during open-endedinterviews at pre- and post-surgery baselines and then at 3-month intervals following DBS activation. Ground-truth severity wasassessed 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 stronglypredicted 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 tomoderate 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 achievedlarge decreases in features and corresponding increases in prediction. The approach could contribute to closed-loop DBS that wouldautomatically titrate DBS based on affect measures

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“…Preliminary evidence suggests that facial AUs and head dynamics may differentiate between different levels of DBS stimulation [45] and predict OCD severity [46].…”

Section: Visual Featuresmentioning

confidence: 99%

Multimodal prediction of obsessive-compulsive disorder, comorbid depression, and energy of deep brain stimulation

Hinduja,

Darzi,

Ertugrul

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Velocity of automatically detected action units and head motion has been strongly related to emotional distress, depression, mania, and autism spectrum disorder [4], [25], [36], [37], [38], [39], [40]. Preliminary evidence suggests that facial AUs and head dynamics may differentiate between different levels of DBS stimulation [41] and predict OCD severity [42].…”

Section: Visual Featuresmentioning

confidence: 99%

Multimodal prediction of obsessive-compulsive disorder, comorbid depression, and energy of deep brain stimulation

Hinduja,

Darzi,

Ertugrul

et al. 2024

Preprint

View full text Add to dashboard Cite

To develop reliable, valid, and efficient measures of severity of OCD and comorbid depression and electrical 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.

show abstract

Long-term ecological assessment of intracranial electrophysiology synchronized to behavioral markers in obsessive-compulsive disorder

Provenza

Sheth

Rijn

et al. 2021

Nat Med

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Automated Detection of Optimal DBS Device Settings

Cited by 5 publications

References 11 publications

Multimodal prediction of obsessive-compulsive disorder, comorbid depression, and energy of deep brain stimulation

Multimodal prediction of obsessive-compulsive disorder, comorbid depression, and energy of deep brain stimulation

Multimodal prediction of obsessive-compulsive disorder, comorbid depression, and energy of deep brain stimulation

Long-term ecological assessment of intracranial electrophysiology synchronized to behavioral markers in obsessive-compulsive disorder

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