Analyzing non-verbal behavior throughout recovery in a sample of depressed patients receiving deep brain stimulation

McCall, Micaela V; Riva‐Posse, Patricio; Garlow, Steven J.; Mayberg, Helen S.; Crowell, Andrea

doi:10.1016/j.npbr.2020.05.002

Cited by 8 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Extensive evidence in psychology and affective computing supports the view that affective communication is multimodal [2], [26], [27], [28], [29]. We briefly review literature relevant to both unimodal and multimodal communication of emotion and emotion disorders (aka internalizing disorders) such as depression, anxiety and OCD.…”

Section: Multimodal Measures Of Affectmentioning

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 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

show abstract

Section: Multimodal Measures Of Affectmentioning

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

“…Extensive evidence in psychology and affective computing supports the view that affective communication is multimodal [2], [22], [23], [24], [25]. We briefly review literature relevant to both unimodal and multimodal communication of emotion and emotion disorders (aka internalizing disorders) such as depression, anxiety and OCD.…”

Section: Multimodal Measures Of Affectmentioning

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

Deep Brain Stimulation for Depression

et al. 2022

View full text Add to dashboard Cite

Analyzing non-verbal behavior throughout recovery in a sample of depressed patients receiving deep brain stimulation

Cited by 8 publications

References 36 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

Deep Brain Stimulation for Depression

Contact Info

Product

Resources

About