Deep Brain Stimulation for Obsessive-Compulsive Disorder: Real World Experience Post-FDA-Humanitarian Use Device Approval

While case series have established the efficacy of deep brain stimulation (DBS) in treating obsessive-compulsive disorder (OCD), it has been our experience that few OCD patients present without comorbidities that affect outcomes associated with DBS treatment. Here we present our experience with DBS therapy for OCD in patients who all have comorbid disease, together with the results of our programming strategies. For this case series, we assessed five patients who underwent ventral capsule/ventral striatum (VC/VS) DBS for OCD between 2015 and 2019 at the University of Colorado Hospital. Every patient in this cohort exhibited comorbidities, including substance use disorders, eating disorder, tic disorder, and autism spectrum disorder. We conducted an IRB-approved, retrospective study of programming modifications and treatment response over the course of DBS therapy. In addition to patients\u27 subjective reports of improvement, we observed significant improvement in the Yale-Brown Obsessive-Compulsive Scale (44%), the Montgomery-Asberg Depression Rating Scale (53%), the Quality of Life Enjoyment and Satisfaction Questionnaire (27%), and the Hamilton Anxiety Rating scales (34.9%) following DBS. With respect to co-morbid disease, there was a significant improvement in a patient with tic disorder\u27s Total Tic Severity Score (TTSS) ( = 0.005). DBS remains an efficacious tool for the treatment of OCD, even in patients with significant comorbidities in whom DBS has not previously been investigated. Efficacious treatment results not only from the accurate placement of the electrodes by the surgeon but also from programming by the psychiatrist

Kernel machine tests of association between brain networks and phenotypes.

Applications of quantitative network analysis to functional brain connectivity have become popular in the last decade due to their ability to describe the general topological principles of brain networks. However, many issues arise when standard statistical analysis techniques are applied to functional magnetic resonance imaging (fMRI) connectivity maps. Frequently, summary measures of these maps, such as global efficiency and clustering coefficients, collapse the changing structures of graph topology from many scales to one. This can result in a loss of whole-brain spatio-temporal pattern information that may be significant in association and prediction analyses. Drawing from the electrical engineering field, the resistance perturbation distance is a quantification of similarity between graphs on the same vertex set that has been shown to identify changes in dynamic graphs, such as those from fMRI, while not being computationally expensive or result in a loss of information. This work proposes a novel kernel-based regression scheme that incorporates the resistance perturbation distance to better understand the association with biological phenotypes from fMRI using both simulated and real datasets