# Adaptive DBS in Non-Motor Neuropsychiatric Disorders: Regulating Limbic Circuit Imbalance

> **NIH NIH UH3** · BAYLOR COLLEGE OF MEDICINE · 2020 · $1,507,081

## Abstract

In patients with intractable Obsessive-Compulsive Disorder (OCD), ventral striatum (VS) deep brain stimulation
(DBS) effectively reduces symptom severity in about 60% of cases. However, there is room for improvement in
both clinical benefits and reduction of DBS-induced behavioral side effects, especially hypomania. A critical
factor may be failure to adaptively adjust DBS in response to phasic changes in negatively and positively
valenced states (i.e., OCD-related distress and hypomania, respectively). New generation adaptive DBS
(aDBS) systems can record, stimulate and use signals from the brain to make responsive adjustments to the
patient's behavioral state. Specific Aim 1 is to train the device to accurately classify acute fluctuations in OCD-
related distress and emergence of hypomania and distinguish these states from other affective states that do
not require adjustments in stimulation. Specific Aim 2 is to develop adaptive control policies that can
automatically adjust stimulation parameters to regulate these undesired states. It is hypothesized that
exacerbations in OCD-related distress will require increased stimulation (higher amplitude or wider pulse width)
whereas hypomania will respond to decreased stimulation. These aims will be executed using a two-phase
Early Feasibility Study of aDBS in 10 adults with intractable OCD. Subjects will enter a 6-month trial of open-
label bilateral aDBS followed by 2 months of adjunctive cognitive behavioral therapy (CBT). Subsequently, they
will enter a 4-week blinded discontinuation period to assess need for ongoing DBS. In Phase I, 5 subjects will
have surgery as per procedures of the FDA Human Device Exemption (HDE) approval for VS DBS in OCD.
Electrode implantation will be optimized and personalized using “precision mapping” of each patient's
anatomical connectivity from high-field tractography in native space. DBS programming sessions will also
serve to train the algorithms to classify different valence states. For example, a symptom provocation paradigm
will elicit different levels of manageable OCD-related distress. During this paradigm multiple streams of time-
locked physiological and behavioral data will be captured to build a classifier: Local Field Potentials (LFPs)
from the VS, Scalp EEG, and Automated Facial Affect Recognition (AFAR), which objectively measures
emotional valence. We hypothesize that classifiers using combined LFP/EEG data will perform better than VS
LFPs alone, but that direct cortical recordings will be needed for accurate classification and creation of a fully
embedded, self-contained, aDBS system. In Phase II, 5 subjects will have VS DBS surgery along with bilateral
subdural placement of electrocorticographic (ECoG) recording leads at a prefrontal target, informed by resting
state functional MRI from Phase I and pre-operative scans. New classifiers will be built based on VS and
ECoG LFPs and adaptive stimulation algorithms tested in the clinic before transfer to t...

## Key facts

- **NIH application ID:** 9769905
- **Project number:** 5UH3NS100549-03
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Wayne K Goodman
- **Activity code:** UH3 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,507,081
- **Award type:** 5
- **Project period:** 2016-09-30 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9769905

## Citation

> US National Institutes of Health, RePORTER application 9769905, Adaptive DBS in Non-Motor Neuropsychiatric Disorders: Regulating Limbic Circuit Imbalance (5UH3NS100549-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9769905. Licensed CC0.

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