Bayesian methods for optimizing deep brain stimulation to enhance cognitive control

Objective Deep brain stimulation (DBS) of the ventral internal capsule/striatum (VCVS) is a potentially effective treatment for several mental health disorders when conventional therapeutics fail. Its effectiveness, however, depends on correct programming to engage VCVS sub-circuits. VCVS programming is currently an iterative, time-consuming process, with weeks between setting changes and reliance on noisy, subjective self-reports. An objective measure of circuit engagement might allow individual settings to be tested in seconds to minutes, reducing the time to response and increasing patient and clinician confidence in the chosen settings. Here, we present an approach to measuring and optimizing that circuit engagement. Approach We leverage prior results showing that effective VCVS DBS engages circuits of cognitive control, that this engagement depends primarily on which contact(s) are activated, and that circuit engagement can be tracked through a state space modeling framework. We combine this framework with an adaptive optimizer to perform a principled exploration of electrode contacts and identify the contacts that maximally improve cognitive control. Main results Using behavioral simulations directly derived from patient data, we show that an Upper Confidence Bound (UCB1) algorithm outperforms other optimizers (roughly 80% probability of convergence to a global optimum). Significance We show that the optimization can converge even with lag between stimulation and effect, and that a complete optimization can be done in a clinically feasible timespan (a few hours). Further, the approach requires no specialized recording or imaging hardware, and thus could be a scalable path to expand the use of DBS in psychiatric and other non-motor applications.