Real-time Bayesian optimization of deep brain stimulation for personalized cognitive control enhancement.

ABSTRACT:

Background

Identifying effective deep brain stimulation (DBS) parameters for psychiatric disorders has historically been a time-consuming and error prone process due to a lack of an objective and rapid readout of target circuit engagement. Cognitive control may have use as a biomarker of treatment efficacy but it has yet to be shown that DBS parameters can be reliably optimized to produce cognitive control improvements in individual subjects.

Objective

We sought to leverage a rat model of DBS-driven cognitive control improvements to determine whether state of the art optimization algorithms could consistently identify effective stimulation amplitudes to enhance cognition.

Methods

We delivered periods of active and inactive DBS-like stimulation at variable parameters w

SUBMITTER: Dastin-van Rijn EM 

PROVIDER: S-EPMC12776293 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature