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

2025 · doi:10.64898/2025.12.30.697057
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The study examined whether real-time optimization algorithms can identify effective deep brain stimulation (DBS) parameters to improve cognitive control in an animal model. In rats performing a set-shifting task with rapid behavioral readouts of reaction time and accuracy, the authors compared predefined stimulation amplitudes to Bayesian Optimization–personalized amplitudes, including an acute optimization approach followed by comparisons to traditional settings. Acute stimulation reduced reaction times without impairing accuracy (N=15), and in a smaller second cohort (N=6) Bayesian Optimization successfully reduced reaction times in all animals with an effect size comparable to historically best settings. The authors’ main limitation is that the work was conducted in rats using task performance as a target-engagement proxy rather than directly testing psychiatric outcomes in humans. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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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 while rats performed a Set-Shifting task that we previously showed to be stimulation-sensitive. We tested both predefined settings of interest and settings that were personalized to individual animals using Bayesian Optimization. Measurements of task performance including reaction time and accuracy were compared between acute, optimized, and traditional settings to evaluate effects on cognitive control. Results Acute stimulation reduced reaction times without hindering accuracy (N=15), replicating the effects previously observed with chronic stimulation. In a second cohort (N=6), optimization of stimulation amplitude successfully reduced reaction times in all animals with comparable effect size to historically best settings. Conclusion These findings confirm that optimization techniques can be effective for improving symptomatically-relevant cognitive markers supporting the feasibility of personalized, quantitatively-informed approaches to neuromodulation and target engagement for psychiatric and/or cognitive disorders. Highlights Proving target engagement is a substantial challenge across brain stimulation modalities, and objective, rapid behavioral read-outs may be a solution to that challenge. Reaction times in cognitive control tasks are an example of a behavioral measure that changes rapidly in response to changes in stimulation parameters, and that also may predict clinical outcomes. Individually optimal stimulation amplitudes for reducing reaction time by stimulating corticofugal fibers passing through the striatum can be determined using Bayesian Optimization. Individually optimized settings discovered in an acute preparation demonstrate consistent effects when applied chronically.
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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 while rats performed a Set-Shifting task that we previously showed to be stimulation-sensitive. We tested both predefined settings of interest and settings that were personalized to individual animals using Bayesian Optimization. Measurements of task performance including reaction time and accuracy were compared between acute, optimized, and traditional settings to evaluate effects on cognitive control.

Results

Acute stimulation reduced reaction times without hindering accuracy (N=15), replicating the effects previously observed with chronic stimulation. In a second cohort (N=6), optimization of stimulation amplitude successfully reduced reaction times in all animals with comparable effect size to historically best settings.

Conclusion

These findings confirm that optimization techniques can be effective for improving symptomatically-relevant cognitive markers supporting the feasibility of personalized, quantitatively-informed approaches to neuromodulation and target engagement for psychiatric and/or cognitive disorders. Highlights Proving target engagement is a substantial challenge across brain stimulation modalities, and objective, rapid behavioral read-outs may be a solution to that challenge. Reaction times in cognitive control tasks are an example of a behavioral measure that changes rapidly in response to changes in stimulation parameters, and that also may predict clinical outcomes. Individually optimal stimulation amplitudes for reducing reaction time by stimulating corticofugal fibers passing through the striatum can be determined using Bayesian Optimization. Individually optimized settings discovered in an acute preparation demonstrate consistent effects when applied chronically. Competing Interest Statement Alik S. Widge has consulted with Abbott on DBS and anonymously to investors interested in psychiatric indications through expert networks that prohibit him from revealing specific clients. None of those clients involves any ongoing relationship, financial, or otherwise. Alik S. Widge has received non-financial research support from Medtronic and Boston Scientific, companies that manufacture deep brain stimulators. This work is indirectly related to patent US11241188B2, "System and methods for monitoring and improving cognitive flexibility" and patent application US20240017069A1, "Systems and methods for measuring and altering brain activity related to flexible behavior", both of which name Alik S. Widge as an inventor.

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