Adaptive Bayesian Optimization for State-Dependent Brain Stimulation

Dabiri, Sina; Cole, Eric R.; Gross, Robert A.

doi:10.1109/ner52421.2023.10123861

2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) 2023

DOI: 10.1109/ner52421.2023.10123861

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Adaptive Bayesian Optimization for State-Dependent Brain Stimulation

Sina Dabiri

Eric R. Cole

Robert A. Gross

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2024

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SAFE-OPT: a Bayesian optimization algorithm for learning optimal deep brain stimulation parameters with safety constraints

Cole,

Connolly,

Ghetiya

et al. 2024

J. Neural Eng.

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Objective:To treat neurological and psychiatric diseases with deep brain stimulation, a trained clinician must select parameters for each patient by monitoring their symptoms and side-effects in a months-long trial-and-error process, delaying optimal clinical outcomes. Bayesian optimization has been proposed as an efficient method to quicklyand automatically search for optimal parameters. However, conventional Bayesianoptimization does not account for patient safety and could trigger unwanted or dangerousside-effects. Approach: In this study we develop SAFE-OPT, a Bayesian optimization algorithm designed to learn subject-specific safety constraints to avoid potentially harmful stimulation settings during optimization. We prototype and validate SAFE-OPT using a rodent multielectrode stimulation paradigm which causes subject-specific performance deficits in a spatial memory task. We first use data from an initial cohort of subjects to build a simulation where we design the best SAFE-OPT configuration for safe and accurate searching in silico.Main results:We then deploy both SAFE-OPT and conventional Bayesian optimization in new subjects in vivo, showing that SAFE-OPT can find an optimally high stimulation amplitude that does not harm task performance with comparable sample efficiency to Bayesian optimization and without selecting amplitude values that exceed the subject’s safety threshold.Conclusion:The incorporation of safety constraints will provide a key step for adopting Bayesian optimization in real-world applications of deep brain stimulation.

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SAFE-OPT: a Bayesian optimization algorithm for learning optimal deep brain stimulation parameters with safety constraints

Cole,

Connolly,

Ghetiya

et al. 2024

J. Neural Eng.

View full text Add to dashboard Cite

show abstract

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Adaptive Bayesian Optimization for State-Dependent Brain Stimulation

Cited by 1 publication

References 13 publications

SAFE-OPT: a Bayesian optimization algorithm for learning optimal deep brain stimulation parameters with safety constraints

SAFE-OPT: a Bayesian optimization algorithm for learning optimal deep brain stimulation parameters with safety constraints

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