Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, United States of America.
Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA 30322, United States of America.
J Neural Eng. 2024 Aug 14;21(4). doi: 10.1088/1741-2552/ad6cf3.
To treat neurological and psychiatric diseases with deep brain stimulation (DBS), 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 quickly and automatically search for optimal parameters. However, conventional Bayesian optimization does not account for patient safety and could trigger unwanted or dangerous side-effects.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 searchingWe then deploy both SAFE-OPT and conventional Bayesian optimization without safety constraints in new subjects, 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.The incorporation of safety constraints will provide a key step for adopting Bayesian optimization in real-world applications of DBS.
为了通过深部脑刺激（DBS）治疗神经和精神疾病，经过训练的临床医生必须通过在长达数月的反复试验过程中监测患者的症状和副作用，为每位患者选择参数，从而延迟最佳的临床效果。贝叶斯优化已被提议作为一种快速自动搜索最佳参数的有效方法。然而，传统的贝叶斯优化并未考虑患者的安全性，并且可能引发不必要或危险的副作用。
在这项研究中，我们开发了 SAFE-OPT，这是一种贝叶斯优化算法，旨在学习特定于主体的安全约束，以在优化过程中避免潜在的有害刺激设置。我们使用啮齿动物多电极刺激范式对 SAFE-OPT 进行了原型设计和验证，该范式在空间记忆任务中导致特定于主体的性能缺陷。我们首先使用来自初始队列的主体数据构建了一个模拟，在该模拟中，我们设计了最佳的 SAFE-OPT 配置，以进行安全准确的搜索。
然后，我们在新主体中部署了 SAFE-OPT 和没有安全约束的传统贝叶斯优化，结果表明，SAFE-OPT 可以找到一个最佳的高刺激幅度，而不会损害任务性能，其样本效率与贝叶斯优化相当，并且不会选择超过主体安全阈值的幅度值。
安全约束的纳入将为在 DBS 的实际应用中采用贝叶斯优化提供关键步骤。
