Dastin-van Rijn Evan M, Widge Alik S
Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minnesota, MN, United States of America.
J Neural Eng. 2025 Jun 13;22(3):036038. doi: 10.1088/1741-2552/ade189.
Precision medicine holds substantial promise for tailoring neuromodulation techniques to the symptomatology of individual patients. Precise selection of stimulation parameters for individual patients requires the development of robust optimization techniques. However, standard optimization approaches, like Bayesian optimization, have historically been assessed and developed for applications with far less noise than is typical in neuro-psychiatric outcome measures and with minimal focus on parameter safety.We conducted a literature review of individual effects in neurological and psychiatric applications to build a series of simulated patient responses of varying signal to noise ratio. Using these simulations, we assessed whether existing standards in Bayesian optimization are sufficient for robustly optimizing such effects.For effect sizes below a Cohen's d of 0.3, standard Bayesian optimization methods failed to consistently identify optimal parameters. This failure primarily results from over-sampling of the boundaries of the space as the number of samples increases, because the variance on the edges becomes disproportionately greater than in the remainder of parameter space. Using a combination of an input warp and a boundary avoiding Iterated Brownian-bridge kernel we demonstrated robust Bayesian optimization performance for problems with a Cohen's d effect size as low as 0.1.Our results demonstrate that caution should be taken when applying standard Bayesian optimization in neuromodulation applications with potentially low effect sizes, as standard algorithms are at high risk of converging to local rather than global optima. Mitigating techniques, like boundary avoidance, are effective and should be used to improve robustness.
精准医学在根据个体患者的症状调整神经调节技术方面具有巨大潜力。为个体患者精确选择刺激参数需要开发强大的优化技术。然而，像贝叶斯优化这样的标准优化方法，历来是针对噪声远低于神经精神疾病结果测量中典型噪声水平且极少关注参数安全性的应用进行评估和开发的。我们对神经学和精神病学应用中的个体效应进行了文献综述，以构建一系列具有不同信噪比的模拟患者反应。利用这些模拟，我们评估了贝叶斯优化中的现有标准是否足以稳健地优化此类效应。对于效应大小低于科恩d值0.3的情况，标准贝叶斯优化方法未能始终如一地识别出最佳参数。这种失败主要是由于随着样本数量增加，空间边界的过度采样导致的，因为边缘处的方差变得比参数空间其余部分大得多。通过结合输入扭曲和避免边界的迭代布朗桥核，我们证明了对于科恩d效应大小低至0.1的问题，贝叶斯优化具有稳健的性能。我们的结果表明，在效应大小可能较低的神经调节应用中应用标准贝叶斯优化时应谨慎，因为标准算法极有可能收敛到局部而非全局最优解。像避免边界这样的缓解技术是有效的，应该用于提高稳健性。
