Meta-Bayesian Optimization for Deep Brain Stimulation.
Meta-Bayesian Optimization for Deep Brain Stimulation.
复制标题
深部脑刺激的元贝叶斯优化。
DOI：
10.1109/embc48229.2022.9871279
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Devergnas,AnnaelleD
中科院分区：
文献类型：
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作者：
Connolly,MarkJ;Opri,Enrico;Miocinovic,Svjetlana;Devergnas,AnnaelleD
脑深部电刺激（DBS）正在成为治疗和研究神经和精神疾病和障碍的基本工具。最近开发的DBS设备和电极允许更灵活和精确的刺激。密集封装的刺激触点可以被独立地刺激以形成电场，瞄准感兴趣的通路，并避免可能引起副作用的通路。然而，这种灵活性是有代价的。每个额外的刺激设置导致潜在刺激设置的数量呈指数增加。最近的工作已经解决了这个问题，使用贝叶斯优化。然而，这种方法从多个主题中学习以提高性能的能力有限。在这项研究中，我们扩展了最近开发的元贝叶斯优化算法的DBS域。我们评估了这种方法相比，经典的贝叶斯优化和随机搜索使用的数据收集从非人灵长类动物在刺激丘脑底核，同时记录诱发电位在运动皮层和局部丘脑底核内。在寻找在生成的目标函数分布中最大化诱发电位的刺激设置的任务中，元贝叶斯优化显著优于其他方法，累积奖励为8.93±0.70，而贝叶斯优化为7.17±1.64（p < 10−9），随机搜索为6.89±1.56（p < 10−9）。此外，该算法在训练期间未使用的目标函数上进行测试时优于贝叶斯优化。这些结果表明，元贝叶斯优化可以利用目标函数分布背后的结构，并学习一种最佳搜索策略，该策略可以推广到不属于训练数据的目标函数之外。临床相关性-这扩展了用于优化DBS刺激设置的元贝叶斯优化方法，其性能优于最新算法24.6%。
Deep brain stimulation (DBS) is becoming a fundamental tool for the treatment and study of neurological and psychiatric diseases and disorders. Recently developed DBS devices and electrodes have allowed for more flexible and precise stimulation. Densely packed stimulation contacts can be independently stimulated to shape the electric field, targeting pathways of interest, and avoiding those that may cause side-effects. However, this flexibility comes at a cost. Each additional stimulation setting causes an exponential increase in the number of potential stimulation settings. Recent works have addressed this problem using Bayesian optimization. However, this approach has a limited ability to learn from multiple subjects to improve performance. In this study we extend a recently developed meta-Bayesian optimization algorithm to the DBS domain. We evaluated this approach compared to classical Bayesian optimization and a random search using data collected from a nonhuman primate during stimulation of the subthalamic nucleus while recording evoked potentials in the motor cortex and locally within the subthalamic nucleus. On the task of finding the stimulation setting that maximized the evoked potential across a distribution of generated objective functions, meta-Bayesian optimization significantly outperformed the other approaches with a cumulative reward of 8.93±0.70, compared to 7.17±1.64 for Bayesian optimization (p < 10−9) and 6.89±1.56 for the random search (p < 10−9). Moreover, the algorithm outperformed Bayesian optimization when tested on an objective function not used during training. These results demonstrate that meta-Bayesian optimization can take advantage of the structure underlying a distribution of objective function and learn an optimal search strategy that can generalize beyond the objective functions that were not part of the training data. Clinical Relevance - This extends a meta-Bayesian optimization approach for optimizing DBS stimulation settings that outperforms state-of-art algorithms by 24.6%.