从黎明到黄昏:神经刺激的时间适应贝叶斯优化.
John E Fleming1, Ines Pont Sanchis2, Oscar Lemmens2
1Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford, United Kingdom.
PLoS computational biology
|December 13, 2023
概括
这项研究引入了时间变化的贝叶斯优化 (TV-BayesOpt),以随着时间的推移调整神经调节治疗设置. TV-BayesOpt有效地跟踪运动障碍的最佳参数,优于静态方法.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
背景情况:
- 神经调节疗法需要精确的刺激设置以获得最佳的疗效.
- 当前的优化方法通常假定静态参数,忽略了时间依赖的因素,如疾病进展或生物节奏.
- 这些随时间变化的因素可以显著影响治疗结果,并需要适应性策略.
研究的目的:
- 开发和评估一种新的时间变化的贝叶斯优化 (TV-BayesOpt) 算法,用于动态跟踪最佳神经调节参数.
- 评估算法在处理最佳刺激设置的渐进和周期变化的性能.
- 为优化神经调节治疗神经和精神疾病提供灵活的框架.
主要方法:
- 开发了一个时间变化的贝叶斯优化 (TV-BayesOpt) 算法,结合了渐进式遗忘和周期性协差函数.
- 评估TV-BayesOpt使用相锁大脑深度刺激的计算模型对振荡症进行评估.
- 将TV-BayesOpt性能与标准的时间不变优化技术进行比较.
主要成果:
- TV-BayesOpt成功地跟踪了随着时间的推移逐渐和周期性变化的最佳刺激设置.
- 该算法在动态参数跟踪中,与时间不变的方法相比,表现出更高的性能.
- 即使最初关于变化的假设与观察到的数据不同时,也保持了强的表现.
结论:
- TV-BayesOpt为优化神经调节疗法提供了一种强大而适应的方法,其中刺激参数随着时间的推移而变化.
- 这个框架可以应用于各种侵入性和非侵入性神经调节策略,用于帕金森病和基本震等疾病.
- 算法的追踪动态参数变化的能力提高了个性化和有效的神经调节治疗的潜力.
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