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闭环深度大脑刺激与强化学习和神经模拟

Chia-Hung Cho, Pin-Jui Huang, Meng-Chao Chen

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

|September 20, 2024

概括

这项研究引入了一种新的闭环深度大脑刺激 (DBS) 系统,使用强化学习 (RL) 进行自适应参数调整. 优化的TD3方法显著降低了功耗,同时保持了对运动障碍的治疗疗效.

科学领域:

计算神经科学是一种神经科学.
生物医学工程生物医学工程
人工智能的人工智能

背景情况:

深度大脑刺激 (DBS) 是对像帕金森病 (PD) 这样的运动障碍的关键治疗方法.
当前的DBS系统往往是开放循环的,缺乏适应性参数调,以获得最佳的能源效率和治疗效果.
临床进步需要一个闭环DBS系统与强化学习 (RL) 进行自动参数优化.

研究的目的:

开发和评估使用RL进行自动参数调节的闭环DBS系统.
为了提高能源效率和恢复thalamic功能在一个模拟的基底-thalamic (BGT) 模型.
为了比较不同RL代理在优化DBS参数方面的性能.

主要方法:

一个基底 - thalamic (BGT) 电路模型被实例化为RL代理人的交互环境.
实施和比较了四种RL算法:软行为体-关键 (SAC),双延迟深确定性政策梯度 (TD3),近距离政策优化 (PPO) 和优势行为体-关键 (A2C).
在闭环DBS系统中,TD3算法被优化为适应性参数调整.

主要成果:

优化的基于TD3的闭环DBS系统实现了与开环系统相比,平均功耗显著减少67%.
该系统成功地保持了模拟的BGT电路的正常响应.
在病理条件下的thalamic错误反应被减轻,防止过度刺激.

结论:

本研究介绍了一种新的自适应参数调节闭环DBS系统,利用RL.
在保持治疗效果的同时,TD3算法在降低功耗方面表现出卓越的性能.
拟议的方法显示了将RL集成到DBS系统中的重大前景,为未来的临床试验优化治疗结果.

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