用可实时测量的脑信号训练强化学习模型，优化帕金森病深部脑刺激参数。
In-Vivo Training for Deep Brain Stimulation
- 基于TD3算法的强化学习代理，根据体内可测脑活动动态调节刺激频率与强度。
- 在基底节模型上训练后，对帕金森病相关生物标志物的抑制效果优于现有临床方案。
- 仅依赖真实患者可测量信号，支持个性化闭环治疗，适合神经调控研究者和临床医生。
深部脑刺激（DBS）是治疗帕金森病（PD）的有效手段。近期研究尝试使用强化学习（RL）调整刺激频率和幅度，但现有模型依赖于无法在患者中直接测量的生物标志物，仅存在于脑芯片（BoC）仿真中。本文提出一种基于强化学习的DBS方法，根据实际可测量的体内脑活动动态调节刺激参数。采用基于TD3的强化学习代理，在基底节区域模型上训练，其对与帕金森病严重程度相关的生物标志物抑制效果优于当前临床标准方案。该方法仅依赖真实环境中可获取的信息，为个性化、自适应的闭环神经调控系统提供了可能，适用于个体化治疗需求。
原文摘要 · Abstract (English)
Deep Brain Stimulation (DBS) is a highly effective treatment for Parkinson's Disease (PD). Recent research uses reinforcement learning (RL) for DBS, with RL agents modulating the stimulation frequency and amplitude. But, these models rely on biomarkers that are not measurable in patients and are only present in brain-on-chip (BoC) simulations. In this work, we present an RL-based DBS approach that adapts these stimulation parameters according to brain activity measurable in vivo. Using a TD3 based RL agent trained on a model of the basal ganglia region of the brain, we see a greater suppression of biomarkers correlated with PD severity compared to modern clinical DBS implementations. Our agent outperforms the standard clinical approaches in suppressing PD biomarkers while relying on information that can be measured in a real world environment, thereby opening up the possibility of training personalized RL agents specific to individual patient needs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。