Deep brain stimulation (DBS) has been proven to be an effective treatment to deal with the symptoms of Parkinson's disease (PD). Currently, the DBS is in an open-loop pattern with which the stimulation parameters remain constant regardless of fluctuations in the disease state, and adjustments of parameters rely mostly on trial and error of experienced clinicians. This could bring adverse effects to patients due to possible overstimulation. Thus closed-loop DBS of which stimulation parameters are automatically adjusted based on variations in the ongoing neurophysiological signals is desired. In this paper, we present a closed-loop DBS method based on reinforcement learning (RL) to regulate stimulation parameters based on a computational model. The network model consists of interconnected biophysically-based spiking neurons, and the PD state is described as distorted relay reliability of thalamus (TH). Results show that the RL-based closed-loop control strategy can effectively restore the distorted relay reliability of the TH but with less DBS energy expenditure.

中文翻译：

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强化学习在帕金森氏病计算模型中对深层脑刺激的应用。

深部脑刺激（DBS）已被证明是治疗帕金森氏病（PD）症状的有效方法。当前，DBS处于开环模式，无论疾病状态如何波动，刺激参数均保持恒定，并且参数的调整主要取决于经验丰富的临床医生的反复试验。由于可能会过度刺激，可能给患者带来不良影响。因此，期望基于进行中的神经生理信号的变化来自动调节刺激参数的闭环DBS。在本文中，我们提出了一种基于强化学习（RL）的闭环DBS方法，以基于计算模型来调节刺激参数。网络模型由相互连接的基于生物物理的尖峰神经元组成，PD状态描述为丘脑（TH）的中继可靠性失真。结果表明，基于RL的闭环控制策略可以有效地恢复TH的失真继电器可靠性，但具有较少的DBS能量消耗。