首个真实神经生理环境，用于评估帕金森自适应脑深刺激算法
Neurophysiologically Realistic Environment for Comparing Adaptive Deep Brain Stimulation Algorithms in Parkinson Disease
- 构建包含15项生理特征的逼真神经模型
- 融合β波活动与实时反馈实现动态调控
- 支持深度强化学习训练，适合智能神经刺激研究者
自适应深部脑刺激（aDBS）是帕金森病（PD）的一种有前景的治疗方法。在aDBS中，植入脑内的电极根据神经生理反馈动态调整刺激参数，但这种侵入式设备限制了离线优化所需的大量数据收集。因此，已有大量关于PD模型和控制算法的合成模型被提出。本文首次提出一个神经生理学上真实的基准测试环境，不仅涵盖基底节环路动力学和病理性振荡，还引入了此前被忽略的15项生理属性，如信号不稳定性、噪声、神经漂移、电极导通变化及个体差异性，均通过脑内β频段活动与反馈机制以时空分布形式建模。同时，本框架专为训练和评估深度强化学习（RL）算法设计，为优化aDBS控制策略开辟新路径，邀请机器学习领域参与智能神经刺激接口的发展。
原文摘要 · Abstract (English)
Adaptive deep brain stimulation (aDBS) has emerged as a promising treatment for Parkinson disease (PD). In aDBS, a surgically placed electrode sends dynamically altered stimuli to the brain based on neurophysiological feedback: an invasive gadget that limits the amount of data one could collect for optimizing the control offline. As a consequence, a plethora of synthetic models of PD and those of the control algorithms have been proposed. Herein, we introduce the first neurophysiologically realistic benchmark for comparing said models. Specifically, our methodology covers not only conventional basal ganglia circuit dynamics and pathological oscillations, but also captures 15 previously dismissed physiological attributes, such as signal instabilities and noise, neural drift, electrode conductance changes and individual variability - all modeled as spatially distributed and temporally registered features via beta-band activity in the brain and a feedback. Furthermore, we purposely built our framework as a structured environment for training and evaluating deep reinforcement learning (RL) algorithms, opening new possibilities for optimizing aDBS control strategies and inviting the machine learning community to contribute to the emerging field of intelligent neurostimulation interfaces.
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。