Suppr超能文献

多时间尺度神经调制策略在帕金森病闭环深部脑刺激中的应用。

Multi-timescale neuromodulation strategy for closed-loop deep brain stimulation in Parkinson's disease.

机构信息

Academy for Engineering and Technology, Fudan University, Shanghai, People's Republic of China.

Shanghai Engineering Research Center of AI & Robotics, Fudan University, Shanghai, People's Republic of China.

出版信息

J Neural Eng. 2024 May 7;21(3). doi: 10.1088/1741-2552/ad4210.

Abstract

Beta triggered closed-loop deep brain stimulation (DBS) shows great potential for improving the efficacy while reducing side effect for Parkinson's disease. However, there remain great challenges due to the dynamics and stochasticity of neural activities. In this study, we aimed to tune the amplitude of beta oscillations with different time scales taking into account influence of inherent variations in the basal ganglia-thalamus-cortical circuit.. A dynamic basal ganglia-thalamus-cortical mean-field model was established to emulate the medication rhythm. Then, a dynamic target model was designed to embody the multi-timescale dynamic of beta power with milliseconds, seconds and minutes. Moreover, we proposed a closed-loop DBS strategy based on a proportional-integral-differential (PID) controller with the dynamic control target. In addition, the bounds of stimulation amplitude increments and different parameters of the dynamic target were considered to meet the clinical constraints. The performance of the proposed closed-loop strategy, including beta power modulation accuracy, mean stimulation amplitude, and stimulation variation were calculated to determine the PID parameters and evaluate neuromodulation performance in the computational dynamic mean-field model.. The Results show that the dynamic basal ganglia-thalamus-cortical mean-field model simulated the medication rhythm with the fasted and the slowest rate. The dynamic control target reflected the temporal variation in beta power from milliseconds to minutes. With the proposed closed-loop strategy, the beta power tracked the dynamic target with a smoother stimulation sequence compared with closed-loop DBS with the constant target. Furthermore, the beta power could be modulated to track the control target under different long-term targets, modulation strengths, and bounds of the stimulation increment.. This work provides a new method of closed-loop DBS for multi-timescale beta power modulation with clinical constraints.

摘要

β 触发闭环深部脑刺激(DBS)在提高疗效的同时降低帕金森病的副作用方面具有很大的潜力。然而,由于神经活动的动力学和随机性,仍然存在很大的挑战。在这项研究中,我们旨在考虑基底神经节-丘脑-皮质回路的固有变化的影响,调节具有不同时间尺度的β 振荡幅度。建立了一个动态的基底神经节-丘脑-皮质平均场模型来模拟药物节律。然后,设计了一个动态目标模型来体现β功率的多时间尺度动态,时间尺度为毫秒、秒和分钟。此外,我们提出了一种基于比例积分微分(PID)控制器的闭环 DBS 策略,具有动态控制目标。此外,还考虑了刺激幅度增量的边界和动态目标的不同参数,以满足临床约束。在计算的动态平均场模型中,计算了所提出的闭环策略的性能,包括β功率调制精度、平均刺激幅度和刺激变化,以确定 PID 参数并评估神经调节性能。结果表明,动态基底神经节-丘脑-皮质平均场模型模拟了最快和最慢的药物节律。动态控制目标反映了β功率从毫秒到分钟的时间变化。使用所提出的闭环策略,与使用恒定目标的闭环 DBS 相比,β功率通过更平滑的刺激序列跟踪动态目标。此外,在不同的长期目标、调制强度和刺激增量边界下,β 功率可以被调制以跟踪控制目标。这项工作为具有临床约束的多时间尺度β功率调制提供了一种新的闭环 DBS 方法。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验