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Closed-Loop Control of Deep Brain Stimulation: A Simulation Study

机译:大脑深部刺激的闭环控制:仿真研究

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Deep brain stimulation (DBS) is an effective therapy to treat movement disorders including essential tremor, dystonia, and Parkinson's disease. Despite over a decade of clinical experience the mechanisms of DBS are still unclear, and this lack of understanding makes the selection of stimulation parameters quite challenging. The objective of this work was to develop a closed-loop control system that automatically adjusted the stimulation amplitude to reduce oscillatory neuronal activity, based on feedback of electrical signals recorded from the brain using the same electrode as implanted for stimulation. We simulated a population of 100 intrinsically active model neurons in the Vim thalamus, and the local field potentials (LFPs) generated by the population were used as the feedback (control) variable for closed loop control of DBS amplitude. Based on the correlation between the spectral content of the thalamic activity and tremor (Hua , 1998), (Lenz , 1988), we implemented an adaptive minimum variance controller to regulate the power spectrum of the simulated LFPs and restore the LFP power spectrum present under tremor conditions to a reference profile derived under tremor free conditions. The controller was based on a recursively identified autoregressive model (ARX) of the relationship between stimulation input and LFP output, and showed excellent performances in tracking the reference spectral features through selective changes in the theta (2–7 Hz), alpha (7–13 Hz), and beta (13–35 Hz) frequency ranges. Such changes reflected modifications in the firing patterns of the model neuronal population, and, differently from open-loop DBS, replaced the tremor-related pathological patterns with patterns similar to those simulated in tremor-free conditions. The closed-loop controller generated a LFP spectrum that approximated more closely the spectrum present in the tremor-free condition than did open loop fixed intensity stimulation -n-nand adapted to match the spectrum after a change in the neuronal oscillation frequency. This computational study suggests the feasibility of closed-loop control of DBS amplitude to regulate the spectrum of the local field potentials and thereby normalize the aberrant pattern of neuronal activity present in tremor.
机译:深部脑刺激(DBS)是一种治疗运动障碍的有效疗法,包括原发性震颤,肌张力障碍和帕金森氏病。尽管有十多年的临床经验,DBS的机制仍不清楚,并且缺乏了解使得刺激参数的选择颇具挑战性。这项工作的目的是开发一个闭环控制系统,该系统基于使用与植入刺激相同的电极从大脑记录的电信号反馈,自动调节刺激幅度以减少振荡神经元活动。我们在Vim丘脑中模拟了100个具有内在活性的模型神经元,并将由该种群产生的局部场电势(LFP)用作DBS振幅闭环控制的反馈(控制)变量。基于丘脑活动的频谱含量与震颤之间的相关性(Hua,1998),(Lenz,1988),我们实现了自适应最小方差控制器,以调节模拟LFP的功率谱并恢复存在下的LFP功率谱。震颤条件下获得的参考曲线是在无震颤条件下得出的。该控制器基于刺激输入和LFP输出之间关系的递归识别自回归模型(ARX),并且在theta(2–7 Hz),α(7–7 13 Hz)和beta(13–35 Hz)频率范围。这种变化反映了模型神经元群体放电模式的改变,并且与开环DBS不同,它用与在无震颤条件下模拟的模式相似的模式代替了与震颤相关的病理模式。闭环控制器生成的LFP频谱比无环固定强度刺激-n-nand更适合于无震颤情况下存在的频谱,后者适应于神经元振荡频率变化后匹配频谱。这项计算研究表明,闭环控制DBS振幅以调节局部场电势的频谱,从而规范震颤中存在的神经元活动的异常模式的可行性。

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