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Characterizing the spiking dynamics of subthalamic nucleus neurons in Parkinson's disease using generalized linear models

机译:使用广义线性模型表征丘脑下丘脑核神经元在帕金森氏病中的突增动态

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摘要

Accurately describing the spiking patterns of neurons in the subthalamic nucleus (STN) of patients suffering from Parkinson's disease (PD) is important for understanding the pathogenesis of the disease and for achieving the maximum therapeutic benefit from deep brain stimulation (DBS). We analyze the spiking activity of 24 subthalamic neurons recorded in Parkinson's patients during a directed hand movement task by using a point process generalized linear model (GLM). The model relates each neuron's spiking probability simultaneously to factors associated with movement planning and execution, directional selectivity, refractoriness, bursting, and oscillatory dynamics. The model indicated that while short-term history dependence related to refractoriness and bursting are most informative in predicting spiking activity, nearly all of the neurons analyzed have a structured pattern of long-term history dependence such that the spiking probability was reduced 20–30 ms and then increased 30–60 ms after a previous spike. This suggests that the previously described oscillatory firing of neurons in the STN of Parkinson's patients during volitional movements is composed of a structured pattern of inhibition and excitation. This point process model provides a systematic framework for characterizing the dynamics of neuronal activity in STN.
机译:准确描述患有帕金森氏病(PD)的患者的丘脑底核(STN)中神经元的突增模式对于理解疾病的发病机理以及实现深部脑刺激(DBS)的最大治疗益处至关重要。我们通过使用点过程广义线性模型(GLM)分析在定向手运动任务期间记录在帕金森氏病患者中的24个丘脑下丘脑神经元的突触活动。该模型将每个神经元的突跳概率同时关联到与运动计划和执行,方向选择性,耐火度,爆发和振荡动力学相关的因素。该模型表明,尽管与耐火度和爆裂有关的短期病史依赖性在预测峰值活动方面最有信息意义,但几乎所有分析的神经元都具有长期病史依赖性的结构化模式,因此峰值概率降低了20–30 ms然后在之前的峰值后增加30–60 ms。这表明,先前描述的帕金森氏病患者自主运动中STN中神经元的振荡放电是由抑制和激发的结构化模式组成的。该点过程模型为表征STN中神经元活动的动力学提供了系统的框架。

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