Characterizing the spiking dynamics of subthalamic nucleus neurons in Parkinson's disease using generalized linear models.
Characterizing the spiking dynamics of subthalamic nucleus neurons in Parkinson's disease using generalized linear models.
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DOI：
10.3389/fnint.2012.00028
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发表时间：
2012
影响因子：
3.5
通讯作者：
Eskandar EN
中科院分区：
文献类型：
--
作者：
Eden UT;Gale JT;Amirnovin R;Eskandar EN
准确描述帕金森病（PD）患者丘脑底核（subthalamic nucleus，DBS）中神经元的尖峰模式对于理解帕金森病的发病机制和实现脑深部电刺激（deep brain stimulation，DBS）的最大治疗益处非常重要。我们使用点过程广义线性模型（GLM）分析了帕金森病患者在定向手部运动任务中记录的24个丘脑底核神经元的放电活动。该模型将每个神经元的尖峰概率同时与运动规划和执行、方向选择性、不应性、爆发和振荡动力学相关的因素相关联。该模型表明，虽然与不应性和爆发相关的短期历史依赖性在预测尖峰活动方面是最有用的，但几乎所有分析的神经元都具有长期历史依赖性的结构化模式，使得尖峰概率在前一个尖峰之后减少20-30 ms，然后增加30-60 ms。这表明，先前描述的振荡发射的神经元在帕金森氏症患者的大脑中的自主运动是由一个结构化的模式的抑制和兴奋。这一点过程模型提供了一个系统的框架，表征神经元活动的动力学行为。
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.