Multiscale autoregressive identification of neuroelectrophysiological systems.
Multiscale autoregressive identification of neuroelectrophysiological systems.
复制标题
神经电生理系统的多尺度自回归识别。
DOI：
10.1155/2012/580795
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发表时间：
2012
影响因子：
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通讯作者：
Jenkins,WKenneth
中科院分区：
文献类型：
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作者：
Gilmour,TimothyP;Subramanian,Thyagarajan;Lagoa,Constantino;Jenkins,WKenneth
由于大脑内路径的复杂性和大量路径，连接的神经核之间的电信号很难建模。 因此经常使用简单的参数模型。最近开发了外源输入自回归（MS-ARX）模型的多尺度版本，该模型允许根据信噪比和可预测程度选择最佳的滤波和抽取量。 在本文中，我们将 MS-ARX 模型应用于从麻醉啮齿动物大脑同时记录的皮层脑电图和底丘脑局部场电位。 我们证明 MS-ARX 模型比传统的 ARX 模型能产生更好的预测。 我们还对 MS-ARX 结果进行了调整，以显示正常大鼠和 6OHDA 诱导的帕金森病大鼠之间核间可预测性的差异，表明该方法可能对其他神经电生理学研究具有广泛的适用性。
Electrical signals between connected neural nuclei are difficult to model because of the complexity and high number of paths within the brain. Simple parametric models are therefore often used. A multiscale version of the autoregressive with exogenous input (MS‐ARX) model has recently been developed which allows selection of the optimal amount of filtering and decimation depending on the signal‐to‐noise ratio and degree of predictability. In this paper, we apply the MS‐ARX model to cortical electroencephalograms and subthalamic local field potentials simultaneously recorded from anesthetized rodent brains. We demonstrate that the MS‐ARX model produces better predictions than traditional ARX modeling. We also adapt the MS‐ARX results to show differences in internuclei predictability between normal rats and rats with 6OHDA‐induced parkinsonism, indicating that this method may have broad applicability to other neuroelectrophysiological studies.