Multiscale Autoregressive Identification of Neuroelectrophysiological Systems

作者：Gilmour Timothy P^*; Subramanian Thyagarajan; Lagoa Constantino; Jenkins W Kenneth

来源：Computational and Mathematical Methods in Medicine, 2012, 580795.

DOI：10.1155/2012/580795

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. Amultiscale 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-ARXmodel 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.

• 出版日期2012