title: Multiscale autoregressive identification of neuroelectrophysiological systems.
abstract: 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.
authors.affiliation: Electrical Engineering Department, Pennsylvania State University, University Park, PA 16802, USA. timgilmour@psu.edu
authors.forename: Timothy P
authors.initials: TP
authors.lastname: Gilmour
authors.forename: Thyagarajan
authors.initials: T
authors.lastname: Subramanian
authors.forename: Constantino
authors.initials: C
authors.lastname: Lagoa
authors.forename: W Kenneth
authors.initials: WK
authors.lastname: Jenkins
mesh_terms: D000818:Animals; D002540:Cerebral Cortex / Q000502:physiology; D004569:Electroencephalography / Q000379:methods; D055724:Electrophysiological Phenomena / Q000502:physiology*; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D010300:Parkinson Disease / Q000503:physiopathology; D051381:Rats; D059629:Signal-To-Noise Ratio; D020530:Subthalamus / Q000502:physiology