title: Multiscale autoregressive identification of neuro-electrophysiological 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 inter-nuclei predictability between normal rats and rats with 6OHDA-induced parkinsonism, indicating that this method may have broad applicability to other neuro-electrophysiological studies.
authors.affiliation: Pennsylvania State University, Electrical Engineering Department, 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
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000473:pathology*; D004195:Disease Models, Animal; D055724:Electrophysiological Phenomena; D004594:Electrophysiology / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D009474:Neurons / Q000473:pathology; D010300:Parkinson Disease / Q000378:metabolism / Q000503:physiopathology; D051381:Rats; D012044:Regression Analysis; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D020531:Subthalamic Nucleus / Q000378:metabolism