| Autor: |
Gilmour TP; Electrical Engineering Department, Pennsylvania State University, University Park, PA 16802, USA. timgilmour@psu.edu, Subramanian T, Lagoa C, Jenkins WK |
| Jazyk: |
angličtina |
| Zdroj: |
Computational and mathematical methods in medicine [Comput Math Methods Med] 2012; Vol. 2012, pp. 580795. Date of Electronic Publication: 2012 Feb 15. |
| DOI: |
10.1155/2012/580795 |
| Abstrakt: |
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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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