Multiscale autoregressive identification of neuroelectrophysiological systems

Timothy P. Gilmour, Thyagarajan Subramanian, Constantino Lagoa, W. Kenneth Jenkins

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number580795
JournalComputational and Mathematical Methods in Medicine
Volume2012
DOIs
StatePublished - 2012

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Applied Mathematics

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