Comput Math Methods Med - Multiscale autoregressive identification of neuroelectrophysiological systems.

Tópicos

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Resumo

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.

Resumo Limpo

electr signal connect neural nuclei difficult model complex high number path within brain simpl parametr model therefor often use multiscal version autoregress exogen input msarx model recent develop allow select optim amount filter decim depend signaltonois ratio degre predict paper appli msarx model cortic electroencephalogram subthalam local field potenti simultan record anesthet rodent brain demonstr msarx model produc better predict tradit arx model also adapt msarx result show differ internuclei predict normal rat rat ohdainduc parkinson indic method may broad applic neuroelectrophysiolog studi

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