Reconstructing rodent brain signals during euthanasia with eigensystem realization algorithm (ERA)

Abstract

Abstract We accurately reconstruct the Local Field Potential time series obtained from anesthetized and awake rats, both before and during CO $$_2$$ 2 euthanasia. We apply the Eigensystem Realization Algorithm to identify an underlying linear dynamical system capable of generating the observed data. Time series exhibiting more intricate dynamics typically lead to systems of higher dimensions, offering a means to assess the complexity of the brain throughout various phases of the experiment. Our results indicate that anesthetized brains possess complexity levels similar to awake brains before CO $$_2$$ 2 administration. This resemblance undergoes significant changes following euthanization, as signals from the awake brain display a more resilient complexity profile, implying a state of heightened neuronal activity or a last fight response during the euthanasia process. In contrast, anesthetized brains seem to enter a more subdued state early on. Our data-driven techniques can likely be applied to a broader range of electrophysiological recording modalities.

Keywords

• Bayesian estimator of abrupt change
• Seasonality
• and Trend (BEAST)
• Brain
• Eigensystem realization algorithm (ERA)
• Euthanasia