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Action Potentials and Waves: A Short Story on Electrophysiological Signal Processing

  • Author(s): Szymanska, Agnieszka Anna Fielder
  • Advisor(s): Nenadic, Zoran
  • et al.
Creative Commons Attribution-NoDerivatives 4.0 International Public License
Abstract

Electrophysiological signal processing is a broad, complex and growing field. Universally, the first steps of any signal analysis are detection and classification. Here we present a flexible matched filter designed to detect spikes from various biological data types, as well as two statistically based approaches for spike classification. We then apply these developed tools to study the effects of deep anesthesia on neuronal network dynamics.

The matched filter was implemented for three different applications: detecting action potentials (APs) from multi-sensor extracellular recordings, detecting depolarization events (DEs) from voltage sensitive dye (VSD) imaged cardiomyocytes, and detecting calcium events (CEs) from calcium imaged neuronal somas as well as dendritic spines. Overall, the presented matched filters could accurately detect spikes from various kinds of biological data, often beating other existing methods, and outperforming manual spike selection.

The classification problems explored here include AP (or spike) sorting, as well as DE classification across different drug administrations. In the case of spike sorting, the MUSIC algorithm was used to extract classification features from multi-sensor extracellular AP recordings. This approach was able to reliably classify tetrode (4 channel) and heptode (7 channel) recorded APs. For DE classification, salient DE features were extracted and then compared across drug treatments using a Kolmogorov-Smirnov test. The drug treated cells were consistently statistically distinguishable from controls. Overall, both methods’ success makes them valuable tools for studying neuronal networks as well as cardiomyocyte drug assays, respectively.

Finally, the matched filter for AP detection as well as the MUSIC-based AP classification scheme were applied to in vivo heptode data collected from M1 of the right hemisphere of anesthetized rats, to assess the effects of increasing anesthesia on neuronal network dynamics during a burst suppression state. We found that higher anesthesia led to higher AP frequency, no change in the number of active single units, and increased cross-hemisphere functional connectivity (as measured by simultaneous ECoG recordings). Additionally, all APs were restricted to ECoG bursts, with no APs occurring during suppressed ECoG states. This work provides valuable insights for the study of neuronal network dynamics as well as coma arousal.

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