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Publication Detail
Impact of DC-Coupled Electrophysiological Recordings for Translational Neuroscience: Case Study of Tracking Neural Dynamics in Rodent Models of Seizures
  • Publication Type:
    Journal article
  • Authors:
    Jafarian A, Wykes RC
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  • Journal:
    Frontiers in Computational Neuroscience
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  • Keywords:
    Science & Technology, Life Sciences & Biomedicine, Mathematical & Computational Biology, Neurosciences, Neurosciences & Neurology, neural mass model, continuous-discrete unscented Kalman filter, DC-coupled electrophysiological recordings, synaptic physiology, infraslow oscillations, RESTING-STATE, BIFURCATION-ANALYSIS, MATHEMATICAL-MODEL, FIRING RATE, SLOW, OSCILLATIONS, NEURONS, SLEEP, IDENTIFICATION, MODULATION
  • Notes:
    © 2022 Jafarian and Wykes. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
We propose that to fully understand biological mechanisms underlying pathological brain activity with transitions (e.g., into and out of seizures), wide-bandwidth electrophysiological recordings are important. We demonstrate the importance of ultraslow potential shifts and infraslow oscillations for reliable tracking of synaptic physiology, within a neural mass model, from brain recordings that undergo pathological phase transitions. We use wide-bandwidth data (direct current (DC) to high-frequency activity), recorded using epidural and penetrating graphene micro-transistor arrays in a rodent model of acute seizures. Using this technological approach, we capture the dynamics of infraslow changes that contribute to seizure initiation (active pre-seizure DC shifts) and progression (passive DC shifts). By employing a continuous-discrete unscented Kalman filter, we track biological mechanisms from full-bandwidth data with and without active pre-seizure DC shifts during paroxysmal transitions. We then apply the same methodological approach for tracking the same parameters after application of high-pass-filtering >0.3Hz to both data sets. This approach reveals that ultraslow potential shifts play a fundamental role in the transition to seizure, and the use of high-pass-filtered data results in the loss of key information in regard to seizure onset and termination dynamics.
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