Imagine trying to understand a complex recipe without fully examining all the ingredients and steps involved. Similarly, in order to grasp the biological mechanisms underlying seizures in the brain, we need to study them from multiple angles. The authors of a recent study advocate for the use of wide-bandwidth electrophysiological recordings that capture ultraslow potential shifts and infraslow oscillations to track neural dynamics during seizures. They conducted experiments on rodent models using DC-coupled electrophysiological recordings, which allowed them to capture the subtle changes that occur before, during, and after a seizure. By applying sophisticated tracking algorithms to their data, they were able to uncover crucial information about seizure initiation and progression. Interestingly, when they applied a high-pass filter to the data, they found that important details regarding seizure onset and termination were lost. This research highlights the significance of wide-bandwidth recordings in elucidating the complexities of epileptic activity and urges further exploration in translational neuroscience.

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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