%0 Journal Article %A Vanessa L. Breton %A Berj L. Bardakjian %A Peter L. Carlen %T Phase Coherent Currents underlying neocortical seizure-like state transitions %D 2019 %R 10.1523/ENEURO.0426-18.2019 %J eneuro %P ENEURO.0426-18.2019 %X In the epileptic brain, phase amplitude cross frequency coupling (CFC) features have been used to objectively classify seizure-related states, and the inter-seizure state has been demonstrated as being random, in contrast to the seizure state being predictable; however, the excitatory and inhibitory networks underlying their dynamics remain unclear. Therefore, the objectives of this study are to classify the dynamics of seizure sub-states labeling seizure-like event (SLE) onset and termination intervals using CFC features and to obtain their underlying excitatory/inhibitory cellular correlates. SLEs were induced in mouse neocortical brain slices using a low-magnesium perfusate, and were recorded in layer II/III using simultaneous local field potential and whole cell voltage clamp electrodes. Classification of onset and termination of SLE transitions was investigated using CFC features in conjunction with an unsupervised 2-state hidden Markov model (HMM). Gamma distributions of their durations indicated that both are predictable. Furthermore, omitting 4Hz from the HMM classifier switched both SLE sub-states from statistically deterministic to random without changing the dynamics of the SLE state. These results were generalized to 4-AP induced SLEs and human seizure traces. Only during these sub-states, both excitatory and inhibitory currents coupled with the field. Where excitatory currents phase locked to a broad range of frequencies between 1 and 12Hz, inhibitory currents dominantly phase locked at 4Hz. We conclude that inhibition underlies the predictability of neocortical CFC-defined SLE transition sub-states.Significance Statement To date, the underlying excitatory and inhibitory bases of objectively identified seizure sub-states have not been determined. Here we identify these sub-states using phase amplitude cross frequency coupling features. This is tested in both human and rodent seizure models. Then, using simultaneous field and whole cell recordings in a mouse model, we investigate the potential for explaining seizure sub-state dynamics from interactions of the excitatory and inhibitory currents with local field network activity. We found that the frequency ranges at which these currents are coherent with field oscillations have a dramatic effect on the predictability of seizure state sub-states. Such information is critical for identifying the mechanisms underlying the dynamics of epileptic seizures. %U https://www.eneuro.org/content/eneuro/early/2019/03/07/ENEURO.0426-18.2019.full.pdf