Loss of neuronal network resilience precedes seizures and determines the ictogenic nature of interictal synaptic perturbations

Abstract

The mechanisms of seizure emergence, and the role of brief interictal epileptiform discharges (IEDs) in seizure generation are two of the most important unresolved issues in modern epilepsy research and clinical epileptology. Our study shows that the transition to seizure is not a sudden phenomenon, but a slow process characterized by the progressive loss of neuronal network resilience. From a dynamical perspective, the slow transition is governed by the principles of critical slowing, a robust natural phenomenon observable in systems characterized by transitions between contrasting dynamical regimes. In epilepsy, this complex process is modulated by the synchronous synaptic input from IEDs. IEDs are external perturbations that produce phasic changes in the slow transition process and can exert opposing effects on the dynamics of a seizuregenerating network, causing either stabilizing anti-seizure or destabilizing pro-seizure effects. We show that the multifaceted nature of IEDs is defined by the dynamical state of the seizuregenerating network at the moment of the discharge occurrence, not necessarily by the existence of distinct cellular mechanisms.

Ensembling crowdsourced seizure prediction algorithms using long‐term human intracranial EEG

“…Moreover, algorithms that directly account for known seizure triggers, such as sleep-wake rhythms, as well as novel measurement approaches may offer alternative routes to performance gains. 2,3,16 In addition, recent work looking at long-term rhythms 18 holds significant promise for seizure forecasting, could be applied in the machine learning context, and suggests that the performance limits seen here may not be tied to potential limits imposed by the physiologic processes underlying seizure occurrence.…”

“…It is also very important to note that as a result of Kaggle contest formats, contestants did not have full timing information available to them for training, such as the time of interictal segments relative to preictal segments, which may significantly limit algorithm performance. Moreover, algorithms that directly account for known seizure triggers, such as sleep‐wake rhythms, as well as novel measurement approaches may offer alternative routes to performance gains . In addition, recent work looking at long‐term rhythms holds significant promise for seizure forecasting, could be applied in the machine learning context, and suggests that the performance limits seen here may not be tied to potential limits imposed by the physiologic processes underlying seizure occurrence.…”

Multiple Oscillatory Push–Pull Antagonisms Constrain Seizure Propagation

“…45 Here, we focused on the mechanism of seizure evolution dynamics after generation rather than seizure emergence. Chang et al found that the transition from interictal to seizure is modulated by interictal epileptiform discharges, which produce phasic changes in the slow transition process and exert opposing effects on the dynamics of a seizure-generating network, causing either antiseizure or proseizure effects.…”

Layer‐ and Cell‐Specific Recruitment Dynamics during Epileptic Seizures In Vivo

“…Several lines of evidence support the possibility that seizure onset has much in common with interictal spikes. First, EEG and ECoG recordings show that many partial and generalized seizures initiate with a single or recurrent spikes . Second, long‐term recordings reveal the frequency of interictal spikes tend to gradually increase prior to seizures .…”

“…First, EEG and ECoG recordings show that many partial and generalized seizures initiate with a single or recurrent spikes. 4,8,[44][45][46][47] Second, long-term recordings reveal the frequency of interictal spikes tend to gradually increase prior to seizures. 48 Third, single cell intracellular voltage recordings show that paroxysmal depolarization shifts underlie both spikes and onset of seizures.…”