Seizure onset location shapes dynamics of initiation

Abstract

ObjectiveIctal electrographic patterns are widely thought to reflect underlying neural mechanisms of seizures. Here we studied the degree to which seizure patterns are consistent in a given patient, relate to particular brain regions and if two candidate biomarkers (high-frequency oscillations, HFOs; infraslow activity, ISA) and network activity, as assessed with cross-frequency interactions, can discriminate between seizure types.MethodsWe analyzed temporal changes in low and high frequency oscillations recorded during seizures, as well as phase-amplitude coupling (PAC) to monitor the interactions between delta/theta and ripple/fast ripple frequency bands at seizure onset.ResultsSeizures of multiple pattern types were observed in a given patient and brain region. While there was an increase in HFO rate across different electrographic patterns, there are specific relationships between types of HFO activity and onset region. Similarly, changes in PAC dynamics were more closely related to seizure onset region than they were to electrographic patterns while ISA was a poor indicator for seizure onset.ConclusionsOur findings suggest that the onset region sculpts neurodynamics at seizure initiation and that unique features of the cytoarchitecture and/or connectivity of that region play a significant role in determining seizure mechanism.SignificanceClinicians should consider more than just overt electrographic patterns when considering seizure mechanisms and regions of onset. Examination of onset pattern in conjunction with the interactions between different oscillatory frequencies in the context of different brain regions might be more informative and lead to more reliable clinical inference as well as novel therapeutic approaches.

Brain network dynamics codify heterogeneity in seizure propagation

“…As one of the most common neurological disorders with roughly 50 million cases world-wide, epilepsy is characterized by its emerging spontaneous seizure activity (17,18). Critically, onethird of the patients do not respond to medications and rely on alternative interventions such as surgical and neuromodulatory approaches which have variable outcomes and subjective success rates (19)(20)(21). Tailoring effective treatment strategies requires biomarkers that can reliably predict not only the temporal complexity but also the dynamical properties of an impending seizure (22,23).…”