Back

From Spike to Seizure: Transformation or Transition?

Aung, T.; Jegou, A.; Chauvel, P.

2025-07-18 neurology
10.1101/2025.07.18.25331676 medRxiv
Show abstract

ObjectiveThe transition from interictal discharges to ictal high-frequency activity (HFA) remains poorly understood. We investigated whether spike-associated high-frequency oscillations (Sp-HFOs) during interictal and preictal periods contribute to the emergence of ictal HFA. MethodsWe retrospectively analyzed the interictal to ictal transition in seizures from six patients with drug-resistant focal epilepsy who underwent stereo-EEG and subsequent surgical resection. Various interictal periods preceding seizure onset were selected for comparison. Time- frequency analysis (TFA) was used to characterize Sp-HFOs and ictal HFA. Frequency overlap was quantified using the I-Fusion metric, and linear regression assessed changes in I-Fusion values over time, with R{superscript 2} indicating correlation strength. ResultsVisual analysis of the time series revealed a preictal phase in all patients, during which brief high-frequency activity gradually emerged within spikes (Sp-HFOs), ultimately transitioning into sustained ictal HFA at the same frequency. TFA demonstrated increasing frequency similarity with time between Sp-HFOs and ictal HFA. I-Fusion values and R{superscript 2} coefficients rose consistently, indicating a progressive convergence in frequency content. Notably, Sp-HFOs and ictal HFA shared narrow-band frequency features within the same electrode contacts, especially in the epileptogenic zone (EZ). InterpretationOur findings support a dynamic, frequency-specific evolution from interictal Sp-HFOs to ictal HFA, suggesting that seizure onset is preceded by a gradual preparatory phase rather than an abrupt transformation. The progressive nature and spectral continuity of Sp-HFOs may reflect increasing neuronal synchrony, providing potential early biomarkers for seizure prediction and improved localization of the EZ.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.