50 Hz cortical stimulation increases interictal epileptiform discharges at the seizure onset zone
Bibliographic record
Abstract
Abstract More than 15 million patients worldwide suffer from drug-resistant epilepsy (DRE). Surgical removal of the seizure onset zone (SOZ)—the brain region(s) from where seizures arise—is the best available treatment for these patients, with post-surgical outcomes depending on the successful identification and resection of the SOZ. Most commonly, SOZ mapping localizes ictal activity occurring spontaneously or evoked by cortical stimulation (CS) during presurgical evaluation of patients with epilepsy using stereoelectroncephalography (SEEG). Mapping events such as interictal epileptiform discharges (IEDs), paroxysmal hypersynchronic electrical discharges that often occur outside ictal discharges or during CS, have been less used for SOZ localization. We test the hypothesis that IEDs triggered by CS via SEEG investigation can contribute to the mapping of the SOZ. We evaluated the impact of CS on IEDs in patients investigated with SEEG on epilepsy surgery investigation. We recorded intracranial signals from 30 DRE patients (seizure-free post-surgery). Bipolar and high frequency (50 Hz) CS was performed with a pulse width of 300 µs and current spanning 1–8 mA. IEDs were automatically detected pre- and post- stimulation, and their normalized absolute changes were quantified within and outside the SOZ (identified by ictal discharges). We found that IED rates significantly increased post-stimulation compared to pre-stimulation within the SOZ, while no significant change was observed outside the SOZ (Linear mixed effect model, p-value <0.001, and AUC=98% for SOZ and 71% for non-SOZ). This effect was present regardless of whether the stimulation was applied to the SOZ or non-SOZ regions, indicating a broader effect of stimulation on the SOZ. Our results offer a quantitative tool for identifying epileptogenic areas in patients with DRE, enhancing the mapping and localization of the SOZ and potentially improving surgical outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".