Ictal phase-amplitude coupling as a biomarker for seizure onset zone in neocortical epilepsy
Bibliographic record
Abstract
Objective To assess ictal phase–amplitude coupling (PAC) dynamics during seizure generation and explore whether ictal PAC analysis can help identify the seizure onset zone (SOZ) in neocortical epilepsy. Methods We analyzed ictal intracranial electroencephalography recordings from 22 seizures in six patients with drug-resistant neocortical epilepsy who achieved International League Against Epilepsy Class I outcomes following resection surgery. PAC strength was quantified using the modulation index (MI), computed by coupling the amplitude of high-frequency bands (ripples: 80–200 Hz; fast ripples: 200–300 Hz) with the phase of slow-wave bands (1–2 Hz, 2–3 Hz, 3–4 Hz and 4–8 Hz). MI values were compared across SOZ, peri-SOZ (adjacent electrodes), and non-SOZ regions. Localization accuracy was evaluated using the area under the curve (AUC) from receiver operating characteristic curve analysis. Results Across all PAC between ripples and slow wave bands, MI values were significantly higher in the SOZ compared to the peri-SOZ and non-SOZ regions (p < 0.001). During the late phase of seizure onset, MIRipples/4–8 Hz was also significantly higher in the peri-SOZ region than in the non-SOZ region (p < 0.001). MIRipples/4–8 Hz (AUC: 0.853) and MIRipples/3–4 Hz (AUC: 0.846) demonstrated strong SOZ localization performance. MIRipples/3–4 Hz distinguished SOZ from peri-SOZ (AUC: 0.800). Conclusion Elevated MI during seizure onset accurately localized the SOZ in neocortical epilepsy. Ictal PAC may be a useful biomarker for identifying SOZ in neocortical epilepsy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".