Fast Ripples Measured From Overnight SEEG Recordings as Markers of the Epileptogenic Zone
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Epilepsy surgery outcomes after intracranial EEG remain suboptimal necessitating the discovery of additional biomarkers to define the epileptogenic zone. Fast ripples (FRs) are a promising, new interictal epilepsy biomarker. By analyzing a multicenter data set consisting of overnight stereo-EEG (SEEG) recordings, we aimed at validating FRs as an accurate marker of the epileptogenic zone. We hypothesized that removing ≥60% of total FR events would significantly increase the odds of good postsurgical outcome (Engel class I). In addition, we compared FRs with spikes, and spikes co-occurring with FRs (spike-FRs) as surgery outcome predictors. METHODS: This retrospective cohort study included consecutive patients from 4 epilepsy surgery centers in Canada, Finland, and Denmark, who underwent SEEG followed by resective surgery or a preplanned ablation procedure separate from the SEEG and had at least 1 year of follow-up. We detected FRs and spikes automatically from overnight SEEG recordings edited for artifacts. To calculate resection ratios of the detected events, we determined resected SEEG contacts by superimposing the peri-implantation and postresection images. We evaluated postsurgical seizure outcomes from medical records. RESULTS: = 0.007, DOR 4.1, 95% CI 1.4-12, accuracy 64%, 95% CI 52%-75%), whereas the spike resection ratio ≥0.6 was not. DISCUSSION: In accordance with our hypothesis, the FR resection ratio ≥0.6 significantly increased the odds of attaining good postsurgical seizure outcome. Although the FR resection ratio ≥0.6 accurately predicted good postsurgical outcome, resecting <0.6 of FRs did not necessarily mean poor outcome. As predictors of postsurgical outcome, spikes fared poorly, whereas spike-FRs were comparable with FRs.
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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 | 0.001 |
| 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".