No association between histopathology and neurophysiology in surgical specimens from pediatric focal epilepsy patients
Bibliographic record
Abstract
OBJECTIVE: Focal epilepsy is caused by focal brain pathologies sometimes with involvement of surrounding or distant tissue. However, within the epileptogenic zone (EZ), which can be resected to cure epilepsy, the contribution of histopathological cells to epileptogenicity remains unknown. We hypothesized that areas showing neurophysiological biomarkers of epileptogenicity would more often contain histopathological cells compared to areas without such biomarkers. METHODS: Pediatric epilepsy patients with nonlesional magnetic resonance imaging (MRI), or with lesions with unclear borders, underwent stereoelectroencephalographic (SEEG) exploration followed by resective surgery of the suspected EZ. Tissue specimens were taken from locations where the SEEG contacts had been, using intraoperative MRI-guided precise neuronavigation. Then, we explored the association between histopathology and rates of interictal epileptic discharges, ripples, and fast ripples (FRs), and the channels of the seizure onset zone (SOZ). RESULTS: The association between histopathology and ictal/interictal activity was low and not statistically significant in 260 specimens from 20 surgeries. Rates of interictal events were slightly lower for pathological samples than in normal tissue (low effect size, Cliff |d| < .15, p > .1). The classification accuracy of tissue as normal or pathological based on interictal activity/SOZ was low (accuracy ≤ 54%). As a secondary outcome, SEEG events were excellent predictors of surgical outcome, FRs leading to perfect prediction (20/20). SIGNIFICANCE: Histopathological tissue initiates epileptogenicity in focal epilepsy. However, our findings suggest that the EZ is not strictly a histopathological entity but a hybrid of abnormal cells and normal-appearing cells. Thus, SEEG events are biomarkers of epileptogenicity and not histopathology. Resecting of electrophysiological biomarkers of epileptogenicity may be more important than resecting all histopathology.
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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 | 0.004 |
| 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 | 0.001 |
| 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".