Single and Multiple Clusters of Magnetoencephalographic Dipoles in Neocortical Epilepsy: Significance in Characterizing the Epileptogenic Zone
Bibliographic record
Abstract
PURPOSE: To characterize the epileptogenic zone in neocortical epilepsy (NE) by using magnetoencephalography (MEG). METHODS: We defined and compared locations of single and multiple clusters of equivalent current dipoles (ECDs) for interictal spikes with MRI findings, ictal-onset zones (IOZs) from subdural electroencephalography (SDEEG), resected areas, and postsurgical outcomes of 20 patients who underwent cortical resection for medically intractable NE. RESULTS: Fourteen patients had single clusters; six had multiple clusters. Overlap of clusters and IOZs defined group A (nine patients), in which a single cluster coincided with the IOZ; group B1 (four patients), in which a single cluster was within or partially overlapped the IOZ; group B2 (five patients), in which multiple-cluster sections overlapped IOZs; group C (two patients; one single; one multiple), in which no overlap was seen. More single clusters (nine of 14) than multiple clusters (none of six) coincided with the IOZ (p = 0.014). More patients with single clusters (10 of 14) than patients with multiple clusters (one of six) had seizure-free outcomes (p = 0.049). Eight of nine patients in group A, versus three of 11 in groups B1, B2, and C, achieved seizure-free outcomes (p = 0.0098). Correlations between MRI findings and postsurgical outcomes were not statistically significant; eight of 13 patients with single lesions, one of four with no lesions, and two of three with multifocal lesions had seizure-free outcomes. CONCLUSIONS: In neocortical epilepsy, MEG ECD clusters correlated with SDEEG IOZs. Single clusters indicated discrete epileptogenic zones that required complete resection for seizure-free outcome. Multiple clusters necessitated that the multiple or extensive epileptogenic zones be completely identified and delineated by SDEEG.
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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 | 0.002 |
| 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 | 0.001 |
| 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".