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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".