MEG Predicts Epileptic Zone in Lesional Extrahippocampal Epilepsy: 12 Pediatric Surgery Cases
Bibliographic record
Abstract
PURPOSE: To discover whether the spatial distribution of spike sources determined by magnetoencephalography (MEG) provides reliable information for planning surgery and predicting outcomes in pediatric patients with lesional extrahippocampal epilepsy. METHODS: We retrospectively studied 12 children with extrahippocampal epilepsy secondary to cortical dysplasia (CD), tumor, or porencephalic cyst. We compared interictal MEG spike source locations and somatosensory evoked fields derived from equivalent-current dipole modeling with intraoperative or extraoperative electrocorticography (ECoG). RESULTS: MEG spike sources were found in proximity to the lesion in all patients and extended from lesions in five patients with CD. Marginal spike sources were noted in three patients with tumors, one patient with a cyst, and one with CD, and extramarginal sources in three patients with tumors. Three patients with tumors underwent lesionectomy only; two had further cortical excisions. One patient with CD underwent lesionectomy only, three had lesionectomy and cortical excisions, and two had lesionectomy and multiple subpial transection. Asymmetric MEG spike sources correlated with ECoG findings in all patients. Residual epileptiform discharges on postexcisional ECoG corresponded to spike sources in three patients with tumors and one patient with a cyst. Eleven patients have been seizure free for 1-6 years (mean, 4 years). One patient had residual seizures after incomplete excision of right temporal CD. CONCLUSIONS: MEG delineated asymmetric epileptogenicity surrounding lesions and the eloquent cortex. Complete tumor resection produced favorable outcomes despite residual postexcisional ECoG spikes and extramarginal MEG spike sources. CD characterized by clusters of MEG spike sources within and extending from lesions seen on magnetic resonance imaging (MRI) should be removed to prevent seizures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".