High-resolution Source Imaging in Mesiotemporal Lobe Epilepsy: A Comparison Between MEG and Simultaneous EEG
Bibliographic record
Abstract
Magnetic source imaging is claimed to have a high accuracy in epileptic focus localization and may be a guide for epilepsy surgery. Non-lesional mesiotemporal lobe epilepsy (MTLE), the most common form of epilepsy operated on, has different etiologies, which may affect the choice of surgical approach. The authors compared whole-head magnetoencephalography (MEG) with high-resolution EEG for source identification in MTLE. Nineteen patients with unilateral, nonlesional MTLE underwent a simultaneous 151-channel CTF MEG (CTF Systems, Inc., Port Coquitlam, British Columbia, Canada) and 64-channel EEG recordings with sleep induction. Three independent observers selected spikes from the EEG and MEG recordings separately. Only when there was interobserver agreement (kappa>0.4) on the presence of spikes in recordings were consensus spikes averaged. EEG and MEG equivalent current dipoles (ECD) were then integrated in the head model of the patient reconstructed from MRI. The results were compared with intraoperative electrocorticography findings. Spikes were detected in 32% of MEGs and 42% of EEGs. No patient showed MEG spikes only. Equivalent current dipole modeling correctly localized the source to the temporal lobe in four out of five MEG and three out of eight EEG recordings. MEG localized sources were more superficial and EEG localized sources were deeper. Unfortunately, basal temporal lobe areas were only partially covered by the sensor helmet of the MEG setup. Best correlation between EEG or MEG findings and electrocorticography findings was between horizontal EEG dipole orientation and prominent neocortical spiking; these patients also had a less favorable prognosis. Magnetic source imaging is currently unlikely to alter the surgical management of MTLE. The yield of spikes is too low, and ECD modeling shows only partial correlation with electrocorticography findings. Moreover, the whole-head MEG helmet provides insufficient coverage of the temporal lobe.
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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 | 0.006 |
| 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 | 0.001 |
| 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".