Epileptic Spikes: Magnetoencephalography versus Simultaneous Electrocorticography
Bibliographic record
Abstract
PURPOSE: To test the sensitivity of extracranial magnetoencephalography (MEG) for epileptic spikes in different cerebral sites. METHODS: We simultaneously recorded MEG and electrocorticography (ECoG) by using subdural electrodes with 1-cm interelectrode distances for one patient with lateral frontal epilepsy and one patient with basal temporal epilepsy. We analyzed MEG spikes associated with ECoG spikes and compared the maximal amplitude and number of electrodes involved. We estimated and evaluated the locations and moments of the equivalent current dipoles (ECDs) of MEG spikes. RESULTS: In patient 1, MEG detected 100 (53%) of 188 ECoG lateral frontal spikes, including 31 (46%) of 67 spikes that activated three subdural electrodes. MEG spike amplitudes correlated with ECoG spike amplitudes and the number of electrodes activated (p < 0.01). ECDs were perpendicular to the superior frontal sulcus. In patient 2, MEG detected 31 (26%) of 121 ECoG basal temporal spikes, but none that activated only three subdural electrodes. ECDs were localized in the entorhinal and parahippocampal gyri, oriented perpendicular to those basal temporal cortical surfaces. The ECD strength was 136.6 +/- 71.5 nAm in the frontal region, but 274.5 +/- 150.6 nAm in the temporal region (p < 0.01). CONCLUSIONS: When lateral frontal ECoG spikes extend >3 cm2 across the fissure, MEG can detect >50%, correlating with spatial activation and voltage. In the basal temporal region, MEG requires higher-amplitude discharges over a more extensive area. MEG shows a significantly higher sensitivity to lateral convexity epileptic discharges than to discharges in isolated deep basal temporal regions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".