Electromagnetic evidence that benign epileptiform transients of sleep are traveling, rotating hippocampal spikes
Bibliographic record
Abstract
OBJECTIVE: Benign epileptiform transients of sleep (BETS) have a unique voltage topography and a posteriorly propagating, inferiorly rotating diphasic EEG pattern. The source generators of BETS have not been definitively identified. We aimed to clarify the cerebral localization of BETS using MEG and electromagnetic source imaging (EMSI). METHODS: We analyzed BETS recorded with simultaneous MEG and EEG in four patients with epilepsy. Magnetic source imaging (MSI) and EMSI using equivalent current, single moving and rotating dipole inverse models was performed on averaged BETS potentials. MEG beamforming was performed in one case with abundant BETS. RESULTS: MSI and EMSI revealed hippocampal dipole source maxima in all cases, with current flow direction rotating from inferomedial to superomedial or superolateral between the first and second BETS peaks. Moving dipole analyses revealed spatiotemporal propagation along the anterior-posterior hippocampal axis and concomitant electromagnetic field rotation. Beamformer source reconstruction revealed an identical hippocampal localization. CONCLUSIONS: Converging evidence from different electromagnetic inverse modeling methods indicates that BETS are traveling, rotating hippocampal spikes, whose diphasic waveform is due to back and forth propagation along the anterior-posterior axis of the hippocampus. SIGNIFICANCE: The hippocampal localization and longitudinal, rotating propagation pattern of BETS raises the possibility of a sleep-related functional role for these hippocampal spikes.
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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 | 0.000 | 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) | 0.002 | 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".