Short-Duration Epileptic Discharges Show a Distinct Phase Preference During Ongoing Hippocampal Slow Oscillations
Bibliographic record
Abstract
Non-REM (slow-wave) sleep has been shown to facilitate temporal lobe epileptiform events, whereas REM sleep seems more restrictive. This state-dependent modulation may be the result of the enhancement of excitatory synaptic transmission and/or the degree of network synchronization expressed within the hippocampus of the temporal lobe. The slow oscillation (SO), a ∼1 Hz oscillatory pattern expressed during non-REM sleep and urethane anesthesia, has been recently shown to facilitate the generation, maintenance, and propagation of stimulus-evoked epileptiform activity in the hippocampus. To further address the state-dependent modulation of epileptic activity during the SO, we studied the properties of short-duration interictal-like activity generated by focal application of penicillin in the hippocampus of urethane-anesthetized rats. Epileptiform spikes were larger but only slightly more prevalent during the SO as opposed to the theta (REM-like) state. More notably, however, epileptic spikes had a significant tendency to occur just following the peak negativity of ongoing SO cycles. Because of the known phase-dependent changes in 1) synaptic excitability (just following the positive peak of the SO) and 2) network synchronization (during the negative peak of the SO), these results suggest that it is the synchrony and not the changes in synaptic excitability that lead to the facilitation of epileptiform activity during sleep-like slow wave states.
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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 | 0.000 |
| 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) | 0.001 | 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".