Paroxysmal Slow Waves Mark Ictal Networks
Bibliographic record
Abstract
Epilepsy diagnosis and treatment monitoring are hindered by the episodic, heterogeneous expression of seizures and by normal-appearing scalp EEG in many patients. We previously described paroxysmal slow-wave events (PSWEs)-brief epochs of broadband slowing detectable on EEG. Here, using intracerebral and epidural recordings in a paraoxon rat model of temporal lobe epilepsy, we show that PSWEs arise preferentially in temporo-frontal networks, co-occur with global slowing, and increase during both spontaneous and pharmacologically induced seizures. Epidurally recorded PSWEs were temporally coupled to deep temporal discharges and were bidirectionally modulated by GABAergic agents (increased with pentylenetetrazol and decreased with pentobarbital). In long-term video-EEG monitoring (LTM) patients with temporal lobe epilepsy, simultaneous stereo-EEG and scalp EEG showed that scalp PSWEs mirrored hippocampal spike-and-wave activity and were more often observed in the preictal and ictal periods than during interictal baseline. These data indicate that surface PSWEs can index remote epileptiform activity and support their use as a quantitative, noninvasive biomarker for detecting EEG-silent deep foci and for pharmacodynamic.
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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 | 0.001 | 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) | 0.002 | 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".