Sleep-related epilepsy through the lens of stereo-EEG: Clinical and research update
Bibliographic record
Abstract
The complex interactions between sleep and epilepsy have drawn increasing attention, and stereo-electroencephalography (SEEG) has become a pivotal tool for investigating their underlying pathophysiological mechanisms. This review highlights key contributions from SEEG studies over the past two decades, with a focus on Sleep-Related Hypermotor Epilepsy (SHE). Considered a disorder of frontal lobe origin, SHE is now recognized as a network-based epilepsy with a broader involvement of cortical regions. Sleep instability in Non-Rapid Eye Movement (NREM) sleep, indexed by the cyclic alternating pattern (CAP), and increased bistability, emerge as critical facilitators of epileptiform discharges. In contrast, rapid eye movement (REM) sleep, particularly its phasic substate, exerts a strong suppressive effect on epileptic activity. SEEG has been instrumental in characterizing these mechanisms and identifying novel biomarkers, including cross-frequency coupling and network-level measures of cortical instability. These findings have implications not only for diagnosis and surgical targeting but also for the development of neuromodulatory and state-based therapeutic approaches. Looking forward, the integration of SEEG with advanced computational tools offers new avenues for real-time brain-state mapping and seizure risk stratification. By bridging clinical neurophysiology with systems neuroscience, SEEG provides a unique platform for advancing the understanding of epilepsy within the dynamic context of sleep.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".