Sleep fragmentation drives local, network-specific epileptic activity in human epilepsy
Bibliographic record
Abstract
Abstract Sleep has complex links with epileptic activity, yet the causal role of sleep instability in driving and modulating pathological discharges in the human brain remains incompletely understood. Here we directly examine this by characterising the fine-scale temporal coupling between experimentally induced sleep arousals and interictal epileptiform discharges (IEDs), using combined stereo-electroencephalography and polysomnography recordings in patients with epilepsy. Sleep arousals triggered rapid IED increases, with effects gated by anatomical region and sleep stage. Increases were confined to neocortical regions and occurred during both non-rapid eye movement stage 2 (N2) and stage 3 (N3) sleep, with a larger effect observed in N2. IED increases did not differ between the seizure-onset zone and surrounding regions. Despite elevating IED counts, arousals did not alter IED spatial propagation, indicating state-dependent enhancement of local cortical excitability without recruitment of broader epileptic networks. These findings establish a causal role for sleep instability in actively driving pathological activity on fine-grained spatiotemporal scales, and highlight sleep stabilisation as a promising therapeutic strategy to reduce epileptic burden and preserve cortical network function.
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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".