The effect of Neuroligin-2 absence on sleep architecture and electroencephalographic activity in mice
Bibliographic record
Abstract
Sleep disorders are comorbid with most psychiatric disorders, but the link between these is not well understood. Neuroligin-2 (NLGN2) is a cell adhesion molecule that plays roles in synapse formation and neurotransmission. Moreover, NLGN2 has been associated with psychiatric disorders, but its implication in sleep remains underexplored. In the present study, the effect of Nlgn2 knockout ( Nlgn2 −/− ) on sleep architecture and electroencephalographic (EEG) activity in mice has been investigated. The EEG and electromyogram (EMG) were recorded in Nlgn2 −/− mice and littermates for 24 h from which three vigilance states (i.e., wakefulness, rapid eye movement [REM] sleep, non-REM [NREM] sleep) were visually identified. Spectral analysis of the EEG was performed for the three states. Nlgn2 −/− mice showed more wakefulness and less NREM and REM sleep compared to wild-type ( Nlgn2 +/+ ) mice, especially during the dark period. This was accompanied by changes in the number and duration of individual episodes of wakefulness and sleep, indexing changes in state consolidation, as well as widespread changes in EEG spectral activity in all states. Abnormal ‘hypersynchronized’ EEG events have also been observed predominantly in Nlgn2 −/− mice. These events were mainly observed during wakefulness and REM sleep. In addition, Nlgn2 −/− mice showed alterations in the daily time course of NREM sleep delta (1–4 Hz) activity, pointing to modifications in the dynamics of sleep homeostasis. These data suggest that NLGN2 participates in the regulation of sleep duration as well as EEG activity during wakefulness and 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".