0119 A DEDICATED BRAINSTEM CIRCUIT CONTROLS REM SLEEP
Bibliographic record
Abstract
It remains unclear which neural circuit triggers REM sleep and REM sleep atonia, but glutamate neurons in the subcoeruleus (SubCGLUT) are hypothesized to control REM sleep as well as REM sleep atonia by activating GABA neurons in the ventral medulla (vMGABA). Here, we aimed to determine how optogenetic activation and inhibition of the SubCGLUT-vMGABA circuit impact REM sleep and REM sleep atonia. To control the neuronal activity of the glutamatergic SubC neurons, we bilaterally infused 200nL of an adeno-associated viral vector (AAV) containing either a light-sensitive excitatory opsin (AAV-EF1α-DIO-ChETA-eYFP) or a light-sensitive inhibitory opsin (AAV- EF1α-DIO-ARCH-eYFP) or an inert control protein (AAV- EF1α-DIO-eYFP) into the SubC of 33 Vglut2-cre mice. Animals were instrumented for EEG and EMG recordings. SubCGLUT neurons were activated or silenced specifically during REM sleep. In another set of animals, the SubCGLUT-vMGABA circuit was inhibited continuously during REM sleep at the level of the vM. Only animals that had histological verification of eYFP expression in the SubC region and projection fibers in the vM were used for analysis. We used Vglut2 fluorescent in situ hybridization and/or Vglut2-tdTomato expressing mice to confirm the specificity of our virally-mediated opsin expression. We found that activation of SubCGLUT neurons increased the length of REM sleep episodes by 77 ± 3% (n=5, p<0.01), and further decreased motor activity during REM sleep (n=5, p<0.01). In contrast, inhibition of SubC cells shortened the duration of REM sleep episodes (n=6, p<0.01), and increased overall motor activity by 26% (n=5, p<0.01). Importantly, silencing SubCGLUT transmission at the vM (SubCGLUT-vMGABA) increased overall motor activity during REM sleep (n=3, p<0.05) without affecting REM sleep amounts (n=3, p=0.639). These results support the hypothesis that neurons in the SubCGLUT-vMGABA circuit control both REM sleep and REM sleep atonia. This research was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC), the Canadian Institutes of Health Research (CIHR), and the CIHR Sleep and Biological Rhythms Toronto.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".