A Focal Inactivation and Computational Study of Ventrolateral Periaqueductal Gray and Deep Mesencephalic Reticular Nucleus Involvement in Sleep State Switching and Bistability
Bibliographic record
Abstract
Abstract Neurons of the ventrolateral periaqueductal gray (vlPAG) and adjacent deep mesencephalic reticular nucleus (DpMe) are implicated in the control of sleep-wake state and are hypothesized components of a flip-flop circuit that maintains sleep bistability by preventing the overexpression of non-rapid eye movement (NREM)/REM sleep intermediary states (NRt). To determine the contribution of vlPAG/DpMe neurons in maintaining sleep bistability we combined computer simulations of flip-flop circuitry with focal inactivation of vlPAG/DpMe neurons by microdialysis delivery of the GABA A receptor agonist muscimol in freely behaving male rats ( n = 25) instrumented for electroencephalographic and electromyographic recording. REM sleep was enhanced by muscimol at the vlPAG/DpMe, consistent with previous studies; however, our analyses of NRt dynamics in vivo and those produced by flop-flop circuit simulations show that current thinking is too narrowly focused on the contribution of REM sleep-inactive populations toward vlPAG/DpMe involvement in REM sleep control. We found that much of the muscimol-mediated increase in REM sleep was more appropriately classified as NRt. This loss of sleep bistability was accompanied by fragmentation of REM sleep, as evidenced by an increased number of short REM sleep bouts. REM sleep fragmentation stemmed from an increased number and duration of NRt bouts originating in REM sleep. By contrast, NREM sleep bouts were not likewise fragmented by vlPAG/DpMe inactivation. In flip-flop circuit simulations, these changes could not be replicated through inhibition of the REM sleep-inactive population alone. Instead, combined suppression of REM sleep active and inactive vlPAG/DpMe subpopulations was required to replicate the changes in NRt dynamics.
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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 | 0.000 |
| 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) | 0.000 | 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 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".