Glutamate neurons in the pedunculopontine tegmental nucleus control arousal state and motor behavior in mice
Bibliographic record
Abstract
The peduculopontine tegmental nucleus (PPT) modulates both sleep-wake states and motor behaviors [1]. However, it remains unclear how glutamatergic, GABAergic, and cholinergic subpopulations within the PPT function to regulate arousal states and motor behaviors [1, 2]. In a recent publication, Kroeger et al. uncovered the biological roles that glutamate PPT (PPTvGlut2) neurons play in modulating arousal and motor behaviors in freely behaving mice (Figure 1). Using a range of sophisticated techniques, they found that PPTvGlut2 neurons not only induce wakefulness (from non-REM sleep), but that they also engage motor activity. Moreover, Kroeger et al. show that PPTvGlut2 neurons control these behaviors through distinct projection pathways within the basal forebrain, hypothalamus, and midbrain. Their findings are important to the fields of neuroscience and sleep biology, as they describe how PPTvGlut2 neurons participate in sleep-wake and motor control in naturally occurring behavior. Although previous research shows that PPTvGlut2 neurons engage arousal [1], it is unclear how they do so. Therefore, to determine how PPTvGlut2 neurons participate in behavioral control, Kroeger et al. started by manipulating the activity of all PPTvGlut2 neurons. Using behavioral, electrophysiological, and optogenetic tools, they found that optically exciting PPTvGlut2 neurons induced rapid awakening from NREM sleep. Intriguingly, however, they found that activation of PPTvGlut2 neurons was unable to induce arousal from REM sleep, suggesting that the wake-promoting effects of PPTvGlut2 neurons are selective for NREM sleep. This finding mirrors results from previous studies showing that the wake-promoting effects of GABA and neurotensin neurons in the lateral hypothalamus [3]—likely through communication with the thalamic reticular nucleus—are also selective for NREM sleep [4–6],
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
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".