State-Dependent Changes in Glutamate, Glycine, GABA, and Dopamine Levels in Cat Lumbar Spinal Cord
Bibliographic record
Abstract
Recent studies have indicated that the glycine receptor antagonist strychnine and the gamma-aminobutyric acid type A (GABA A) receptor antagonist bicuculline reduced the rapid-eye-movement (REM) sleep-specific inhibition of sensory inflow via the dorsal spinocerebellar tract (DSCT). These findings imply that the spinal release of glycine and GABA may be due directly to the REM sleep-specific activation of reticulospinal neurons and/or glutamate-activated last-order spinal interneurons. This study used in vivo microdialysis and high-performance liquid chromatography analysis techniques to provide evidence for these possibilities. Microdialysis probes were stereotaxically positioned in the L3 spinal cord gray matter corresponding to sites where maximal cerebellar-evoked field potentials or individual DSCT and nearby spinoreticular tract (SRT) neurons could be recorded. Glutamate, glycine, and GABA levels significantly increased during REM sleep by approximately 48, 48, and 14%, respectively, compared with the control state of wakefulness. In contrast, dopamine levels significantly decreased by about 28% during REM sleep compared with wakefulness. During the state of wakefulness, electrical stimulation of the nucleus reticularis gigantocellularis (NRGc) at intensities sufficient to inhibit DSCT neuron activity, also significantly increased glutamate and glycine levels by about 69 and 45%, respectively, but not GABA or dopamine levels. We suggest that the reciprocal changes in the release of glutamate, glycine, and GABA versus dopamine during REM sleep contribute to the reduction of sensory inflow to higher brain centers via the DSCT and nearby SRT during this behavioral state. The neural pathways involved in this process likely include reticulo- and diencephalospinal and spinal interneurons.
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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".