The atypical dopamine receptor agonist <scp>SKF</scp> 83959 enhances hippocampal and prefrontal cortical neuronal network activity in a rat model of cognitive dysfunction
Bibliographic record
Abstract
Deficits in neuronal network synchrony in hippocampus and prefrontal cortex have been widely demonstrated in disorders of cognitive dysfunction, including schizophrenia and Alzheimer's disease. The atypical dopamine agonist SKF 83959 has been shown to increase brain-derived neurotrophic factor signalling and suppress activity of glycogen synthase kinase-3 in PFC, two processes important to learning and memory. The purpose of this study was to therefore evaluate the impact of SKF 83959 on oscillatory deficits in methylazoxymethanol acetate (MAM) rat model of schizophrenia. To achieve this, local field potentials were recorded simultaneously from the hippocampus and prefrontal cortex of anesthetized rats at 15 and 90 min following both acute and repeated administration of SKF 83959 (0.4 mg/kg). In MAM rats, but not controls, repeated SKF 83959 treatment increased signal amplitude in hippocampus and enhanced the spectral power of low frequency delta and theta oscillations in this region. In PFC, SKF 83959 increased delta, theta and gamma spectral power. Increased HIP-PFC theta coherence was also evident following acute and repeated SKF 83959. In apparent contradiction to these oscillatory effects, in MAM rats, SKF 83959 inhibited spatial learning and induced a significant increase in thigmotactic behaviour. These findings have uncovered a previously unknown role for SKF 83959 in the positive regulation of hippocampal-prefrontal cortical oscillatory network activity. As SKF 83959 is known to have affinity for a number of receptors, delineating the receptor mechanisms that mediate the positive drug effects on neuronal oscillations could have significant future implications in disorders associated with cognitive dysfunction.
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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 | 0.000 |
| 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 | 0.001 |
| 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".