Cannabis Vapour Exposure Alters Neural Circuit Oscillatory Activity In A Neurodevelopmental Model Of Schizophrenia: Exploring The Differential Impact Of Cannabis Constituents
Bibliographic record
Abstract
Abstract Cannabis use is highly prevalent in patients with schizophrenia and worsens the course of the disorder. To understand the causal impacts of cannabis on schizophrenia-related oscillatory disruptions, we herein investigated the impact of exposure to cannabis vapour (containing delta-9-tetrahydrocannabinol [THC] or balanced THC and cannabidiol [CBD]) on oscillatory activity in the neonatal ventral hippocampal lesion (NVHL) rat model of schizophrenia. Male Sprague Dawley rats underwent NVHL or sham surgeries on postnatal day 7. In adulthood, electrodes were implanted targeting the cingulate cortex (Cg), the prefrontal cortex (PFC), the dorsal hippocampus (HIP), and the nucleus accumbens (NAc). Local field potential recordings were obtained following exposure to two strains of vapourized cannabis flower (with ~10% THC or ~10% balanced THC:CBD) in a cross-over design with a two-week wash-out period between exposures. Compared to controls, NVHL rats had reduced baseline gamma power in the Cg, dHIP, and NAc, and reduced high-gamma coherence between the dHIP-Cg. THC-only vapour broadly suppressed oscillatory power and coherence, even beyond the baseline suppressions observed in NHVL rats. Balanced THC:CBD vapour appeared to ameliorate the THC-induced impacts on power and coherence in both sham and NVHL rats. For NVHL rats, THC-only vapour also normalized the baseline dHIP-Cg high-gamma coherence deficits. NHVL rats also demonstrated a 20ms delay in dHIP theta to high-gamma phase coupling, which was ameliorated by both exposures in the PFC and NAc. In conclusion, THC-only cannabis vapour suppressed oscillatory activity in NVHL and sham rats, while balanced THC:CBD vapour may ameliorate some of these effects.
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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".