Cannabis Vapor Exposure Alters Neural Circuit Oscillatory Activity in a Neurodevelopmental Model of Schizophrenia: Exploring the Differential Impact of Cannabis Constituents
Bibliographic record
Abstract
Cannabis use is highly prevalent in patients with schizophrenia and worsens the course of the disorder. To understand how exposure to cannabis changes schizophrenia-related oscillatory disruptions, we investigated the impact of administering cannabis vapor containing either Δ9-tetrahydrocannabinol (THC) or balanced THC/cannabidiol (CBD) on oscillatory activity in the neonatal ventral hippocampal lesion (NVHL) rat model of schizophrenia. Male Sprague Dawley rats underwent lesion or sham surgeries on postnatal day 7. In adulthood, electrodes were implanted targeting the cingulate cortex (Cg), the prelimbic cortex (PrLC), the hippocampus (HIP), and the nucleus accumbens (NAc). Local field potential recordings were obtained after rats were administered either the "THC-only" cannabis vapor (8-18% THC/0% CBD) or the "Balanced THC:CBD" cannabis vapor (4-11% THC/8.5-15.5% CBD) in a cross-over design with a 2-week wash-out period between exposures. Compared to controls, NVHL rats had reduced baseline gamma power in the Cg, HIP, and NAc, and reduced HIP-Cg high-gamma coherence. THC-only vapor exposure broadly suppressed oscillatory power and coherence, even beyond the baseline reductions observed in NHVL rats. Balanced THC:CBD vapor, however, did not suppress oscillatory power and coherence, and in some instances enhanced power. For NVHL rats, THC-only vapor normalized the baseline HIP-Cg high-gamma coherence deficits. NHVL rats demonstrated a 20 ms delay in HIP theta to high-gamma phase coupling, which was not apparent in the PrLC and NAc after both exposures. In conclusion, cannabis vapor exposure has varying impacts on oscillatory activity in NVHL rats, and the relative composition of naturally occurring cannabinoids may contribute to this variability.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".