Glutamine and GABA alterations in cingulate cortex may underlie alcohol drinking in a rat model of co-occurring alcohol use disorder and schizophrenia: an 1H-MRS study
Bibliographic record
Abstract
Alcohol use disorder commonly occurs in patients with schizophrenia and significantly worsens the clinical course of the disorder. The neurobiological underpinnings of alcohol drinking are not well understood. Magnetic resonance spectroscopy (MRS) has been used to assess the neurochemical substrates that may be associated with alcohol drinking in patients; however, the causal impact of these findings remains elusive, highlighting the need for studies in animal models. This study performed MRS in the neonatal ventral hippocampal lesioned (NVHL) rat model, a model of co-occurring schizophrenia and substance use disorders. NVHL lesions (or sham surgeries) were performed on post-natal day 7 and animals were given brief exposure to alcohol during adolescence (10% v/v in a 2-bottle choice design). Animals were re-exposed to alcohol during adulthood (20% v/v) until a stable drinking baseline was established, and then forced into abstinence to control for the effects of differential alcohol drinking. Animals were scanned for MRS after one month of abstinence. NVHL rats consumed significantly more alcohol than sham rats and in the cingulate cortex showed significantly higher levels of GABA and glutamine. Significantly lower GABA levels were observed in the nucleus accumbens. No differences between the NVHL and sham animals were observed in the hippocampus. Correlation analysis revealed that GABA and glutamine concentrations in the cingulate cortex significantly correlated with the rats' alcohol drinking prior to 30 days of forced abstinence. These findings suggest that a potential dysfunction in the glutamate/GABA-glutamine cycle may contribute to alcohol drinking in a rat model of schizophrenia, and this dysfunction could be targeted in future treatment-focused studies.
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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) | 0.001 | 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".