Prefrontal GABA and glutamate levels correlate with impulsivity and cognitive function of prescription opioid addicts: A <sup>1</sup>H‐magnetic resonance spectroscopy study
Bibliographic record
Abstract
Aim Prescription opioids are psychoactive substances that can elicit many neuropsychological effects. There are no studies that directly demonstrate the effects of prescription opioid addiction (POA) on the human brain. This study aimed to quantify γ‐aminobutyric acid (GABA) and glutamate (Glu) levels in the prefrontal cortex (PFC) of POA patients using proton magnetic resonance spectroscopy (1H‐MRS), and to explore their association with impulsive behavior and cognitive impairment. Methods Thirty‐five patients with a definitive clinical diagnosis of codeine‐containing cough syrup dependence and 35 matched healthy controls underwent neuropsychological assessments, namely the Barratt Impulsiveness Scale (BIS‐11) and the Montreal Cognitive Assessment Scale (MoCA). Point‐resolved spectroscopy was performed to detect GABA and glutamate within the medial PFC, and the corresponding levels were estimated using jMRUI and corrected for fraction of cerebrospinal fluid in the 1H‐MRS voxel. The difference in metabolite levels between groups and the correlation between metabolite levels and psychometric scores in patients were analyzed statistically. Results The peak level predominantly consisting of GABA with a relatively small influence of other chemicals (GABA+) was lower and that of glutamate was higher in the PFC of POA patients than in healthy controls. GABA+ levels correlated negatively with BIS‐11 scores but correlated positively with MoCA scores. In contrast, glutamate levels showed a positive correlation with BIS‐11 scores but no significant correlation with MoCA scores. Conclusion The quantitative in vivo measurement of GABA and glutamate levels in the PFC by 1H‐MRS could be a reliable way to evaluate impulsivity and cognitive function of POA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".