Brain structural magnetic resonance imaging predictors of brief intervention response in individuals with alcohol use disorder.
Bibliographic record
Abstract
AIMS: Magnetic resonance imaging (MRI) studies have identified brain structural predictors of treatment response in individuals with alcohol use disorder (AUD) but with varying findings and primarily in male veterans. The present study investigated cortical surface area and thickness (CT) as predictors of brief intervention response in community-based adults with AUD. METHODS: Sixty-five non-treatment-seeking adults with AUD (44.6% male, aged 33.2 ± 1.3 years) underwent an MRI and received a brief intervention comprising personalized feedback and motivational interviewing, with follow-up ~6-8 weeks later to quantify changes in drinks/week (DPW), the primary outcome. Eighteen bilateral a priori regions of interest (ROIs) were used to predict DPW at follow-up, adjusting for baseline drinking. Significant predictors were examined with secondary outcomes, percent drinking and heavy drinking days, and in relation to out-of-scanner measures of impulsivity and comorbidities. RESULTS: Participants exhibited significant decreases in alcohol consumption in response to the brief intervention. Eight bilateral CT ROIs in the frontal, temporal, and occipital lobes, most notably medial orbitofrontal, middle temporal, and lateral occipital gyri, predicted DPW; however, only three predicted the secondary outcomes. Significant associations were observed between CT in frontal and occipital regions and impulsivity (delay discounting, lack of premeditation), executive functioning, anxiety, and stress. CONCLUSIONS: Thinner frontal, temporal, and occipital ROIs predicted poorer brief intervention response, with notable overlap with brain regions previously implicated in AUD. Clarifying whether these regions reflect premorbid or acquired differences and, if the latter, the potential for recovery of cortical gray matter following drinking reductions are future priorities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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".