Proposed Model of the Neurobiological Mechanisms Underlying Psychosocial Alcohol Interventions: The Example of Motivational Interviewing
Bibliographic record
Abstract
OBJECTIVE: Despite the prevalence and profound consequences of alcohol use disorders, psychosocial alcohol interventions have widely varying outcomes. The range of behavior following psychosocial alcohol treatment indicates the need to gain a better understanding of active ingredients and how they may operate. Although this is an area of great interest, at this time there is a limited understanding of how in-session behaviors may catalyze changes in the brain and subsequent alcohol use behavior. Thus, in this review, we aim to identify the neurobiological routes through which psychosocial alcohol interventions may lead to post-session behavior change as well as offer an approach to conceptualize and evaluate these translational relationships. METHOD: PubMed and PsycINFO searches identified studies that successfully integrated functional magnetic resonance imaging and psychosocial interventions. RESULTS: Based on this research, we identified potential neurobiological substrates through which behavioral alcohol interventions may initiate and sustain behavior change. In addition, we proposed a testable model linking within-session active ingredients to outside-of-session behavior change. CONCLUSIONS: Through this review, we present a testable translational model. Additionally, we illustrate how the proposed model can help facilitate empirical evaluations of psychotherapeutic factors and their underlying neural mechanisms, both in the context of motivational interviewing and in the treatment of alcohol use disorders.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".