Modulation of orbitofrontal-striatal reward activity by dopaminergic functional polymorphisms contributes to a predisposition to alcohol misuse in early adolescence
Bibliographic record
Abstract
BACKGROUND: Abnormalities in reward circuit function are considered a core feature of addiction. Yet, it is still largely unknown whether these abnormalities stem from chronic drug use, a genetic predisposition, or both. METHODS: In the present study, we investigated this issue using a large sample of adolescent children by applying structural equation modeling to examine the effects of several dopaminergic polymorphisms of the D1 and D2 receptor type on the reward function of the ventral striatum (VS) and orbital frontal cortex (OFC), and whether this relationship predicted the propensity to engage in early alcohol misuse behaviors at 14 years of age and again at 16 years of age. RESULTS: The results demonstrated a regional specificity with which the functional polymorphism rs686 of the D1 dopamine receptor (DRD1) gene and Taq1A of the ANKK1 gene influenced medial and lateral OFC activation during reward anticipation, respectively. Importantly, our path model revealed a significant indirect relationship between the rs686 of the DRD1 gene and early onset of alcohol misuse through a medial OFC × VS interaction. CONCLUSIONS: These findings highlight the role of D1 and D2 in adjusting reward-related activations within the mesocorticolimbic circuitry, as well as in the susceptibility to early onset of alcohol misuse.
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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 | 0.000 | 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) | 0.002 | 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".