Independent brain cortical signatures of risk for adolescent cannabis use and consequences of such use are moderated by sex
Bibliographic record
Abstract
Abstract There is an accumulation of evidence linking adolescent cannabis use with variations in brain structure and function, however it remains poorly understood whether cannabis-associated variations in brain structure represent pre-existing risk factors or consequences of cannabis use. We investigated whether cannabis use propensity and within-person variations of cannabis use were associated with cortical thickness during adolescence. Adolescents ( n = 136, 74 female) completed three neuroimaging sessions and annual assessments from 12 until 17 years of age (with 90% follow-up). Cannabis use was disaggregated into between- (vulnerability) and within-person (time-varying) components using longitudinal multi-level modelling, controlling for age, sex and alcohol use. Across the whole sample, cortical thickness was lower in years when participants’ cannabis use exceeded their own average level of cannabis use ( F 1,25663.3 = 3.96, p = 0.047; mean: −0.0023 mm/once-per-week increase). This effect was stronger in males ( F 1,11447.7 = 9.83, p = 0.0017), such that each once-per-week increase in cannabis use was associated with a 0.005 mm reduction in cortical thickness, comparable to 17.9% of the annual rate of cortical thinning (−0.028 mm/year). The strongest within-person effects of cannabis were observed in regions with the greatest expression of CNR1 , the gene that codes for the CB1 receptor (sample: rho = −0.33, p spin = .025; males: rho = −0.5, p spin = .005). At the between-person level, males (but not females) also exhibited a stable cortical thickness signature associated with propensity towards cannabis use and this signature was present before cannabis exposure. These results highlight the importance of longitudinal analyses using multi-level modelling to disaggregate potential risk factors from potential consequences of substance use.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".