CANNABIS AND TOBACCO CO-USE AND ITS ASSOCIATION WITH STRIATAL BRAIN MORPHOMETRY: LEVERAGING DATA FROM THE ENIGMA ADDICTION WORKING GROUP
Bibliographic record
Abstract
BACKGROUND: The striatum plays a central role in the pathophysiology of addiction. Studies report that cannabis use is associated with greater striatal gray matter volume (GMV), while tobacco use is associated with both greater and lower striatal GMV. However, their combined effects on striatal GMV remain unclear. METHODS: Using two MRI processing methods, we investigated associations between these substances and striatal GMV. Men and women from eight ENIGMA Addiction sites (N = 302) were parsed into 4 groups; individuals with: co-use (CT, n = 38); cannabis-only use (CO, n = 34); tobacco-only use (TO, n = 61), and controls (CTL, n = 169). Striatal GMV was assessed using (1) Freesurfer-extracted region-specific estimates and (2) voxel-based morphometry using FSL within a striatal mask. Analyses employed 2 × 2 ANCOVAs. RESULTS: With Freesurfer, a main effect of cannabis use (CT and CO) emerged in the right nucleus accumbens, and was replicated using FSL (ps ≤ 0.05). With FSL, main effects of cannabis use were found in the putamen and caudate (ps ≤ 0.01), and were modulated by tobacco use (cannabis-tobacco interaction ps ≤ 0.02). Post-hoc comparisons revealed: CT = CTL > TO in the putamen and caudate (ps ≤ 0.04); and CTL > CO (ps ≤ 0.06) and CT > CO (p = 0.03) in the caudate. CONCLUSIONS: Results demonstrate that in the putamen and caudate, individuals who co-use have greater GMV compared to those who use either substance alone, whereas individuals using either substance alone have lower GMV compared to controls. Future research should replicate our novel findings and investigate the clinical correlates of this unique pattern to clarify the functional significance of these findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".