Brain volume in male patients with recent onset schizophrenia with and without cannabis use disorders
Bibliographic record
Abstract
BACKGROUND: Schizophrenia is highly comorbid with cannabis use disorders (CUDs), and this comorbidity is associated with an unfavourable course. Early onset or frequent cannabis use may influence brain structure. A key question is whether comorbid CUDs modulate brain morphology alterations associated with schizophrenia. METHODS: We used surface-based analysis to measure the brain volume, cortical thickness and cortical surface area of a priori-defined brain regions (hippocampus, amygdala, thalamus, caudate, putamen, orbitofrontal cortex, anterior cingulate cortex, insula, parahippocampus and fusiform gyrus) in male patients with schizophrenia or related disorders with and without comorbid CUDs and matched healthy controls. Associations between age at onset and frequency of cannabis use with regional grey matter volume were explored. RESULTS: We included 113 patients with (CUD, n = 80) and without (NCUD, n = 33) CUDs and 84 controls in our study. As expected, patients with schizophrenia (with or without a CUD) had smaller volumes of most brain regions (amygdala, putamen, insula, parahippocampus and fusiform gyrus) than healthy controls, and differences in cortical volume were mainly driven by cortical thinning. Compared with the NCUD group, the CUD group had a larger volume of the putamen, possibly driven by polysubstance use. No associations between age at onset and frequency of use with regional grey matter volumes were found. LIMITATIONS: We were unable to correct for possible confounding effects of smoking or antipsychotic medication. CONCLUSION: Patients with psychotic disorders and comorbid CUDs have larger putamen volumes than those without CUDs. Future studies should elaborate whether a large putamen represents a risk factor for the development of CUDs or whether (poly)substance use causes changes in putamen volume.
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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 | 0.001 | 0.001 |
| 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".