Evaluating the impact of cannabis use on thalamic connectivity in youth at clinical high risk of psychosis
Bibliographic record
Abstract
BACKGROUND: Disruptions in thalamic functional connectivity have been observed in people with schizophrenia and in youth at clinical high risk (CHR) of psychosis. However, the impact of environmental risk factors for psychosis on thalamic dysconnectivity is poorly understood. We tested whether thalamic dysconnectivity is related to patterns of cannabis use in a CHR sample. METHODS: 162 CHR and 105 control participants were assessed on cannabis use severity, frequency, and age at onset of first use as part of the North American Prodrome Longitudinal Study and completed resting-state fMRI scans. Whole-brain thalamic functional connectivity maps were generated using individual subjects' anatomically defined thalamic seeds. RESULTS: Thalamic connectivity did not significantly correlate with current cannabis use severity or frequency in either CHR or controls. In CHR cannabis users, a significant correlation emerged between attenuated thalamic connectivity with left sensory/motor cortex and a younger age at onset of cannabis use. CHR who used cannabis before age 15 did not differ on thalamic connectivity as compared to CHR who used after age 15 or CHR who were cannabis naïve. No group differences in thalamic connectivity emerged when comparing CHR separated by moderate/high use frequency, low-frequency or cannabis naïve. CONCLUSIONS: Although a younger age at onset of cannabis use may be associated with disrupted thalamo-cortical coupling, cannabis use does not appear to be an identifying characteristic for thalamic connectivity in CHR with moderate/high use frequency compared to low-frequency users or CHR who are cannabis naïve.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".