Stress‐induced cortical dopamine response is altered in subjects at clinical high risk for psychosis using cannabis
Bibliographic record
Abstract
Abstract Stress and cannabis use are risk factors for the development of psychosis. We have previously shown that subjects at clinical high risk for psychosis (CHR) exhibit a higher striatal dopamine response to stress compared with healthy volunteers (HV), with chronic cannabis use blunting this response. However, it is unknown if this abnormal dopamine response extends to the prefrontal cortex (PFC). Here, we investigated dorsolateral PFC (dlPFC) and medial PFC (mPFC) dopamine release using [11C]FLB457 positron emission tomography (PET) and a validated stress task. Thirty‐three participants completed two PET scans (14 CHR without cannabis use, eight CHR regular cannabis users [CHR‐CUs] and 11 HV) while performing a Sensory Motor Control Task (control scan) and the Montreal Imaging Stress Task (stress scan). Stress‐induced dopamine release (ΔBPND) was defined as percent change in D2/3 receptor binding potential between both scans using a novel correction for injected mass of [11C]FLB457. ΔBPND was significantly different between groups in mPFC (F(2,30) = 5.40, .010), with CHR‐CUs exhibiting lower ΔBPND compared with CHR (.008). Similarly, salivary cortisol response (ΔAUCI) was significantly lower in CHR‐CU compared with CHR (F(2,29) = 5.08, .013; post hoc .018) and positively associated with ΔBPND. Furthermore, CHR‐CUs had higher attenuated psychotic symptoms than CHR following the stress task, which were negatively associated with ΔBPND. Length of cannabis use was negatively associated with ΔBPND in mPFC when controlling for current cannabis use. Given the global trend to legalize cannabis, this study is important as it highlights the effects of regular cannabis use on cortical dopamine function in high‐risk youth.
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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".