The contributions of the endocannabinoid system and stress on the neural processing of reward stimuli
Bibliographic record
Abstract
The brain's endocannabinoid system plays a crucial role in reward processes by mediating appetitive learning and encoding the reinforcing properties of substances. Evidence also suggests that endocannabinoids are an important constituent of neuronal substrates involved in emotional responses to stress. Thus, it is critical to understand how the endocannabinoid system and stress may affect reward processes given their importance in substance use disorders. We examined the relationship between factors that regulate endocannabinoid system signaling (i.e., cannabinoid receptor genes and prolonged cannabis exposure) and stress on fMRI BOLD response to reward cues using multivariate statistical analysis. We found that proxies for endocannabinoid system signaling (i.e., endocannabinoid genes and chronic exposure to cannabis) and stress have differential effects on neural response to cannabis cues. Specifically, a single nucleotide polymorphism (SNP) variant in the cannabinoid receptor 1 (CNR1) gene, early life stress, and current perceived stress modulated reward responsivity in long-term, heavy cannabis users, while a variant in the fatty acid amide hydrolase (FAAH) gene and current perceived stress modulated cue-elicited response in non-using controls. These associations were related to distinct neural responses to cannabis-related cues compared to natural reward cues. Understanding the contributions of endocannabinoid system factors and stress that lead to downstream effects on neural mechanisms underlying sensitivity to rewards, such as cannabis, will contribute towards a better understanding of endocannabinoid-targeted therapies as well as individual risks for cannabis use disorder.
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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) | 0.001 | 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".