The endocannabinoid system in humans: significant associations between anandamide, brain function during reward feedback and a personality measure of reward dependence
Bibliographic record
Abstract
Preclinical evidence indicates that the endocannabinoid system is involved in neural responses to reward. This study aimed to investigate associations between basal serum concentrations of the endocannabinoids anandamide (AEA) and 2-arachidonylglycerol (2-AG) with brain functional reward processing. Additionally, a personality measure of reward dependence was obtained. Brain functional data were obtained of 30 right-handed adults by conducting fMRI at 3 Tesla using a reward paradigm. Reward dependence was obtained using the subscale reward dependence of the Tridimensional Personality Questionnaire (TPQ). Basal concentrations of AEA and 2-AG were determined in serum. Analyzing the fMRI data, for AEA and 2-AG ANCOVAs were calculated using a full factorial model, with condition (reward > control, loss > control) and concentrations for AEA and 2-AG as factors. Regression analyses were conducted for AEA and 2-AG on TPQ-RD scores. A whole-brain analysis showed a significant interaction effect of AEA concentration by condition (positive vs. negative) within the putamen ( x = 26, y = 16, z = −8, F13.51, TFCE (1, 54) = 771.68, k = 70, P FWE = 0.044) resulting from a positive association of basal AEA concentrations and putamen activity to rewarding stimuli, while this association was absent in the loss condition. AEA concentrations were significantly negatively correlated with TPQ reward dependence scores ( r spearman = −0.56, P = 0.001). These results show that circulating AEA may modulate brain activation during reward feedback and that the personality measure reward dependence is correlated with AEA concentrations in healthy human volunteers. Future research is needed to further characterize the nature of the lipids’ influence on reward processing, the impact on reward anticipation and outcome, and on vulnerability for psychiatric disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".