TouchScreen-based phenotyping: altered stimulus/reward association and lower perseveration to gain a reward in mu opioid receptor knockout mice
Bibliographic record
Abstract
While the contribution of Mu Opioid Receptors (MORs) to hedonic aspects of reward processing is well-established, the notion that these receptors may also regulate motivation to gain a reward, and possibly other related cognitive dimensions, has been less investigated. The prefrontal cortex (PFC) is a critical site for these processes. Our previous functional magnetic resonance imaging study found alterations of functional connectivity (FC) in reward/aversion networks in MOR knockout mice. Here we pursued voxelwise seed-based FC analyses using the same dataset with a focus on the PFC. We observed significant reduction of PFC FC in mutant mice, predominantly with the nucleus accumbens, supporting the notion of altered reward-driven top-down controls. We tested motivation for palatable food in a classical operant self-administration paradigm, and found delayed performance for mutant mice. We then evaluated motivational and cognitive abilities of MOR knockout mice in TouchScreen-based behavioral tests. Learning was delayed and stimulus/reward association was impaired, suggesting lower hedonic reward value and reduced motivation. Perseverative responses were decreased, while discriminatory behavior and attention were unchanged, indicative of increased inhibitory controls with otherwise intact cognitive performance. Together, our data suggest that MORs contribute to enhance reward-seeking and facilitate perseverative behaviors. The possibility that MOR blockade could reduce maladaptive compulsivity deserves further investigation in addiction and self-control disorder research.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".