Dissociable patterns of dopamine dynamics and causal contributions to stimulus-response behaviors across striatal subregions
Bibliographic record
Abstract
Abstract Rationale Midbrain dopamine (DA) neurons project principally towards the striatum, serving as key regulators of movement, motivation, and cognition. Different striatal dopaminergic (DA) pathways may regulate different aspects of cognition. Objectives We investigated how different striatal DA pathways contribute to a known function of the striatum, visuomotor conditional learning. Methods Using fiber photometry, we recorded DA transients in these regions as mice learned the touchscreen Visuo-Motor Conditional Learning task. Results DA transients in all regions dynamically tracked task events, but differed in the timing of peak responses and ramp-like activity preceding a choice, indicating region-specific temporal dynamics across learning. Manipulations of reward probability revealed DA transients in all regions during reward delivery and omission that are consistent with an interpretation in terms of reward prediction error. Thus, DA dynamics in all regions could indicate involvement in visuomotor conditional learning. Therefore, to determine whether nigrostriatal or mesolimbic DA is necessary for learning, we chemogenetically inhibited DA striatal afferents, revealing that only DLS-projecting nigrostriatal DA, and not NAc-projecting mesolimbic striatal DA, was necessary for learning the task. Conclusion These findings demonstrate functional heterogeneity of aspects of striatal DA signaling, and selective causal roles in the learning of visuomotor conditional learning.
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 | 0.001 |
| 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".