Reward integration in prefrontal-cortical and ventral-hippocampal nucleus accumbens inputs cooperatively modulates engagement
Bibliographic record
Abstract
Abstract The NAc, a highly integrative brain region controlling motivated behavior, is thought to receive distinct information from various glutamatergic inputs yet strong evidence of functional specialization of inputs is lacking. While circuit neuroscience commonly seeks specific functions for specific circuits, redundancy can be highly adaptive and is a critical motif in circuit organization. Using dual-site fiber photometry in an operant reward task, we simultaneously recorded from two NAc glutamatergic afferents to assess circuit specialization. We identify a common neural motif that integrates reward history in medial prefrontal cortex (mPFC) and ventral hippocampus (vHip) inputs to NAc. Then, by systematically degrading task complexity, dissociating reward from choice and action, we identify key circuit-specificity in the behavioral conditions that recruit encoding. While mPFC-NAc invariantly encodes reward, vHip-NAc encoding is uniquely anchored to loss. Ultimately, using optogenetic stimulation we demonstrate that both inputs co-operatively modulate task engagement. We illustrate how similar encoding, with differential gating by behavioral state, supports state-sensitive tuning of reward-motivated behavior.
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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 | 0.000 |
| 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".