NMDA receptor ablation in medial prefrontal cortex disrupts value updating and reward history integration
Bibliographic record
Abstract
Schizophrenia, a serious mental illness, is associated with evidence of NMDA receptor (NMDAR) dysfunction and characterized by cognitive impairments that reflect impaired value updating and feedback-driven control. However, the cellular and circuit-level mechanisms underlying these disruptions remain unclear. Here, we test how NMDA receptor (NMDAR) signaling in the medial prefrontal cortex (mPFC) contributes to adaptive decision-making by combining targeted genetic ablation in mice and systemic pharmacology. Using a CRISPR-Cas9 approach to eliminate the obligate GluN1 subunit, we induced NMDAR hypofunction centered on the mPFC and compared its effects to systemic pharmacological blockade with the NMDAR antagonist MK-801 during performance of a touchscreen-based restless bandit task. Prefrontal NMDAR ablation impaired value discrimination and weakened the use of negative feedback, indicating disrupted feedback-guided decision making. Reinforcement-learning models incorporating a choice-kernel term best captured behavior and revealed that NMDAR ablation selectively altered experience-dependent choice updating. Systemic MK-801 produced widespread impairments in control animals, reducing performance and disrupting reward-history integration, while producing more limited additional effects in animals with prefrontal NMDAR ablation. Simulations using fitted model parameters reproduced these patterns, showing convergence toward a shared impaired behavioral regime under MK-801 despite residual differences in underlying decision processes. Together, these findings indicate that prefrontal NMDAR signaling supports key components of value-based decision making, while systemic NMDAR hypofunction engages broader mechanisms that further disrupt the integration of recent experience. This work provides a mechanistic account of how localized and distributed glutamatergic dysfunction contribute to distinct components of reinforcement-learning deficits relevant to schizophrenia.
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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 | 0.001 |
| 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".