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Vinogradov, S.

Publications and source records attributed to Vinogradov, S..

2 recordsLinked to original sources

NMDA receptor ablation in medial prefrontal cortex disrupts value updating and reward history integration

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 spatially confined NMDAR hypofunction in 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, weakened the use of negative feedback, and reduced mutual information between recent outcomes and current choices, indicating disrupted reward-history integration. Reinforcement-learning models incorporating a choice-kernel term best captured behavior and revealed that NMDAR ablation selectively dampened learning and choice-history parameters governing flexible updating. Systemic MK-801 produced broad impairments in control animals, reducing accuracy, mutual information, and outcome sensitivity, yet exerted only modest additional effects after NMDAR ablation, suggesting that prefrontal NMDAR loss occluded much of the pharmacological disruption. Simulations using fitted RLCK parameters reproduced these patterns, showing convergent flattening of choice dynamics under MK-801 and persistent deficits in GluN1 ablated animals. Together, these findings demonstrate that prefrontal NMDAR signaling is necessary for effective value updating and feedback-driven learning, and that its loss recapitulates core features of systemic NMDAR hypofunction. This work establishes a mechanistic bridge between localized cortical glutamatergic dysfunction and the reinforcement-learning disturbances characteristic of schizophrenia.

neuroscience↗

Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions

BackgroundCognitive gains following cognitive training interventions (CT) are associated with improved functioning in people with schizophrenia (SCZ). However, considerable inter-individual variability is observed. Here, we evaluate the sensitivity of brain structural features to predict functional response to auditory-based cognitive training (ABCT) at a single subject level. MethodsWe employed whole-brain multivariate pattern analysis (MVPA) with support vector machine (SVM) modeling to identify grey matter (GM) patterns that predicted higher vs. lower functioning after 40 hours of ABCT at the single subject level in SCZ patients. The generalization capacity of the SVM model was evaluated by applying the original model through an Out-Of-Sample Cross Validation analysis (OOCV) to unseen SCZ patients from an independent sample that underwent 50 hours of ABCT. ResultsThe whole-brain GM volume-based pattern classification predicted higher vs. lower functioning at follow-up with a balanced accuracy (BAC) of 69.4% (sensitivity 72.2%, specificity 66.7%) as determined by nested cross-validation. The neuroanatomical model was generalizable to an independent cohort with a BAC of 62.1% (sensitivity 90.9%, specificity 33.3%). ConclusionsIn particular, greater baseline GM volume in regions within superior temporal gyrus, thalamus, anterior cingulate and cerebellum -- predicted improved functioning at the single-subject level following ABCT in SCZ participants.

neuroscience↗