About

Matthew Chafee, PhD, is a professor in the Department of Neuroscience at the University of Minnesota Twin Cities. His research focuses on understanding intelligent behavior in higher primates and humans, particularly the ability to act based on internally generated abstract information rather than solely sensory input. His work investigates executive control, which involves the brain's capacity to flexibly process the same sensory input to produce different, context-appropriate actions by changing goals, rules, or principles. In his laboratory, he records electrical activity from individual neurons in different cortical areas to study how the brain applies various rules to compute context-dependent information. His projects include examining the network basis of executive control over spatial cognitive processing, comparing neural activity in the prefrontal and posterior parietal cortex, and exploring how neuronal information processing related to executive control is disrupted in schizophrenia. His research aims to extend understanding of how schizophrenia affects information processing at the level of single prefrontal neurons, contributing to cognitive deficits characteristic of the disease.

Research topics

• Neuroscience
• Psychiatry
• Psychology
• Biology
• Cognitive psychology

Selected publications

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

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-12-10 · 1 citations

  articleOpen access

  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.

  PublisherOA PDFDOI

• Explore-exploit instability reveals computational decision-making heterogeneity in early psychosis

  medRxiv · 2025-05-01

  preprintOpen access

  Abstract Background and Hypothesis Psychosis spectrum illnesses are characterized by impaired goal-directed behavior and significant clinical and neurophysiological heterogeneity. This study investigated cognitive heterogeneity by applying computational modeling to trial-wise decision making task behavior. Study Design 75 participants with Early Psychosis (EP) and 68 controls completed a dynamic decision-making task during two baseline sessions as part of a larger longitudinal fMRI study. Study Results Consistent with prior studies, EP exhibited more choice switching. However, this was not explained by reward learning deficits as no group difference in reward acquisition was found. Instead, a Hidden Markov model fit to choice sequences revealed increased exploration as a result of higher probability of transition from exploitation to exploration in EP, leaving a favorable option too soon. A Bayesian learner model that estimates both value and uncertainty characterized EP behavior better than traditional RL models with fixed learning rates. Results of computational modeling implicated elevated uncertainty sensitivity and decision noise as independent contributors to suboptimal transition into exploration among EP. Task strategy yielded three computational subtypes (normative, uncertainty-sensitive, high decision-noise) with unique cognitive and symptom profiles. Conclusions These specific microcognitive disruptions underlying the distinct neurocomputational subtypes are individually measurable and may have the potential for targeted interventions.

  PublisherOA PDFDOI

• Monkey Prefrontal Cortex Learns to Minimize Sequence Prediction Error

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-02-28

  preprintOpen access

  In this study, we develop a novel recurrent neural network (RNN) model of pre-frontal cortex that predicts sensory inputs, actions, and outcomes at the next time step. Synaptic weights in the model are adjusted to minimize sequence prediction error, adapting a deep learning rule similar to those of large language models. The model, called Sequence Prediction Error Learning (SPEL), is a simple RNN that predicts world state at the next time step, but that differs from standard RNNs by using its own prediction errors from the previous state predictions as inputs to the hidden units of the network. We show that the time course of sequence prediction errors generated by the model closely matched the activity time courses of populations of neurons in macaque prefrontal cortex. Hidden units in the model responded to combinations of task variables and exhibited sensitivity to changing stimulus probability in ways that closely resembled monkey prefrontal neurons. Moreover, the model generated prolonged response times to infrequent, unexpected events as did monkeys. The results suggest that prefrontal cortex may generate internal models of the temporal structure of the world even during tasks that do not explicitly depend on temporal expectation, using a sequence prediction error minimization learning rule to do so. As such, the SPEL model provides a unified, general-purpose theoretical framework for modeling the lateral prefrontal cortex.

  PublisherOA PDFDOI

• Author response: A prefrontal network model operating near steady and oscillatory states links spike desynchronization and synaptic deficits in schizophrenia

  2023-09-19

  peer-reviewOpen access

  Cortical network model suggests a mechanism explaining the link between NMDAR synaptic and spike synchrony deficits observed in a pharmacological monkey model of prefrontal network failure in schizophrenia.

  PublisherDOI

• A prefrontal network model operating near steady and oscillatory states links spike desynchronization and synaptic deficits in schizophrenia

  eLife · 2023-12-21 · 6 citations

  articleOpen access

  Schizophrenia results in part from a failure of prefrontal networks but we lack full understanding of how disruptions at a synaptic level cause failures at the network level. This is a crucial gap in our understanding because it prevents us from discovering how genetic mutations and environmental risks that alter synaptic function cause prefrontal network to fail in schizophrenia. To address that question, we developed a recurrent spiking network model of prefrontal local circuits that can explain the link between NMDAR synaptic and 0-lag spike synchrony deficits we recently observed in a pharmacological monkey model of prefrontal network failure in schizophrenia. We analyze how the balance between AMPA and NMDA components of recurrent excitation and GABA inhibition in the network influence oscillatory spike synchrony to inform the biological data. We show that reducing recurrent NMDAR synaptic currents prevents the network from shifting from a steady to oscillatory state in response to extrinsic inputs such as might occur during behavior. These findings strongly parallel dynamic modulation of 0-lag spike synchrony we observed between neurons in monkey prefrontal cortex during behavior, as well as the suppression of this 0-lag spiking by administration of NMDAR antagonists. As such, our cortical network model provides a plausible mechanism explaining the link between NMDAR synaptic and 0-lag spike synchrony deficits observed in a pharmacological monkey model of prefrontal network failure in schizophrenia.

  PublisherDOI

• Shared Neural Activity But Distinct Neural Dynamics for Cognitive Control in Monkey Prefrontal and Parietal Cortex

  Journal of Neuroscience · 2023-03-09 · 6 citations

  articleOpen accessSenior author

  To better understand how prefrontal networks mediate forms of cognitive control disrupted in schizophrenia, we translated a variant of the AX continuous performance task that measures specific deficits in the human disease to 2 male monkeys and recorded neurons in PFC and parietal cortex during task performance. In the task, contextual information instructed by cue stimuli determines the response required to a subsequent probe stimulus. We found parietal neurons encoding the behavioral context instructed by cues that exhibited nearly identical activity to their prefrontal counterparts (Blackman et al., 2016). This neural population switched their preference for stimuli over the course of the trial depending on whether the stimuli signaled the need to engage cognitive control to override a prepotent response. Cues evoked visual responses that appeared in parietal neurons first, whereas population activity encoding contextual information instructed by cues was stronger and more persistent in PFC. Increasing cognitive control demand biased the representation of contextual information toward the PFC and augmented the temporal correlation of task-defined information encoded by neurons in the two areas. Oscillatory dynamics in local field potentials differed between cortical areas and carried as much information about task conditions as spike rates. We found that, at the single-neuron level, patterns of activity evoked by the task were nearly identical between the two cortical areas. Nonetheless, distinct population dynamics in PFC and parietal cortex were evident. suggesting differential contributions to cognitive control. SIGNIFICANCE STATEMENT We recorded neural activity in PFC and parietal cortex of monkeys performing a task that measures cognitive control deficits in schizophrenia. This allowed us to characterize computations performed by neurons in the two areas to support forms of cognitive control disrupted in the disease. Subpopulations of neurons in the two areas exhibited parallel modulations in firing rate; and as a result, all patterns of task-evoked activity were distributed between PFC and parietal cortex. This included the presence in both cortical areas of neurons reflecting proactive and reactive cognitive control dissociated from stimuli or responses in the task. However, differences in the timing, strength, synchrony, and correlation of information encoded by neural activity were evident, indicating differential contributions to cognitive control.

  PublisherOA PDFDOI

• Unmasking Schizophrenia: Synaptic Pruning in Adolescence Reveals a Latent Physiological Vulnerability in Prefrontal Recurrent Networks

  Biological Psychiatry · 2022-08-22 · 23 citations

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• A prefrontal network model operating near steady and oscillatory states links spike desynchronization and synaptic deficits in schizophrenia

  bioRxiv (Cold Spring Harbor Laboratory) · 2022-06-13

  preprintOpen accessCorresponding

  Abstract Schizophrenia results in part from a failure of prefrontal networks but we lack full understanding of how disruptions at a synaptic level cause failures at the network level. This is a crucial gap in our understanding because it prevents us from discovering how genetic mutations and environmental risks that alter synaptic function cause prefrontal network to fail in schizophrenia. To address that question, we developed a recurrent spiking network model of prefrontal local circuits that can explain the link between NMDAR synaptic and spike timing deficits we recently observed in a pharmacological monkey model of prefrontal network failure in schizophrenia. We analyze how the balance between AMPA and NMDA components of recurrent excitation and GABA inhibition in the network influence spike timing to inform the biological data. We show that reducing recurrent NMDAR synaptic currents prevents the network from shifting from a steady to oscillatory state in response to extrinsic inputs such as might occur during behavior. This explains how NMDAR synaptic deficits, implicated by genetic evidence as causal in schizophrenia, could prevent the emergence of 0-lag synchronous spiking in prefrontal local circuits during behavior, potentially disconnecting those circuits via spike-timing dependent mechanisms in the human disease.

  PublisherOA PDFDOI

• Prefrontal cortex

  Current Biology · 2022 · 47 citations

  1st authorCorresponding
  • Neuroscience
  • Biology
  PublisherOA PDFDOI

• Psychosis spectrum illnesses as disorders of prefrontal critical period plasticity

  Neuropsychopharmacology · 2022 · 41 citations

  • Neuroscience
  • Psychology
  • Psychiatry

  Emerging research on neuroplasticity processes in psychosis spectrum illnesses-from the synaptic to the macrocircuit levels-fill key gaps in our models of pathophysiology and open up important treatment considerations. In this selective narrative review, we focus on three themes, emphasizing alterations in spike-timing dependent and Hebbian plasticity that occur during adolescence, the critical period for prefrontal system development: (1) Experience-dependent dysplasticity in psychosis emerges from activity decorrelation within neuronal ensembles. (2) Plasticity processes operate bidirectionally: deleterious environmental and experiential inputs shape microcircuits. (3) Dysregulated plasticity processes interact across levels of scale and time and include compensatory mechanisms that have pathogenic importance. We present evidence that-given the centrality of progressive dysplastic changes, especially in prefrontal cortex-pharmacologic or neuromodulatory interventions will need to be supplemented by corrective learning experiences for the brain if we are to help people living with these illnesses to fully thrive.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R01MH077779

  NIH · $1.4M · 2015

• Interactions between prefrontal and insula circuits during normal and disrupted state representation processes in mice

  NIH · $36.7M · 2020–2030

• Circuit and synaptic basis of cognitive control in monkey prefrontal cortex

  NIH · $1.6M · 2015–2021

Frequent coauthors

• David A. Crowe

  University of Augsburg

  52 shared

• Bruno B. Averbeck

  37 shared

• Rachael K. Blackman

  University of Minnesota Medical Center

  30 shared

• Apostolos P. Georgopoulos

  University of Minnesota Medical Center

  25 shared

• Adele L. DeNicola

  University of Minnesota

  21 shared

• Angus W. MacDonald

  University of Minnesota

  18 shared

• Scott R. Sponheim

  University of Minnesota

  17 shared

• Paul J. Reber

  15 shared