A predictive coding account of MMN reduction in schizophrenia

Area of Science:

• Neuroscience
• Computational Psychiatry
• Auditory Neuroscience

Background:

• Mismatch negativity (MMN) reflects auditory prediction and deviance detection, reduced in schizophrenia.
• NMDA receptor (NMDA-R) dysfunction is hypothesized to cause reduced MMN in schizophrenia.
• No prior neuronal models have tested NMDA-R dysfunction's effect on MMN.

Purpose of the Study:

• To implement NMDA-R dysfunction in a neuronal model of MMN.
• To investigate the interaction of predictive coding and synaptic adaptation in MMN generation.
• To assess the implications for MMN use in schizophrenia research.

Main Methods:

• Developed a neuronal model of MMN based on predictive coding principles.
• Simulated NMDA receptor dysfunction within the model.
• Analyzed the interplay between predictive processes and synaptic adaptation.

Main Results:

• NMDA-R dysfunction in the model led to reduced MMN, consistent with clinical observations.
• Predictive coding and synaptic adaptation interact to shape MMN generation.
• Model simulations highlight potential confounds in MMN paradigms for schizophrenia.

Conclusions:

• NMDA-R dysfunction provides a plausible neuronal mechanism for reduced MMN in schizophrenia.
• Interactions between prediction and adaptation are crucial for understanding MMN.
• Findings inform the interpretation and application of MMN paradigms in schizophrenia research.