Using dynamic causal modeling and Bayesian model selection on data from a double-blind, placebo-controlled, crossover ketamine study, the authors investigated how the NMDA-receptor antagonist ketamine reduces mismatch negativity (MMN) amplitudes. Guided by a predictive coding framework that unifies adaptation and model adjustment theories, they compared models allowing different expressions of neuronal adaptation and synaptic plasticity. Results replicated that both adaptation and short-term plasticity are necessary for MMN generation. Ketamine significantly affected synaptic plasticity but not adaptation, with a selective effect on the forward connection from left primary auditory cortex to superior temporal gyrus. This model-based estimate of ketamine's effect on synaptic plasticity correlated with ratings of ketamine-induced impairments in cognition and control, suggesting a concrete mechanism linking ketamine effects on MMN to drug-induced psychopathology.
The auditory mismatch negativity (MMN) is reduced in schizophrenia and can also be reduced by NMDA receptor (NMDAR) antagonists, suggesting impaired predictive coding. This study tested the theory that perceptual inference depends on NMDAR-dependent hierarchical precision-weighted prediction errors (PEs). Using a hierarchical Bayesian model on single-trial EEG data from healthy volunteers given the NMDAR antagonist S-ketamine in a placebo-controlled, double-blind, within-subject design, the analysis showed that low-level PEs (about stimulus transitions) appear early (102-207 ms), while high-level PEs (about transition probability) appear later (152-199 and 215-277 ms). Ketamine significantly diminished high-level PE responses, indicating NMDAR antagonism disrupts inference on abstract statistical regularities and impairs hierarchical Bayesian inference about the world's statistical structure.