Modeling Ketamine Effects on Synaptic Plasticity During the Mismatch Negativity
André Schmidt, Andreea O. Diaconescu, Michael Kometer, Karl Friston, Klaas Ε. Stephan, Franz X. Vollenweider
Cerebral Cortex August 8, 2012 DOI: 10.1093/cercor/bhs238 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Cross-over, double-blind, placebo-controlled design Peer reviewed |
|---|---|
| Intervention | Ketamine |
| Topics | Ketamine Neuroplasticity Esketamine |
| Keywords | Mismatch negativity Nmda receptor Electroencephalography |
| Citations | 110 |
| Key points | Ketamine reduces MMN amplitudes by affecting synaptic plasticity, not adaptation, and this effect correlates with drug-induced cognitive impairments. |
Abstract
This paper presents a model-based investigation of mechanisms underlying the reduction of mismatch negativity (MMN) amplitudes under the NMDA-receptor antagonist ketamine. We applied dynamic causal modeling and Bayesian model selection to data from a recent ketamine study of the roving MMN paradigm, using a cross-over, double-blind, placebo-controlled design. Our modeling was guided by a predictive coding framework that unifies contemporary "adaptation" and "model adjustment" MMN theories. Comparing a series of dynamic causal models that allowed for different expressions of neuronal adaptation and synaptic plasticity, we obtained 3 major results: 1) We replicated previous results that both adaptation and short-term plasticity are necessary to explain MMN generation per se; 2) we found significant ketamine effects on synaptic plasticity, but not adaptation, and a selective ketamine effect on the forward connection from left primary auditory cortex to superior temporal gyrus; 3) this model-based estimate of ketamine effects on synaptic plasticity correlated significantly with ratings of ketamine-induced impairments in cognition and control. Our modeling approach thus suggests a concrete mechanism for ketamine effects on MMN that correlates with drug-induced psychopathology. More generally, this demonstrates the potential of modeling for inferring on synaptic physiology, and its pharmacological modulation, from electroencephalography data.