Modeling Ketamine Effects on Synaptic Plasticity During the Mismatch Negativity
Cerebral Cortex – August 08, 2012
Source: OpenAlex
Summary
Ketamine significantly alters brain neuroplasticity, specifically affecting synaptic plasticity by targeting the NMDA receptor. Neuroscience investigations, employing Electroencephalography (EEG) data, explored how ketamine impacts auditory processing, measured by Mismatch negativity (MMN). This work, relevant to Functional Brain Connectivity Studies, revealed ketamine's effects on synaptic plasticity correlated with impairments in Psychology-related cognitive functions. Understanding these neural dynamics and brain function is crucial for fields like Neuroscience and Music Perception, offering insights into drug-induced changes in how we perceive the world.
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.