Influence of NMDA and GABA synaptic dysfunction on the evoked gamma oscillations in a computational model of schizophrenia

Abstract

Background. Schizophrenia is a psychiatric disorder which is characterized by delusions and hallucinations, and affects thoughts, behaviour and emotions. Major neuronal degeneration is not observed in schizophrenic patients, but abnormalities in cortical circuits are present. These abnormalities are reflected in impaired EEG gamma frequency (30–80 Hz), being crucial for many processes including sensation, perception, working memory, and attention. NMDA and GABA synaptic dysfunction is proposed as one of the possible mechanisms underlying the gamma oscillatory deficits in schizophrenia. Materials and Methods. We used a computational modeling approach to investigate the joint influence of NMDA and GABA synaptic dysfunction on gamma oscillations in cortex. We employed a computational model of a spiking neural network composed of 800 pyramidal neurons, 150 regular-spiking interneurons, and 50 fast-spiking interneurons. All cells were randomly interconnected. Network neurons received independent Poisson noise input at 4 Hz and 40 Hz drive excitatory stimulation. Fast-spiking interneuron GABA receptor-gated channel time constant was increased and NMDA receptor-gated channel synaptic conductance was decreased to represent synaptic dysfunction in schizophrenia. Results. Reducing NMDA conductance enhanced gamma power, and increasing decay time constant of GABA receptorgated channel attenuated gamma generation in a network. The effect of synaptic GABA alteration was more profound. Conclusions. NMDA and GABA synaptic dysfunction leads to the impaired gamma frequency oscillations in a spiking neural network of cortex. Computational modeling approach is a powerful tool to understand complex non-linear dynamical systems and intrinsic mechanisms of neuronal network activity in healthy and diseased brain.