The functional consequences of cortical circuit abnormalities on gamma oscillations in schizophrenia: insights from computational modeling

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Abstract
Schizophrenia is characterized by cortical circuit abnormalities, which might be reflected in γ-frequency (30-100 Hz) oscillations in the electroencephalogram. Here we used a computational model of cortical circuitry to examine the effects that neural circuit abnormalities might have on γ generation and network excitability. The model network consisted of 1000 leaky integrate-and-fire neurons with realistic connectivity patterns and proportions of neuron types (pyramidal cells [PCs], regular-spiking inhibitory interneurons, and fast-spiking interneurons [FSIs]). The network produced a γ oscillation when driven by noise input. We simulated reductions in 1) recurrent excitatory inputs to PCs; 2) both excitatory and inhibitory inputs to PCs; 3) all possible connections between cells; 4) reduced inhibitory output from FSIs; and 5) reduced NMDA input to FSIs. Reducing all types of synaptic connectivity sharply reduced γ power and phase synchrony. Network excitability was reduced when recurrent excitatory connections were deleted, but the network showed disinhibition effects when inhibitory connections were deleted. Reducing FSI output impaired γ generation to a lesser degree than reducing synaptic connectivity, and increased network excitability. Reducing FSI NMDA input also increased network excitability, but increased γ power. The results of this study suggest that a multimodal approach, combining non-invasive neurophysiological and structural measures, might be able to distinguish between different neural circuit abnormalities in schizophrenia patients. Computational modeling may help to bridge the gaps between post-mortem studies, animal models, and experimental data in humans, and facilitate the development of new therapies for schizophrenia and neuropsychiatric disorders in general.