The Functional Consequences of Cortical Circuit Abnormalities on Gamma Oscillations in Schizophrenia: Insights from Computational Modeling
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The Functional Consequences of Cortical Circuit Abnormalities on Gamma Oscillations in Schizophrenia: Insights from Computational Modeling
| Title: | The Functional Consequences of Cortical Circuit Abnormalities on Gamma Oscillations in Schizophrenia: Insights from Computational Modeling |
| Author: | Spencer, Kevin M. |
| Citation: | Spencer, Kevin M. 2009. The functional consequences of cortical circuit abnormalities on gamma oscillations in schizophrenia: insights from computational modeling. Frontiers in Human Neuroscience 3: 33. |
| Full Text & Related Files: |
2769552.pdf (1.894Mb; PDF) 
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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. |
| Published Version: | doi://10.3389/neuro.09.033.2009 |
| Other Sources: | http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2769552/pdf/ |
| Terms of Use: | This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA |
| Citable link to this page: | http://nrs.harvard.edu/urn-3:HUL.InstRepos:5361119 |
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