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Investigating the Neural Correlates of Pathological Cortical Networks in Alzheimer's Disease Using Heterogeneous Neuronal Models

Abuhassan, Kamal, Coyle, Damien and Maguire, LP (2012) Investigating the Neural Correlates of Pathological Cortical Networks in Alzheimer's Disease Using Heterogeneous Neuronal Models. IEEE Transactions on Biomedical Engineering, 59 (3). pp. 890-896. [Journal article]

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URL: http://dx.doi.org/10.1109/TBME.2011.2181843

DOI: doi:10.1109/TBME.2011.2181843


This paper describes an investigation into thepathophysiological causes of abnormal cortical oscillations inAlzheimer’s disease (AD) using two heterogeneous neuronal networkmodels. The effect of excitatory circuit disruption on the betaband power (13–30 Hz) using a conductance-based network modelof 200 neurons is assessed. Then, the neural correlates of abnormalcortical oscillations in different frequency bands based on alarger network model of 1000 neurons consisting of different typesof cortical neurons are also analyzed. EEG studies in AD patientshave shown that beta band power (13–30 Hz) decreased in the earlystages of the disease with a parallel increase in theta band power(4–7 Hz). This abnormal change progresses with the later stagesof the disease but with decreased power spectra in other fast frequencybands plus an increase in delta band power (1–3 Hz). Ourresults show that, despite the heterogeneity of the network models,the beta band power is significantly affected by excitatory neuraland synaptic loss. Second, the results of modeling a functionalimpairment in the excitatory circuit shows that beta band powerexhibits the most decrease compared with other bands. Previousbiological experiments on different types of cultural excitatory neuronsshowthat cortical neuronal death ismediated by dysfunctionalionic behavior that might specifically contribute to the pathogenesisof β-amyloid-peptide-induced neuronal death in AD. Our studyalso shows that beta band power was the first affected componentwhen the modeled excitatory circuit begins to lose neurons andsynapses.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:21343
Deposited By: Prof Damien Coyle
Deposited On:09 Mar 2012 15:08
Last Modified:17 Oct 2017 16:02

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