Ulster University Logo

Ulster Institutional Repository

Alpha and theta rhythm abnormality in Alzheimer's Disease: a study using a computational model.

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Bhattacharya, Basab, Coyle, DH and Maguire, LP (2011) Alpha and theta rhythm abnormality in Alzheimer's Disease: a study using a computational model. Advances in experimental medicine and biology, 718 . pp. 57-73. [Journal article]

Full text not available from this repository.

Abstract

Electroencephalography (EEG) studies in Alzheimer's Disease (AD) patients show an attenuation of average power within the alpha band (7.5-13 Hz) and an increase of power in the theta band (4-7 Hz). Significant body of evidence suggest that thalamocortical circuitry underpin the generation and modulation of alpha and theta rhythms. The research presented in this chapter is aimed at gaining a better understanding of the neuronal mechanisms underlying EEG band power changes in AD which may in the future provide useful biomarkers towards early detection of the disease and for neuropharmaceutical investigations. The study is based on a classic computational model of the thalamocortical circuitry which exhibits oscillation within the theta and the alpha bands. We are interested in the change in model oscillatory behaviour corresponding with changes in the connectivity parameters in the thalamocortical as well as sensory input pathways. The synaptic organisation as well as the connectivity parameter values in the model are modified based on recent experimental data from the cat thalamus. We observe that the inhibitory population in the model plays a crucial role in mediating the oscillatory behaviour of the model output. Further, increase in connectivity parameters in the afferent and efferent pathways of the inhibitory population induces a slowing of the output power spectra. These observations may have implications for extending the model for further AD research.

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
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:21066
Deposited By:Dr Damien Coyle
Deposited On:14 Feb 2012 16:50
Last Modified:14 Feb 2012 16:50

Repository Staff Only: item control page