Spectral and non-linear analysis of thalamocortical neural mass model oscillatory dynamics

Sen Bhattacharya, Basabdatta, Serap Sengor, Neslihan, Cakir, Yuksel, Maguire, Liam and Coyle, Damien (2014) Spectral and non-linear analysis of thalamocortical neural mass model oscillatory dynamics. In: Advanced computational approaches to biomedical engineering. Springer Verlag, pp. 87-112. ISBN 9783642415388

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Abstract

The chapter is organised in two parts: In the first part, the focus is on
a combined power spectral and non-linear behavioural analysis of a neural mass
model of the thalamocortical circuitry. The objective is to study the effectiveness
of such ‘multi-modal’ analytical techniques in model-based studies investigating
the neural correlates of abnormal brain oscillations in Alzheimer’s disease (AD).
The power spectral analysis presented here is a study of the ‘slowing’ (decreasing
dominant frequency of oscillation) within the alpha frequency band (8 – 13 Hz),
a hallmark of Electroencephalogram (EEG) dynamics in AD. Analysis of the nonlinear
dynamical behaviour focuses on the bifurcating property of the model. The
results show that the alpha rhythmic content is maximal at close proximity to the
bifurcation point — an observation made possible by the ‘multi-modal’ approach
adopted herein. Furthermore, a slowing in alpha rhythm is observed for increasing
inhibitory connectivity — a consistent feature of our research into neuropathological
oscillations associated with AD. In the second part, we have presented power
spectral analysis on a model that implements multiple feed-forward and feed-back
connectivities in the thalamo-cortico-thalamic circuitry, and is thus more advanced
in terms of biological plausibility. This study looks at the effects of synaptic connectivity
variation on the power spectra within the delta (1 – 3 Hz), theta (4 – 7 Hz),
alpha (8 – 13 Hz) and beta (14 – 30 Hz) bands. An overall slowing of EEG with decreasing synaptic connectivity is observed, indicated by a decrease of power within
alpha and beta bands and increase in power within the theta and delta bands. Thus,
the model behaviour conforms to longitudinal studies in AD indicating an overall
slowing of EEG.

Additional Information:Submitted February 6th
Keywords:brain oscillations, non-linear analysis, neural mass model, thalamocortical circuitry
Subjects:G Mathematical and Computer Sciences > G730 Neural Computing
Divisions:College of Science > School of Engineering
ID Code:10604
Deposited On:05 Jul 2013 08:25

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