Brain network, modelling and corresponding EEG patterns for health and disease states

PhD Thesis


Al-Hossenat, Auhood Hadi Jabbar. 2020. Brain network, modelling and corresponding EEG patterns for health and disease states. PhD Thesis Doctor of Philosophy. University of Southern Queensland. https://doi.org/10.26192/7xxq-3k23
Title

Brain network, modelling and corresponding EEG patterns for health and disease states

TypePhD Thesis
Authors
AuthorAl-Hossenat, Auhood Hadi Jabbar
SupervisorWen, Paul
Li, Yan
Tao, Xiaohui
Institution of OriginUniversity of Southern Queensland
Qualification NameDoctor of Philosophy
Number of Pages176
Year2020
Digital Object Identifier (DOI)https://doi.org/10.26192/7xxq-3k23
Abstract

EEG is a significant tool used to capture normal and abnormal cerebral electrical activities in human brain. To understand and test complex hypotheses about the mechanisms of their generation, various model and modelling approaches have been proposed and developed.

Among these models and approaches, a new type of network model has emerged known as large-scale brain network model (LSBNM). LSBNM is becoming increasingly important in understanding, studying and testing the mechanisms of the generation of normal and abnormal oscillatory activities of the human brain. It also offers unique predictive tools for studying disease states and brain abnormalities. However, there are still many limitations in the existing LSBNM approaches. Hence, developing novel methods for LSBNM leads to the exploration, generation and prediction of a new and rich repertoire of healthy and disease rhythmic activities in the human brain.

The aim of this project is to develop LSBNM to include new versions of network models comprising various human cerebral areas in the left and right hemispheres. First, two network models at multi scale are developed to generate EEG patterns for health states: alpha rhythms with a low frequency at 7Hz and, and the alpha band of EEG rhythms at different ranges of frequencies 7–8 Hz, 8 9 Hz and 10–11 Hz. Second, a new network model for simulating multi-bands of EEG patterns: delta–range frequency of (1-4 Hz), theta at a frequency of (4-7Hz) and diverse narrowband oscillations ranging from delta to theta (0-5Hz) is introduced. Third, novel brain network models are simulated and used to predict the abnormal electrical activity such as oscillations observed in the epileptic brain.

The design and simulation of each of the network models are implemented using the unique neuro informatics platform: The Virtual Brain (TVB). This project made significant contributions to brain modelling, in particularly to the understanding of neural activity in the human brain at multi levels of scale. Further, it
emphasises the role of structural connectivity of the connectome on emerging normal and abnormal dynamics of brain oscillations, as well as affirming that modelling with TVB can provide reliable neuroimaging data such as EEGS for the healthy and diseased brain. In particular, the results of this study help researchers and physicians studying large-scale brain activity associated with lower and higher alpha oscillations and the delta waves of Stages 3 and 4 of the sleep and theta waves of Stages 1 and 2 of sleep. Moreover, they will be able to assist researchers and clinical doctors in the field of epilepsy to understand the complex neural mechanisms generating abnormal oscillatory activities and, thus, may open up new avenues towards the discovery of new clinical interventions related to these types of activities.

Keywordslarge­scale brain network modelling, neural mass model, Normal EEG activity, abnormal EEG activity, TVB
ANZSRC Field of Research 2020460207. Modelling and simulation
Byline AffiliationsSchool of Sciences
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https://research.usq.edu.au/item/q5z8w/brain-network-modelling-and-corresponding-eeg-patterns-for-health-and-disease-states

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Novel large scale brain network models for EEG epileptic pattern generations
Al-Hossenat, Auhood, Song, Bo, Wen, Peng and Li, Yan. 2022. "Novel large scale brain network models for EEG epileptic pattern generations." Expert Systems with Applications. 194. https://doi.org/10.1016/j.eswa.2021.116477
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