Novel large scale brain network models for EEG epileptic pattern generations

Al-Hossenat, Auhood and Song, Bo and Wen, Peng ORCID: https://orcid.org/0000-0003-0939-9145 and Li, Yan ORCID: https://orcid.org/0000-0002-4694-4926 (2022) Novel large scale brain network models for EEG epileptic pattern generations. Expert Systems with Applications, 194:116477. ISSN 0957-4174


Abstract

Background: Unlike normal EEG patterns, the epileptiform abnormal pattern is characterized by different morphologies such as the high-frequency oscillations (HFOs) of ripples on spikes, spikes and waves, continuous and sporadic spikes, and ploy2 spikes. Several studies have reported that HFOs can be novel biomarkers in human epilepsy study.

Method: To regenerate and investigate these patterns, we have proposed three large scale brain network models (BNMS) by linking the neural mass model (NMM) of Stefanescu-Jirsa 2D (S-J 2D) with our own structural connectivity derived from the realistic biological data, so called, large-scale connectivity connectome. These models include multiple network connectivity of brain regions at different lobes from both hemispheres (left and right). The network nodes of
these models were simulated based on the local dynamics of the S-J 2D model, which were generated by adjusting the global coupling between the excitatory and inhibitory populations. The connection strength between the inhibitory and excitatory neurons of the local model was also adjusted to investigate different morphology patterns.

Results: The proposed network models were developed and evaluated by simulations. Different abnormal patterns of EEG brain activities such as HFOS ripples on spikes, spikes, continuous spikes, sporadic spikes and ploy2 spikes ranging from 94-144 Hz were regenerated. Different morphology patterns of abnormality were generated from novel BNMs and the epileptiform abnormal pattern obtained in actual EEG and other computational models were also compared.

Significant: This study is able to assist researchers and clinical doctors in the field of epilepsy to better understand the complex neural mechanisms behind the abnormal oscillatory activities, which may lead to the discovery of new clinical interventions in epilepsy.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: No
Item Status: Live Archive
Additional Information: Permanent restricted access to published version in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Engineering (1 Jan 2022 -)
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Engineering (1 Jan 2022 -)
Date Deposited: 31 Jan 2022 06:38
Last Modified: 11 Feb 2022 05:26
Uncontrolled Keywords: low and high gamma EEG, EEG seizure-like signal, connectome-based brain network modelling, temporal dynamics of Stefanescu-Jirsa 2D, TVB
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
09 Engineering > 0903 Biomedical Engineering > 090302 Biomechanical Engineering
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460207 Modelling and simulation
40 ENGINEERING > 4003 Biomedical engineering > 400399 Biomedical engineering not elsewhere classified
Identification Number or DOI: https://doi.org/10.1016/j.eswa.2021.116477
URI: http://eprints.usq.edu.au/id/eprint/46319

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