Novel large scale brain network models for EEG epileptic pattern generations

Abstract

BackgroundUnlike 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. MethodTo 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. ResultsThe 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 to 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. SignificantThis 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.