Large-scale brain network model and multi-band electroencephalogram rhythm simulations

Electroencephalogram (EEG) alpha oscillations play a considerable role in understanding cognitive and physiological aspects of human life, and in diagnosing neurocognitive disorders such as Alzheimer's disease (AD) and dementia. In this work, we developed a large-scale brain network model (LSBNM) to simulate multi-alpha band EEG rhythms. This model includes six cortical areas in the left hemisphere and each area is implemented as a local Jansen and Rit (JR) network. The proposed model is developed using the biologically realistic, large-scale connectivity connectome. The implementation and simulations were performed on the neuroinformatics platform, the virtual brain (TVB v1.5.4). Experimental results show that the proposed brain network model enables the generation of the multi-alpha band of EEG rhythms at different ranges of frequencies 7-8 Hz, 8-9 Hz and 10-11 Hz by combining the local dynamics of the JR model with connectome. This model can help physicians to understand the general mechanism of EEG rhythms.