Network analysis has been used to investigate the network dynamics of neurological diseases such as AD and mild cognitive impairment [39,40,41]. Resting state connectivity and network topology are increasingly being studied to understand the effect of aging on specific brain regions connected in a resting state. Previous studies have shown that reduced small world configuration and increased path length reduce clustering in the resting state network in healthy aging EEG signals [6,18]. It has been recommended that network indices of graph theory can be used to investigate the age-related characteristic of functional networks of the brain [42]. Moreover, graph theory has been applied to investigate age-related alterations during an n-back test using a clustering coefficient, a small world coefficient, and characteristic path length [43]. In order to explore how a resting state network configuration involving regions is different from a WM state configuration, our current work focuses on age-related differences in networks in a resting state and under visual working memory conditions. However, to the best of our knowledge, network analysis has not been applied in a visual working memory task to investigate age-related changes in middle-aged and elderly populations and understand the mechanism of cognitive aging.
