evaluating the effect of increasing working memory load on eeg-based functional brain networks

Purpose: working memory (wm) plays a crucial role in many cognitive functions of the human brain. examining how the inter-regional connectivity and characteristics of functional brain networks modulate with increasing wm load could lead to a more in-depth understanding of the wm system. materials and methods: to investigate the effect of wm load alterations on the inter-regional synchronization and functional network characteristics, we used electroencephalogram (eeg) data recorded from 21 healthy participants during an n-back task with three load levels (0-back, 2-back, and 3-back). the networks were constructed based on the weighted phase lag index (wpli) in the theta, alpha, beta, low-gamma, and high-gamma frequency bands. after constructing the fully connected, weighted, and undirected networks, the node-to-node connections, graph-theory metrics consisting of mean clustering coefficient (c), characteristic path length (l), and node strength were analyzed by statistical tests. results: it was revealed that in the presence of wm load (2- and 3-back tasks) compared with the wm-free condition (0-back task) within the alpha range, the inter-regional functional connectivity (irfc), functional integration, functional segregation, and node strength in channels located at the frontal, parietal and occipital regions were significantly reduced. in the high-gamma band, irfc was significantly higher in the difficult task (3-back) compared to the easy and moderate tasks (0- and 2-back). besides, locally clustered connections were significantly increased in 3-back relative to the 2-back task. conclusion: inter-regional alpha synchronization and alpha-band network metrics can distinguish between the wm and wm-free tasks. in contrast, phase synchronization of high-gamma oscillations can differentiate between the levels of wm load, which demonstrates the potential of the phase-based functional connectivity and brain network metrics for predicting the wm load level.