Evaluating the Effect of Increasing Working Memory Load on EEG-Based Functional Brain Networks

Susan Samiei; Mehdi Delrobaei; Ali  Khadem

doi:10.18502/fbt.v9i3.9641

Susan Samiei Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Mehdi Delrobaei Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Ali Khadem Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran

DOI: https://doi.org/10.18502/fbt.v9i3.9641

Keywords: Electroencephalogram; Working Memory; Functional Connectivity; Weighted Phase Lag Index; Graph Theory.

Abstract

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.