Graph analysis of functional brain networks under varying working memory load: An EEG-based minimum spanning tree approach

Abstract

Conventional graph theory approach implemented in prior studies to understand brain topology during performance of cognitive tasks is biased and limits comparison of results across studies. This study employed minimum spanning tree (MST) analysis, an unbiased approach, to quantify changes in brain network organization under varying working memory (WM) load. We used 28-channel electroencephalography dataset of 26 subjects performing cognitive (0-, 2- and 3-back) tasks. We computed functional connectivity across channels using weighted phase lag index (wPLI) in multiple frequency bands and constructed MSTs of brain networks under varying WM conditions. Results reveal 0- back had significantly higher parietal alpha wPLI, lower frontal theta wPLI and lower central theta wPLI, than other conditions. In theta band, we observed more star-like topology in 0- back condition than 2- back or 3- back conditions. Our findings suggest sensitivity of these metrics to alterations in brain network topology under varying cognitive demand.