Graph theoretical analysis
Graph theoretical analyses were performed by GRETNA (Wang et al., 2015) in MATLAB R2017b. Graph theory is the study of graphs, which are mathematical structures used to model pairwise relations between objects. The graph is made up of nodes and edges. In the current study, nodes represent ROIs, while edges represent functional connectivity, which is the lagged linear coherence calculated using eLORETA, which was used for graph theory in a previous study (Vecchio et al., 2021). The small world index (SWI) calculated from graph theoretical analysis was used for further analysis due to the association with cognition (Vecchio et al., 2014; Zeng et al., 2015). A previous study reported that increased hippocampal volume reflected increased small-world characteristics in gamma frequency band of connectivity in Alzheimer’s disease (Vecchio et al., 2017). The study by Vecchio et al. reported higher gamma small world characteristics during the resting state and better performance in short-term memory (Vecchio et al., 2016). The small world network combines high levels of local clustering among nodes and short path length (Bullmore & Sporns, 2009). SWI was defined as the ratio of the normalized clustering coefficient (Cw) and the normalized path length (Lw) (Rubinov & Sporns, 2010; Vecchio et al., 2018). Brain networks have a small world property (Bassett & Bullmore, 2006). The density threshold was set from 0.15 to 0.50, with a 0.05 interval. The upper limit of the density threshold was defined by the size of the connectivity matrix (82, 82) (Supplemental Method 1). SWI in 0.30–0.50 thresholds showed similar trends in each group (Supplemental Fig. 1). We chose the data at a density threshold of 0.45 for further analyses.