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.