Weighted Phase Lag Index and Graph Analysis: Preliminary Investigation of Functional Connectivity during Resting State in Children

Abstract

Resting state functional connectivity of MEG data was studied in 29 children (9-10 years old). The weighted phase lag index (WPLI) was employed for estimating connectivity and compared to coherence. To further evaluate the network structure, a graph analysis based on WPLI was used to determine clustering coefficient (C) and betweenness centrality (BC) as local coefficients as well as the characteristic path length (L) as a parameter for global interconnectedness. The network's modular structure was also calculated to estimate functional segregation. A seed region was identified in the central occipital area based on the power distribution at the sensor level in the alpha band. WPLI reveals a specific connectivity map different from power and coherence. BC and modularity show a strong level of connectedness in the occipital area between lateral and central sensors. C shows different isolated areas of occipital sensors. Globally, a network with the shortest L is detected in the alpha band, consistently with the local results. Our results are in agreement with findings in adults, indicating a similar functional network in children at this age in the alpha band. The integrated use of WPLI and graph analysis can help to gain a better description of resting state networks.

Resting-state EEG power predicts conflict-related brain activity in internally guided but not in externally guided decision-making

“…S4(a), one-to-all connectivity). The results of EC resting-state functional connectivity were similar to those reported from a previous study (Ortiz et al, 2012). One-factor repeated-measures ANOVA (EC vs. EO vs. ITI) for the averaged WPLI value over Oz-to-all channel pairs revealed main effects of the baseline condition (F(2, 46) = 9.07, p b 0.001, η 2 = 0.28).…”

Atypical resting synchrony in autism spectrum disorder

“…It has been shown that wPLI outperforms PLI, coherence, and imaginary coherence in attenuating volume conduction using real local field potential data [Vinck et al 2011]. Moreover, wPLI has been demonstrated for measurement of inter-regional MEG synchrony in children [Dimitriadis et al 2013], including graph analysis of resting-state connectivity [Ortiz et al 2012].…”

Graph theoretical analysis of resting-state MEG data: Identifying interhemispheric connectivity and the default mode

“…A variety of connectivity metrics have been developed to circumvent these problems. For example, the phase coherence (PC), the phase lag index (PLI), and imaginary coherence (icoh) of the MEG time courses have been used as connectivity measures (Hillebrand et al, 2012; Ortiz et al, 2012; Stam et al, 2009; Stam et al, 2007; Tewarie et al, 2013; Vinck et al, 2011). There have been several studies using these metrics to identify MEG source space networks.…”