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

Abstract EN

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.