weighted phase lag index and graph analysis: preliminary investigation of functional connectivity during resting state in children

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ID: 226849
2012
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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.
Reference Key
ortiz2012computationalweighted Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Erick Ortiz, Krunoslav Stingl, Jana Münßinger, Christoph Braun, Hubert Preissl, Paolo Belardinelli
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Journal advanced functional materials
Year 2012
DOI
10.1155/2012/186353
URL
http://dx.doi.org/10.1155/2012/186353
Keywords
medical informatics computer applications to medicine

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