Name
Abstract | ||
|---|---|---|
Graph theory provides many metrics of complex network organization that can be applied to analysis of brain networks derived from neuroimaging data. Here we investigated the test–retest reliability of graph metrics of functional networks derived from magnetoencephalography (MEG) data recorded in two sessions from 16 healthy volunteers who were studied at rest and during performance of the n-back working memory task in each session. For each subject's data at each session, we used a wavelet filter to estimate the mutual information (MI) between each pair of MEG sensors in each of the classical frequency intervals from γ to low δ in the overall range 1–60 Hz. Undirected binary graphs were generated by thresholding the MI matrix and 8 global network metrics were estimated: the clustering coefficient, path length, small-worldness, efficiency, cost-efficiency, assortativity, hierarchy, and synchronizability. Reliability of each graph metric was assessed using the intraclass correlation (ICC). Good reliability was demonstrated for most metrics applied to the n-back data (mean ICC=0.62). Reliability was greater for metrics in lower frequency networks. Higher frequency γ- and β-band networks were less reliable at a global level but demonstrated high reliability of nodal metrics in frontal and parietal regions. Performance of the n-back task was associated with greater reliability than measurements on resting state data. Task practice was also associated with greater reliability. Collectively these results suggest that graph metrics are sufficiently reliable to be considered for future longitudinal studies of functional brain network changes. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.neuroimage.2009.05.035 | NeuroImage |
Keywords | Field | DocType |
working memory,clustering coefficient,complex network,magnetoencephalography,cost efficiency,mutual information,resting state,intraclass correlation,meg,graph theory | Data mining,Assortativity,Path length,Computer science,Resting state fMRI,Cognitive psychology,Complex network,Artificial intelligence,Clustering coefficient,Graph theory,Pattern recognition,Mutual information,Magnetoencephalography | Journal |
Volume | Issue | ISSN |
47 | 4 | 1053-8119 |
Citations | PageRank | References |
47 | 6.22 | 7 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lorena Deuker | 1 | 47 | 6.22 |
| Ed Bullmore | 2 | 1331 | 150.94 |
| Marie Smith | 3 | 47 | 6.22 |
| Soren Christensen | 4 | 47 | 6.22 |
| Pradeep J. Nathan | 5 | 65 | 9.16 |
| Brigitte Rockstroh | 6 | 82 | 10.35 |
| Danielle Bassett | 7 | 776 | 57.28 |