Eigenvector Centrality of the Visual Network exceeds the Default Mode Network during Rest

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The default mode network (DMN) is one of the resting state networks (RSNs) in the brain that have been identified by investigating temporal correlations of spontaneous activity fluctuations in resting state fMRI (rsfMRI). The DMN is crucial for efficient cognitive functioning, although evidently decreasing in activity during many cognitive tasks. Even though the DMN is typically identified by independent component analysis (ICA), other methods have been used to extract and analyze the network as well and their relation to ICA has been explored. However, no comparison of ICA and eigenvector centrality mapping, another data-driven, but graph-theory based method has been reported yet. Here, we used 100 rsfMRI data sets to show that the medial visual network, rather than the DMN, was the most central network during rest and that its eigenvector centrality correlated negatively with the centrality of the DMN. Accordingly, the most central areas during rest did not conform with the DMN extracted by ICA. Our results suggest that the visual RSNs play a more versatile and not strictly modular function during rest and that the investigation of their individual variations is more important than previously believed. Keywords: DMN, visual network, eigenvector centrality, ICA, rsfMRI
Faculteit der Sociale Wetenschappen