Abstract
Typical brain networks consist of many peripheral regions and a few highly central ones, i.e., hubs, playing key functional roles in cerebral inter-regional interactions. Studies have shown that networks, obtained from the analysis of specific frequency components of brain activity, present peculiar architectures with unique profiles of region centrality. However, the identification of hubs in networks built from different frequency bands simultaneously is still a challenging problem, remaining largely unexplored. Here we identify each frequency component with one layer of a multiplex network and face this challenge by exploiting the recent advances in the analysis of multiplex topologies. First, we show that each frequency band carries unique topological information, fundamental to accurately model brain functional networks. We then demonstrate that hubs in the multiplex network, in general different from those ones obtained after discarding or aggregating the measured signals as usual, provide a more accurate map of brain's most important functional regions, allowing to distinguish between healthy and schizophrenic populations better than conventional network approaches.
Highlights
The brain functional network is generally built by interconnecting brain regions according to some measure of functional connectivity (Bassett and Bullmore, 2006; Bullmore and Sporns, 2009, 2012)
We use a publicly available Center for Biomedical Research Excellence (COBRE) data set of resting state fMRI, consisting of 71 patients affected by Schizophrenia and 74 healthy controls
The upper bound of this frequency range corresponds to the Nyquist frequency of fMRI signals, while the lower bound is obtained by following conventional way to eliminate long term drift (Cordes et al, 2002)
Summary
The brain functional network is generally built by interconnecting brain regions according to some measure of functional connectivity (Bassett and Bullmore, 2006; Bullmore and Sporns, 2009, 2012). It has been shown that networks with unique hub regions can be built from different frequency ranges (Sasai et al, 2014) and that region centrality might largely fluctuate depending on frequency cuts (Thompson and Fransson, 2015), with components above 0.1 Hz contributing to functional connectivity with unique topological information (Bassett et al, 2006; Mantini et al, 2007; Supekar et al, 2008; Chavez et al, 2010; Liao et al, 2013; Chen and Glover, 2015) Such an evidence impels the development of a novel framework to account for full information from all frequency bands separately and simultaneously, without discarding any particular component or aggregating some of them to build single networks
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