Abstract
Visibility algorithms are a family of methods that map time series into graphs, such that the tools of graph theory and network science can be used for the characterization of time series. This approach has proved a convenient tool, and visibility graphs have found applications across several disciplines. Recently, an approach has been proposed to extend this framework to multivariate time series, allowing a novel way to describe collective dynamics. Here we test their application to fMRI time series, following two main motivations, namely that (a) this approach allows vs to simultaneously capture and process relevant aspects of both local and global dynamics in an easy and intuitive way, and (b) this provides a suggestive bridge between time series and network theory that nicely fits the consolidating field of network neuroscience. Our application to a large open dataset reveals differences in the similarities of temporal networks (and thus in correlated dynamics) across resting-state networks, and gives indications that some differences in brain activity connected to psychiatric disorders could be picked up by this approach.
Highlights
We present the first application of multivariate visibility graphs to fMRI data
Multivariate time series, as those encountered in neuroscience, and in fMRI in particular, can be seen as a multiplex network, in which each layer represents a time series
We report the method, we describe some relevant aspects of its application to BOLD time series, and we discuss the analogies and differences with existing methods
Summary
We used the public dataset described in Poldrack et al (2016). These data were obtained from the OpenfMRI database, with accession number ds000030. The volumes were corrected for motion, after which slice timing correction was applied to correct for temporal alignment. We averaged the signal in 278 ROIs using the template described in Shen, Tokoglu, Papademetris, & Constable (2013). In order to localize the results within the intrinsic connectivity network of the resting brain, we assigned each of these ROIs to one of the nine resting-state networks (seven cortical networks, plus subcortical regions and cerebellum) as described in Yeo et al (2011)
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