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Visibility graphs for fMRI data : multiplex temporal graphs and their modulations across resting state networks

(2017) NETWORK NEUROSCIENCE. 1(3). p.208-221
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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 (i) this approach allows to simultaneously capture and process relevant aspects of both local and global dynamics in an easy and intuitive way, and (ii) this provides a suggestive bridge between time series and network theory which 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.
Keywords
resting state fmri, signal processing, visibility graphs

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Please use this url to cite or link to this publication:

Chicago
Sannino, Speranza, Sebastiano Stramaglia, Lucas Lacasa, and Daniele Marinazzo. 2017. “Visibility Graphs for fMRI Data : Multiplex Temporal Graphs and Their Modulations Across Resting State Networks.” Network Neuroscience 1 (3): 208–221.
APA
Sannino, S., Stramaglia, S., Lacasa, L., & Marinazzo, D. (2017). Visibility graphs for fMRI data : multiplex temporal graphs and their modulations across resting state networks. NETWORK NEUROSCIENCE, 1(3), 208–221.
Vancouver
1.
Sannino S, Stramaglia S, Lacasa L, Marinazzo D. Visibility graphs for fMRI data : multiplex temporal graphs and their modulations across resting state networks. NETWORK NEUROSCIENCE. MIT Press - Journals; 2017;1(3):208–21.
MLA
Sannino, Speranza, Sebastiano Stramaglia, Lucas Lacasa, et al. “Visibility Graphs for fMRI Data : Multiplex Temporal Graphs and Their Modulations Across Resting State Networks.” NETWORK NEUROSCIENCE 1.3 (2017): 208–221. Print.
@article{8519672,
  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 (i) this approach allows to simultaneously capture and process relevant aspects of both local and global dynamics in an easy and intuitive way, and (ii) this provides a suggestive bridge between time series and network theory which 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.},
  author       = {Sannino, Speranza and Stramaglia, Sebastiano and Lacasa, Lucas and Marinazzo, Daniele},
  issn         = {2472-1751},
  journal      = {NETWORK NEUROSCIENCE},
  keyword      = {resting state fmri,signal processing,visibility graphs},
  language     = {eng},
  number       = {3},
  pages        = {208--221},
  publisher    = {MIT Press - Journals},
  title        = {Visibility graphs for fMRI data : multiplex temporal graphs and their modulations across resting state networks},
  url          = {http://dx.doi.org/10.1162/NETN\_a\_00012},
  volume       = {1},
  year         = {2017},
}

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