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Open AccessArticle

Quantifying the Variability in Resting-State Networks

1
Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, AB T2N 1N4, Canada
2
Institute of Computer Science, The Czech Academy of Sciences, 117 20 Prague, Czech Republic
3
National Institute of Mental Health, 250 67 Klecany, Czech Republic
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Department of Computing and Control Engineering, University of Chemistry and Technology, 166 28 Prague, Czech Republic
5
Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(9), 882; https://doi.org/10.3390/e21090882
Received: 31 July 2019 / Revised: 27 August 2019 / Accepted: 6 September 2019 / Published: 11 September 2019
(This article belongs to the Special Issue Complex Networks from Information Measures)
Recent precision functional mapping of individual human brains has shown that individual brain organization is qualitatively different from group average estimates and that individuals exhibit distinct brain network topologies. How this variability affects the connectivity within individual resting-state networks remains an open question. This is particularly important since certain resting-state networks such as the default mode network (DMN) and the fronto-parietal network (FPN) play an important role in the early detection of neurophysiological diseases like Alzheimer’s, Parkinson’s, and attention deficit hyperactivity disorder. Using different types of similarity measures including conditional mutual information, we show here that the backbone of the functional connectivity and the direct connectivity within both the DMN and the FPN does not vary significantly between healthy individuals for the AAL brain atlas. Weaker connections do vary however, having a particularly pronounced effect on the cross-connections between DMN and FPN. Our findings suggest that the link topology of single resting-state networks is quite robust if a fixed brain atlas is used and the recordings are sufficiently long—even if the whole brain network topology between different individuals is variable. View Full-Text
Keywords: resting-state networks; network inference; network topology resting-state networks; network inference; network topology
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Oliver, I.; Hlinka, J.; Kopal, J.; Davidsen, J. Quantifying the Variability in Resting-State Networks. Entropy 2019, 21, 882.

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