The role of nonlinearity in computing graph-theoretical properties of resting-state functional magnetic resonance imaging brain networks

DOI10.1063/1.3553181zbMath1345.92088OpenAlexW2081685059WikidataQ33941094 ScholiaQ33941094MaRDI QIDQ2821513

Milan Paluš, David Hartman, Dante Mantini, M. Corbetta, Jaroslav Hlinka

Publication date: 21 September 2016

Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc4108645

zbMATH Keywords

functional connectivityfunctional magnetic resonance imagingmultivariate linear Gaussian surrogate data

Mathematics Subject Classification ID

Biomedical imaging and signal processing (92C55) Neural networks for/in biological studies, artificial life and related topics (92B20) Large-scale systems (93A15)

Related Items (2)

Analyzing complex functional brain networks: fusing statistics and network science to understand the brain ⋮ A review on epileptic foci localization using resting-state functional magnetic resonance imaging

Cites Work

• Measuring synchronization in coupled model systems: a comparison of different approaches
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• Detecting nonlinearity in multivariate time series
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• Nonlinearity in normal human EEG: cycles, temporal asymmetry, nonstationarity and randomness, not chaos
• A faster algorithm for betweenness centrality*
• Emergence of Scaling in Random Networks
• Collective dynamics of ‘small-world’ networks

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