Počet záznamů: 1

The Role of Nonlinearity in Computing Graph-Theoretical Properties of Resting-State Functional Magnetic Resonance Imaging Brain Networks

  1. 1.
    0358939 - UIVT-O 2012 RIV US eng J - Článek v odborném periodiku
    Hartman, David - Hlinka, Jaroslav - Paluš, Milan - Mantini, D. - Corbetta, M.
    The Role of Nonlinearity in Computing Graph-Theoretical Properties of Resting-State Functional Magnetic Resonance Imaging Brain Networks.
    Chaos. Roč. 21, č. 1 (2011), art.no 013119 ISSN 1054-1500
    Grant CEP: GA MŠk 7E08027
    Výzkumný záměr: CEZ:AV0Z10300504
    Klíčová slova: complex network * fMRI * brain connectivity * nonlinear * mutual information * correlation
    Kód oboru RIV: BB - Aplikovaná statistika, operační výzkum
    Impakt faktor: 2.076, rok: 2011

    We present a comparison of network analysis results for the brain connectivity graphs capturing either linear and nonlinear or only linear connectivity using 24 sessions of human resting-state fMRI. For comparison, connectivity matrices for multivariate linear Gaussian surrogate data preserving the correlations, but removing any nonlinearity are generated. Subsequent binarization with multiple thresholds generate graphs corresponding to linear and full nonlinear interactions. The effect of neglecting nonlinearity is then assessed by comparing the values of a range of graph-theoretical measures evaluated for both types of graphs. Statistical comparisons suggest a potential effect of nonlinearity on the local measures - clustering coefficient and betweenness centrality. A subsequent quantitative comparison shows that this effect is practically negligible when compared to the intersubject variability. Further, on the group-average graph level, the nonlinearity effect is unnoticeable.
    Trvalý link: http://hdl.handle.net/11104/0196837