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

0358939 - ÚI 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. E-ISSN 1089-7682
Grant CEP: GA MŠMT 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