The Role of Nonlinearity in Computing Graph-Theoretical Properties of Resting-State Functional Magnetic Resonance Imaging Brain Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F11%3A00358939" target="_blank" >RIV/67985807:_____/11:00358939 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1063/1.3553181" target="_blank" >http://dx.doi.org/10.1063/1.3553181</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1063/1.3553181" target="_blank" >10.1063/1.3553181</a>
Alternative languages
Result language
angličtina
Original language name
The Role of Nonlinearity in Computing Graph-Theoretical Properties of Resting-State Functional Magnetic Resonance Imaging Brain Networks
Original language description
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 multivariatelinear 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.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Result continuities
Project
<a href="/en/project/7E08027" target="_blank" >7E08027: Large scale interactions in brain networks and their breakdown in brain diseases</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Chaos
ISSN
1054-1500
e-ISSN
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Volume of the periodical
21
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
13
Pages from-to
"art.no 013119"
UT code for WoS article
000289149100019
EID of the result in the Scopus database
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