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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

<a href="/cs/project/7E08027" target="_blank" >7E08027: Large scale interactions in brain networks and their breakdown in brain diseases</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Chaos

ISSN

1054-1500

e-ISSN

—

Svazek periodika

21

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

"art.no 013119"

Kód UT WoS článku

000289149100019

EID výsledku v databázi Scopus

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