Functional connectivity in resting-state fMRI: Is linear correlation sufficient?

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F11%3A00356655" target="_blank" >RIV/67985807:_____/11:00356655 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Functional connectivity in resting-state fMRI: Is linear correlation sufficient?

Popis výsledku v původním jazyce

Functional connectivity (FC) analysis is a prominent approach to analyzing fMRI data, especially acquired in resting state. The commonly used linear correlation bears an implicit assumption of Gaussianity of the dependence structure. To assess the suitability of linear correlation and the general potential of nonlinear FC measures, we present a framework for testing and estimating the deviation from Gaussianity by comparing mutual information in the data and its Gaussianized counterpart. We apply this method to 24 sessions of human resting state fMRI. While the group-level tests confirmed non-Gaussianity in the FC, the quantitative assessment revealed that the portion of mutual information neglected by linear correlation is relatively minor - on average only about 5% of the total mutual information. We conclude that for this type of data, practical relevance of nonlinear methods trying to improve over linear correlation is limited by the fact that the data are almost Gaussian.

Název v anglickém jazyce

Functional connectivity in resting-state fMRI: Is linear correlation sufficient?

Popis výsledku anglicky

Functional connectivity (FC) analysis is a prominent approach to analyzing fMRI data, especially acquired in resting state. The commonly used linear correlation bears an implicit assumption of Gaussianity of the dependence structure. To assess the suitability of linear correlation and the general potential of nonlinear FC measures, we present a framework for testing and estimating the deviation from Gaussianity by comparing mutual information in the data and its Gaussianized counterpart. We apply this method to 24 sessions of human resting state fMRI. While the group-level tests confirmed non-Gaussianity in the FC, the quantitative assessment revealed that the portion of mutual information neglected by linear correlation is relatively minor - on average only about 5% of the total mutual information. We conclude that for this type of data, practical relevance of nonlinear methods trying to improve over linear correlation is limited by the fact that the data are almost Gaussian.

Druh

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

CEP obor

FH - Neurologie, neurochirurgie, neurovědy

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

Neuroimage

ISSN

1053-8119

e-ISSN

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Svazek periodika

54

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

8

Strana od-do

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Kód UT WoS článku

000286302000044

EID výsledku v databázi Scopus

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