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

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    SYSNO ASEP0356655
    Document TypeJ - Journal Article
    R&D Document TypeJournal Article
    Subsidiary JČlánek ve WOS
    TitleFunctional connectivity in resting-state fMRI: Is linear correlation sufficient?
    Author(s) Hlinka, Jaroslav (UIVT-O) RID, SAI, ORCID
    Paluš, Milan (UIVT-O) RID, SAI, ORCID
    Vejmelka, Martin (UIVT-O) SAI, RID, ORCID
    Mantini, D. (BE)
    Corbetta, M. (IT)
    Source TitleNeuroimage. - : Elsevier - ISSN 1053-8119
    Roč. 54, č. 3 (2011), s. 2218-2225
    Number of pages8 s.
    Languageeng - English
    CountryUS - United States
    KeywordsfMRI ; functional connectivity ; Gaussianity ; nonlinearity ; correlation ; mutual information
    Subject RIVFH - Neurology
    R&D Projects7E08027 GA MŠMT - Ministry of Education, Youth and Sports (MEYS)
    CEZAV0Z10300504 - UIVT-O (2005-2011)
    UT WOS000286302000044
    EID SCOPUS78650180896
    DOI10.1016/j.neuroimage.2010.08.042
    AnnotationFunctional 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.
    WorkplaceInstitute of Computer Science
    ContactTereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800
    Year of Publishing2011
Number of the records: 1  

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