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Functional connectivity in resting-state fMRI: Is linear correlation sufficient?
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SYSNO ASEP 0356655 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Functional 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 Title Neuroimage. - : Elsevier - ISSN 1053-8119
Roč. 54, č. 3 (2011), s. 2218-2225Number of pages 8 s. Language eng - English Country US - United States Keywords fMRI ; functional connectivity ; Gaussianity ; nonlinearity ; correlation ; mutual information Subject RIV FH - Neurology R&D Projects 7E08027 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) CEZ AV0Z10300504 - UIVT-O (2005-2011) UT WOS 000286302000044 EID SCOPUS 78650180896 DOI 10.1016/j.neuroimage.2010.08.042 Annotation 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2011
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