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

0356655 - ÚI 2011 RIV US eng J - Journal Article
Hlinka, Jaroslav - Paluš, Milan - Vejmelka, Martin - Mantini, D. - Corbetta, M.
Functional connectivity in resting-state fMRI: Is linear correlation sufficient?
Neuroimage. Roč. 54, č. 3 (2011), s. 2218-2225. ISSN 1053-8119. E-ISSN 1095-9572
R&D Projects: GA MŠMT 7E08027
EU Projects: European Commission(XE) 200728 - BRAINSYNC
Institutional research plan: CEZ:AV0Z10300504
Keywords : fMRI * functional connectivity * Gaussianity * nonlinearity * correlation * mutual information
Subject RIV: FH - Neurology
Impact factor: 5.895, year: 2011

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.
Permanent Link: http://hdl.handle.net/11104/0195122