title: Kernel Granger causality mapping effective connectivity on FMRI data.
abstract: Although it is accepted that linear Granger causality can reveal effective connectivity in functional magnetic resonance imaging (fMRI), the issue of detecting nonlinear connectivity has hitherto not been considered. In this paper, we address kernel Granger causality (KGC) to describe effective connectivity in simulation studies and real fMRI data of a motor imagery task. Based on the theory of reproducing kernel Hilbert spaces, KGC performs linear Granger causality in the feature space of suitable kernel functions, assuming an arbitrary degree of nonlinearity. Our results demonstrate that KGC captures effective couplings not revealed by the linear case. In addition, effective connectivity networks between the supplementary motor area (SMA) as the seed and other brain areas are obtained from KGC.
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Liao
authors.forename: Daniele
authors.initials: D
authors.lastname: Marinazzo
authors.forename: Zhengyong
authors.initials: Z
authors.lastname: Pan
authors.forename: Qiyong
authors.initials: Q
authors.lastname: Gong
authors.forename: Huafu
authors.initials: H
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015999:Multivariate Analysis; D016011:Normal Distribution; D015203:Reproducibility of Results