On multivariate spectral analysis of fMRI time series

Most of functional magnetic resonance imaging (fMRI) time series analysis i s based on single voxel data evaluation using parametric statistical tests. The result of such an analysis is a statistical parametric map. Voxels wit h a high significance value in the parametric test are interpreted as activ ation regions stimulated by the experimental task. However, for the investi gation of functional connectivities it would be interesting to get some det ailed information about the temporal dynamics of the blood oxygen level-dep endent (BOLD) signal. For investigating that behavior, a method for fMRI da ta analysis has been developed that is based on Wiener theory of spectral a nalysis for multivariate time series. Spectral parameters such as coherence measure and phase lead can be estimated. The resulting maps give detailed information on brain regions that belong to a network structure and also sh ow the temporal behavior of the BOLD response function. This paper describe s the method and presents a visual fMRI experiment as an example to demonst rate the results. (C) 2001 Academic Press.