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Journal Article

On multivariate spectral analysis of fMRI time series

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Müller,  Karsten
MPI of Cognitive Neuroscience (Leipzig, -2003), The Prior Institutes, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Lohmann,  Gabriele
MPI of Cognitive Neuroscience (Leipzig, -2003), The Prior Institutes, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Bosch,  Volker
MPI of Cognitive Neuroscience (Leipzig, -2003), The Prior Institutes, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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von Cramon,  D. Yves
MPI of Cognitive Neuroscience (Leipzig, -2003), The Prior Institutes, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

Müller, K., Lohmann, G., Bosch, V., & von Cramon, D. Y. (2001). On multivariate spectral analysis of fMRI time series. NeuroImage, 14(2), 347-356. doi:10.1006/nimg.2001.0804.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0010-CE0D-3
Abstract
Most of functional magnetic resonance imaging (fMRI) time series analysis is based on single voxel data evaluation using parametric statistical tests. The result of such an analysis is a statistical parametric map. Voxels with a high significance value in the parametric test are interpreted as activation regions stimulated by the experimental task. However, for the investigation of functional connectivities it would be interesting to get some detailed information about the temporal dynamics of the blood oxygen level-dependent (BOLD) signal. For investigating that behavior, a method for fMRI data analysis has been developed that is based on Wiener theory of spectral analysis 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 show the temporal behavior of the BOLD response function. This paper describes the method and presents a visual fMRI experiment as an example to demonstrate the results.