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  On multivariate spectral analysis of fMRI time series

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

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Müller, K, Author
Lohmann, G1, Author           
Bosch, V, Author
von Cramon, DY, Author
Affiliations:
1External Organizations, ou_persistent22              

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Free keywords: fMRI; Functional connectivities; Spectral analysis; Coherence; Phase lead
 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.

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Language(s): eng - English
 Dates: 2001-08
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1006/nimg.2001.0804
 Degree: -

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Title: NeuroImage
Source Genre: Journal
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Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 14 (2) Sequence Number: - Start / End Page: 347 - 356 Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166