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  Realignment strategies for awake-monkey fMRI data

Stoewer, S., Goense, J., Keliris, G., Bartels, A., Logothetis, N., Duncan, J., et al. (2011). Realignment strategies for awake-monkey fMRI data. Magnetic Resonance Imaging, 29(10), 1390-1400. doi:10.1016/j.mri.2011.05.003.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-B8C6-C Version Permalink: http://hdl.handle.net/21.11116/0000-0001-AD7A-4
Genre: Journal Article

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Stoewer, S1, 2, Author              
Goense, J1, 2, Author              
Keliris, GA1, 2, Author              
Bartels, A1, 2, Author              
Logothetis, NK1, 2, Author              
Duncan, J, Author
Sigala, N, Author              
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Functional magnetic resonance imaging (fMRI) experiments with awake nonhuman primates (NHPs) have recently seen a surge of applications. However, the standard fMRI analysis tools designed for human experiments are not optimal for NHP data collected at high fields. One major difference is the experimental setup. Although real head movement is impossible for NHPs, MRI image series often contain visible motion artifacts. Animal body movement results in image position changes and geometric distortions. Since conventional realignment methods are not appropriate to address such differences, algorithms tailored specifically for animal scanning become essential. We have implemented a series of high-field NHP specific methods in a software toolbox, fMRI Sandbox (http://kyb.tuebingen.mpg.de/~stoewer/), which allows us to use different realignment strategies. Here we demonstrate the effect of different realignment strategies on the analysis of awake-monkey fMRI data acquired at high field (7 T). We show that the advantage of using a nonstandard realignment algorithm depends on the amount of distortion in the dataset. While the benefits for less distorted datasets are minor, the improvement of statistical maps for heavily distorted datasets is significant.

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 Dates: 2011-12
 Publication Status: Published in print
 Pages: -
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 Rev. Method: -
 Identifiers: DOI: 10.1016/j.mri.2011.05.003
BibTex Citekey: StoewerGKBLDS2011_2
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Title: Magnetic Resonance Imaging
Source Genre: Journal
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Publ. Info: New York : Elsevier
Pages: - Volume / Issue: 29 (10) Sequence Number: - Start / End Page: 1390 - 1400 Identifier: ISSN: 0730-725X
CoNE: /journals/resource/954925533026