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Meeting Abstract

Blind spots in functional magnetic resonance imaging

MPS-Authors
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Stelzer,  J
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bause,  J
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Ehses,  P
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Scheffler,  K
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Lohmann,  G
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Stelzer, J., Bause, J., Ehses, P., Bazin, P.-L., Scheffler, K., & Lohmann, G. (2017). Blind spots in functional magnetic resonance imaging. In 18th Conference of Junior Neuroscientists of Tübingen (NeNa 2017) (pp. 10).


Cite as: http://hdl.handle.net/21.11116/0000-0001-00FD-2
Abstract
Are brain functions located in only a few segregated areas, or do they involve widespread parts of the brain? Traditional human fMRI research clearly leans towards small sets of areas that become activated. Recently, however, this view has been challenged and appears to be caused by low power and deficient analysis methods. I will present fMRI data at 9.4T that has been analyzed with state-of-the-art network analysis methods and compare the results to a 3T dataset, which was analyzed by traditional means. Indeed, at 9.4T we find that most parts of grey matter reconfigure their connectivity, while we only find a small set of activated regions at 3T. I will discuss the implications of this and other findings in terms of scientific inference.