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Abstract #2238

Frequency Characteristics of Large Scale Resting State Networks Using 7T Spin Echo EPI

Erik van Oort1, 2, Peter J. Koopmans3, Rasim Boyacioglu4, Markus Barth4, Christian F. Beckmann1, 2, David Norris5

1Statistical Imaging Neuroscience, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Nijmegen, Gelderland, Netherlands; 2Statistical Imaging Neuroscience, Radboud University Nijmegen, Donders Center for Cognitive Neuroimaging, Nijmegen, Nederland, Netherlands; 3MR Techniques in Brain Function, Radboud University Nijmegen, Donders Center for Cognitive Neuroimaging, Nijmegen, Nederland, Netherlands; 4MR Techniques in Brain Function, Radboud University, Nijmegen, Nederland, Netherlands; 5MR Techniques in Brain Function, Radboud University Nijmegen, Donders Center for Cognitive Neuroimaging, Nijmegen, Gelderland, Netherlands

Frequency characteristics of large scale Resting State Networks were investigated using full brain 7T Spin Echo EPI. Previous results of GE EPI data show peak identifiability and estimation of RSNs at frequencies above 0.1Hz, with decreasing performance at higher frequencies. The SE performance also peaks above 0.1Hz, but plateaus instead of dropping off. This is in accordance with the reported more linear nature of SE EPI, when compared to GE EPI. This causes less attenuation of high frequency contributions in SE EPI, which show in a monotonically rising performance, until reaching a plateau.

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Keywords

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