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Abstract #1617

Model-Based & Data-Driven Analysis of Whole Brain EVI Demonstrates Increased Statistical Power Compared to EPI at 3T

Radu Mutihac1,2, Elena Ackley1, Jochen Rick3, Akio Yoshimoto4, Maxim Zaitsev3, Oliver Speck5, Stefan Posse1,6

1Department of Neurology, University of New Mexico, Albuquerque, NM, United States; 2Department of Electricity & Biophysics, University of Bucharest, Bucharest, Romania; 3Department of Radiology - Medical Physics, University Medical Center Freiburg, Freiburg, Germany; 4Polytechnic Institute of New York University, New York, United States; 5Department Biomedical Magnetic Resonance, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany; 6Department of Physics & Astronomy, University of New Mexico, Albuquerque, NM, United States

Whole brain multiple-slab echo-volumar-imaging (EVI) is a novel methodology that provides up to an order of magnitude higher temporal resolution compared to multi-slice echo-planar imaging (EPI). However, fMRI sensitivity of EVI and EPI has not yet been systematically compared using neither hypothesis driven inferential statistics like statistical parametric mapping (SPM) nor exploratory methods like spatial independent component analysis (ICA) or temporal fuzzy clustering analysis (FCA). In this study, we statistically assess the extent and maximum T-score of activation elicited by an auditory-gated visual-motor task for both modalities using SPM8. Furthermore, the finer time course information available in EVI lends itself to data-driven analysis to identify physiological noise sources and spurious activation investigated by spatial ICA.