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Reducing the unwanted draining vein BOLD contribution in fMRI with statistical post-processing methods., and . NeuroImage, 37 (1): 177-188 (2007)Quantification of the Statistical Effects of Spatiotemporal Processing of Nontask fMRI Data., , , and . Brain Connect., 4 (9): 649-661 (2014)Two-Axis Acceleration of Functional Connectivity Magnetic Resonance Imaging by Parallel Excitation of Phase-Tagged Slices and Half k-Space Acceleration., , , and . Brain Connect., 1 (1): 81-90 (2011)Improving robustness and reliability of phase-sensitive fMRI analysis using temporal off-resonance alignment of single-echo timeseries (TOAST)., , and . NeuroImage, 44 (3): 742-752 (2009)Functional connectivity of the cortical swallowing network in humans., , , , , , , and . NeuroImage, (2013)Using Low-Frequency Oscillations to Detect Temporal Lobe Epilepsy with Machine Learning., , , , , , , , , and 18 other author(s). Brain Connect., 9 (2): 184-193 (2019)Deep-learning based Tools for Automated Protocol Definition of Advanced Diagnostic Imaging Exams., , , , and . CoRR, (2021)Value CMR: Towards a Comprehensive, Rapid, Cost-Effective Cardiovascular Magnetic Resonance Imaging., , , , , , , , , and 4 other author(s). Int. J. Biomed. Imaging, (2021)Effective Connectivity Within the Default Mode Network in Left Temporal Lobe Epilepsy: Findings from the Epilepsy Connectome Project., , , , , , , , , and 17 other author(s). Brain Connect., 9 (2): 174-183 (2019)Classification before Segmentation: Improved U-Net Prostate Segmentation., , , , , , , , , and 1 other author(s). BHI, page 1-4. IEEE, (2019)