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Thickness of Deep Layers in the Fusiform Face Area Predicts Face Recognition.

, , , , and . J. Cogn. Neurosci., 32 (7): 1316-1329 (2020)

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Multi-modal and targeted imaging improves automated mid-brain segmentation., , , , , , and . Medical Imaging: Image Processing, volume 10133 of SPIE Proceedings, page 101330J. SPIE, (2017)MRI correlates of chronic symptoms in mild traumatic brain injury., , , , , , , and . Medical Imaging: Image Processing, volume 11313 of SPIE Proceedings, page 113132Q. SPIE, (2020)Learning implicit brain MRI manifolds with deep learning., , , , , and . Medical Imaging: Image Processing, volume 10574 of SPIE Proceedings, page 105741L. SPIE, (2018)SHARD: spherical harmonic-based robust outlier detection for HARDI methods., , , , , , , , , and 1 other author(s). Medical Imaging: Image Processing, volume 10574 of SPIE Proceedings, page 105740X. SPIE, (2018)Evaluation of inter-site bias and variance in diffusion-weighted MRI., , , , , , , , , and . Medical Imaging: Image Processing, volume 10574 of SPIE Proceedings, page 1057413. SPIE, (2018)Ultra-high-resolution mapping of cortical layers 3T-guided 7T MRI., , , , , , , , , and 1 other author(s). Medical Imaging: Image Processing, volume 12032 of SPIE Proceedings, SPIE, (2022)Inter-Scanner Harmonization of High Angular Resolution DW-MRI using Null Space Deep Learning., , , , , , , , , and 8 other author(s). CoRR, (2018)On the fallacy of quantitative segmentation for T1-weighted MRI., , , , , , , , and . Medical Imaging: Image Processing, volume 9784 of SPIE Proceedings, page 978416. SPIE, (2016)Thickness of Deep Layers in the Fusiform Face Area Predicts Face Recognition., , , , and . J. Cogn. Neurosci., 32 (7): 1316-1329 (2020)Measurement of steady state functional connectivity in the human brain using functional magnetic resonance imaging.. Vanderbilt University, Nashville, Tennessee, USA, (2009)base-search.net (ftvanderbilt:oai:ir.vanderbilt.edu:1803/11757).