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Abstract #4127

Bayesian shrinkage as an alternative to spatial smoothing for multi-echo BOLD fMRI

Feng Xu 1,2 , Joseph S. Gillen 1,2 , Hongjun Liu 3 , Ann Choe 1,2 , Hua Jun 1,2 , Craig K. Jones 1,2 , Suresh E. Joel 1 , Brian S. Caffo 4 , Martin A. Lindquist 4 , Ciprian M. Crainiceanu 4 , Peter C. van Zijl 1,2 , and James J. Pekar 1,2

1 Russell H. Morgan Department of Radiology, Johns Hopkins University, Baltimore, MD, United States, 2 F.M. Kirby Research Center, Kennedy Krieger Institute, Baltimore, MD, United States, 3 Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China, 4 Biostatistics, School of Public Health, Johns Hopkins University, Baltimore, MD, United States

Spatial smoothing is the most popular way to enhance sensitivity in fMRI analysis, at a cost of coarsened spatial specificity. Multi-echo acquisitions can enhance specificity in fMRI by allowing analysis of effective transverse relaxation rate (R2*) via least-squares (LS) fitting to each voxels echo decay. Bayesian shrinkage improves parallel simultaneous estimation of many similar parameters by borrowing strength from parallel measurements. Here, we shrink over grey matter by applying Bayesian shrinkage to estimation of R2* in grey matter voxels, and show that shrinkage increases fMRI sensitivity (with respect to LS fitting) without the blurring caused by spatial smoothing.

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