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Abstract #3257

Empirical Bayesian Estimation Improves Analysis of Resting-State Functional Connectivity from Multi-Echo BOLD Data

Feng Xu1, 2, Suresh E. Joel, 12, Jun Hua1, 2, Craig K. Jones, 12, Brian S. Caffo3, Martin A. Lindquist4, Ciprian M. Crainiceanu4, Peter C.M. van Zijl1, 2, James J. Pekar1, 2

1Radiology, Johns Hopkins University, School of Medicine, Baltimore, MD, United States; 2F. M. Kirby Research Center, Kennedy Krieger Institute, Baltimore, MD, United States; 3Biostatistics, Johns Hopkins University, Baltimore, MD, United States; 4Biostatistics, Johns Hopkins University, School of Public Health, Baltimore, MD, United States

Multi-echo acquisitions can improve the sensitivity and specificity of resting-state functional connectivity MRI compared with conventional EPI. Analysis of multi-echo decays to estimate transverse relaxation in each voxel is a case of parallel estimation; statistical theory states that popular maximum likelihood (least-squares) methods are in some ways inferior to empirical Bayesian approaches. In this study we show that the James-Stein estimator yields modest increases in the spatial extent and inter-subject concordance of functional networks estimated from multi-echo resting BOLD data acquired at 7 Tesla.