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.