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In this paper, we use data from the Human Connectome Project (N=461) toinvestigate the effect of scan length on reliability of resting-statefunctional connectivity (rsFC) estimates produced from resting-state functionalmagnetic resonance imaging (rsfMRI). Additionally, we study the benefits ofempirical Bayes shrinkage, in which subject-level estimates borrow strengthfrom the population average by trading a small increase in bias for a greaterreduction in variance. For each subject, we compute raw and shrinkage estimatesof rsFC between 300 regions identified through independent components analysis(ICA) based on rsfMRI scans varying from 3 to 30 minutes in length. The timecourse for each region is determined using dual regression, and rsFC isestimated as the Pearson correlation between each pair of time courses.Shrinkage estimates for each subject are computed as a weighted average betweenthe raw subject-level estimate and the population average estimate, where theweight is determined for each connection by the relationship of within-subjectvariance to between-subject variance. We find that shrinkage estimates exhibitgreater reliability than raw estimates for most connections, with 30-40%improvement using scans less than 10 minutes in length and 10-20% improvementusing scans of 20-30 minutes. We also observe significant spatial variabilityin reliability of both raw and shrinkage estimates, with connections within thedefault mode and motor networks exhibiting the greatest reliability andbetween-network connections exhibiting the poorest reliability. We concludethat the scan length required for reliable estimation of rsFC depends on thespecific connections of interest, and shrinkage can be used to increasereliability of rsFC, even when produced from long, high-quality rsfMRI scans.

Effects of Scan Length and Shrinkage on Reliability of Resting-State Functional Connectivity in the Human Connectome Project