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Large brain imaging databases contain a wealth of information on brainorganization in the populations they target, and on individual variability.While such databases have been used to study group-level features ofpopulations directly, they are currently underutilized as a resource to informsingle-subject analysis. Here, we propose leveraging the information containedin large functional magnetic resonance imaging (fMRI) databases by establishingpopulation priors to employ in an empirical Bayesian framework. We focus onestimation of brain networks as source signals in independent componentanalysis (ICA). We formulate a hierarchical "template" ICA model where sourcesignals---including known population brain networks and subject-specificsignals---are represented as latent variables. For estimation, we derive anexpectation maximization (EM) algorithm having an explicit solution. However,as this solution is computationally intractable, we also consider anapproximate subspace algorithm and a faster two-stage approach. Throughextensive simulation studies, we assess performance of both methods and comparewith dual regression, a popular but ad-hoc method. The two proposed algorithmshave similar performance, and both dramatically outperform dual regression. Wealso conduct a reliability study utilizing the Human Connectome Project andfind that template ICA achieves substantially better performance than dualregression, achieving 75-250% higher intra-subject reliability.

Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks using Big Data Population Priors