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Brain functional connectivity (FC), the temporal synchrony between brainnetworks, is essential to understand the functional organization in the brainand to identify changes due to neurological disorders, development, treatment,and other phenomena. Independent component analysis (ICA) is a matrixdecomposition method used extensively for simultaneous estimation of functionalbrain topography and connectivity. However, estimation of FC via ICA is oftensub-optimal due to the use of ad-hoc estimation methods or temporal dimensionreduction prior to ICA. Bayesian ICA methods can avoid dimension reduction,produce more accurate estimates of latent variables and model parameters, andfacilitate inference via posterior distributions. In this paper, we develop anovel, computationally feasible Bayesian ICA method with population-derivedpriors on both the spatial ICs and their temporal correlation. For the latterwe consider two priors: the inverse-Wishart, which is designed for covariancematrices and has limitations for modeling correlation matrices; and a novelinformative prior for correlation matrices. For both choices of prior, wederive a variational Bayes algorithm to estimate the model variables and obtainposterior variances or distributions of quantities of interest. Throughextensive realistic simulation studies, we evaluate the performance of theproposed methods and compare them with existing approaches. Finally, we analyzefMRI data from over 400 healthy adults in the Human Connectome Project. We findthat our Bayesian ICA algorithms produce highly accurate measures of functionalconnectivity and spatial brain features. Our informative prior for correlationmatrices outperforms the inverse-Wishart, but comes with a higher computationalburden. The proposed framework is applicable to single-subject analysis, makingit potentially clinically viable.

Leveraging population information in brain connectivity via Bayesian ICA with a novel informative prior for correlation matrices