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Resting state brain functional connectivity quantifies the similarity betweenbrain regions, each of which consists of voxels at which dynamic signals areacquired via neuroimaging techniques such as blood-oxygen-level-dependentsignals in functional magnetic resonance imaging. Pearson correlation andsimilar metrics have been adopted by neuroscientists to estimate inter-regionalconnectivity, usually after averaging of signals within regions. However,dependencies between signals within each region and the presence of noise couldcontaminate such inter-regional correlation estimates. We propose amixed-effects model with a novel covariance structure that explicitly isolatesthe different sources of variability in the observed BOLD signals, includingcorrelated regional signals, local spatiotemporal variability, and measurementerror. Methods for tackling the computational challenges associated withrestricted maximum likelihood estimation will be discussed. Large sampleproperties are discussed and used for uncertainty quantification. Simulationresults demonstrate that the parameters of the proposed model parameters can beaccurately estimated and is superior to the Pearson correlation of averages inthe presence of spatiotemporal noise. The proposed model is also applied to areal data set of BOLD signals collected from rats to construct individual brainnetworks.

A Mixed Model Approach for Estimating Regional Functional Connectivity from Voxel-level BOLD Signals