Statistical Approaches for Estimation and Comparison of Brain Functional Connectivity

Drug addiction can lead to many health-related problems and social concerns. Functional connectivity obtained from functional magnetic resonance imaging (fMRI) data promotes a variety of fundamental understandings in such association. Due to its complex correlation structure and large dimensionality, the modeling and analysis of the functional connectivity from neuroimage are challenging. By proposing a spatio-temporal model for multi-subject neuroimage data, we incorporate voxel-level spatio-temporal dependencies of whole-brain measurements to improve the accuracy of statistical inference. To tackle large-scale spatio-temporal neuroimage data, we develop a computationally efficient algorithm to estimate the parameters. Our method is used to identify functional connectivity and detect the effect of cocaine use disorder (CUD) on functional connectivity between different brain regions. The functional connectivity identified by our spatio-temporal model matches existing studies on brain networks, and further indicates that CUD may alter the functional connectivity in the medial orbitofrontal cortex subregions and the supplementary motor areas. We further propose a method that jointly estimates the graphical models which share the common structure, while allowing for differences between categories in the data. By assigning different tuning parameters for the contrast of each categorical factor, our method could estimate the effects of multiple treatments or factors across brain regions accurately and achieve computational efficiency at the same time. Simulation studies suggest our method achieves better accuracy in network estimation compared with the joint graphical lasso method. We apply our method to the cocaine-use disorder data and identify functional connectivity in brain affected by cocaine use disorder and gender