A Spatio-temporal Model for Detecting the Effect of Cocaine Use Disorder on Functional Connectivity

Abstract

Drug addiction can lead to many health-related problems and social concerns. Researchers are interested in the association between long-term drug usage and abnormal functional connectivity. Functional connectivity obtained from functional magnetic resonance imaging data promotes a variety of fundamental understandings in such association. Due to the 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 computational efficient algorithm to estimate the parameters. Our method is used to first identify functional connectivity, and then 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.

Keywords: EM algorithm; functional connectivity; functional magnetic resonance imaging (fMRI); spatio-temporal dependency.

Figure C.1:

Histograms of sill and range parameters on each directions.

Figure F.2:

Histogram of separately estimated white noise variances for each subject in the cocaine use disorder multi-subject FMRI. The blue vertical line is the estimated white noise variance when assuming homogeneity.

Figure F.3:

Histogram of all parameter estimates of AR(2) model at all voxel locations for real data AR(2) time series models.