Mapping the connectivity with structural equation modeling in an fMRI study of shape-from-motion task

Abstract

In this fMRI study, we explore the connectivity among brain regions in a shape-from-motion task using the causal mapping analysis of structural equation modeling (SEM). An important distinction of our approach is that we have adapted SEM from its traditional role in confirmatory analysis to provide utility as an exploratory mapping technique. Our current approaches include (I) detecting brain regions that fit well in a hypothesized neural network model, and (II) identifying the best connectivity model at each brain region. We demonstrate that SEM effectively detects the dorsal and ventral visual pathways from the covariance structure in fMRI data, confirming previous neuroscience results. Further, our SEM mapping methodology found that the two pathways interact through specific cortical areas such as the superior lateral occipital cortex in the perception of shape from motion.