Modeling Dynamic Functional Connectivity with Latent Factor Gaussian\n Processes

Abstract

Dynamic functional connectivity, as measured by the time-varying covariance\nof neurological signals, is believed to play an important role in many aspects\nof cognition. While many methods have been proposed, reliably establishing the\npresence and characteristics of brain connectivity is challenging due to the\nhigh dimensionality and noisiness of neuroimaging data. We present a latent\nfactor Gaussian process model which addresses these challenges by learning a\nparsimonious representation of connectivity dynamics. The proposed model\nnaturally allows for inference and visualization of time-varying connectivity.\nAs an illustration of the scientific utility of the model, application to a\ndata set of rat local field potential activity recorded during a complex\nnon-spatial memory task provides evidence of stimuli differentiation.\n