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