Abstract
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 connectivity dynamics. 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.
This work was supported by NIH award R01-MH115697 (B.S., H.O., N.J.F), NSF award DMS-1622490 (B.S.), Whitehall Foundation Award 2010-05-84 (N.J.F.), NSF CAREER award IOS-1150292 (N.J.F.), NSF award BSC-1439267 (N.J.F.), and KAUST research fund (H.O.). We would like to thank Michele Guindani (UC-Irvine), Weining Shen (UC-Irvine), and Moo Chung (Univ. of Wisconsin) for their helpful comments regarding this work.