Abstract
Because multivariate autoregressive models have failed to adequately account for the complexity of neural signals, researchers have predominantly relied on non-parametric methods when studying the relations between brain and behavior. Using a database of medial temporal lobe (MTL) recordings from 96 neurosurgical patients, we show that time series models with volatility described by a multivariate stochastic latent variable process and lagged interactions between signals in different brain regions provide new insights into the dynamics of brain function. We estimate both the parameters describing the latent variable processes and the directional correlations in volatility between brain regions using Bayesian sampling techniques. The implied volatility inferred from our process positively correlates with high-frequency spectral activity, a signal that correlates with neuronal activity and is widely used to study brain function. We show that volatility features derived from our model can reliably decode good vs. poor memory states, and that this classifier performs as well as those using spectral features. Using the multivariate stochastic volatility model, we uncovered hippocampal-perirhinal bidirectional connections in the MTL regions that are associated with successful memory encoding.