Multivariate stochastic volatility modeling of neural data.

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 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. The implied volatility inferred from our process positively correlates with high-frequency spectral activity, a signal that correlates with neuronal activity. We show that volatility features derived from our model can reliably decode memory states, and

SUBMITTER: Phan TD 

PROVIDER: S-EPMC6697415 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature