Dr. Hernando Ombao (UC Irvine)

Title: Modeling Complex Oscillatory Cross-Dependence in Multivariate Time Series

Abstract:In this talk, we shall discuss approaches for characterizing dependence between components of a multivariate time series (e.g., between brain regions). My own interest in this area stems from a growing body of evidence suggesting that various neurological disorders, including Alzheimer’s disease, depression, and Parkinson’s disease may be associated with altered brain connectivity.

Dependence may be portrayed in a number of ways. Here, we focus on measures that depict interactions between oscillatory activities at different brain regions. The classical notion of coherence pertains only to contemporaneous single-frequency interactions between signals. To generalize this notion, we introduce the time-lagged dual-frequency coherence which measures, as a specific example, oscillatory interactions between alpha activity on a current time block at one channel and beta activity on a future time block at another channel. We develop formal methods for statistical inference under the framework of harmonizable processes. This new approach will be applied to analyze an electroencephalographic data set to investigate dependence between the hippocampus and nucleus accumbens in a macaque monkey from local field potentials recorded during a learning task.