Exploring Spectral Dependence in Multivariate Time Series

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Advances in imaging technology have given neuroscientists unprecedented access to examine various facets of how the brain “works”. Brain activity is complex. A full understanding of brain activity requires careful study of its multi-scale spatial-temporal organization (from neurons to regions of interest; and from transient events to long-term temporal dynamics). Motivated by these challenges, we will explore some characterizations of dependence between components of a multivariate time series and then apply these to the study of brain functional connectivity.  This is potentially interesting for brain scientists because functional brain networks are associated with cognitive function and mental and neurological diseases.

There is no single measure of dependence that can capture all facets of brain connectivity. In this talk, we shall present a general framework for exploring dependence through the oscillatory activities derived from each component of the tine series. In fact, some of the classical notions of spectral dependence such as coherence, partial coherence and dual-frequency coherence can be derived from this framework. Moreover, this framework provides a starting point for exploring potential non-linear cross-frequency interactions. These interactions include the impact of phase of one oscillatory activity in one component on the amplitude of another oscillation. The proposed approach captures lead-lag relationships and hence can be used as a general framework for spectral causality.  This is joint work with Marco Pinto (KAUST and Oslo Metropolitan University).

Brief Biography

Hernando Ombao is Professor of Statistics at KAUST and currently Chair of the Statistics Program. He is the PI of the Biostatistics Research Group [https://biostats.kaust.edu.sa]. His research is on statistical models for time series with dynamic and complex structures. Prior to coming to KAUST, he was a faculty member at UC Irvine, Brown University, University of Illinois and the University of Pittsburgh. At UC-Irvine, he was a recipient of the Mid-Career Distinguished Research Award. He is Co-Editor of the Handbook of Statistical Methods for Neuroimaging. He was PI of several US NSF awards. He is an Elected Fellow of the American Statistical Association (ASA). He serves the community as Associate Editor of Statistics journals (Metron, JASA, JRSS-B, CSDA) and as a permanent member of the US National Institutes of Health Biostatistics Study Section. He is a founding member and previous chair of the Statistics in Imaging Section of the ASA.

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