Thursday, May 26, 2022 • 11:05 am–noon (CT) • Light Hall, Room 202
Organizer: Hernando Ombao, King Abdullah University of Science and Technology (KAUST)
Chair: Tingting Zhang, University of Pittsburgh
Comparing populations of high-dimensional time series spectra
Robert Krafty, Emory University
Marie Tuft, University of Pittsburgh
Zeda Li, Baruch College
Fabio Ferrarelli, University of Pittsburgh
Technological advances have led to an increase in the breadth and number of studies that collect high-dimensional time series signals from multiple groups and whose scientific goal is to understand differences in time series spectra between the groups. Although methods have been proposed for comparing populations of power spectra, often referred to as analysis of power (ANOPOW), none exist when time series are high-dimensional. In this talk, we discuss a non-parametric Bayesian approach for ANOPOW with high-dimensional time series. The method models the collection of time series through a mixture of experts where the experts are spectral-domain factor models that overcome the high-dimensionality of the data. Weight functions are modeled through multinomial logistic regression containing fixed group effects and random subject effects, which can capture spectral differences between groups while accounting for within-group spectral variability. The approach is motivated by and used to analyze resting-state high-dimensional EEG in patients hospitalized for a first psychotic episode to understand how their electrophysiology differs from that of healthy controls.
Exploring non-linear spectral dependence in multichannel EEGs
Hernando Ombao, KAUST
Marco Pinto, KAUST and Oslo Metropolitan University
Brain activity over the entire network 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 some new models for exploring potential interactions between oscillations in multivariate time series including non-linear dynamics. The proposed approach captures lead-lag relationships and hence can be used as a general framework for spectral causality.