Session: Technical Session II

Date & Time: Friday, May 22 — 1:30 PM–3:45 PM

Talk Title

Spectral Transfer Entropy: Modeling Brain Functional Connectivity

Abstract

Modeling dependence between nodes in a brain network is central to understanding underlying neural mechanisms. In this talk, we present a broad range of statistical methods for characterizing dependence in a brain network. We first review a general framework which decomposes each signal into various frequency components and then characterize the dependence properties through these oscillatory activities. In the first part of the talk, some of the most prominent frequency domain measures such as coherence, partial coherence, and dual-frequency coherence can be derived as special cases under this general framework. Coherence can be viewed in the proposed framework as the maximal squared correlation between oscillations from a pair of nodes. It gives a more specific information that correlation because it identifies the frequency bands that drive the dependence between nodes.

In the second part of the talk, we will briefly describe current work on information-theoretic measures of connectivity. This class of measures are based on joint (and conditional) densities rather than just the first two moments. The proposed spectral transfer entropy (STE) quantifies the magnitude and direction of information flow from a certain frequency-band oscillation of a channel to an oscillation of another channel. A novel contribution is a simple, yet efficient estimation method based on vine copula theory that enables estimates to capture zero (boundary point) without the need for bias adjustments. With the vine copula representation, a null copula model, which exhibits zero STE, is defined, making significance testing for STE straightforward through a standard resampling approach. Lastly, we illustrate the advantage of our proposed measure through some numerical experiments and provide interesting and novel findings on the analysis of EEG recordings linked to a visual task.

This is joint work with Paolo Redondo and Raphael Huser at KAUST.