American Statistical Association
New York City
Metropolitan Area Chapter

Levin Lecture Series: Spring 2019 Colloquium Seminars
Department of Biostatistics
Mailman School of Public Health
Columbia University



SPECTRAL AND HERITABILITY ANALYSIS
OF EEG TIME SERIES DATA
USING A NESTED DIRICHLET PROCESS

by

Mark Fiecas
Assistant Professor
School of Public Health
Division of Biostatistics
University of Minnesota

Host: Dr. Todd Ogden


Abstract

In this talk, we will analyze the spectral features of resting-state EEG time series data collected from twins enrolled in the Minnesota Twin Family Study (MTFS). Our goal is to calculate the heritability of the spectral features of the resting EEG data. Due to the twin design of the MTFS, the time series will have similar underlying characteristics across individuals. To account for this, we develop a Bayesian nonparametric modeling approach for estimating the spectral densities of the EEG data. In our methodology, we use Bernstein polynomials and a Dirichlet process (DP) to estimate each subject-specific spectral density. In order to estimate the spectral densities for the entire sample, we nest this model using a nested DP process. Thus, the top-level DP clusters individuals with similar spectral densities and the bottom-level dependent DP fits a functional curve to the individuals within that cluster. We then extract relevant spectral features from the estimates of the spectral densities and estimate their heritability.

This is joint work with PhD student Brian Hart (Univ. of Minnesota),
Dr. Michele Guindani (UC Irvine), and Dr. Stephen Malone (Univ. of Minnesota).


Date: Thursday, April 4, 2019
Time: 11:45 A.M. - 12:45 P.M.
Location: Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
AR Building
8th Floor Auditorium
New York, New York

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