Modeling Region-Referenced Longitudinal Functional Electroencephalography Data

February 12, 2019
4 to 5pm

Aaron Wolf Scheffler, Doctoral Candidate, Department of Biostatistics, University of California, Los Angeles

Highly structured data collected in a variety of biomedical applications such as electroencephalography (EEG) are discrete samples of a smooth functional process observed across both temporal and spatial dimensions.  Specifically, I consider EEG data as region-referenced longitudinal functional data in which the functional dimension captures local signal dynamics, the longitudinal dimension tracks changes over the course of an experiment, and the regional dimension indexes spatial information across electrodes on the scalp.  This complex data structure exhibits intricate dependencies with rich information but its dimensionality and size produce significant obstacles for interpretation, estimation, and inference.  Motivated by a series of EEG studies in children with autism spectrum disorder (ASD), I present a set of computationally efficient methods for these high-dimensional data structures that both maintain information along each dimension and yield interpretable components and inferences.  Proposed methods not only help identify neurodevelopmental differences between typically developing and ASD children but can also be used to study the heterogeneity within children with ASD

Event Type: 
Biostatistics and Bioinformatics Seminar