Covariate-Adjusted Region-Referenced Generalized Functional Linear Model for EEG Data

• Activity Number: 212 - Scientifically and Clinically Motivated Statistical Methods for Human Brain Data Analysis
• Type: Invited
• Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Damla Senturk* and Aaron Scheffler and Donatello Telesca and Catherine Sugar and Shafali Jeste and Abigail Dickinson and Charlotte DiStefano
• Companies: UCLA and UCLA and UCLA and UCLA and UCLA and UCLA and UCLA
• Keywords: Autism spectrum disorder; electroencephalography; functional data analysis ; peak alpha frequency; penalized regression

Abstract

Electroencephalography (EEG) studies produce region-referenced functional data in the form of EEG signals recorded across electrodes on the scalp. In our motivating study, resting state EEG is collected on both typically developing (TD) children and children with Autism Spectrum Disorder (ASD) aged two to twelve years old. The peak alpha frequency (PAF), defined as the location of a prominent peak in the alpha frequency band of the spectral density, is an important biomarker linked to neurodevelopment and is known to shift from lower to higher frequencies as children age. To retain the most amount of information from the data, we consider the oscillations in the spectral density within the alpha band, rather than just the peak location, as a functional predictor of diagnostic status (TD vs. ASD), adjusted for chronological age. A covariate-adjusted region-referenced generalized functional linear model (CARR-GFLM) is proposed for modeling scalar outcomes from region-referenced functional predictors, which utilizes a tensor basis to estimate functional effects across a discrete regional domain while simultaneously adjusting for additional non-functional covariates, such as age.