Online Program Home
My Program

Abstract Details

Activity Number: 88
Type: Invited
Date/Time: Sunday, July 31, 2016 : 6:00 PM to 8:00 PM
Sponsor: Section on Statistics in Imaging
Abstract #320046
Title: Modeling Connectivity in High-Dimensional Time Series Data via Factor Analysis
Author(s): Hernando Ombao* and Yuxiao Wang and Chee-Ming Ting
Companies: University of California at Irvine and University of California at Irvine and Universiti Teknologi Malaysia
Keywords: Electroencephalograms ; Factor Analysis ; Partial Directed Coherence

We develop a novel approach to modeling connectivity in high dimensional brain signals. In the approach, we model the cortical activity using linear mixture of latent factor activities that follows a vector autoregressive (VAR) process. The frequency-specific connectivity on the cortical surface can be characterized by the latent factor activity and its loading matrix.

The primary motivation for this work is modeling connectivity among regions on the cortical surface using multi-channel scalp electroencephalograms (EEG). Modeling connectivity between brain regions is difficult under high dimensionality of the anatomical parcellation on the cortical surface.

We present a modeling procedure that addresses a number of challenges in high dimensional brain signals. In the first step, we estimate the sources using imaging method with anatomical constraints. In the second step, to estimate temporal dependency between regions on the cortex, we fit a latent process with a vector autoregressive (VAR) structure. From the VAR parameters, we produce different measures of cortical connectivity. We apply this new approach to modeling EEG data during resting state and task.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

Copyright © American Statistical Association