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Activity Number: 536 - Contributed Poster Presentations: Section on Statistics in Imaging
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Imaging
Abstract #330642
Title: Genetic Analysis of Imaging Data Using Random-Effect Tensor Regression
Author(s): Tong Shen*
Companies: University of California, Irvine
Keywords: brain signal; genetic factor; human behavior; random-effect tensor regression; brain connectivity; working memory
Abstract:

Various brain recordings and biological techniques have been developed to systematically examine how genetic factors and brain function jointly affect human behaviors and cognitive functions. These types of multi-modal data are large, high dimensional, endowed with a complicated dependence structure and low signal-to-noise ratio. To efficiently model the genetic effects on brain signal data, we developed a novel random-effect tensor regression with separable covariance. We derived and compared several estimating algorithms to estimate genetic parameters of interest, such as genetic heritability. Furthermore, we investigated the genetic contribution to brain connectivity, which characterizes the dependence between different brain regions, during a working memory study.


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

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