Adaptive Testing of SNP-Brain Functional Connectivity Associations Using Modular Network Structures

• Activity Number: 635 - Advanced Machine Learning Methods for Large-Scale Imaging Data
• Type: Invited
• Date/Time: Thursday, August 3, 2017 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistical Learning and Data Science
• Author(s): Wei Pan*
• Companies: University of Minnesota
• Keywords: aSPU ; imaging genetics ; networks ; sequencing data ; GWAS

Abstract

There is accumulating evidence showing that many complex neurodegenerative and psychiatric diseases like Alzheimer's are due to disrupted brain functional networks, with which it is of great interest to identify genetic variants associated. Although several methods exist for estimating brain functional networks based on resting state fMRI (rs-fMRI) data, such as the sample correlation matrix or graphical lasso for a sparse precision matrix, they may not yield network estimates with scale-free topology and/or network modularity, which have been demonstrated to be present in brain function networks. In particular, alteration of brain modularity is observed in patients suffering from various types of brain malfunctions. We propose a method to identify modular structures in brain functional networks, which are then used to construct phenotypes and to detect associated genetic variants. We demonstrate its application to the ADNI rs-fMRI and sMRI data.