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Activity Number: 661 - Statistical Approaches to High-Dimensional Modeling and Real-World Problems
Type: Contributed
Date/Time: Thursday, August 3, 2017 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract #324870 View Presentation
Title: Non-parametric Association Testing of fMRI Functional Connectivity and Genetic Similarity
Author(s): Dustin Pluta* and Hernando Ombao and Zhaoxia Yu and Tong Shen
Companies: University of California, Irvine and KAUST and UC Irvine and University of California, Irvine and University of California, Irvine
Keywords: Genetics ; Neuroimaging ; Connectomics ; Non-parametric Testing
Abstract:

With the increasing availability of multi-modal data sets from biological and medical studies, there is a need for general methods to assess the agreement of similarity of subjects across many different data modalities. In the field of imaging genetics in particular, it is of interest to test the association of genetic similarity with similarity of neural connectivity. We here propose a flexible non-parametric test for multi-modal data sets, based on Mantel's test from evolutionary biology. We examine the power and performance of this test through a simulation study, propose modified forms to enhance the statistical performance, and illustrate its use with an application to a real world imaging genetics data set. Lastly, we compare the concordance of genetic similarity with similarity derived from a variety of neural connectivity measures and fMRI experimental settings.


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

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