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Activity Number: 528 - Contributed Poster Presentations: Section on Statistics in Imaging
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Imaging
Abstract #306570
Title: Identification of Differences in Cortical Thickness in Multiple Sclerosis Patients Based on Race
Author(s): Jiajing Niu* and Andrew Brown and Jagannadha R Avasarala
Companies: Clemson University and Clemson University and Greenville Health Syatem
Keywords: Analysis of variance; deep gray matter; empirical Bayes; false discovery rate; generalized linear models; magnetic resonance imaging
Abstract:

Large, multicenter longitudinal studies show deep gray matter (DGM) volume loss drives disability severity in multiple sclerosis (MS) patients. When evaluating treatment effects of therapeutic interventions, the differences in DGM atrophy between ethnicities is still an open question. We aim to analyze the cortical thickness in deep gray matter of MS patients as measured by magnetic resonance images. The goal is to identify any systematic differences between ethnicities. We explore the effects of ethnicity on the cortical thickness measurements based on analysis of variance by thresholding the t-statistics using local false discovery rate control based on empirically null distributions. In this analysis, there is a loss of power due to small sample size and large number of simultaneous hypothesis tests. We apply tools traditionally used for differential analysis of count data in comparative high-throughput sequencing assays. The method is based on generalized linear models and uses empirical Bayes to improve the stability compared to maximum-likelihood based outcomes. We find this method can find more potentially interesting regions in DGM compared to a more traditional approach.


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

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