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Activity Number: 221 - Contributed Poster Presentations: Section on Statistics in Imaging
Type: Contributed
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistics in Imaging
Abstract #314434
Title: Discovering Alzheimer’s Disease pathology by neuroimaging and genetic data
Author(s): Kristen Knight* and Nicole Lazar and Liang Liu
Companies: University of Georgia, Department of Statistics and University of Georgia and University of Georgia

The emerging field of imaging genetics combines the neuroimaging power of MRI and the sequencing of the complex human genome to produce a unified approach for the acquisition and progression of psychiatric illnesses. Despite many attempts for discovery in the past decade, the statistical groundwork for imaging genetics remains in its infancy. Numerous challenges exist for this BIG data problem, such as easing the computational burden, minimizing the prevalence of false positives, all while accounting for natural human heterogeneity. In this presentation, I will outline the current direction of imaging genetics research and highlight the short-comings of past studies through simulation and replication studies. Then, I will introduce my own approach by providing a applicable framework for data fusion and analysis of structural MRI, functional MRI and genetic data. Data gleaned from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) will be analyzed with this new approach. Results will be compared among healthy, mild cognitive impaired and Alzheimer’s Disease diagnosed subjects.

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

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