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Activity Number: 132 - Statistical Advances in Dimension Reduction and Feature Interpretability in Neuroimaging
Type: Topic Contributed
Date/Time: Monday, August 8, 2022 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract #322045
Title: Multimodal Data Fusion for Imaging Genomics
Author(s): Nicole A. Lazar* and Krissy Knight and Liang Liu
Companies: Pennsylvania State University and UC San Francisco and University of Georgia
Keywords: machine learning; high dimensional data; neuroimaging

Imaging genomics combines data from multiple sources, registered in multiple modalities, to gain better understanding of brain function. The large size of imaging genomics data across many dimensions, together with the differing modalities, present challenges to statistical analysis. In this talk, we will describe some typical imaging genomics data and discuss data fusion as a way to combine multiple sources of information. Data fusion results are interpretable, generalizable, and can be integrated with additional data analysis pathways.

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

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