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Activity Number:
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240
- Statistical Analysis of Complex Imaging Data
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Type:
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Invited
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Date/Time:
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Tuesday, August 4, 2020 : 1:00 PM to 2:50 PM
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Sponsor:
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WNAR
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Abstract #309303
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Title:
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Covariance Regression for Connectome Outcomes
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Author(s):
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Brian Caffo* and Rossi Luo and Yi Zhao and Bingkai Wang
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Companies:
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Johns Hopkins University and The University of Texas Health Science Center and Indiana University and Johns Hopkins Bloomberg School of Public Health
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Keywords:
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Imaging;
fMRI;
linear models;
graphs;
connectivity;
correlation
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Abstract:
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In this talk, we cover methodology for jointly analyzing a collection of covariance or correlation matrices that depend on other variables. This covariance-as-an-outcome regression problem arises commonly in the study of brain imaging, where the covariance matrix in question is an estimate of functional or structural connectivity. Two main approaches to covariance regression exists: outer product models and joint diagonalization approaches. We investigate joint diagonalization approaches and discuss the benefits and costs of this solution. We distinguish between diagonalization approaches where the eigenvectors are selected in the absence of covariate information and those that chose the eigenvectors so that the result regression model holds best. The methods are applied to resting state functional magnetic resonance imaging data in a study of aphasia and potential interventions.
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Authors who are presenting talks have a * after their name.
