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Activity Number:
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271
- 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, July 30, 2019 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Imaging
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Abstract #300025
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Title:
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Fréchet Regression for Time-Varying Covariance Matrices of Myelination and Regional Co-Evolution Networks in the Developing Brain
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Author(s):
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Hans Mueller* and Alexander Petersen and Sean Deoni and Xiongtao Dai and Jane-Ling Wang
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Companies:
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UC Davis and University of California, Santa Barbara and Brown University and Iowa State University and University of California, Davis
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Keywords:
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MRI;
Random Objects;
Cognitive Development ;
BAMBAM study
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Abstract:
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Fréchet Regression provides an extension of Fréchet means to the case of conditional Fréchet means when studying the evolution of random objects in a metric space (Petersen & Müller 2019). In cross-sectional studies on neurodevelopment one observes p-vectors of signals extracted from magnetic resonance imaging (MRI) over p brain regions and is interested in the p x p covariance or correlation matrix as a function of time. For a given metric on the space of covariance matrices, Fréchet regression generates a matrix function where at each fixed time the matrix is a non-negative definite covariance or correlation matrix of the p-vectors. We demonstrate this approach for MRI-extracted measurements of the myelin contents of various brain regions, which characterizes regional brain development, for a sample of small infants of various ages from the BAMBAM study. The time-dynamic correlation matrix of myelination between brain regions can then be quantified. Further applications of this approach include the prediction of cognitive scores from regional myelination and the construction of regional co-evolution networks.
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Authors who are presenting talks have a * after their name.
