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Activity Number:
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151
- Recent Advances in Bayesian Approaches to Neuroimaging
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Type:
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Invited
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Date/Time:
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Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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WNAR
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Abstract #315528
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Title:
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A Bayesian Regression Framework for Brain Imaging Data with Multiple Structural- and Network-Valued Predictors
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Author(s):
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Aaron Wolfe Scheffler* and Giovanni Battistella and Maria Luisa Mandelli and Maria Luisa Gorno-Tempini and Rajarshi Guhaniyogi
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Companies:
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University of California, San Francisco and Department of Neurology, University of California, San Francisco and Department of Neurology, University of California, San Francisco and Department of Neurology, University of California, San Francisco and University of California, Santa Cruz
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Keywords:
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Bayesian regression;
Hierarchical models;
Multi-modal imaging ;
Primary Progressive Aphasia;
Neuroimaging
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Abstract:
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Clinical researchers collect multiple images from separate modalities (sources) to investigate questions of human health that are inadequately explained by considering one image source at a time. Viewing the collection of images as multi-objects, the successful integration of multi-object data produces a sum of information greater than the individual parts, but this integration can be hindered by the data complexity. Each image contains structural information, indexing spatial information, or network information, indexing connectivity among the image, which reinforce each other but are challenging to merge. We propose a Bayesian regression framework that provides inference and prediction for a scalar outcome as a function of a multi-object predictor. Our framework will accommodate multiple image predictors having different structures and identify image regions influencing the response jointly via efficient hierarchical prior structures that scale to high-resolution image data volume. A working example is provided for prediction of language comprehension scores from multi-object image data to explore the neural underpinnings of language loss in primary progressive aphasia patients.
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Authors who are presenting talks have a * after their name.
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