A1264
Title: Bayesian multi-object data integration in the study of primary progressive aphasia
Authors: Aaron Scheffler - University of California, San Francisco (United States) [presenting]
Rajarshi Guhaniyogi - Texas A & M university (United States)
Rene Gutierrez - Texas A and M University (United States)
Abstract: 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 the data complexity can hinder this integration. Each image contains structural information, indexing spatial information, or network information, and indexing connectivity among the image, reinforcing each other but challenging to merge. A Bayesian regression framework that provides inference and prediction for a multi-object outcome as a function of a scalar predictor. The framework will accommodate multiple image outcomes with different structures and jointly identify image regions associated with the scalar predictor via efficient hierarchical prior structures that scale to high-resolution image data volume. A working example is provided for the association of language comprehension scores with multi-object image data to explore the neural underpinnings of language loss in primary progressive aphasia patients.
