Title: Bayesian multi-object data integration in the study of primary progressive aphasia
Authors: Rajarshi Guhaniyogi - Texas A & M university (United States)
Rene Gutierrez - Texas A and M University (United States)
Aaron Scheffler - University of California, San Francisco (United States) [presenting]
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. Still, this integration can be hindered by data complexity. Each image contains structural information, indexing spatial information, or network information, indexing connectivity among the image, which reinforce each other but is challenging to merge. We propose a Bayesian regression framework that provides inference and prediction for a multi-object outcome as a function of a scalar predictor. Our framework will accommodate multiple image outcomes having different structures and identify image regions associated with the scalar predictor jointly 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.