Abstract #0660

BrainVAE: Exploring role of white matter BOLD in preclinical Alzheimer’s disease classification

Yikang Li^1,2, Lyuan Xu^2,3, Yukie Chang⁴, Lianrui Zuo³, Zhaohua Ding^2,3, Adam W. Anderson^1,2,5, Kurt G. Schilling^2,5, John C. Gore^1,2,5, and Yurui Gao^1,2

¹Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States, ²Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States, ³Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States, ⁴Department of Computer Science, Pomona College, Claremont, CA, United States, ⁵Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, United States

Synopsis

Keywords: Task/Intervention Based fMRI, Alzheimer's Disease

Motivation: Characteristics of white matter (WM) BOLD signals have been reported as potential indicators for classifying preclinical Alzheimer’s disease (pre-AD), but their performance and interpretation remain unclear.

Goal(s): Develop a novel model for pre-AD classification incorporating WM BOLD signals and quantify WM’s contribution versus gray matter (GM).

Approach: We introduce BrainVAE, a transformer-based Variational Autoencoder utilizing WM and GM BOLD input and compare its performance against nine models with WM-only, GM-only, and combined inputs, assessing the impact of incorporating WM information.

Results: BrainVAE achieved superior accuracy with combined inputs, and WM contributed significantly to success in classification across all models.

Impact: This study highlights the potential importance of including analyses of white matter BOLD signals to distinguish subjects with preclinical AD from normal controls subjects, suggesting a critical role of degenerative changes in WM in the etiology of disease.

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