Dissertation

Multimodal Representation Learning Frameworks for Modeling Progression and Heterogeneity in Alzheimer’s Disease

Kumar, Sayantan

2024

Overview

Alzheimer’s Disease (AD) is the leading cause of dementia, characterized by cognitive and functional impairments that disrupt daily activities. Different clinical modalities such as neuroimaging biomarkers, cognitive assessments, fluid biomarkers and genetic data provide unique and complementary information, contributing to a more comprehensive understanding of disease progression and heterogeneity in disease characteristics. With recent advancements in computational capabilities, particularly in deep learning, multimodal representation learning frameworks aim to integrate diverse clinical modalities into a cohesive framework, capturing the most significant patterns within each modality. Existing data-driven multimodal representation learning frameworks in AD research have two major limitations. First, AD progresses gradually from early preclinical stages to severe impairment, requiring dynamic, longitudinal assessments for timely interventions, rather than single-endpoint predictions. Additionally, its significant heterogeneity in symptoms, progression, and pathology is often overlooked by traditional ML/DL methods, which rely on group averages. Precision medicine demands a shift toward characterizing disease abnormalities at the individual level.Towards this end, three research aims were pursued. (1) First, we developed HiMAL, a novel multimodal Hierarchical Multi-task Auxiliary Learning framework that predicts cognitive composite scores as auxiliary tasks to estimate the longitudinal risk of progression from Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD). HiMAL also provides interpretable, longitudinal explanations of disease progression to support clinical decision-making (2) Next, we designed data-driven unsupervised machine learning frameworks to explore inter-individual heterogeneity within AD cohorts. This includes an unsupervised clustering framework leveraging cognitive assessments and neuroimaging biomarkers to identify subtypes within heterogeneous dementia populations. Additionally, we developed a deep learning-based normative modeling framework to examine AD heterogeneity using multimodal neuroimaging biomarkers of neuropathology and neurodegeneration.(3) Finally, building on this, we created a multimodal introspective variational autoencoder to enhance normative modeling and improve the detection of individual-level disease abnormalities.The three aims in this dissertation collectively advances the state of multimodal representation learning by developing computational frameworks to address two key challenges of heterogeneity and progression in AD. While Aim 1 establishes a multimodal longitudinal framework to predict cognitive decline, setting the stage for personalized disease monitoring, Aims 2 and 3 extends this foundation by investigating inter-individual variability using unsupervised frameworks, delving deeper into the heterogeneous nature of AD. Together, these aims form a cohesive narrative, advancing our understanding of AD and proposing tools for precision diagnostics.

• Publisher: ProQuest Dissertations & Theses
• Subject: Artificial intelligence / Bioinformatics / Computer Engineering / Computer science
• ISBN: 9798346865247