Multi-Head Graph Convolutional Network for Structural Connectome Classification

We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain connectivity input graph and processes the data separately through a parallel GCN mechanism with multiple heads. The proposed network is a simple design that employs different heads involving graph convolutions focused on edges and nodes, capturing representations from the input data thoroughly. To test the ability of our model to extract complementary and representative features from brain connectivity data, we chose the task of sex classification. This quantifies the degree to which the connectome varies depending on the sex, which is important for improving our understanding of health and disease in both sexes. We show experiments on two publicly available datasets: PREVENT-AD (347 subjects) and OASIS3 (771 subjects). The proposed model demonstrates the highest performance compared to the existing machine-learning algorithms we tested, including classical methods and (graph and non-graph) deep learning. We provide a detailed analysis of each component of our model

Transcriptomic Profiling in Mild Cognitive Impairment and Alzheimer's Disease Using Neuroimaging Endophenotypes

Indiana University-Purdue University Indianapolis (IUPUI)Alzheimer’s disease (AD) is a devastating neurodegenerative disease affecting more than 6 million Americans and 50 million people worldwide currently. It is an irreversible neurodegenerative disease which causes decline in memory, cognition, personality, and other functions which eventually lead to death due to complete brain failure. Recently there has been a lot of research that has focused on enabling early intervention and disease prevention in AD which could have a significant impact on this disease, be crucial for life management, assessment of risk for future generations, and assistance in end-of-life preparation. For a late-life complex multifactorial disease, such as AD, where both genetic and environmental factors are involved, integrating multiple layers of genetic, imaging, and other biomarker data is a critical step for therapeutic discovery and building predictive risk assessment tools. The multifactorial nature of AD suggests that multiple therapeutic targets need to be identified and tested together. Hence, we need a systems-level approach to build biomarker profiles which can be used for drug discovery and screening/risk assessment. The research presented in this dissertation focuses on utilizing a systems level approach to identify promising imaging genetics biomarkers that provide insight into dysregulated biological pathways in AD pathogenesis and identify critical mRNA measures that can be investigated further within the scope of novel therapeutics, as well as input variables in predictive models for AD risk, screening, and diagnosis. The overall research goal was the development of systems level, imaging genetics biomarker signatures to serve as tools for risk analysis and therapeutic discovery in AD. The specific outcomes of the analyses were characterization of patterns in gene expression at systems level using neuroimaging endophenotypes, and identification of specific driver genes and genotypic variants, which can inform predictive modeling for diagnosis, risk, and pathogenic profiling in AD