Development of a Neuroinformatics Pipeline and its Application to Gene-environment Interaction in Neurodegenerative Disease

Abstract

BackgroundAlzheimer’s disease is a neurodegenerative disease and the sixth-leading cause of death in the United States. While there has been a greater understanding of Alzheimer’s disease (AD) processes in the last two decades, clinical trials in AD have not been successful, suggesting that further research is needed to understand key questions pertaining to the underpinnings of the disease. Alzheimer’s disease is a complex disease with significant heterogeneity in the disease progression and expression of clinical symptoms. The presence of the ϵ4 allele of the Apolipoprotein (APOE4) increases the risk of Alzheimer’s disease and is associated with earlier onset of Alzheimer’s disease pathology. On the other hand, variability in “Resilience,” i.e., the ability to cope with Alzheimer’s disease pathology, is associated with the differences in the expression of clinical symptoms. Objective The work presented draws on the methodological strengths of health informatics, biostatistics, and neuroscience, to achieve two specific aims: (1) Deployment of a neuroinformatics pipeline for the replicable collection, manipulation, and analysis of AD data (2) Employment of the neuroinformatics pipeline to evaluate the potential impact of specific allele carriership on what is recognized as a resilience mechanisms in the context of AD. Specifically, the neuroscience questions are: (1) Does the effect of cognitive reserve on GMD differ by APOE4 genotype? (2) Does APOE4 carrier status impact clinical functioning, and is this effect mediated by global efficiency? (3) Do APOE4 carriers as compared to non-carriers demonstrate differences in network recruitment (specifically, global efficiency of the default mode network)? Methods To evaluate the complex interplay of gene-environment interactions in AD, we investigated the impact of APOE4 and education on brain structure in the first study. In our second study, we used a core construct from graph theory to compute global efficiency on single-subject 3T MRI scans and evaluated the interplay between pathology, APOE4, education, and global efficiency and their impact on clinical functioning. In our third study, we applied causal inference models to investigate the causal relationships between pathology, APOE4, education, and global efficiency, considered drivers of clinical functioning in AD. Conclusion This work uniquely contributes to health informatics through the construction of a neuroinformatics pipeline that combines multimodal biomedical data (neuroimaging, genomics, cognition, and clinical), employs database management, automated computing, graph theory, and biostatistics to answer clinical questions. This work contributes to science by proposing a method to measure and monitor brain health, providing additional insight into the mechanistic underpinnings of APOE4 allele carriership underlying AD pathology.