Integrative Predictive Modeling of Alzheimer's Disease

点击翻译按钮获取中文摘要

Project Abstract Alzheimer’s disease (AD) is a medical emergency that has, to date, proven impossible to defeat. Being able to accurately predict disease progression in the early symptomatic stages will critically advance our field. Predictive models abiding to a salient disease feature (e.g. amyloid deposition) would, by design, offer narrow- scope advances and will invariably come short of accurate disease modeling and outcome prediction. The present application proposes a systems-level, multimodal approach to identify promising imaging-genetics biomarkers that will reliably predict cognitive decline at early disease stages. Our long-term research goal is to develop a method for cost-efficient risk assessment and predictive modeling and to implement it in therapeutic drug development. The overall objective of this application is to develop an integrative predictive framework for mild cognitive impairment (MCI) based on biomarker signatures. Our central hypothesis is that our state-of-the- art statistical and topological multimodal data analysis will significantly improve the diagnostic and predictive accuracy in MCI and help close this knowledge gap in AD pathogenesis. To this end, we propose to accomplish the following specific aims using existing clinical, cognitive, imaging, genomic and transcriptomic data: 1) Characterize the gene expression patterns and neuroimaging endophenotypes in MCI using persistent homology; 2) Develop a Bayesian multi-kernel learning framework for diagnostic prediction of MCI and its progression to AD dementia; and 3) Estimate the relative contribution of different data modalities in terms of their effectiveness regarding early prediction and diagnosis. The methods proposed in this application offer significant advances over the status-quo by utilizing contemporary state-of -the-art analytic approaches such as persistent homology-based topological surface analysis and Bayesian multi-kernel learning framework. We will also rely on the integration of prior knowledge, whereby augmenting the strength of data-driven methods with interpretable domain expertise. The positive impact of our work is significant because it will help advance our understanding of the complex interactions between several biomarker modalities in AD, lead to the identification of sensitive multimodal biomarker set for AD risk assessment and potentially uncover novel critical disease-related pathways that might result in the discovery of new therapeutic targets.