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Principal Investigator: Sheng Luo
Organization: DUKE UNIVERSITY
Fiscal Year: 2024
Award: $458,223
Funding agency: National Institute on Aging
Project Summary/Abstract
The proposed R01 grant is in direct response to PAR-18-352 “Methodology and Measurement in the
Behavioral and Social Sciences (R01)”. Alzheimer's disease (AD) is a progressive, neurodegenerative disorder
that causes impairment in multiple domains (e.g., cognition, behavior, and quality of life) and progresses
heterogeneously in time and across domains and individuals. No single biomarker provides sufficient
information to capture the underlying severity of disease across the entire spectrum. Hence, AD studies collect
data from multiple sources (e.g., clinical, neuroimaging, and genetic; multi-modal data). We propose a novel
integrative modeling framework to provide statistically-principled inference, accurate personalized prediction of
disease progression, and dynamic prediction update, based on new subject-specific data. This novel model
development is important to identify risk and protective factors for AD and target high risk individuals, as well
as to personalize the management, prognosis, and treatment selections. The overall objectives are to: (1)
develop a multivariate functional mixed model (MFMM) for the integrative modeling of the longitudinal clinical
data; (2) use such model to provide personalized prediction of future outcome trajectories and risks of target
events; (3) advance the integrative model by incorporating the high-dimensional neuroimaging and genetic
data; (4) make this methodology easily accessible via professional software development and web
deployment. Our methods can be broadly applied to other clinical studies with similar multi-modal data
structure.
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