# Integrative Predictive Modeling of Alzheimer's Disease

> **NIH NIH R21** · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · 2021 · $446,124

## Abstract

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.

## Key facts

- **NIH application ID:** 10195195
- **Project number:** 1R21AG072101-01
- **Recipient organization:** UNIVERSITY OF VERMONT & ST AGRIC COLLEGE
- **Principal Investigator:** Alice Patania
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $446,124
- **Award type:** 1
- **Project period:** 2021-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10195195

## Citation

> US National Institutes of Health, RePORTER application 10195195, Integrative Predictive Modeling of Alzheimer's Disease (1R21AG072101-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10195195. Licensed CC0.

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