# Multi-dimensional network framework for AD detection and progression

> **NIH NIH R21** · STANFORD UNIVERSITY · 2020 · $195,625

## Abstract

PROJECT SUMMARY
Alzheimer's disease (AD) is the most common form of dementia with significant impact on patients, families
and the public health system. An estimated 5.7 million Americans have Alzheimer's in 2018 1.
At the time of clinical manifestation of the disease, significant irreversible brain damage is already present,
rendering the diagnosis of AD at early stages of the disease an urgent prerequisite for potential therapies to
delay or prevent symptoms2. It is estimated that early and accurate AD detection could save up to $7.9 trillion
in medical and care costs1. Further, early AD detection and progression is crucial for monitoring the effect of
experimental treatments as well as for informing developing efficient treatments. It is a pressing clinical need to
improve early AD detection and progression. With recent advances in neurobiology of AD, our understanding
of the disease has moved from one based on clinical symptoms to a biological construct that is multifactorial
and heterogeneous and that cannot be explained by any single available biomarkers. NIH has devoted billions
of dollars in the past decades to fund several centers and data initiatives on large cohorts of older adults;
resulting in a wealth of multi-modal neuroimaging, cognitive, clinical, biospecimen, and genetic data. However,
less effort has been made to implement innovative integrative methods for aggregating data across modalities
to capture the heterogeneity of AD. To fill the gap in the analysis paradigm of multi-modal AD data, the
overarching goals of the proposed study are to test and validate a multi-dimensional network framework for
aggregating data across modalities in a single model to capture the heterogeneity of AD and to further
enhance AD detection and progression. Our central hypothesis – backed by previous evidence and preliminary
data – is that the proposed framework will enhance AD detection and progression by improving the ability to
detect common as well as complementary signals across multiple data types and by reducing the effect of
differences in scale, collection bias and noise in each modality. We will integrate behavioral, clinical, MR
imaging, Aβ and Tau markers, and neurodegeneration markers from ADNI and Stanford ADRC data to test
and validate the proposed multi-dimensional network framework for integration of different data types for early
detection of AD (Aim1) as well as to characterize AD progression by applying multi-dimensional network
framework to longitudinal changes in various measurements (Aim 2). To our knowledge, this is the first study
that integrates various AD data in a multi-dimensional network model to characterize AD to further enhance AD
detection and progression. If proven successful, this high-risk high-reward proposal will have a large impact on
AD characterization, early detection and progression with significant health and economical impact. Moreover,
successful completion of this study will provide critical tools fo...

## Key facts

- **NIH application ID:** 9986600
- **Project number:** 5R21AG064263-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Hadi Hosseini
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $195,625
- **Award type:** 5
- **Project period:** 2019-08-01 → 2021-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9986600

## Citation

> US National Institutes of Health, RePORTER application 9986600, Multi-dimensional network framework for AD detection and progression (5R21AG064263-02). Retrieved via AI Analytics 2026-06-24 from https://api.ai-analytics.org/grant/nih/9986600. Licensed CC0.

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