# Identification of Multi-modal Imaging Biomarkers for Early Prediction of MCI-AD Conversion via Multigraph Representation

> **NIH NIH R03** · MASSACHUSETTS GENERAL HOSPITAL · 2022 · $329,200

## Abstract

Summary
Alzheimer’s disease (AD) is the most common form of neurodegenerative dementia and has an
astounding impact at individual and societal levels. As the early-stage cognitive degeneration,
mild cognitive impairment (MCI) has a high chance to convert to AD. Effective and early
prediction of such conversion is of great importance for risk stratification, patient management,
and possible symptomatic treatments. Identification of an MCI-AD conversion end point is also
important for clinical trials for better evaluating the effectiveness of therapeutic interventions.
Recent studies have shown that multi-modalities neuroimaging can offer a more comprehensive
characterization for the MCI-AD conversion, revealing the physiologic underpinning of the
clinical states, and ultimately result in higher prediction accuracy based on the multi-modal
imaging biomarker. Advancement in deep learning, especially deep Graph Convolutional
Networks, has provided us with powerful tools in modeling the multi-modal neuroimaging data
on the brain networks. However, despite the high prediction accuracy of AD in literature, multi-
modal imaging diagnostic still lacks generalizability and robustness in dealing with data from
other sites/populations due to the combined effect of relatively smaller sample sizes and
potential bias in the sample labels.
In this proposal, we will investigate the interaction among structural, functional, and
proteinopathies networks in MCI and AD patients via a contrastive learning-based, multigraph
representation framework on the multi-modal neuroimaging data of MRI, fMRI and PET
modalities. The proposed framework will be used to identify and evaluate a multi-modal image
biomarker for the AD conversion in MCI population from a multi-site dataset. By analyzing the
spatial and populational patterns of the identified multi-modal image biomarker, we will be able
to discover novel neuroscientific and biological mechanisms of the MCI-AD conversion.

## Key facts

- **NIH application ID:** 10510971
- **Project number:** 1R03AG078625-01
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Xiang Li
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $329,200
- **Award type:** 1
- **Project period:** 2022-08-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10510971

## Citation

> US National Institutes of Health, RePORTER application 10510971, Identification of Multi-modal Imaging Biomarkers for Early Prediction of MCI-AD Conversion via Multigraph Representation (1R03AG078625-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10510971. Licensed CC0.

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