# Large-scale harmonization and integration of multi-modal ADNI data for the early detection of Alzheimer's disease and related dementias

> **NIH NIH R01** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2022 · $778,492

## Abstract

Alzheimer’s disease (AD) and Alzheimer’s Disease Related Dementia (ADRD) are highly heterogeneous in
pathology with mixed signatures on clinical biomarkers, making the early diagnosis challenging. Over the past
few decades, large cohorts of multi-modal data have been collected to identify the interactions between these
key pathologies. However, the utility of such cohorts has been compromised by the heterogeneity of the
data collected from multiple sites and scanners, creating technical variability that can introduce noise and
bias. Without comprehensive data harmonization and aggregation, these non-biological sources of variability
can systematically bias the results of data-driven efforts in biomarker development. Our long-term goal is to
identify specific AD and ADRD disease pathology markers and how they evolve. This project aims to improve the
early detection of AD and ADRD so that future disease-modifying therapy can be allocated more efficiently to
patients. To achieve this objective, we aim to harmonize trans-national cohorts of the Alzheimer’s Disease
Neuroimaging Initiative (ADNI) to improve the diagnostic classification of AD and ADRD. The central
hypothesis of our study is that by harmonizing the multi-modal American ADNI (versions 1, 2, 3, and GO) and
Japanese ADNI datasets and building state of the art predictive models from each modality integrated into
comprehensive ensembles, we can identify novel classifiers and features for early AD diagnosis and
differentiation from ADRD. The central hypothesis will be tested by pursuing three specific aims: 1)
Harmonization of multi-modal ADNI data, 2) Development of a suite of effective classifiers from diverse,
harmonized ADNI data modalities, 3) Integration of multi-modal predictors into an ensemble model for
AD/ADRD/healthy control classification, validation of the model in international ADNI cohorts, and sharing of
the data and software products. We will pursue these aims by applying innovative computational approaches
that combine traditional machine learning and more recent deep learning methods for unstructured
neuroimaging and structured clinical data in ADNI. Moreover, we will leverage ensemble learning
techniques to effectively combine models built from these diverse data modalities to optimize for robust
classifiers of AD, ADRD, and the health status of patients. The results from this proposal will have a significant
impact on better understanding the spatial dynamics and other mechanisms of AD and ADRD pathogenesis.
Importantly, this project will create publicly available resources for multi-modal data harmonization and predictive
modeling that can be used to explore further AD, ADRD, and other neurological disorders in future studies.

## Key facts

- **NIH application ID:** 10515212
- **Project number:** 1R01NS128486-01
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Jeiran Choupan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $778,492
- **Award type:** 1
- **Project period:** 2022-07-15 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10515212

## Citation

> US National Institutes of Health, RePORTER application 10515212, Large-scale harmonization and integration of multi-modal ADNI data for the early detection of Alzheimer's disease and related dementias (1R01NS128486-01). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10515212. Licensed CC0.

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