# Multivariate methods for identifying multi-task/multimodal brain imaging biomarkers

> **NIH NIH R01** · GEORGIA STATE UNIVERSITY · 2021 · $360,634

## Abstract

ABSTRACT:
The focus of this supplement request is to leverage and reinforce our ongoing biomarker identification work with
methods specifically focusing on Alzheimer's disease (AD) and related disorders (ADRD). Deep learning methods that we
are developing in the parent grant can produce an optimal performance based on learning end-to-end directly from the
data. Our goal is to leverage models trained to classify AD from the full brain fMRI dynamics for capturing novel
dynamic biomarkers of AD via trained model introspection. However, it is notoriously difficult to train models to predict
directly from full brain dynamics without prior dimensionality reduction. To overcome this difficulty, we will develop
self-supervised approaches that would take advantage of unrelated datasets and provide a performance boost that
would allow obtaining reliable classification improvements even on small data. This improved classification directly
transfers into more reliable introspection of why the model classifies subjects to AD. We plan to improve the robustness
of these predictive/introspective methods and study these full-brain fMRI dynamic measures in younger adults who
have CSF risk markers assessed for AD in order to evaluate the potential for leveraging such models as biomarkers of AD.

## Key facts

- **NIH application ID:** 10289426
- **Project number:** 3R01EB006841-14S1
- **Recipient organization:** GEORGIA STATE UNIVERSITY
- **Principal Investigator:** VINCE D CALHOUN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $360,634
- **Award type:** 3
- **Project period:** 2007-04-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10289426

## Citation

> US National Institutes of Health, RePORTER application 10289426, Multivariate methods for identifying multi-task/multimodal brain imaging biomarkers (3R01EB006841-14S1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10289426. Licensed CC0.

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