# SCH: Novel and Interpretable Statistical Learning for Brain Images in AD/ADRDs

> **NIH NIH R01** · GEORGE MASON UNIVERSITY · 2024 · $276,364

## Abstract

Biomedical imaging technology has undergone rapid advancements over the last several decades, 
producing large volumes of multimodal imaging data that hold great promise as biomarkers for agingrelated diseases such as Alzheimer’s. Current imaging biomarkers are primarily based on specific 
extracted one-dimensional measures that may not fully capture the richness of imaging data. Utilizing 
three-dimensional (3D) or higher imaging information directly may facilitate the identification of more 
effective disease biomarkers to inform diagnosis, prognosis, and treatment. However, this also brings 
significant challenges, such as analyzing ir-regularly shaped 3D objects, managing high-dimensional and 
high-resolution data, addressing noisiness and complexity, quantifying uncertainty, and ensuring the 
interpretability of the results. Our multi-institutional, inter-disciplinary team of investigators will develop 
efficient statistical learning approaches and scalable computing tools to extract and assess biomarkers 
from large-scale brain imaging studies. We will also incorporate genetic and clinical information in 
constructing the biomarkers. Specifically, our proposal comprises five interrelated research aims carried 
out by investigators with complementary expertise from three institutions. Aim 1 focuses on developing an 
interpretable model for genome-wide association studies (GWAS) with brain imaging pheno-types and 
non-visual contextual information. Aim 2 targets to develop novel nonparametric distributed learning 
methods for analyzing 3D brain imaging data using an innovative domain decomposition strategy to 
improve computing performance. Aim 3 quantifies the bias effect in image processing and develops 
inference methods to reveal the underlying signal from brain imaging data and identify significant brain 
regions among different diagnosis groups. Aims 4-5 aim to develop statistical methods for obtaining and 
evaluating imaging-adjusted biomarkers for disease diagnosis and prognosis and assess the incremental 
value of imaging information over genetic biomarkers on diagnosis and prediction accuracy. The efficacy 
of the methods developed in this pro-posal will be tested by data collected from studies in Alzheimer’s 
disease and brain sciences. The proposed research will address critical gaps in current biomarker 
development and analysis by utilizing advanced sta-tistical learning approaches and computing tools to 
directly utilize the 3D or higher imaging information. This innovative approach holds the potential to provide 
more effective disease biomarkers, leading to improved accuracy in diagnosis, prognosis, and treatment 
for Alzheimer’s disease and related dementias.

## Key facts

- **NIH application ID:** 10911372
- **Project number:** 5R01AG085616-02
- **Recipient organization:** GEORGE MASON UNIVERSITY
- **Principal Investigator:** XIAO SONG
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $276,364
- **Award type:** 5
- **Project period:** 2023-09-01 → 2027-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10911372

## Citation

> US National Institutes of Health, RePORTER application 10911372, SCH: Novel and Interpretable Statistical Learning for Brain Images in AD/ADRDs (5R01AG085616-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10911372. Licensed CC0.

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