Wang studying novel & interpretable statistical learning for brain imaging data

Wang Studying Novel & Interpretable Statistical Learning For Brain Imaging Data  

Lily Wang, Professor, Statistics,  has received a total grant of $1,199,772 ($299,987 for the first year) from the National Institutes of Health for the project: "SCH: Novel and Interpretable Statistical Learning for Brain Images in AD/ADRDs." This funding began in Sept. 2023 and will end in late April 2027. This grant was reviewed by the joint NSF/NIH Smart Health and Biomedical Research in the Era of Artificial Intelligence and Advanced Data Science (SCH) program. 

Alzheimer’s Disease, the most common form of dementia and a leading cause of death, poses a growing concern. In 2020, the US Centers for Disease Control and Prevention reported about 5.8 million Americans living with Alzheimer’s, with projections of nearly 14 million by 2060. Regrettably, at present, there exists no known cure. 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 aging-related 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. Directly utilizing three-dimensional (3D) or higher imaging information may facilitate the identification of more effective disease biomarkers to inform diagnosis, prognosis, and treatment. However, this also brings significant challenges, such as analyzing irregularly shaped 3D objects, managing high-dimensional and high-resolution data, addressing noisiness and complexity, quantifying uncertainty, and ensuring the interpretability of the results. 

Under Dr. Wang’s leadership, a cross-institutional research team from George Mason University, William & Mary, and the University of Georgia will develop efficient statistical learning approaches and scalable computing tools to extract and assess biomarkers from large-scale brain imaging studies. By using advanced statistical learning approaches and computing tools to leverage 3D or higher imaging information directly, the proposed research can facilitate the identification of biomarkers for Alzheimer’s Disease and related dementias (AD/RD). This project holds the potential to provide more effective disease biomarkers, leading to improved accuracy in diagnosis, prognosis, and treatment for AD/RD. 

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About George Mason University

George Mason University is Virginia's largest public research university. Located near Washington, D.C., Mason enrolls 38,000 students from 130 countries and all 50 states. Mason has grown rapidly over the last half-century and is recognized for its innovation and entrepreneurship, remarkable diversity and commitment to accessibility. Learn more at http://www.gmu.edu.

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