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Alzheimer’s Disease remains an elusive specter of old age, threatening to take one’s personality, memory, and eventually body. The most common form of dementia, the disease is the seventh leading cause of death in the nation, as stated by the Centers for Disease Control (CDC).
“According to CDC, in 2020, we had as many as 5.8 million Americans living with the common disease, and this number is projected to triple by 2026,” said Lily Wang, a professor of statistics at George Mason University. “Unfortunately, even now, scientists still do not fully understand the causes of Alzheimer disease, and there's currently no known cure.”
Motivated to study the disease, Wang and colleagues at other universities are using new biomedical imaging techniques in hope of recognizing Alzheimer’s earlier. Their work has resulted in several publications in journals such as the Journal of Alzheimer Disease.
Now, Wang is the Principal Investigator and George Mason is the leading institution for a grant of $1,199,772 from a program called Smart Health and Biomedical Research in the Era of Artificial Intelligence and Advanced Data Science. The interagency program is run by the National Science Foundation and the National Institutes of Health and is meant to “support the development of transformative high-risk, high-reward advances in computer and information science, engineering, mathematics, statistics, behavioral and/or cognitive research to address pressing questions in the biomedical and public health communities,” per its website.
Of Wang's project, titled “SCH: Novel and Interpretable Statistical Learning for Brain Images in AD/ADRDs,” she further explained, “In current imaging, biomarkers are primarily focused on one-dimensional measures . . .that may not fully capture the richness of imaging data. So, what we propose is to utilize 3D- or 4D-imaging information that may facilitate the identification of more effective disease biomarkers to inform diagnosis, prognosis, and treatment [of Alzheimer’s Disease].”
The project’s interdisciplinary team will comprise investigators from William & Mary and the University of Georgia who will “develop efficient statistical learning approaches and scalable computing tools to extract and assess biomarkers from large-scale brain imaging studies,” according to the project’s abstract. The team will also include both genetic and clinical information in their creation of said biomarkers.
Wang hopes the application of advanced statistical learning approaches now underway to study Alzheimer’s will benefit not only those suffering from the disease, but also those studying or experiencing related dementias.