ATLANTA—The National Institutes of Health has awarded a five-year, $3.5 million grant to Vince Calhoun, Distinguished University Professor of Psychology at Georgia State University, to develop methods that can capture dynamic connectivity in the brain — changes in the shape, size or location of brain networks, as well as changes in the connections between brain networks.
The researchers will apply these methods towards identifying biomarkers that clinicians could use for the early detection of Alzheimer’s disease.
There are no robust, accurate and reliable biomarkers for identifying preclinical Alzheimer’s. Examining the brain’s dynamic functional activity and connectivity could lead to a better understanding of how Alzheimer’s affects the brain in its continuously changing state, Calhoun said.
“There’s already some evidence showing that methods that can capture moment-to-moment connectivity demonstrate greater accuracy for individualized prediction than measures that rely on averaging brain connectivity over a relatively long period.”
The researchers will develop novel tools and techniques that can track connectivity in real time. They will use these methods to analyze a large existing neuroimaging dataset of adults, including many with preclinical Alzheimer’s disease. The goal is to identify patterns that can predict future cognitive decline and changes in amyloid and tau, proteins that collect in the brains of Alzheimer’s patients.
“These approaches have been shown to be especially promising most likely due to the highly dynamic nature of the brain,” said Calhoun, who is a Georgia Research Alliance Eminent Scholar and the director of the Center for Translational Research in Neuroimaging and Data Science (TReNDS), a partnership among Georgia State, Georgia Tech and Emory University. “Yet there are no methods that can provide a full characterization of temporal, spatial, and spatiotemporal dynamics across the entire brain. Once developed, our models will likely be considerably more sensitive and more accurate in determining differences between people who are at high-risk of decline and people who are at low-risk.”
The researchers will make the tools and data available throughout the project’s duration, allowing scientists to compare the methods with existing models and to apply the new techniques to a large variety of brain disorders.
“Our tools also have wide application to the study of the healthy brain as well as many other disorders,” said Calhoun. “The research is an important first step towards providing an opportunity to develop and evaluate early interventions that can have a positive impact on long-term prognosis.”
Tulay Adali, Distinguished University Professor at the University of Maryland Baltimore County (UMBC) and Calhoun’s longtime collaborator, is co-principal investigator on the project.
“This new project will let us demonstrate the great promise of our data-driven approach for the early detection of Alzheimer’s disease” said Adali, who is director of UMBC’s Machine Learning for Signal Processing Lab.
They will work with Ihab Hajjar, professor of medicine and neurology and director of the Clinical Trial Program for the Goizueta Alzheimer’s Disease Research Center at Emory. He noted there is a critical need to identify individuals who have preclinical Alzheimer’s disease, a stage that may precede the symptomatic phases by more than a decade.
“Many ongoing studies are being conducted to identify treatments that might prevent the progression of preclinical Alzheimer’s disease,” said Hajjar. “Developing imaging-related biomarkers would offer a great opportunity to identify candidates for interventions that might prevent this devastating illness.”
Distinguished University Professor
Calhoun is the founding director of the Center for Translational Research in Neuroimaging and Data Science (TReNDS), which is focused on improving our understanding of the human brain using advanced analytic approaches.