AMHERST, Mass. ⎯ An estimated 6.7 million American adults over the age of 65 have Alzheimer’s disease. One of the major challenges with this condition is that there currently is no established, universally recognized method for predicting its onset, and emerging treatments are considered to be effective only in the early stages of the disease. Now, however, researchers from the University of Massachusetts Amherst have received a grant from the National Institutes of Health to develop new deep-learning models for the early prediction of Alzheimer’s using clinical data, including brain MRIs taken in real-world settings.

The ultimate goal of this research is to enable earlier detection of Alzheimer’s—ideally two years or more before onset of symptoms—and identify patient populations at risk for developing the condition using MRI data, allowing researchers to test interventions and medications that interrupt the course of the disease. To create these predictive models, the researchers will use multimodal clinical data, including brain MRIs.

“This research brings us closer to putting people in clinical trials at a point where the brain biology is still intact and something can be done,” says Madalina Fiterau, assistant professor in the Manning College of Information and Computer Sciences at UMass Amherst and principal investigator and project leader of the study. “Sixty percent of a patient’s brain matter disappears by the time of diagnosis, and at that stage it’s irretrievable. What we would like to do is identify those changes early, at least two years before onset, and then, based on that, figure out which treatments work.”
The study’s other principal investigator, Joyita Dutta, associate professor of biomedical engineering, echoes this excitement of possible treatment on the horizon. “We would not have been able to say this three years back, but now that many new drug candidates are emerging, we are at the point where forecasting techniques can actually be deployed to identify potential subjects for a disease-modifying therapy.”

Previous research has aimed to create deep-learning models for predicting degenerative disease using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Unfortunately, this poses a challenge for making the findings generalizable since these ADNI-based models use engineered data. For instance, instead of incorporating actual images of the cerebral cortex, other studies use software to extract average cortical thickness.

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