A Binghamton graduate student has found new ways for doctors to detect Alzheimer’s before symptoms set in.
Wenna Duan, a doctoral student in computer science, uses magnetization transfer rate (MTR) as a visual biomarker for brain tissue health.
MTR is a measurement most commonly used in magnetic resonance imaging (MRI) when looking at the brain. An MRI shoots energy into tissue cells, disorienting them, and then is turned off. MTR measures the amount of time the tissue cells take to dissipate the energy and reorient themselves.
Duan determined that brain tissue, specifically white matter, dissipates the energy more slowly in a brain that is more likely to suffer from Alzheimer’s. By comparing the MTR of brains suffering from different stages of Alzheimer’s with undiagnosed scans, doctors would be able to diagnose before the patient suffers from symptoms such as memory loss.
It’s a potentially vital development in the fight against Alzheimer’s disease, which the Centers for Disease Control and Prevention reported led to the deaths of more than 120,000 Americans in 2017.
The International Society for Magnetic Resonance in Medicine (ISMRM) accepted Duan’s research for its 2019 annual conference, where it received a magna cum laude award, which is given to the top 15% of thousands of projects.
The process of comparing scans was not a quick one. Duan had to create a template made up from the average of thousands of scans from a specific stage of Alzheimer’s. Then, because brain shape and size varies, she had to manipulate the undiagnosed scans, one by one, to fit the size of the template.
“The most challenging part of it all was in the preprocessing, especially because I had to teach myself the science behind the data to use it,” Duan says. “It took around two months to process all of the brain scans, and there were some very late nights.”
The brain data was collected as both longitudinal, where one subject was observed over several years, and cross-sectional, where one time-point was observed in different stages of Alzheimer’s, by the Cardiovascular Health Study at the University of Pittsburgh.
Weiying Dai, an assistant professor of computer science who previously taught at the University of Pittsburgh, supervised Duan’s research at Binghamton and provided the database. Dai says Duan’s research has the potential to make big changes in the clinical field.
“Her research can also help to identify potential treatment groups when a new drug comes to play as we do not currently have a cure for Alzeihmer’s,” Dai says. “Her work is clearly application oriented. If successful, she will make a huge contribution to both method development and clinical application.”
Duan is continuing her research with hopes of reconfirming her findings until this process is used regularly to help patients.
Her collaboration with Dai began when she took a machine learning course Dai taught.
“I did research with her and didn’t know it was all MR related, but when I turned in the MR project, she introduced how significant those findings are in the clinical area,” Duan says. “I thought, ‘You are really impacting somebody,’ so I got very excited and I decided to continue with that.”