Joseph Gullett, Ph.D., is an assistant professor in the Department of Clinical and Health Psychology
My work focuses on the use of multimodal neuroimaging and neuropsychological performance data to predict response to intervention or disease progression with machine learning tools.
The exciting part about this is that with accurate prediction models, we can save patients time, money and energy by developing a personalized medicine plan for what intervention might be effective for them based on their brain and cognitive performance characteristics.
I was able to secure National Institutes of Health funding through the National Institute on Aging for a five-year career development award human subjects clinical trial project. This was integrated into a larger R01 project within our center, which I then took over as principal investigator in August 2024.
I am currently running a separate device trial under an investigational device exemption through the Food and Drug Administration to determine the effectiveness of transcranial pulse stimulation in patients with early Alzheimer’s disease dementia. If effective, the pilot data from this trial will be combined with the data from my NIH Career Development Award to propose my first R01 study in 2028.
As far as new developments in the AI world, I am particularly intrigued by generative adversarial networks regarding their ability to create new and accurate brain imaging data based on large subsets of existing brain imaging or cognitive performance data.