Computer Science Assistant Professor Hua Wang has received an NSF CAREER Award for a research project to create a new machine-learning model for mining various kinds of data that could lead to easier, earlier and less-costly detection of neurological diseases such as Alzheimer’s or Parkinson’s.

The project, called “Robust Brain Imaging Genomics Data Mining Framework for Improved Cognitive Health,” will receive $409,641 over five years.

Wang will develop algorithms aimed at revealing the relationships between people’s genetic information, how their brains appear in scans that measure volume and function and their performances in cognitive tests. “The algorithms can extract information from large amounts of data that cannot be directly analyzed by ourselves,” Wang said.

Determining one person’s full genetic profile can cost several thousand dollars. If Wang’s project determines a link, for example, between a disease and a small section of that long genetic chain, testing one’s likelihood of developing the disease would be much cheaper.

The project could also determine which cognitive tests are most effective in diagnosing diseases, again saving patients and doctors money, time and effort. Early detection is important in Alzheimer’s, for example, because while the disease is currently irreversible, there are therapies that can slow down its progress significantly. Discovering these relationships could also contribute to cures for such diseases down the road.

The project will contribute to the BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative, a public-private research partnership that includes numerous government entities, universities, corporations and other institutions. The initiative seeks to create a better understanding of how exactly the brain—with its nearly 100 billion neurons and 100 trillion connections—functions.