Predicting Change in the Alzheimer’s Brain

MIT researchers are developing a computer system that uses genetic, demographic, and clinical data to help predict the effects of disease on brain anatomy.

In experiments, they trained a machine-learning system on MRI data from patients with neurodegenerative diseases and found that supplementing that training with other patient information improved the system’s predictions. In the cases of patients with drastic changes in brain anatomy, the additional data cut the predictions’ error rate in half, from 20 percent to 10 percent.

“This is the first paper that we’ve ever written on this,” said Polina Golland, a professor of electrical engineering and computer science at MIT and the senior author on the new paper. “Our goal is not to prove that our model is the best model to do this kind of thing; it’s to prove that the information is actually in the data. So what we’ve done is, we take our model, and we turn off the genetic information and the demographic and clinical information, and we see that with combined information, we can predict anatomical changes better.”