Machine Learning on Stampede2 Supercomputer Bolsters Brain Research

May 31, 2017

Donna Loveland

In our ongoing quest to understand the human mind and banish abnormalities that interfere with life we’ve always drawn upon the most advanced science available. During the last century, neuroimaging – most recently, the Magnetic Resonance Imaging scan (MRI) – has held the promise of showing the connection between brain structure and brain function.

Just last year, cognitive neuroscientist David Schnyer and colleagues Peter Clasen, Christopher Gonzalez, and Christopher Beevers published a compelling new proof of concept in Psychiatry Research: Neuroimaging. It suggests that machine learning algorithms running on high-performance computers to classify neuroimaging data may deliver the most reliable insights yet.

Their analysis of brain data from a group of treatment-seeking individuals with depression and heathy controls predicted major depressive disorder with a remarkable 75 percent accuracy.

Making More of MRI

Since MRI first appeared as a diagnostic tool, Dr. Schnyer observes, the hope has been that running a person through a scanner would reveal psychological as well as physical problems. However, the vast majority of MRI research done on depression, for example, has been primarily descriptive. While it tells how individual brains differ across various characteristics, it doesn’t predict who might have a disorder or who might be vulnerable to developing one.

To appreciate the role the software can play, consider the most familiar path to prediction.

As Dr. Schnyer points out, researchers might acquire a variety of scans of individuals at a single time and wait 20 years to see who develops a disorder like depression. Then they’d go back and try to determine which aspects of their neuroimaging data would predict who ended up becoming depressed. In addition to the obvious problem of long duration, they’d…

Read the full article from the Source…

Back to Top