Depression affects more than 15 million American adults, or about 6.7 percent of the U.S. population, each year. It is the leading cause of disability for those between the ages of 15 and 44. Is it possible to detect who might be vulnerable to the illness before its onset using brain imaging? David Schnyer, a cognitive neuroscientist and professor of psychology at The University of Texas at Austin, believes it may be. But identifying its tell-tale signs is no simpler matter.

Depression can be hard to diagnose, let alone to predict in advance. This machine learning system could highlight individuals who may be vulnerable to depression -- based only on their MRI scans. Depression can be a crippling disorder, affecting upward of 15 million American adults and representing the leading cause of disability for people between 15-44. New research coming out of the University of Texas at Austin could make it easier to diagnose, however -- or even to highlight individuals who could be vulnerable to depression prior to its onset. "There's a whole lot of literature that's emerging in the field of cognitive neuroscience and psychiatry that looks at using in vivo brain imaging techniques in humans to examine differences that might be associated with mental disorders," David Schnyer, a cognitive neuroscientist and professor of psychology at the University of Texas at Austin, told Digital Trends.

A new study from the University of Texas suggests machine learning with a supercomputer may help identify people susceptible to developing depression. Depression is the leading cause of disability for people between the ages of 15 and 44, and affects more than 15 million American adults each year. Researchers have studied mental illness by identifying the relationship between brain function and structure using neuroimaging data for years. "One difficulty with that work is that it's primarily descriptive," David Schnyer, a cognitive neuroscientist at the University of Texas, said in a press release. "The brain networks may appear to differ between two groups, but it doesn't tell us about what patterns actually predit which group you will fall into. We're looking for diagnostic measures that are predictive for outcomes like vulnerability to depression or dementia."

Depression affects more than 15 million American adults, or about 6.7 percent of the U.S. population, each year. It is the leading cause of disability for those between the ages of 15 and 44. Is it possible to detect who might be vulnerable to the illness before its onset using brain imaging? David Schnyer, a cognitive neuroscientist and professor of psychology at The University of Texas at Austin, believes it may be. But identifying its tell-tale signs is no simpler matter.