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. He is using the Stampede supercomputer at the Texas Advanced Computing Center (TACC) to train a machine learning algorithm that can identify commonalities among hundreds of patients using Magnetic Resonance Imaging (MRI) brain scans, genomics data and other relevant factors, to provide accurate predictions of risk for those with depression and anxiety.
“One difficulty with that work is that it’s primarily descriptive. The brain networks may appear to differ between two groups, but it doesn’t tell us about what patterns actually predict which group you will fall into,” Schnyer says. “We’re looking for diagnostic measures that are predictive for outcomes like vulnerability to depression or dementia.”
In March 2017, Schnyer, working with Peter Clasen (University of Washington School of Medicine), Christopher Gonzalez (University of California, San Diego) and Christopher Beevers (UT Austin), published their analysis of a proof-of-concept study in Psychiatry Research: Neuroimaging that used a machine learning approach to classify individuals with major depressive disorder with roughly 75 percent accuracy.
Machine learning is a subfield of computer science that involves the construction of algorithms that can “learn” by building a model from sample data inputs, and then make independent predictions on new data.
The type of machine learning that Schnyer and his team tested is called Support Vector Machine Learning. The researchers provided a set of training examples, each marked as belonging to either healthy individuals or those who have been diagnosed with depression. Schnyer and his team labelled features in their data that were meaningful, and these examples were used to train the system. A computer then scanned the data, found subtle connections between disparate parts, and built a model that assigns new examples to one category or the other.
In the recent study, Schnyer analyzed brain data from 52 treatment-seeking participants with depression, and 45 heathy control participants. To compare the two, a subset of depressed participants was matched with healthy individuals based on age and gender, bringing the sample size to 50.
Read more at: https://www.tacc.utexas.edu/-/psychologists-enlist-machine-learning-to-help-diagnose-depression
Source: Aaron Dubrow, TACC