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Psychologists Enlist Machine Learning to Help Diagnose Depression


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 Region of anterior thalamic radiation that revealed significantly lower axial diffusivity and mean diffusivity values in those with major depressive disorder relative to healthy control participants.

David Schnyer, a cognitive neuroscientist and professor of psychology at The University of Texas at Austin, believes it may be possible to detect who might be vulnerable to depression before its onset using brain imaging.

Credit: David M Schnyer, Peter C. Clasen, Christopher Gonzalez, Christopher G. Beevers

Researchers at the University of Texas at Austin (UT Austin) are using the Texas Advanced Computing Center's Stampede supercomputer to recognize patterns in neuroimaging data that are predictive for mental disorders such as depression.

They are training a machine-learning algorithm that can spot common features among hundreds of patients using magnetic resonance imaging brain scans, genomics data, and other pertinent factors, to provide accurate predictions of risk for people with depression and anxiety.

One recent study analyzed brain data from 52 treatment-seeking participants with depression and 45 healthy controls. "We feed in whole-brain data or a subset and predict disease classifications or any potential behavioral measure such as measures of negative information bias," says UT Austin professor David Schnyer.

UT Austin professor Christopher Beevers notes, "one of the benefits of machine learning...is that machine learning...should generalize to new data."

From Texas Advanced Computing Center
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Abstracts Copyright © 2017 Information Inc., Bethesda, Maryland, USA


 

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