Researchers have discovered a neural signature that predicts whether individuals with depression are likely to benefit from sertraline, a commonly prescribed antidepressant medication. The findings, published in Nature Biotechnology, suggest that new machine learning techniques can identify complex patterns in a person’s brain activity that correlate with meaningful clinical outcomes. The research was funded by the National Institute of Mental Health (NIMH), part of the National Institutes of Health.
“There is a great need in psychiatry today for objective tests that can inform treatment and go beyond some of the limitations of our diagnostic system. Our findings are exciting because they reflect progress made toward this clinical goal, and they also show the potential of bringing sophisticated data analytic methods to psychiatry,” explained senior author Amit Etkin, M.D., Ph.D., a professor of psychiatry and behavioral sciences at Stanford University and CEO of Alto Neuroscience, Los Altos, California.