School of Medicine researchers have shown that brain scans can predict which patients with clinical depression are most likely to benefit from a specific kind of talk therapy.
 

In a new study published in the journal Neuropsychopharmacology, researchers from the University of North Carolina School of Medicine demonstrated that a technique known as “resting-state functional brain connectivity MRI” can identify differences in brain wiring that predict therapeutic responses to talk therapy.
 

The findings suggest brain scans could ultimately be used as a diagnostic tool to determine the best course of treatment for the millions of Americans that suffer from depression.
 

"In the future, we will be able to use non-invasive brain imaging technology to match patients with the treatment option that has the best chance of lifting their depression," said researcher Gabriel S. Dichter, associate professor of psychiatry and psychology. "In my mind, that's as important as developing new treatments. We already have a lot of excellent treatments but no way to know which one is best for a particular patient."
 

Dichter added that if doctors can identify the best treatment immediately, then doctors and patients could avoid months of trial and error, reducing the often debilitating effects of depression for patients and their families.
 

Major depressive disorder, also known as clinical depression, is now the second leading cause of disability worldwide. Approximately one in six people experience at least one bout of depression, and many will suffer multiple bouts over the course of their lives.
 

Although antidepressant medications, different kinds of talk therapies, and brain stimulation can be effective, 40 percent of people are not helped by the first treatment they try. As a result, Dichter said, it can take patients multiple attempts with different treatments before they experience any relief.
 

"It's a long road to find the right treatment for each patient," Dichter said. "Our goal is to develop a road map, to use this type of information to predict which patients will respond to which treatments."