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New Stanford study takes steps toward integrating brain imaging into psychiatric care

Leanne Williams, PhD, professor of psychiatry and behavioral sciences at Stanford, envisions a time when a clinician can order a brain scan to help with the diagnosis and treatment of mental health disorders, much like a orthopedic surgeon now orders X-rays or MRIs for a broken bone.

In a study that appears in Translational Psychiatry, Williams and other colleagues at Stanford say they have taken an important first step toward achieving that goal by developing a standard metric for healthy brain network functioning that can be compared against individual patients’ scan results.

“The idea is for psychiatrists to get the brain scan, to know what it means and how to use it,” said Tali Ball, PhD, a postdoctoral scholar and lead author of the study. Williams, senior author of the study, is shown in the photo above at left with Ball and research coordinator Catherine Kircos.

“Right now, brain imaging is not the standard of care in psychiatry,” Ball added. “A big reason for this is that we don’t have a way to interpret a single patient’s brain profile. We’re trying to solve that problem.”

Although advances in neuroimaging have provided insights into human brain networks and their relevance to psychiatric and neurological disorders, there has been no way to translate that knowledge into care for individual patients, the study says.

To come up with a standard metric, researchers examined brain scans from 322 healthy individuals and measured data for average connectivity of the “default mode network,” a well-established brain network often studied for its ties to anxiety and depression.

“We figured out a metric to summarize how that brain circuit is working and characterized it down to a single number,” Ball says. That number can now be used to understand a single patient's MRI results, she says.

To help show how this would work in clinical practice, the authors included a case study of a 39-year-old male with major depressive disorder. By comparing the MRI measurements of his brain networks to the normative metric developed in this study, care providers could see that he would be a good candidate for antidepressant medication.

“There are a lot of good treatments for disorders such as anxiety and depression,” Ball says. “Knowing how to get the right person to the right treatment is the problem. If we know which brain network is dysfunctioning, it can help pinpoint the best treatment plan. This paper is the first step to getting us there.”

Previously: Using brain scans and personal history to predict best antidepressant choice and Stanford brain scientist’s quest to personalize mental health care
Photo by Dickson Chow

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