Using EEG to measure brain activity, Stanford researchers and their collaborators applied artificial intelligence to help determine the best depression treatment for individual patients.
A new method of interpreting brain activity could be used in clinics to help determine the best treatment options for depression, according to a study led by researchers at the Stanford School of Medicine.
Stanford researchers and their collaborators used electroencephalography, a tool for monitoring electrical activity in the brain, and an algorithm to identify a brain-wave signature in individuals with depression who will most likely respond to sertraline, an antidepressant marketed as Zoloft.
A paper describing the work will be published Feb. 10 in Nature Biotechnology.
The study emerged from a decades-long effort funded by the National Institute of Mental Health to create biologically-based approaches, such as blood tests and brain imaging, to help personalize the treatment of depression and other mental disorders. Currently, there are no such tests to objectively diagnose depression or guide its treatment.
“This study takes previous research showing that we can predict who benefits from an antidepressant and actually brings it to the point of practical utility,” said Amit Etkin, MD, PhD, professor of psychiatry and behavioral sciences at Stanford. “I will be surprised if this isn’t used by clinicians within the next five years.”
Instead of functional magnetic resonance imaging, an expensive technology often used in studies to image brain activity, the scientists turned to electroencephalography, or EEG, a much less costly technology.
Etkin shares senior authorship of the paper with Madhukar Trivedi, MD, professor of psychiatry at the University of Texas-Southwestern. Wei Wu, PhD, an instructor of psychiatry at Stanford, is the lead author.
The paper is one of several based on data from a federally funded depression study launched in 2011 — the largest randomized, placebo-controlled clinical trial on antidepressants ever conducted with brain imaging — which tested the use of sertraline in 309 medication-free patients. The multicenter trial was called Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care, or EMBARC. Led by Trivedi, it was designed to advance the goal of improving the trial-and-error method of treating depression that is still in use today.
“It often takes many steps for a patient with depression to get better,” Trivedi said. “We went into this thinking, ‘Wouldn’t it be better to identify at the beginning of treatment which treatments would be best for which patients?’”
Most common mental disorder
Major depression is the most common mental disorder in the United States, affecting about 7% of adults in 2017, according to the National Institute of Mental Health. Among those, about half never get diagnosed. For those who do, finding the right treatment can take years, Trivedi said. He pointed to one of his past studies that showed only about 30% of depressed patients saw any remission of symptoms after their first treatment with an antidepressant.
Current methods for diagnosing depression are simply too subjective and imprecise to guide clinicians in quickly identifying the right treatment, Etkin said. In addition to a variety of antidepressants, there are several other types of treatments for depression, including psychotherapy and brain stimulation, but figuring out which treatment will work for which patients is based on educated guessing.
To diagnose depression, clinicians rely on a patient reporting at least 5 of 9 common symptoms of the disease. The list includes symptoms such as feelings of sadness or hopelessness, self-doubt, sleep disturbances — ranging from insomnia to sleeping too much — low energy, unexplained body aches, fatigue, and changes in appetite, ranging from overeating to undereating. Patients often vary in both the severity and types of symptoms they experience, Etkin said.