A new method for interpreting brain waves could potentially help determine the best depression treatment, according to a new study.
The researchers 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.
The study emerged from a decades-long effort 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,” says Amit Etkin, professor of psychiatry and behavioral sciences at Stanford University. “I will be surprised if this isn’t used by clinicians within the next five years.”
The problem with depression treatment
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.
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 trial was called Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care, or EMBARC. The researchers designed the trial 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,” says co-senior author Madhukar Trivedi, professor of psychiatry at the University of Texas-Southwestern who led the research team.
“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?'”
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 (NIMH). Among those, about half never get diagnosed.
For those who do, finding the right treatment can take years, Trivedi says. He points to one of his past studies that showed only about 30% of patients with depression 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 says. 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 says.
“As a psychiatrist, I know these patients differ a lot,” Etkin says. “But we put them all under the same umbrella, and we treat them all the same way.”
Treating people with depression often begins with prescribing them an antidepressant. If one doesn’t work, a second antidepressant is prescribed. Each of these “trials” often takes at least eight weeks to assess whether the drug worked and symptoms are alleviated.
If an antidepressant doesn’t work, other treatments, such as psychotherapy or occasionally transcranial magnetic stimulation, may work. Often, doctors combine multiple treatments, Etkin says, but figuring out which combination works can take a while.
“People often feel a lot of dejection each time a treatment doesn’t work, creating more self-doubt for those whose primary symptom is most often self-doubt,” Trivedi says.
Predicting whether Zoloft will work
The EMBARC trial enrolled 309 people with depression who randomly received either sertraline or a placebo.
For their study, Etkin and his colleagues set out to find a brain-wave pattern to help predict which depressed participants would respond to sertraline. First, the researchers collected EEG data on the participants before they received any drug treatment. The goal was to obtain a baseline measure of brain-wave patterns.
Next, using insights from neuroscience and bioengineering, the investigators analyzed the EEG using a novel artificial intelligence technique they developed and identified signatures in the data that predicted which participants would respond to treatment based on their individual EEG scans.
The researchers found that this technique reliably predicted which of the patients did, in fact, respond to sertraline and which responded to placebo. They replicated the results at four different clinical sites.
Further research suggests that participants who researchers predicted would show little improvement with sertraline were more likely to respond to treatment involving transcranial magnetic stimulation, or TMS, in combination with psychotherapy.
“Using this method, we can characterize something about an individual person’s brain,” Etkin says. “It’s a method that can work across different types of EEG equipment, and thus more apt to reach the clinic.”
“Part of getting these study results used in clinical care is, I think, that society has to demand it,” Trivedi says. “That is the way things get put into practice. I don’t see a downside to putting this into clinical use soon.”
Data guiding care
When researchers launched EMBARC, it was part of a broader effort by the NIMH to push for improvements in mental health care by using advances in fields such as genetics, neuroscience, and biotechnology, says Thomas Insel, who served as director of that institute from 2002 to 2015.
“We went into EMBARC saying anything is possible,” Insel says. “Let’s see if we can come up with clinically actionable techniques.” He didn’t think it would take this long, but he remains optimistic.
“I think this study is a particularly interesting application of EMBARC,” he says. “It leverages the power of modern data science to predict at the individual level who is likely to respond to an antidepressant.”
In addition to improving care, the researchers say they see a possible side benefit to the use of biologically based approaches: It could reduce the stigma associated with depression and other mental health disorders that prevents many people from seeking appropriate medical care.
“I’d love to think scientific evidence will help to counteract this stigma, but it hasn’t so far,” says Insel. “It’s been over 160 years since Abraham Lincoln says that melancholy ‘is a misfortune, not a fault.’ We still have a long way to go before most people will understand that depression is not someone’s fault.” (President Lincoln suffered bouts of depression.)
A paper on the work appears in Nature Biotechnology. Additional researchers from South China University of Technology, the Netherlands Research Institute, Harvard Medical School, the New York State Psychiatric Institute, Columbia University, and the Netherlands neuroCare Group contributed to the work.
Etkin is on leave from Stanford, working as the founder and CEO of the startup Alto Neuroscience, a company based in Los Altos, California that aims to build on these findings and develop a new generation of biologically based diagnostic tests to personalize mental health treatments with a high degree of clinical utility. Insel is an investor in Alto Neuroscience.
Funding came from the National Institutes of Health, the Stanford Neurosciences Institute, the Hersh Foundation, the National Key Research and Development Plan of China, and the National Natural Science Foundation of China.
Source: Stanford University