Stanford study identifies six depression biotypes for personalized treatment

In the near future, rapid brain imaging may be used to screen for depression to determine the best treatment.

A combination of brain imaging and machine learning can identify subtypes of depression and anxiety, according to a new study led by Stanford Medicine scientists. The study, to be published in the journal Nature Medicine, divides depression into six biological subtypes, or “biotypes,” and identifies which treatments are more or less likely to be effective for three of those subtypes.

The need for better treatment selection methods

Better methods for tailoring treatments are urgently needed, said lead study author Lynn Williams, PhD, a professor of psychiatry and behavioral sciences and director of the Center for Precision Psychiatry and Well-Being at Stanford Medicine. Williams, who lost her partner to depression in 2015, has focused her work on pioneering research in precision psychiatry.

About 30% of people with depression have what's called treatment-resistant depression, meaning multiple types of medication or therapy have failed to improve their symptoms. For two-thirds of people with depression, treatment fails to completely eliminate symptoms to a healthy level.

This is partly because there is no reliable way to determine which antidepressant or type of therapy will help a particular patient. Medications are prescribed through trial and error, so it can take months or years to find an effective treatment, if it happens at all. And trying different treatments for a long time without getting relief can make depression symptoms worse.

"The goal of our work is to figure out how to get it right the first time. It's very frustrating to work in the field of depression and not have a better alternative to a one-size-fits-all approach," Dr Williams said.

Biotypes predict response to treatment

To better understand the biology of depression and anxiety, Williams and her colleagues evaluated 801 study participants previously diagnosed with depression or anxiety using an imaging technique known as functional MRI (fMRI) to measure brain activity. They scanned the volunteers’ brains at rest and while they performed various tasks designed to test their cognitive and emotional function. The scientists focused on areas of the brain and the connections between them that are already known to play a role in depression.

Using a machine learning approach known as cluster analysis to group images of patients' brains, they identified six distinct patterns of activity in the brain regions examined.

The researchers also randomly assigned 250 study participants to receive one of three widely used antidepressants or behavioral therapy. Patients with one subtype, characterized by increased activity in cognitive areas of the brain, responded best to the antidepressant venlafaxine (known as Effexor) compared with other biotypes. Those with another subtype, whose resting brains showed increased activity in three areas associated with depression and problem solving, responded better to behavioral therapy. And those with a third subtype, with decreased resting activity in a brain circuit that controls attention, were less likely to see improvement in symptoms from behavioral therapy compared with other biotypes.

Importance of the study

"To our knowledge, this is the first time we've been able to show that depression can be explained by different abnormalities in brain function," Williams said. "This is essentially a demonstration of a personalized medicine approach to mental health based on objective measures of brain function."

In another recent study, Williams and her team showed that using fMRI to image the brain improves their ability to identify people who are likely to respond to antidepressant treatment. In this study, the researchers focused on a subtype they call the cognitive biotype of depression, which affects more than a quarter of people with depression and is less likely to respond to standard antidepressants. By identifying people with the cognitive biotype using fMRI, the researchers accurately predicted the likelihood of remission in 63% of patients, compared with 36% accuracy without using brain imaging. This improvement in accuracy means that doctors are more likely to prescribe the right treatment the first time. The researchers are now exploring new treatments for this biotype in hopes of finding more options for those who don’t respond to standard antidepressants.

Further Research on Depression

Different biotypes also correlated with differences in symptoms and task performance among study participants. For example, those with increased activity in cognitive areas of the brain had higher levels of anhedonia (the inability to experience pleasure) than other biotypes; they also performed worse on executive function tasks. Those with the subtype that responded best to behavioral therapy also made errors on executive function tasks but performed well on cognitive tasks.

One of the six biotypes identified in the study showed no discernible differences in brain activity in the imaged regions compared to activity in people without depression. Williams believes they likely haven’t explored the full range of brain biology underlying the disorder. Their study focused on areas known to be involved in depression and anxiety, but there may be other types of dysfunction in that biotype that their imaging didn’t capture.

Williams and her team are expanding the imaging study to include more participants. She also wants to test more treatments across all six biotypes, including drugs that haven’t traditionally been used for depression.

Her colleague Laura Hack, MD, PhD, assistant professor of psychiatry and behavioral sciences, has begun using the imaging technique in her clinical practice at Stanford Medicine through an experimental protocol. The team also wants to establish easy-to-implement standards for the method so that other mental health practitioners can begin implementing it.

"To really move the field toward precision psychiatry, we need to identify the treatments that are most likely to be effective for patients and get them on those treatments as soon as possible," Ma said. "Having information about their brain function, particularly the proven signatures we assessed in this study, will help inform more precise treatments and prescriptions for individuals."