Researchers at Stanford Medicine have identified six biological subtypes of depression using brain imaging and machine learning, paving the way for precision psychiatry. The results show that some antidepressants and behavioral therapies are more effective for specific subtypes, offering hope for more targeted and effective treatment strategies. Credit: SciTechDaily.com
A Stanford Medicine study reveals six subtypes of depression, identified through brain imaging and machine learning. These subtypes display unique patterns of brain activity, helping to predict which patients will benefit from antidepressants or behavioral therapies. This approach aims to personalize and improve the efficacy of depression treatment.
In the not-too-distant future, a quick brain scan during a screening assessment for depression may identify the best treatment.
Brain imaging combined with a type of AI called machine learning can detect subtypes of depression and anxiety, according to a new study led by researchers at Stanford Medicine. The study, to be published today (June 17) in the journal Nature Medicineclassifies depression into six biological subtypes or “biotypes” and identifies treatments that are more or less likely to work for three of these subtypes.
Advances in Precision Psychiatry
Better methods for matching patients to treatments are desperately needed, said the study’s senior author, Leanne Williams, PhD, Vincent VC Woo Professor, a professor of psychiatry and behavioral sciences, and director of the Stanford Medicine Center for Health Mental Precision and Wellness. Williams, who lost her partner to depression in 2015, has focused her work on pioneering the field of precision psychiatry.
About 30% of people with depression have what is known as treatment-resistant depression, meaning that multiple types of medication or therapy have failed to improve their symptoms. And for up to two-thirds of people with depression, treatment fails to fully return their symptoms to healthy levels.
This is partly because there is no good way to know which antidepressant or type of therapy might help a particular patient. Medications are prescribed through a trial-and-error method, so it can take months or years to get a drug that works—if ever. And trying treatment after treatment for so long, only to experience no relief, can make depression symptoms worse.
“The goal of our work is to figure out how we can get it right the first time,” Williams said. “It’s very frustrating to be in the depression field and not have a better alternative to this one-size-fits-all approach.”
Biotypes predict treatment response
To better understand the biology underlying depression and anxiety, Williams and her colleagues evaluated 801 study participants who had previously been diagnosed with depression or anxiety using imaging technology known as functional MRI, or fMRI, to measure brain activity. They scanned the brains of volunteers at rest and when they were engaged in various tasks designed to test their cognitive and emotional functioning. The scientists narrowed down the brain regions and the connections between them that were already known to play a role in depression.
Using a machine learning approach known as cluster analysis to group the patients’ brain images, they identified six distinct patterns of activity in the brain regions they studied.
The scientists also randomly assigned 250 of the study participants to receive one of three commonly used antidepressants or behavioral talk therapy. Patients with one subtype, which is characterized by overactivity in cognitive regions of the brain, experienced the best response to the antidepressant venlafaxine (commonly known as Effexor) compared to those with other biotypes. Those with another subtype, whose brains at rest had higher levels of activity among three regions associated with depression and problem solving, had better symptom relief with behavioral talk therapy. And those with a third subtype, who had lower levels of resting activity in the brain circuit that controls attention, were less likely to see improvement in their symptoms with talk therapy than those with other biotypes.
Exploring treatment efficacy based on brain activity
Biotypes and their response to behavioral therapy make sense based on what they know about these brain regions, said Jun Ma, MD, PhD, the Beth and George Vitoux Professor of Medicine at the University of Illinois Chicago and one of the study’s authors. The type of therapy used in their trial teaches patients skills to better deal with everyday problems, so high levels of activity in these brain regions may allow patients with that biotype to pick up new skills more easily. . As for those with lower activity in the region associated with attention and engagement, Ma said it’s possible that pharmaceutical treatment that addresses that lower activity first could help those patients benefit more from talk therapy. .
“To our knowledge, this is the first time we’ve been able to demonstrate that depression can be explained by different disruptions in brain function,” Williams said. “Basically, it’s a demonstration of a personalized medicine approach to mental health based on objective measures of brain function.”
Improving the prediction of antidepressant treatment
In another recently published study, Williams and her team showed that using fMRI brain imaging improves their ability to identify individuals who may respond to antidepressant treatment. In that study, scientists focused on a subtype they call the cognitive biotype of depression, which affects more than a quarter of those with depression and is less likely to respond to standard antidepressants. By identifying those with the cognitive biotype using fMRI, the researchers accurately predicted the likelihood of remission in 63% of patients, compared to 36% ACCURATELY without using brain imaging. This improved accuracy means providers can be more likely to get the right treatment the first time. Scientists are now studying new treatments for this biotype in hopes of finding more options for those who don’t respond to standard antidepressants.
Further explorations of depression
Different biotypes are also associated with differences in symptoms and task performance among test participants. Those with overactive cognitive brain regions, for example, had higher levels of anhedonia (inability to feel pleasure) than those with other biotypes; they also performed worse on executive function tasks. Those with the subtype who responded best to talk therapy also made errors on executive function tasks but performed well on cognitive tasks.
One of the six biotypes detected in the study showed no obvious differences in brain activity in the imaged regions from the activity of non-depressed people. Williams believes they likely haven’t explored the full range of brain biology underlying the disorder – their study focused on regions known to be involved in depression and anxiety, but there may be other types. dysfunction in this biotype that their images did not capture. .
Williams and her team are expanding the imaging study to include more participants. She also wants to test more types of treatments across the six biotypes, including drugs not traditionally used for depression.
Her colleague Laura Hack, MD, PhD, an 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-follow standards for the method so that other practicing psychiatrists can start 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 that treatment as quickly as possible,” Ma said. “Having information about their brain function, in particular the validated signatures we evaluated in this study, would help inform more accurate treatment and prescriptions for individuals.”
Reference: “Personalized Brain Circuitry Results Identify Clinically Distinct Biotypes in Depression and Anxiety” by Leonardo Tozzi, Xue Zhang, Adam Pines, Alisa M. Olmsted, Emily S. Zhai, Esther T. Anene, Megan Chesnut, Bailey Holt -Gosselin, Sarah Chang, Patrick C. Stetz, Carolina A. Ramirez, Laura M. Hack, Mayuresh S. Korgaonkar, Max Wintermark, Ian H. Gotlib, Jun Ma, and Leanne M. Williams, 17 Jun 2024, Nature Medicine.
DOI: 10.1038/s41591-024-03057-9
Researchers from Columbia University; Yale University School of Medicine; University of California, Los Angeles; UC San Francisco; THE University of Sydney; University of Texas MD Anderson; and the University of Illinois at Chicago also contributed to the study.
The data in the study were funded by National Institutes of Health (grant numbers R01MH101496, UH2HL132368, U01MH109985 and U01MH136062) and by Brain Resource Ltd.
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