Researchers at Stanford University School of Medicine have used brain imaging and machine learning to identify six biological subtypes of depression, paving the way for precision psychiatry. The results show that certain antidepressants and behavioral therapies are more effective against certain subtypes, offering hope for more targeted and effective treatment strategies. Credit: SciTechDaily.com
A study from the Stanford University School of Medicine found that brain imaging and Machine LearningThese subtypes exhibit unique brain activity patterns that may help predict which patients will benefit from specific antidepressants or behavioral therapies. This approach aims to individualize and improve the effectiveness of depression treatments.
In the not-too-distant future, a simple brain scan during a screening evaluation for depression may be able to identify the best treatment.
Brain imaging combined with an AI technique called machine learning can help identify subtypes of depression and anxiety, according to a new study led by researchers at the Stanford University School of Medicine and published today (June 17) in the journal Neuroscience. Nature Medicineclassifies depression into six biological subtypes, or “biotypes,” and identifies treatments that are more or less effective for three of the subtypes.
Advances in precision psychiatry
“We desperately need better ways to match patients with treatments,” said lead study author Dr. Leanne Williams, the Vincent V. C. Wu Professor of Psychiatry and Behavioral Sciences and director of the Center for Precision Mental Health and Wellness at the Stanford University School of Medicine. Dr. Williams, who lost her partner to depression in 2015, has focused her research on pioneering the field of precision psychiatry.
Approximately 30% of people with depression Treatment-resistant depressionThis means that people have tried multiple types of medications and therapies without success, and up to two-thirds of people with depression never fully recover to healthy levels of depression, even with treatment.
One reason is that there’s no good way to know which antidepressants or treatments will work for a particular patient: Medications are prescribed on a trial-and-error basis, so it can take months or years to land on one that works (or maybe not). And if you go a long time without seeing relief after multiple attempts at treatment, your depression symptoms can get worse.
“The goal of our research is to think about how we can get it right the first time,” Williams said. “It’s really frustrating in the depression field that there aren’t any better alternatives to this one-size-fits-all approach.”
Biotype predicts treatment response
To better understand the biology underlying depression and anxiety, Williams and his colleagues used an imaging technique called functional MRI to assess 801 participants who had been diagnosed with depression or anxiety. Functional MRIto measure brain activity. They scanned the volunteers’ brains at rest and while they engaged in a variety of tasks designed to test cognitive and emotional functioning. The scientists zeroed in on areas of the brain already known to play a role in depression, and the connections between them.
The researchers used a machine learning technique called cluster analysis to group the patients’ brain images and 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 interview therapy. Patients with one subtype, characterized by overactivity in cognitive brain regions, responded best to the antidepressant venlafaxine (commonly known as Effexor) compared with patients with the other biotypes. Patients with another subtype, who had higher resting levels of activity in three brain regions related to depression and problem solving, saw better symptom relief with behavioral interview therapy. And patients with a third subtype, who had lower resting levels of activity in the brain circuits that control attention, saw less improvement from interview therapy than patients with the other biotypes.
Exploring therapeutic effects based on brain activity
The biotype and response to behavioral therapy make sense based on what we know about these regions of the brain, said Jun Ma, MD, the Beth and George Vittou Professor of Medicine at the University of Illinois at Chicago and one of the study’s authors. The type of therapy used in their trial teaches patients skills to better cope with everyday problems, and higher levels of activity in these brain regions may allow patients with that biotype to adopt new skills more easily. For people with lower activity in areas related to attention and engagement, medications to address that reduced activity could help them get more out of talk therapy, Ma said.
“To our knowledge, this is the first time we’ve been able to demonstrate that depression can be explained by multiple disturbances in brain function,” Williams said. “In essence, this is a demonstration of a personalized medicine approach to mental health based on objective measurements of brain function.”
Improving antidepressant treatment predictions
another Recently published studiesWilliams and her team found that using fMRI brain imaging improves the ability to identify individuals who are likely to respond to antidepressant treatment. In their study, the scientists focused on a subtype of depression called the cognitive biotype, which affects more than a quarter of depressed patients and is less likely to respond to standard antidepressants. By using fMRI to identify patients with the cognitive biotype, the researchers accurately predicted the likelihood of remission in 63% of patients, compared with 36%. Accuracy This biotype can now be diagnosed without the use of brain imaging, and increased accuracy means healthcare professionals are more likely to provide the right treatment the first time. Scientists are currently researching new treatments for this biotype, in the hope of finding more options for patients for whom standard antidepressants do not work.
Further exploration of depression
The different biotypes also correlated with differences in the study participants’ symptoms and performance on tasks. For example, people with overactivity in cognitive areas of the brain had higher levels of anhedonia (the inability to feel pleasure) and performed worse on executive function tasks than people with other biotypes. People in the subtype who responded best to talk therapy also made mistakes on executive function tasks but performed better on cognitive tasks.
In one of the six biotypes found in the study, the regions imaged showed no noticeable difference in brain activity from those in people without depression. Williams believes they haven’t explored the full range of brain biology underlying the disorder. While the study focused on areas known to be involved in depression and anxiety, it’s possible that this biotype has other kinds of dysfunction that imaging didn’t capture.
Williams and her team are expanding their imaging study to include more participants, and they also hope to test more types of treatments across all six biotypes, including drugs that haven’t previously been used for depression.
Her colleague, Laura Hack, MD, PhD, assistant professor of psychiatry and behavioral sciences, is beginning to use the imaging technique in her clinical practice at Stanford University School of Medicine through experimental protocols. The team also hopes to establish an easy-to-understand standard for the technique so that other psychiatrists can put it into practice.
“To truly move the field forward 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,” Marr said. “Having information about a patient’s brain function, particularly the validated signature that we assessed in this study, will help us more precisely tailor and prescribe treatments to individuals.”
Reference: “Personalized Brain Circuit Scores Identify Clinically Distinct Biotypes of Depression and Anxiety” Leonardo Tozzi, Xue Zhang, Adam Pines, Alisa M. Olmstead, Emily S. Zai, Esther T. Annen, Megan Chestnut, Bailey Holt-Gosselin, Sarah Chang, Patrick C. Stetz, Carolina A. Ramirez, Laura M. Hack, Mayuresh S. Korgaonkar, Max Wintermark, Ian H. Gotlib, Jun Ma, Leanne M. Williams, June 17, 2024, Nature Medicine.
Publication date: 10.1038/s41591-024-03057-9
The researchers Columbia University; Yale University School of Medicine, University of California, Los Angeles, University of California, San Francisco, University of SydneyThe University of Texas MD Anderson and The University of Illinois at Chicago also contributed to the study.
The dataset for this study is National Institutes of Health (Grant Numbers R01MH101496, UH2HL132368, U01MH109985, U01MH136062) and Brain Resource Ltd.
