Brain Imaging and Machine Learning Identify Six Depression Subtypes

Researchers at Stanford Medicine have discovered that brain imaging combined with machine learning can reveal six biological subtypes, or “biotypes,” of depression and anxiety. The study, published in the journal Nature Medicine, also identifies treatments that are more likely or less likely to work for three of these subtypes.

Leanne Williams, PhD, the study’s senior author and director of Stanford Medicine’s Center for Precision Mental Health and Wellness, emphasizes the need for better methods to match patients with treatments. Currently, around 30% of people with depression have treatment-resistant depression, and treatment fails to fully reverse symptoms in up to two-thirds of patients.

Identifying Brain Patterns Associated with Depression and Anxiety

The researchers assessed 801 study participants previously diagnosed with depression or anxiety using functional MRI (fMRI) to measure brain activity at rest and during cognitive and emotional tasks. Using a machine learning approach called cluster analysis, they identified six distinct patterns of activity in brain regions known to play a role in depression.

Among the 250 participants randomly assigned to receive one of three antidepressants or behavioral talk therapy, the researchers found that specific biotypes responded better to certain treatments. Patients with overactivity in cognitive brain regions experienced the best response to the antidepressant venlafaxine, while those with higher activity in regions associated with depression and problem-solving had better symptom alleviation with behavioral talk therapy. Patients with lower activity in the brain circuit that controls attention were less likely to see improvement with talk therapy.

Personalized Medicine Approach for Mental Health

The study demonstrates that depression can be explained by different disruptions to brain function, paving the way for a personalized medicine approach in mental health based on objective measures of brain function. In another recently published study, Williams and her team showed that using fMRI brain imaging improves their ability to identify individuals likely to respond to antidepressant treatment, particularly for the cognitive biotype of depression, which affects more than a quarter of those with depression and is less likely to respond to standard antidepressants.

The different biotypes also correlate with differences in symptoms and task performance among the trial participants. The researchers plan to expand the imaging study to include more participants and test more kinds of treatments in all six biotypes. They also aim to establish easy-to-follow standards for the method so that other practicing psychiatrists can begin implementing it.

“To really move the field toward precision psychiatry, we need to identify treatments most likely to be effective for patients and get them on that treatment as soon as possible,” said Jun Ma, MD, PhD, one of the study’s authors. “Having information on their brain function, in particular the validated signatures we evaluated in this study, would help inform more precise treatment and prescriptions for individuals.”


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