Six distinct types of depression identified in Stanford Medicine-led study

https://med.stanford.edu/news/all-news/2024/06/depression-biotypes.html

Six distinct types of depression identified in Stanford Medicine-led study
June 17, 2024 - By Rachel Tompa

In the not-too-distant future, a screening assessment for depression could include a quick brain scan to identify the best treatment. Brain imaging combined with machine learning can reveal subtypes of depression and anxiety, according to a new study led by researchers at Stanford Medicine. The study, published June 17 in the journal Nature Medicine, sorts depression into six biological subtypes, or “biotypes,” and identifies treatments that are more likely or less likely to work for three of these subtypes.
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Around 30% of people with depression have what’s known as treatment-resistant depression, meaning multiple kinds of medication or therapy have failed to improve their symptoms. And for up to two-thirds of people with depression, treatment fails to fully reverse their symptoms to healthy levels.

That’s in part because there’s no good way to know which antidepressant or type of therapy could help a given patient. Medications are prescribed through a trial-and-error method, so it can take months or years to land on a drug that works — if it ever happens. And spending so long trying treatment after treatment, only to experience no relief, can worsen depression symptoms.
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Biotypes predict treatment response
To better understand the biology underlying depression and anxiety, Williams and her colleagues assessed 801 study participants who were previously diagnosed with depression or anxiety using the imaging technology known as functional MRI, or fMRI, to measure brain activity. They scanned the volunteers’ brains at rest and when they were engaged in different tasks designed to test their cognitive and emotional functioning. The scientists narrowed in on regions of the brain, 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.
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