Scientists spot 6 distinct biological types of depression
For the first time, researchers reported today that they have categorized depression into six biological subtypes, or “biotypes,” and pinpointed treatments that are more or less effective for three of these subtypes.
A study from Stanford Medicine, suggests that brain imaging coupled with machine learning can help identify different subtypes of depression and anxiety. By employing a machine learning technique called cluster analysis to analyze brain images of patients, the researchers discovered six unique brain activity patterns.
“Better methods for matching patients with treatments are desperately needed,” stated Leanne Williams, director of Stanford Medicine’s Center for Precision Mental Health and Wellness.
The research indicated that patients with a specific subtype, characterized by overactivity in cognitive brain regions, showed the best response to the antidepressant venlafaxine (commonly known as Effexor) compared to other subtypes. Another subtype, with increased activity in three brain regions associated with depression and problem-solving, responded better to behavioral talk therapy. However, those in a third subtype, with lower activity in the brain circuit that controls attention, did not see significant symptom improvement from talk therapy compared to other biotypes, according to the study findings.
“This is the first time we have been able to show that depression may be due to different disruptions in brain function,” Williams explained.
In a previous study, Williams and her team demonstrated that using fMRI brain imaging enhances their ability to identify individuals who are likely to benefit from antidepressant treatment.
The team is now expanding the imaging study to include more participants. “Our aim is to improve the accuracy of treatment choices from the outset,” Williams added. “It’s very frustrating to work in the field of depression without a better solution than the current one-size-fits-all approach.”
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