A Stanford Medicine study reveals six subtypes of depression, identified through brain imaging and machine learning. These subtypes exhibit unique brain activity patterns, helping predict which patients will benefit from specific antidepressants or behavioral therapies. This approach aims to personalize and improve depression treatment efficacy.
In the not-too-distant future, a quick brain scan during a screening assessment for depression could identify the best treatment.
According to a new study led by researchers at Stanford Medicine, brain imaging combined with a type of AI called machine learning can reveal subtypes of depression and anxiety. The study, to be published today (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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