Researchers at Stanford Medicine have identified six different biological subtypes, or “biotypes,” of depression and anxiety using new brain imaging and machine-learning techniques. Senior author Leeanne Williams, director of the Stanford Center for Precision Mental Health and Wellness at the Stanford University School of Medicine, said better methods for matching patients with treatments are “desperately needed,” according to a news release. Since losing her partner to depression in 2015, Ms.
Williams has focused her work on the field of precision psychiatry. The study authors evaluated brain images of 801 people diagnosed with depression or anxiety and identified six patterns of brain activity. They found that these brain activity clusters were associated with different responses to medication and therapy and were triggered by different stimuli.
“The goal of our work is figuring out how we can get it right the first time,” Ms. Williams said in the press release. Currently, around 30 percent of people with depression do not see improvements with pharmaceutical drugs, and for two-thirds of them, medications and therapy fail to fully return symptoms to normal levels.
Part of the reason for this, according to Ms. William, is because medication therapy is typically prescribed through a trial-and-error method, which could take months or years to find the right drug. The research team also used machine learning to identify and group patterns into clusters.
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