WASHINGTON (dpa-AFX) - In a groundbreaking study conducted by Stanford Medicine researchers, it was discovered that a combination of brain imaging and machine learning techniques has the potential to categorize different subtypes of depression and anxiety.
Published in Nature Medicine, the study divides depression into six distinct biological subtypes, each characterized by unique patterns of brain activity.
According to Leanne Williams, the senior author of the study and a professor of psychiatry and behavioral sciences, as well as the director of Stanford Medicine's Center for Precision Mental Health and Wellness, this marks the first demonstration of different disruptions to brain functioning in explaining depression.
The study involved the evaluation of 801 participants diagnosed with depression or anxiety, utilizing functional MRI technology to monitor brain activity during rest and cognitive tasks.
Through cluster analysis of the participants' brain images, the research team identified six distinct brain activity patterns. Importantly, the study found that patients with different depression subtypes responded differently to specific treatments, such as antidepressants or behavioral therapy.
For example, individuals with the first subtype, characterized by heightened activity in cognitive brain regions, exhibited the most favorable response to the antidepressant venlafaxine (Effexor) compared to other subtypes.
Conversely, those with the second subtype, showing increased activity in brain regions associated with depression and problem-solving during rest, responded better to behavioral therapy for symptom relief. Patients with the third subtype, with decreased brain activity in the attention control circuit during rest, were less likely to experience symptom improvement with talk therapy compared to other subtypes.
Moving forward, the research team aims to expand the imaging study to include a larger participant pool and explore a wider array of treatments across the identified subtypes to enhance personalized depression treatment. This promising research offers hope for the development of more targeted and effective treatment methods for depression, potentially revolutionizing the approach to mental health care.
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