A study identifies six distinct types of depression: towards personalized treatment?

Recent advances in understanding depressive disorders pave the way for personalized mental health medicine. A study led by researchers from Stanford Medicine has identified six biological subtypes of depression using brain imaging and machine learning. This discovery holds the promise of revolutionizing depression treatment by tailoring therapies to the specific brain characteristics of each patient.


Nature Medicine published this study on June 17, which distinguishes six biotypes of depression by analyzing the brain activity of participants using functional MRI (fMRI). This method allows the measurement of brain activity both at rest and during cognitive and emotional tasks. Researchers were thus able to determine which treatments are most effective for three of these biotypes.

Leanne Williams, a professor of psychiatry and behavioral sciences at Stanford Medicine, highlights the significance of this breakthrough. Approximately 30% of people suffering from depression have treatment-resistant depression, meaning that several medications or therapies have not improved their symptoms. For up to two-thirds of patients, treatments fail to completely restore their mental health. Currently, antidepressants are prescribed by trial and error, a process that can take months or even years.

To better understand the underlying biology of depression and anxiety, Leanne Williams and her team studied 801 participants diagnosed with these disorders. Using a machine learning approach, they identified six distinct patterns of brain activity. For example, patients with hyperactivity in cognitive regions responded better to the antidepressant venlafaxine, while those with high activity in regions associated with depression and problem-solving showed better results with behavioral therapy.

Jun Ma, a professor at the University of Illinois at Chicago and co-author of the study, explains that this correlation between biotypes and therapeutic response is consistent with current knowledge about these brain regions. He suggests that patients with low activity in the attention circuit could benefit from medication before engaging in behavioral therapy.

This study is the first to demonstrate that depression can be explained by different disruptions in brain function. Leanne Williams and her team continue their research to explore new treatments suited to these biotypes, including non-traditional medications for depression.

Laura Hack, an assistant professor at Stanford Medicine, already uses this imaging technique in her clinical practice. The goal is to set standards so that other psychiatrists can adopt this method and improve the precision of treatments.

To advance precision psychiatry, it is essential to quickly identify the most effective treatments for each patient based on objective measures of brain function. This research promises to significantly improve treatment success rates and offer new hope to those suffering from depression.