Brain imaging and machine learning reveal six subtypes of depression
A groundbreaking study led by Stanford Medicine researchers has unveiled six distinct biological subtypes of depression using brain imaging and machine learning techniques. This discovery could change the approach to diagnosing and treating depression, potentially leading to more effective, personalised interventions.
Addressing the treatment-resistant challenge
Depression remains a significant global health concern, with approximately 30% of patients experiencing treatment-resistant depression. Current treatment methods often rely on a trial-and-error approach, which can be time-consuming and potentially exacerbate symptoms. Professor Leanne Williams, the study’s senior author and director of Stanford Medicine’s Center for Precision Mental Health and Wellness, emphasised the urgent need for improvement:
“The goal of our work is figuring out how we can get it right the first time,” Williams said. “It’s very frustrating to be in the field of depression and not have a better alternative to this one-size-fits-all approach.”
Methodology and findings
The research team employed functional MRI (fMRI) to assess brain activity in 801 participants previously diagnosed with depression or anxiety. Scans were conducted both at rest and during tasks designed to test cognitive and emotional functioning. Using cluster analysis, a machine learning approach, the researchers identified six distinct patterns of brain activity in regions known to be associated with depression.
The study also randomly assigned 250 participants to receive one of three common antidepressants or behavioural talk therapy. This allowed the researchers to correlate brain activity patterns with treatment responses, revealing promising insights for personalised treatment strategies.
Biotypes and treatment responses
Three of the six identified biotypes showed particular promise in predicting treatment responses:
- Overactive cognitive regions: Patients with this subtype responded best to the antidepressant venlafaxine (Effexor).
- Higher resting activity in depression-associated regions: This group showed better symptom alleviation with behavioural talk therapy.
- Lower resting activity in attention control circuits: These patients were less likely to benefit from talk therapy compared to other biotypes.
Dr Jun Ma, a study co-author from the University of Illinois Chicago, noted that these findings align with current understanding of brain function in depression: “To our knowledge, this is the first time we’ve been able to demonstrate that depression can be explained by different disruptions to the functioning of the brain,” Williams added. “In essence, it’s a demonstration of a personalised medicine approach for mental health based on objective measures of brain function.”
Implications for clinical practice
The research team is now expanding their imaging study to include more participants and explore a broader range of treatments across all six biotypes. Dr Laura Hack, an assistant professor at Stanford Medicine, has begun implementing the imaging technique in clinical practice through an experimental protocol.
The ultimate goal is to establish standardised protocols that would allow psychiatrists to incorporate brain imaging into their diagnostic and treatment planning processes. This could significantly improve the accuracy of treatment selection and reduce the time patients spend trying ineffective interventions.
Future directions
While the study represents a significant step forward in understanding depression, the researchers acknowledge that there is still more to explore. One of the identified biotypes showed no noticeable differences in brain activity compared to non-depressed individuals, suggesting that there may be additional neural mechanisms underlying depression that were not captured in this study.
The research team is committed to further investigations, including testing novel treatments and expanding the range of brain regions examined. They hope that this work will contribute to the development of more precise and effective treatments for depression, ultimately improving outcomes for patients worldwide.
References:
- Tozzi, L., Zhang, X., Pines, A. et al. Personalized brain circuit scores identify clinically distinct biotypes in depression and anxiety. Nat Med (2024).
https://doi.org/10.1038/s41591-024-03057-9
