Global Deep Learning to Predict Depression Treatment Response
Official title: Global Deep Learning Initiative to Understand Outcomes in Major Depression
Funded at University of Southern California · NIH-11415542 · Data updated 2026-11-12
Researchers are using advanced AI on brain scans to predict which people with major depressive disorder will respond to rTMS treatment.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Southern California NIH-funded |
| Lab location | 1 site (Los Angeles, UNITED STATES) |
| Project start | June 1, 2023 |
| Project end | March 31, 2028 |
| Funding year | 2026 |
| Award (this year) | $491,367 |
| NIH project number | 5R01MH131806-04 |
| Project ID | NIH-11415542 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults with major depressive disorder being considered for rTMS, including those with treatment-resistant depression.
✕Less likely to help
Patients not treated with rTMS or those without detectable brain biomarkers linked to response may see limited benefit.
What this research studies
This project will combine international experts in depression, brain imaging, and AI to build predictive models that link brain features to rTMS outcomes. They will use multimodal brain images (MRI, diffusion, and resting-state fMRI) and novel deep learning methods to find patterns associated with better responses. The effort includes privacy-protecting techniques like transfer learning to leverage existing datasets while keeping personal data secure. Results could guide personalized treatment choices and help clinicians decide who should receive rTMS.
Why it matters
Potential benefit: If successful, patients could receive more effective, personalized rTMS treatments with higher chances of improvement.
How similar studies have gone: Early studies show promise for imaging-based predictors, but this approach is novel in its scale and integration of multimodal data.
Where this research is happening
-
University of Southern California
Los Angeles, United States Active
Researchers
- Principal investigator: Thompson, Paul M — University of Southern California
- Study coordinator: Thompson, Paul M
Related research
Other NIH-funded projects on the same topics.
-
Global Deep Learning effort to predict depression treatment response
A patient-focused project uses AI on brain scans to predict who with major depression will respond to rTMS treatment.
-
Trans-Nordic look at extreme major depression genetics
A large genetics study is exploring what makes extreme forms of major depression unique, by studying many thousands of people and their DNA.
-
Genetic factors behind alcohol and opioid use disorders and depression in veterans
A study using genetic data from veterans to understand how genes contribute to co-occurring alcohol use, prescription opioid use, and depression.
-
Understanding immune dysfunction and social stress in veterans with depression
A study in U.S. veterans with major depressive disorder looks at how stress-related immune changes may be linked to depression and heart health.
-
Risk pathways to major depression using big Danish and Swedish data
A large study using health and genetic data from Denmark and Sweden will help understand what genetic and environmental factors contribute to major depression across how people grow up.
-
Understanding genetic and environmental pathways to major depression in Nordic health records
A large study will look at how genes and life experiences combine over time to influence major depression, using Danish and Swedish population data.
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.