Rapid, reliable tests for thinking, cravings, and mood in addiction
Official title: Utility of adaptive design optimization for developing rapid and reliable behavioral paradigms for substance use disorders
Funded at Virginia Commonwealth University · NIH-11386166 · Data updated 2026-11-12
This project uses smart, computer-driven testing to make shorter, more reliable tasks that measure thinking skills, reward responses, and negative emotions in people with substance use problems.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Virginia Commonwealth University NIH-funded |
| Lab location | 1 site (Richmond, United States) |
| Project start | September 1, 2023 |
| Project end | June 30, 2028 |
| Funding year | 2025 |
| Award (this year) | $53,128 |
| NIH project number | 3R01DA058038-03S1 |
| Project ID | NIH-11386166 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults with current or past substance use disorders (including alcohol) who can complete computerized behavioral tasks are the ideal participants.
✕Less likely to help
People without substance use problems or those seeking immediate clinical treatment rather than participating in research are unlikely to receive direct benefit from this work.
What this research studies
You would complete computer-based tasks that target three brain-behavior areas linked to addiction: executive function (thinking and self-control), incentive salience (craving and reward), and negative emotionality (stress and mood). The team will apply a machine-learning method called Bayesian adaptive design optimization to select the most informative questions in real time so each task can be much shorter. The aim is to replace long, burdensome assessment batteries with brief probes that still capture meaningful individual differences. These improved tasks are intended to be more reliable over time and more useful for research and eventual clinical decision-making.
Why it matters
Potential benefit: If successful, patients could get faster, less tiring tests that more accurately identify the brain processes behind their substance use and help guide personalized care and research.
How similar studies have gone: Adaptive Bayesian testing has shown promise in other cognitive and decision-making tasks, but applying it broadly to addiction-related batteries is relatively new.
Where this research is happening
-
Virginia Commonwealth University
Richmond, United States Active
Researchers
- Principal investigator: Vassileva, Jasmin L — Virginia Commonwealth University
- Study coordinator: Vassileva, Jasmin L
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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.