Using advanced imaging and AI to enhance deep brain stimulation for better treatment outcomes
Official title: Functional Magnetic Resonance Imaging and Deep Learning to Improve Deep Brain Stimulation Therapy
Funded at Ge Medical Systems Information Technologies, INC · NIH-10930962 · Data updated 2026-11-12
This study is looking to make deep brain stimulation therapy for Parkinson's disease quicker and easier by using advanced brain imaging and smart technology, so patients can find the best treatment settings faster and with less hassle.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Ge Medical Systems Information Technologies, INC NIH-funded |
| Lab location | 1 site (Niskayuna, United States) |
| Project start | September 19, 2023 |
| Project end | November 30, 2028 |
| Funding year | 2025 |
| Award (this year) | $547,547 |
| NIH project number | 5R01NS133433-03 |
| Project ID | NIH-10930962 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients diagnosed with Parkinson's disease who are considering or currently undergoing deep brain stimulation therapy.
✕Less likely to help
Patients with conditions other than Parkinson's disease or those who are not candidates for deep brain stimulation therapy may not benefit from this research.
What this research studies
This research focuses on improving deep brain stimulation (DBS) therapy for conditions like Parkinson's disease by utilizing functional magnetic resonance imaging (fMRI) and deep learning techniques. The goal is to optimize the stimulation parameters more efficiently than current methods, which often rely on trial and error and can take up to a year for patients to find the right settings. By employing advanced imaging and AI, the researchers aim to reduce the time and financial burden associated with DBS therapy, making it more accessible and effective for patients. The study will explore the use of newer DBS electrodes that allow for more precise stimulation of brain tissue, potentially leading to better clinical outcomes.
Why it matters
Potential benefit: If successful, this research could significantly improve the effectiveness and accessibility of deep brain stimulation therapy for patients with Parkinson's disease.
How similar studies have gone: Other research has shown promise in using advanced imaging and AI techniques to optimize medical treatments, suggesting that this approach could lead to significant advancements in DBS therapy.
Where this research is happening
-
Ge Medical Systems Information Technologies, INC
Niskayuna, United States Active
Researchers
- Principal investigator: Ajala, Afis — Ge Medical Systems Information Technologies, INC
- Study coordinator: Ajala, Afis
Related research
Other NIH-funded projects on the same topics.
-
Improving deep brain stimulation with MRI and AI
We use brain scans and artificial intelligence to speed up and personalize deep brain stimulation settings for people getting DBS.
-
MRI and AI to personalize deep brain stimulation for Parkinson's
This project uses brain MRI and artificial intelligence to help personalize deep brain stimulation settings so people with Parkinson's get faster, safer symptom relief.
-
Understanding heart fibrosis with AI, MRI, and genomics
A doctor-scientist is using artificial intelligence, advanced heart MRI, and genetic data to learn how scar tissue forms in the aging heart and how to prevent it.
-
Improving MRI safety for patients with deep brain stimulators
This study is working to make MRI scans safer for people with deep brain stimulation devices, so they can get the imaging they need without worrying about their implants, helping doctors better understand their health and treatment.
-
Integrative histo-genomic analysis to guide cancer risk and targeted therapies with interpretable AI
Researchers are developing an advanced AI-powered approach to map genetic and cellular patterns across breast and cervical cancers to help tailor treatments.
-
Predicting how antimalarial drug resistance spreads with AI
A powerful AI model will be built to forecast how resistance to antimalarial drugs might spread in Africa and beyond, helping clinicians and public health teams stay ahead of outbreaks.
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.