Faster, more accurate reading of muscle nerve responses after brain or nerve stimulation
Enhancing Speed and Accuracy of Motor Evoked Potential Recruitment Curve Analysis Using Hierarchical Bayesian Modeling
This project builds improved computer tools to get clearer, faster readings of muscle nerve responses for people who have nerve or brain injuries or who get neuromodulation.
Quick facts
| Grant type | R03 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Columbia University Health Sciences NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project ID | NIH-11309199 on NIH RePORTER |
What this research studies
This work improves how motor evoked potentials (MEPs)—the small electrical responses in muscles after brain or nerve stimulation—are analyzed to better track nerve function. The team is expanding hbMEP, a Python toolbox, to use hierarchical Bayesian models that borrow strength across muscles and subjects and to run both during experiments and after data are collected. That approach aims to give more precise estimates of key parameters like stimulation thresholds and off-target excitability changes, raising the chance of detecting true effects of treatments such as neuromodulation. Researchers will test and validate the methods using recorded MEP data and simulation studies before wider clinical use.
Who could benefit from this research
Good fit: People undergoing MEP testing—for example those with stroke, spinal cord injury, traumatic brain injury, or receiving neuromodulation—would be the most relevant candidates.
Not a fit: People without motor pathway problems or those who will not undergo MEP testing are unlikely to receive direct benefit from this work.
Why it matters
Potential benefit: If successful, patients could get more reliable measurements of nerve function that help clinicians track recovery and tailor neuromodulation or rehabilitation plans.
How similar studies have performed: Bayesian methods have improved signal estimates in related neurophysiology work, but applying a hierarchical, online/offline recruitment-curve tool specifically to MEPs is relatively new.
Where this research is happening
New York, United States
- Columbia University Health Sciences — New York, United States (Active)
Researchers
- Principal investigator: Mcintosh, James Robert — Columbia University Health Sciences
- Study coordinator: Mcintosh, James Robert
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 below.