Columbia University Irving Medical Center
New York, 10032, United States
Location contact
James R McIntosh, PhD
CONTACT
James R McIntosh, PhD
PRINCIPAL_INVESTIGATOR
Not Yet Recruiting
NCT Number: NCT07561372
Adaptive Recruitment Curve Analysis Using Bayesian Modeling
The purpose of this study is to better understand how electrical or magnetic stimulation affect the nervous system by optimizing the way researchers measure muscle responses. The relationship between stimulation intensity and muscle response is described by "neural recruitment curves," which are critical for monitoring the state of the nervous system during therapies like transcranial magnetic stimulation (TMS) and spinal cord stimulation (SCS).
This study tests a new, real-time computational approach based on our previously developed methods (Hierarchical Bayesian models) to estimate these recruitment curves more efficiently. The primary goal is to use this model to dynamically guide the experiment, automatically selecting the optimal stimulation intensities to test.
The investigators hypothesize that this optimized approach will accurately estimate the entire recruitment curve, or specific targets components of it like the motor threshold, using significantly fewer samples than standard methods. By reducing the number of measurements required, this approach aims to decrease experimental time and minimize participant burden, making future TMS and SCS therapies and experiments more feasible and efficient.
Not Yet Recruiting
Trial opening soon.
Get NotifiedKey information
Conditions
Age range
18 year–90 year
Sex eligibility
All sexes
Study type
Interventional
Phase
Not applicable
Primary location
About this study
Transcranial magnetic stimulation and other types of neurostimulation play a crucial role in advancing the understanding and manipulation of neural activity for both research and therapeutic purposes. The proposed approach to sampling recruitment curves in real-time promises to significantly improve the efficiency and precision of experiments that use electrical or electromagnetic stimulation techniques, reducing the experimental burden for participants as well as experimenters. By enhancing experimental efficiency in multiple experimental settings and techniques, this research directly contributes to accelerating the translation of scientific discoveries into clinical applications. This study will benchmark the relative performance of different methods against each other by testing existing and proposed algorithms using neurostimulation in people, and comparing the resultant estimates in recruitment curve parameters.
Who can participate
Healthy volunteers accepted: Yes
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
- Healthy adults
Exclusion criteria
- Presence of any neurological disorder
- History of seizures
- History of autonomic dysfunction
- Current use of seizure-threshold lowering medications
- Presence of metal implants
- History of prior neurosurgical interventions
Treatment and study plan
Algorithm: Uniform Sampling
OtherStandard uniform distribution sampling used as a baseline comparison.
Algorithm: hbMEP-adaptive algorithm (version 1)
OtherAn active sampling algorithm for recruitment curve estimation.
Algorithm: hbMEP-adaptive algorithm (version 2)
OtherAn alternative active sampling algorithm for recruitment curve estimation.
ML-PEST
OtherAlgorithm: Adaptive threshold hunting using the Parameter Estimation by Sequential Testing (PEST) algorithm.
MagPro X100 Transcranial Magnetic Stimulation
DeviceThe proposed algorithms will deliver stimulation by using this magnetic stimulation methodology.
Digitimer DS8R Transcutaneous Electrical stimulation
DeviceThe proposed algorithms will deliver stimulation by using this electrical stimulation methodology.
Primary outcomes
-
Mean absolute threshold error
Time frame: Through completion of the study visit, an average of 1 hour.
The threshold error of the methods under comparison, with the ground truth computed from recruitment curves fitted subsequent to sampling using aggregated data.
Study contacts
Contact information is provided by the study sponsor or research team.
Sponsors and collaborators
Lead sponsor
Columbia University
Other
Collaborators
- National Institute of Neurological Disorders and Stroke (NINDS)
Registry information
Official study title
Enhancing Speed and Accuracy of Motor Evoked Potential Recruitment Curve Analysis Using Hierarchical Bayesian Modeling
Important dates
- Study start
- 2026
- Primary completion
- 2027
- Study completion
- 2027
- First posted
- May 1, 2026
- Registry last updated
- Jun 18, 2026
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.