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Adaptive Recruitment Curve Analysis Using Bayesian Modeling

Enhancing Speed and Accuracy of Motor Evoked Potential Recruitment Curve Analysis Using Hierarchical Bayesian Modeling

Who can join

Ages 18 to 90 · All sexes · Healthy volunteers welcome

Inclusion Criteria:

* Healthy adult volunteers aged 18 years and older.
* Able to understand study procedures and provide written informed consent.

Exclusion Criteria:

* 1\. History of adverse reaction to Transcranial Magnetic Stimulation (TMS) or non-invasive neurostimulation.
* 2\. History of seizures, epilepsy, or family history of epilepsy.
* 3\. History of stroke, brain injury, or illness causing brain injury.
* 4\. History of head injury or neurosurgery.
* 5\. History of neurological diseases, or central nervous system lesions.
* 6\. Presence of metallic implants or foreign bodies in the head (outside of dental work/fillings).
* 7\. Presence of implanted electronic or medical devices (e.g., cardiac pacemakers, medical pumps, implanted stimulators).
* 8\. Current pregnancy or possibility of pregnancy.
* 9\. Currently taking medications that alter cortical excitability or lower seizure threshold.

About the study

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.

What is being tested

• Algorithm: Uniform Sampling (other)
• Algorithm: hbMEP-adaptive algorithm (version 1) (other)
• Algorithm: hbMEP-adaptive algorithm (version 2) (other)
• ML-PEST (other)
• MagPro X100 Transcranial Magnetic Stimulation (device)
• Digitimer DS8R Transcutaneous Electrical stimulation (device)

Sponsor: Columbia University · Participants: 14 · Started: Sep 1, 2026

Contact the study team

• James R McIntosh, PhD · Phone: +19294352335

Official record on ClinicalTrials.gov — NCT07561372

Locations in the U.S.

From ClinicalTrials.gov, data retrieved Sep 30, 2026. Each study sets its own eligibility; the study team decides who can join.