Recruiting

TMS & SCS

Sponsor:

Columbia University

Code:

NCT07561372

Conditions

Modeling of Recruitment Curves

Eligibility Criteria

Sex: All

Age: 18 - 70+

Healthy Volunteers: Accepted

Interventions

Algorithm: Uniform Sampling

Algorithm: hbMEP-adaptive algorithm (version 1)

Algorithm: hbMEP-adaptive algorithm (version 2)

ML-PEST

MagPro X100 Transcranial Magnetic Stimulation

Study Details

Brief summary:

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.

Conditions

Modeling of Recruitment Curves

Study ID

NCT07561372

Start date

Sep 1, 2026

Status verified date

Aug, 2026

Completion date

Mar 31, 2027

Anticipated

Primary completion date

Mar 31, 2027

Anticipated

Eligibility Criteria

Eligibility Criteria

Sex: All

Age: 18 - 70+

Healthy Volunteers: Accepted

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.

Study Design

Enrollment

14 participants

Anticipated

Intervention Model

Single group

Primary purpose

Basic Science

Interventions and Outcome Measures

Arms

experimental: Test of developed methods

Participants undergo distinct experiments within a single session to compare different neurostimulation sampling algorithms. Each experiment involves recruitment curve sampling with different methods (e.g., Uniform, Expected Information Gain) to evaluate the accuracy and efficiency of motor threshold.

Interventions

Algorithm: Uniform Sampling

Standard uniform distribution sampling used as a baseline comparison.

Algorithm: hbMEP-adaptive algorithm (version 1)

An active sampling algorithm for recruitment curve estimation.

Algorithm: hbMEP-adaptive algorithm (version 2)

An alternative active sampling algorithm for recruitment curve estimation.

ML-PEST

Algorithm: Adaptive threshold hunting using the Parameter Estimation by Sequential Testing (PEST) algorithm.

MagPro X100 Transcranial Magnetic Stimulation

The proposed algorithms will deliver stimulation by using this magnetic stimulation methodology.

Digitimer DS8R Transcutaneous Electrical stimulation

The proposed algorithms will deliver stimulation by using this electrical stimulation methodology.

Primary outcome measure

  • Number of stimuli to reach a pre-defined threshold error [ Time Frame: Through completion of the study visit, 2-4 hours. ]
  • Number of stimuli to reach a pre-defined predictive curve error [ Time Frame: Through completion of the study visit, 2-4 hours. ]

Central Contacts and Locations

Central contacts

James R McIntosh, PhD

+19294352335jrm2263@cumc.columbia.edu

Locations

Columbia University Irving Medical Center

Recruiting

New York, New York, United States, 10032

Contacts

James R McIntosh, PhD

9294352335jrm2263@cumc.columbia.edu

Principal Investigator:

James R McIntosh, PhD

More Information

Sponsor

Columbia University

Last update posted

Aug 14, 2026

Last verified

Aug, 2026

Keywords

  • TMS
  • SCS
  • SCI
  • Hierarchical Bayesian
  • Motor threshold
  • Transcranial Magnetic Stimulation
  • Spinal cord stimulation
  • Evoked Potentials

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-10. This information was provided to ClinicalTrials.gov by Columbia University on 2026-08-14.