Adaptive Recruitment Curve Analysis Using Bayesian Modeling

May 15, 2026 updated by: James McIntosh, Columbia University

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

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

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.

Study Type

Interventional

Enrollment (Estimated)

10

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • United States
    • New York
      • New York, New York, United States, 10032
        • Columbia University Irving Medical Center
        • Contact:
        • Principal Investigator:
          • James R McIntosh, PhD

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Basic Science
  • Allocation: N/A
  • Interventional Model: Single Group Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
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.
Other: Algorithm: Uniform Sampling
Standard uniform distribution sampling used as a baseline comparison.
Other: ML-PEST
Algorithm: Adaptive threshold hunting using the Parameter Estimation by Sequential Testing (PEST) algorithm.
Device: MagPro X100 Transcranial Magnetic Stimulation
The proposed algorithms will deliver stimulation by using this magnetic stimulation methodology.
Device: Digitimer DS8R Transcutaneous Electrical stimulation
The proposed algorithms will deliver stimulation by using this electrical stimulation methodology.
Other: Algorithm: hbMEP-adaptive algorithm (version 1)
An active sampling algorithm for recruitment curve estimation.
Other: Algorithm: hbMEP-adaptive algorithm (version 2)
An alternative active sampling algorithm for recruitment curve estimation.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
Through completion of the study visit, an average of 1 hour.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Columbia University

Collaborators

National Institute of Neurological Disorders and Stroke (NINDS)

Investigators

  • Principal Investigator: James R McIntosh, PhD, Columbia University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

May 11, 2026

Primary Completion (Estimated)

March 31, 2027

Study Completion (Estimated)

March 31, 2027

Study Registration Dates

First Submitted

April 20, 2026

First Submitted That Met QC Criteria

April 24, 2026

First Posted (Actual)

May 1, 2026

Study Record Updates

Last Update Posted (Actual)

May 19, 2026

Last Update Submitted That Met QC Criteria

May 15, 2026

Last Verified

May 1, 2026

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • AAAV6853
  • 1R03NS141040-01A1 (U.S. NIH Grant/Contract)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

IPD Plan Description

De-identified MEP data and analysis code and algorithms.

IPD Sharing Time Frame

Together with publication at the end of this study (04/2027).

IPD Sharing Access Criteria

Open access repository (e.g. Zenodo and Github).

IPD Sharing Supporting Information Type

  • ANALYTIC_CODE

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

Yes

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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