Adaptive Recruitment Curve Analysis Using Bayesian Modeling

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

Interventions

OTHER

Algorithm: Uniform Sampling

Standard uniform distribution sampling used as a baseline comparison.

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.

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.

Sponsors & Collaborators

• National Institute of Neurological Disorders and Stroke (NINDS)

  collaborator NIH

• Columbia University

  lead OTHER

Principal Investigators

• James R McIntosh, PhD · Columbia University

Study Design

Allocation

NA

Purpose

BASIC_SCIENCE

Masking

NONE

Model

SINGLE_GROUP

Eligibility

Min Age

18 Years

Max Age

90 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2026-05-11

Primary Completion

2027-03-31

Completion

2027-03-31

FDA Device

Yes

Countries

• United States

Study Locations