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Not yet recruiting NCT07561372

Adaptive Recruitment Curve Analysis Using Bayesian Modeling

No phase Interventional Modeling of Recruitment Curves

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: Algorithm: Uniform Sampling, Algorithm: hbMEP-adaptive algorithm (version 1), Algorithm: hbMEP-adaptive algorithm (version 2), ML-PEST.
Who it may be relevant to
Registry conditions: Modeling of Recruitment Curves. Basic parameters: 18 years — 90 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
United States
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

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Official title

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

Overview

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.

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.

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.

Primary outcome measures

  • Mean absolute threshold error [Time frame: Through completion of the study visit, an average of 1 hour.]

Eligibility criteria

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

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Allocation
N/A
Model
Single group
Masking
Open label
Primary purpose
Basic science

Study locations

United States · 1 center
  • Columbia University Irving Medical Center — New York

Publications

  • Tyagi V, Murray LM, Asan AS, Mandigo C, Virk MS, Harel NY, Carmel JB, McIntosh JR. Hierarchical Bayesian estimation of motor-evoked potential recruitment curves yields accurate and robust estimates. Brain Stimul. 2025 Nov-Dec;18(6):1855-1870. doi: 10.1016/j.brs.2025.09.008. Epub 2025 Sep 18. PMID 40975380

Identifiers

NCT: NCT07561372 · AAAV6853 · 1R03NS141040-01A1

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗