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Набор скоро начнётся NCT07561372

Adaptive Recruitment Curve Analysis Using Bayesian Modeling

Без фазы С лечением Modeling of Recruitment Curves

Ориентир для пациента и семьи

Простыми словами

Автоматическая сводка по структурированным данным реестра. Она помогает сориентироваться, но не заменяет официальный протокол или оценку врача.

Что изучают
В протоколе указаны: Algorithm: Uniform Sampling, Algorithm: hbMEP-adaptive algorithm (version 1), Algorithm: hbMEP-adaptive algorithm (version 2), ML-PEST.
Кому может быть актуально
Состояния в реестре: Modeling of Recruitment Curves. Базовые параметры: 18 лет — 90 лет · Все.
Что важно проверить
Возраст, диагноз и пол — только базовые ориентиры. Предыдущее лечение, анализы и другие обязательные условия указаны ниже в критериях участия.
Где проводится
США
Следующий шаг
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Официальное название

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

Обзор

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.

Подробное описание

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.

Вмешательства

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

Первичные конечные точки

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

Критерии участия

Критерии включения

\- Healthy adults

Критерии исключения

  • 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

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Распределение
Не применимо
Модель
Одна группа
Маскирование
Открытое
Основная цель
Фундаментальное исследование

Центры проведения

США · 1 центр
  • Columbia University Irving Medical Center — New York

Публикации

  • 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

Идентификаторы

NCT: NCT07561372 · AAAV6853 · 1R03NS141040-01A1

Первоисточники (государственные реестры)

Открыть это исследование на ClinicalTrials.gov ↗