Enhancing Speed and Accuracy of Motor Evoked Potential Recruitment Curve Analysis Using Hierarchical Bayesian Modeling
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 14
- 试验地点
- 1
- 主要终点
- Mean absolute threshold error
研究概览
简要总结
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.
详细描述
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, and the number of samples required to reach predefined tolerances on these parameters.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Basic Science
- 盲法
- None
盲法说明
Participants are functionally masked to the specific interventions, as the stimulation parameters generated by the different algorithms are randomly interleaved pulse-by-pulse.
入排标准
- 年龄范围
- 18 Years 至 90 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Healthy adult volunteers aged 18 years and older.
- •Able to understand study procedures and provide written informed consent.
排除标准
- •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.
研究组 & 干预措施
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.
干预措施: Digitimer DS8R Transcutaneous Electrical stimulation (Device)
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.
干预措施: Algorithm: hbMEP-adaptive algorithm (version 2) (Other)
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.
干预措施: ML-PEST (Other)
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.
干预措施: Algorithm: Uniform Sampling (Other)
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.
干预措施: Algorithm: hbMEP-adaptive algorithm (version 1) (Other)
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.
干预措施: MagPro X100 Transcranial Magnetic Stimulation (Device)
结局指标
主要结局
Mean absolute threshold error
时间窗: 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.
Number of stimuli to reach a pre-defined threshold error
时间窗: Through completion of the study visit, 2-4 hours.
Number of stimuli required for the compared methods to reach a pre-defined error threshold relative to the ground truth, computed from recruitment curves fitted after sampling using aggregated data.
Number of stimuli to reach a pre-defined predictive curve error
时间窗: Through completion of the study visit, 2-4 hours.
Number of stimuli required for the compared methods to reach a pre-defined error threshold relative to the ground truth, computed from recruitment curves fitted after sampling using aggregated data.
次要结局
- Mean absolute error in a given parameter (e.g. threshold, predictive curve, slope) for a given number of stimuli(Through completion of the study visit, 2-4 hours.)
研究者
James McIntosh
Associate Research Scientist
Columbia University