Adaptive Recruitment Curve Analysis Using Bayesian Modeling
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
Conditions
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, and the number of samples required to reach predefined tolerances on these parameters.
Study Type
Enrollment (Estimated)
Phase
- Not Applicable
Contacts and Locations
Study Contact
- Name: James R McIntosh, PhD
- Phone Number: +19294352335
- Email: jrm2263@cumc.columbia.edu
Study Locations
-
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New York
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New York, New York, United States, 10032
- Recruiting
- Columbia University Irving Medical Center
-
Contact:
- James R McIntosh, PhD
- Phone Number: 9294352335
- Email: jrm2263@cumc.columbia.edu
-
Principal Investigator:
- James R McIntosh, PhD
-
-
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Description
Inclusion Criteria:
- Healthy adult volunteers aged 18 years and older.
- Able to understand study procedures and provide written informed consent.
Exclusion Criteria:
- 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.
Study Plan
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. |
Standard uniform distribution sampling used as a baseline comparison. An active sampling algorithm for recruitment curve estimation. An alternative active sampling algorithm for recruitment curve estimation. Algorithm: Adaptive threshold hunting using the Parameter Estimation by Sequential Testing (PEST) algorithm. The proposed algorithms will deliver stimulation by using this magnetic stimulation methodology. The proposed algorithms will deliver stimulation by using this electrical stimulation methodology. |
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Number of stimuli to reach a pre-defined threshold error
Time Frame: 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. |
Through completion of the study visit, 2-4 hours.
|
|
Number of stimuli to reach a pre-defined predictive curve error
Time Frame: 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. |
Through completion of the study visit, 2-4 hours.
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Mean absolute error in a given parameter (e.g. threshold, predictive curve, slope) for a given number of stimuli
Time Frame: Through completion of the study visit, 2-4 hours.
|
The 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, 2-4 hours.
|
Collaborators and Investigators
Sponsor
Investigators
- Principal Investigator: James R McIntosh, PhD, Columbia University
Publications and helpful links
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
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)?
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
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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