Adaptive Recruitment Curve Analysis Using Bayesian Modeling

ConditionModeling of Recruitment Curves

Trial statusNot yet recruiting

Trial phaseNot applicable

Trial typeInterventional

Biological sexAll

Age18-90

SponsorColumbia University

About this trial

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.

Eligibility criteria

This trial accepts healthy volunteers

Qualifiers

None

Disqualifiers

Presence of any neurological disorder

History of seizures

History of autonomic dysfunction

Current use of seizure-threshold lowering medications

Trial design

Design model

Single group

Treatments tested in this trial

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

Treatment groups

10 Participants

are divided into 1 treatment group

Group A: Test of developed methodsExperimental treatment 6 interventions

Trial outcomes

Primary outcomes

Mean absolute threshold error

The threshold error of the methods under comparison, with the ground truth computed from recruitment curves fitted subsequent to sampling using aggregated data.

Time frame

Through completion of the study visit, an average of 1 hour.

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

Columbia University

Lead sponsor

National Institute of Neurological Disorders and Stroke (NINDS)

Collaborator

Source ClinicalTrials.gov

This trial is not recruiting at the moment. You can still explore other options: