Adaptive Recruitment Curve Analysis Using Bayesian Modeling

Conditions

• Modeling of Recruitment Curves

Eligibility Criteria

Sex

ALL

Age

18 Years - 90 Years

Healthy Volunteers

Accepted

Interventions

• Algorithm: Uniform Sampling — OTHER
  Standard uniform distribution sampling used as a baseline comparison.
• Algorithm: hbMEP-adaptive algorithm (version 1) — OTHER
  An active sampling algorithm for recruitment curve estimation.
• Algorithm: hbMEP-adaptive algorithm (version 2) — OTHER
  An alternative active sampling algorithm for recruitment curve estimation.
• ML-PEST — OTHER
  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.

Study Details

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.

Key Dates

Start date

May 11, 2026

Status verified

May 2026

Primary completion

Mar 31, 2027

Completion

Mar 31, 2027

Study Design

Enrollment

10 participants (estimated)

Allocation

NA

Intervention model

SINGLE_GROUP

Primary purpose

BASIC_SCIENCE

Arms

• 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.

Primary Outcome Measure

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

Central Contacts

• James R McIntosh, PhD
  +19294352335
  [email protected]

Locations (1)

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