Hierarchical Bayesian estimation of motor-evoked potential recruitment curves yields accurate and robust estimates.

ABSTRACT:

Purpose

We aim to develop a robust method to improve the estimation accuracy of motor-evoked potential (MEP) recruitment curves (RCs), including motor threshold, in small-sample settings which typically involve fewer than 40 stimuli.

Methods

We present a hierarchical Bayesian (HB) method to model MEP size as a rectified-logistic function of stimulation intensity. This method is designed to account for small samples, handle outliers without discarding data, quantify estimation uncertainty, and simulate synthetic data that closely matches real observations, useful for optimizing experimental design. We validate its performance on transcranial magnetic stimulation (TMS), epidural spinal cord stimulation (SCS), and synthetic TMS datasets, and provide an open-source library for P

SUBMITTER: Tyagi V 

PROVIDER: S-EPMC12739467 | biostudies-literature | 2025 Nov-Dec

REPOSITORIES: biostudies-literature