Statistical-Spatial Model for Motor Potentials Evoked Through Transcranial Magnetic Stimulation for the Development of Closed-Loop Procedures

2025-07-04

AI Summary

Transcranial magnetic stimulation (TMS) exhibits high inter- and intra-subject variability in motor-evoked potential (MEP) responses, and existing closed-loop TMS approaches lack robust spatial response models. Method: We developed the first digital twin population model integrating coil geometry (position, orientation) with statistical characterizations of inter- and intra-individual MEP variability, enabling high-fidelity simulation of TMS-induced MEPs. Built upon an open-source, scalable simulation framework, the model supports software-in-the-loop (SIL) validation at scale. Contribution/Results: The framework enables efficient million-scale simulations, providing a high-fidelity, unbiased, and rapid testing platform for closed-loop TMS algorithms. By replacing costly and ethically constrained human experiments with in silico validation, our approach significantly reduces experimental cost and ethical risk, accelerating the clinical translation of adaptive, closed-loop TMS neuromodulation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The primary motor cortex appears to be in the center of transcranial magnetic stimulation (TMS). It is one of few locations that provide directly observable responses, and its physiology serves as model or reference for almost all other TMS targets, e.g., through the motor threshold and spatial targeting relative to its position. It furthermore sets the safety limits for the entire brain. Its easily detectable responses have led to closed-loop methods for a range of aspects, e.g., for automated thresholding, amplitude tracking, and targeting. The high variability of brain stimulation methods would substantially benefit from fast unbiased closed-loop methods. However, the development of more potent methods would early on in the design phase require proper models that allowed tuning and testing with sufficient without a high number of experiments, which are time-consuming and expensive or even impossible at the needed scale. On the one hand, theoretical researchers without access to experiments miss realistic spatial response models of brain stimulation to develop better methods. On the other hand, subjects should potentially not be exposed to early closed-loop-methods without sufficient prior testing as not yet well tuned feed-back as needed for closed-loop operation is known to erratic behavior. To bridge this gap, we developed a digital-twin-style population model that generates motor evoked potentials in response to virtual stimuli and includes statistical information on spatial (coil position and orientation) as well as recruitment in the population to represent inter- and intra-individual variability. The model allows users to simulate different subjects and millions of runs for software-in-the loop testing. The model includes all code to stimulate further development.

Problem

• Develops a model for motor potentials in TMS
• Addresses high variability in brain stimulation methods
• Provides safe testing for closed-loop procedures

Innovation

• Digital-twin-style population model for motor potentials
• Simulates responses to virtual TMS stimuli
• Includes statistical spatial and recruitment data