A probabilistic transcranial magnetic stimulation localization method.

Area of Science:

• Neuroscience
• Biophysics
• Computational Biology

Background:

• Transcranial magnetic stimulation (TMS) is a noninvasive technique for brain stimulation.
• The precise parameters of neuronal activation by TMS remain largely undefined.
• Understanding these parameters is crucial for optimizing TMS applications.

Purpose of the Study:

• To propose a novel neuronal activation model for TMS.
• To develop a method for inferring TMS activation parameters from motor evoked potential (MEP) signals.
• To characterize the location, preferred activation direction, and threshold electric field of neuronal sites.

Main Methods:

• Modeling the relationship between induced electric fields and motor thresholds.
• Employing Bayesian inference with Markov chain Monte Carlo (MCMC) sampling to estimate model parameters.
• Validating the model with synthetic data and applying it to MEP measurements from the first dorsal interosseus muscle.

Main Results:

• Generated probability distributions for TMS activation locations.
• Derived minimal 95% probability volumes for activation, approximately 100 mm³.
• Identified activation sites consistent with the pre-central gyrus and perpendicular to the central sulcus, with ambiguity between grey and white matter.

Conclusions:

• The developed method provides a robust framework for analyzing TMS activation mechanisms.
• It enables precise characterization of neuronal activation sites.
• This work lays the foundation for more accurate and personalized TMS therapies.