Efficient high-resolution TMS mapping of the human motor cortex by nonlinear regression

Area of Science:

• Neuroscience
• Computational Neuroscience
• Brain Imaging

Background:

• Transcranial Magnetic Stimulation (TMS) is crucial for studying brain function but faces challenges in precisely identifying stimulated neuronal populations due to complex electric field distributions.
• Accurate localization of stimulated areas is essential for understanding causal structure-function relationships in the human brain.

Purpose of the Study:

• To develop a rapid, feasible method for individual-subject cortical localization of TMS-induced effects.
• To quantify the relationship between the induced electric field and behavioral outcomes to identify the cortical origin of modulated effects.

Main Methods:

• Recorded motor evoked potentials (MEPs) from three finger muscles during random TMS coil positioning around the primary motor area.
• Combined experimental MEP data with numerically modeled electric fields.
• Used nonlinear regression to link induced electric fields with elicited MEPs for cortical origin identification.

Main Results:

• Achieved high spatial resolution in distinguishing cortical muscle representations, primarily locating them on the precentral gyrus.
• Determined that approximately 180 random stimulations are needed for stable and reliable results.
• Demonstrated the method's exponential convergence with an increasing number of stimulations.

Conclusions:

• The developed method enables precise cortical localization of TMS effects at the individual level.
• This approach establishes a functional link between TMS-modulated effects and the induced electric field, enhancing TMS potential.
• The protocol is easy to implement, fast, robust, and suitable for practical and clinical applications.