An integrative transcranial magnetic stimulation mapping technique using non-linear curve fitting.

Area of Science:

• Neuroscience
• Biophysics
• Medical Imaging

Background:

• Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation technique.
• Analyzing TMS mapping data, particularly one-dimensional studies, requires robust mathematical methods.
• Current methods may not fully capture the complex spatial distribution of TMS responses.

Purpose of the Study:

• To develop a simple and accurate method for analyzing one-dimensional TMS mapping studies in humans.
• To mathematically characterize TMS curves using non-linear curve fitting.
• To identify the optimal mathematical function for describing TMS mapping data.

Main Methods:

• Recorded motor evoked potentials (MEP) from the abductor pollicis brevis (APB) muscle.
• Stimulated nine scalp positions along a line through the APB hot spot and vertex.
• Applied non-linear curve fitting (Levenberg-Marquardt algorithm) to averaged MEP amplitude data.
• Evaluated symmetrical and asymmetrical peak functions for best fit.

Main Results:

• The symmetric complementary error function (erfc) provided the best fit to the experimental TMS data across all subjects.
• The erfc function, with three parameters (amplitude, position, width), accurately described the TMS curves.
• Erfc parameters showed high correlation with the hot spot amplitude and the center of gravity of the TMS curve.

Conclusions:

• Non-linear curve fitting is an accurate method for the mathematical characterization of one-dimensional TMS curves.
• The erfc function offers a robust model for analyzing TMS mapping data.
• This method provides simultaneous information on amplitude, position, and width of TMS responses.