Statistical method accounts for microscopic electric field distortions around neurons when simulating activation...

Area of Science:

• Neuroscience
• Computational Biology
• Biophysics

Background:

• Computational models of neuromodulation show discrepancies with experimental activation thresholds.
• Transcranial Magnetic Stimulation (TMS) of the primary motor cortex generates motor evoked potentials (MEPs).
• Macroscopic electric fields predicted by models are lower than simulated neuronal thresholds.

Purpose of the Study:

• To investigate the role of brain microstructure in electrical field warping.
• To reconcile the mismatch between simulated and experimental neuromodulation thresholds.
• To develop a statistically derived scaling factor for improved computational models.

Main Methods:

• Combined detailed neural threshold simulations with microscopic electric field calculations.
• Utilized a novel statistical approach to derive a scaling factor.
• Incorporated brain-region specific microstructure metrics.

Main Results:

• A single statistical scaling factor accurately predicts neuronal thresholds when applied to macroscopic electric fields.
• The statistical methods align with experimental TMS thresholds for cortical samples.
• Microstructure significantly influences electric field distribution and neuronal activation.

Conclusions:

• The developed statistical approach bridges the gap between simulated and experimental neuromodulation thresholds.
• This method offers a computationally tractable solution for neuromodulation models.
• The approach is broadly applicable to various neuromodulation modeling scenarios.