Modeling motor-evoked potentials from neural field simulations of transcranial magnetic stimulation

Abstract IntroductionUnderstanding how transcranial magnetic stimulation (TMS) interacts with cortical circuits is enhanced through biophysical modeling. Existing population-based models of TMS-evoked activity and plasticity evaluate changes in cortical synaptic weights. However, it remains unclear how these changes impact motor-evoked potentials (MEPs), an experimental measure inferring the cortical response to TMS. ObjectivesTo develop a population-based biophysical model of MEPs following TMS. MethodsWe combined an existing MEP model with population-based cortical modeling. Layer 2/3 excitatory and inhibitory neurons are stimulated with TMS and feed layer 5 corticospinal neurons, which also couple directly but weakly to the TMS pulse. The layer 5 output controls mean motoneuron responses, which generate a series of single motor-unit action potentials that are summed to estimate a MEP. ResultsA MEP waveform was generated comparable to those observed experimentally. The model captured TMS phenomena including a sigmoidal input-output curve, common paired pulse effects (SICI, ICF, LICI) including responses to pharmacological interventions, and a cortical silent period. Changes in MEP amplitude following theta burst paradigms were observed including variability in outcome direction. ConclusionsBetter interpretation of population-based TMS modeling approaches is achieved by interpreting output via MEPs, providing a quantitative link between theoretical models and experimental outcomes. HighlightsA model of motor-evoked potential formation gives a realistic electromyogram in response to TMS.The model reproduces effects of SICI, ICF and LICI.A link between existing neural field modeling and realistic outcome measures of TMS is provided.