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

      Wilson, Marcus T.; Moezzi, Bahar; Rogasch, Nigel C.
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      mep_manuscript_12a.pdf
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      DOI
       10.1016/j.clinph.2020.10.032
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      Wilson, M. T., Moezzi, B., & Rogasch, N. C. (2021). Modeling motor-evoked potentials from neural field simulations of transcranial magnetic stimulation. Clinical Neurophysiology, 132(2), 412–428. https://doi.org/10.1016/j.clinph.2020.10.032
      Permanent Research Commons link: https://hdl.handle.net/10289/14180
      Abstract
      Objective

      To develop a population-based biophysical model of motor-evoked potentials (MEPs) following transcranial magnetic stimulation (TMS).

      Methods

      We combined an existing MEP model with population-based cortical modeling. Layer 2/3 excitatory and inhibitory neural populations, modeled with neural-field theory, 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.

      Results

      A MEP waveform was generated comparable to those observed experimentally. The model captured TMS phenomena including a sigmoidal input–output curve, common paired pulse effects (short interval intracortical inhibition, intracortical facilitation, long interval intracortical inhibition) 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.

      Conclusions

      The model reproduces effects seen in common TMS paradigms.

      Significance

      The model allows population-based modeling of changes in cortical dynamics due to TMS protocols to be assessed in terms of changes in MEPs, thus allowing a clear comparison between population-based modeling predictions and typical experimental outcome measures.
      Date
      2021
      Type
      Journal Article
      Publisher
      Elsevier
      Rights
      © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
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      • Science and Engineering Papers [3117]
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