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      Calcium dependent plasticity applied to repetitive transcranial magnetic stimulation with a neural field model

      Wilson, Marcus T.; Fung, P.K.; Robinson, P.A.; Shemmell, J.; Reynolds, J.N.J.
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      tms_two_c.pdf
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      DOI
       10.1007/s10827-016-0607-7
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      Wilson, M. T., Fung, P. K., Robinson, P. A., Shemmell, J., & Reynolds, J. N. J. (2016). Calcium dependent plasticity applied to repetitive transcranial magnetic stimulation with a neural field model. Journal of Computational Neuroscience, 41(1), 107–125. https://doi.org/10.1007/s10827-016-0607-7
      Permanent Research Commons link: https://hdl.handle.net/10289/12885
      Abstract
      The calcium dependent plasticity (CaDP) approach to the modeling of synaptic weight change is applied using a neural field approach to realistic repetitive transcranial magnetic stimulation (rTMS) protocols. A spatially-symmetric nonlinear neural field model consisting of populations of excitatory and inhibitory neurons is used. The plasticity between excitatory cell populations is then evaluated using a CaDP approach that incorporates metaplasticity. The direction and size of the plasticity (potentiation or depression) depends on both the amplitude of stimulation and duration of the protocol. The breaks in the inhibitory theta-burst stimulation protocol are crucial to ensuring that the stimulation bursts are potentiating in nature. Tuning the parameters of a spike-timing dependent plasticity (STDP) window with a Monte Carlo approach to maximize agreement between STDP predictions and the CaDP results reproduces a realistically-shaped window with two regions of depression in agreement with the existing literature. Developing understanding of how TMS interacts with cells at a network level may be important for future investigation.
      Date
      2016
      Type
      Journal Article
      Publisher
      Springer
      Rights
      This is an author’s accepted version of an article published in the journal: Journal of Computational Neuroscience. © Springer
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      • Science and Engineering Papers [3142]
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