Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/137641
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Type: Journal article
Title: Finding Synaptic Couplings from a Biophysical Model of Motor Evoked Potentials after Theta-Burst Transcranial Magnetic Stimulation
Author: Wilson, M.T.
Goldsworthy, M.
Vallence, A.-M.
Fornito, A.
Rogasch, N.
Citation: Brain Research, 2023; 1801:148205-1-148205-14
Publisher: Elsevier
Issue Date: 2023
ISSN: 0006-8993
1872-6240
Statement of
Responsibility: 
Marcus T. Wilson, Mitchell R. Goldsworthy, Ann-Maree Vallence, Alex Fornito, Nigel C. Rogasch
Abstract: Objective: We aimed to use measured input-output (IO) data to identify the best fitting model for motor evoked potentials. Methods: We analyzed existing IO data before and after intermittent and continuous theta-burst stimulation (iTBS & cTBS) from a small group of subjects (18 for each). We fitted individual synaptic couplings and sensitivity parameters using variations of a biophysical model. A best performing model was selected and analyzed. Results: cTBS gives a broad reduction in MEPs for amplitudes larger than resting motor threshold (RMT). Close to threshold, iTBS gives strong potentiation. The model captures individual IO curves. There is no change to the population average synaptic weights post TBS but the change in excitatory-to-excitatory synaptic coupling is strongly correlated with the experimental post-TBS response relative to baseline. Conclusions: The model describes population-averaged and individual IO curves, and their post-TBS change. Variation among individuals is accounted for with variation in synaptic couplings, and variation in sensitivity of neural response to stimulation. Significance: The best fitting model could be applied more broadly and validation studies could elucidate underlying biophysical meaning of parameters.
Keywords: Motor Evoked Potential
transcranial magnetic stimulation
cortical plasticity
modeling
neural field theory
theta burst stimulation
Rights: © 2022 Elsevier B.V. All rights reserved.
DOI: 10.1016/j.brainres.2022.148205
Grant ID: http://purl.org/au-research/grants/arc/DE180100741
http://purl.org/au-research/grants/arc/DE200100575
http://purl.org/au-research/grants/arc/DE190100694
http://purl.org/au-research/grants/nhmrc/1197431
http://purl.org/au-research/grants/nhmrc/1146292
http://purl.org/au-research/grants/arc/DP200103509
Published version: http://dx.doi.org/10.1016/j.brainres.2022.148205
Appears in Collections:Molecular and Biomedical Science publications

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