CNS*2025 Florence

P046 Model Parameter Estimation for TMS-induced MEPs

Shih-Cheng Chien*1, Christian Röse2, Peng Wang2,3, Helmut Schmidt1, Jaroslav Hlinka1,4, Thomas R. Knösche2, Konstantin Weise2


1Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic
2Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
3Institute of Psychology, University of Greifswald, Greifswald, Germany
4National Institute of Mental Health, Klecany, Czech Republic

*Email:chien@cs.cas.cz

Introduction

TMS-induced MEPs are widely utilized in both basic research and clinical practice. The MEP parameters, such as input-output (I/O) curves, often exhibit significant variability across individuals, both in healthy populations and in patients. Understanding the sources of this variability is critical for improving the precision of motor-related diagnoses. Previously, we developed a biologically inspired model capable of reproducing MEP waveforms. In this study, we apply model fitting to an open MEP dataset of ten healthy participants [1] and investigate the distribution of model parameters underlying the variability of I/O curves.

Methods
The model incorporates the descending motor pathways from the spinal cord to the hand muscles, with synthetic D- and I-waves serving as inputs. The spinal cord component consists of 100 conductance-based leaky integrate-and-fire alpha motor neurons (aMNs), which interact with a population of Renshaw cells (RCs) that function as a common inhibitory pool. The aMNs are connected to 100 motor units in the hand muscle component. Each motor unit generates a motor unit action potential (MUAP) in response to spikes from its corresponding aMN. The simulated MEP is computed as the sum of these time-shifted MUAPs.
Results
The resting motor threshold (RMT) across individuals in the dataset was 41.3 ± 6.0% of the maximum stimulator output (MSO). Peak latencies showed no significant variation with MEP peak-to-peak amplitude. Fitting the model to individual MEP waveforms provided insights into the neuronal interactions underlying MEP generation. The DI-waves, after convolution with synaptic kernels (AMPA and NMDA), produced sustained inputs to the αMNs. Renshaw cells played a critical role in suppressing excessive spikes, particularly under high TMS intensities, preventing excessive oscillations in the MEP waveform.
Discussion
We employed a computationally efficient and biologically plausible model to explain the variability in individual TMS-induced MEPs. The fitting procedure relied on synthesizing common DI-waves for healthy participants, which may introduce additional errors in parameter estimation. Future work will validate this approach using patient data, where individual DI-waves are available, to improve accuracy and robustness in parameter fitting.




Acknowledgements

The publication was supported by a Lumina-Quaeruntur fellowship (LQ100302301) by the Czech Academy of Sciences (awarded to HS) and ERDF-Project Brain Dynamics, No. CZ.02.01.01/00/22_008/0004643.
References
[1]https://doi.org/10.1016/j.brs.2022.06.013