Advanced search
Add to list

Simulating the variability of TMS responses in a speech mapping case study

Author
Organization
Abstract
When delivered over a specific cortical site, TMS can temporarily disrupt the ongoing process in that area. This allows for mapping motor and speech-related cortical areas for preoperative evaluation with recent promising clinical outcomes [1,2]. Speech corresponds to an extended, complex, highly individualized neural network [3]. We aimed to numerically explain the observed variability of TMS responses during a speech mapping experiment, performed on a healthy, right-handed male subject following Lioumis’ approach [4]. We selected four study cases with very small differences in coil position and orientation. In one case (E) a naming error occurred, while in the other three cases (NEa,b,c) the subject appointed the object images as smoothly as without TMS. T1-weighted and diffusion-weighted MRI were acquired from the subject and post-processed to construct a realistic 2-mm resolution anisotropic head model. The induced electric field distributions were computed, with the coil configuration parameters retrieved from the neuronavigation system, using the anisotropic independent impedance method [5]. Whole brain tractography was performed using the graphical toolbox ExploreDTI [6]. 35 relevant tracts were identified in a region of interest obtained from the electric field distribution. Finally, the spatio-temporal variation of the membrane potentials along these tracts was computed for all four case using the compartmental cable equation [7], with passive and active neural components. One tract is activated for all coil positions. Another tract is only triggered for case E. NEa induced action potentials in 13 tracts, while NEb stimulated 11 tracts and NEc only one. The calculated results are certainly sensitive to the coil specifications, confirming the observed variability in this speech mapping study. However, even though one neural tract only appears to be triggered for the error case, we do not want to draw strong conclusions from this. Further research is needed on the location and functional meaning of this tract in terms of the speech-related network and on refining the neural model with synapses and network connections. We believe case- and subject-specific modelling is necessary to accurately capture the electromagnetic and neurophysiologic phenomena triggered by TMS, certainly when the stimulation interacts with complex neural networks that can differ significantly from person to person. References: [1] Krieg, S.M. et al. (2012) J. Neurosurgery, 116:994-1001. [2] Picht, T. et al. (2013) Neurosurgery, 72:808–819. [3] Catani, M. et al. (2005) Ann. Neurol., 57:8–16. [4] Lioumis, P. et al. (2012) J. Neurosci. Meth., 204:349–354. [5] De Geeter, N. et al. (2012) Phys. Med. Biol., 57:2169-2188. [6] Leemans, A. et al. (2009) Proceedings of 17th Annual Meeting of Intl. Soc. Mag. Reson. Med. [7] Salvador, R. (2009) Numerical modelling in transcranial magnetic stimulation. PhD thesis.
Keywords
Speech mapping, Transcranial magnetic stimulation, Simulations

Citation

Please use this url to cite or link to this publication:

MLA
De Geeter, Nele, Pantelis Lioumis, Guillaume Crevecoeur, et al. “Simulating the Variability of TMS Responses in a Speech Mapping Case Study.” International Conference on Basic and Clinical Multimodal Imaging, Abstracts. 2015. Print.
APA
De Geeter, N., Lioumis, P., Crevecoeur, G., & Dupré, L. (2015). Simulating the variability of TMS responses in a speech mapping case study. International Conference on Basic and Clinical Multimodal Imaging, Abstracts. Presented at the International Conference on Basic and Clinical Multimodal Imaging (BaCI).
Chicago author-date
De Geeter, Nele, Pantelis Lioumis, Guillaume Crevecoeur, and Luc Dupré. 2015. “Simulating the Variability of TMS Responses in a Speech Mapping Case Study.” In International Conference on Basic and Clinical Multimodal Imaging, Abstracts.
Chicago author-date (all authors)
De Geeter, Nele, Pantelis Lioumis, Guillaume Crevecoeur, and Luc Dupré. 2015. “Simulating the Variability of TMS Responses in a Speech Mapping Case Study.” In International Conference on Basic and Clinical Multimodal Imaging, Abstracts.
Vancouver
1.
De Geeter N, Lioumis P, Crevecoeur G, Dupré L. Simulating the variability of TMS responses in a speech mapping case study. International Conference on Basic and Clinical Multimodal Imaging, Abstracts. 2015.
IEEE
[1]
N. De Geeter, P. Lioumis, G. Crevecoeur, and L. Dupré, “Simulating the variability of TMS responses in a speech mapping case study,” in International Conference on Basic and Clinical Multimodal Imaging, Abstracts, Utrecht, The Netherlands, 2015.
@inproceedings{6976676,
  abstract     = {When delivered over a specific cortical site, TMS can temporarily disrupt the ongoing process in that area. This allows for mapping motor and speech-related cortical areas for preoperative evaluation with recent promising clinical outcomes [1,2].
Speech corresponds to an extended, complex, highly individualized neural network [3]. We aimed to numerically explain the observed variability of TMS responses during a speech mapping experiment, performed on a healthy, right-handed male subject following Lioumis’ approach [4]. We selected four study cases with very small differences in coil position and orientation. In one case (E) a naming error occurred, while in the other three cases (NEa,b,c) the subject appointed the object images as smoothly as without TMS. T1-weighted and diffusion-weighted MRI were acquired from the subject and post-processed to construct a realistic 2-mm resolution anisotropic head model. The induced electric field distributions were computed, with the coil configuration parameters retrieved from the neuronavigation system, using the anisotropic independent impedance method [5]. Whole brain tractography was performed using the graphical toolbox ExploreDTI [6]. 35 relevant tracts were identified in a region of interest obtained from the electric field distribution. Finally, the spatio-temporal variation of the membrane potentials along these tracts was computed for all four case using the compartmental cable equation [7], with passive and active neural components. 
One tract is activated for all coil positions. Another tract is only triggered for case E. NEa induced action potentials in 13 tracts, while NEb stimulated 11 tracts and NEc only one. The calculated results are certainly sensitive to the coil specifications, confirming the observed variability in this speech mapping study. However, even though one neural tract only appears to be triggered for the error case, we do not want to draw strong conclusions from this. Further research is needed on the location and functional meaning of this tract in terms of the speech-related network and on refining the neural model with synapses and network connections.
We believe case- and subject-specific modelling is necessary to accurately capture the electromagnetic and neurophysiologic phenomena triggered by TMS, certainly when the stimulation interacts with complex neural networks that can differ significantly from person to person.

References: 
[1] Krieg, S.M. et al. (2012) J. Neurosurgery, 116:994-1001.
[2] Picht, T. et al. (2013) Neurosurgery, 72:808–819. 
[3] Catani, M. et al. (2005) Ann. Neurol., 57:8–16.
[4] Lioumis, P. et al. (2012) J. Neurosci. Meth., 204:349–354.
[5] De Geeter, N. et al. (2012) Phys. Med. Biol., 57:2169-2188.
[6] Leemans, A. et al. (2009) Proceedings of 17th Annual Meeting of Intl. Soc. Mag. Reson. Med.
[7] Salvador, R. (2009) Numerical modelling in transcranial magnetic stimulation. PhD thesis.},
  author       = {De Geeter, Nele and Lioumis, Pantelis and Crevecoeur, Guillaume and Dupré, Luc},
  booktitle    = {International Conference on Basic and Clinical Multimodal Imaging, Abstracts},
  keywords     = {Speech mapping,Transcranial magnetic stimulation,Simulations},
  language     = {eng},
  location     = {Utrecht, The Netherlands},
  title        = {Simulating the variability of TMS responses in a speech mapping case study},
  year         = {2015},
}