Browsing by Subject "speech mapping"

Sort by: Order: Results:

Now showing items 1-2 of 2
  • Hernandez-Pavon, Julio C.; Makela, Niko; Lehtinen, Henri; Lioumis, Pantelis; Makela, Jyrki P. (2014)
  • De Geeter, N.; Lioumis, P.; Laakso, A.; Crevecoeur, G.; Dupre, L. (2016)
    When delivered over a specific cortical site, TMS can temporarily disrupt the ongoing process in that area. This allows mapping of speech-related areas for preoperative evaluation purposes. We numerically explore the observed variability of TMS responses during a speech mapping experiment performed with a neuronavigation system. We selected four cases with very small perturbations in coil position and orientation. In one case (E) a naming error occurred, while in the other cases (NEA, B, C) the subject appointed the images as smoothly as without TMS. A realistic anisotropic head model was constructed of the subject from T1-weighted and diffusion-weighted MRI. The induced electric field distributions were computed, associated to the coil parameters retrieved from the neuronavigation system. Finally, the membrane potentials along relevant white matter fibre tracts, extracted from DTI-based tractography, were computed using a compartmental cable equation. While only minor differences could be noticed between the induced electric field distributions of the four cases, computing the corresponding membrane potentials revealed different subsets of tracts were activated. A single tract was activated for all coil positions. Another tract was only triggered for case E. NEA induced action potentials in 13 tracts, while NEB stimulated 11 tracts and NEC one. The calculated results are certainly sensitive to the coil specifications, demonstrating the observed variability in this study. However, even though a tract connecting Broca's with Wernicke's area is only triggered for the error case, further research is needed on other study cases and on refining the neural model with synapses and network connections. Case-and subject-specific modelling that includes both electromagnetic fields and neuronal activity enables demonstration of the variability in TMS experiments and can capture the interaction with complex neural networks.