Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (2/2 displayed)

  • 2023Hybrid modeling of an adhesive bonding process, case study : polyphenylene sulfidecitations
  • 2012A DTI-based model for TMS using the independent impedance method with frequency-dependent tissue parameters44citations

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Van Doninck, Bart
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Jordens, Jeroen
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Khatiry Goharoodi, Saeideh
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Leemans, Alexander
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Dupré, Luc
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Geeter, Nele De
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Van Hecke, Wim
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2023
2012

Co-Authors (by relevance)

  • Van Doninck, Bart
  • Jordens, Jeroen
  • Khatiry Goharoodi, Saeideh
  • Leemans, Alexander
  • Dupré, Luc
  • Geeter, Nele De
  • Van Hecke, Wim
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article

A DTI-based model for TMS using the independent impedance method with frequency-dependent tissue parameters

  • Leemans, Alexander
  • Dupré, Luc
  • Geeter, Nele De
  • Crevecoeur, Guillaume
  • Van Hecke, Wim
Abstract

Accurate simulations on detailed realistic head models are necessary to gain a better understanding of the response to transcranial magnetic stimulation (TMS). Hitherto, head models with simplified geometries and constant isotropic material properties are often used, whereas some biological tissues have anisotropic characteristics which vary naturally with frequency. Moreover, most computational methods do not take the tissue permittivity into account. Therefore, we calculate the electromagnetic behaviour due to TMS in a head model with realistic geometry and where realistic dispersive anisotropic tissue properties are incorporated, based on T1-weighted and diffusion-weighted magnetic resonance images. This paper studies the impact of tissue anisotropy, permittivity and frequency dependence, using the anisotropic independent impedance method. The results show that anisotropy yields differences up to 32% and 19% of the maximum induced currents and electric field, respectively. Neglecting the permittivity values leads to a decrease of about 72% and 24% of the maximum currents and field, respectively. Implementing the dispersive effects of biological tissues results in a difference of 6% of the maximum currents. The cerebral voxels show limited sensitivity of the induced electric field to changes in conductivity and permittivity, whereas the field varies approximately linearly with frequency. These findings illustrate the importance of including each of the above parameters in the model and confirm the need for accuracy in the applied patient-specific method, which can be used in computer-assisted TMS.

Topics
  • simulation
  • anisotropic
  • isotropic