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 (7/7 displayed)

  • 2023Automated model discovery for human brain using Constitutive Artificial Neural Networks.73citations
  • 2022Correlating the microstructural architecture and macrostructural behaviour of the brain.18citations
  • 2022Automated model discovery for human brain using Constitutive Artificial Neural Networks6citations
  • 2020Towards microstructure-informed material models for human brain tissue84citations
  • 2015Emerging Brain Morphologies from Axonal Elongation99citations
  • 2009Stress-strain behavior of mitral valve leaflets in the beating ovine heart63citations
  • 2008Material properties of the ovine mitral valve anterior leaflet in vivo from inverse finite element analysis83citations

Places of action

Chart of shared publication
Pierre, Sarah R.
1 / 1 shared
Linka, Kevin
2 / 2 shared
Püllmann, Denise
1 / 1 shared
Hoppstädter, Mayra
1 / 1 shared
Seydewitz, Robert
1 / 1 shared
Böl, Markus
1 / 2 shared
Budday, Silvia
1 / 4 shared
Sarem, M.
1 / 2 shared
Starck, L.
1 / 1 shared
Sommer, Gerhard
1 / 4 shared
Paulsen, F.
1 / 3 shared
Shastri, V. P.
1 / 1 shared
Steinmann, P.
1 / 11 shared
Pfefferle, J.
1 / 1 shared
Holzapfel, Gerhard
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Phunchago, N.
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Holland, Maria A.
1 / 1 shared
Miller, Kyle E.
1 / 1 shared
Krishnamurthy, Gaurav
2 / 2 shared
Karlsson, Matts
2 / 4 shared
Bothe, Wolfgang
2 / 4 shared
Ingels, Neil B.
2 / 2 shared
Itoh, Akinobu
2 / 2 shared
Swanson, Julia C.
2 / 2 shared
Miller, D. Craig
2 / 2 shared
Ennis, Daniel B.
1 / 1 shared
Chart of publication period
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2022
2020
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Co-Authors (by relevance)

  • Pierre, Sarah R.
  • Linka, Kevin
  • Püllmann, Denise
  • Hoppstädter, Mayra
  • Seydewitz, Robert
  • Böl, Markus
  • Budday, Silvia
  • Sarem, M.
  • Starck, L.
  • Sommer, Gerhard
  • Paulsen, F.
  • Shastri, V. P.
  • Steinmann, P.
  • Pfefferle, J.
  • Holzapfel, Gerhard
  • Phunchago, N.
  • Holland, Maria A.
  • Miller, Kyle E.
  • Krishnamurthy, Gaurav
  • Karlsson, Matts
  • Bothe, Wolfgang
  • Ingels, Neil B.
  • Itoh, Akinobu
  • Swanson, Julia C.
  • Miller, D. Craig
  • Ennis, Daniel B.
OrganizationsLocationPeople

article

Correlating the microstructural architecture and macrostructural behaviour of the brain.

  • Püllmann, Denise
  • Hoppstädter, Mayra
  • Seydewitz, Robert
  • Kuhl, Ellen
  • Böl, Markus
Abstract

The computational simulation of pathological conditions and surgical procedures, for example the removal of cancerous tissue, can contribute crucially to the future of medicine. Especially for brain surgery, these methods can be important, as the ultra-soft tissue controls vital functions of the body. However, the microstructural interactions and their effects on macroscopic material properties remain incompletely understood. Therefore, we investigated the mechanical behaviour of brain tissue under three different deformation modes, axial tension, compression, and semi-confined compression, in different anatomical regions, and for varying axon orientation. In addition, we characterised the underlying microstructure in terms of myelin, cells, glial cells and neuron area fraction, and density. The correlation of these quantities with the material parameters of the anisotropic Ogden model reveals a decrease in shear modulus with increasing myelin area fraction. Strikingly, the tensile shear modulus correlates positively with cell and neuronal area fraction (Spearman's correlation coefficient of rs=0.40 and rs=0.33), whereas the compressive shear modulus decreases with increasing glial cell area (rs=-0.33). Our study finds that tissue non-linearity significantly depends on the myelin area fraction (rs=0.47), cell density (rs=0.41) and glial cell area (rs=0.49). Our results provide an important step towards understanding the micromechanical load transfer that leads to the non-linear macromechanical behaviour of the brain. STATEMENT OF SIGNIFICANCE: Within this article, we investigate the mechanical behaviour of brain tissue under three different deformation modes, in different anatomical regions, and for varying axon orientation. Further, we characterise the underlying microstructure in terms of various constituents. The correlation of these quantities with the material parameters of the anisotropic Ogden model reveals a decrease in shear modulus with increasing myelin area fraction. Strikingly, the tensile shear modulus correlates positively with cell and neuronal area fraction, whereas the compressive shear modulus decreases with increasing glial cell area. Our study finds that tissue non-linearity significantly depends on the myelin area fraction, cell density, and glial cell area. Our results provide an important step towards understanding the micromechanical load transfer that leads to the non-linear macromechanical behaviour of the brain.

Topics
  • density
  • microstructure
  • simulation
  • anisotropic