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 (4/4 displayed)

  • 2020Towards microstructure-informed material models for human brain tissue84citations
  • 2017Mechanical characterization of human brain tissue543citations
  • 2016Microstructure and mechanics of healthy and aneurysmatic abdominal aortas176citations
  • 2016Mechanical strength of aneurysmatic and dissected human thoracic aortas at different shear loading modes82citations

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Chart of shared publication
Budday, Silvia
1 / 4 shared
Sarem, M.
1 / 2 shared
Starck, L.
1 / 1 shared
Kuhl, Ellen
1 / 7 shared
Paulsen, F.
2 / 3 shared
Shastri, V. P.
1 / 1 shared
Steinmann, P.
2 / 11 shared
Pfefferle, J.
1 / 1 shared
Holzapfel, Gerhard
4 / 4 shared
Phunchago, N.
1 / 1 shared
Budday, S.
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Bauer, M.
1 / 19 shared
Kuhl, E.
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Kohnert, J.
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Haybaeck, J.
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Langkammer, C.
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Birkl, C.
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Niestrawska, Justyna Anna
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Regitnig, Peter
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Cohnert, Tina U.
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Viertler, Christian
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Sherifova, Selda
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Oberwalder, Peter J.
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Dapunt, Otto E.
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Ursomanno, Patricia A.
1 / 1 shared
Deanda, Abe
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Griffith, Boyce E.
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2020
2017
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Co-Authors (by relevance)

  • Budday, Silvia
  • Sarem, M.
  • Starck, L.
  • Kuhl, Ellen
  • Paulsen, F.
  • Shastri, V. P.
  • Steinmann, P.
  • Pfefferle, J.
  • Holzapfel, Gerhard
  • Phunchago, N.
  • Budday, S.
  • Bauer, M.
  • Kuhl, E.
  • Kohnert, J.
  • Haybaeck, J.
  • Langkammer, C.
  • Birkl, C.
  • Niestrawska, Justyna Anna
  • Regitnig, Peter
  • Cohnert, Tina U.
  • Viertler, Christian
  • Sherifova, Selda
  • Oberwalder, Peter J.
  • Dapunt, Otto E.
  • Ursomanno, Patricia A.
  • Deanda, Abe
  • Griffith, Boyce E.
OrganizationsLocationPeople

article

Towards microstructure-informed material models for human brain tissue

  • Budday, Silvia
  • Sarem, M.
  • Starck, L.
  • Kuhl, Ellen
  • Sommer, Gerhard
  • Paulsen, F.
  • Shastri, V. P.
  • Steinmann, P.
  • Pfefferle, J.
  • Holzapfel, Gerhard
  • Phunchago, N.
Abstract

<p>Emerging evidence suggests that the mechanical behavior of the brain plays a critical role in development, disease, and aging. Recent studies have begun to characterize the mechanical behavior of gray and white matter tissue and to identify sets of material models that best reproduce the stress-strain behavior of different brain regions. Yet, these models are mainly phenomenological in nature, their parameters often lack clear physical interpretation, and they fail to correlate the mechanical behavior to the underlying microstructural composition. Here we make a first attempt towards identifying general relations between microstructure and mechanics with the ultimate goal to develop microstructurally motivated constitutive equations for human brain tissue. Using histological staining, we analyze the microstructure of brain specimens from different anatomical regions, the cortex, basal ganglia, corona radiata, and corpus callosum, and identify the regional stiffness and viscosity under multiple loading conditions, simple shear, compression, and tension. Strikingly, our study reveals a negative correlation between cell count and stiffness, a positive correlation between myelin content and stiffness, and a negative correlation between proteoglycan content and stiffness. Additionally, our analysis shows a positive correlation between lipid and proteoglycan content and viscosity. We demonstrate how understanding the microstructural origin of the macroscopic behavior of the brain can help us design microstructure-informed material models for human brain tissue that inherently capture regional heterogeneities. This study represents an important step towards using brain tissue stiffness and viscosity as early diagnostic markers for clinical conditions including chronic traumatic encephalopathy, Alzheimer's and Parkinson's disease, or multiple sclerosis. Statement of significance: The complex and heterogeneous mechanical properties of brain tissue play a critical role for brain function. To understand and predict how brain tissue properties vary in space and time, it will be key to link the mechanical behavior to the underlying microstructural composition. Here we use histological staining to quantify area fractions of microstructural components of mechanically tested specimens and evaluate their individual contributions to the nonlinear macroscopic mechanical response. We further propose a microstructure-informed material model for human brain tissue that inherently captures regional heterogeneities. The current work provides unprecedented insights into the biomechanics of human brain tissue, which are highly relevant to develop refined computational models for brain tissue behavior or to advance neural tissue engineering.</p>

Topics
  • microstructure
  • stress-strain behavior
  • viscosity
  • aging
  • aging