Linking viscoelastic Characteristics of 17 Human Brain Regions with their Microstructural Composition
Please login to view abstract download link
Experimental data from human brain tissue is essential for calibrating model parameters for finite element simulations of the brain undergoing development, aging, disease, surgical intervention, or accidents. It has been shown in the past that the mechanical behavior of brain tissue differs across anatomical regions [1] and that whether or not these differences are taken into account in simulations has an influence on the results [2,3]. However, so far, viscoelastic data have only been published for a small number of brain regions. Here, we present experimental data for 17 anatomical regions from 16 body donor brains that were tested in compression, tension, and torsional shear and subjected to both cyclic loading and stress relaxation tests. In addition to mechanical experiments, we have performed histological analyses of all samples that allow us to link the observed mechanical behavior to the region-specific microstructure of each sample. The mechanical dataset offers a valuable resource for researchers who model the mechanical behavior of the human brain in computer simulations. Further, our insights into how mechanical characteristics are related to the tissue’s microstructure could contribute to a better understanding of brain mechanics and thus improved, microstructure-informed models of brain aging and disease. REFERENCES [1] Hinrichsen, J. et al. Inverse identification of region-specific hyperelastic material parameters for human brain tissue. Biomech Model Mechanobiol 22, 1729–1749 (2023). [2] Griffiths, E., Hinrichsen, J., Reiter, N. & Budday, S. On the importance of using region-dependent material parameters for full-scale human brain simulations. European Journal of Mechanics - A/Solids 99, 104910 (2023). [3] Tueni, N., Griffiths, E., Weickenmeier, J., Rampp, S. & Budday, S. Region-dependent mechanical parameters in simulating cerebral atrophy. APL Bioeng. 10, 016104 (2026).
