Shear relaxation during one loading cycle in four regions of the human brain: cortex (C), basal ganglia (BG), corona radiata (CR), corpus callosum (CC). Image from Budday et al. (2017).

Computer modeling of brain tissue makes it easier to find regions with extreme tissue kinematics. Here the local displacement field in an accelerated porcine brain slice and the equivalent von Mises strain field are shown. Image from Goriely et al. (2015).

Computational modeling in biomechanics facilitates important insights into the underlying mechanisms of cerebral pathologies and neurological disorders. Understanding and characterizing the mechanical behavior of brain tissue and linking this to its microstructure is essential for developing reliable material models.

Human brain tissue is suspected to comprise a complex porous compound of solid and liquid phase. This makes it a polyphasic material with its mechanical properties determined not only by the viscoelastic behavior, but also by the porous nature of the tissue. The aim of this D-A-CH project is therefore to characterize the mechanical response of brain tissue by developing a biphasic constitutive model, based on a comprehensive set of experimental data.

To achieve this, we defined four specific objectives: (i) We will develop a new experimental set-up to adequately characterize the proposed poro-viscoelastic nature of brain tissue. We aim to fit multiple loading conditions simultaneously for the identified model parameters to produce accurate computational results. (ii) We will elucidate the relation between the macroscopic mechanical response and the tissue microstructure through microstructural investigations, and, potentially, identify structural model parameters. (iii) We will develop a poro-viscoelastic model to capture, at the continuum level, the individual effects of the fluid and solid components, and their interaction. The experimental findings and the structural parameters will enable us to replace formerly phenomenological constitutive equations with comprehensive microstructurally motivated material laws. A robust finite element framework will allow for the successful implementation of the proposed model. (iv) We will accurately calibrate the model parameters through an inverse material parameter identification scheme and evaluate their physical meaning considering the observed porous and viscous phenomena.

The outcome of the project will be a better understanding of the role porous and viscous effects have in the response of brain tissue to mechanical loading. We will have linked the microstructure of the tissue to its macroscopic behavior via experimental and computational investigations and use these findings as a base for further explorations of the human brain.

Funding: Austrian Science Fund (FWF)