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Tueni, N.

Publications and source records attributed to Tueni, N..

2 recordsLinked to original sources

Towards patient-specific biomechanical human brain models

Reliable characterization of spatial variations in brain tissue stiffness is essential for predictive biomechanical modeling, yet most current methods rely on coarse regional parameter assignments based on invasive mechanical testing. In this study, we propose a new approach to obtain subject-specific mechanical properties at voxel resolution from in vivo diffusion tensor magnetic resonance imaging (DTI) based on a linear regression between the fractional anisotropy (FA) from DTI and experimentally measured stiffness values. To assess how such heterogeneity in mechanical properties influences simulated brain deformation, we construct a finite element model based on two material parameterizations of the same human brain: one employing nine anatomically defined regions, each with uniform material parameters, and another in which the shear modulus is assigned voxel-wise on the corresponding FA value. Applying this FA-stiffness mapping yields a smoothly varying mechanical property distribution that better captures local microstructural differences not represented by region-wise parameterizations. Both parameterizations are subjected to an identical atrophy-driven loading scenario. They exhibit comparable overall volume loss, but diverge in regional behavior. The voxel-resolved parameterization predicts more pronounced ventricular expansion and differs in the displacement and stretch distributions, indicating that variability in stiffness can alter local predicted responses even when global outcomes appear similar. This work presents a pipeline for estimating individualized mechanical properties directly from imaging protocols that are routinely performed on patients, with important implications for brain biomechanics. While the approach depends on a simplified linear FA-stiffness relation and assumes isotropic constitutive behavior, it provides a framework for integrating imaging-based microstructure into subject-specific simulations. Future validation against in vivo or experimental deformation data is needed to determine the fidelity and clinical utility of FA-derived stiffness fields.

neuroscience↗

Fractional Anisotropy as a Surrogate Marker of Brain Mechanics

Understanding the mechanical properties of brain tissue may provide crucial insights into brain development, injury, disease and surgical planning. Conventionally, these properties are measured ex vivo or in vivo during surgical procedures, while non-invasive in vivo alternatives are sparse. This study investigates whether fractional anisotropy (FA) derived from diffusion-weighted magnetic resonance imaging can serve as a surrogate marker for brain tissue stiffness in healthy human brains. MRI data were collected from three body donor brains, 28 healthy adults, and a publicly available independent dataset of 26 adults. FA values were compared with mechanical properties from ex vivo mechanical testing of brain tissue. Statistical analysis revealed a strong negative correlation between FA and the mechanical response for small strains expressed as shear modulus of a one-term hyperelastic Ogden model, indicating that higher FA values are associated with lower tissue stiffness. The nonlinearity parameter alpha exhibited a qualitatively similar, but considerably weaker correlation with FA. These findings were consistent across datasets. The findings suggest that FA can be a robust, non-invasive marker for estimating mechanical properties of brain tissue, with potential applications in clinical diagnosis and computational modeling of brain mechanics and the study of brain development. Further research is needed to clarify the relationship in lesional tissues and to optimize clinical utility.

neuroscience↗