Biology subjects

Hinrichsen, J.

Publications and source records attributed to Hinrichsen, J..

4 recordsLinked to original sources

bioRxiv2026

The Mechanical Fingerprint of Hippocampal Sclerosis Linking Neuronal Cell Loss and Gliosis to Tissue Stiffness

Hippocampal sclerosis (HS) is the most common pathology in drug-resistant temporal lobe epilepsy (TLE). However, clinical diagnosis, prevalent epileptogenicity, and drug drug-resistance in individuals with HS remain an ongoing challenge demanding multidisciplinary research efforts. In this study, we examined the mechanical properties of neurosurgically en bloc resected HS specimens (n=8) ex vivo under compression, tension, and torsional shear. We fitted a two-term Ogden hyperelastic model to the measured mechanical responses to quantify nonlinear mechanical tissue properties. The resulting parameters revealed higher strain stiffening under compression in HS compared to hippocampus obtained post mortem (n=7). The distinction was most noticeable in the large-strain regime, which has important implications for using mechanical tissue properties as valuable diagnostic biomarker. Furthermore, we correlated the tissue microstructure with mechanical parameters. We trained a deep-learning histopathology classifier to detect and classify neurons and glial cells from hematoxylin-stained whole slide images (WSI). We identified a strong association between the small-strain stiffness (shear modulus {micro}) and the overall cell density as well as the glial cell density. The negative relationship between the neuron-to-glia ratio and shear modulus is consistent with the hypothesis that neuronal cell loss and gliosis drives tissue stiffening, respectively. Magnetic resonance imaging (MRI) analysis of the specimens confirmed the previously reported negative association between MRI-derived fractional anisotropy and shear modulus {micro}. Taken together, our study establishes a direct link between tissue mechanics and microstructure, suggesting nonlinear continuum mechanics models as promising new tools for clinical diagnosis and novel research strategies.

bioengineering↗

bioRxiv2026

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↗

bioRxiv2026

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↗

bioRxiv2022

Inverse identification of region-specific hyperelastic material parameters for human brain tissue

The identification of material parameters accurately describing the region-dependent mechanical behavior of human brain tissue is crucial for computational models used to assist, e.g., the development of safety equipment like helmets or the planning and execution of brain surgery. While the division of the human brain into different anatomical regions is well established, knowledge about regions with distinct mechanical properties remains limited. Here, we establish an inverse parameter identification scheme using a hyperelastic Ogden model and experimental data from multi-modal testing of tissue from 19 anatomical human brain regions to identify mechanically distinct regions and provide the corresponding material parameters. We assign the 19 anatomical regions to nine governing regions based on similar parameters and microstructures. Statistical analyses confirm differences between the regions and indicate that at least the corpus callosum and the corona radiata should be assigned different material parameters in computational models of the human brain. We provide a total of four parameter sets based on the two initial Poissons ratios of 0.45 and 0.49 as well as the pre- and unconditioned experimental responses, respectively. Our results highlight the close interrelation between the Poissons ratio and the remaining model parameters. The identified parameters will contribute to more precise computational models enabling spatially resolved predictions of the stress and strain states in human brains under complex mechanical loading conditions.

bioengineering↗