Multimodal mechanical characterization pipeline for spinal cord tissue

The ultrasoft nature and intricate geometry of the 'butterfly-shaped' spinal cord gray matter, surrounded by white matter, pose major challenges for experimental and computational characterization of tissue mechanics. With growing interest in modeling spinal cord injury, disease, and regeneration, reliable mechanical data are increasingly needed. To meet this demand, we developed a multimodal characterization pipeline, combining mesoscale spherical indentation of gray and white matter, macroscale rheometer-based compression-tension tests, nonlinear continuum mechanics modeling, and inverse finite element simulations, using Hertzian, neo-Hookean, and Ogden constitutive laws. Both testing modalities were applied sequentially on samples extracted along the cranio-caudal axis of porcine spinal cords to assess regional variations in mechanical properties. Compared with Hertzian and neo-Hookean models, Ogden models more accurately captured the nonlinear material response. For comparable loading rates, shear moduli from mesoscale indentation are consistent with those from macroscale large-strain testing, demonstrating the robustness of the multimodal approach. Based on these observations, we demonstrate the effectiveness of identifying nonlinear material parameters for spinal cord gray and white matter tissue through the simultaneous integration of indentation and large-strain data. Like this, we for the first time include information on both, tissue-type-specific properties as well as large-strain nonlinear effects. Our results demonstrate that a multimodal characterization pipeline can yield a more robust and representative material parameters with important implications for advanced treatment strategies for spinal cord injuries and disease. Statement of Significance: In the growing body of research on the mechanical characterization of brain and spinal cord tissue, discrepancies between measured properties obtained from different testing modalities remain a major challenge. In this study, we use two complementary testing methods - spherical indentation and large-strain compression-tension loading - applied to the same samples. We demonstrate that when the experimental data are analyzed using nonlinear material modeling and finite element simulations, the results from both methods agree well. We for the first time integrate both indentation and large-strain testing data to identify reliable individual material parameters for spinal cord gray and white matter tissue. Our results highlight the robustness of the presented multimodal characterization pipeline for central nervous system tissue.