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Updated: Jun 6, 2026

An In Vitro Organ Culture Model of the Murine Intervertebral Disc
Published on: April 11, 2017
Non-linear stiffness and damping properties of human intervertebral discs in compression and flexion at high-rates of
Lucas Low1, Spyros D Masouros2, Nicolas Newell2
1Dyson School of Design Engineering, Imperial College London, UK.
Abstract:
High-loading rate events such as automotive collisions, aircraft ejection, and underbody blast can result in severe spinal injuries. Computational models aim to predict injuries to enable future safety improvements but require a detailed understanding of the high-rate mechanical response of the spine's structures, particularly the intervertebral discs. This study aimed to characterise the compressive and flexion stiffness properties of human intervertebral discs across all levels of the spine, using an inverse modelling approach. Vertebral body-disc-vertebral body segments from each level of four human cadaveric spines were subjected to increasing rates of loading in both compression and flexion using a servo-hydraulic machine for lower rates, and a drop tower for higher rates. A multibody model was developed for each segment (two degree-of-freedom spring), and an inverse method was used to calculate the non-linear disc response that matched the one measured experimentally. Compressive loads were applied at mean strain rates of 0.82, 4.54, 6.71 and 30.35/s and flexion loads were applied at mean rates of 0.28, 1.92, 4.19 and 12.46 rad/s. Differences were observed between spinal regions (lumbar vs thoracic vs cervical), with inferior thoracic spine segments demonstrating higher compressive and flexion stiffnesses compared to other levels. An increase in compressive and flexion stiffnesses were seen with loading rate, with greater variations in stiffness at higher loading rates, and increased degeneration grades. The 3rd order polynomial disc stiffnesses under high-rate axial compression and flexion, calculated according to spinal region and loading rate, can be used to inform physical and computational surrogate spinal models that aim to improve strategies to prevent spinal injuries.
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