Computational Modeling of the Cervical Spinal Cord: Integration into a Human Body Model to Investigate Response to Impact

Acute spinal cord injuries (SCI) have a global annual occurrence rate of 14 to 40 per million population with considerable societal cost. The primary mechanism of injury involves physical damage to the nervous tissues, such as spinal cord compression resulting from fracture or dislocation of the vertebra. However, experimental findings have indicated that neurological sequela can occur without radiographic abnormalities of the neural tissues. In addition, studies have suggested that the cerebrospinal fluid (CSF) layer may play a protective role for the spinal cord during impact. Yet, there are significant limitations to examining SCI experimentally, resulting in large gaps in understanding. Computational Human Body Models (HBM) are an alternative and potentially important tool to investigate the risk of SCI. A key challenge in applying contemporary HBM to study SCI is the need for a biofidelic model of the spinal cord, which accurately predicts the loading and response of the cervical neural tissues in relevant impact scenarios. This thesis developed and validated a finite element model of the cervical spinal cord and associated tissues and integrated this model within a contemporary HBM to achieve two aims: (1) to provide a tool for the assessment of spinal cord response in impact scenarios; and (2) to create an improved physical boundary condition for the brain and brain stem, which is a limitation of current HBM. The geometry of the cervical neural tissues was defined using subject-specific magnetic resonance imaging and literature data. The salient mechanical properties of cervical neural tissues were identified, and experimental data were used to fit appropriate constitutive material models for each tissue. Experimental pellet impact tests and indentation tests on the spinal cord were simulated to validate the tissue mechanical properties, verify finite element mesh refinement and assess numerical representation of the CSF. The developed material models and meshes of the cervical neural tissues were integrated into a contemporary HBM. Lastly, the contemporary HBM with implemented cervical neural tissues was simulated in frontal, lateral, rear, and oblique impact scenarios. A comprehensive assessment of the spinal cord influence on brain tissue deformation was undertaken. In general, the presence of the spinal cord in the HBM model increased the strains observed in the brain tissue. The brain stem tissue observed the largest average increase of 17% in strain. Results from this work provided the first validated finite element model of the cervical neural tissues and cerebrospinal fluid layer integrated into a state-of-the-art full-body HBM for transient impact simulations. This model enabled the prediction of spinal cord response for impact scenarios, improved anatomic boundary conditions for connection to the brain tissue, and ultimately will assist in assessing safety systems to mitigate catastrophic human injuries