Age-related skull fracture patterns in infants after low-height falls.

BACKGROUND: Prior research and experience has increased physician understanding of infant skull fracture prediction. However, patterns related to fracture length, nonlinearity, and features of complexity remain poorly understood, and differences across infant age groups have not been previously explored. METHODS: To determine how infant and low-height fall characteristics influence fracture patterns, we collected data from 231 head CT 3D reconstructions and quantified length and nonlinearity using a custom image processing code. Regression analysis was used to determine the effects of age and fall characteristics on nonlinearity, length, and features of fracture complexity. RESULTS: While impact surface had an important role in the number of cracks present in a fracture, younger infants and greater fall heights significantly affected most features of fracture complexity, including suture-to-suture spanning and biparietal involvement. In addition, increasing fracture length with increasing fall height supports trends identified by prior finite-element modeling. Finally, this study yielded results supporting the presence of soft tissue swelling as a function of fracture location rather than impact site. CONCLUSIONS: Age-related properties of the infant skull confer unique fracture patterns following head impact. Further characterization of these properties, particularly in infants <4 months of age, will improve our understanding of the infant skull's response to trauma. IMPACT: Younger infant age and greater fall heights have significant effects on many features of fracture complexity resulting from low-height falls. Incorporating multiple crack formation and multiple bone involvement into computational models of young infant skull fractures may result in increased biofidelity. Drivers of skull fracture complexity are not well understood, and skull fracture patterns in real-world data across infant age groups have not been previously described. Understanding fracture complexity relative to age in accidental falls will improve the understanding of accidental and abusive head trauma.

Spatial distribution of human arachnoid trabeculae.

Traumatic brain injury (TBI) is a common injury modality affecting a diverse patient population. Axonal injury occurs when the brain experiences excessive deformation as a result of head impact. Previous studies have shown that the arachnoid trabeculae (AT) in the subarachnoid space significantly influence the magnitude and distribution of brain deformation during impact. However, the quantity and spatial distribution of cranial AT in humans is unknown. Quantification of these microstructural features will improve understanding of force transfer during TBI, and may be a valuable dataset for microneurosurgical procedures. In this study, we quantify the spatial distribution of cranial AT in seven post-mortem human subjects. Optical coherence tomography (OCT) was used to conduct in situ imaging of AT microstructure across the surface of the human brain. OCT images were segmented to quantify the relative amounts of trabecular structures through a volume fraction (VF) measurement. The average VF for each brain ranged from 22.0% to 29.2%. Across all brains, there was a positive spatial correlation, with VF significantly greater by 12% near the superior aspect of the brain (p < .005), and significantly greater by 5%-10% in the frontal lobes (p < .005). These findings suggest that the distribution of AT between the brain and skull is heterogeneous, region-dependent, and likely contributes to brain deformation patterns. This study is the first to image and quantify human AT across the cerebrum and identify region-dependencies. Incorporation of this spatial heterogeneity may improve the accuracy of computational models of human TBI and enhance understanding of brain dynamics.

Mechanical characterization of the human pia-arachnoid complex.

Traumatic brain injury (TBI) is a significant problem in global health that affects a wide variety of patients. Mild forms of TBI, commonly referred to as concussion, are a result of rapid accelerations of the head from either direct or indirect impacts. Kinetic energy from the impact is transferred into deformation of the brain, leading to cellular disruption. This transfer of energy is in part mediated by the pia-arachnoid complex (PAC), a layer of anatomical structures that forms the physical connection between the brain and the skull. The importance of properly quantifying the mechanics of the PAC for use in computational models of TBI has been understood for some time, but data from human subjects has been unavailable. In this study, we quantify the normal traction modulus of the PAC in five post-mortem human subjects using hydrostatic fluid pressurization in combination with optical coherence tomography. Testing at multiple locations across each brain reveals that brain-skull stiffness is heterogeneously distributed. The material response to traction loading was linear, with a mean normal traction modulus of 12.6 ± 4.8 kPa. Modulus was 21% greater in superior regions of the brain compared to inferior regions. Comparisons with regional microstructural data suggests a potential relationship between the volume fraction of arachnoid trabeculae and modulus. Comparisons to coincident measurements of microstructural properties showed a positive correlation between arachnoid membrane thickness and normal traction modulus. This study is the first to characterize the mechanics of the human pia-arachnoid complex and quantify material properties in situ. These findings suggest implementing a heterogeneous model of the brain-skull interface in computational models of TBI may lead to more realistic injury prediction.