With an increasing focus on long-term consequences of concussive brain injuries, there is a new emphasis on developing tools that can accurately predict the mechanical response of the brain to impact loading. Although finite element models (FEM) estimate the brain response under dynamic loading, these models are not capable of delivering rapid (∼seconds) estimates of the brain's mechanical response. In this study, we develop a multibody spring-mass-damper model that estimates the regional motion of the brain to rotational accelerations delivered either about one anatomic axis or across three orthogonal axes simultaneously. In total, we estimated the deformation across 120 locations within a 50th percentile human brain. We found the multibody model (MBM) correlated, but did not precisely predict, the computed finite element response (average relative error: 18.4 ± 13.1%). We used machine learning (ML) to combine the prediction from the MBM and the loading kinematics (peak rotational acceleration, peak rotational velocity) and significantly reduced the discrepancy between the MBM and FEM (average relative error: 9.8 ± 7.7%). Using an independent sports injury testing set, we found the hybrid ML model also correlated well with predictions from a FEM (average relative error: 16.4 ± 10.2%). Finally, we used this hybrid MBM-ML approach to predict strains appearing in different locations throughout the brain, with average relative error estimates ranging from 8.6% to 25.2% for complex, multi-axial acceleration loading. Together, these results show a rapid and reasonably accurate method for predicting the mechanical response of the brain for single and multiplanar inputs, and provide a new tool for quickly assessing the consequences of impact loading throughout the brain.

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Understanding the risk of skin injury caused by impacts from blunt less lethal weapons (LLW) is critical for designing safer devices, but the tolerances of injury are still understudied. Previous research has utilized post-mortem human surrogates (PMHS) to investigate the injury thresholds of various soft tissues; however, PMHS testing is often limited by low sample size and questions surrounding the biofidelity of the tissue for approximating the living response. Animal surrogates are often used to supplement these known limitations. In this study, eight in situ ovine specimens were tested under high-rate, blunt impacts to assess the viability of this model as a surrogate for human skin. All tests were conducted using a 6.5 g, 19 mm (0.75 in.) diameter cylindrical impactor with impact velocities up to 162 m/s. Injury assessments were performed to study the effect of body region, tissue storage condition, and flesh temperature on the likelihood of skin injury. Injury risk functions (IRFs) were developed relating skin injury risk to impactor velocity and the impacted body region. Impact location was a significant factor for injury risk, with the tissues in the shoulder and ribs demonstrating lower tolerances for skin injury than in the stomach and rump. By the comparing IRF responses to that of PMHS, the cadaveric ovine tissue showed viability as a potential surrogate for human skin (from the upper chest and abdomen regions) under high-rate blunt impacts. Furthermore, there were no significant differences in IRFs produced with fresh and frozen-thawed tissue. Anecdotally, variation in flesh temperature produced different injury results, but statistically this effect was insignificant due to the limited sample size of the study.

The white matter tracts forming the intricate wiring of the brain are subject-specific; this heterogeneity can complicate studies of brain function and disease. Here we collapse tractography data from the Human Connectome Project (HCP) into structural connectivity (SC) matrices and identify groups of similarly wired brains from both sexes. To characterize the significance of these architectural groupings, we examined how similarly wired brains led to distinct groupings of neural activity dynamics estimated with Kuramoto oscillator models (KMs). We then lesioned our networks to simulate traumatic brain injury (TBI) and finally we tested whether these distinct architecture groups’ dynamics exhibited differing responses to simulated TBI. At each of these levels we found that brain structure, simulated dynamics, and injury susceptibility were all related to brain grouping. We found four primary brain architecture groupings (two male and two female), with similar architectures appearing across both sexes. Among these groupings of brain structure, two architecture types were significantly more vulnerable than the remaining two architecture types to lesions. These groups suggest that mesoscale brain architecture types exist, and these architectural differences may contribute to differential risks to TBI and clinical outcomes across the population.