Subconcussive (SC) impacts have become a growing concern within the neuroscience community regarding the immediate and long-lasting effects of sports-related injuries. While a single low-level impact, i.e., a subconcussion, may not cause cerebral perturbations, it has been increasingly recognized that repeated SC exposure can induce deleterious effects. Therefore, determining the lower limits of systematic perturbation resulting from multiple SC impacts is of critical importance in expanding our understanding of cerebral vulnerability and recovery. Currently, there is a lack of correlation between a mild traumatic brain injury (mTBI) and repeated SC impacts with respect to injury biomechanics. Moreover, the cumulative threshold for repetitive low-level impacts is currently undefined. Thus, this research was designed to determine the pathophysiological differences between a single impact of an mTBI and repeated SC impacts with a subdivided cumulative kinetic energy of the single mTBI impact. In order to address this gap in knowledge, the present investigation employed a surgery-free, closed-head, weight drop injury device capable of producing repeatable, head impacts within a rat model. General locomotion and anxiety-like behavior were assessed using an Open Field Test and motor coordination dysfunction was measured using the rotarod assay. Neuroinflammation was measured using immunohistochemical assessment of astrogliosis (GFAP) and microgliosis (Iba-1) within the hippocampus. Additionally, immunohistochemical assessment of neuronal loss (NeuN) was measured within the hippocampus. To investigate the tolerance and the persistence of cerebral vulnerability following a single mTBI and repeated subconcussive impacts, measurement outcomes were assessed over two-time points (3- and 7-days) post final impact. Although injury groups were not statistically different from their associated sham groups with respect to behavioral outcomes; on average, RSC injury rats displayed a significant increase in anxious-like behavior after 7-days of recovery compared to the single mTBI group. From an inflammatory perspective, both mTBI and RSC injury groups led to extensive microgliosis in the gray matter following 3-days post-impact. Overall, this work’s findings do not provide evidence in support of the notion that repeated subconcussive impacts do result in behavioral disturbances and neuroinflammation, that do not manifest following a single mTBI of the same energy input.

<div>Adolescent collision-sport athletes may be exposed to repetitive head impacts over years of practices and competitions without immediately observable symptoms. Despite the growing concerns, these athletes often continue play while at risk. Concrete objective measurements are desired to inform prompt and effective preventative strategies for this vulnerable population. However, adolescent brains are rapidly developing and the accrual of brain injury is often subtle. Prospective screening with sensitive biomarkers is challenging and requires advanced technologies, rigorous data processing, and the interdisciplinary expertise of engineering, neurobiology, and cognitive sciences.</div><div><br></div><div>To address the challenge, we first developed population-specific brain atlases to facilitate reproducible and meaningful statistical analyses. The atlases better characterized the neuroanatomy of early-to-middle adolescent (ages 13-19) collision-sport athletes, reduced deformation introduced during spatial normalization, and exhibited higher sensitivity in image analysis compared to standardized adult or age-appropriate brain templates. The atlases can be further applied to monitor the neuroanatomical trajectory and can serve as a coordinate reference system to retrospectively harmonize data collected from different sites and imaging acquisition parameters, facilitating group analysis at large scale.</div><div><br></div><div>Next, to assess whether the changes of white matter microstructure can be attributed to repetitive head impacts and are reflected by cognitive performance, we analysed the diffusion tensor imaging (DTI) data of high school men’s football and women's soccer across a single season, with accompanying data from head impact sensors and neurocognitive assessments. Within multiple brain regions, we observed significantly altered DTI metrics, both transiently over a season and chronically with more years of high school experience. For the football players, hits with peak translational acceleration over 37 <i>g</i> were sufficient to alter the distributions of DTI changes, and deficits in white matter microstructure correlated with poorer performance of anti-saccade task at one month post-season, suggesting increased vulnerability for inhibitory control. Monitoring repetitive head impacts thus provides a temporal profile for identifying at-risk individuals during the competitive season, informing prompt interventional strategies, therefore protecting the brain and cognitive health of early-to-middle adolescent collision-sport athletes in the long run.</div>

Diffusion Tensor Imaging (DTI) is a magnetic resonance imaging (MRI)-based technique that has frequently been used for the identification of brain biomarkers of neurodevelopmental and neurodegenerative disorders because of its ability to assess the structural organization of brain tissue. In this work, I present (1) preclinical findings of a longitudinal DTI study that investigated asymptomatic high school football athletes who experienced repetitive head impact and (2) an automated pipeline for assessing the quality of DTI images that uses a convolutional neural network (CNN) and transfer learning. The first section addresses the effects of repetitive subconcussive head trauma on the white matter of adolescent brains. Significant concerns exist regarding sub-concussive injury in football since many studies have reported that repetitive blows to the head may change the microstructure of white matter. This is more problematic in youth-aged athletes whose white matter is still developing. Using DTI and head impact monitoring sensors, regions of significantly altered white matter were identified and within-season effects of impact exposure were characterized by identifying the volume of regions showing significant changes for each individual. The second section presents a novel pipeline for DTI quality control (QC). The complex nature and long acquisition time associated with DTI make it susceptible to artifacts that often result in inferior diagnostic image quality. We propose an automated QC algorithm based on a deep convolutional neural network (DCNN). Adaptation of transfer learning makes it possible to train a DCNN with a relatively small dataset in a short time. The QA algorithm detects not only motion- or gradient-related artifacts, but also various erroneous acquisitions, including images with regional signal loss or those that have been incorrectly imaged or reconstructed.