Sport-related concussion (SRC) is a major health problem, affecting millions of athletes each year. While the clinical effects of SRC (e.g., symptoms and functional impairments) typically resolve within several days, increasing evidence suggests persistent neurophysiological abnormalities beyond the point of clinical recovery after injury. This study aimed to evaluate cerebral blood flow (CBF) changes in acute SRC, as measured using advanced arterial spin labeling (ASL) magnetic resonance imaging (MRI). We compared CBF maps assessed in 18 concussed football players (age, 17.8 ± 1.5 years) obtained within 24 h and at 8 days after injury with a control group of 19 matched non-concussed football players. While the control group did not show any changes in CBF between the two time-points, concussed athletes demonstrated a significant decrease in CBF at 8 days relative to within 24 h. Scores on the clinical symptom (Sport Concussion Assessment Tool 3, SCAT3) and cognitive measures (Standardized Assessment of Concussion [SAC]) demonstrated significant impairment (vs. pre-season baseline levels) at 24 h (SCAT, p < 0.0001; SAC, p < 0.01) but returned to baseline levels at 8 days. Two additional computerized neurocognitive tests, the Automated Neuropsychological Assessment Metrics and Immediate Post-Concussion and Cognitive Testing, showed a similar pattern of changes. These data support the hypothesis that physiological changes persist beyond the point of clinical recovery after SRC. Our results also indicate that advanced ASL MRI methods might be useful for detecting and tracking the longitudinal course of underlying neurophysiological recovery from concussion.

Objective To study longitudinal recovery trajectories of white matter after sports-related concussion (SRC) by performing diffusion tensor imaging (DTI) on collegiate athletes who sustained SRC. Methods Collegiate athletes (n = 219, 82 concussed athletes, 68 contact-sport controls, and 69 non–contact-sport controls) were included from the Concussion Assessment, Research and Education Consortium. The participants completed clinical assessments and DTI at 4 time points: 24 to 48 hours after injury, asymptomatic state, 7 days after return-to-play, and 6 months after injury. Tract-based spatial statistics was used to investigate group differences in DTI metrics and to identify white-matter areas with persistent abnormalities. Generalized linear mixed models were used to study longitudinal changes and associations between outcome measures and DTI metrics. Cox proportional hazards model was used to study effects of white-matter abnormalities on recovery time. Results In the white matter of concussed athletes, DTI-derived mean diffusivity was significantly higher than in the controls at 24 to 48 hours after injury and beyond the point when the concussed athletes became asymptomatic. While the extent of affected white matter decreased over time, part of the corpus callosum had persistent group differences across all the time points. Furthermore, greater elevation of mean diffusivity at acute concussion was associated with worse clinical outcome measures (i.e., Brief Symptom Inventory scores and symptom severity scores) and prolonged recovery time. No significant differences in DTI metrics were observed between the contact-sport and non–contact-sport controls. Conclusions Changes in white matter were evident after SRC at 6 months after injury but were not observed in contact-sport exposure. Furthermore, the persistent white-matter abnormalities were associated with clinical outcomes and delayed recovery time.

We evaluated cerebral blood flow (CBF) in chronic pediatric mild traumatic brain injury (mTBI) using arterial spin labeling (ASL) magnetic resonance imaging perfusion. mTBI patients showed lower CBF than controls in bilateral frontotemporal regions, with no between-group cognitive differences. Findings suggest ASL may be useful to assess functional abnormalities in pediatric mTBI.

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain‐age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain‐age has highlighted the need for robust and publicly available brain‐age models pre‐trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain‐age model. Here we expand this work to develop, empirically validate, and disseminate a pre‐trained brain‐age model to cover most of the human lifespan. To achieve this, we selected the best‐performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain‐age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre‐trained models were tested for cross‐dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age‐bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain‐age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open‐science, web‐based platform for individualized neuroimaging metrics.