Simultaneous MEG and EEG source imaging of electrophysiological activity in response to acute transcranial photobiomodulation.

Introduction: Transcranial photobiomodulation (tPBM) is a non-invasive neuromodulation technique that improves human cognition. The effects of tPBM of the right forehead on neurophysiological activity have been previously investigated using EEG in sensor space. However, the spatial resolution of these studies is limited. Magnetoencephalography (MEG) is known to facilitate a higher spatial resolution of brain source images. This study aimed to image post-tPBM effects in brain space based on both MEG and EEG measurements across the entire human brain. Methods: MEG and EEG scans were concurrently acquired for 6 min before and after 8-min of tPBM delivered using a 1,064-nm laser on the right forehead of 25 healthy participants. Group-level changes in both the MEG and EEG power spectral density with respect to the baseline (pre-tPBM) were quantified and averaged within each frequency band in the sensor space. Constrained modeling was used to generate MEG and EEG source images of post-tPBM, followed by cluster-based permutation analysis for family wise error correction (p < 0.05). Results: The 8-min tPBM enabled significant increases in alpha (8-12 Hz) and beta (13-30 Hz) powers across multiple cortical regions, as confirmed by MEG and EEG source images. Moreover, tPBM-enhanced oscillations in the beta band were located not only near the stimulation site but also in remote cerebral regions, including the frontal, parietal, and occipital regions, particularly on the ipsilateral side. Discussion: MEG and EEG results shown in this study demonstrated that tPBM modulates neurophysiological activity locally and in distant cortical areas. The EEG topographies reported in this study were consistent with previous observations. This study is the first to present MEG and EEG evidence of the electrophysiological effects of tPBM in the brain space, supporting the potential utility of tPBM in treating neurological diseases through the modulation of brain oscillations.

Magnetoencephalography Atlas Viewer for Dipole Localization and Viewing.

Magnetoencephalography (MEG) is a noninvasive neuroimaging technique widely recognized for epilepsy and tumor mapping. MEG clinical reporting requires a multidisciplinary team, including expert input regarding each dipole's anatomic localization. Here, we introduce a novel tool, the "Magnetoencephalography Atlas Viewer" (MAV), which streamlines this anatomical analysis. The MAV normalizes the patient's Magnetic Resonance Imaging (MRI) to the Montreal Neurological Institute (MNI) space, reverse-normalizes MNI atlases to the native MRI, identifies MEG dipole files, and matches dipoles' coordinates to their spatial location in atlas files. It offers a user-friendly and interactive graphical user interface (GUI) for displaying individual dipoles, groups, coordinates, anatomical labels, and a tri-planar MRI view of the patient with dipole overlays. It evaluated over 273 dipoles obtained in clinical epilepsy subjects. Consensus-based ground truth was established by three neuroradiologists, with a minimum agreement threshold of two. The concordance between the ground truth and MAV labeling ranged from 79% to 84%, depending on the normalization method. Higher concordance rates were observed in subjects with minimal or no structural abnormalities on the MRI, ranging from 80% to 90%. The MAV provides a straightforward MEG dipole anatomic localization method, allowing a nonspecialist to prepopulate a report, thereby facilitating and reducing the time of clinical reporting.

Magnetoencephalography (MEG) Data Processing in Epilepsy Patients with Implanted Responsive Neurostimulation (RNS) Devices.

Drug-resistant epilepsy (DRE) is often treated with surgery or neuromodulation. Specifically, responsive neurostimulation (RNS) is a widely used therapy that is programmed to detect abnormal brain activity and intervene with tailored stimulation. Despite the success of RNS, some patients require further interventions. However, having an RNS device in situ is a hindrance to the performance of neuroimaging techniques. Magnetoencephalography (MEG), a non-invasive neurophysiologic and functional imaging technique, aids epilepsy assessment and surgery planning. MEG performed post-RNS is complicated by signal distortions. This study proposes an independent component analysis (ICA)-based approach to enhance MEG signal quality, facilitating improved assessment for epilepsy patients with implanted RNS devices. Three epilepsy patients, two with RNS implants and one without, underwent MEG scans. Preprocessing included temporal signal space separation (tSSS) and an automated ICA-based approach with MNE-Python. Power spectral density (PSD) and signal-to-noise ratio (SNR) were analyzed, and MEG dipole analysis was conducted using single equivalent current dipole (SECD) modeling. The ICA-based noise removal preprocessing method substantially improved the signal-to-noise ratio (SNR) for MEG data from epilepsy patients with implanted RNS devices. Qualitative assessment confirmed enhanced signal readability and improved MEG dipole analysis. ICA-based processing markedly enhanced MEG data quality in RNS patients, emphasizing its clinical relevance.

MEG measured delta waves increase in adolescents after concussion.

INTRODUCTION: The purpose of this study is to determine if delta waves, measured by magnetoencephalography (MEG), increase in adolescents due to a sports concussion. METHODS: Twenty-four adolescents (age 14-17) completed pre- and postseason MRI and MEG scanning. MEG whole-brain delta power was calculated for each subject and normalized by the subject's total power. In eight high school football players diagnosed with a concussion during the season (mean age = 15.8), preseason delta power was subtracted from their postseason scan. In eight high school football players without a concussion (mean age = 15.7), preseason delta power was subtracted from postseason delta power and in eight age-matched noncontact controls (mean age = 15.9), baseline delta power was subtracted from a 4-month follow-up scan. ANOVA was used to compare the mean differences between preseason and postseason scans for the three groups of players, with pairwise comparisons based on Student's t-test method. RESULTS: Players with concussions had significantly increased delta wave power at their postseason scans than nonconcussed players (p = .018) and controls (p = .027). CONCLUSION: We demonstrate that a single concussion during the season in adolescent subjects can increase MEG measured delta frequency power at their postseason scan. This adds to the growing body of literature indicating increased delta power following a concussion.

Cumulative strain-based metrics for predicting subconcussive head impact exposure-related imaging changes in a cohort of American youth football players.

OBJECTIVE: Youth football athletes are exposed to repetitive subconcussive head impacts during normal participation in the sport, and there is increasing concern about the long-term effects of these impacts. The objective of the current study was to determine if strain-based cumulative exposure measures are superior to kinematic-based exposure measures for predicting imaging changes in the brain. METHODS: This prospective, longitudinal cohort study was conducted from 2012 to 2017 and assessed youth, male football athletes. Kinematic data were collected at all practices and games from enrolled athletes participating in local youth football organizations in Winston-Salem, North Carolina, and were used to calculate multiple risk-weighted cumulative exposure (RWE) kinematic metrics and 36 strain-based exposure metrics. Pre- and postseason imaging was performed at Wake Forest School of Medicine, and diffusion tensor imaging (DTI) measures, including fractional anisotropy (FA), and its components (CL, CP, and CS), and mean diffusivity (MD), were investigated. Included participants were youth football players ranging in age from 9 to 13 years. Exclusion criteria included any history of previous neurological illness, psychiatric illness, brain tumor, concussion within the past 6 months, and/or contraindication to MRI. RESULTS: A total of 95 male athletes (mean age 11.9 years [SD 1.0 years]) participated between 2012 and 2017, with some participating for multiple seasons, resulting in 116 unique athlete-seasons. Regression analysis revealed statistically significant linear relationships between the FA, linear coefficient (CL), and spherical coefficient (CS) and all strain exposure measures, and well as the planar coefficient (CP) and 8 strain measures. For the kinematic exposure measures, there were statistically significant relationships between FA and RWE linear (RWEL) and RWE combined probability (RWECP) as well as CS and RWEL. According to area under the receiver operating characteristic (ROC) curve (AUC) analysis, the best-performing metrics were all strain measures, and included metrics based on tensile, compressive, and shear strain. CONCLUSIONS: Using ROC curves and AUC analysis, all exposure metrics were ranked in order of performance, and the results demonstrated that all the strain-based metrics performed better than any of the kinematic metrics, indicating that strain-based metrics are better discriminators of imaging changes than kinematic-based measures. Studies relating the biomechanics of head impacts with brain imaging and cognitive function may allow equipment designers, care providers, and organizations to prevent, identify, and treat injuries in order to make football a safer activity.

Relationship Between Time-Weighted Head Impact Exposure on Directional Changes in Diffusion Imaging in Youth Football Players.

Approximately 3.5 million youth and adolescents in the US play football, a sport with one of the highest rates of concussion. Repeated subconcussive head impact exposure (HIE) may lead to negative neurological sequelae. To understand HIE as an independent predictive variable, quantitative cumulative kinematic metrics have been developed to capture the volume (i.e., number), severity (i.e., magnitude), and frequency (i.e., time-weighting by the interval between head impacts). In this study, time-weighted cumulative HIE metrics were compared with directional changes in diffusion tensor imaging (DTI) metrics. Changes in DTI conducted on a per-season, per-player basis were assessed as a dependent variable. Directional changes were defined separately as increases and decreases in the number of abnormal voxels relative to non-contact sport controls. Biomechanical and imaging data from 117 athletes (average age 11.9 ± 1.0 years) enrolled in this study was analyzed. Cumulative HIE metrics were more strongly correlated with increases in abnormal voxels than decreases in abnormal voxels. Additionally, across DTI sub-measures, increases and decreases in mean diffusivity (MD) had the strongest relationships with HIE metrics (increases in MD: average R2 = 0.1753, average p = 0.0002; decreases in MD: average R2 = 0.0997, average p = 0.0073). This encourages further investigation into the physiological phenomena represented by directional changes.

Analysis of longitudinal head impact exposure and white matter integrity in returning youth football players.

OBJECTIVE: The objective of this study was to characterize changes in head impact exposure (HIE) across multiple football seasons and to determine whether changes in HIE correlate with changes in imaging metrics in youth football players. METHODS: On-field head impact data and pre- and postseason imaging data, including those produced by diffusion tensor imaging (DTI), were collected from youth football athletes with at least two consecutive seasons of data. ANCOVA was used to evaluate HIE variations (number of impacts, peak linear and rotational accelerations, and risk-weighted cumulative exposure) by season number. DTI scalar metrics, including fractional anisotropy, mean diffusivity, and linear, planar, and spherical anisotropy coefficients, were evaluated. A control group was used to determine the number of abnormal white matter voxels, which were defined as 2 standard deviations above or below the control group mean. The difference in the number of abnormal voxels between consecutive seasons was computed for each scalar metric and athlete. Linear regression analyses were performed to evaluate relationships between changes in HIE metrics and changes in DTI scalar metrics. RESULTS: There were 47 athletes with multiple consecutive seasons of HIE, and corresponding imaging data were available in a subsample (n = 19) of these. Increases and decreases in HIE metrics were observed among individual athletes from one season to the next, and no significant differences (all p > 0.05) in HIE metrics were observed by season number. Changes in the number of practice impacts, 50th percentile impacts per practice session, and 50th percentile impacts per session were significantly positively correlated with changes in abnormal voxels for all DTI metrics. CONCLUSIONS: These results demonstrate a significant positive association between changes in HIE metrics and changes in the numbers of abnormal voxels between consecutive seasons of youth football. Reducing the number and frequency of head impacts, especially during practice sessions, may decrease the number of abnormal imaging findings from one season to the next in youth football.

Regional White Matter Diffusion Changes Associated with the Cumulative Tensile Strain and Strain Rate in Nonconcussed Youth Football Players.

The purpose of this study is to assess the relationship between regional white matter diffusion imaging changes and finite element strain measures in nonconcussed youth football players. Pre- and post-season diffusion-weighted imaging was performed in 102 youth football subject-seasons, in which no concussions were diagnosed. The diffusion data were normalized to the IXI template. Percent change in fractional anisotropy (%ΔFA) images were generated. Using data from the head impact telemetry system, the cumulative maximum principal strain one times strain rate (CMPS1 × SR), a measure of the cumulative tensile brain strain and strain rate for one season, was calculated for each subject. Two linear regression analyses were performed to identify significant positive or inverse relationships between CMPS1 × SR and %ΔFA within the international consortium for brain mapping white matter mask. Age, body mass index, days between pre- and post-season imaging, previous brain injury, attention disorder diagnosis, and imaging protocol were included as covariates. False discovery rate correction was used with corrected alphas of 0.025 and voxel thresholds of zero. Controlling for all covariates, a significant, positive linear relationship between %ΔFA and CMPS1 × SR was identified in the bilateral cingulum, fornix, internal capsule, external capsule, corpus callosum, corona radiata, corticospinal tract, cerebral and middle cerebellar peduncle, superior longitudinal fasciculus, and right superior fronto-occipital fasciculus. Post hoc analyses further demonstrated significant %ΔFA differences between high-strain football subjects and noncollision control athletes, no significant %ΔFA differences between low-strain subjects and noncollision control athletes, and that CMPS1 × SR significantly explained more %ΔFA variance than number of head impacts alone.

Mapping default mode connectivity alterations following a single season of subconcussive impact exposure in youth football.

Repetitive head impact (RHI) exposure in collision sports may contribute to adverse neurological outcomes in former players. In contrast to a concussion, or mild traumatic brain injury, "subconcussive" RHIs represent a more frequent and asymptomatic form of exposure. The neural network-level signatures characterizing subconcussive RHIs in youth collision-sport cohorts such as American Football are not known. Here, we used resting-state functional MRI to examine default mode network (DMN) functional connectivity (FC) following a single football season in youth players (n = 50, ages 8-14) without concussion. Football players demonstrated reduced FC across widespread DMN regions compared with non-collision sport controls at postseason but not preseason. In a subsample from the original cohort (n = 17), players revealed a negative change in FC between preseason and postseason and a positive and compensatory change in FC during the offseason across the majority of DMN regions. Lastly, significant FC changes, including between preseason and postseason and between in- and off-season, were specific to players at the upper end of the head impact frequency distribution. These findings represent initial evidence of network-level FC abnormalities following repetitive, non-concussive RHIs in youth football. Furthermore, the number of subconcussive RHIs proved to be a key factor influencing DMN FC.

Brain Strain: Computational Model-Based Metrics for Head Impact Exposure and Injury Correlation.

Athletes participating in contact sports are exposed to repetitive subconcussive head impacts that may have long-term neurological consequences. To better understand these impacts and their effects, head impacts are often measured during football to characterize head impact exposure and estimate injury risk. Despite widespread use of kinematic-based metrics, it remains unclear whether any single metric derived from head kinematics is well-correlated with measurable changes in the brain. This shortcoming has motivated the increasing use of finite element (FE)-based metrics, which quantify local brain deformations. Additionally, quantifying cumulative exposure is of increased interest to examine the relationship to brain changes over time. The current study uses the atlas-based brain model (ABM) to predict the strain response to impacts sustained by 116 youth football athletes and proposes 36 new, or derivative, cumulative strain-based metrics that quantify the combined burden of head impacts over the course of a season. The strain-based metrics developed and evaluated for FE modeling and presented in the current study present potential for improved analytics over existing kinematically-based and cumulative metrics. Additionally, the findings highlight the importance of accounting for directional dependence and expand the techniques to explore spatial distribution of the strain response throughout the brain.

BrainNET: Inference of Brain Network Topology Using Machine Learning.

Background: To develop a new functional magnetic resonance image (fMRI) network inference method, BrainNET, that utilizes an efficient machine learning algorithm to quantify contributions of various regions of interests (ROIs) in the brain to a specific ROI. Methods: BrainNET is based on extremely randomized trees to estimate network topology from fMRI data and modified to generate an adjacency matrix representing brain network topology, without reliance on arbitrary thresholds. Open-source simulated fMRI data of 50 subjects in 28 different simulations under various confounding conditions with known ground truth were used to validate the method. Performance was compared with correlation and partial correlation (PC). The real-world performance was then evaluated in a publicly available attention-deficit/hyperactivity disorder (ADHD) data set, including 134 typically developing children (mean age: 12.03, males: 83), 75 ADHD inattentive (mean age: 11.46, males: 56), and 93 ADHD combined (mean age: 11.86, males: 77) subjects. Network topologies in ADHD were inferred using BrainNET, correlation, and PC. Graph metrics were extracted to determine differences between the ADHD groups. Results: BrainNET demonstrated excellent performance across all simulations and varying confounders in identifying the true presence of connections. In the ADHD data set, BrainNET was able to identify significant changes (p < 0.05) in graph metrics between groups. No significant changes in graph metrics between ADHD groups were identified using correlation and PC. Conclusion: We describe BrainNET, a new network inference method to estimate fMRI connectivity that was adapted from gene regulatory methods. BrainNET out-performed Pearson correlation and PC in fMRI simulation data and real-world ADHD data. BrainNET can be used independently or combined with other existing methods as a useful tool to understand network changes and to determine the true network topology of the brain under various conditions and disease states. Impact statement Developed a new functional magnetic resonance image (fMRI) network inference method named as BrainNET using machine learning. BrainNET out-performed Pearson correlation and partial correlation in fMRI simulation data and real-world attention-deficit/hyperactivity disorder data. BrainNET does not need to be pretrained and can be applied to infer fMRI network topology independently on individual subjects and for varying number of nodes.

Evaluation of head impact exposure measured from youth football game plays.

OBJECTIVE: There is a growing body of literature informing efforts to improve the safety of football; however, research relating on-field activity to head impacts in youth football is limited. Therefore, the objective of this study was to compare head impact exposure (HIE) measured in game plays among 3 youth football teams. METHODS: Head impact and video data were collected from athletes (ages 10-13 years) participating on 3 youth football teams. Video analysis was performed to verify head impacts and assign each to a specific play type. Each play was categorized as a down, punt, kickoff, field goal, or false start. Kickoffs and punts were classified as special teams. Downs were classified as running, passing, or other. HIE was quantified by play type in terms of mean, median, and 95th percentile linear and rotational acceleration. Mixed-effects models were used to assess differences in acceleration among play types. Contact occurring on special teams plays was evaluated using a standardized video abstraction form. RESULTS: A total of 3003 head impacts over 27.5 games were analyzed and paired with detailed video coding of plays. Most head impacts were attributed to running (79.6%), followed by passing (14.0%), and special teams (6.4%) plays. The 95th percentile linear acceleration measured during each play type was 52.6g, 50.7g, and 65.5g, respectively. Special teams had significantly greater mean linear acceleration than running and passing plays (both p = 0.03). The most common kick result on special teams was a deep kick, of which 85% were attempted to be returned. No special teams plays resulted in a touchback, and one resulted in a fair catch. One-third of all special teams plays and 92% of all nonreturned kicks resulted in athletes diving toward the ball. CONCLUSIONS: The results demonstrate a trend toward higher head impact magnitudes on special teams than for running and passing plays, but a greater number of impacts were measured during running plays. Deep kicks were most common on special teams, and many returned and nonreturned kicks resulted in athletes diving toward the ball. These results support policy changes to youth special teams plays, including modifying the yard line the ball is kicked from and coaching proper return technique. Further investigation into biomechanical exposure measured during game impact scenarios is needed to inform policy relevant to the youth level.

In-Season Variations in Head Impact Exposure among Youth Football Players.

Head impact exposure (HIE) is often summarized by the total exposure measured during the season and does not indicate how the exposure was accumulated, or how it varied during the season. Therefore, the objective of this study was to compare HIE during pre-season, the first and second halves of the regular season, and playoffs in a sample of youth football players (n = 119, aged 9-13 years). Athletes were divided into one of four exposure groups based on quartiles computed from the distribution of risk-weighted cumulative exposure (RWECP). Mean impacts per session and mean 95th percentile linear and rotational acceleration in practices and games were compared across the four exposure groups and time frames using mixed effects models. Within games, the mean 95th percentile accelerations for the entire sample ranged from 47.2g and 2331.3 rad/sec2 during pre-season to 52.1g and 2533.4 rad/sec2 during the second half of regular season. Mean impacts per practice increased from pre-season to the second half of regular season and declined into playoffs among all exposure groups; however, the variation between time frames was not greater than two impacts per practice. Time of season had a significant relationship with mean 95th percentile linear and rotational acceleration in games (both, p = 0.01) but not with practice accelerations or impacts per session. The in-practice mean levels of 95th percentile linear and rotational acceleration remained fairly constant across the four time frames, but in games these changed over time depending on exposure group (interactions, p ≤ 0.05). The results of this study improve our understanding of in-season variations in HIE in youth football and may inform important opportunities for future interventions.

Microstructural correlates of 3D steady-state inhomogeneous magnetization transfer (ihMT) in the human brain white matter assessed by myelin water imaging and diffusion tensor imaging.

PURPOSE: To compare the recently introduced inhomogeneous magnetization transfer (ihMT) technique with more established MRI techniques including myelin water imaging (MWI) and diffusion tensor imaging (DTI), and to evaluate the microstructural attributes correlating with this new contrast method in the human brain white matter. METHODS: Eight adult healthy volunteers underwent T1 -weighted, ihMT, MWI, and DTI imaging on a 3T human scanner. The ihMT ratio (ihMTR), myelin water fraction (MWF), fractional anisotropy (FA), radial diffusivity (RD), axial diffusivity (AD), and mean diffusivity (MD) values were calculated from different white matter tracts. The angle ( θ ) between the directions of the principal eigenvector, as measured by DTI, and the main magnetic field was calculated for all voxels from various fiber tracts. The ihMTR was correlated with MWF and DTI metrics. RESULTS: A strong correlation was found between ihMTR and MWF (ρ = 0.77, P < 0.0001). This was followed by moderate to weak correlations between ihMTR and DTI metrics: RD (ρ = -0.30, P < 0.0001), FA (ρ = 0.20, P < 0.0001), MD (ρ = -0.19, P < 0.0001), AD (ρ = 0.02, P < 0.0001). A strong correlation was found between ihMTR and θ (ρ = -0.541, P < 0.0001). CONCLUSION: The strong correlation with myelin water imaging and its low coefficient of variation suggest that ihMT has the potential to become a new structural imaging marker of myelin. The substantial orientational dependence of ihMT should be taken into account when evaluating and quantitatively interpreting ihMT results.

Applications of Resting State Functional MR Imaging to Traumatic Brain Injury.

Traumatic brain injury (TBI) is an important public health issue. TBI includes a broad spectrum of injury severities and abnormalities. Functional MR imaging (fMR imaging), both resting state (rs) and task, has been used often in research to study the effects of TBI. Although rs-fMR imaging is not currently applicable in clinical diagnosis of TBI, computer-aided tools are making this a possibility for the future. Specifically, graph theory is being used to study the change in networks after TBI. Machine learning methods allow researchers to build models capable of predicting injury severity and recovery trajectories.

Head Impact Exposure in Youth Football: Comparing Age- and Weight-Based Levels of Play.

Approximately 5,000,000 athletes play organized football in the United States, and youth athletes constitute the largest proportion with ∼3,500,000 participants. Investigations of head impact exposure (HIE) in youth football have been limited in size and duration. The objective of this study was to evaluate HIE of athletes participating in three age- and weight-based levels of play within a single youth football organization over four seasons. Head impact data were collected using the Head Impact Telemetry (HIT) System. Mixed effects linear models were fitted, and Wald tests were used to assess differences in head accelerations and number of impacts among levels and session type (competitions vs. practices). The three levels studied were levels A (n = 39, age = 10.8 ± 0.7 years, weight = 97.5 ± 11.8 lb), B (n = 48, age = 11.9 ± 0.5 years, weight = 106.1 ± 13.8 lb), and C (n = 32, age = 13.0 ± 0.5 years, weight = 126.5 ± 18.6 lb). A total of 40,538 head impacts were measured. The median/95th percentile linear head acceleration for levels A, B, and C was 19.8/49.4g, 20.6/51.0g, and 22.0/57.9g, respectively. Level C had significantly greater mean linear acceleration than both levels A (p = 0.005) and B (p = 0.02). There were a significantly greater number of impacts per player in a competition than in a practice session for all levels (A, p = 0.0005, B, p = 0.0019, and C, p < 0.0001). Athletes at lower levels experienced a greater percentage of their high magnitude impacts (≥ 80g) in practice, whereas those at the highest level experienced a greater percentage of their high magnitude impacts in competition. These data improve our understanding of HIE within youth football and are an important step in making evidence-based decisions to reduce HIE.

Subconcussive Head Impact Exposure and White Matter Tract Changes over a Single Season of Youth Football.

Purpose To examine the effects of subconcussive impacts resulting from a single season of youth (age range, 8-13 years) football on changes in specific white matter (WM) tracts as detected with diffusion-tensor imaging in the absence of clinically diagnosed concussions. Materials and Methods Head impact data were recorded by using the Head Impact Telemetry system and quantified as the combined-probability risk-weighted cumulative exposure (RWECP). Twenty-five male participants were evaluated for seasonal fractional anisotropy (FA) changes in specific WM tracts: the inferior fronto-occipital fasciculus (IFOF), inferior longitudinal fasciculus, and superior longitudinal fasciculus (SLF). Fiber tracts were segmented into a central core and two fiber terminals. The relationship between seasonal FA change in the whole fiber, central core, and the fiber terminals with RWECP was also investigated. Linear regression analysis was conducted to determine the association between RWECP and change in fiber tract FA during the season. Results There were statistically significant linear relationships between RWEcp and decreased FA in the whole (R2 = 0.433; P = .003), core (R2 = 0.3649; P = .007), and terminals (R2 = 0.5666; P < .001) of left IFOF. A trend toward statistical significance (P = .08) in right SLF was observed. A statistically significant correlation between decrease in FA of the right SLF terminal and RWECP was also observed (R2 = 0.2893; P = .028). Conclusion This study found a statistically significant relationship between head impact exposure and change of FA fractional anisotropy value of whole, core, and terminals of left IFOF and right SLF's terminals where WM and gray matter intersect, in the absence of a clinically diagnosed concussion. © RSNA, 2016.

Abnormalities in Diffusional Kurtosis Metrics Related to Head Impact Exposure in a Season of High School Varsity Football.

The purpose of this study was to determine whether the effects of cumulative head impacts during a season of high school football produce changes in diffusional kurtosis imaging (DKI) metrics in the absence of clinically diagnosed concussion. Subjects were recruited from a high school football team and were outfitted with the Head Impact Telemetry System (HITS) during all practices and games. Biomechanical head impact exposure metrics were calculated, including: total impacts, summed acceleration, and Risk Weighted Cumulative Exposure (RWE). Twenty-four players completed pre- and post-season magnetic resonance imaging, including DKI; players who experienced clinical concussion were excluded. Fourteen subjects completed pre- and post-season Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT). DKI-derived metrics included mean kurtosis (MK), axial kurtosis (K axial), and radial kurtosis (K radial), and white matter modeling (WMM) parameters included axonal water fraction, tortuosity of the extra-axonal space, extra-axonal diffusivity (De axial and radial), and intra-axonal diffusivity (Da). These metrics were used to determine the total number of abnormal voxels, defined as 2 standard deviations above or below the group mean. Linear regression analysis revealed a statistically significant relationship between RWE combined probability (RWECP) and MK. Secondary analysis of other DKI-derived and WMM metrics demonstrated statistically significant linear relationships with RWECP after covariate adjustment. These results were compared with the results of DTI-derived metrics from the same imaging sessions in this exact same cohort. Several of the DKI-derived scalars (Da, MK, K axial, and K radial) explained more variance, compared with RWECP, suggesting that DKI may be more sensitive to subconcussive head impacts. No significant relationships between DKI-derived metrics and ImPACT measures were found. It is important to note that the pathological implications of these metrics are not well understood. In summary, we demonstrate a single season of high school football can produce DKI measurable changes in the absence of clinically diagnosed concussion.

Subconcussive impacts and imaging findings over a season of contact sports.

The effect of repeated subconcussive head impacts in youth and high school sports on the developing brain is poorly understood. Emerging neuroimaging data correlated with biomechanical exposure metrics are beginning to demonstrate relationships across a variety of modalities. The long-term consequences of these changes are unknown. A review of the currently available literature on the effect of subconcussive head impacts on youth and high school-age male football players provides compelling evidence for more focused studies of these effects in these vulnerable populations.

Abnormal white matter integrity related to head impact exposure in a season of high school varsity football.

The aim of this study was to determine whether the cumulative effects of head impacts from a season of high school football produce magnetic resonance imaging (MRI) measureable changes in the brain in the absence of clinically diagnosed concussion. Players from a local high school football team were instrumented with the Head Impact Telemetry System (HITS™) during all practices and games. All players received pre- and postseason MRI, including diffusion tensor imaging (DTI). Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) was also conducted. Total impacts and risk-weighted cumulative exposure (RWE), including linear (RWELinear), rotational (RWERotational), and combined components (RWECP), were computed from the sensor data. Fractional, linear, planar, and spherical anisotropies (FA, CL, CP, and CS, respectively), as well as mean diffusivity (MD), were used to determine total number of abnormal white matter voxels defined as 2 standard deviations above or below the group mean. Delta (post-preseason) ImPACT scores for each individual were computed and compared to the DTI measures using Spearman's rank correlation coefficient. None of the players analyzed experienced clinical concussion (N=24). Regression analysis revealed a statistically significant linear relationship between RWECP and FA. Secondary analyses demonstrated additional statistically significant linear associations between RWE (RWECP and RWELinear) and all DTI measures. There was also a strong correlation between DTI measures and change in Verbal Memory subscore of the ImPACT. We demonstrate that a single season of football can produce brain MRI changes in the absence of clinical concussion. Similar brain MRI changes have been previously associated with mild traumatic brain injury.