Resting State Electroencephalography and Sports-Related Concussion: A Systematic Review

I won't let you down; why 20% of Men's and Women's Super League players underreported suspected concussions

OBJECTIVES

Quantify and identify factors associated with concussion underreporting in Super League rugby league players.

DESIGN

Cross sectional survey.

METHODS

During the 2022 season preseason, 422 Men's and Women's Super League players completed an online survey quantifying player demographics, rugby playing history, concussion history, prevalence of, and reasons for underreporting concussion, concussion knowledge and long-term implications and perceptions of concussion.

RESULTS

Overall, 20% of respondents stated they did not report concussion-related symptoms to medical staff during the 2020 and 2021 seasons. The two most common reasons for underreporting concussion were 'didn't want to be ruled out of a match' (35%) and 'didn't want to let down team' (24%). 65% of players reported an appropriate level of knowledge about concussion and potential long-term implications at the start of their senior rugby career, versus 89% now. In relation to concussion knowledge, symptoms were correctly identified on 74% of occasions. 57% of players surveyed were concerned about the potential long-term implications from concussion, and 11% of players would encourage their/family members' children to not play rugby league.

CONCLUSIONS

The proportion of Super League players who did not report concussion symptoms was similar to rugby league players in Australia. The main reasons for not reporting concussion appeared to be due to perceptions of what is beneficial for the team, suggesting both performance and medical staff should collectively encourage players to report concussion. A player's attitude towards concussion is potentially an individual modifiable risk factor and should be considered within the concussion management of players.

The effects of sports-related concussion history on female adolescent brain activity and connectivity for bilateral lower extremity knee motor control

Sports-related concussions (SRCs) are associated with neuromuscular control deficits in athletes following return to play. However, the connection between SRC and potentially disrupted neural regulation of lower extremity motor control has not been investigated. The purpose of this study was to investigate brain activity and connectivity during a functional magnetic resonance imaging (fMRI) lower extremity motor control task (bilateral leg press) in female adolescent athletes with a history of SRC. Nineteen female adolescent athletes with a history of SRC and nineteen uninjured (without a history of SRC) age- and sport-matched control athletes participated in this study. Athletes with a history of SRC exhibited less neural activity in the left inferior parietal lobule/supramarginal gyrus (IPL) during the bilateral leg press compared to matched controls. Based upon signal change detected in the brain activity analysis, a 6 mm region of interest (seed) was defined to perform secondary connectivity analyses using psychophysiological interaction (PPI) analyses. During the motor control task, the left IPL (seed) was significantly connected to the right posterior cingulate gyrus/precuneus cortex and right IPL for athletes with a history of SRC. The left IPL was significantly connected to the left primary motor cortex (M1) and primary somatosensory cortex (S1), right inferior temporal gyrus, and right S1 for matched controls. Altered neural activity in brain regions important for sensorimotor integration and motor attention, combined with unique connectivity to regions responsible for attentional, cognitive, and proprioceptive processing, indicate compensatory neural mechanisms may underlie the lingering neuromuscular control deficits associated with SRC.

Electrophysiological variability during tests of executive functioning: A comparison of athletes with and without concussion and sedentary control participants

OBJECTIVE

Sport participation may benefit executive functioning (EF), but EF can also be adversely affected by concussion, which can occur during sport participation. Neural variability is an emerging proxy of brain health that indexes the brain's range of possible responses to incoming stimuli (i.e., dynamic range) and interconnectedness, but has yet to be characterized following concussion among athletes. This study examined whether neural variability was enhanced by athletic participation and attenuated by concussion.

METHOD

Seventy-seven participants (18-25 years-old) were classified as sedentary controls (n = 33), athletes with positive concussion history (n = 21), or athletes without concussion (n = 23). Participants completed tests of attention switching, response inhibition, and updating working memory while undergoing electroencephalography recordings to index neural variability.

RESULTS

Compared to sedentary controls and athletes without concussion, athletes with concussion exhibited a restricted whole-brain dynamic range of neural variability when completing a test of inhibitory control. There were no group differences observed for either the switching or working memory tasks.

CONCLUSIONS

A history of concussion was related to reduced dynamic range of neural activity during a task of response inhibition in young adult athletes. Neural variability may have value for evaluating brain health following concussion.

An Evaluation of the Emerging Techniques in Sports-Related Concussion

SUMMARY

Sports-related concussion is now in public awareness more than ever before. Investigations into underlying pathophysiology and methods of assessment have correspondingly increased at an exponential rate. In this review, we aim to highlight some of the evidence supporting emerging techniques in the fields of neurophysiology, neuroimaging, vestibular, oculomotor, autonomics, head sensor, and accelerometer technology in the setting of the current standard: clinical diagnosis and management. In summary, the evidence we reviewed suggests that (1) head impact sensors and accelerometers may detect possible concussions that would not otherwise receive evaluation; (2) clinical diagnosis may be aided by sideline vestibular, oculomotor, and portable EEG techniques; (3) clinical decisions on return-to-play eligibility are currently not sensitive at capturing the neurometabolic, cerebrovascular, neurophysiologic, and microstructural changes that biomarkers have consistently detected days and weeks after clinical clearance. Such biomarkers include heart rate variability, quantitative electroencephalography, as well as functional, metabolic, and microstructural neuroimaging. The current challenge is overcoming the lack of consistency and replicability of any one particular technique to reach consensus.

Neurophysiological Markers to Guide Return to Sport After Sport-Related Concussion

SUMMARY

Sport-related concussion (SRC) has been defined as a subset of mild traumatic brain injury (mTBI), without structural abnormalities, reflecting a functional disturbance. Over the past decade, SRC has gained increasing awareness and attention, which coincides with an increase in incidence rates. Because this injury has been considered one of the most challenging encounters for clinicians, there is a need for objective biomarkers to aid in diagnosis (i.e., presence/severity) and management (i.e., return to sport) of SRC/mTBI.The primary aim of this article was to present state-of-the-art neurophysiologic methods (e.g., electroencephalography, magnetoencephalography, transcranial magnetic stimulation, and autonomic nervous system) that are appropriate to investigate the complex pathophysiological process of a concussion. A secondary aim was to explore the potential for evidence-based markers to be used in clinical practice for SRC management. The article concludes with a discussion of future directions for SRC research with specific focus on clinical neurophysiology.

Time for Brain Medicine

Unprecedented knowledge of the brain is inevitably contributing to the convergence of neurology and psychiatry. However, clinical training continues to follow a divergent approach established in the 19th century. An etiological approach will continue to shift more psychiatric patients to the care of neurologists who are untrained in psychiatric management. At the same time, this new era of diagnostic biomarkers and neuroscience-based precision treatments requires skills not readily available to those trained in psychiatry. The challenges in training the next generation of doctors include establishing competence involving aspects of the whole brain, fostering the subspecialized expertise needed to remain current, and developing programs that are feasible in duration and practical in implementation. A new 4-year residency training program proposed in this article could replace existing residency programs. The program includes 2 years of common and urgent training in various aspects of neurology and psychiatry followed by 2 years of elective subspecialty tracks. The concept is similar to internal medicine residencies and fellowships. No changes to existing departmental structures are necessary. In concert with the emerging biological approach to the brain, "brain medicine" is proposed as a new name to denote this practice in the simplest terms: a focus on all aspects of the brain.

Concussed Neural Signature is Substantially Different than Fatigue Neural Signature in Non-concussed Controls

Traumatic brain injuries can result in short-lived and long-lasting neurological impairment. Identifying the correct recovery timeframe is challenging, as balance-based metrics may be negatively impacted if testing is performed soon after exercise. Thirty-two healthy controls and seventeen concussed individuals performed a series of balance challenges, including virtual reality optical flow perturbation. The control group completed a backpacking protocol to induce moderate fatigue. Concussed participants had lower spectral power in the motor cortex and central sulcus when compared to fatigued controls. Moreover, concussed participants experienced a decrease in overall theta band spectral power while fatigued controls showed an increase in theta band spectral power. This neural signature may be useful to distinguish between concussed and non-concussed fatigued participants in future assessments.

Towards defining biomarkers to evaluate concussions using virtual reality and a moving platform (BioVRSea)

Current diagnosis of concussion relies on self-reported symptoms and medical records rather than objective biomarkers. This work uses a novel measurement setup called BioVRSea to quantify concussion status. The paradigm is based on brain and muscle signals (EEG, EMG), heart rate and center of pressure (CoP) measurements during a postural control task triggered by a moving platform and a virtual reality environment. Measurements were performed on 54 professional athletes who self-reported their history of concussion or non-concussion. Both groups completed a concussion symptom scale (SCAT5) before the measurement. We analyzed biosignals and CoP parameters before and after the platform movements, to compare the net response of individual postural control. The results showed that BioVRSea discriminated between the concussion and non-concussion groups. Particularly, EEG power spectral density in delta and theta bands showed significant changes in the concussion group and right soleus median frequency from the EMG signal differentiated concussed individuals with balance problems from the other groups. Anterior–posterior CoP frequency-based parameters discriminated concussed individuals with balance problems. Finally, we used machine learning to classify concussion and non-concussion, demonstrating that combining SCAT5 and BioVRSea parameters gives an accuracy up to 95.5%. This study is a step towards quantitative assessment of concussion.

A Routine Electroencephalography Monitoring System for Automated Sports-Related Concussion Detection

Cases of concussions in the United States keep increasing and are now up to 2 million to 3 million incidents per year. Although concussions are recoverable and usually not life-threatening, the degree and rate of recovery may vary depending on age, severity of the injury, and past concussion history. A subsequent concussion before full recovery may lead to more-severe brain damage and poorer outcomes. Electroencephalography (EEG) recordings can identify brain dysfunctionality and abnormalities, such as after a concussion. Routine EEG monitoring can be a convenient method for reducing unreported injuries and preventing long-term damage, especially among groups with a greater risk of experiencing a concussion, such as athletes participating in contact sports. Because of the relative availability of EEG compared to other brain-imaging techniques (e.g., functional magnetic resonance imaging), the use of EEG monitoring is growing for various neurological disorders. In this longitudinal study, EEG was analyzed from 4 football athletes before their athletic season and also within 7 days of concussion. Compared to a control group of 4 additional athletes, a concussion was detected with up to 99.5% accuracy using EEG recordings in the Theta-Alpha band. Classifiers that use data from only a subset of the EEG electrodes providing reliable detection are also proposed. The most effective classifiers used EEG recordings from the Central scalp region in the Beta band and over the Temporal scalp region using the Theta-Alpha band. This proof-of-concept study and preliminary findings suggest that EEG monitoring may be used to identify a sports-related concussion occurrence with a high level of accuracy and thus reduce the chance of unreported concussion.

Deep Learning Recurrent Neural Network for Concussion Classification in Adolescents Using Raw Electroencephalography Signals: Toward a Minimal Number of Sensors

Artificial neural networks (ANNs) are showing increasing promise as decision support tools in medicine and particularly in neuroscience and neuroimaging. Recently, there has been increasing work on using neural networks to classify individuals with concussion using electroencephalography (EEG) data. However, to date the need for research grade equipment has limited the applications to clinical environments. We recently developed a deep learning long short-term memory (LSTM) based recurrent neural network to classify concussion using raw, resting state data using 64 EEG channels and achieved high accuracy in classifying concussion. Here, we report on our efforts to develop a clinically practical system using a minimal subset of EEG sensors. EEG data from 23 athletes who had suffered a sport-related concussion and 35 non-concussed, control athletes were used for this study. We tested and ranked each of the original 64 channels based on its contribution toward the concussion classification performed by the original LSTM network. The top scoring channels were used to train and test a network with the same architecture as the previously trained network. We found that with only six of the top scoring channels the classifier identified concussions with an accuracy of 94%. These results show that it is possible to classify concussion using raw, resting state data from a small number of EEG sensors, constituting a first step toward developing portable, easy to use EEG systems that can be used in a clinical setting.

Steady-state visual evoked potentials are unchanged following physical and cognitive exertion paradigms

Background

There is a need for objective biomarkers of sports-related concussion that are unaffected by physical and cognitive exertion. Electroencephalography-based biomarkers such as steady-state visually evoked potentials (SSVEPs) have been proposed as one such biomarker. The aim of this study was to investigate the effects of cognitive and physical exertion on SSVEP signal-to-noise ratio (SNR).

Methods

This study involved two experiments. The first experiment was performed in a controlled laboratory environment and involved a treadmill run designed to induce physical fatigue and a Stroop task designed to induce mental fatigue, completed in a randomized order on two separate visits. SSVEPs were evoked using a 15-Hz strobe using a Nurochek headset before and after each task. Changes in the 15-Hz SSVEP SNR and self-reported fatigue (visual analog scales) were assessed. In the second experiment, SSVEP SNR was measured before and after real-world boxing matches. Paired t-tests compared pre- and post-task SSVEP SNR and fatigue scores.

Results

Eighteen participants were recruited for experiment 1. Following the treadmill run, participants reported higher physical fatigue, mental fatigue, and overall fatigue ( p ≤ 0.005; d ≥ 0.90). Following the Stroop task, participants reported higher mental fatigue and overall fatigue ( p < 0.001; d ≥ 1.16), but not physical fatigue. SSVEP SNR scores were unchanged following either the Stroop task ( p = 0.059) or the treadmill task ( p = 0.590). Seven participants were recruited for experiment 2. SSVEP SNR scores were unchanged following the boxing matches ( p = 0.967).

Conclusions

The results of both experiments demonstrate that SSVEP SNR scores were not different following the treadmill run, Stroop task or amateur boxing match. These findings provide preliminary evidence that SSVEP fidelity may not be significantly affected by physical and cognitive exertion paradigms.

Brain Vital Signs in Elite Ice Hockey: Towards Characterizing Objective and Specific Neurophysiological Reference Values for Concussion Management

Background: Prior concussion studies have shown that objective neurophysiological measures are sensitive to detecting concussive and subconcussive impairments in youth ice-hockey. These studies monitored brain vital signs at rink-side using a within-subjects design to demonstrate significant changes from pre-season baseline scans. However, practical clinical implementation must overcome inherent challenges related to any dependence on a baseline. This requires establishing the start of normative reference data sets.

Methods: The current study collected specific reference data for N = 58 elite, youth, male ice-hockey players and compared these with a general reference dataset from N = 135 of males and females across the lifespan. The elite hockey players were recruited to a select training camp through CAA Hockey, a management agency for players drafted to leagues such as the National Hockey League (NHL). The statistical analysis included a test-retest comparison to establish reliability, and a multivariate analysis of covariance to evaluate differences in brain vital signs between groups with age as a covariate.

Findings: Test-retest assessments for brain vital signs evoked potentials showed moderate-to-good reliability (Cronbach’s Alpha > 0.7, Intraclass correlation coefficient > 0.5) in five out of six measures. The multivariate analysis of covariance showed no overall effect for group (p = 0.105), and a significant effect of age as a covariate was observed (p < 0.001). Adjusting for the effect of age, a significant difference was observed in the measure of N100 latency (p = 0.022) between elite hockey players and the heterogeneous control group.

Interpretation: The findings support the concept that normative physiological data can be used in brain vital signs evaluation in athletes, and should additionally be stratified for age, skill level, and experience. These can be combined with general norms and/or individual baseline assessments where appropriate and/or possible. The current results allow for brain vital sign evaluation independent of baseline assessment, therefore enabling objective neurophysiological evaluation of concussion management and cognitive performance optimization in ice-hockey.

Review of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuries

Mild traumatic brain injuries (mTBIs) are the most common type of brain injury. Timely diagnosis of mTBI is crucial in making 'go/no-go' decision in order to prevent repeated injury, avoid strenuous activities which may prolong recovery, and assure capabilities of high-level performance of the subject. If undiagnosed, mTBI may lead to various short- and long-term abnormalities, which include, but are not limited to impaired cognitive function, fatigue, depression, irritability, and headaches. Existing screening and diagnostic tools to detect acute andearly-stagemTBIs have insufficient sensitivity and specificity. This results in uncertainty in clinical decision-making regarding diagnosis and returning to activity or requiring further medical treatment. Therefore, it is important to identify relevant physiological biomarkers that can be integrated into a mutually complementary set and provide a combination of data modalities for improved on-site diagnostic sensitivity of mTBI. In recent years, the processing power, signal fidelity, and the number of recording channels and modalities of wearable healthcare devices have improved tremendously and generated an enormous amount of data. During the same period, there have been incredible advances in machine learning tools and data processing methodologies. These achievements are enabling clinicians and engineers to develop and implement multiparametric high-precision diagnostic tools for mTBI. In this review, we first assess clinical challenges in the diagnosis of acute mTBI, and then consider recording modalities and hardware implementation of various sensing technologies used to assess physiological biomarkers that may be related to mTBI. Finally, we discuss the state of the art in machine learning-based detection of mTBI and consider how a more diverse list of quantitative physiological biomarker features may improve current data-driven approaches in providing mTBI patients timely diagnosis and treatment.

Clinical Application of Combined EEG-qEEG Functional Neuroimaging in the Practice of Pediatric Neuroscience: A Personal Perspective

This brief article is an overview of my personal experience over the past almost 10 years of the clinical use of EEG and quantitative EEG (qEEG) functional neuroimaging in a busy pediatric neurology practice. The concomitant use of surface EEG and functional electromagnetic EEG neuroimaging/qEEG in clinical practice provides significant additional clinical and neurophysiologic information. The qEEG is a noninvasive, inexpensive, portable technique with high temporal resolution (milliseconds) and improving spatial resolution (down to 3 mm³) and is an appropriate and validated tool for investigation of abnormal brain dynamics and connectivity of neuronal networks in clinical disorders of the brain. This article describes the daily applicability and utility of this modality in assisting diagnosis and clinical management of patients with a wide variety of presenting symptoms, including headaches, tics, autism spectrum disorder, inattention, sleep dysregulation, anxiety, and depression. The ease of data acquisition and analysis in clinical practices, coupled with skilled interpretation and clinical application, makes this tool one of the most valuable clinical tools to complement a thorough history and examination process.

Validation of a Machine Learning Brain Electrical Activity-Based Index to Aid in Diagnosing Concussion Among Athletes

IMPORTANCE

An objective, reliable indicator of the presence and severity of concussive brain injury and of the readiness for the return to activity has the potential to reduce concussion-related disability.

OBJECTIVE

To validate the classification accuracy of a previously derived, machine learning, multimodal, brain electrical activity-based Concussion Index in an independent cohort of athletes with concussion.

DESIGN, SETTING, AND PARTICIPANTS

This prospective diagnostic cohort study was conducted at 10 clinical sites (ie, US universities and high schools) between February 4, 2017, and March 20, 2019. A cohort comprising a consecutive sample of 207 athletes aged 13 to 25 years with concussion and 373 matched athlete controls without concussion were assessed with electroencephalography, cognitive testing, and symptom inventories within 72 hours of injury, at return to play, and 45 days after return to play. Variables from the multimodal assessment were used to generate a Concussion Index at each time point. Athletes with concussion had experienced a witnessed head impact, were removed from play for 5 days or more, and had an initial Glasgow Coma Scale score of 13 to 15. Participants were excluded for known neurologic disease or history within the last year of traumatic brain injury. Athlete controls were matched to athletes with concussion for age, sex, and type of sport played.

MAIN OUTCOMES AND MEASURES

Classification accuracy of the Concussion Index at time of injury using a prespecified cutoff of 70 or less (total range, 0-100, where ≤70 indicates it is likely the individual has a concussion and >70 indicates it is likely the individual does not have a concussion).

RESULTS

Of 580 eligible participants with analyzable data, 207 had concussion (124 male participants [59.9%]; mean [SD] age, 19.4 [2.5] years), and 373 were athlete controls (187 male participants [50.1%]; mean [SD] age, 19.6 [2.2] years). The Concussion Index had a sensitivity of 86.0% (95% CI, 80.5%-90.4%), specificity of 70.8% (95% CI, 65.9%-75.4%), negative predictive value of 90.1% (95% CI, 86.1%-93.3%), positive predictive value of 62.0% (95% CI, 56.1%-67.7%), and area under receiver operator characteristic curve of 0.89. At day 0, the mean (SD) Concussion Index among athletes with concussion was significantly lower than among athletes without concussion (75.0 [14.0] vs 32.7 [27.2]; P < .001). Among athletes with concussion, there was a significant increase in the Concussion Index between day 0 and return to play, with a mean (SD) paired difference between these time points of -41.2 (27.0) (P < .001).

CONCLUSIONS AND RELEVANCE

These results suggest that the multimodal brain activity-based Concussion Index has high classification accuracy for identification of the likelihood of concussion at time of injury and may be associated with the return to control values at the time of recovery. The Concussion Index has the potential to aid in the clinical diagnosis of concussion and in the assessment of athletes' readiness to return to play.

The Neurophysiological Responses of Concussive Impacts: A Systematic Review and Meta-Analysis of Transcranial Magnetic Stimulation Studies

Aim: This systematic review and meta-analysis investigated neurophysiological responses using transcranial magnetic stimulation (TMS) following a concussion or sub-concussion.

Methods: A systematic searching of relevant databases for peer-reviewed literature quantifying motor evoked potentials from TMS between 1999 and 2019 was performed. A meta-analysis quantified pooled data for measures including motor threshold, motor latency, and motor evoked potential amplitude and for inhibitory measures such as cortical silent period duration, short-interval intracortical inhibition (SICI), and long-interval intracortical inhibition (LICI) ratios.

Results: Fifteen articles met the inclusion criteria. The studies were arbitrarily classified into the groups, based on time post-concussion, “acute” (subjects 0–3 months post-injury, n = 8) and “post-acute” (3 months−2 years post-concussion, n = 7). A TMS quality of study checklist rated studies from moderate to high in methodological quality; however, the risk of bias analysis found that the included studies were categorised as high risk of bias, particularly for a lack of allocation concealment and blinding of participants in the methodologies. A meta-analysis showed no differences in excitability measures, apart from a decreased motor threshold that was observed in the concussed group (SMD −0.28, 95% CI −0.51 to −0.04; P = 0.02) for the post-acute time frame. Conversely, all inhibitory measures showed differences between groups. Cortical silent period duration was found to be significantly increased in the acute (SMD 1.19, 95% CI 0.58–1.81; P < 0.001) and post-acute (SMD 0.55, 95% CI 0.12–0.98; P = 0.01) time frames. The SICI (SMD −1.15, 95% CI −1.95 to −0.34; P = 0.005) and LICI (SMD −1.95, 95% CI −3.04 to −0.85; P = 0.005) ratios were reduced, inferring increased inhibition, for the post-acute time frame.

Conclusion: This systematic review and meta-analysis demonstrates that inhibitory pathways are affected in the acute period post-concussion. However, persistent alterations in cortical excitability remain, with increased intracortical inhibition. While TMS should be considered as a reliable technique to measure the functional integrity of the central nervous system, the high risk of bias and heterogeneity in data suggest that future studies should aim to incorporate standardised methodological techniques, particularly with threshold determination and stimulus intervals for paired-pulse measures.

Acute cognitive postconcussive symptoms follow longer recovery trajectories than somatic postconcussive symptoms in young children

Objective: To investigate somatic and cognitive postconcussive symptoms (PCS) using the symptom evaluation subtest (cSCAT3-SE) of the Child Sports Concussion Assessment Tool 3 (Child SCAT) in tracking PCS up to 2 weeks postinjury.Methods: A total of 96 participants aged 5 to 12 years (M_age = 9.55, SD = 2.20) completed three assessment time points: 48 h postinjury (T0), 2 to 4 days postinjury (T1), and 2 weeks postinjury (T2). The Wilcoxon signed-rank test was used to analyze differences between cognitive and somatic symptoms over time, while the Friedman test was used to analyze differences within symptom type over time.Results: Cognitive PCS were found to be significantly higher than somatic PCS at all assessment time points and were also found to significantly decline from 4 days onwards postinjury; in contrast, somatic PCS significantly declined as early as 48 hpostinjury.Discussion: Differences between cognitive and somatic PCS emerge as early as a few days postinjury, with cognitive PCS being more persistent than somatic PCS across 2 weeks. Research in symptom-specific interventions may be of benefit in helping young children manage severe PCS as early as 2 weeks postinjury.

Frontal theta-gamma ratio is a sensitive index of concussion history in athletes on tasks of visuo-motor control

Patients with mTBI often show deficits in executive function and changes in neural activity. Similar changes in those with a history of mTBI (i.e. concussion), however, have not been consistently reported. Frontal theta-to-gamma frequency ratio has shown promise in EEG research in predicting performance on working memory tasks. In the present study we explored the sensitivity of the frontal theta-to-gamma relative power spectral density (PSD) ratio to the history of concussion in 81 youth athletes (18 with a history of concussion, ages 13–18) during the tests of the Nike Sensory Training Station that vary in working memory and processing speed demands and motor output requirements. The results showed that the theta-to-gamma relative PSD ratio was significantly lower in the concussion history group on the tests of target capture, perception span and hand reaction time. A principle component analysis further indicated that this metric reflects an underlying dimension shared by several visuo-motor control tests of the Nike battery. The results suggested persistent deficits in psychomotor ability in the athletes with a history of concussion that may have implications for diagnosis, rehabilitation and athletic training.

Idarucizumab in major trauma patients: a single centre real life experience

INTRODUCTION

Trauma care providers are facing an increasing number of elderly patients on direct oral anticoagulants prior to injury. For dabigatran etexilate (DAB), the specific antagonist idarucizumab (IDA) has been approved since 2015 as a reversal agent. However, only limited data regarding the use of IDA in trauma patients are available.

METHODS

We performed a retrospective analysis of trauma patients under DAB for whom IDA administration was deemed necessary to reverse DAB's antithrombotic effect.

RESULTS

A total of 15 (9 male) patients were treated with IDA during the study period. The mean age was 81 ± 10 years. Intracranial haemorrhage (n = 7) and long bone fractures (n = 5) were the most common types of injury. Three patients were diagnosed as polytrauma. In all but one patient, atrial fibrillation was the indication for DAB intake. The median dose of IDA was 2.5 g (IQR 2.5-5). IDA administration decreased DAB plasma levels from 112.4 (IQR 73.4-123.4) to 5 (IQR 4-12) ng/mL (p = 0.031), thrombin time from 114.8 ± 48.3 to 16.2 ± 0.5 s (p < 0.0001) and activated partial thromboplastin time form 45.4 ± 11.3 to 34.2 ± 7.0 s (p = 0.0025). No thromboembolic events or side effects attributed to IDA were observed. All patients survived until hospital discharge.

CONCLUSIONS

In trauma patients under DAB prior to injury, IDA decreased DAB plasma levels and normalized coagulation parameters. IDA appears to be safe, and no serious side effects were observed in this small cohort of patients.

Objective Classification of mTBI Using Machine Learning on a Combination of Frontopolar Electroencephalography Measurements and Self-reported Symptoms

BACKGROUND

The reliable diagnosis of a mild traumatic brain injury (mTBI) is a pervasive problem in sports and in the military. The frequency and severity of each occurrence, while difficult to quantify, may impact long term cognitive function and quality of life. Despite the new revelations concerning brain disfunction from head injuries, individuals still feel pressure to remain on the field despite a debilitating injury. In this study, we evaluated the accuracy of a system that could be employed on the sidelines or in the locker room to provide an immediate objective mTBI assessment.

METHODS

Participants consisted of 38 individuals with a recent mTBI and 47 controls with no history of mTBI within the last 5 years. Participants were administered a simple symptom questionnaire, behavioral tests, and resting state EEG was measured using three frontopolar electrodes. An advanced machine learning algorithm called boosting was utilized to classify subjects into either injured or controls using power spectral densities on 1-min of resting EEG and the symptom questionnaire.

RESULTS

Results based on leave-one-out cross-validation revealed that the addition of EEG measurements boosted the accuracy to approximately 91 ± 2% compared to 82 ± 4% from the symptom questionnaire alone.

CONCLUSION

This study demonstrated the potential benefit of including EEG measurements to diagnose suspected brain injury patients. This is a step toward accurate and objective classification measurements that can be implemented on the field as a future injury assessment tool.