A multimodal biomarker for concussion identification, prognosis and management

Point-of-Care Electroencephalography in Acute Neurological Care: A Narrative Review

Point-of-care electroencephalography (POC-EEG) systems are rapid-access, reduced-montage devices designed to address the limitations of conventional EEG (conv-EEG), enabling faster neurophysiological assessment in acute settings. This review evaluates their clinical impact, diagnostic performance, and feasibility in non-convulsive status epilepticus (NCSE), traumatic brain injury (TBI), stroke, and delirium. A comprehensive search of Medline, Scopus, and Embase identified 69 studies assessing 15 devices. In suspected NCSE, POC-EEG facilitates rapid seizure detection and prompt diagnosis, making it particularly effective in time-sensitive and resource-limited settings. Its after-hours availability and telemedicine integration ensure continuous coverage. AI-assisted tools enhance interpretability and accessibility, enabling use by non-experts. Despite variability in accuracy, it supports triaging, improving management, treatment decisions and outcomes while reducing hospital stays, transfers, and costs. In TBI, POC-EEG-derived quantitative EEG (qEEG) indices reliably detect structural lesions, support triage, and minimize unnecessary CT scans. They also help assess concussion severity and predict recovery. For strokes, POC-EEG aids triage by detecting large vessel occlusions (LVOs) with high feasibility in hospital and prehospital settings. In delirium, spectral analysis and AI-assisted models enhance diagnostic accuracy, broadening its clinical applications. Although POC-EEG is a promising screening tool, challenges remain in diagnostic variability, technical limitations, and AI optimization, requiring further research.

Identification of Concussion Subtypes Based on Intrinsic Brain Activity

Importance

The identification of brain activity-based concussion subtypes at time of injury has the potential to advance the understanding of concussion pathophysiology and to optimize treatment planning and outcomes.

Objective

To investigate the presence of intrinsic brain activity-based concussion subtypes, defined as distinct resting state quantitative electroencephalography (qEEG) profiles, at the time of injury.

Design, Setting, and Participants

In this retrospective, multicenter (9 US universities and high schools and 4 US clinical sites) cohort study, participants aged 13 to 70 years with mild head injuries were included in longitudinal cohort studies from 2017 to 2022. Patients had a clinical diagnosis of concussion and were restrained from activity by site guidelines for more than 5 days, with an initial Glasgow Coma Scale score of 14 to 15. Participants were excluded for known neurological disease or history of traumatic brain injury within the last year. Patients were assessed with 2 minutes of artifact-free EEG acquired from frontal and frontotemporal regions within 120 hours of head injury. Data analysis was performed from July 2021 to June 2023.

Main Outcomes and Measures

Quantitative features characterizing the EEG signal were extracted from a 1- to 2-minute artifact-free EEG data for each participant, within 120 hours of injury. Symptom inventories and days to return to activity were also acquired.

Results

From the 771 participants (mean [SD] age, 20.16 [5.75] years; 432 male [56.03%]), 600 were randomly selected for cluster analysis according to 471 qEEG features. Participants and features were simultaneously grouped into 5 disjoint subtypes by a bootstrapped coclustering algorithm with an overall agreement of 98.87% over 100 restarts. Subtypes were characterized by distinctive profiles of qEEG measure sets, including power, connectivity, and complexity, and were validated in the independent test set. Subtype membership showed a statistically significant association with time to return to activity.

Conclusions and Relevance

In this cohort study, distinct subtypes based on resting state qEEG activity were identified within the concussed population at the time of injury. The existence of such physiological subtypes supports different underlying pathophysiology and could aid in personalized prognosis and optimization of care path.

Median Time to Return to Sports After Concussion Is Within 21 Days in 80% of Published Studies

PURPOSE

To perform a systematic review of the literature and evaluate the return to play (RTP) time frame after a concussion diagnosis. Our secondary purpose was to analyze and compare different prognostic variables affecting concussions, time to return to school, time to symptom resolution of concussive symptoms, and time each patient spent in the RTP protocol.

METHODS

A PubMed, Scopus, Medline, Embase, and Cochrane Library database literature review was performed in August 2022. The studies needed to report, in days, the length of time a patient/athlete was removed from play due to concussion management. The Risk of Bias in Non-Randomized Studies of Interventions tool was used for risk of bias for each study, and Methodological Index for Non-Randomized Studies criteria were used for quality assessment.

RESULTS

There were 65 studies included in the systematic review and a total of 21,966 patients evaluated. The RTP time intervals ranged from 1 to 1,820 days, with 80.7% of the median RTP time frames for each study within 21 days. Preconcussion risk factors for prolonged RTP included female sex, younger age, presence of psychiatric disorders, and history of previous concussion. Postconcussion risk factors included severe symptom scores at initial clinic visit, loss of consciousness, nonelite athletes, and delayed removal from competition. The most common sports resulting in concussion were contact sports, most commonly football and soccer. Median time to return to school was 3 to 23 days. Median time to symptom resolution ranged from 2 to 11 days. Median time in RTP protocol was 1 to 6 days.

CONCLUSIONS

Median time to return to sports after concussion is within 21 days in 80% of published studies.

LEVEL OF EVIDENCE

IV, systematic review of Level I to IV studies.

Long-Term Changes in Brain Connectivity Reflected in Quantitative Electrophysiology of Symptomatic Former National Football League Players

Exposure to repetitive head impacts (RHI) has been associated with long-term disturbances in cognition, mood, and neurobehavioral dysregulation, and reflected in neuroimaging. Distinct patterns of changes in quantitative features of the brain electrical activity (quantitative electroencephalogram [qEEG]) have been demonstrated to be sensitive to brain changes seen in neurodegenerative disorders and in traumatic brain injuries (TBI). While these qEEG biomarkers are highly sensitive at time of injury, the long-term effects of exposure to RHI on brain electrical activity are relatively unexplored. Ten minutes of eyes closed resting EEG data were collected from a frontal and frontotemporal electrode montage (BrainScope Food and Drug Administration-cleared EEG acquisition device), as well as assessments of neuropsychiatric function and age of first exposure (AFE) to American football. A machine learning methodology was used to derive a qEEG-based algorithm to discriminate former National Football League (NFL) players (n = 87, 55.40 ± 7.98 years old) from same-age men without history of RHI (n = 68, 54.94 ± 7.63 years old), and a second algorithm to discriminate former players with AFE <12 years (n = 33) from AFE ≥12 years (n = 54). The algorithm separating NFL retirees from controls had a specificity = 80%, a sensitivity = 60%, and an area under curve (AUC) = 0.75. Within the NFL population, the algorithm separating AFE <12 from AFE ≥12 resulted in a sensitivity = 76%, a specificity = 52%, and an AUC = 0.72. The presence of a profile of EEG abnormalities in the NFL retirees and in those with younger AFE includes features associated with neurodegeneration and the disruption of neuronal transmission between regions. These results support the long-term consequences of RHI and the potential of EEG as a biomarker of persistent changes in brain function.

The Power of Public-Private Partnership in Medical Technology Innovation: Lessons From the Development of Fda-Cleared Medical Devices for Assessment of Concussion

Given the convergence of the long and challenging development path for medical devices with the need for diagnostic capabilities for mild traumatic brain injury (mTBI/concussion), the effective role of public–private partnership (PPP) can be demonstrated to yield Food and Drug Administration (FDA) clearances and innovative product introductions. An overview of the mTBI problem and landscape was performed. A detailed situation analysis of an example of a PPP yielding an innovative product was further demonstrated. The example of PPP has led to multiple FDA clearances and product introductions in the TBI diagnostic product category where there was an urgent military and public need. Important lessons included defining the primary public and military health objective for new product introduction, the importance of the government–academia–industry PPP triad with a “collaboration towards solutions” Quality-by-Design (QbD) mindset to assure clinical validity with regulatory compliance, the development of device comparators and integration of measurements into a robust, evidence-based statistical and FDA pathway, and the utility of top-down, flexible, practical action while operating within governmental guidelines and patient safety.

Putative Concussion Biomarkers Identified in Adolescent Male Athletes Using Targeted Plasma Proteomics

Sport concussions can be difficult to diagnose and if missed, they can expose athletes to greater injury risk and long-lasting neurological disabilities. Discovery of objective biomarkers to aid concussion diagnosis is critical to protecting athlete brain health. To this end, we performed targeted proteomics on plasma obtained from adolescent athletes suffering a sports concussion. A total of 11 concussed male athletes were enrolled at our academic Sport Medicine Concussion Clinic, as well as 24 sex-, age- and activity-matched healthy control subjects. Clinical evaluation was performed and blood was drawn within 72 h of injury. Proximity extension assays were performed for 1,472 plasma proteins; a total of six proteins were considered significantly different between cohorts (P < 0.01; five proteins decreased and one protein increased). Receiver operating characteristic curves on the six individual protein biomarkers identified had areas-under-the-curves (AUCs) for concussion diagnosis ≥0.78; antioxidant 1 copper chaperone (ATOX1; AUC 0.81, P = 0.003), secreted protein acidic and rich in cysteine (SPARC; AUC 0.81, P = 0.004), cluster of differentiation 34 (CD34; AUC 0.79, P = 0.006), polyglutamine binding protein 1 (PQBP1; AUC 0.78, P = 0.008), insulin-like growth factor-binding protein-like 1 (IGFBPL1; AUC 0.78, P = 0.008) and cytosolic 5'-nucleotidase 3A (NT5C3A; AUC 0.78, P = 0.009). Combining three of the protein biomarkers (ATOX1, SPARC and NT5C3A), produced an AUC of 0.98 for concussion diagnoses (P < 0.001; 95% CI: 0.95, 1.00). Despite a paucity of studies on these three identified proteins, the available evidence points to their roles in modulating tissue inflammation and regulating integrity of the cerebral microvasculature. Taken together, our exploratory data suggest that three or less novel proteins, which are amenable to a point-of-care immunoassay, may be future candidate biomarkers for screening adolescent sport concussion. Validation with protein assays is required in larger cohorts.

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.

Phybrata Sensors and Machine Learning for Enhanced Neurophysiological Diagnosis and Treatment

Concussion injuries remain a significant public health challenge. A significant unmet clinical need remains for tools that allow related physiological impairments and longer-term health risks to be identified earlier, better quantified, and more easily monitored over time. We address this challenge by combining a head-mounted wearable inertial motion unit (IMU)-based physiological vibration acceleration (“phybrata”) sensor and several candidate machine learning (ML) models. The performance of this solution is assessed for both binary classification of concussion patients and multiclass predictions of specific concussion-related neurophysiological impairments. Results are compared with previously reported approaches to ML-based concussion diagnostics. Using phybrata data from a previously reported concussion study population, four different machine learning models (Support Vector Machine, Random Forest Classifier, Extreme Gradient Boost, and Convolutional Neural Network) are first investigated for binary classification of the test population as healthy vs. concussion (Use Case 1). Results are compared for two different data preprocessing pipelines, Time-Series Averaging (TSA) and Non-Time-Series Feature Extraction (NTS). Next, the three best-performing NTS models are compared in terms of their multiclass prediction performance for specific concussion-related impairments: vestibular, neurological, both (Use Case 2). For Use Case 1, the NTS model approach outperformed the TSA approach, with the two best algorithms achieving an F1 score of 0.94. For Use Case 2, the NTS Random Forest model achieved the best performance in the testing set, with an F1 score of 0.90, and identified a wider range of relevant phybrata signal features that contributed to impairment classification compared with manual feature inspection and statistical data analysis. The overall classification performance achieved in the present work exceeds previously reported approaches to ML-based concussion diagnostics using other data sources and ML models. This study also demonstrates the first combination of a wearable IMU-based sensor and ML model that enables both binary classification of concussion patients and multiclass predictions of specific concussion-related neurophysiological impairments.

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.

Concussion assessment potentially aided by use of an objective multimodal concussion index

Objective

Prompt, accurate, objective assessment of concussion is crucial as delays can lead to increased short and long-term consequences. The purpose of this study was to derive an objective multimodal concussion index (CI) using EEG at its core, to identify concussion, and to assess change over time throughout recovery.

Methods

Male and female concussed ( N = 232) and control ( N = 206) subjects 13–25 years were enrolled at 12 US colleges and high schools. Evaluations occurred within 72 h of injury, 5 days post-injury, at return-to-play (RTP), 45 days after RTP (RTP + 45); and included EEG, neurocognitive performance, and standard concussion assessments. Concussed subjects had a witnessed head impact, were removed from play for ≥ 5 days using site guidelines, and were divided into those with RTP < 14 or ≥14 days. Part 1 describes the derivation and efficacy of the machine learning derived classifier as a marker of concussion. Part 2 describes significance of differences in CI between groups at each time point and within each group across time points.

Results

Sensitivity = 84.9%, specificity = 76.0%, and AUC = 0.89 were obtained on a test Hold-Out group representing 20% of the total dataset. EEG features reflecting connectivity between brain regions contributed most to the CI. CI was stable over time in controls. Significant differences in CI between controls and concussed subjects were found at time of injury, with no significant differences at RTP and RTP + 45. Within the concussed, differences in rate of recovery were seen.

Conclusions

The CI was shown to have high accuracy as a marker of likelihood of concussion. Stability of CI in controls supports reliable interpretation of CI change in concussed subjects. Objective identification of the presence of concussion and assessment of readiness to return to normal activity can be aided by use of the CI, a rapidly obtained, point of care assessment tool.

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.

Sideline Concussion Assessment: The Current State of the Art

More than 200 million American adults and children participate in organized physical activity. Growing awareness has highlighted that concussion, especially when repeated, may be associated with prolonged neurological, cognitive, and/or neuropsychiatric sequelae. Objective diagnosis of concussion remains challenging. Although some concussion symptoms may be apparent even to nonmedical observers, diagnosis and removal from play for evaluation depend on validated assessment tools and trained, vigilant healthcare personnel. Over the past 2 decades, sideline concussion measures have undergone significant revision and augmentation to become more comprehensive batteries in order to detect a wide spectrum of symptomatology, eg, neurocognitive function, postconcussive symptoms, gait/balance, and saccadic eye movements. This review summarizes the current state-of-the-art concussion evaluation instruments, ranging from the Sports Concussion Assessment Tool (SCAT) and tools that may enhance concussion detection, to near-term blood-based biomarkers and emerging technology (eg, head impact sensors, vestibulo-ocular/eye-tracking, and mobile applications). Special focus is directed at feasibility, utility, generalizability, and challenges to implementation of each measure on-field and on the sidelines. This review finds that few instruments beyond the SCAT provide guidance for removal from play, and establishing thresholds for concussion detection and removal from play in qualification/validation of future instruments is of high importance. Integration of emerging sideline concussion evaluation tools should be supported by resources and education to athletes, caregivers, athletic staff, and medical professionals for standardized administration as well as triage, referral, and prevention strategies. It should be noted that concussion evaluation instruments are used to assist the clinician in sideline diagnosis, and no single test can diagnose concussion as a standalone investigation.

Concussion Acutely Decreases Plasma Glycerophospholipids in Adolescent Male Athletes

Concussions are frequent in sports and can contribute to significant and long-lasting neurological disability. Adolescents are particularly susceptible to concussions, with accurate determination of the injury challenging. Our previous study demonstrated that concussion diagnoses could be aided by metabolomics profiling and machine learning, with particular weighting on changes in plasma glycerophospholipids (PCs). Here, our aim was to report directional change of PCs after concussion and develop a diagnostic concussion panel utilizing a minimum number of plasma PCs. To this end, we enrolled 12 concussed male athletes at our academic Sport Medicine Concussion Clinic, as well as 17 sex-, age-, and activity-matched healthy controls. Blood was drawn and 71 plasma PCs were measured for statistically significant changes within 72 h of injury, and individual PCs were further analyzed with receiver operating characteristic (ROC) curves. Our data demonstrated that 26 of 71 PCs measured were significantly decreased after sports-related concussion (p < 0.01). None of the PCs increased in plasma after concussion. ROC curve analyses identified the top four PCs with areas under the curve (AUCs) ≥0.86 for concussion diagnosis: PCaeC36:0 (0.92; p < 0.001); PCaaC42:6 (0.90; p < 0.001); PCaeC36:2 (0.86; p = 0.001), and PCaaC32:0 (0.86; p = 0.001). Cut-off values in μM were ≤0.31, 0.22, 5.07, and 4.63, respectively. Importantly, combining these four PCs produced an AUC of 0.96 for concussion diagnoses (p < 0.001; 95% confidence interval, 0.89, 1.00). Our data suggest that as few as four circulating PCs may provide excellent diagnostic potential for adolescent concussion. External validation is required in larger cohorts.

Physiological Vibration Acceleration (Phybrata) Sensor Assessment of Multi-System Physiological Impairments and Sensory Reweighting Following Concussion

Objective

To assess the utility of a head-mounted wearable inertial motion unit (IMU)-based physiological vibration acceleration (“phybrata”) sensor to support the clinical diagnosis of concussion, classify and quantify specific concussion-induced physiological system impairments and sensory reweighting, and track individual patient recovery trajectories.

Methods

Data were analyzed from 175 patients over a 12-month period at three clinical sites. Comprehensive clinical concussion assessments were first completed for all patients, followed by testing with the phybrata sensor. Phybrata time series data and spatial scatter plots, eyes open (Eo) and eyes closed (Ec) phybrata powers, average power (Eo+Ec)/2, Ec/Eo phybrata power ratio, time-resolved phybrata spectral density (TRPSD) distributions, and receiver operating characteristic (ROC) curves are compared for individuals with no objective impairments and those clinically diagnosed with concussions and accompanying vestibular impairment, other neurological impairment, or both vestibular and neurological impairments. Finally, pre- and post-injury phybrata case report results are presented for a participant who was diagnosed with a concussion and subsequently monitored during treatment, rehabilitation, and return-to-activity clearance.

Results

Phybrata data demonstrate distinct features and patterns for individuals with no discernable clinical impairments, diagnosed vestibular pathology, and diagnosed neurological pathology. ROC curves indicate that the average power (Eo+Ec)/2 may be utilized to support clinical diagnosis of concussion, while Eo and Ec/Eo may be utilized as independent measures to confirm accompanying neurological and vestibular impairments, respectively. All 3 measures demonstrate area under the curve (AUC), sensitivity, and specificity above 90% for their respective diagnoses. Phybrata spectral analyses demonstrate utility for quantifying the severity of concussion-induced physiological impairments, sensory reweighting, and subsequent monitoring of improvements throughout treatment and rehabilitation.

Conclusion

Phybrata testing assists with objective concussion diagnosis and provides an important adjunct to standard concussion assessment tools by objectively ascertaining neurological and vestibular impairments, guiding targeted rehabilitation strategies, monitoring recovery, and assisting with return-to-sport/work/learn decision-making.

Preliminary investigation of a multimodal enhanced brain function index among high school and collegiate concussed male and female athletes

Objective: The primary purpose of this study was to examine the longitudinal effects of sports-related concussion (SRC) on a multi-faceted assessment battery which included neuropsychological testing, symptom reporting, and enhanced brain function index (eBFI) among athletes with and without SRC. A secondary purpose was to explore longitudinal sex differences among these measures in athletes with and without SRC. Methods: A case-control, repeated-measures design was used for this study. A total of 186 athletes (concussed group:n= 87 controls:n= 99) participated in the study. A repeated-measures design was used in which each athlete was tested at four time points following an SRC: within 72 h of injury (Day 0; 2.0 ± 0.9 days following injury), 5 days following injury (Day 5; 5.0 ± 0.0), at return to play (RTP; 18.3 ± 13.8 days following injury), and within 45 days following RTP (RTP45; 66.2 ± 19.0 days following injury). All analyses were conducted separately using a 2 (Group: concussed, control) × 2 (Sex: male, female) × 4 (Time:Day 0, Day 5, RTP, RTP45) univariate multi-level model including the random intercept for each participant. A higher eBFI score indicates a better performance. Alpha level was set aprior at .05. This study was registered on clinicaltrials.gov (Objective Brain Function Assessment of mTBI/Concussion in College/high school Athletes NCT02477943, NCT02661633, CAS 13-25 NCT03963804). Results: Concussed athletes exhibited impaired eBFI within 72 h of SRC and at Day 5 compared to controls (p<.001). Analysis of eBFI scores between male and female athletes revealed a main effect of sex (p=.05), with female athletes exhibiting lower eBFI (33.9 ± 30.7) relative to male athletes (40.4 ± 33.0), however, it did not indicate interactions between sex, group, and time (p's ≥ 0.786). Conclusion: The eBFI appears to be a useful tool in determining concussed athletes during the acute stages of an SRC. However, this index may lack the sensitivity to detect sex-related differences between groups at various time points during recovery.

Classification of Non-Severe Traumatic Brain Injury from Resting-State EEG Signal Using LSTM Network with ECOC-SVM

Traumatic brain injury (TBI) is one of the common injuries when the human head receives an impact due to an accident or fall and is one of the most frequently submitted insurance claims. However, it is often always misused when individuals attempt an insurance fraud claim by providing false medical conditions. Therefore, there is a need for an instant brain condition classification system. This study presents a novel classification architecture that can classify non-severe TBI patients and healthy subjects employing resting-state electroencephalogram (EEG) as the input, solving the immobility issue of the computed tomography (CT) scan and magnetic resonance imaging (MRI). The proposed architecture makes use of long short term memory (LSTM) and error-correcting output coding support vector machine (ECOC-SVM) to perform multiclass classification. The pre-processed EEG time series are supplied to the network by each time step, where important information from the previous time step will be remembered by the LSTM cell. Activations from the LSTM cell is used to train an ECOC-SVM. The temporal advantages of the EEG were amplified and able to achieve a classification accuracy of 100%. The proposed method was compared to existing works in the literature, and it is shown that the proposed method is superior in terms of classification accuracy, sensitivity, specificity, and precision.

Cutting to the Pathophysiology Chase: Translating Cutting-Edge Neuroscience to Rehabilitation Practice in Sports-Related Concussion Management

Mild traumatic brain injury, or concussion, is a common sports injury. Concussion involves physical injury to brain tissue and vascular and axonal damage that manifests as transient and often nonspecific clinical symptoms. Concussion diagnosis is challenging, and the relationship between brain injury and clinical symptoms is unclear. The purpose of this commentary was to translate cutting-edge neuroscience to rehabilitation practice. We (1) highlight potential biomarkers that may improve our understanding of concussion and its recovery, (2) explain why researchers must address the paucity of concussion research in female athletes, and (3) present female-specific factors that should be accounted for in future studies. Integrating objective, quantitative measures of concussion pathophysiology with concussion history, genetics, and genomics will help caregivers identify concussed athletes, tailor recovery protocols, and protect athletes from potential long-term effects of cumulative head impact. J Orthop Sports Phys Ther 2019;49(11):811-818. Epub 1 Jun 2019. doi:10.2519/jospt.2019.8884.

Toward development of clinically translatable diagnostic and prognostic metrics of traumatic brain injury using animal models: A review and a look forward

Traumatic brain injury is a leading cause of cognitive and behavioral deficits in children in the US each year. There is an increasing interest in both clinical and pre-clinical studies to discover biomarkers to accurately diagnose traumatic brain injury (TBI), predict its outcomes, and monitor its progression especially in the developing brain. In humans, the heterogeneity of TBI in terms of clinical presentation, injury causation, and mechanism has contributed to the many challenges associated with finding unifying diagnosis, treatment, and management practices. In addition, findings from adult human research may have little application to pediatric TBI, as age and maturation levels affect the injury biomechanics and neurophysiological consequences of injury. Animal models of TBI are vital to address the variability and heterogeneity of TBI seen in human by isolating the causation and mechanism of injury in reproducible manner. However, a gap between the pre-clinical findings and clinical applications remains in TBI research today. To take a step toward bridging this gap, we reviewed several potential TBI tools such as biofluid biomarkers, electroencephalography (EEG), actigraphy, eye responses, and balance that have been explored in both clinical and pre-clinical studies and have shown potential diagnostic, prognostic, or monitoring utility for TBI. Each of these tools measures specific deficits following TBI, is easily accessible, non/minimally invasive, and is potentially highly translatable between animals and human outcomes because they involve effort-independent and non-verbal tasks. Especially conspicuous is the fact that these biomarkers and techniques can be tailored for infants and toddlers. However, translation of preclinical outcomes to clinical applications of these tools necessitates addressing several challenges. Among the challenges are the heterogeneity of clinical TBI, age dependency of some of the biomarkers, different brain structure, life span, and possible variation between temporal profiles of biomarkers in human and animals. Conducting parallel clinical and pre-clinical research, in addition to the integration of findings across species from several pre-clinical models to generate a spectrum of TBI mechanisms and severities is a path toward overcoming some of these challenges. This effort is possible through large scale collaborative research and data sharing across multiple centers. In addition, TBI causes dynamic deficits in multiple domains, and thus, a panel of biomarkers combining these measures to consider different deficits is more promising than a single biomarker for TBI. In this review, each of these tools are presented along with the clinical and pre-clinical findings, advantages, challenges and prospects of translating the pre-clinical knowledge into the human clinical setting.