Diffusion Tensor Imaging Indicators of White Matter Injury Are Correlated with a Multimodal Electroencephalography-Based Biomarker in Slow Recovering, Concussed Collegiate Athletes

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.

Quantitative Electroencephalography Objectivity and Reliability in the Diagnosis and Management of Traumatic Brain Injury: A Systematic Review

Background. Persons with a history of traumatic brain injury (TBI) may exhibit short- and long-term cognitive deficits as well as psychiatric symptoms. These symptoms often reflect functional anomalies in the brain that are not detected by standard neuroimaging. In this context, quantitative electroencephalography (qEEG) is more suitable to evaluate non-normative activity in a wide range of clinical settings. Method. We searched the literature using the "Medline" and "Web of Science" online databases. The search was concluded on February 23, 2023, and revised on July 12, 2023. It returned 134 results from Medline and 4 from Web of Science. We then applied the PRISMA method, which led to the selection of 31 articles, the most recent one published in March 2023. Results. The qEEG method can detect functional anomalies in the brain occurring immediately after and even years after injury, revealing in most cases abnormal power variability and increases in slow (delta and theta) versus decreases in fast (alpha, beta, and gamma) frequency activity. Moreover, other findings show that reduced beta coherence between frontoparietal regions is associated with slower processing speed in patients with recent mild TBI (mTBI). More recently, machine learning (ML) research has developed highly reliable models and algorithms for the detection of TBI, some of which are already integrated into commercial qEEG equipment. Conclusion. Accumulating evidence indicates that the qEEG method may improve the diagnosis and management of TBI, in many cases revealing long-term functional anomalies in the brain or even neuroanatomical insults that are not revealed by standard neuroimaging. While FDA clearance has been obtained only for some of the commercially available equipment, the qEEG method allows for systematic, cost-effective, non-invasive, and reliable investigations at emergency departments. Importantly, the automated implementation of intelligent algorithms based on multimodally acquired, clinically relevant measures may play a key role in increasing diagnosis reliability.

Role of biomarkers and emerging technologies in defining and assessing neurobiological recovery after sport-related concussion: a systematic review

OBJECTIVE

Determine the role of fluid-based biomarkers, advanced neuroimaging, genetic testing and emerging technologies in defining and assessing neurobiological recovery after sport-related concussion (SRC).

DESIGN

Systematic review.

DATA SOURCES

Searches of seven databases from 1 January 2001 through 24 March 2022 using keywords and index terms relevant to concussion, sports and neurobiological recovery. Separate reviews were conducted for studies involving neuroimaging, fluid biomarkers, genetic testing and emerging technologies. A standardised method and data extraction tool was used to document the study design, population, methodology and results. Reviewers also rated the risk of bias and quality of each study.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

Studies were included if they: (1) were published in English; (2) represented original research; (3) involved human research; (4) pertained only to SRC; (5) included data involving neuroimaging (including electrophysiological testing), fluid biomarkers or genetic testing or other advanced technologies used to assess neurobiological recovery after SRC; (6) had a minimum of one data collection point within 6 months post-SRC; and (7) contained a minimum sample size of 10 participants.

RESULTS

A total of 205 studies met inclusion criteria, including 81 neuroimaging, 50 fluid biomarkers, 5 genetic testing, 73 advanced technologies studies (4 studies overlapped two separate domains). Numerous studies have demonstrated the ability of neuroimaging and fluid-based biomarkers to detect the acute effects of concussion and to track neurobiological recovery after injury. Recent studies have also reported on the diagnostic and prognostic performance of emerging technologies in the assessment of SRC. In sum, the available evidence reinforces the theory that physiological recovery may persist beyond clinical recovery after SRC. The potential role of genetic testing remains unclear based on limited research.

CONCLUSIONS

Advanced neuroimaging, fluid-based biomarkers, genetic testing and emerging technologies are valuable research tools for the study of SRC, but there is not sufficient evidence to recommend their use in clinical practice.

PROSPERO REGISTRATION NUMBER

CRD42020164558.

Cerebrovascular Reactivity and Concussion

Cerebrovascular reactivity (CVR) reflects the change in cerebral blood flow in response to vasodilatory stimuli enabling assessment of the health of the cerebral vasculature. Recent advances in the quantitative delivery of CO2 stimuli with computer-controlled sequential gas delivery have enabled mapping of the speed and magnitude of response to flow stimuli. These CVR advances when applied to patients with acute concussion have unexpectedly shown faster speed and greater magnitude of responses unseen in other diseases that typically show the opposite effects. The strength of the CVR alterations have diagnostic potential in single subjects with AUC values in the 0.90-0.94 range.

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.

Altered white matter diffusivity and subtle motor function in a pilot cohort of adolescents with sports-related concussion

Background and objective:

Adolescents with sports-related concussion (SRC) demonstrate acute and persistent deficits in subtle motor function. However, there is limited research examining related neurological underpinnings. This pilot study examined changes in motor-associated white matter pathways using diffusion tensor imaging (DTI) and their relationship with subtle motor function.

Methods:

Twelve adolescents with SRC (12–17 years) within two-weeks post-injury and 13 never-injured neurotypical peers completed DTI scanning. A subset of 6 adolescents with SRC returned for a follow-up visit post-medical clearance from concussion. Subtle motor function was evaluated using the Physical and Neurological Examination of Subtle Signs (PANESS).

Results:

Adolescents with SRC showed higher mean diffusivity (MD) of the superior corona radiata and greater subtle motor deficits compared to controls. Across all participants, greater subtle motor deficits were associated with higher (more atypical) MD of the superior corona radiata. Preliminary longitudinal analysis indicated reduction in fractional anisotropy of the corpus callosum but no change in the MD of the superior corona radiata from the initial visit to the follow-up visit post-medical clearance.

Conclusions:

These findings support preliminary evidence for a brain–behavior relationship between superior corona radiata microstructure and subtle motor deficits in adolescents with SRC that merits further investigation.

Cerebrovascular reactivity changes in acute concussion: a controlled cohort study

Background

Evidence suggests that cerebrovascular reactivity (CVR) increases within the first week after the incidence of concussion, indicating a disruption of normal autoregulation. We sought to extend these findings by investigating the effects of acute concussion on the speed of CVR response and by visualizing global and regional impairments in individual patients with acute concussion.

Methods

Twelve patients aged 18-40 years who experienced concussion less than a week before this prospective study were included. Twelve age and sex-matched healthy subjects constituted the control group. In all subjects, CVR was assessed using blood oxygenation level-dependent (BOLD) echo-planar imaging with a 3.0T MRI scanner, in combination with changes in end-tidal partial pressure of CO₂ (P_ETCO₂). In each subject, we calculated the CVR amplitude and CVR response time in the gray and white matter using a step and ramp P_ETCO₂ challenge. In addition, a separate group of 39 healthy controls who underwent the same evaluation was used to create atlases with voxel-wise mean and standard deviation of CVR amplitude and CVR response time. This allowed us to convert each metric of the 12 patients with concussion and the 12 healthy controls into z-score maps. These maps were then used to generate and compare z-scores for each of the two groups. Group differences were calculated using an unpaired t-test.

Results

All studies were well tolerated without any serious adverse events. Anatomical MRI was normal in all study subjects. No differences in CO₂ stimulus and O₂ targeting were observed between the two participant groups during BOLD MRI. With regard to the gray matter, the CVR magnitude step (P=0.117) and ramp + 10 (P=0.085) were not significantly different between patients with concussion and healthy controls. However, the tau value was significantly lower in patients with concussion than in the healthy controls (P=0.04). With regard to the white matter, the CVR magnitude step (P=0.003) and ramp + 10 (P=0.031) were significantly higher and the tau value (P=0.024) was significantly shorter in patients with concussion than in healthy controls. After z-score transformation, the z tau value was significantly lower in patients with concussion than in healthy controls (Grey matter P=0.021, White matter P=0.003). Comparison of the three parameters, z ramp + 10, z step, and z tau, between the two groups showed that z step (Grey matter P=0.035, White matter P=0.005) was the most sensitive parameter and that z ramp + 10 (Grey matter P=0.073, White matter P=0.126) was the least sensitive parameter.

Conclusions

Concussion is associated with patient-specific abnormalities in BOLD cerebrovascular responsiveness that occur in the setting of normal global CVR. This study demonstrates that the measurement of CVR using BOLD MRI and precise CO₂ control is a safe, reliable, reproducible, and clinically useful method for evaluating the state of patients with concussion. It has the potential to be an important tool for assessing the severity and duration of symptoms after concussion.

Diffusion Tensor Imaging in Sport-Related Concussion: A Systematic Review Using an a priori Quality Rating System

Diffusion tensor imaging (DTI) of brain white matter (WM) may be useful for characterizing the nature and degree of brain injury after sport-related concussion (SRC) and assist in establishing objective diagnostic and prognostic biomarkers. This study aimed to conduct a systematic review using an a priori quality rating strategy to determine the most consistent DTI-WM changes post-SRC. Articles published in English (until June 2020) were retrieved by standard research engine and gray literature searches (N = 4932), using PRISMA guidelines. Eligible studies were non-interventional naturalistic original studies that conducted DTI within 6 months of SRC in current athletes from all levels of play, types of sports, and sex. A total of 29 articles were included in the review, and after quality appraisal by two raters, data from 10 studies were extracted after being identified as high quality. High-quality studies showed widespread moderate-to-large WM differences when SRC samples were compared to controls during the acute to early chronic stage (days to weeks) post-SRC, including both increased and decreased fractional anisotropy and axial diffusivity and decreased mean diffusivity and radial diffusivity. WM differences remained stable in the chronic stage (2-6 months post-SRC). DTI metrics were commonly associated with SRC symptom severity, although standardized SRC diagnostics would improve future research. This indicates that microstructural recovery is often incomplete at return to play and may lag behind clinically assessed recovery measures. Future work should explore interindividual trajectories to improve understanding of the heterogeneous and dynamic WM patterns post-SRC.

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.

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.