Traumatic brain injury detection using electrophysiological methods

Physical exercise for brain plasticity promotion an overview of the underlying oscillatory mechanism

The global recognition of the importance of physical exercise (PE) for human health has resulted in increased research on its effects on cortical activity. Neural oscillations, which are prominent features of brain activity, serve as crucial indicators for studying the effects of PE on brain function. Existing studies support the idea that PE modifies various types of neural oscillations. While EEG-related literature in exercise science exists, a comprehensive review of the effects of exercise specifically in healthy populations has not yet been conducted. Given the demonstrated influence of exercise on neural plasticity, particularly cortical oscillatory activity, it is imperative to consolidate research on this phenomenon. Therefore, this review aims to summarize numerous PE studies on neuromodulatory mechanisms in the brain over the past decade, covering (1) effects of resistance and aerobic training on brain health via neural oscillations; (2) how mind-body exercise affects human neural activity and cognitive functioning; (3) age-Related effects of PE on brain health and neurodegenerative disease rehabilitation via neural oscillation mechanisms; and (4) conclusion and future direction. In conclusion, the effect of PE on cortical activity is a multifaceted process, and this review seeks to comprehensively examine and summarize existing studies' understanding of how PE regulates neural activity in the brain, providing a more scientific theoretical foundation for the development of personalized PE programs and further research.

Prognostic value of early EEG abnormalities in severe stroke patients requiring mechanical ventilation: a pre-planned analysis of the SPICE prospective multicenter study

INTRODUCTION

Prognostication of outcome in severe stroke patients necessitating invasive mechanical ventilation poses significant challenges. The objective of this study was to assess the prognostic significance and prevalence of early electroencephalogram (EEG) abnormalities in adult stroke patients receiving mechanical ventilation.

METHODS

This study is a pre-planned ancillary investigation within the prospective multicenter SPICE cohort study (2017-2019), conducted in 33 intensive care units (ICUs) in the Paris area, France. We included adult stroke patients requiring invasive mechanical ventilation, who underwent at least one intermittent EEG examination during their ICU stay. The primary endpoint was the functional neurological outcome at one year, determined using the modified Rankin scale (mRS), and dichotomized as unfavorable (mRS 4-6, indicating severe disability or death) or favorable (mRS 0-3). Multivariable regression analyses were employed to identify EEG abnormalities associated with functional outcomes.

RESULTS

Of the 364 patients enrolled in the SPICE study, 153 patients (49 ischemic strokes, 52 intracranial hemorrhages, and 52 subarachnoid hemorrhages) underwent at least one EEG at a median time of 4 (interquartile range 2-7) days post-stroke. Rates of diffuse slowing (70% vs. 63%, p = 0.37), focal slowing (38% vs. 32%, p = 0.15), periodic discharges (2.3% vs. 3.7%, p = 0.9), and electrographic seizures (4.5% vs. 3.7%, p = 0.4) were comparable between patients with unfavorable and favorable outcomes. Following adjustment for potential confounders, an unreactive EEG background to auditory and pain stimulations (OR 6.02, 95% CI 2.27-15.99) was independently associated with unfavorable outcomes. An unreactive EEG predicted unfavorable outcome with a specificity of 48% (95% CI 40-56), sensitivity of 79% (95% CI 72-85), and positive predictive value (PPV) of 74% (95% CI 67-81). Conversely, a benign EEG (defined as continuous and reactive background activity without seizure, periodic discharges, triphasic waves, or burst suppression) predicted favorable outcome with a specificity of 89% (95% CI 84-94), and a sensitivity of 37% (95% CI 30-45).

CONCLUSION

The absence of EEG reactivity independently predicts unfavorable outcomes at one year in severe stroke patients requiring mechanical ventilation in the ICU, although its prognostic value remains limited. Conversely, a benign EEG pattern was associated with a favorable outcome.

A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning

The electroencephalogram (EEG) serves as an essential tool in exploring brain activity and holds particular importance in the field of mental health research. This review paper examines the application of artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), for classifying schizophrenia (SCZ) through EEG. It includes a thorough literature review that addresses the difficulties, methodologies, and discoveries in this field. ML approaches utilize conventional models like Support Vector Machines and Decision Trees, which are interpretable and effective with smaller data sets. In contrast, DL techniques, which use neural networks such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), are more adaptable to intricate EEG patterns but require significant data and computational power. Both ML and DL face challenges concerning data quality and ethical issues. This paper underscores the importance of integrating various techniques to enhance schizophrenia diagnosis and highlights AI's potential role in this process. It also acknowledges the necessity for collaborative and ethically informed approaches in the automated classification of SCZ using AI.

Adolescent Sport-Related Concussion and the Associated Neurophysiological Changes: A Systematic Review

BACKGROUND

Sport-related concussion (SRC) has been shown to induce cerebral neurophysiological deficits, quantifiable with electroencephalography (EEG). As the adolescent brain is undergoing rapid neurodevelopment, it is fundamental to understand both the short- and long-term ramifications SRC may have on neuronal functioning. The current systematic review sought to amalgamate the literature regarding both acute/subacute (≤28 days) and chronic (>28 days) effects of SRC in adolescents via EEG and the diagnostic accuracy of this tool.

METHODS

The review was registered within the Prospero database (CRD42021275256). Search strategies were created and input into the PubMed database, where three authors completed all screening. Risk of bias assessments were completed using the Scottish Intercollegiate Guideline Network and Methodological Index for Non-Randomized Studies.

RESULTS

A total of 128 articles were identified; however, only seven satisfied all inclusion criteria. The studies ranged from 2012 to 2021 and included sample sizes of 21 to 81 participants, albeit only ∼14% of the included athletes were females. The studies displayed low-to-high levels of bias due to the small sample sizes and preliminary nature of most investigations. Although heterogeneous methods, tasks, and analytical techniques were used, 86% of the studies found differences compared with control athletes, in both the symptomatic and asymptomatic phases of SRC. One study used raw EEG data as a diagnostic indicator demonstrating promise; however, more research and standardization are a necessity.

CONCLUSIONS

Collectively, the findings highlight the utility of EEG in assessing adolescent SRC; however, future studies should consider important covariates including biological sex, maturation status, and development.

A comprehensive characterization of cognitive performance, clinical symptoms, and cortical activity following mild traumatic brain injury (mTBI)

OBJECTIVE

The objective of this study was to investigate clinical symptoms, cognitive performance and cortical activity following mild traumatic brain injury (mTBI).

METHODS

We recruited 30 individuals in the sub-acute phase post mTBI and 28 healthy controls with no history of head injury and compared these groups on clinical, cognitive and cortical activity measures. Measures of cortical activity included; resting state electroencephalography (EEG), task related EEG and combined transcranial magnetic stimulation with electroencephalography (TMS-EEG). Primary analyses investigated clinical, cognitive and cortical activity differences between groups. Exploratory analyses investigated the relationships between these measures.

RESULTS

At 4 weeks' post injury, mTBI participants exhibited significantly greater post concussive and clinical symptoms compared to controls; as well as reduced cognitive performance on verbal learning and working memory measures. mTBI participants demonstrated alterations in cortical activity while at rest and in response to stimulation with TMS.

CONCLUSIONS

The present study comprehensively characterized the multidimensional effect of mTBI in the sub-acute phase post injury, showing a broad range of differences compared to non-mTBI participants. Further research is needed to explore the relationship between these pathophysiologies and clinical/cognitive symptoms in mTBI.

Histological and electroencephalographic demonstration of probiotic effect for reduce of oxidative stress and apoptosis in experimental traumatic brain injury

BACKGROUND

The gut microbiota modulates nervous system function. In the literature, it has been shown that this modula-tion is used in many nervous system injuries through oxidative stress (OS) and apoptosis mechanisms. In this study, it was aimed to investigate the neuroprotective effects of probiotic (PB) treatment in a rat traumatic brain injury (TBI) model with histological and electroencephalographic (EEG) data.

METHODS

Forty male Wistar albino rats were divided into four groups. Group 1 was the control group (CONTROL, n=10) and no trauma was applied. Group 2 was the trauma group with the weight-drop technique (TBH, n=10). Group 3 was the sham group (SHAM), (TBH+sterile saline [SS], n=10) rats were given 500 µL of SS per day by oral gavage. Group 4 was the PB treatment group, (TBH+PB, n=10) rats were treated daily for 7 days with 500 µL of PB oral gavage. Brain samples were collected 7 days after trauma. Histopathological evaluation of brain samples was done with HE. OS with Endothelial nitric oxide synthase, vascularization with Vas-cular Endothelial Growth Factor, gliosis with S100, and apoptosis with caspase 3 were evaluated immunohistochemically. Apoptotic index was determined with TUNEL. In addition, EEG and somatosensory evoked potential (SEP) recording findings were compared.

RESULTS

It was determined by HE staining that there was a significant (P<0.001) damage in the TBI and sham groups compared to the control group. It was found that PB treatment provided a significant (P<0.01) improvement in the damage created. While OS (P<0.01), gliosis (P<0.01), and apoptosis (P<0.05) decreased with PB treatment, angiogenesis (P<0.01) increased. In support of these findings, in the software-mediated EEG and SUP examination; Delta wave power and theta/alpha ratio increased with TBI and de-creased with PB treatment.

CONCLUSION

The results showed that PB treatment provided a significant improvement in rats by reducing OS, apoptosis, and gliosis and increasing vascularity. To the best of our knowledge in the literature, it was shown for the 1st time that histological results for the treatment of PB were supported by software-mediated EEG and SEP analysis.

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.

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.

Altered Auditory and Visual Evoked Potentials following Single and Repeated Low-Velocity Head Rotations in 4-Week-Old Swine

Auditory and visually evoked potentials (EP) have the ability to monitor cognitive changes after concussion. In the literature, decreases in EP are commonly reported; however, a subset of studies shows increased cortical activity after injury. We studied auditory and visual EP in 4-week-old female Yorkshire piglets (N = 35) divided into anesthetized sham, and animals subject to single (sRNR) and repeated (rRNR) rapid non-impact head rotations (RNR) in the sagittal direction. Two-tone auditory oddball tasks and a simple white-light visual stimulus were evaluated in piglets pre-injury, and at days 1, 4- and 7 post injury using a 32-electrode net. Traditional EP indices (N1, P2 amplitudes and latencies) were extracted, and a piglet model was used to source-localize the data to estimate brain regions related to auditory and visual processing. In comparison to each group's pre-injury baselines, auditory Eps and brain activity (but not visual activity) were decreased in sham. In contrast, sRNR had increases in N1 and P2 amplitudes from both stimuli. The rRNR group had decreased visual N1 amplitudes but faster visual P2 latencies. Auditory and visual EPs have different change trajectories after sRNR and rRNR, suggesting that injury biomechanics are an important factor to delineate neurofunctional deficits after concussion.

AI-Based Decision Support System for Traumatic Brain Injury: A Survey

Traumatic brain injury (TBI) is one of the major causes of disability and mortality worldwide. Rapid and precise clinical assessment and decision-making are essential to improve the outcome and the resulting complications. Due to the size and complexity of the data analyzed in TBI cases, computer-aided data processing, analysis, and decision support systems could play an important role. However, developing such systems is challenging due to the heterogeneity of symptoms, varying data quality caused by different spatio-temporal resolutions, and the inherent noise associated with image and signal acquisition. The purpose of this article is to review current advances in developing artificial intelligence-based decision support systems for the diagnosis, severity assessment, and long-term prognosis of TBI complications.

The Use of Electroencephalography as an Informative Tool in Assisting Early Clinical Management after Sport-Related Concussion: a Systematic Review

Sport-related concussion (SRC) is managed primarily through serial clinical evaluations throughout recovery. However, studies suggest that clinical measures may not be suitable to detect subtle alterations in functioning and are limited by numerous internal and external factors. Electroencephalography (EEG) has been used for over eight decades to discern altered function following illnesses and injuries, including traumatic brain injury. This study evaluated the associations between EEG measures and clinical presentation within three-months following SRC. A systematic review of the literature was performed in Medline, Embase, PsycINFO, CINAHL and Web of Science databases following Preferred Reporting Items for Systematic Reviews and Meta Analyses guidelines, yielding a total of 13 peer-reviewed articles. Most studies showed low to moderate bias and moderate to high quality. The majority of the existing literature on the impact of concussion within the first 3 months post-injury suggests that individuals with concussion show altered brain function, with EEG abnormalities outlasting clinical dysfunction. Of all EEG biomarkers evaluated, P300 shows the most promise and should be explored further. Despite the relatively high quality of included articles, significant limitations are still present within this body of literature, including potential conflicts of interest and proprietary algorithms, making it difficult to draw strong and meaningful conclusions on the use of EEG in the early stages of SRC. Therefore, further exploration of the relationship between EEG measures and acute clinical presentation is warranted to determine if EEG provides additional benefits over current clinical assessments and is a feasible tool in clinical settings.

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.

Electrophysiological trajectories of concussion recovery: From acute to prolonged stages in late teenagers

PURPOSE

Numerous studies have reported electrophysiological differences between concussed and non-concussed groups, but few studies have systematically explored recovery trajectories from acute concussion to symptom recovery and the transition from acute concussion to prolonged phases. Questions remain about recovery prognosis and the extent to which symptom resolution coincides with injury resolution. This study therefore investigated the electrophysiological differences in recoveries between simple and complex concussion.

METHODS

Student athletes with acute concussion from a previous study (19(2) years old) were tracked from pre-injury baseline, 24-48 hours after concussion, and through in-season recovery. The electroencephalography (EEG) with P300 evoked response trajectories from this acute study were compared to an age-matched population of 71 patients (18(2) years old) with prolonged post-concussive symptoms (PPCS), 61 (SD 31) days after concussion.

RESULTS

Acute, return-to-play, and PPCS groups all experienced a significant deficit in P300 amplitude compared to the pre-injury baseline group. The PPCS group, however, had significantly different EEG spectral and coherence patterns from every other group.

CONCLUSION

These data suggest that while the evoked response potentials deficits of simple concussion may persist in more prolonged stages, there are certain EEG measures unique to PPCS. These metrics are readily accessible to clinicians and may provide useful parameters to help predict trajectories, characterize injury (phenotype), and track the course of injury.

Traumatic Brain Injury (TBI) Detection: Past, Present, and Future

Traumatic brain injury (TBI) can produce temporary biochemical imbalance due to leaks through cell membranes or disruption of the axoplasmic flow due to the misalignment of intracellular neurofilaments. If untreated, TBI can lead to Alzheimer's, Parkinson's, or total disability. Mild TBI (mTBI) accounts for about about 90 percent of all TBI cases. The detection of TBI as soon as it happens is crucial for successful treatment management. Neuroimaging-based tests provide only a structural and functional mapping of the brain with poor temporal resolution. Such tests may not detect mTBI. On the other hand, the electroencephalogram (EEG) provides good spatial resolution and excellent temporal resolution of the brain activities beside its portability and low cost. The objective of this paper is to provide clinicians and scientists with a one-stop source of information to quickly learn about the different technologies used for TBI detection, their advantages and limitations. Our research led us to conclude that even though EEG-based TBI detection is potentially a powerful technology, it is currently not able to detect the presence of a mTBI with high confidence. The focus of the paper is to review existing approaches and provide the reason for the unsuccessful state of EEG-based detection of mTBI.

Pathological changes of brain oscillations following ischemic stroke

Brain oscillations recorded in the extracellular space are among the most important aspects of neurophysiology data reflecting the activity and function of neurons in a population or a network. The signal strength and patterns of brain oscillations can be powerful biomarkers used for disease detection and prediction of the recovery of function. Electrophysiological signals can also serve as an index for many cutting-edge technologies aiming to interface between the nervous system and neuroprosthetic devices and to monitor the efficacy of boosting neural activity. In this review, we provided an overview of the basic knowledge regarding local field potential, electro- or magneto- encephalography signals, and their biological relevance, followed by a summary of the findings reported in various clinical and experimental stroke studies. We reviewed evidence of stroke-induced changes in hippocampal oscillations and disruption of communication between brain networks as potential mechanisms underlying post-stroke cognitive dysfunction. We also discussed the promise of brain stimulation in promoting post stroke functional recovery via restoring neural activity and enhancing brain plasticity.

Severe Acquired Brain Injury: Prognostic Factors of Discharge Outcome in Older Adults

Severe Acquired Brain Injury (sABI) is a leading cause of disability and requires intensive rehabilitation treatment. Discharge from the rehabilitation ward is a key moment in patient management. Delays in patient discharge can adversely affect hospital productivity and increase healthcare costs. The discharge should be structured from the hospital admission toward the most appropriate environment. The purpose of our study is to investigate early predictors of outcome for discharge in older adults with sABI. A retrospective study was performed on 22 patients who were admitted to an intensive neurorehabilitation unit between June 2019 and December 2021. Patients were divided into two outcome categories, good outcome (GO) or poor outcome (PO), based on discharge destination, and the possible prognostic factors were analyzed at one and two months after admission. Among the factors analyzed, changes in the Disability Rating Scale (DRS) and Level of Cognitive Functioning (LCF) at the first and second month of hospitalization were predictive of GO at discharge (DRS, p = 0.025; LCF, p = 0.011). The presence of percutaneous endoscopic gastrostomy at two months after admission was also significantly associated with PO (p = 0.038). High Body Mass Index (BMI) and the presence of sepsis at one month after admission were possible predictors of PO (BMI p = 0.048; sepsis p = 0.014). An analysis of dynamic predictors could be useful to guarantee an early evaluation of hospital discharge in frail patients with sABI.

Cautionary Observations Concerning the Introduction of Psychophysiological Biomarkers into Neuropsychiatric Practice

The combination of statistical learning technologies with large databases of psychophysiological data has appropriately generated enthusiastic interest in future clinical applicability. It is argued here that this enthusiasm should be tempered with the understanding that significant obstacles must be overcome before the systematic introduction of psychophysiological measures into neuropsychiatric practice becomes possible. The objective of this study is to identify challenges to this effort. The nonspecificity of psychophysiological measures complicates their use in diagnosis. Low test-retest reliability complicates use in longitudinal assessment, and quantitative psychophysiological measures can normalize in response to placebo intervention. Ten cautionary observations are introduced and, in some instances, possible directions for remediation are suggested.

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.

Transcranial direct current stimulation modulates working memory and prefrontal-insula connectivity after mild-moderate traumatic brain injury

Background: Persistent posttraumatic symptoms (PPS) may manifest after a mild-moderate traumatic brain injury (mmTBI) even when standard brain imaging appears normal. Transcranial direct current stimulation (tDCS) represents a promising treatment that may ameliorate pathophysiological processes contributing to PPS. Objective/Hypothesis: We hypothesized that in a mmTBI population, active tDCS combined with training would result in greater improvement in executive functions and post-TBI cognitive symptoms and increased resting state connectivity of the stimulated region, i.e., left dorsolateral prefrontal cortex (DLPFC) compared to control tDCS. Methods: Thirty-four subjects with mmTBI underwent baseline assessments of demographics, symptoms, and cognitive function as well as resting state functional magnetic resonance imaging (rsfMRI) in a subset of patients (n = 24). Primary outcome measures included NIH EXAMINER composite scores, and the Neurobehavioral Symptom Inventory (NSI). All participants received 10 daily sessions of 30 min of executive function training coupled with active or control tDCS (2 mA, anode F3, cathode right deltoid). Imaging and assessments were re-obtained after the final training session, and assessments were repeated after 1 month. Mixed-models linear regression and repeated measures analyses of variance were calculated for main effects and interactions. Results: Both active and control groups demonstrated improvements in executive function (EXAMINER composite: p < 0.001) and posttraumatic symptoms (NSI cognitive: p = 0.01) from baseline to 1 month. Active anodal tDCS was associated with greater improvements in working memory reaction time compared to control (p = 0.007). Reaction time improvement correlated significantly with the degree of connectivity change between the right DLPFC and the left anterior insula (p = 0.02). Conclusion: Anodal tDCS improved reaction time on an online working memory task in a mmTBI population, and decreased connectivity between executive network and salience network nodes. These findings generate important hypotheses for the mechanism of recovery from PPS after mild-moderate TBI.

Investigation of Machine Learning and Deep Learning Approaches for Detection of Mild Traumatic Brain Injury from Human Sleep Electroencephalogram

Traumatic Brain Injury (TBI) is a highly prevalent and serious public health concern. Most cases of TBI are mild in nature, yet some individuals may develop following-up persistent disability. The pathophysiologic causes for those with persistent postconcussive symptoms are most likely multifactorial and the underlying mechanism is not well understood, although it is clear that sleep disturbances feature prominently in those with persistent disability. The sleep electroencephalogram (EEG) provides a direct window into neuronal activity during an otherwise highly stereotyped behavioral state, and represents a promising quantitative measure for TBI diagnosis and prognosis. With the ever-evolving domain of machine learning, deep convolutional neural networks, and the development of better architectures, these approaches hold promise to solve some of the long entrenched challenges of personalized medicine for uses in recommendation systems and/or in health monitoring systems. In particular, advanced EEG analysis to identify putative EEG biomarkers of neurological disease could be highly relevant in the prognostication of mild TBI, an otherwise heterogeneous disorder with a wide range of affected phenotypes and disability levels. In this work, we investigate the use of various machine learning techniques and deep neural network architectures on a cohort of human subjects with sleep EEG recordings from overnight, in-lab, diagnostic polysomnography (PSG). An optimal scheme is explored for the classification of TBI versus non-TBI control subjects. The results were promising with an accuracy of ∼95% in random sampling arrangement and ∼70% in independent validation arrangement when appropriate parameters were used using a small number of subjects (10 mTBI subjects and 9 age- and sex-matched controls). We are thus confident that, with additional data and further studies, we would be able to build a generalized model to detect TBI accurately, not only via attended, in-lab PSG recordings, but also in practical scenarios such as EEG data obtained from simple wearables in daily life.

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.

Practice Guideline: Use of Quantitative EEG for the Diagnosis of Mild Traumatic Brain Injury: Report of the Guideline Committee of the American Clinical Neurophysiology Society

SUMMARY

Despite many decades of research, controversy regarding the utility of quantitative EEG (qEEG) for the accurate diagnosis of mild traumatic brain injury (mTBI) remains. This guideline is meant to assist clinicians by providing an expert review of the clinical usefulness of qEEG techniques for the diagnosis of mTBI. This guideline addresses the following primary aim: For patients with or without posttraumatic symptoms (abnormal cognition or behavior), does qEEG either at the time of injury or remote from the injury, as compared with current clinical diagnostic criteria, accurately identify those patients with mTBI (i.e., concussion)? Secondary aims included differentiating between mTBI and other diagnoses, detecting mTBI in the presence of central nervous system medications, and pertinence of statistical methods for measurements of qEEG components. It was found that for patients with or without symptoms of abnormal cognition or behavior, current evidence does not support the clinical use of qEEG either at the time of the injury or remote from the injury to diagnose mTBI (level U). In addition, the evidence does not support the use of qEEG to differentiate mTBI from other diagnoses or detect mTBI in the presence of central nervous system medications, and suitable statistical methods do not exist when using qEEG to identify patients with mTBI. Based upon the current literature review, qEEG remains an investigational tool for mTBI diagnosis (class III evidence).

Quantitative multimodal imaging in traumatic brain injuries producing impaired cognition

PURPOSE OF REVIEW

Cognitive impairments are a devastating long-term consequence following traumatic brain injury (TBI). This review provides an update on the quantitative mutimodal neuroimaging studies that attempt to elucidate the mechanism(s) underlying cognitive impairments and their recovery following TBI.

RECENT FINDINGS

Recent studies have linked individual specific behavioural impairments and their changes over time to physiological activity and structural changes using EEG, PET and MRI. Multimodal studies that combine measures of physiological activity with knowledge of neuroanatomical and connectivity damage have also illuminated the multifactorial function-structure relationships that underlie impairment and recovery following TBI.

SUMMARY

The combined use of multiple neuroimaging modalities, with focus on individual longitudinal studies, has the potential to accurately classify impairments, enhance sensitivity of prognoses, inform targets for interventions and precisely track spontaneous and intervention-driven recovery.

Predicting outcome following mild traumatic brain injury: protocol for the longitudinal, prospective, observational Concussion Recovery (CREST) cohort study

INTRODUCTION

Mild traumatic brain injury (mTBI) is a complex injury with heterogeneous physical, cognitive, emotional and functional outcomes. Many who sustain mTBI recover within 2 weeks of injury; however, approximately 10%-20% of individuals experience mTBI symptoms beyond this 'typical' recovery timeframe, known as persistent post-concussion symptoms (PPCS). Despite increasing interest in PPCS, uncertainty remains regarding its prevalence in community-based populations and the extent to which poor recovery may be identified using early predictive markers.

OBJECTIVE

(1) Establish a research dataset of people who have experienced mTBI and document their recovery trajectories; (2) Evaluate a broad range of novel and established prognostic factors for inclusion in a predictive model for PPCS.

METHODS AND ANALYSIS

The Concussion Recovery Study (CREST) is a prospective, longitudinal observational cohort study conducted in Perth, Western Australia. CREST is recruiting adults aged 18-65 from medical and community-based settings with acute diagnosis of mTBI. CREST will create a state-wide research dataset of mTBI cases, with data being collected in two phases. Phase I collates data on demographics, medical background, lifestyle habits, nature of injury and acute mTBI symptomatology. In Phase II, participants undergo neuropsychological evaluation, exercise tolerance and vestibular/ocular motor screening, MRI, quantitative electroencephalography and blood-based biomarker assessment. Follow-up is conducted via telephone interview at 1, 3, 6 and 12 months after injury. Primary outcome measures are presence of PPCS and quality of life, as measured by the Post-Concussion Symptom Scale and the Quality of Life after Brain Injury questionnaires, respectively. Multivariate modelling will examine the prognostic value of promising factors.

ETHICS AND DISSEMINATION

Human Research Ethics Committees of Royal Perth Hospital (#RGS0000003024), Curtin University (HRE2019-0209), Ramsay Health Care (#2009) and St John of God Health Care (#1628) have approved this study protocol. Findings will be published in peer-reviewed journals and presented at scientific conferences.

TRIAL REGISTRATION NUMBER

ACTRN12619001226190.

Elevated and Slowed EEG Oscillations in Patients with Post-Concussive Syndrome and Chronic Pain Following a Motor Vehicle Collision

(1) Background: Mild traumatic brain injury produces significant changes in neurotransmission including brain oscillations. We investigated potential quantitative electroencephalography biomarkers in 57 patients with post-concussive syndrome and chronic pain following motor vehicle collision, and 54 healthy nearly age- and sex-matched controls. (2) Methods: Electroencephalography processing was completed in MATLAB, statistical modeling in SPSS, and machine learning modeling in Rapid Miner. Group differences were calculated using current-source density estimation, yielding whole-brain topographical distributions of absolute power, relative power and phase-locking functional connectivity. Groups were compared using independent sample Mann–Whitney U tests. Effect sizes and Pearson correlations were also computed. Machine learning analysis leveraged a post hoc supervised learning support vector non-probabilistic binary linear kernel classification to generate predictive models from the derived EEG signatures. (3) Results: Patients displayed significantly elevated and slowed power compared to controls: delta (p = 0.000000, r = 0.6) and theta power (p < 0.0001, r = 0.4), and relative delta power (p < 0.00001) and decreased relative alpha power (p < 0.001). Absolute delta and theta power together yielded the strongest machine learning classification accuracy (87.6%). Changes in absolute power were moderately correlated with duration and persistence of symptoms in the slow wave frequency spectrum (<15 Hz). (4) Conclusions: Distributed increases in slow wave oscillatory power are concurrent with post-concussive syndrome and chronic pain.

A Raspberry Pi-Based Traumatic Brain Injury Detection System for Single-Channel Electroencephalogram

Traumatic Brain Injury (TBI) is a common cause of death and disability. However, existing tools for TBI diagnosis are either subjective or require extensive clinical setup and expertise. The increasing affordability and reduction in the size of relatively high-performance computing systems combined with promising results from TBI related machine learning research make it possible to create compact and portable systems for early detection of TBI. This work describes a Raspberry Pi based portable, real-time data acquisition, and automated processing system that uses machine learning to efficiently identify TBI and automatically score sleep stages from a single-channel Electroencephalogram (EEG) signal. We discuss the design, implementation, and verification of the system that can digitize the EEG signal using an Analog to Digital Converter (ADC) and perform real-time signal classification to detect the presence of mild TBI (mTBI). We utilize Convolutional Neural Networks (CNN) and XGBoost based predictive models to evaluate the performance and demonstrate the versatility of the system to operate with multiple types of predictive models. We achieve a peak classification accuracy of more than 90% with a classification time of less than 1 s across 16–64 s epochs for TBI vs. control conditions. This work can enable the development of systems suitable for field use without requiring specialized medical equipment for early TBI detection applications and TBI research. Further, this work opens avenues to implement connected, real-time TBI related health and wellness monitoring systems.

Quantitative EEG Analysis in Clinical Practice: Concussion Injury

Concussion is a common brain injury. The American Academy of Neurology provides a definition of concussion: "Concussion is a traumatically, or biomechanically, induced alteration of brain function. Emphasis is placed on a pathophysiological process, or functional disruption, as opposed to anatomic, structural, or tissue injury.". The incidence of mild traumatic brain injury (mTBI) is estimated at 200 per 100 000. The Centers for Disease Control and Prevention (CDC) estimates 3.8 million sport and recreational mTBIs occurring in the United States each year. A more recent CDC assessment estimates 2.5 million concussion injuries in high school sports alone. The controlled environment and opportunity for direct surveillance and observation has made the sports arena the scientific "wet lab" for the study of mTBI natural history, short- and long-term consequences and opportunities to intervene. Quantitative EEG methods have been utilized in the assessment and management of mTBI and lends to provide a cost-effective procedure that has the sensitivities needed to identify pathology where routine visual inspection of the EEG has failed.

Evaluating Performance of EEG Data-Driven Machine Learning for Traumatic Brain Injury Classification

Objectives:

Big data analytics can potentially benefit the assessment and management of complex neurological conditions by extracting information that is difficult to identify manually. In this study, we evaluated the performance of commonly used supervised machine learning algorithms in the classification of patients with traumatic brain injury (TBI) history from those with stroke history and/or normal EEG.

Methods:

Support vector machine (SVM) and K-nearest neighbors (KNN) models were generated with a diverse feature set from Temple EEG Corpus for both two-class classification of patients with TBI history from normal subjects and three-class classification of TBI, stroke and normal subjects.

Results:

For two-class classification, an accuracy of 0.94 was achieved in 10-fold cross validation (CV), and 0.76 in independent validation (IV). For three-class classification, 0.85 and 0.71 accuracy were reached in CV and IV respectively. Overall, linear discriminant analysis (LDA) feature selection and SVM models consistently performed well in both CV and IV and for both two-class and three-class classification. Compared to normal control, both TBI and stroke patients showed an overall reduction in coherence and relative PSD in delta frequency, and an increase in higher frequency (alpha, mu, beta and gamma) power. But stroke patients showed a greater degree of change and had additional global decrease in theta power.

Conclusions:

Our study suggests that EEG data-driven machine learning can be a useful tool for TBI classification.

Significance:

Our study provides preliminary evidence that EEG ML algorithm can potentially provide specificity to separate different neurological conditions.

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.

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.

Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice

Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today’s world, robust detection of TBI has become more significant than ever. In this work, we investigate several machine learning approaches to assess their performance in classifying electroencephalogram (EEG) data of TBI in a mouse model. Algorithms such as decision trees (DT), random forest (RF), neural network (NN), support vector machine (SVM), K-nearest neighbors (KNN) and convolutional neural network (CNN) were analyzed based on their performance to classify mild TBI (mTBI) data from those of the control group in wake stages for different epoch lengths. Average power in different frequency sub-bands and alpha:theta power ratio in EEG were used as input features for machine learning approaches. Results in this mouse model were promising, suggesting similar approaches may be applicable to detect TBI in humans in practical scenarios.

Assessment of glutamatergic synaptic transmission and plasticity in brain slices: relevance to bioelectronic approaches

Background

Glutamatergic neurons represent the largest neuronal class in the brain and are responsible for the bulk of excitatory synaptic transmission and plasticity. Abnormalities in glutamatergic neurons are linked to several brain disorders and their modulation represents a potential opportunity for emerging bioelectronic medicine (BEM) approaches. Here, we have used a set of electrophysiological assays to identify the effect of the pyrimidine nucleoside uridine on glutamatergic systems in ex vivo brain slices. An improved understanding of glutamatergic synaptic transmission and plasticity, through this type of examination, is critical to the development of potential neuromodulation strategies.

Methods

Ex vivo hippocampal slices (400 μm thick) were prepared from mouse brain. We recorded field excitatory postsynaptic potentials (fEPSP) in the CA1’s stratum radiatum by stimulation of the CA3 Schaeffer collateral/commissural axons. Uridine was applied at concentrations (3, 30, 300 μM) representing the physiological range present in brain tissue. Synaptic function was studied with input-output (I-O) functions, as well as paired-pulse facilitation (PPF). Synaptic plasticity was studied by applying tetanic stimulation to induce post-tetanic potentiation (PTP), short-term potentiation (STP) and long-term potentiation (LTP). Additionally, we determined whether uridine affected synaptic responses carried solely by n-methyl-d-aspartate receptors (NMDARs), particularly during the oxygen-glucose deprivation (OGD) paradigm.

Results

The presence of uridine altered glutamatergic synaptic transmission and plasticity. We found that uridine affected STP and LTP in a concentration-dependent manner. Low-dose uridine (3 μM) had no effect, but higher doses (30 and 300 μM) impaired STP and LTP. Moreover, uridine (300 μM) decreased NMDAR-mediated synaptic responses. Conversely, uridine (at all concentrations tested) had a negligible effect on PPF and basal synaptic transmission, which is mediated primarily by α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs). In addition, uridine (100 μM) exerted a protective effect when the hippocampal slices were challenged with OGD, a widely used model of cerebral ischemia.

Conclusions

Using a wide set of electrophysiological assays, we identify that uridine interacts with glutamatergic neurons to alter NMDAR-mediated responses, impair synaptic STP and LTP in a dose-dependent manner, and has a protective effect against OGD insult. This work outlines a strategy to identify deficits in glutamatergic mechanisms for signaling and plasticity that may be critical for targeting these same systems with BEM device-based approaches. To improve the efficacy of potential neuromodulation approaches for treating brain dysfunction, we need to improve our understanding of glutamatergic systems in the brain, including the effects of modulators such as uridine.

Hamid AI, Abdullah MZ, Azman A, Abdullah JM. Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography

Traumatic brain injury (TBI) is one of the injuries that can bring serious consequences if medical attention has been delayed. Commonly, analysis of computed tomography (CT) or magnetic resonance imaging (MRI) is required to determine the severity of a moderate TBI patient. However, due to the rising number of TBI patients these days, employing the CT scan or MRI scan to every potential patient is not only expensive, but also time consuming. Therefore, in this paper, we investigate the possibility of using electroencephalography (EEG) with computational intelligence as an alternative approach to detect the severity of moderate TBI patients. EEG procedure is much cheaper than CT or MRI. Although EEG does not have high spatial resolutions as compared with CT and MRI, it has high temporal resolutions. The analysis and prediction of moderate TBI from EEG using conventional computational intelligence approaches are tedious as they normally involve complex preprocessing, feature extraction, or feature selection of the signal. Thus, we propose an approach that uses convolutional neural network (CNN) to automatically classify healthy subjects and moderate TBI patients. The input to this computational intelligence system is the resting-state eye-closed EEG, without undergoing preprocessing and feature selection. The EEG dataset used includes 15 healthy volunteers and 15 moderate TBI patients, which is acquired at the Hospital Universiti Sains Malaysia, Kelantan, Malaysia. The performance of the proposed method has been compared with four other existing methods. With the average classification accuracy of 72.46%, the proposed method outperforms the other four methods. This result indicates that the proposed method has the potential to be used as a preliminary screening for moderate TBI, for selection of the patients for further diagnosis and treatment planning.

Brain dysfunction underlying prolonged post-concussive syndrome: A systematic review

BACKGROUND

One out of 4 patients who sustains a mild traumatic brain injury (mTBI) experiences persistent complaints, despite the absence of structural brain damage on conventional neuroimaging. Susceptibility to develop post concussive symptoms (PCS) is thought to originate from occult brain dysfunction. However, the influence of such neural changes on the development of persistent PCS is poorly characterized.

METHODS

In this article, we aim to integrate findings from longitudinal studies that investigated across the spectrum of neuroimaging modalities the changes within the first twelve months following a mTBI, with the goal of identifying possible predictors or biomarkers of persistent PCS.

RESULTS

Nine studies met inclusion criteria: 5 that used resting state functional MRI, 2 that used Diffusion Weighted Imaging, and 2 that used ¹H-MR Spectroscopy. All studies indicate significant structural, functional and/or metabolic aberrations that occur in the acute and early subacute phases following a mTBI. However, in patients with persistent PCS, these mTBI-induced damages linger and relate to the severity of PCS. These biomarkers include: decreased diffusion along white matter fiber tracts, alteration of perfusion, disrupted metabolism, and reduced connectivity within several resting state networks. Additionally, in PCS patients, disruptions of brain function can manifest exclusively in the chronic phase.

CONCLUSION

This review support the ongoing use of neuroimaging modalities to understand the brain changes that occur throughout the time course of mTBI. Based on the complexity of mTBI, however, more work is required to characterize injury and recovery mechanisms that could impact the emergence and persistence of PCS.

Aberrant Whole-Brain Transitions and Dynamics of Spontaneous Network Microstates in Mild Traumatic Brain Injury

Dynamic Functional Connectivity (DFC) analysis is a promising approach for the characterization of brain electrophysiological activity. In this study, we investigated abnormal alterations due to mild Traumatic Brain Injury (mTBI) using DFC of the source reconstructed magnetoencephalographic (MEG) resting-state recordings. Brain activity in several well-known frequency bands was first reconstructed using beamforming of the MEG data to determine ninety anatomical brain regions of interest. A DFC graph was formulated using the imaginary part of phase-locking values, which were obtained from 30 mTBI patients and 50 healthy controls (HC). Subsequently, we estimated normalized Laplacian transformations of individual, statistically and topologically filtered quasi-static graphs. The corresponding eigenvalues of each node synchronization were then computed and through the neural-gas algorithm, we quantized the evolution of the eigenvalues resulting in distinct network microstates (NMstates). The discrimination level between the two groups was assessed using an iterative cross-validation classification scheme with features either the NMstates in each frequency band, or the combination of the so-called chronnectomics (flexibility index, occupancy time of NMstate, and Dwell time) with the complexity index over the evolution of the NMstates across all frequency bands. Classification performance based on chronnectomics showed 80% accuracy, 99% sensitivity, and 49% specificity. However, performance was much higher (accuracy: 91-97%, sensitivity: 100%, and specificity: 77-93%) when focusing on the microstates. Exploring the mean node degree within and between brain anatomical networks (default mode network, frontoparietal, occipital, cingulo-opercular, and sensorimotor), a reduced pattern occurred from lower to higher frequency bands, with statistically significant stronger degrees for the HC than the mTBI group. A higher entropic profile on the temporal evolution of the modularity index was observed for both NMstates for the mTBI group across frequencies. A significant difference in the flexibility index was observed between the two groups for the β frequency band. The latter finding may support a central role of the thalamus impairment in mTBI. The current study considers a complete set of frequency-dependent connectomic markers of mTBI-caused alterations in brain connectivity that potentially could serve as markers to assess the return of an injured subject back to normality.

In-clinic event related potentials after sports concussion: A 4-year study

PURPOSE

Electrophysiological event-related potentials (ERP's) have been reported to change after concussion. The objective of this study is to use a simple 2-tone auditory P300 ERP in routine clinical settings to measure changes from baseline after concussion and to determine if these changes persist at return to play when other standard measures have normalized.

METHODS

Three-hundred sixty-four (364) student athletes, aged 17-23 years, participating in contact sports were tracked over consecutive years. In this blinded study P300, plus physical reaction times and Trail Making tests, were collected alongside standard clinical evaluations. Changes in these measures after concussion were compared to clinical outcomes over various stages of post-injury recovery.

RESULTS

Concussed players experienced significant reaction time and/or P300 amplitude changes compared to pre-concussion baseline measurements (p< 0.005). P300 changes persisted in 38% of the players after standard measures, including reaction times, had cleared. Many of those players slow to normalize were part of the sub-concussive symptom group and/or appeared more prone to repeat concussions.

CONCLUSION

These data suggest significant P300 amplitude changes after concussion that are quantifiable and consistent. These changes often normalized slower than other standard assessments. More data are needed to determine if slow normalization relates to sub-concussive or repeated events.

Electrophysiological Markers of Visuospatial Attention Recovery after Mild Traumatic Brain Injury

Objective: Attentional problems are amongst the most commonly reported complaints following mild traumatic brain injury (mTBI), including difficulties orienting and disengaging attention, sustaining it over time, and dividing attentional resources across multiple simultaneous demands. The objective of this study was to track, using a single novel electrophysiological task, various components associated with the deployment of visuospatial selective attention. Methods: A paradigm was designed to evoke earlier visual evoked potentials (VEPs), as well as attention-related and visuocognitive ERPs. Data from 36 individuals with mTBI (19 subacute, 17 chronic) and 22 uninjured controls are presented. Postconcussion symptoms (PCS), anxiety (BAI), depression (BDI-II) and visual attention (TEA Map Search, DKEFS Trail Making Test) were also assessed. Results: Earlier VEPs (P1, N1), as well as processes related to visuospatial orientation (N2pc) and encoding in visual short-term memory (SPCN), appear comparable in mTBI and control participants. However, there appears to be a disruption in the spatiotemporal dynamics of attention (N2pc-Ptc, P2) in subacute mTBI, which recovers within six months. This is also reflected in altered neuropsychological performance (information processing speed, attentional shifting). Furthermore, orientation of attention (P3a) and working memory processes (P3b) are also affected and remain as such in the chronic post-mTBI period, in co-occurrence with persisting postconcussion symptomatology. Conclusions: This study adds original findings indicating that such a sensitive and rigorous ERP task implemented at diagnostic and follow-up levels could allow for the identification of subtle but complex brain activation and connectivity deficits that can occur following mTBI.

Longitudinal Functional Assessment of Brain Injury Induced by High-Intensity Ultrasound Pulse Sequences

Exposure of the brain to high-intensity stress waves creates the potential for long-term functional deficits not related to thermal or cavitational damage. Possible sources of such exposure include overpressure from blast explosions or high-intensity focused ultrasound (HIFU). While current ultrasound clinical protocols do not normally produce long-term neurological deficits, the rapid expansion of potential therapeutic applications and ultrasound pulse-train protocols highlights the importance of establishing a safety envelope beyond which therapeutic ultrasound can cause neurological deficits not detectable by standard histological assessment for thermal and cavitational damage. In this study, we assessed the neuroinflammatory response, behavioral effects, and brain micro-electrocorticographic (µECoG) signals in mice following exposure to a train of transcranial pulses above normal clinical parameters. We found that the HIFU exposure induced a mild regional neuroinflammation not localized to the primary focal site, and impaired locomotor and exploratory behavior for up to 1 month post-exposure. In addition, low frequency (δ) and high frequency (β, γ) oscillations recorded by ECoG were altered at acute and chronic time points following HIFU application. ECoG signal changes on the hemisphere ipsilateral to HIFU exposure are of greater magnitude than the contralateral hemisphere, and persist for up to three months. These results are useful for describing the upper limit of transcranial ultrasound protocols, and the neurological sequelae of injury induced by high-intensity stress waves.

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.

Effects of RF-EMF on the Human Resting-State EEG-the Inconsistencies in the Consistency. Part 1: Non-Exposure-Related Limitations of Comparability Between Studies

The results of studies on possible effects of radiofrequency electromagnetic fields (RF-EMFs) on human waking electroencephalography (EEG) have been quite heterogeneous. In the majority of studies, changes in the alpha-frequency range in subjects who were exposed to different signals of mobile phone-related EMF sources were observed, whereas other studies did not report any effects. In this review, possible reasons for these inconsistencies are presented and recommendations for future waking EEG studies are made. The physiological basis of underlying brain activity, and the technical requirements and framework conditions for conducting and analyzing the human resting-state EEG are discussed. Peer-reviewed articles on possible effects of EMF on waking EEG were evaluated with regard to non-exposure-related confounding factors. Recommendations derived from international guidelines on the analysis and reporting of findings are proposed to achieve comparability in future studies. In total, 22 peer-reviewed studies on possible RF-EMF effects on human resting-state EEG were analyzed. EEG power in the alpha frequency range was reported to be increased in 10, decreased in four, and not affected in eight studies. All reviewed studies differ in several ways in terms of the methodologies applied, which might contribute to different results and conclusions about the impact of EMF on human resting-state EEG. A discussion of various study protocols and different outcome parameters prevents a scientifically sound statement on the impact of RF-EMF on human brain activity in resting-state EEG. Further studies which apply comparable, standardized study protocols are recommended. Bioelectromagnetics. 2019;40:291-318. © 2019 The Authors. Bioelectromagnetics Published by Wiley Periodicals, Inc.

ERPs predict symptomatic distress and recovery in sub-acute mild traumatic brain injury

Mild traumatic brain injury (mTBI) can affect high-level executive functioning long after somatic symptoms resolve. We tested if simple EEG responses within an oddball paradigm could capture variance relevant to this clinical problem. The P3a and P3b components reflect bottom-up and top-down processes driving engagement with exogenous stimuli. Since these features are related to primitive decision abilities, abnormal amplitudes following mTBI may account for problems in the ability to exert executive control. Sub-acute (<2 weeks) mTBI participants (N = 38) and healthy controls (N = 24) were assessed at an initial session as well as a two-month follow-up (sessions 1 and 2). We contrasted the initial assessment to a comparison group of participants with chronic symptomatology following brain injury (N = 23). There were no group differences in P3a or P3b amplitudes. Yet in the sub-acute mTBI group, higher symptomatology on the Frontal Systems Behavior scale (FrSBe), a questionnaire validated as measuring symptomatic distress related to frontal lobe injury, correlated with lower P3a in session 1. This relationship was replicated in session 2. These findings were distinct from chronic TBI participants, who instead expressed a relationship between increased FrSBe symptoms and a lower P3b component. In the sub-acute group, P3b amplitudes in the first session correlated with the degree of symptom change between sessions 1 and 2, above and beyond demographic predictors. Controls did not show any relationship between FrSBe symptoms and P3a or P3b. These findings identify symptom-specific alterations in neural systems that vary along the time course of post-concussive symptomatology.

Slow-gamma frequencies are optimally guarded against effects of neurodegenerative diseases and traumatic brain injuries

We introduce a computational model for the cellular level effects of firing rate filtering due to the major forms of neuronal injury, including demyelination and axonal swellings. Based upon experimental and computational observations, we posit simple phenomenological input/output rules describing spike train distortions and demonstrate that slow-gamma frequencies in the 38-41 Hz range emerge as the most robust to injury. Our signal-processing model allows us to derive firing rate filters at the cellular level for impaired neural activity with minimal assumptions. Specifically, we model eight experimentally observed spike train transformations by discrete-time filters, including those associated with increasing refractoriness and intermittent blockage. Continuous counterparts for the filters are also obtained by approximating neuronal firing rates from spike trains convolved with causal and Gaussian kernels. The proposed signal processing framework, which is robust to model parameter calibration, is an abstraction of the major cellular-level pathologies associated with neurodegenerative diseases and traumatic brain injuries that affect spike train propagation and impair neuronal network functionality. Our filters are well aligned with the spectrum of dynamic memory fields including working memory, visual consciousness, and other higher cognitive functions that operate in a frequency band that is - at a single cell level - optimally guarded against common types of pathological effects. In contrast, higher-frequency neural encoding, such as is observed with short-term memory, are susceptible to neurodegeneration and injury.

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.

Mirror and Vibration Therapies Effects on the Upper Limbs of Hemiparetic Patients after Stroke: A Pilot Study

Background/Aim

To evaluate, in this pilot study, the effects of the mirror (MT) and vibration therapies (VT) on the functionality of hemiparesis patients after stroke.

Materials and Methods

Twenty-one individuals after stroke with upper limb hemiparesis were randomized into control group (CG), Mirror Therapy Group (MTG), and Vibration Therapy Group (VTG). The functionality was evaluated before and after 12 sessions with three tests (i) Mobility Index Rivermead, (ii) Motor Function Wolf Test (time, functional ability), and (iii) Jebsen Taylor Test.

Results

Significant findings were observed for MTG or VTG when compared to the CG, obtaining improvements in the three functional tests: Mobility Index Rivermead, Motor Function Test Wolf (time) and Motor Function Test Wolf (functional ability), and Jebsen Test Taylor.

Conclusions

MT or VT showed enhancements on the functionality of subjects with poststroke hemiparesis. In consequence, these interventions may be used in the rehabilitation of these individuals in order to promote improvements of the affected upper limb functionality. Probably, neuromuscular responses of the used therapies would be related to these desirable effects. However, it is necessary conducting further controlled studies with more subjects.