EEG slow waves in traumatic brain injury: Convergent findings in mouse and man

Novel murine closed-loop auditory stimulation paradigm elicits macrostructural sleep benefits in neurodegeneration

Boosting slow-wave activity (SWA) by modulating slow waves through closed-loop auditory stimulation (CLAS) might provide a powerful non-pharmacological tool to investigate the link between sleep and neurodegeneration. Here, we established mouse CLAS (mCLAS)-mediated SWA enhancement and explored its effects on sleep deficits in neurodegeneration, by targeting the up-phase of slow waves in mouse models of Alzheimer's disease (AD, Tg2576) and Parkinson's disease (PD, M83). We found that tracking a 2 Hz component of slow waves leads to highest precision of non-rapid eye movement (NREM) sleep detection in mice, and that its combination with a 30° up-phase target produces a significant 15-30% SWA increase from baseline in wild-type (WTAD) and transgenic (TGAD) mice versus a mock stimulation group. Conversely, combining 2 Hz with a 40° phase target yields a significant increase ranging 30-35% in WTPD and TGPD mice. Interestingly, these phase-target-triggered SWA increases are not genotype dependent but strain specific. Sleep alterations that may contribute to disease progression and burden were described in AD and PD lines. Notably, pathological sleep traits were rescued by mCLAS, which elicited a 14% decrease of pathologically heightened NREM sleep fragmentation in TGAD mice, accompanied by a steep decrease in microarousal events during both light and dark periods. Overall, our results indicate that model-tailored phase targeting is key to modulate SWA through mCLAS, prompting the acute alleviation of key neurodegeneration-associated sleep phenotypes and potentiating sleep regulation and consolidation. Further experiments assessing the long-term effect of mCLAS in neurodegeneration may majorly impact the establishment of sleep-based therapies.

Comorbidity of concussion and depression alters brain functional connectivity in collegiate student-athletes

Depression and concussion are highly prevalent neuropsychological disorders that often occur simultaneously. However, due to the high degree of symptom overlap between the two events, including but not limited to headache, sleep disturbances, appetite changes, fatigue, and difficulty concentrating, they may be treated in isolation. Thus, clinical awareness of additive symptom load may be missed. This study measures neuropsychological and electroencephalography (EEG) alpha band coherence differences in collegiate student-athletes with history of comorbid depression and concussion, in comparison to those with a single morbidity and healthy controls (HC). 35 collegiate athletes completed neuropsychological screenings and EEG measures. Participants were grouped by concussion and depression history. Differences in alpha band coherence were calculated using two-way ANOVA with post hoc correction for multiple comparisons. Comorbid participants scored significantly worse on neuropsychological screening, BDI-FS, and PCSS than those with a single morbidity and HC. Two-way ANOVA by group revealed significant main effects of alpha band coherence for concussion, depression, and their interaction term. Post-hoc analysis showed that comorbid participants had more abnormal alpha band coherence than single morbidity, when compared to HC. Comorbidity of concussion and depression increased symptom reporting and revealed more altered alpha band coherence than single morbidity, compared to HC. The abnormalities of the comorbid group exclusively showed decreased alpha band coherence in comparison to healthy controls. The comorbidity of depression and SRC has a compounding effect on depression symptoms, post-concussion symptoms, and brain functional connectivity. This research demonstrates a promising objective measure in comorbid individuals, previously only measured via subjective symptom reporting.

Electrophysiology Methods for Assessing of Neurodegenerative and Post-Traumatic Processes as Applied to Translational Research

Electrophysiological studies have long established themselves as reliable methods for assessing the functional state of the brain and spinal cord, the degree of neurodegeneration, and evaluating the effectiveness of therapy. In addition, they can be used to diagnose, predict functional outcomes, and test the effectiveness of therapeutic and rehabilitation programs not only in clinical settings, but also at the preclinical level. Considering the urgent need to develop potential stimulators of neuroregeneration, it seems relevant to obtain objective data when modeling neurological diseases in animals. Thus, in the context of the application of electrophysiological methods, not only the comparison of the basic characteristics of bioelectrical activity of the brain and spinal cord in humans and animals, but also their changes against the background of neurodegenerative and post-traumatic processes are of particular importance. In light of the above, this review will contribute to a better understanding of the results of electrophysiological assessment in neurodegenerative and post-traumatic processes as well as the possibility of translating these methods from model animals to humans.

Electroencephalogram pattern predicting neurological outcomes of children with seizures secondary to abusive head trauma

BACKGROUND

The clinical presentations of abusive head trauma can abruptly worsen, so the occurrence of seizures and changes of EEG can be variable according to patients' conditions. Since the changes of EEG background waves reflect the cortical function of children, we aimed to find out whether the timing of EEG background, epileptiform discharges and seizure patterns were associated with the outcomes of patients with AHT.

MATERIAL AND METHODS

Using seizure type and acute stage electroencephalographic (EEG) characteristics to assess adverse neurological outcomes in children with seizures secondary to abusive head trauma (AHT). Children who were hospitalized with AHT at a tertiary referral hospital from October 2000 to April 2010 were evaluated retrospectively. A total of 50 children below 6 years of age admitted due to AHT were included. KOSCHI outcome scale was used to evaluate the primary outcome and neurological impairment was used as secondary outcome after 6 months discharge.

RESULTS

Children with apnea, cardiac arrest, reverse blood flow and skull fracture in clinic had a higher mortality rate even in the no-seizure group (3/5 [60%] vs. 3/45 [6.7%], odds ratio [OR] = 11; 95% CI = 2.3-52; p = 0.025). Seizure occurrence reduced mostly at the second day after admission in seizure groups; but children with persistent seizures for 1 week showed poor neurological outcomes. The occurrence of initial seizure was frequency associated with younger age; focal seizure, diffuse cortical dysfunction in acute-stage EEG, and low Glasgow Coma Scale (GCS) score were significantly related to poor outcomes after 6 months. Diffuse cortical dysfunction was also associated with motor, speech, and cognitive dysfunction.

CONCLUSIONS

Diffuse cortical dysfunction in acute-stage EEG combined with low GCS score and focal seizure may related to poor outcomes and neurological dysfunctions in children with AHT.

Sleep, inflammation, and hemodynamics in rodent models of traumatic brain injury

Traumatic brain injury (TBI) can induce dysregulation of sleep. Sleep disturbances include hypersomnia and hyposomnia, sleep fragmentation, difficulty falling asleep, and altered electroencephalograms. TBI results in inflammation and altered hemodynamics, such as changes in blood brain barrier permeability and cerebral blood flow. Both inflammation and altered hemodynamics, which are known sleep regulators, contribute to sleep impairments post-TBI. TBIs are heterogenous in cause and biomechanics, which leads to different molecular and symptomatic outcomes. Animal models of TBI have been developed to model the heterogeneity of TBIs observed in the clinic. This review discusses the intricate relationship between sleep, inflammation, and hemodynamics in pre-clinical rodent models of TBI.

An Open-Source Mouse Chronic EEG Array System with High-Density MXene-Based Skull Surface Electrodes

Electroencephalography (EEG) is an indispensable tool in epilepsy, sleep, and behavioral research. In rodents, EEG recordings are typically performed with metal electrodes that traverse the skull into the epidural space. In addition to requiring major surgery, intracranial EEG is difficult to perform for more than a few electrodes, is time-intensive, and confounds experiments studying traumatic brain injury. Here, we describe an open-source cost-effective refinement of this technique for chronic mouse EEG recording. Our alternative two-channel (EEG2) and sixteen-channel high-density EEG (HdEEG) arrays use electrodes made of the novel, flexible 2D nanomaterial titanium carbide (Ti3C2T x ) MXene. The MXene electrodes are placed on the surface of the intact skull and establish an electrical connection without conductive gel or paste. Fabrication and implantation times of MXene EEG electrodes are significantly shorter than the standard approach, and recorded resting baseline and epileptiform EEG waveforms are similar to those obtained with traditional epidural electrodes. Applying HdEEG to a mild traumatic brain injury (mTBI) model in mice of both sexes revealed that mTBI significantly increased spike-wave discharge (SWD) preictal network connectivity with frequencies of interest in the β-spectral band (12-30 Hz). These findings indicate that the fabrication of MXene electrode arrays is a cost-effective, efficient technology for multichannel EEG recording in mice that obviates the need for skull-penetrating surgery. Moreover, increased preictal β-frequency network connectivity may contribute to the development of early post-mTBI SWDs.

Long-term resting EEG correlates of repetitive mild traumatic brain injury and loss of consciousness: alterations in alpha-beta power

Objective

Long-term changes to EEG spectra after mild traumatic brain injury (mTBI, i.e., concussion) have been reported; however, the role of injury characteristics in long-term EEG changes is unclear. It is also unclear how any chronic EEG changes may underlie either subjective or objective cognitive difficulties, which might help explain the variability in recovery after mTBI.

Methods

This study included resting-state high-density electroencephalography (EEG) and mTBI injury data from 340 service members and veterans collected on average 11 years after injury as well as measures of objective and subjective cognitive functioning. The average absolute power within standard bands was computed across 11 spatial regions of the scalp. To determine how variation in brain function was accounted for by injury characteristics and aspects of cognition, we used regression analyses to investigate how EEG power was predicted by mTBI history characteristics [number, number with post-traumatic amnesia and witnessed loss of consciousness (PTA + LOC), context of injury (combat or non-combat), potentially concussive blast exposures], subjective complaints (TBIQOL General Cognitive and Executive Function Concerns), and cognitive performance (NIH Toolbox Fluid Intelligence and premorbid IQ).

Results

Post-traumatic amnesia (PTA) and loss of consciousness (LOC), poorer cognitive performance, and combat experience were associated with reduced power in beta frequencies. Executive function complaints, lower premorbid IQ, poorer cognitive performance, and higher psychological distress symptoms were associated with greater power of delta frequencies. Multiple regression confirmed the relationship between PTA + LOC, poor cognitive performance, cognitive complaints, and reduced power in beta frequencies and revealed that repetitive mTBI was associated with a higher power in alpha and beta frequencies. By contrast, neither dichotomous classification of the presence and absence of mTBI history nor blast exposures showed a relationship with EEG power variables.

Conclusion

Long-term alterations in resting EEG spectra measures of brain function do not appear to reflect any lasting effect of a history of mTBI or blast exposures. However, power in higher frequencies reflects both injury characteristics and subjective and objective cognitive difficulties, while power in lower frequencies is related to cognitive functions and psychological distress associated with poor long-term outcomes after mTBI.

N-acetylcysteine treatment mitigates loss of cortical parvalbumin-positive interneuron and perineuronal net integrity resulting from persistent oxidative stress in a rat TBI model

Traumatic brain injury (TBI) increases cerebral reactive oxygen species production, which leads to continuing secondary neuronal injury after the initial insult. Cortical parvalbumin-positive interneurons (PVIs; neurons responsible for maintaining cortical inhibitory tone) are particularly vulnerable to oxidative stress and are thus disproportionately affected by TBI. Systemic N-acetylcysteine (NAC) treatment may restore cerebral glutathione equilibrium, thus preventing post-traumatic cortical PVI loss. We therefore tested whether weeks-long post-traumatic NAC treatment mitigates cortical oxidative stress, and whether such treatment preserves PVI counts and related markers of PVI integrity and prevents pathologic electroencephalographic (EEG) changes, 3 and 6 weeks after fluid percussion injury in rats. We find that moderate TBI results in persistent oxidative stress for at least 6 weeks after injury and leads to the loss of PVIs and the perineuronal net (PNN) that surrounds them as well as of per-cell parvalbumin expression. Prolonged post-TBI NAC treatment normalizes the cortical redox state, mitigates PVI and PNN loss, and - in surviving PVIs - increases per-cell parvalbumin expression. NAC treatment also preserves normal spectral EEG measures after TBI. We cautiously conclude that weeks-long NAC treatment after TBI may be a practical and well-tolerated treatment strategy to preserve cortical inhibitory tone post-TBI.

Pathological Slow-Wave Activity and Impaired Working Memory Binding in Post-Traumatic Amnesia

Associative binding is key to normal memory function and is transiently disrupted during periods of post-traumatic amnesia (PTA) following traumatic brain injury (TBI). Electrophysiological abnormalities, including low-frequency activity, are common following TBI. Here, we investigate associative memory binding during PTA and test the hypothesis that misbinding is caused by pathological slowing of brain activity disrupting cortical communication. Thirty acute moderate to severe TBI patients (25 males; 5 females) and 26 healthy controls (20 males; 6 females) were tested with a precision working memory paradigm requiring the association of object and location information. Electrophysiological effects of TBI were assessed using resting-state EEG in a subsample of 17 patients and 21 controls. PTA patients showed abnormalities in working memory function and made significantly more misbinding errors than patients who were not in PTA and controls. The distribution of localization responses was abnormally biased by the locations of nontarget items for patients in PTA, suggesting a specific impairment of object and location binding. Slow-wave activity was increased following TBI. Increases in the δ-α ratio indicative of an increase in low-frequency power specifically correlated with binding impairment in working memory. Connectivity changes in TBI did not correlate with binding impairment. Working memory and electrophysiological abnormalities normalized at 6 month follow-up. These results show that patients in PTA show high rates of misbinding that are associated with a pathological shift toward lower-frequency oscillations.SIGNIFICANCE STATEMENT How do we remember what was where? The mechanism by which information (e.g., object and location) is integrated in working memory is a central question for cognitive neuroscience. Following significant head injury, many patients will experience a period of post-traumatic amnesia (PTA) during which this associative binding is disrupted. This may be because of electrophysiological changes in the brain. Using a precision working memory test and resting-state EEG, we show that PTA patients demonstrate impaired binding ability, and this is associated with a shift toward slower-frequency activity on EEG. Abnormal EEG connectivity was observed but was not specific to PTA or binding ability. These findings contribute to both our mechanistic understanding of working memory binding and PTA pathophysiology.

Electroencephalographic Patterns in taVNS: A Systematic Review

Transcutaneous auricular vagus nerve stimulation (taVNS) is a newer delivery system using a non-invasive stimulation device placed at the ear. taVNS research is focused on clinical trials showing potential therapeutic benefits, however the neurophysiological effects of this stimulation on brain activity are still unclear. We propose a systematic review that aims to describe the effects of taVNS on EEG measures and identify taVNS parameters that can potentially lead to consistent EEG-mediated biomarkers for this therapy. A systematic literature review was carried out following the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) and the Cochrane handbook for systematic reviews. Clinical trials examining EEG parameters were considered, including absolute and relative power, coherence, degree of symmetry, evoked potentials, and peak frequency of all bands. According to our criteria, 18 studies (from 122 articles) were included. Our findings show a general trend towards increased EEG power spectrum activity in lower frequencies, and changes on early components of the ERP related to inhibitory tasks. This review suggests that quantitative electroencephalography can be used to assess the effects of taVNS on brain activity, however more studies are needed to systematically establish the specific effects and metrics that would reflect the non-invasive stimulation through the auricular branch of the vagus nerve.

MEG measured delta waves increase in adolescents after concussion

INTRODUCTION

The purpose of this study is to determine if delta waves, measured by magnetoencephalography (MEG), increase in adolescents due to a sports concussion.

METHODS

Twenty-four adolescents (age 14-17) completed pre- and postseason MRI and MEG scanning. MEG whole-brain delta power was calculated for each subject and normalized by the subject's total power. In eight high school football players diagnosed with a concussion during the season (mean age = 15.8), preseason delta power was subtracted from their postseason scan. In eight high school football players without a concussion (mean age = 15.7), preseason delta power was subtracted from postseason delta power and in eight age-matched noncontact controls (mean age = 15.9), baseline delta power was subtracted from a 4-month follow-up scan. ANOVA was used to compare the mean differences between preseason and postseason scans for the three groups of players, with pairwise comparisons based on Student's t-test method.

RESULTS

Players with concussions had significantly increased delta wave power at their postseason scans than nonconcussed players (p = .018) and controls (p = .027).

CONCLUSION

We demonstrate that a single concussion during the season in adolescent subjects can increase MEG measured delta frequency power at their postseason scan. This adds to the growing body of literature indicating increased delta power following a concussion.

The Effect of Traumatic Brain Injury on Sleep Architecture and Circadian Rhythms in Mice—A Comparison of High-Frequency Head Impact and Controlled Cortical Injury

Simple Summary

Traumatic brain injury (TBI) is a significant risk factor for the development of sleep and circadian rhythm impairments. In order to understand if TBI models with different injury mechanism, severity and pathology have different sleep and circadian rhythm disruptions, we performed a detailed sleep and circadian analysis of the high-frequency head impact TBI model (a mouse model that mimics sports-related head impacts) and the controlled cortical impact TBI model (a mouse model that mimics severe brain trauma). We found that both TBI models disrupt the ability of brain cells to maintain circadian rhythms; however, both injury groups could still maintain circadian behavior patterns. Both the mild head impact model and the severe brain injury model had normal amount of sleep at 7 d after injury; however, the severe brain injury mice had disrupted brain wave patterns during sleep. We conclude that different types of TBI have different patterns of sleep disruptions.

Abstract

Traumatic brain injury (TBI) is a significant risk factor for the development of sleep and circadian rhythm impairments. In this study we compare the circadian rhythms and sleep patterns in the high-frequency head impact (HFHI) and controlled cortical impact (CCI) mouse models of TBI. These mouse models have different injury mechanisms key differences of pathology in brain regions controlling circadian rhythms and EEG wave generation. We found that both HFHI and CCI caused dysregulation in the diurnal expression of core circadian genes (Bmal1, Clock, Per1,2, Cry1,2) at 24 h post-TBI. CCI mice had reduced locomotor activity on running wheels in the first 7 d post-TBI; however, both CCI and HFHI mice were able to maintain circadian behavior cycles even in the absence of light cues. We used implantable EEG to measure sleep cycles and brain activity and found that there were no differences in the time spent awake, in NREM or REM sleep in either TBI model. However, in the sleep states, CCI mice have reduced delta power in NREM sleep and reduced theta power in REM sleep at 7 d post-TBI. Our data reveal that different types of brain trauma can result in distinct patterns of circadian and sleep disruptions and can be used to better understand the etiology of sleep disorders after TBI.

Label Alignment Improves EEG-based Machine Learning-based Classification of Traumatic Brain Injury

Machine learning and deep learning algorithms have paved the way for improved analysis of biomedical data which has led to a better understanding of various biological conditions. However, one major hindrance to leveraging the potential of machine learning models is the requirement of huge datasets. In the biomedical domain, this becomes extremely difficult due to uncertainties in collecting high-quality data as well as, in the case of human subjects data, privacy. Further, when it comes to biomedical data, inter-subject variability has been a long-entrenched issue. The data obtained from different individuals will differ to a considerable extent that it becomes difficult to find population differences in small datasets. In this work, we investigate the use of label alignment techniques on an EEG-based Traumatic Brain Injury (TBI) classification task to overcome inter-subject variability, thereby increasing the classification accuracy. We show an increase in accuracy of around 6% in some cases as compared to our previous results. In the end, we also propose a methodology to incorporate TBI data from a different species (e.g., mice) after domain adaptation, which might further improve the performance by increasing the amount of training datasets available for the classification model.

Dietary Supplementation With Branched Chain Amino Acids to Improve Sleep in Veterans With Traumatic Brain Injury: A Randomized Double-Blind Placebo-Controlled Pilot and Feasibility Trial

Study Objectives

Traumatic brain injury (TBI) is associated with chronic sleep disturbances and cognitive impairment. Our prior preclinical work demonstrated dietary supplementation with branched chain amino acids (BCAA: leucine, isoleucine, and valine), precursors to de novo glutamate production, restored impairments in glutamate, orexin/hypocretin neurons, sleep, and memory in rodent models of TBI. This pilot study assessed the feasibility and preliminary efficacy of dietary supplementation with BCAA on sleep and cognition in Veterans with TBI.

Methods

Thirty-two Veterans with TBI were prospectively enrolled in a randomized, double-blinded, placebo-controlled trial comparing BCAA (30 g, b.i.d. for 21-days) with one of two placebo arms (microcrystalline cellulose or rice protein, both 30 g, b.i.d. for 21-days). Pre- and post-intervention outcomes included sleep measures (questionnaires, daily sleep/study diaries, and wrist actigraphy), neuropsychological testing, and blood-based biomarkers related to BCAA consumption.

Results

Six subjects withdrew from the study (2/group), leaving 26 remaining subjects who were highly adherent to the protocol (BCAA, 93%; rice protein, 96%; microcrystalline, 95%; actigraphy 87%). BCAA were well-tolerated with few side effects and no adverse events. BCAA significantly improved subjective insomnia symptoms and objective sleep latency and wake after sleep onset on actigraphy.

Conclusion

Dietary supplementation with BCAA is a mechanism-based, promising intervention that shows feasibility, acceptability, and preliminary efficacy to treat insomnia and objective sleep disruption in Veterans with TBI. A larger scale randomized clinical trial is warranted to further evaluate the efficacy, dosing, and duration of BCAA effects on sleep and other related outcome measures in individuals with TBI.

Clinical Trial Registration

[http://clinicaltrials.gov/], identifier [NCT03990909].

Local sleep: A new concept in brain plasticity

Traditionally, sleep and wakefulness have been considered as two global, mutually exclusive states. However, this view has been challenged by the discovery that sleep and wakefulness are actually locally regulated and that islands of these two states may often coexist in the same individual. Importantly, such a local regulation seems to be the key for many essential functions of sleep, including the maintenance of cognitive efficiency and the consolidation of new skills and memories. Indeed, local changes in sleep-related oscillations occur in brain areas that are used and involved in learning during wakefulness. In turn, these changes directly modulate experience-dependent brain adaptations and the consolidation of newly acquired memories. In line with these observations, alterations in the regional balance between wake- and sleep-like activity have been shown to accompany many pathologic conditions, including psychiatric and neurologic disorders. In the last decade, experimental research has started to shed light on the mechanisms involved in the local regulation of sleep and wakefulness. The results of this research have opened new avenues of investigation regarding the function of sleep and have revealed novel potential targets for the treatment of several pathologic conditions.

Plasticity impairment exposes CA3 vulnerability in a hippocampal network model of mild traumatic brain injury

Proper function of the hippocampus is critical for executing cognitive tasks such as learning and memory. Traumatic brain injury (TBI) and other neurological disorders are commonly associated with cognitive deficits and hippocampal dysfunction. Although there are many existing models of individual subregions of the hippocampus, few models attempt to integrate the primary areas into one system. In this work, we developed a computational model of the hippocampus, including the dentate gyrus, CA3, and CA1. The subregions are represented as an interconnected neuronal network, incorporating well-characterized ex vivo slice electrophysiology into the functional neuron models and well-documented anatomical connections into the network structure. In addition, since plasticity is foundational to the role of the hippocampus in learning and memory as well as necessary for studying adaptation to injury, we implemented spike-timing-dependent plasticity among the synaptic connections. Our model mimics key features of hippocampal activity, including signal frequencies in the theta and gamma bands and phase-amplitude coupling in area CA1. We also studied the effects of spike-timing-dependent plasticity impairment, a potential consequence of TBI, in our model and found that impairment decreases broadband power in CA3 and CA1 and reduces phase coherence between these two subregions, yet phase-amplitude coupling in CA1 remains intact. Altogether, our work demonstrates characteristic hippocampal activity with a scaled network model of spiking neurons and reveals the sensitive balance of plasticity mechanisms in the circuit through one manifestation of mild traumatic injury.

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.

Roadmap for Advancing Pre-Clinical Science in Traumatic Brain Injury

Pre-clinical models of disease have long played important roles in the advancement of new treatments. However, in traumatic brain injury (TBI), despite the availability of numerous model systems, translation from bench to bedside remains elusive. Integrating clinical relevance into pre-clinical model development is a critical step toward advancing therapies for TBI patients across the spectrum of injury severity. Pre-clinical models include in vivo and ex vivo animal work-both small and large-and in vitro modeling. The wide range of pre-clinical models reflect substantial attempts to replicate multiple aspects of TBI sequelae in humans. Although these models reveal multiple putative mechanisms underlying TBI pathophysiology, failures to translate these findings into successful clinical trials call into question the clinical relevance and applicability of the models. Here, we address the promises and pitfalls of pre-clinical models with the goal of evolving frameworks that will advance translational TBI research across models, injury types, and the heterogenous etiology of pathology.

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.

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.

Classification of Electroencephalogram in a Mouse Model of Traumatic Brain Injury Using Machine Learning Approaches

Traumatic Brain Injury (TBI) is highly prevalent, affecting ~1% of the U.S. population, with lifetime economic costs estimated to be over $75 billion. In the U.S., there are about 50,000 deaths annually related to TBI, and many others are permanently disabled. However, it is currently unknown which individuals will develop persistent disability following TBI and what brain mechanisms underlie these distinct populations. The pathophysiologic causes for those are most likely multifactorial. Electroencephalogram (EEG) has been used as a promising quantitative measure for TBI diagnosis and prognosis. The recent rise of advanced data science approaches such as machine learning and deep learning holds promise to further analyze EEG data, looking for EEG biomarkers of neurological disease, including TBI. In this work, we investigated various machine learning approaches on our unique 24-hour recording dataset of a mouse TBI model, in order to look for an optimal scheme in classification of TBI and control subjects. The epoch lengths were 1 and 2 minutes. The results were promising with accuracy of ~80-90% when appropriate features and parameters were used using a small number of subjects (5 shams and 4 TBIs). We are thus confident that, with more data and studies, we would be able to detect TBI accurately, not only via long-term recordings but also in practical scenarios, with EEG data obtained from simple wearables in the daily life.

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.

Predicting outcome in patients with moderate to severe traumatic brain injury using electroencephalography

BACKGROUND

Better outcome prediction could assist in reliable quantification and classification of traumatic brain injury (TBI) severity to support clinical decision-making. We developed a multifactorial model combining quantitative electroencephalography (qEEG) measurements and clinically relevant parameters as proof of concept for outcome prediction of patients with moderate to severe TBI.

METHODS

Continuous EEG measurements were performed during the first 7 days of ICU admission. Patient outcome at 12 months was dichotomized based on the Extended Glasgow Outcome Score (GOSE) as poor (GOSE 1-2) or good (GOSE 3-8). Twenty-three qEEG features were extracted. Prediction models were created using a Random Forest classifier based on qEEG features, age, and mean arterial blood pressure (MAP) at 24, 48, 72, and 96 h after TBI and combinations of two time intervals. After optimization of the models, we added parameters from the International Mission for Prognosis And Clinical Trial Design (IMPACT) predictor, existing of clinical, CT, and laboratory parameters at admission. Furthermore, we compared our best models to the online IMPACT predictor.

RESULTS

Fifty-seven patients with moderate to severe TBI were included and divided into a training set (n = 38) and a validation set (n = 19). Our best model included eight qEEG parameters and MAP at 72 and 96 h after TBI, age, and nine other IMPACT parameters. This model had high predictive ability for poor outcome on both the training set using leave-one-out (area under the receiver operating characteristic curve (AUC) = 0.94, specificity 100%, sensitivity 75%) and validation set (AUC = 0.81, specificity 75%, sensitivity 100%). The IMPACT predictor independently predicted both groups with an AUC of 0.74 (specificity 81%, sensitivity 65%) and 0.84 (sensitivity 88%, specificity 73%), respectively.

CONCLUSIONS

Our study shows the potential of multifactorial Random Forest models using qEEG parameters to predict outcome in patients with moderate to severe TBI.

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.

QEEG correlates of cognitive processing speed in children and adolescents with traumatic brain injuries

Both quantitative electroencephalography (qEEG) and cognitive measures have been used to understand the underlying brain changes that occur in individuals after experiencing a traumatic brain injury, however, research exploring the relationship between qEEG patterns and cognitive test performance is scarcely studied in school-aged populations. The purpose of the present study was to explore first, the neuropsychological and academic deficits in young individuals with TBI; and second, the underlying relationship between qEEG patterns and cognitive test performance. Analyses included 21 school-aged participants whom have experienced a recent TBI and 15 school-aged participants whom have never experienced a TBI. Mean subtest and composite scores were compared and regression analyses were used to determine whether alpha band and beta band qEEG coherence values predicted processing speed measures. Results suggest that young individuals who experienced a recent TBI exhibit general deficits in cognition and academic skills beyond what would be expected in the general population. Further, beta band coherence with the frontal brain regions significantly predicted processing speed scores, providing evidence of a relationship between qEEG patterns and processing speed. This outlines a relatively inexpensive method for utilizing neural connectivity to verify cognitive deficits for school-aged individuals with a recent TBI.

Frequency-Dependent Changes in Resting State Electroencephalogram Functional Networks after Traumatic Brain Injury in Piglets

Traumatic brain injury (TBI) is a major health concern in children, as it can cause chronic cognitive and behavioral deficits. The lack of objective involuntary metrics for the diagnosis of TBI makes prognosis more challenging, especially in the pediatric context, in which children are often unable to articulate their symptoms. Resting state electroencephalograms (EEG), which are inexpensive and non-invasive, and do not require subjects to perform cognitive tasks, have not yet been used to create functional brain networks in relation to TBI in children or non-human animals; here we report the first such study. We recorded resting state EEG in awake piglets before and after TBI, from which we generated EEG functional networks from the alpha (8-12 Hz), beta (16.5-25 Hz), broad (1-35 Hz), delta (1-3.5 Hz), gamma (30-35 Hz), sigma (13-16 Hz), and theta (4-7.5 Hz) frequency bands. We hypothesize that mild TBI will induce persistent frequency-dependent changes in the 4-week-old piglet at acute and chronic time points. Hyperconnectivity was found in several frequency band networks after TBI. This study serves as proof of concept that the study of EEG functional networks in awake piglets may be useful for the development of diagnostic metrics for TBI in children.

Hypocretin Mediates Sleep and Wake Disturbances in a Mouse Model of Traumatic Brain Injury

Traumatic brain injury (TBI) is a major cause of disability worldwide. Post-TBI sleep and wake disturbances are extremely common and difficult for patients to manage. Sleep and wake disturbances contribute to poor functional and emotional outcomes from TBI, yet effective therapies remain elusive. A more comprehensive understanding of mechanisms underlying post-TBI sleep and wake disturbance will facilitate development of effective pharmacotherapies. Previous research in human patients and animal models indicates that altered hypocretinergic function may be a major contributor to sleep-wake disturbance after TBI. In this study, we further elucidate the role of hypocretin by determining the impact of TBI on sleep-wake behavior of hypocretin knockout (HCRT KO) mice. Adult male C57BL/6J and HCRT KO mice were implanted with electroencephalography recording electrodes, and pre-injury baseline recordings were obtained. Mice were then subjected to either moderate TBI or sham surgery. Additional recordings were obtained and sleep-wake behavior determined at 3, 7, 15, and 30 days after TBI or sham procedures. At baseline, HCRT KO mice had a significantly different sleep-wake phenotype than control C57BL/6J mice. Post-TBI sleep-wake behavior was altered in a genotype-dependent manner: sleep of HCRT KO mice was not altered by TBI, whereas C57BL/6J mice had more non-rapid eye movement sleep, less wakefulness, and more short wake bouts and fewer long wake bouts. Numbers of hypocretin-positive cells were reduced in C57BL/6J mice by TBI. Collectively, these data indicate that the hypocretinergic system is involved in the alterations in sleep-wake behavior that develop after TBI in this model, and suggest potential therapeutic interventions.

Frequency of helping friends and helping strangers is explained by different neural signatures

Acts of helping friends and strangers are part of everyday life. However, people vary significantly with respect to how often they help others and with respect to whom they actually help on a day-to-day basis. Despite everyday helping being so pervasive, these individual differences are poorly understood. Here, we used source-localized resting electroencephalography to measure objective and stable individual differences in neural baseline activation in combination with an ecologically valid method that allows assessment of helping behavior in the field. Results revealed that neural baseline activation in the right dorsolateral prefrontal cortex (DLPFC) - a brain region associated with self-control and strategic social behavior - predicts the daily frequency of helping friends, whereas the daily frequency of helping strangers was predicted by neural baseline activation in the dorsomedial prefrontal cortex (DMPFC) - a brain region associated with social cognition processes. These findings offer evidence that distinct neural signatures and associated psychological and cognitive processes may underlie the propensity to help friends and strangers in daily life.

Frequency of everyday pro-environmental behaviour is explained by baseline activation in lateral prefrontal cortex

Humankind faces a plethora of environmental problems, many of which are directly influenced by individual human behaviour. To better understand pro-environmental behaviour, we here try to identify interindividual markers that explain variance in the frequency of every-day pro-environmental behaviour. So far, research on this topic has mainly relied on subjective self-report measures and has yielded mixed results. In this study, we applied a neural trait approach to assess stable, objective individual differences. Using source-localised electroencephalography, we measured cortical activation at rest and combined our neural task-independent data with an ecologically valid assessment of everyday pro-environmental behaviour. We find whole-brain-corrected evidence that task-independent baseline activation in the right lateral prefrontal cortex, a brain area known to be involved in cognitive control and self-control processes, explains individual differences in pro-environmental behaviour. The higher the cortical baseline activation in this area, the higher the frequency of everyday pro-environmental behaviour. Implications for the promotion of pro-environmental behaviour are discussed.

Noise-sustained patterns in a model of volume-coupled neural tissue

Computational neuroscience operates on models based on several important paradigms. Among them is the assumption that coupling in neural ensembles is provided by chemical or electrical synapses. This assumption works well under normal conditions. However, there is a growing body of data that show the importance of other communication pathways caused by bi-directional transport of substances between the cells and the intercellular space. This type of interaction is called "volume transmission" and has not been rarely addressed in the model studies. The volume transmission pathway naturally appears in multidimensional quantitative models of cellular processes, but is not sufficiently represented at the level of lumped and computationally effective neural models. In this paper, we propose a simple model that allows one to study the features of volume transmission coupling at various spatial scales and taking into account various inhomogeneities. This model is obtained by the extension of the well-known FitzHugh-Nagumo system by the addition of the nonlinear terms and equations to describe, at a qualitative level, the release of potassium into the intercellular space, its diffusion, and the reverse effect on the neurons. The study of model dynamics in various spatial configurations has revealed a number of characteristic spatio-temporal types of behavior that include self-organizing bursting and phase-locked firing patterns, different scenarios of excitation spreading, noise-sustained target patterns, and long-living slow moving wave segments.

Neural signatures of different behavioral types in fairness norm compliance

Fairness norm compliance is critical in any society. However, norm compliant behavior is very heterogeneous. Some people are reliably fair (voluntary compliers). Some are fair to avoid sanctions (sanction-based compliers), and some are reliably unfair (non-compliers). These types play divergent roles in society. However, they remain poorly understood. Here, we combined neural measures (resting electroencephalography and event-related potentials) and economic paradigms to better understand these types. We found that voluntary compliers are characterized by higher baseline activation in the right temporo-parietal junction, suggesting better social cognition capacity compared to sanction-based compliers and non-compliers. The latter two types are differentiated by (a) baseline activation in the dorso-lateral prefrontal cortex, a brain area known to be involved in self-control processes, and (b) event-related potentials in a classic self-control task. Both results suggest that sanction-based compliers have better self-control capacity than non-compliers. These findings improve our understanding of fairness norm compliance. Broadly, our findings suggest that established training techniques that boost self-control might help non-compliers adhere to fairness norms.

Concussion As a Multi-Scale Complex System: An Interdisciplinary Synthesis of Current Knowledge

Traumatic brain injury (TBI) has been called "the most complicated disease of the most complex organ of the body" and is an increasingly high-profile public health issue. Many patients report long-term impairments following even "mild" injuries, but reliable criteria for diagnosis and prognosis are lacking. Every clinical trial for TBI treatment to date has failed to demonstrate reliable and safe improvement in outcomes, and the existing body of literature is insufficient to support the creation of a new classification system. Concussion, or mild TBI, is a highly heterogeneous phenomenon, and numerous factors interact dynamically to influence an individual's recovery trajectory. Many of the obstacles faced in research and clinical practice related to TBI and concussion, including observed heterogeneity, arguably stem from the complexity of the condition itself. To improve understanding of this complexity, we review the current state of research through the lens provided by the interdisciplinary field of systems science, which has been increasingly applied to biomedical issues. The review was conducted iteratively, through multiple phases of literature review, expert interviews, and systems diagramming and represents the first phase in an effort to develop systems models of concussion. The primary focus of this work was to examine concepts and ways of thinking about concussion that currently impede research design and block advancements in care of TBI. Results are presented in the form of a multi-scale conceptual framework intended to synthesize knowledge across disciplines, improve research design, and provide a broader, multi-scale model for understanding concussion pathophysiology, classification, and treatment.

Mild Traumatic Brain Injury Chronically Impairs Sleep- and Wake-Dependent Emotional Processing

Study Objectives

A single traumatic brain injury (TBI), even when mild (ie, concussion), can cause lasting consequences. Individuals with a history of chronic (>1-year prior) mild TBI have an increased risk of mood disturbances (eg, depression, suicide). This population also has lingering sleep alterations, including poor sleep quality and changes in sleep stage proportions. Given these sleep deficits, we aimed to test whether sleep-dependent emotional memory consolidation is reduced in this population. We utilized a mild TBI group (3.7 ± 2.9 years post injury) and an uninjured (non-TBI) population.

Methods

Participants viewed negative and neutral images both before and after a 12-hour period containing sleep ("Sleep" group) or an equivalent period of time spent awake ("Wake" group). Participants rated images for valence/arousal at both sessions, and memory recognition was tested at session two.

Results

The TBI group had less rapid eye movement (REM), longer REM latency, and more sleep complaints. Sleep-dependent memory consolidation of nonemotional images was present in all participants. However, consolidation of negative images was only present in the non-TBI group. A lack of differentiation between the TBI Sleep and Wake groups was due to poor performance in the sleep group and, unexpectedly, enhanced performance in the wake group. Additionally, although the non-TBI participants habituated to negative images over a waking period, the TBI participants did not.

Conclusions

We propose disrupted sleep- and wake-dependent emotional processing contributes to poor emotional outcomes following chronic, mild TBI. This work has broad implications, as roughly one-third of the US population will sustain a mild TBI during their lifetime.