Local and large-scale beta oscillatory dysfunction in males with mild traumatic brain injury

Biofluid, Imaging, Physiological, and Functional Biomarkers of Mild Traumatic Brain Injury and Subconcussive Head Impacts

Post-concussive symptoms are frequently reported by individuals who sustain mild traumatic brain injuries (mTBIs) and subconcussive head impacts, even when evidence of intracranial pathology is lacking. Current strategies used to evaluate head injuries, which primarily rely on self-report, have a limited ability to predict the incidence, severity, and duration of post-concussive symptoms that will develop in an individual patient. In addition, these self-report measures have little association with the underlying mechanisms of pathology that may contribute to persisting symptoms, impeding advancement in precision treatment for TBI. Emerging evidence suggests that biofluid, imaging, physiological, and functional biomarkers associated with mTBI and subconcussive head impacts may address these shortcomings by providing more objective measures of injury severity and underlying pathology. Interest in the use of biomarker data has rapidly accelerated, which is reflected by the recent efforts of organizations such as the National Institute of Neurological Disorders and Stroke and the National Academies of Sciences, Engineering, and Medicine to prioritize the collection of biomarker data during TBI characterization in acute-care settings. Thus, this review aims to describe recent progress in the identification and development of biomarkers of mTBI and subconcussive head impacts and to discuss important considerations for the implementation of these biomarkers in clinical practice.

The Effect of Intranasal Plus Transcranial Photobiomodulation on Neuromuscular Control in Individuals with Repetitive Head Acceleration Events

Objective: This proof-of-concept study was to investigate the relationship between photobiomodulation (PBM) and neuromuscular control. Background: The effects of concussion and repetitive head acceleration events (RHAEs) are associated with decreased motor control and balance. Simultaneous intranasal and transcranial PBM (itPBM) is emerging as a possible treatment for cognitive and psychological sequelae of brain injury with evidence of remote effects on other body systems. Methods: In total, 43 (39 male) participants, age 18-69 years (mean, 49.5; SD, 14.45), with a self-reported history of concussive and/or RHAE and complaints of their related effects (e.g., mood dysregulation, impaired cognition, and poor sleep quality), completed baseline and posttreatment motor assessments including clinical reaction time, grip strength, grooved pegboard, and the Mini Balance Evaluation Systems Test (MiniBEST). In the 8-week interim, participants self-administered itPBM treatments by wearing a headset comprising four near-infrared light-emitting diodes (LED) and a near-infrared LED nasal clip. Results: Posttreatment group averages in reaction time, MiniBEST reactive control subscores, and bilateral grip strength significantly improved with effect sizes of g = 0.75, g = 0.63, g = 0.22 (dominant hand), and g = 0.34 (nondominant hand), respectively. Conclusion: This study provides a framework for more robust studies and suggests that itPBM may serve as a noninvasive solution for improved neuromuscular health.

A Siamese Convolutional Neural Network for Identifying Mild Traumatic Brain Injury and Predicting Recovery

Timely diagnosis of mild traumatic brain injury (mTBI) remains challenging due to the rapid recovery of acute symptoms and the absence of evidence of injury in static neuroimaging scans. Furthermore, while longitudinal tracking of mTBI is essential in understanding how the diseases progresses/regresses over time for enhancing personalized patient care, a standardized approach for this purpose is not yet available. Recent functional neuroimaging studies have provided evidence of brain function alterations following mTBI, suggesting mTBI-detection models can be built based on these changes. Most of these models, however, rely on manual feature engineering, but the optimal set of features for detecting mTBI may be unknown. Data-driven approaches, on the other hand, may uncover hidden relationships in an automated manner, making them suitable for the problem of mTBI detection. This paper presents a data-driven framework based on Siamese Convolutional Neural Network (SCNN) to detect mTBI and to monitor the recovery state from mTBI over time. The proposed framework is tested on the cortical images of Thy1-GCaMP6s mice, obtained via widefield calcium imaging, acquired in a longitudinal study. Results show that the proposed model achieves a classification accuracy of 96.5%. To track the state of the injured brain over time, a reference distance map is constructed, which together with the SCNN model, are employed to assess the recovery state in subsequent sessions after injury, revealing that the recovery progress varies among subjects. The promising results of this work suggest that a similar approach could be potentially applicable for monitoring recovery from mTBI, in humans.

Using normative modeling and machine learning for detecting mild traumatic brain injury from magnetoencephalography data

New biomarkers are urgently needed for many brain disorders; for example, the diagnosis of mild traumatic brain injury (mTBI) is challenging as the clinical symptoms are diverse and nonspecific. EEG and MEG studies have demonstrated several population-level indicators of mTBI that could serve as objective markers of brain injury. However, deriving clinically useful biomarkers for mTBI and other brain disorders from EEG/MEG signals is hampered by the large inter-individual variability even across healthy people. Here, we used a multivariate machine-learning approach to detect mTBI from resting-state MEG measurements. To address the heterogeneity of the condition, we employed a normative modeling approach and modeled MEG signal features of individual mTBI patients as deviations with respect to the normal variation. To this end, a normative dataset comprising 621 healthy participants was used to determine the variation in power spectra across the cortex. In addition, we constructed normative datasets based on age-matched subsets of the full normative data. To discriminate patients from healthy control subjects, we trained support-vector-machine classifiers on the quantitative deviation maps for 25 mTBI patients and 20 controls not included in the normative dataset. The best performing classifier made use of the full normative data across the entire age and frequency ranges. This classifier was able to distinguish patients from controls with an accuracy of 79%. Inspection of the trained model revealed that low-frequency activity in the theta frequency band (4-8 Hz) is a significant indicator of mTBI, consistent with earlier studies. The results demonstrate the feasibility of using normative modeling of MEG data combined with machine learning to advance diagnosis of mTBI and identify patients that would benefit from treatment and rehabilitation. The current approach could be applied to a wide range of brain disorders, thus providing a basis for deriving MEG/EEG-based biomarkers.

Modified mouse model of repeated mild traumatic brain injury through a thinned-skull window and fluid percussion

Mild traumatic brain injury (mTBI) is a clinically highly heterogeneous neurological disorder, none of the existing animal models can replicate the entire sequelae. This study aimed to develop a modified closed head injury (CHI) model of repeated mTBI (rmTBI) for investigating Ca2+ fluctuations of the affected neural network, the alternations of electrophysiology, and behavioral dysfunctions. The transcranial Ca2+ study protocol includes AAV-GCaMP6s infection in the right motor cortex, thinned-skull preparation, and two-photon laser scanning microscopy (TPLSM) imaging. The CHI rmTBI model is fabricated using the thinned-skull site and applying 2.0 atm fluid percussion with 48-h interval. The neurological dysfunction, minor motor performance, evident mood, spatial working, and reference deficits we found in this study mimic the clinically relevant syndromes after mTBI. Besides, our study revealed that there was a trend of transition from Ca2+ singlepeak to multipeak and plateau, and the total Ca2+ activities of multipeaks and plateaus (p < .001 vs. pre-rmTBI value) were significantly increased in ipsilateral layer 2/3 motor neurons after rm TBI. In parallel, there is a low-frequency power shift from delta to theta band (p < .01 vs. control) in the ipsilateral layer 2/3 of motor cortex of the rmTBI mice, and the overall firing rates significantly increased (p < .01 vs. control). Moreover, rmTBI causes slight cortical and hippocampal neuron damage and possibly induces neurogenesis in the dentate gyrus (DG). The alternations of Ca2+ and electrophysiological characteristics in layer 2/3 neuronal network, histopathological changes, and possible neurogenesis may play concertedly and partially contribute to the functional outcome post-rmTBI.

Sensor-level MEG combined with machine learning yields robust classification of mild traumatic brain injury patients

OBJECTIVE

Diagnosis of mild traumatic brain injury (mTBI) is challenging despite its high incidence, due to the unspecificity and variety of symptoms and the frequent lack of structural imaging findings. There is a need for reliable and simple-to-use diagnostic tools that would be feasible across sites and patient populations.

METHODS

We evaluated linear machine learning (ML) methods' ability to separate mTBI patients from healthy controls, based on their sensor-level magnetoencephalographic (MEG) power spectra in the subacute phase (<2 months) after a head trauma. We recorded resting-state MEG data from 25 patients and 25 age-sex matched controls and utilized a previously collected data set of 20 patients and 20 controls from a different site. The data sets were analyzed separately with three ML methods.

RESULTS

The median classification accuracies varied between 80 and 95%, without significant differences between the applied ML methods or data sets. The classification accuracies were significantly higher with ML than with traditional sensor-level MEG analysis based on detecting pathological low-frequency activity.

CONCLUSIONS

Easily applicable linear ML methods provide reliable and replicable classification of mTBI patients using sensor-level MEG data.

SIGNIFICANCE

Power spectral estimates combined with ML can classify mTBI patients with high accuracy and have high promise for clinical use.

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.

Spautin-1 administration mitigates mild TBI-induced cognitive and memory dysfunction in mice via activation of caspase-3

BACKGROUND

Cognitive and memory dysfunction, a common sequela of traumatic brain injury (TBI), places a heavy social and economic burden on individuals, families, communities, and countries. Although the potent anti-tumor effects of spautin-1, a novel autophagy inhibitor, have been documented in malignant melanoma, little is known regarding its efficacy on alleviation of cognitive and memory dysfunction. Here, we describe the effect of spautin-1 administration on cognitive and memory impairment post-TBI, and reveal its underlying mechanism of action.

METHODS

We first induced mild TBI in mice through Feeney's weight-drop model, then immediately administered spautin-1 (10 mmol/μl, 2 μl) into the left lateral ventricle. Behavioral and pathological changes were assessed at 24 h, 7 and 30 days after TBI by analyzing neurological severity scores (NSS), novel objective recognition (NOR), Morris water maze (MWM) test, recording of local field potential (LFP), as well as western blot, and immunofluorescence assays.

RESULTS

Mild TBI not only reduced recognition index and times crossing platform, but also aggravated neuronal injury, including reduced MAP2, GAD2, VGlut2, and CHAT intensity. It also elevated activated microglia and CD86-occupied areas in TMEM119-positive cells, but suppressed θ, β, and γ oscillation power in the hippocampal CA1. However, spautin-1 administration significantly reversed these changes, whereas AC-DEVD-CHO an inhibitor of caspase-3 partially blocked the neuroprotective effects of spautin-1.

CONCLUSION

Spautin-1 administration mitigates mild TBI-induced cognitive and memory dysfunction in mice, potentially through activation of caspase-3.

Multimodal magnetic resonance imaging of youth sport-related concussion reveals acute changes in the cerebellum, basal ganglia, and corpus callosum that resolve with recovery

Magnetic resonance imaging (MRI) can provide a number of measurements relevant to sport-related concussion (SRC) symptoms; however, most studies to date have used a single MRI modality and whole-brain exploratory analyses in attempts to localize concussion injury. This has resulted in highly variable findings across studies due to wide ranging symptomology, severity and nature of injury within studies. A multimodal MRI, symptom-guided region-of-interest (ROI) approach is likely to yield more consistent results. The functions of the cerebellum and basal ganglia transcend many common concussion symptoms, and thus these regions, plus the white matter tracts that connect or project from them, constitute plausible ROIs for MRI analysis. We performed diffusion tensor imaging (DTI), resting-state functional MRI, quantitative susceptibility mapping (QSM), and cerebral blood flow (CBF) imaging using arterial spin labeling (ASL), in youth aged 12-18 years following SRC, with a focus on the cerebellum, basal ganglia and white matter tracts. Compared to controls similar in age, sex and sport (N = 20), recent SRC youth (N = 29; MRI at 8 ± 3 days post injury) exhibited increased susceptibility in the cerebellum (p = 0.032), decreased functional connectivity between the caudate and each of the pallidum (p = 0.035) and thalamus (p = 0.021), and decreased diffusivity in the mid-posterior corpus callosum (p < 0.038); no changes were observed in recovered asymptomatic youth (N = 16; 41 ± 16 days post injury). For recent symptomatic-only SRC youth (N = 24), symptom severity was associated with increased susceptibility in the superior cerebellar peduncles (p = 0.011) and reduced activity in the cerebellum (p = 0.013). Fewer days between injury and MRI were associated with reduced cerebellar-parietal functional connectivity (p < 0.014), reduced activity of the pallidum (p = 0.002), increased CBF in the caudate (p = 0.005), and reduced diffusivity in the central corpus callosum (p < 0.05). Youth SRC is associated with acute cerebellar inflammation accompanied by reduced cerebellar activity and cerebellar-parietal connectivity, as well as structural changes of the middle regions of the corpus callosum accompanied by functional changes of the caudate, all of which resolve with recovery. Early MRI post-injury is important to establish objective MRI-based indicators for concussion diagnosis, recovery assessment and prediction of outcome.

Is frontoparietal electroencephalogram activity related to the level of functional disability in patients emerging from a minimally conscious state? A preliminary study

Objective

When regaining consciousness, patients who emerge from a minimally conscious state (EMCS) present with different levels of functional disability, which pose great challenges for treatment. This study investigated the frontoparietal activity in EMCS patients and its effects on functional disability.

Materials and methods

In this preliminary study, 12 EMCS patients and 12 healthy controls were recruited. We recorded a resting-state scalp electroencephalogram (EEG) for at least 5 min for each participant. Each patient was assessed using the disability rating scale (DRS) to determine the level of functional disability. We analyzed the EEG power spectral density and sensor-level functional connectivity in relation to the patient’s functional disability.

Results

In the frontoparietal region, EMCS patients demonstrated lower relative beta power (P < 0.01) and higher weighted phase lag index (wPLI) values in the theta (P < 0.01) and gamma (P < 0.01) bands than healthy controls. The frontoparietal theta wPLI values of EMCS patients were positively correlated with the DRS scores (r_s = 0.629, P = 0.029). At the whole-brain level, EMCS patients only had higher wPLI values in the theta band (P < 0.01) than healthy controls. The whole-brain theta wPLI values of EMCS patients were also positively correlated with the DRS scores (r_s = 0.650, P = 0.022). No significant difference in the power and connectivity between the frontoparietal region and the whole brain in EMCS patients was observed.

Conclusion

EMCS patients still experience neural dysfunction, especially in the frontoparietal region. However, the theta connectivity in the frontoparietal region did not increase specifically. At the level of the whole brain, the same shift could also be seen. Theta functional connectivity in the whole brain may underlie different levels of functional disability.

Contextual Effects of Traumatic Brain Injury on the Connectome: Differential Effects of Deployment- and Non-Deployment-Acquired Injuries

OBJECTIVE

To identify differential effects of mild traumatic brain injury (TBI) occurring in a deployment or nondeployment setting on the functional brain connectome.

SETTING

Veterans Affairs Medical Center.

PARTICIPANTS

In total, 181 combat-exposed veterans of the wars in Iraq and Afghanistan (n = 74 with deployment-related mild TBI, average time since injury = 11.0 years, SD = 4.1).

DESIGN

Cross-sectional observational study.

MAIN MEASURES

Mid-Atlantic MIRECC (Mid-Atlantic Mental Illness Research, Education, and Clinical Center) Assessment of TBI, Clinician-Administered PTSD Scale, connectome metrics.

RESULTS

Linear regression adjusting for relevant covariates demonstrates a significant (P < .05 corrected) association between deployment mild TBI with reduced global efficiency (nonstandardized β = -.011) and degree of the K-core (nonstandardized β = -.79). Nondeployment mild TBI was significantly associated with a reduced number of modules within the connectome (nonstandardized β = -2.32). Finally, the interaction between deployment and nondeployment mild TBIs was significantly (P < .05 corrected) associated with increased mean (nonstandardized β = 9.92) and mode (nonstandardized β = 14.02) frequency at which connections occur.

CONCLUSIONS

These results demonstrate distinct effects of mild TBI on the functional brain connectome when sustained in a deployment versus nondeployment context. This is consistent with findings demonstrating differential effects in other areas such as psychiatric diagnoses and severity, pain, sleep, and cognitive function. Furthermore, participants were an average of 11 years postinjury, suggesting these represent chronic effects of the injury. Overall, these findings add to the growing body of evidence, suggesting the effects of mild TBI acquired during deployment are different and potentially longer lasting than those of mild TBI acquired in a nondeployment context.

Safeguarding Athletes Against Head Injuries Through Advances in Technology: A Scoping Review of the Uses of Machine Learning in the Management of Sports-Related Concussion

Sports injury prevention is an important part of the athlete welfare and safeguarding research field. In sports injury prevention, sport-related concussion (SRC) has proved to be one of the most difficult and complex injuries to manage in terms of prevention, diagnosis, classification, treatment and rehabilitation. SRC can cause long-term health issues and is a commonly reported injury in both adult and youth athletes around the world. Despite increased knowledge of the prevalence of SRC, very few tools are available for diagnosing SRC in athletic settings. Recent technological innovations have resulted in different machine learning and deep learning methodologies being tested to improve the management of this complex sports injury. The purpose of this article is to summarize and map the existing research literature on the use of machine learning in the management of SRC, ascertain where there are gaps in the existing research and identify recommendations for future research. This is explored through a scoping review. A systematic search in the three electronic databases SPORTDiscus, PubMed and Scopus identified an initial 522 studies, of which 24 were included in the final review, the majority of which focused on machine learning for the prediction and prevention of SRC (N = 10), or machine learning for the diagnosis and classification of SRC (N = 11). Only 3 studies explored machine learning approaches for the treatment and rehabilitation of SRC. A main finding is that current research highlights promising practical uses (e.g., more accurate and rapid injury assessment or return-to-sport participation criteria) of machine learning in the management of SRC. The review also revealed a narrow research focus in the existing literature. As current research is primarily conducted on male adolescents or adults from team sports in North America there is an urgent need to include wider demographics in more diverse samples and sports contexts in the machine learning algorithms. If research datasets continue to be based on narrow samples of athletes, the development of any new diagnostic and predictive tools for SRC emerging from this research will be at risk. Today, these risks appear to mainly affect the health and safety of female athletes.

Repeated subconcussive exposure alters low-frequency neural oscillation in memory retrieval processing

Repeated subconcussive head impacts are frequently experienced by athletes involved in competitive sports, such as boxing. The objective of the present study was to investigate the changes in working memory performance and memory retrieval-related neural oscillations in boxing athletes who experienced repeated subconcussive head impacts. Twenty-one boxing athletes (boxing group) and twenty-five matched controls (control group) completed a modified visual working memory task, and their continuous scalp electroencephalography (EEG) data were collected simultaneously. The behavioral measures and retrieval-related low-frequency neural oscillations were analyzed at each working memory set size in both groups. Subjects in the boxing group showed a reduced mean accuracy, diminished capacity estimates, and slower reaction time at demanding set sizes and a marginally increased intraindividual coefficient of variation (ICV) for overall set sizes. Additionally, decreased event-related frontal theta synchronization, parieto-occipital alpha desynchronization, and frontal low beta synchronization were observed in the boxing group, suggesting underlying working memory dysfunction for efficient neurocognitive resource employment, inhibition of distracting stimuli, and post-retrieval control in the boxing group. Moreover, a negative correlation was found between frontal beta synchronization and reaction time for most set sizes in both groups. The present study was the first to reveal the underlying working memory deficits caused by the cumulative effects of boxing-related subconcussive head impacts from the perspective of behavior and EEG time-frequency oscillations. Joint analysis of EEG low-frequency oscillations and the innovative task with multiple challenging load conditions may serve as a promising way to detect concealed deficiencies within working memory processing. Keywords: repeated subconcussive head impacts, working memory, modified Sternberg task, event-related desynchronization, event-related synchronization, boxing athletes.

Mild traumatic brain injury impairs the coordination of intrinsic and motor-related neural dynamics

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MTBI is poorly understood and lacks objective diagnostic and prognostic tools.

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Abnormal neural oscillations are found in subjects with a history of mTBI.

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We identify transient bursts in MEG data using a Hidden Markov Model.

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We explain a deficit in beta connectivity and power in terms of transient bursts.

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Data-driven feature selection identifies symptom-relevant functional connections.

Mild traumatic brain injury (mTBI) poses a considerable burden on healthcare systems. Whilst most patients recover quickly, a significant number suffer from sequelae that are not accompanied by measurable structural damage. Understanding the neural underpinnings of these debilitating effects and developing a means to detect injury, would address an important unmet clinical need. It could inform interventions and help predict prognosis. Magnetoencephalography (MEG) affords excellent sensitivity in probing neural function and presents significant promise for assessing mTBI, with abnormal neural oscillations being a potential specific biomarker. However, growing evidence suggests that neural dynamics are (at least in part) driven by transient, pan-spectral bursting and in this paper, we employ this model to investigate mTBI. We applied a Hidden Markov Model to MEG data recorded during resting state and a motor task and show that previous findings of diminished intrinsic beta amplitude in individuals with mTBI are largely due to the reduced beta band spectral content of bursts, and that diminished beta connectivity results from a loss in the temporal coincidence of burst states. In a motor task, mTBI results in diminished burst amplitude, altered modulation of burst probability during movement, and a loss in connectivity in motor networks. These results suggest that, mechanistically, mTBI disrupts the structural framework underlying neural synchrony, which impairs network function. Whilst the damage may be too subtle for structural imaging to see, the functional consequences are detectable and persist after injury. Our work shows that mTBI impairs the dynamic coordination of neural network activity and proposes a potent new method for understanding mTBI.

Improved neuropathological identification of traumatic brain injury through quantitative neuroimaging and neural network analyses: Some practical approaches for the neurorehabilitation clinician

BACKGROUND

Quantitative neuroimaging analyses have the potential to provide additional information about the neuropathology of traumatic brain injury (TBI) that more thoroughly informs the neurorehabilitation clinician.

OBJECTIVE

Quantitative neuroimaging is typically not covered in the standard radiological report, but often can be extracted via post-processing of clinical neuroimaging studies, provided that the proper volume acquisition sequences were originally obtained.

METHODS

Research and commercially available quantitative neuroimaging methods provide region of interest (ROI) quantification metrics, lesion burden volumetrics and cortical thickness measures, degree of focal encephalomalacia, white matter (WM) abnormalities and residual hemorrhagic pathology. If present, diffusion tensor imaging (DTI) provides a variety of techniques that aid in evaluating WM integrity. Using quantitatively identified structural and ROI neuropathological changes are most informative when done from a neural network approach.

RESULTS

Viewing quantitatively identifiable damage from a neural network perspective provides the neurorehabilitation clinician with an additional tool for linking brain pathology to understand symptoms, problems and deficits as well as aid neuropsychological test interpretation. All of these analyses can be displayed in graphic form, including3-D image analysis. A case study approach is used to demonstrate the utility of quantitative neuroimaging and network analyses in TBI.

CONCLUSIONS

Quantitative neuroimaging may provide additional useful information for the neurorehabilitation clinician.

Teasing apart trauma: neural oscillations differentiate individual cases of mild traumatic brain injury from post-traumatic stress disorder even when symptoms overlap

Post-traumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI) are highly prevalent and closely related disorders. Affected individuals often exhibit substantially overlapping symptomatology - a major challenge for differential diagnosis in both military and civilian contexts. According to our symptom assessment, the PTSD group exhibited comparable levels of concussion symptoms and severity to the mTBI group. An objective and reliable system to uncover the key neural signatures differentiating these disorders would be an important step towards translational and applied clinical use. Here we explore use of MEG (magnetoencephalography)-multivariate statistical learning analysis in identifying the neural features for differential PTSD/mTBI characterisation. Resting state MEG-derived regional neural activity and coherence (or functional connectivity) across seven canonical neural oscillation frequencies (delta to high gamma) were used. The selected features were consistent and largely confirmatory with previously established neurophysiological markers for the two disorders. For regional power from theta, alpha and high gamma bands, the amygdala, hippocampus and temporal areas were identified. In line with regional activity, additional connections within the occipital, parietal and temporal regions were selected across a number of frequency bands. This study is the first to employ MEG-derived neural features to reliably and differentially stratify the two disorders in a multi-group context. The features from alpha and beta bands exhibited the best classification performance, even in cases where distinction by concussion symptom profiles alone were extremely difficult. We demonstrate the potential of using 'invisible' neural indices of brain functioning to understand and differentiate these debilitating conditions.

Magnetoencephalography abnormalities in adult mild traumatic brain injury: A systematic review

BACKGROUND

The global incidence of traumatic brain injuries is rising, with at least 80% being classified as mild. These mild injuries are not visible on routine clinical imaging. The potential clinical role of a specific imaging biomarker be it diagnostic, prognostic or directing and monitoring progress of personalised treatment and rehabilitation has driven the exploration of several new neuroimaging modalities. This systematic review examined the evidence for magnetoencephalography (MEG) to provide an imaging biomarker in mild traumatic brain injury (mTBI).

METHODS

Our review was prospectively registered on PROSPERO: CRD42019151387. We searched EMBASE, MEDLINE, trial registers, PsycINFO, Cochrane Library and conference abstracts and identified 37 papers describing MEG changes in mTBI eligible for inclusion. Since meta-analysis was not possible, based on the heterogeneity of reported outcomes, we provide a narrative synthesis of results.

RESULTS

The two most promising MEG biomarkers are excess resting state low frequency power, and widespread connectivity changes in all frequency bands. These may represent biomarkers with potential for diagnostic application, which reflect time sensitive changes, or may be capable of offering clinically relevant prognostic information. In addition, the rich data that MEG produces are well-suited to new methods of machine learning analysis, which is now being actively explored.

INTERPRETATION

MEG reveals several promising biomarkers, in the absence of structural abnormalities demonstrable with either computerised tomography or magnetic resonance imaging. This review has not identified sufficient evidence to support routine clinical use of MEG in mTBI currently. However, verifying MEG's potential would help meet an urgent clinical need within civilian, sports and military medicine.

Mild traumatic brain injury is associated with dysregulated neural network functioning in children and adolescents

Mild traumatic brain injury is highly prevalent in paediatric populations, and can result in chronic physical, cognitive and emotional impairment, known as persistent post-concussive symptoms. Magnetoencephalography has been used to investigate neurophysiological dysregulation in mild traumatic brain injury in adults; however, whether neural dysrhythmia persists in chronic mild traumatic brain injury in children and adolescents is largely unknown. We predicted that children and adolescents would show similar dysfunction as adults, including pathological slow-wave oscillations and maladaptive, frequency-specific, alterations to neural connectivity. Using magnetoencephalography, we investigated regional oscillatory power and distributed brain-wide networks in a cross-sectional sample of children and adolescents in the chronic stages of mild traumatic brain injury. Additionally, we used a machine learning pipeline to identify the most relevant magnetoencephalography features for classifying mild traumatic brain injury and to test the relative classification performance of regional power versus functional coupling. Results revealed that the majority of participants with chronic mild traumatic brain injury reported persistent post-concussive symptoms. For neurophysiological imaging, we found increased regional power in the delta band in chronic mild traumatic brain injury, predominantly in bilateral occipital cortices and in the right inferior temporal gyrus. Those with chronic mild traumatic brain injury also showed dysregulated neuronal coupling, including decreased connectivity in the delta range, as well as hyper-connectivity in the theta, low gamma and high gamma bands, primarily involving frontal, temporal and occipital brain areas. Furthermore, our multivariate classification approach combined with functional connectivity data outperformed regional power in terms of between-group classification accuracy. For the first time, we establish that local and large-scale neural activity are altered in youth in the chronic phase of mild traumatic brain injury, with the majority presenting persistent post-concussive symptoms, and that dysregulated interregional neural communication is a reliable marker of lingering paediatric ‘mild’ traumatic brain injury.