Mild Traumatic Brain Injury Affects Cognitive Processing and Modifies Oscillatory Brain Activity during Attentional Tasks

Slowing of Parameterized Resting-State Electroencephalography After Mild Traumatic Brain Injury

Reported changes in electroencephalography (EEG)-derived spectral power after mild traumatic brain injury (mTBI) remains inconsistent across existing literature. However, this may be a result of previous analyses depending solely on observing spectral power within traditional canonical frequency bands rather than accounting for the aperiodic activity within the collected neural signal. Therefore, the aim of this study was to test for differences in rhythmic and arrhythmic time series across the brain, and in the cognitively relevant frontoparietal (FP) network, and observe whether those differences were associated with cognitive recovery post-mTBI. Resting-state electroencephalography (rs-EEG) was collected from 88 participants (56 mTBI and 32 age- and sex-matched healthy controls) within 14 days of injury for the mTBI participants. A battery of executive function (EF) tests was collected at the first session with follow-up metrics collected approximately 2 and 4 months after the initial visit. After spectral parameterization, a significant between-group difference in aperiodic-adjusted alpha center peak frequency within the FP network was observed, where a slowing of alpha peak frequency was found in the mTBI group in comparison to the healthy controls. This slowing of week 2 (collected within 2 weeks of injury) aperiodic-adjusted alpha center peak frequency within the FP network was associated with increased EF over time (evaluated using executive composite scores) post-mTBI. These findings suggest alpha center peak frequency within the FP network as a candidate prognostic marker of EF recovery and may inform clinical rehabilitative methods post-mTBI.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

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.

Gamma frequency entrainment rescues cognitive impairment by decreasing postsynaptic transmission after traumatic brain injury

INTRODUCTION

The relationship between oscillatory activity in hippocampus and cognitive impairment in traumatic brain injury (TBI) remains unclear. Although TBI decreases gamma oscillations and 40 Hz light flicker improves TBI prognosis, the effects and mechanism of rhythmic flicker on TBI remain unclear.

AIMS

In this study, we aimed to explore whether light flicker could reverse cognitive deficits, and further explore its potential mechanisms in TBI mouse model.

METHODS

The Morris water maze test (MWM), step-down test (SDT), and novel object recognition test (NOR) were applied to evaluate the cognitive ability. The local field potential (LFP) recording was applied to measure low gamma reduction of CA1 in hippocampus after TBI. And electrophysiological experiments were applied to explore effects of the gamma frequency entrainment on long-term potentiation (LTP), postsynaptic transmission, and intrinsic excitability of CA1 pyramidal cells (PCs) in TBI mice. Immunofluorescence staining and western blotting were applied to explore the effects of 40 Hz light flicker on the expression of PSD95 in hippocampus of TBI mice.

RESULTS

We found that 40 Hz light flicker restored low gamma reduction of CA1 in hippocampus after TBI. And 40 Hz, but not random or 80 Hz light flicker, reversed cognitive impairment after TBI in behavioral tests. Moreover, 40 Hz light flicker improved N-methyl-D-aspartate (NMDA) receptor-dependent LTP (LTP_NMDAR ) and L-type voltage-gated calcium channel-dependent LTP (LTP_L-VGCC ) after TBI treatment. And gamma frequency entrainment decreased excitatory postsynaptic currents (EPSCs) of CA1 PCs in TBI mice. Our results have illustrated that 40 Hz light flicker could decrease intrinsic excitability of PCs after TBI treatment in mice. Furthermore, 40 Hz light flicker decreased the expression of PSD95 in hippocampus of TBI mice.

CONCLUSION

These results demonstrated that 40 Hz light flicker rescues cognitive impairment by decreasing postsynaptic transmission in PCs after TBI treatment in mice.

Speech error rates after a sports-related concussion

Background

Alterations in speech have long been identified as indicators of various neurologic conditions including traumatic brain injury, neurodegenerative diseases, and stroke. The extent to which speech errors occur in milder brain injuries, such as sports-related concussions, is unknown. The present study examined speech error rates in student athletes after a sports-related concussion compared to pre-injury speech performance in order to determine the presence and relevant characteristics of changes in speech production in this less easily detected neurologic condition.

Methods

A within-subjects pre/post-injury design was used. A total of 359 Division I student athletes participated in pre-season baseline speech testing. Of these, 27 athletes (18-22 years) who sustained a concussion also participated in speech testing in the days immediately following diagnosis of concussion. Picture description tasks were utilized to prompt connected speech samples. These samples were recorded and then transcribed for identification of errors and disfluencies. These were coded by two trained raters using a 6-category system that included 14 types of error metrics.

Results

Repeated measures analysis of variance was used to compare the difference in error rates at baseline and post-concussion. Results revealed significant increases in the speech error categories of pauses and time fillers (interjections/fillers). Additionally, regression analysis showed that a different pattern of errors and disfluencies occur after a sports-related concussion (primarily time fillers) compared to pre-injury (primarily pauses).

Conclusion

Results demonstrate that speech error rates increase following even mild head injuries, in particular, sports-related concussion. Furthermore, the speech error patterns driving this increase in speech errors, rate of pauses and interjections, are distinct features of this neurological injury, which is in contrast with more severe injuries that are marked by articulation errors and an overall reduction in verbal output. Future studies should consider speech as a diagnostic tool for concussion.

Neuropsychological Profile of Traumatic Brain Injury Patients with Medicolegal Cases: A Pilot Study

Introduction Traumatic brain injury (TBI) is a global health problem and is a silent epidemic of the modern times. Studies indicate litigation is a prominent factor that accounts for poor outcome and prolonged recovery from mild TBI. Depression is the most frequently diagnosed psychiatric disorder after TBI. Postconcussion symptoms, litigation, and suboptimal effort could contribute to the neuropsychological functioning of TBI patients medicolegal cases (MLCs). With increase in TBI and medicolegal cases, there is a requirement for comprehensive neuropsychological assessment.

Method The aim of the study was to evaluate the cognitive functions, postconcussion, and depressive symptoms in TBI patients with MLC and without MLCs (non-MLC). Patients were also assessed on electrophysiological parameters. An observational cross-sectional design was adopted, the sample size was 30 TBI patients in total, 15 (MLC) and 15 (non-MLC), and 11 patients from each group for electrophysiological assessment. The patients were in the age range of 18 to 50 years.

Results The MLC group had poor performance compared with the non-MLC group on both neuropsychological and electrophysiological measures. There was evidence of significant difference in verbal working memory, verbal learning, and memory and visuoconstructive ability. In the MLC group, postconcussion and depressive scores were negatively correlated with visuospatial span.

Conclusion Findings from this study indicate differences in the neuropsychological performance and electroencephalographic measures in between MLC and non-MLC groups. The results could be indicative of persistent cognitive problems associated with TBI for patients pursuing litigation. Poor performance could also be attributed to suboptimal level of effort. However, being a preliminary study with a small sample size, the findings need to be treated with caution.

Theta and gamma oscillatory dynamics in mouse models of Alzheimer's disease: A path to prospective therapeutic intervention

Understanding the neural basis of cognitive deficits, a key feature of Alzheimer's disease (AD), is imperative for achieving the therapy of the disease. Rhythmic oscillatory activities in neural systems are a fundamental mechanism for diverse brain functions, including cognition. In several neurological conditions like AD, aberrant neural oscillations have been shown to play a central role. Furthermore, manipulation of brain oscillations in animals has confirmed their impact on cognition and disease. In this article, we review the evidence from mouse models that shows how synchronized oscillatory activity is intricately linked to AD machinery. We primarily focus on recent reports showing abnormal oscillatory activities at theta and gamma frequencies in AD condition and their influence on cellular disturbances and cognitive impairments. A thorough comprehension of the role that neuronal oscillations play in AD pathology should pave the way to therapeutic interventions that can curb the disease.

Quantitative multimodal imaging in traumatic brain injuries producing impaired cognition

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

SUMMARY

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

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.

An Analysis of the External Validity of EEG Spectral Power in an Uncontrolled Outdoor Environment during Default and Complex Neurocognitive States

Traditionally, quantitative electroencephalography (QEEG) studies collect data within controlled laboratory environments that limit the external validity of scientific conclusions. To probe these validity limits, we used a mobile EEG system to record electrophysiological signals from human participants while they were located within a controlled laboratory environment and an uncontrolled outdoor environment exhibiting several moderate background influences. Participants performed two tasks during these recordings, one engaging brain activity related to several complex cognitive functions (number sense, attention, memory, executive function) and the other engaging two default brain states. We computed EEG spectral power over three frequency bands (theta: 4-7 Hz, alpha: 8-13 Hz, low beta: 14-20 Hz) where EEG oscillatory activity is known to correlate with the neurocognitive states engaged by these tasks. Null hypothesis significance testing yielded significant EEG power effects typical of the neurocognitive states engaged by each task, but only a beta-band power difference between the two background recording environments during the default brain state. Bayesian analysis showed that the remaining environment null effects were unlikely to reflect measurement insensitivities. This overall pattern of results supports the external validity of laboratory EEG power findings for complex and default neurocognitive states engaged within moderately uncontrolled environments.

Residual and compensatory changes of resting‐state EEG in successful recovery after moderate TBI

Purpose:

Even in years after recovery from moderate traumatic brain injury (moderate TBI), patients complain about residual cognitive impairment and fatigue. We hypothesized that non‐linear and linear resting‐state electroencephalography (rsEEG) features might also reflect neural underpinnings of these deficits.

Methods:

We analyzed a 10‐minute rsEEG in 77 moderate TBI‐survivors and 151 healthy volunteers after cognitive and psychological assessment. The rsEEG analysis included linear measures, such as power spectral density and peak alpha frequency, and non‐linear parameters such as Higuchi fractal dimension, envelope frequency, and Hjorth complexity.

Results:

The patients with moderate TBI had higher scores for fatigue and sleepiness and lower scores for mood and life satisfaction than controls. The behavioral test for directed attention showed a smaller and non‐significant between‐group difference. In rsEEG patterns, moderate TBI‐group had significantly higher deltaand theta‐rhythm power, which correlated with higher sleepiness and fatigue scores. The higher beta and lower alpha power were associated with a higher attention level in moderate TBI patients. Non‐linear rsEEG features were significantly higher in moderate TBI patients than in healthy controls but correlated with sleepiness and fatigue scores in both controls and patients.

Conclusion:

The rsEEG patterns may reflect compensatory processes supporting directed attention and residual effect of moderate TBI causing subjective fatigue in patients even after full physiological recovery.

Disrupted brain functional hub and causal connectivity in acute mild traumatic brain injury

There have been an increasing number of functional magnetic resonance imaging (fMRI) reports on brain abnormalities in mild traumatic brain injury (mTBI) at different phases. However, the neural bases and cognitive impairment after acute mTBI are unclear. This study aimed to identify brain functional hubs and connectivity abnormalities in acute mTBI patients and their correlations with deficits in cognitive performance. Within seven days after brain injury, mTBI patients (n=55) and age-, sex-, and educational -matched healthy controls (HCs) (n=41) underwent resting-state fMRI scans and cognitive assessments. We derived functional connectivity (FC) strength of the whole-brain network using degree centrality (DC) and performed Granger causality analysis (GCA) to analyze causal connectivity patterns in acute mTBI. Compared with HCs, acute mTBI patients had significantly decreased network centrality in the left middle frontal gyrus (MFG). Additionally, acute mTBI showed decreased inflows from the left MFG to bilateral middle temporal gyrus (MTG), left medial superior frontal gyrus (mSFG), and left anterior cingulate cortex (ACC). Correlation analyses revealed that changes in network centrality and causal connectivity were associated with deficits in cognitive performance in mTBI. Our findings may help to provide a new perspective for understanding the neuropathophysiological mechanism of acute cognitive impairment after mTBI.