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.

Decoupling of structural and functional connectivity in hubs and cognitive impairment after mild traumatic brain injury

INTRODUCTION

Mild traumatic brain injury (mild TBI) exhibited abnormal brain network topologies associated with cognitive dysfunction. However, it was still unclear which aspects of network organization were critical underlying the key pathology of mild TBI. Here, a multi-imaging strategy was applied to capture dynamic topological features of both structural and functional connectivity networks (SCN and FCN), to provide more sensitive detection of altered FCN from its anatomical backbone and identify novel biomarkers of mild TBI outcomes.

METHODS

62 mild TBI patients (30 subjects as an original sample with 3-12 months follow-up, 32 subjects as independent replicated sample), and 37 healthy controls were recruited. Both diffusion tensor imaging (DTI) and resting-state fMRI were used to create global connectivity matrices in the same individuals. Global and regional network analyses were applied to identify group differences and correlations with clinical assessments.

RESULTS

Most global network properties were conserved in both SCNs and FCNs in subacute mild TBI, whereas SCNs presented decreased global efficiency and characteristic path length at follow-up. Specifically, some hubs in healthy brain networks typically became non-hubs in patients and vice versa, such as the medial prefrontal cortex, superior temporal gyrus, middle frontal gyrus. The relationship between structural and functional connectivity (SC and FC) in patients also showed salient decoupling as a function of time, primarily located in the hubs.

CONCLUSIONS

These results suggested mild TBI influences the relationship between SCN and FCN, and the SC-FC coupling strength may be used as a potential biomarker to predict long-term outcomes after injury.

Magnetoencephalography in the Detection and Characterization of Brain Abnormalities Associated with Traumatic Brain Injury: A Comprehensive Review

Magnetoencephalography (MEG) is a functional brain imaging technique with high temporal resolution compared with techniques that rely on metabolic coupling. MEG has an important role in traumatic brain injury (TBI) research, especially in mild TBI, which may not have detectable features in conventional, anatomical imaging techniques. This review addresses the original research articles to date that have reported on the use of MEG in TBI. Specifically, the included studies have demonstrated the utility of MEG in the detection of TBI, characterization of brain connectivity abnormalities associated with TBI, correlation of brain signals with post-concussive symptoms, differentiation of TBI from post-traumatic stress disorder, and monitoring the response to TBI treatments. Although presently the utility of MEG is mostly limited to research in TBI, a clinical role for MEG in TBI may become evident with further investigation.

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

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

Theta-Band Oscillations as an Indicator of Mild Traumatic Brain Injury

Mild traumatic brain injury (mTBI) patients continue to pose a diagnostic challenge due to their diverse symptoms without trauma-specific changes in structural imaging. We addressed here the possible early changes in spontaneous oscillatory brain activity after mTBI, and their feasibility as an indicator of injury in clinical evaluation. We recorded resting-state magnetoencephalography (MEG) data in both eyes-open and eyes-closed conditions from 26 patients (11 females and 15 males, aged 20–59) with mTBI 6 days–6 months after the injury, and compared their spontaneous oscillatory activity to corresponding data from 139 healthy controls. Twelve of the patients underwent a follow-up measurement at 6 months. Ten of all patients were without structural lesions in MRI. At single-subject level, aberrant 4–7 Hz (theta) band activity exceeding the + 2 SD limit of the healthy subjects was visible in 7 out of 26 patients; three out of the seven patients with abnormal theta activity were without any detectable lesions in MRI. Of the patients that participated in the follow-up measurements, five showed abnormal theta activity in the first recording, but only two in the second measurement. Our results suggest that aberrant theta-band oscillatory activity can provide an early objective sign of brain dysfunction after mTBI. In 3/7 patients, the slow-wave activity was transient and visible only in the first recording, urging prompt timing for the measurements in clinical settings.

Source Connectivity Analysis Can Assess Recovery of Acute Mild Traumatic Brain Injury Patients

In this study we investigated whether source connectivity analysis of resting-state Magnetoencephalographic (MEG) activity could separate mild traumatic brain injury (mTBI) patients from age-and sex-matched controls. For each subject, we used artifact-free data recorded on three sessions to estimate intracranial sources which were then projected onto a standardized brain atlas for common reference. The statistical and topological properties of functional brain networks, estimated using Granger causality, were analyzed using MANOVA, with group and recording session as the independent variables and number of in-going and out-going connections in each atlas region as dependent variables. Overall, mTBI subjects showed a larger number of stronger connections compared to controls. The number and topology of in-going and out-going connections were significantly different across the two groups in areas involved with spatial memory, perception of visual space, emotion formation and processing, learning, and memory. Additionally, differences between patients and controls were decreasing across the three sessions indicating patient improvement. Our results suggest that connectivity analysis may be used as a reliable biomarker of mTBI and can also help with the diagnosis and assessment of patient recovery.

Altered Rich-Club and Frequency-Dependent Subnetwork Organization in Mild Traumatic Brain Injury: A MEG Resting-State Study

Functional brain connectivity networks exhibit "small-world" characteristics and some of these networks follow a "rich-club" organization, whereby a few nodes of high connectivity (hubs) tend to connect more densely among themselves than to nodes of lower connectivity. The Current study followed an "attack strategy" to compare the rich-club and small-world network organization models using Magnetoencephalographic (MEG) recordings from mild traumatic brain injury (mTBI) patients and neurologically healthy controls to identify the topology that describes the underlying intrinsic brain network organization. We hypothesized that the reduction in global efficiency caused by an attack targeting a model's hubs would reveal the "true" underlying topological organization. Connectivity networks were estimated using mutual information as the basis for cross-frequency coupling. Our results revealed a prominent rich-club network organization for both groups. In particular, mTBI patients demonstrated hyper-synchronization among rich-club hubs compared to controls in the δ band and the δ-γ1, θ-γ1, and β-γ2 frequency pairs. Moreover, rich-club hubs in mTBI patients were overrepresented in right frontal brain areas, from θ to γ1 frequencies, and underrepresented in left occipital regions in the δ-β, δ-γ1, θ-β, and β-γ2 frequency pairs. These findings indicate that the rich-club organization of resting-state MEG, considering its role in information integration and its vulnerability to various disorders like mTBI, may have a significant predictive value in the development of reliable biomarkers to help the validation of the recovery from mTBI. Furthermore, the proposed approach might be used as a validation tool to assess patient recovery.

Brain activation profiles in mTBI: evidence from ERP activity of working memory response

In this study we analyzed event related potentials (ERPs) obtained in an N-back working memory test that varied in difficulty from 0- to 2-back. We collected 21 channels of activity from 11 mild traumatic brain injury (mTBI) patients and 7 normal controls, on three different visits, and used the amplitude and latency of the P300 component to characterize the subjects. A preprocessing procedure based on independent component analysis was used first to identify and eliminate electrophysiological noise on a single trial basis. Then to obtain more reliable statistics, the recording electrodes were lumped into five main groups corresponding roughly to frontal, central, parietal, and left and right temporal brain regions. For each subject, the P300 amplitude and latency were measured after averaging the activity of all channels in each group. Group analyses showed that latencies in the central region were significantly shorter in controls, at every visit for the 2-back test. The lack of significant differences across the three visits for the mTBI group indicates that mTBI subjects are not improving at the rate that might have been expected, confirming previous reports that mTBI deficits may persist for years.

Early Asymmetric Cardio-Cerebral Causality and Outcome after Severe Traumatic Brain Injury

The brain and heart are two vital systems in health and disease, increasingly recognized as a complex, interdependent network with constant information flow in both directions. After severe traumatic brain injury (TBI), the causal, directed interactions between the brain, heart, and autonomic nervous system have not been well established. Novel methods are needed to probe unmeasured, potentially prognostic information in complex biological networks that are not revealed by traditional means. In this study, we examined potential bidirectional causality between intracranial pressure (ICP), mean arterial pressure (MAP), and heart rate (HR) and its relationship to mortality in a 24-h period early post-TBI. We applied Granger causality (GC) analysis to cardio-cerebral monitoring data from 171 severe TBI patients admitted to a single neurocritical care center over a 10-year period. There was significant bidirectional causality between ICP and MAP, MAP and HR, and ICP and HR in the majority of patients (p < 0.01). MAP influenced both ICP and HR to a greater extent (higher GC, p < 0. 00001), but there was no dominant unidirectional causality between ICP and HR (p = 0.85). Those who died had significantly lower GC for ICP causing MAP and HR causing ICP (p = 0.006 and p = 0.004, respectively) and were predictors of mortality independent of age, sex, and traditional intracranial variables (ICP, cerebral perfusion pressure, GCS, and pressure reactivity index). Examining the brain and heart with GC-based features for the first time in severe TBI patients has confirmed strong interdependence and reveals a significant relationship between select causality pairs and mortality. These results support the notion that impaired causal information flow between the cerebrovascular, autonomic, and cardiovascular systems are of central importance in severe TBI.

Brain activation profiles in mTBI: Evidence from combined resting-state EEG and MEG activity

In this study, we compared the brain activation profiles obtained from resting state Electroencephalographic (EEG) and Magnetoencephalographic (MEG) activity in six mild traumatic brain injury (mTBI) patients and five orthopedic controls, using power spectral density (PSD) analysis. We first estimated intracranial dipolar EEG/MEG sources on a dense grid on the cortical surface and then projected these sources on a standardized atlas with 68 regions of interest (ROIs). Averaging the PSD values of all sources in each ROI across all control subjects resulted in a normative database that was used to convert the PSD values of mTBI patients into z-scores in eight distinct frequency bands. We found that mTBI patients exhibited statistically significant overactivation in the delta, theta, and low alpha bands. Additionally, the MEG modality seemed to better characterize the group of individual subjects. These findings suggest that resting-state EEG/MEG activation maps may be used as specific biomarkers that can help with the diagnosis of and assess the efficacy of intervention in mTBI patients.

Comparison of brain network models using cross-frequency coupling and attack strategies

Several neuroimaging studies have suggested that functional brain connectivity networks exhibit "small-world" characteristics, whereas recent studies based on structural data have proposed a "rich-club" organization of brain networks, whereby hubs of high connection density tend to connect among themselves compared to nodes of lower density. In this study, we adopted an "attack strategy" to compare the rich-club and small-world organizations and identify the model that describes best the topology of brain connectivity. We hypothesized that the highest reduction in global efficiency caused by a targeted attack on each model's hubs would reveal the organization that better describes the topology of the underlying brain networks. We applied this approach to magnetoencephalographic data obtained at rest from neurologically intact controls and mild traumatic brain injury patients. Functional connectivity networks were computed using phase-to-amplitude cross-frequency coupling between the δ and β frequency bands. Our results suggest that resting state MEG connectivity networks follow a rich-club organization.

Mining cross-frequency coupling microstates from resting state MEG: An application to mild traumatic brain injury

Recent studies have investigated the possible role of dynamic functional connectivity and the role of cross-frequency coupling (CFC) to provide the substrate for reliable biomarkers of brain disorders. In this study, we analyzed time-varying CFC profiles from resting state Magnetoencephal-ographic recordings of 30 mild Traumatic Brain Injury (mTBI) patients and 50 normal controls. Interactions among sensors at specific pairs of frequency bands were computed via estimation of phase-to-amplitude couplings. We then computed time-varying functional connectivity graphs that were described in terms of segregation (local efficiency, LE) and integration (global efficiency, GE) and mapped those graphs to time series of GE/LE estimates. The resulting dynamic network revealed transitions between a limited number of microstates for mTBI subjects compared to controls. The significant differences in transition probability between the two groups, along with the limited repertoire of possible states, can form the basis for a robust dynamic connectomic biomarker for the diagnosis of mTBI.

Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury

Cross-frequency coupling (CFC) is thought to represent a basic mechanism of functional integration of neural networks across distant brain regions. In this study, we analyzed CFC profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 30 mild traumatic brain injury (mTBI) patients and 50 controls. We used mutual information (MI) to quantify the phase-to-amplitude coupling (PAC) of activity among the recording sensors in six nonoverlapping frequency bands. After forming the CFC-based functional connectivity graphs, we employed a tensor representation and tensor subspace analysis to identify the optimal set of features for subject classification as mTBI or control. Our results showed that controls formed a dense network of stronger local and global connections indicating higher functional integration compared to mTBI patients. Furthermore, mTBI patients could be separated from controls with more than 90% classification accuracy. These findings indicate that analysis of brain networks computed from resting-state MEG with PAC and tensorial representation of connectivity profiles may provide a valuable biomarker for the diagnosis of mTBI.

Functional connectivity changes detected with magnetoencephalography after mild traumatic brain injury

Mild traumatic brain injury (mTBI) may affect normal cognition and behavior by disrupting the functional connectivity networks that mediate efficient communication among brain regions. In this study, we analyzed brain connectivity profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 31 mTBI patients and 55 normal controls. We used phase-locking value estimates to compute functional connectivity graphs to quantify frequency-specific couplings between sensors at various frequency bands. Overall, normal controls showed a dense network of strong local connections and a limited number of long-range connections that accounted for approximately 20% of all connections, whereas mTBI patients showed networks characterized by weak local connections and strong long-range connections that accounted for more than 60% of all connections. Comparison of the two distinct general patterns at different frequencies using a tensor representation for the connectivity graphs and tensor subspace analysis for optimal feature extraction showed that mTBI patients could be separated from normal controls with 100% classification accuracy in the alpha band. These encouraging findings support the hypothesis that MEG-based functional connectivity patterns may be used as biomarkers that can provide more accurate diagnoses, help guide treatment, and monitor effectiveness of intervention in mTBI.

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We analyzed resting state connectivity profiles in 31 mTBI patients and 55 controls.

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We quantified frequency-specific connectivity couplings using phase-locking values.

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Normal control networks showed dense local and sparse long-range connections.

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TBI patient networks showed sparse local and dense long-range connections.

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Tensor subspace analysis could classify subjects with 100% accuracy in the α band

Delayed and disorganised brain activation detected with magnetoencephalography after mild traumatic brain injury

BACKGROUND

Awareness to neurocognitive issues after mild traumatic brain injury (mTBI) is increasing, but currently no imaging markers are available for mTBI. Advanced structural imaging recently showed microstructural tissue changes and axonal injury, mild but likely sufficient to lead to functional deficits. Magnetoencephalography (MEG) has high temporal and spatial resolution, combining structural and electrophysiological information, and can be used to examine brain activation patterns of regions involved with specific tasks.

METHODS

16 adults with mTBI and 16 matched controls were submitted to neuropsychological testing (Wechsler Abbreviated Scale of Intelligence (WASI); Conners; Alcohol Use Disorders Identification Test (AUDIT); Generalised Anxiety Disorder Seven-item Scale (GAD-7); Patient Health Questionnaire (PHQ-9); Symptom Checklist and Symptom Severity Score (SCAT2)) and MEG while tested for mental flexibility (Intra-Extra Dimensional set-shifting tasks). Three-dimensional maps were generated using synthetic aperture magnetometry beamforming analyses to identify differences in regional activation and activation times. Reaction times and accuracy between groups were compared using 2×2 mixed analysis of variance.

FINDINGS

While accuracy was similar, patients with mTBI reaction time was delayed and sequence of activation of brain regions disorganised, with involvement of extra regions such as the occipital lobes, not used by controls. Examination of activation time showed significant delays in the right insula and left posterior parietal cortex in patients with mTBI.

CONCLUSIONS

Patients with mTBI showed significant delays in the activation of important areas involved in executive function. Also, more regions of the brain are involved in an apparent compensatory effort. Our study suggests that MEG can detect subtle neural changes associated with cognitive dysfunction and thus, may eventually be useful for capturing and tracking the onset and course of cognitive symptoms associated with mTBI.

Mild traumatic brain injury in translation

This Introduction to a Special Issue on Mild Traumatic Brain Injury (mTBI) highlights the methodological challenges in outcome studies and clinical trials involving patients who sustain mTBI. Recent advances in brain imaging and portable, computerized cognitive tasks have contributed to protocols that are sensitive to the effects of mTBI and efficient in time for completion. Investigation of civilian mTBI has been extended to single and repeated injuries in athletes and blast-related mTBI in service members and veterans. Despite differences in mechanism of injury, there is evidence for similar effects of acceleration-deceleration and blast mechanisms of mTBI on cognition. Investigation of repetitive mTBI suggests that the effects may be cumulative and that repeated mTBI and repeated subconcussive head trauma may lead to neurodegenerative conditions. Although animal models of mTBI using cortical impact and fluid percussion injury in rodents have been able to reproduce some of the cognitive deficits frequently exhibited by patients after mTBI, modeling post-concussion symptoms is difficult. Recent use of closed head and blast injury animal models may more closely approximate clinical mTBI. Translation of interventions that are developed in animal models to patients with mTBI is a priority for the research agenda. This Special Issue on mTBI integrates basic neuroscience studies using animal models with studies of human mTBI, including the cognitive sequelae, persisting symptoms, brain imaging, and host factors that facilitate recovery.