Alteration of cortical functional connectivity as a result of traumatic brain injury revealed by graph theory, ICA, and sLORETA analyses of EEG signals

Functional connectivity, tissue microstructure and T2 at 11.1 Tesla distinguishes neuroadaptive differences in two traumatic brain injury models in rats: A Translational Outcomes Project in NeuroTrauma (TOP-NT) UG3 phase study

The damage caused by contusive traumatic brain injuries (TBIs) is thought to involve breakdown in neuronal communication through focal and diffuse axonal injury along with alterations to the neuronal chemical environment, which adversely affects neuronal networks beyond the injury epicenter(s). In the present study, functional connectivity along with brain tissue microstructure coupled with T2 relaxometry were assessed in two experimental TBI models in rat, controlled cortical impact (CCI) and lateral fluid percussive injury (LFPI). Rats were scanned on an 11.1 Tesla scanner on days 2 and 30 following either CCI or LFPI. Naive controls were scanned once and used as a baseline comparison for both TBI groups. Scanning included functional magnetic resonance imaging (fMRI), diffusion weighted images (DWI), and multi-echo T2 images. fMRI scans were analyzed for functional connectivity across laterally and medially located region of interests (ROIs) across the cortical mantle, hippocampus, and dorsal striatum. DWI scans were processed to generate maps of fractional anisotropy, mean, axial, and radial diffusivities (FA, MD, AD, RD). The analyses focused on cortical and white matter (WM) regions at or near the TBI epicenter. Our results indicate that rats exposed to CCI and LFPI had significantly increased contralateral intra-cortical connectivity at 2 days post-injury. This was observed across similar areas of the cortex in both groups. The increased contralateral connectivity was still observed by day 30 in CCI, but not LFPI rats. Although both CCI and LFPI had changes in WM and cortical FA and diffusivities, WM changes were most predominant in CCI and cortical changes in LFPI. Our results provide support for the use of multimodal MR imaging for different types of contusive and skull-penetrating injury.

Brain functional connectivity in children with a mild traumatic brain injury: A scoping review

INTRODUCTION

The occurrence of mild traumatic brain injury(mTBI) is estimated at 0,2-0,3% cases annually. Following a mTBI, some children experience persistent symptoms, and functional connectivity(FC) changes may be implicated. However, characteristics of FC have not been widely described in this population. This scoping review aimed to identify and understand the impacts of mTBI on EEG-measured FC in children, provide an overview of the available literature, detail analysis techniques, and describe gaps in the research.

METHODS

PubMed, Web of Science, Medline, Embase, ProQuest and CINAHL were searched up to June 25, 2023, with the terms child, mTBI, EEG, FC, and their synonyms. Ten studies were identified.

RESULTS

Five studies reported significant differences between the mTBI group and controls. In addition to group differences, six studies reported significant variation over time. Brain Network Analysis(BNA), utilized in seven studies, was the primary FC analysis recorded. Two of the five studies that reported significant differences following mTBI utilized the BNA. The other three applied alternative analysis methods.

DISCUSSION

FC assessment based on EEG can identify some differences in children with mTBI. BNA was more useful in following changes over time. Further research is suggested, considering the limited age range and number of retrieved studies.

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.

Relaxed Alert Electroencephalography Screening for Mild Traumatic Brain Injury in Athletes

Due to the mildness of initial injury, many athletes with recurrent mild traumatic brain injury (mTBI) are misdiagnosed with other neuropsychiatric illnesses. This study was designed as a proof-of-principle feasibility trial for athletic trainers at a sports facility to generate electroencephalograms (EEGs) from student athletes for discriminating (mTBI) associated EEGs from uninjured ones. A total of 47 EEGs were generated, with 30 athletes recruited at baseline (BL) pre-season, after a concussive injury (IN), and post-season (PS). Outcomes included: 1) visual analyses of EEGs by a neurologist; 2) support vector machine (SVM) classification for inferences about whether particular groups belonged to the three subgroups of BL, IN, or PS; and 3) analyses of EEG synchronies including phase locking value (PLV) computed between pairs of distinct electrodes. All EEGs were visually interpreted as normal. SVM classification showed that BL and IN could be discriminated with 81% accuracy using features of EEG synchronies combined. Frontal inter-hemispheric phase synchronization measured by PLV was significantly lower in the IN group. It is feasible for athletic trainers to record high quality EEGs from student athletes. Also, spatially localized metrics of EEG synchrony can discriminate mTBI associated EEGs from control EEGs.

Neurophysiological Markers to Guide Return to Sport After Sport-Related Concussion

SUMMARY

Sport-related concussion (SRC) has been defined as a subset of mild traumatic brain injury (mTBI), without structural abnormalities, reflecting a functional disturbance. Over the past decade, SRC has gained increasing awareness and attention, which coincides with an increase in incidence rates. Because this injury has been considered one of the most challenging encounters for clinicians, there is a need for objective biomarkers to aid in diagnosis (i.e., presence/severity) and management (i.e., return to sport) of SRC/mTBI.The primary aim of this article was to present state-of-the-art neurophysiologic methods (e.g., electroencephalography, magnetoencephalography, transcranial magnetic stimulation, and autonomic nervous system) that are appropriate to investigate the complex pathophysiological process of a concussion. A secondary aim was to explore the potential for evidence-based markers to be used in clinical practice for SRC management. The article concludes with a discussion of future directions for SRC research with specific focus on clinical neurophysiology.

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

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

An exploratory study on functional connectivity after mild traumatic brain injury: Preserved global but altered local organization

INTRODUCTION

This study aimed to investigate alterations in whole-brain functional connectivity after a concussion using graph-theory analysis from global and local perspectives and explore the association between changes in the functional network properties and cognitive performance.

METHODS

Individuals with mild traumatic brain injury (mTBI, n = 29) within a month after injury, and age- and sex-matched healthy controls (n = 29) were included. Graph-theory measures on functional connectivity assessed using resting state functional magnetic resonance imaging data were acquired from each participant. These included betweenness centrality, strength, clustering coefficient, local efficiency, and global efficiency. Multi-domain cognitive functions were correlated with the graph-theory measures.

RESULTS

In comparison to the controls, the mTBI group showed preserved network characteristics at a global level. However, in the local network, we observed decreased betweenness centrality, clustering coefficient, and local efficiency in several brain areas, including the fronto-parietal attention network. Network strength at the local level showed mixed-results in different areas. The betweenness centrality of the right parahippocampus showed a significant positive correlation with the cognitive scores of the verbal learning test only in the mTBI group.

CONCLUSION

The intrinsic functional connectivity after mTBI is preserved globally, but is suboptimally organized locally in several areas. This possibly reflects the neurophysiological sequelae of a concussion. The present results may imply that the network property could be used as a potential indicator for clinical outcomes after mTBI.

A Routine Electroencephalography Monitoring System for Automated Sports-Related Concussion Detection

Cases of concussions in the United States keep increasing and are now up to 2 million to 3 million incidents per year. Although concussions are recoverable and usually not life-threatening, the degree and rate of recovery may vary depending on age, severity of the injury, and past concussion history. A subsequent concussion before full recovery may lead to more-severe brain damage and poorer outcomes. Electroencephalography (EEG) recordings can identify brain dysfunctionality and abnormalities, such as after a concussion. Routine EEG monitoring can be a convenient method for reducing unreported injuries and preventing long-term damage, especially among groups with a greater risk of experiencing a concussion, such as athletes participating in contact sports. Because of the relative availability of EEG compared to other brain-imaging techniques (e.g., functional magnetic resonance imaging), the use of EEG monitoring is growing for various neurological disorders. In this longitudinal study, EEG was analyzed from 4 football athletes before their athletic season and also within 7 days of concussion. Compared to a control group of 4 additional athletes, a concussion was detected with up to 99.5% accuracy using EEG recordings in the Theta-Alpha band. Classifiers that use data from only a subset of the EEG electrodes providing reliable detection are also proposed. The most effective classifiers used EEG recordings from the Central scalp region in the Beta band and over the Temporal scalp region using the Theta-Alpha band. This proof-of-concept study and preliminary findings suggest that EEG monitoring may be used to identify a sports-related concussion occurrence with a high level of accuracy and thus reduce the chance of unreported concussion.

Deep Learning Recurrent Neural Network for Concussion Classification in Adolescents Using Raw Electroencephalography Signals: Toward a Minimal Number of Sensors

Artificial neural networks (ANNs) are showing increasing promise as decision support tools in medicine and particularly in neuroscience and neuroimaging. Recently, there has been increasing work on using neural networks to classify individuals with concussion using electroencephalography (EEG) data. However, to date the need for research grade equipment has limited the applications to clinical environments. We recently developed a deep learning long short-term memory (LSTM) based recurrent neural network to classify concussion using raw, resting state data using 64 EEG channels and achieved high accuracy in classifying concussion. Here, we report on our efforts to develop a clinically practical system using a minimal subset of EEG sensors. EEG data from 23 athletes who had suffered a sport-related concussion and 35 non-concussed, control athletes were used for this study. We tested and ranked each of the original 64 channels based on its contribution toward the concussion classification performed by the original LSTM network. The top scoring channels were used to train and test a network with the same architecture as the previously trained network. We found that with only six of the top scoring channels the classifier identified concussions with an accuracy of 94%. These results show that it is possible to classify concussion using raw, resting state data from a small number of EEG sensors, constituting a first step toward developing portable, easy to use EEG systems that can be used in a clinical setting.

Cognitive Decline Risk Stratification in People with Late-Onset Epilepsy of Unknown Etiology: An Electroencephalographic Connectivity and Graph Theory Pilot Study

BACKGROUND

Although people with late onset epilepsy of unknown etiology (LOEU) are at higher risk of cognitive decline compared to the general population, we still lack affordable tools to predict and stratify their risk of dementia.

OBJECTIVE

This pilot-study investigates the potential application of electroencephalography (EEG) network small-world (SW) properties in predicting cognitive decline among patients with LOEU.

METHODS

People diagnosed with LOEU and normal cognitive examination at the time of epilepsy diagnosis were included. Cerebrospinal fluid biomarkers, brain imaging, and neuropsychological assessment were performed at the time of epilepsy diagnosis. Baseline EEG was analyzed for SW properties. Patients were followed-up over time with neuropsychological testing to define the trajectory of cognitive decline.

RESULTS

Over 5.1 years of follow-up, among 24 patients diagnosed with LOEU, 62.5% were female, mean age was 65.3 years, thirteen developed mild cognitive impairment (MCI), and four developed dementia. Patients with LOEU developing MCI had lower values of SW coefficients in the delta (p = 0.03) band and higher SW values in the alpha frequency bands (p = 0.02) compared to patients having normal cognition at last follow-up. The two separate ANOVAs, for low and alpha bands, confirmed an interaction between SW and cognitive decline at follow-up. A similar gradient was confirmed for patients developing dementia compared to those with normal cognitive function as well as to those developing MCI.

CONCLUSION

Baseline EEG analysis through SW is worth investigating as an affordable, widely available tool to stratify LOEU patients for their risk of cognitive decline.

EEG-connectivity: A fundamental guide and checklist for optimal study design and evaluation

Brain connectivity can be estimated through many analyses applied to electroencephalographic (EEG) data. However, substantial heterogeneity in the implementation of connectivity methods exist. Heterogeneity in conceptualization of connectivity measures, data collection, or data pre-processing may be associated with variability in robustness of measurement. While it is difficult to compare the results of studies using different EEG connectivity measures, standardization of processing and reporting may facilitate the task. We discuss how factors such as referencing, epoch length and number, controls for volume conduction, artefact removal, and statistical control of multiple comparisons influence the EEG connectivity estimate for connectivity measures, and what can be done to control for potential confounds associated with these factors. Based on the results reported in previous literature, this article presents recommendations and a novel checklist developed for quality assessment of EEG connectivity studies. This checklist and its recommendations are made in an effort to draw attention to factors that may influence connectivity estimates and factors that need to be improved in future research. Standardization of procedures and reporting in EEG connectivity may lead to EEG connectivity studies to be made more synthesisable and comparable despite variations in the methodology underlying connectivity estimates.

Graph Theory on Brain Cortical Sources in Parkinson's Disease: The Analysis of 'Small World' Organization from EEG

Parkinson’s disease (PD) is the second most common neurodegenerative disease in the elderly population. Similarly to other neurodegenerative diseases, the early diagnosis of PD is quite difficult. The current pilot study aimed to explore the differences in brain connectivity between PD and NOrmal eLDerly (Nold) subjects to evaluate whether connectivity analysis may speed up and support early diagnosis. A total of 26 resting state EEGs were analyzed from 13 PD patients and 13 age-matched Nold subjects, applying to cortical reconstructions the graph theory analyses, a mathematical representation of brain architecture. Results showed that PD patients presented a more ordered structure at slow-frequency EEG rhythms (lower value of SW) than Nold subjects, particularly in the theta band, whereas in the high-frequency alpha, PD patients presented more random organization (higher SW) than Nold subjects. The current results suggest that PD could globally modulate the cortical connectivity of the brain, modifying the functional network organization and resulting in motor and non-motor signs. Future studies could validate whether such an approach, based on a low-cost and non-invasive technique, could be useful for early diagnosis, for the follow-up of PD progression, as well as for evaluating pharmacological and neurorehabilitation treatments.

Graph-theoretical analysis of EEG functional connectivity during balance perturbation in traumatic brain injury: A pilot study

Traumatic brain injury (TBI) often results in balance impairment, increasing the risk of falls, and the chances of further injuries. However, the underlying neural mechanisms of postural control after TBI are not well understood. To this end, we conducted a pilot study to explore the neural mechanisms of unpredictable balance perturbations in 17 chronic TBI participants and 15 matched healthy controls (HC) using the EEG, MRI, and diffusion tensor imaging (DTI) data. As quantitative measures of the functional integration and segregation of the brain networks during the postural task, we computed the global graph-theoretic network measures (global efficiency and modularity) of brain functional connectivity derived from source-space EEG in different frequency bands. We observed that the TBI group showed a lower balance performance as measured by the center of pressure displacement during the task, and the Berg Balance Scale (BBS). They also showed reduced brain activation and connectivity during the balance task. Furthermore, the decrease in brain network segregation in alpha-band from baseline to task was smaller in TBI than HC. The DTI findings revealed widespread structural damage. In terms of the neural correlates, we observed a distinct role played by different frequency bands: theta-band modularity during the task was negatively correlated with the BBS in the TBI group; lower beta-band network connectivity was associated with the reduction in white matter structural integrity. Our future studies will focus on how postural training will modulate the functional brain networks in TBI.

Contribution of Graph Theory Applied to EEG Data Analysis for Alzheimer's Disease Versus Vascular Dementia Diagnosis

BACKGROUND

Most common progressive brain diseases in the elderly are Alzheimer's disease (AD) and vascular dementia (VaD). They present with relatively similar clinical symptoms of cognitive decline, but the underlying pathophysiological mechanisms are different.

OBJECTIVE

The aim is to explore the brain connectivity differences between AD and VaD patients compared to mild cognitive impairment (MCI) and normal elderly (Nold) subjects applying graph theory, in particular the Small World (SW) analysis.

METHODS

274 resting state EEGs were analyzed in 100 AD, 80 MCI, 40 VaD, and 54 Nold subjects. Graph theory analyses were applied to undirected and weighted networks obtained by lagged linear coherence evaluated by eLORETA tool.

RESULTS

VaD and AD patients presented more ordered low frequency structure (lower value of SW) than Nold and MCI subjects, and more random organization (higher value of SW) in low and high frequency alpha rhythms. Differences between patients have been found in high frequency alpha rhythms in VaD (higher value of SW) with respect to AD, and in theta band with a trend which is more similar to MCI and Nold than to AD. MCI subjects presented a network organization which is intermediate, in low frequency bands, between Nold and patients.

CONCLUSION

Graph theory applied to EEG data has proved very useful in identifying differences in brain network patterns in subjects with dementia, proving to be a valid tool for differential diagnosis. Future studies will aim to validate this method to diagnose especially in the early stages of the disease and at single subject level.

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.

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.

On the time-course of functional connectivity: theory of a dynamic progression of concussion effects

The current literature presents a discordant view of mild traumatic brain injury and its effects on the human brain. This dissonance has often been attributed to heterogeneities in study populations, aetiology, acuteness, experimental paradigms and/or testing modalities. To investigate the progression of mild traumatic brain injury in the human brain, the present study employed data from 93 subjects (48 healthy controls) representing both acute and chronic stages of mild traumatic brain injury. The effects of concussion across different stages of injury were measured using two metrics of functional connectivity in segments of electroencephalography time-locked to an active oddball task. Coherence and weighted phase-lag index were calculated separately for individual frequency bands (delta, theta, alpha and beta) to measure the functional connectivity between six electrode clusters distributed from frontal to parietal regions across both hemispheres. Results show an increase in functional connectivity in the acute stage after mild traumatic brain injury, contrasted with significantly reduced functional connectivity in chronic stages of injury. This finding indicates a non-linear time-dependent effect of injury. To understand this pattern of changing functional connectivity in relation to prior evidence, we propose a new model of the time-course of the effects of mild traumatic brain injury on the brain that brings together research from multiple neuroimaging modalities and unifies the various lines of evidence that at first appear to be in conflict.

A Dose Relationship Between Brain Functional Connectivity and Cumulative Head Impact Exposure in Collegiate Water Polo Players

A growing body of evidence suggests that chronic, sport-related head impact exposure can impair brain functional integration and brain structure and function. Evidence of a robust inverse relationship between the frequency and magnitude of repeated head impacts and disturbed brain network function is needed to strengthen an argument for causality. In pursuing such a relationship, we used cap-worn inertial sensors to measure the frequency and magnitude of head impacts sustained by eighteen intercollegiate water polo athletes monitored over a single season of play. Participants were evaluated before and after the season using computerized cognitive tests of inhibitory control and resting electroencephalography. Greater head impact exposure was associated with increased phase synchrony [r₍₁₆₎ > 0.626, p < 0.03 corrected], global efficiency [r₍₁₆₎ > 0.601, p < 0.04 corrected], and mean clustering coefficient [r₍₁₆₎ > 0.625, p < 0.03 corrected] in the functional networks formed by slow-wave (delta, theta) oscillations. Head impact exposure was not associated with changes in performance on the inhibitory control tasks. However, those with the greatest impact exposure showed an association between changes in resting-state connectivity and a dissociation between performance on the tasks after the season [r₍₁₆₎ = 0.481, p = 0.043] that could also be attributed to increased slow-wave synchrony [F_{(4, 135)} = 113.546, p < 0.001]. Collectively, our results suggest that athletes sustaining the greatest head impact exposure exhibited changes in whole-brain functional connectivity that were associated with altered information processing and inhibitory control.

Human brain networks: a graph theoretical analysis of cortical connectivity normative database from EEG data in healthy elderly subjects

Moving from the hypothesis that aging processes modulate brain connectivity networks, 170 healthy elderly volunteers were submitted to EEG recordings in order to define age-related normative limits. Graph theory functions were applied to exact low-resolution electromagnetic tomography on cortical sources in order to evaluate the small-world parameter as a representative model of network architecture. The analyses were carried out in the whole brain-as well as for the left and the right hemispheres separately-and in three specific resting state subnetworks defined as follows: attentional network (AN), frontal network (FN), and default mode network (DMN) in the EEG frequency bands (delta, theta, alpha 1, alpha 2, beta 1, beta 2, gamma). To evaluate the stability of the investigated parameters, a subgroup of 32 subjects underwent three separate EEG recording sessions in identical environmental conditions after a few days interval. Results showed that the whole right/left hemispheric evaluation did not present side differences, but when individual subnetworks were considered, AN and DMN presented in general higher SW in low (delta and/or theta) and high (gamma) frequency bands in the left hemisphere, while for FN, the alpha 1 band was lower in the left with respect to the right hemisphere. It was also evident the test-retest reliability and reproducibility of the present methodology when carried out in clinically stable subjects.Evidences from the present study suggest that graph theory represents a reliable method to address brain connectivity patterns from EEG data and is particularly suitable to study the physiological impact of aging on brain functional connectivity networks.

Objective Classification of mTBI Using Machine Learning on a Combination of Frontopolar Electroencephalography Measurements and Self-reported Symptoms

BACKGROUND

The reliable diagnosis of a mild traumatic brain injury (mTBI) is a pervasive problem in sports and in the military. The frequency and severity of each occurrence, while difficult to quantify, may impact long term cognitive function and quality of life. Despite the new revelations concerning brain disfunction from head injuries, individuals still feel pressure to remain on the field despite a debilitating injury. In this study, we evaluated the accuracy of a system that could be employed on the sidelines or in the locker room to provide an immediate objective mTBI assessment.

METHODS

Participants consisted of 38 individuals with a recent mTBI and 47 controls with no history of mTBI within the last 5 years. Participants were administered a simple symptom questionnaire, behavioral tests, and resting state EEG was measured using three frontopolar electrodes. An advanced machine learning algorithm called boosting was utilized to classify subjects into either injured or controls using power spectral densities on 1-min of resting EEG and the symptom questionnaire.

RESULTS

Results based on leave-one-out cross-validation revealed that the addition of EEG measurements boosted the accuracy to approximately 91 ± 2% compared to 82 ± 4% from the symptom questionnaire alone.

CONCLUSION

This study demonstrated the potential benefit of including EEG measurements to diagnose suspected brain injury patients. This is a step toward accurate and objective classification measurements that can be implemented on the field as a future injury assessment tool.

Global Synchronization Index as an Indicator for Tracking Cognitive Function Changes in a Traumatic Brain Injury Patient: A Case Report

Traumatic brain injury is a main cause of long-term neurological disability, and many patients suffer from cognitive impairment for a lengthy period. Cognitive impairment is a fatal malady to that limits active rehabilitation, and functional recovery in patients with traumatic brain injury. In severe cases, it is impossible to assess cognitive function precisely, and severe cognitive impairment makes it difficult to establish a rehabilitation plan, as well as evaluate the course of rehabilitation. Evaluation of cognitive function is essential for establishing a rehabilitation plan, as well as evaluating the course of rehabilitation. We report a case of the analysis of electroencephalography with global synchronization index and low-resolution brain electromagnetic tomography applied, for evaluation of cognitive function that was difficult with conventional tests, due to severe cognitive impairment in a 77-year-old male patient that experienced traumatic brain injury.

Modified Dominant Mode Rejection Beamformer for Localizing Brain Activities When Data Covariance Matrix Is Rank Deficient

OBJECTIVE

Minimum variance beamformer (MVB) and its extensions fail in localizing short time brain activities particularly evoked potentials because of rank deficiency or inaccurate estimation of a data covariance matrix. In this paper, the conventional dominant mode rejection (DMR) adaptive beamformer is modified to localize brain short time activities.

METHODS

In the modified DMR, it is attempted to obtain a well-conditioned covariance matrix by dividing the eigenvalues of the data covariance matrix into dominant, medium, and small eigenvalues and then modifying medium and small parts. The performance of the proposed approach is compared with diagonal loading MVB (DL_MVB) and fast fully adaptive (FFA) beamformer by using simulated event-related potentials and real event-related field data. Eigenspace versions of DL_MVB and modified DMR are also implemented.

RESULTS

In all simulations, the modified DMR obtains the least localization error (0-5 mm) and spread radius (0-8 mm) when the signal-to-noise ratio (SNR) varies from 0 to 10 dB with step 1 dB. In real data, the new approach in comparison to two other ones attains the most concentrated power spectrum. Eigenspace projection of DL_MVB presents better results than DL_MVB but worse results than the modified DMR. Applying eigenspace projection on the proposed method improves its performance at high SNR levels.

CONCLUSION

Empirical results illustrate the superiority of the proposed DMR method to the DL_MVB and FFA in localizing brain short time activities.

SIGNIFICANCE

The proposed method can be utilized in source localization of epilepsy for presurgical clinical evaluation purpose and also in applications dealing with the localization of evoked potentials and fields.

Resting State Electroencephalography and Sports-Related Concussion: A Systematic Review

Sports-related concussion is associated with a range of short-term functional deficits that are commonly thought to recover within a two-week post-injury period for most, but certainly not all, persons. Resting state electroencephalography (rs-EEG) may prove to be an affordable, accessible, and sensitive method of assessing severity of brain injury and rate of recovery after a concussion. This article presents a systematic review of rs-EEG in sports-related concussion. A systematic review of articles published in the English language, up to June 2017, was retrieved via PsychINFO, Medline, Medline In Process, Embase, SportDiscus, CINAHL, and Cochrane Library, Reviews, and Trials. The following key words were used for database searches: electroencephalography, quantitative electroencephalography, qEEG, cranio-cerebral trauma, mild traumatic brain injury, mTBI, traumatic brain injury, brain concussion, concussion, brain damage, sport, athletic, and athlete. Observational, cohort, correlational, cross-sectional, and longitudinal studies were all included in the current review. Sixteen articles met inclusion criteria, which included data on 504 athletes and 367 controls. All 16 articles reported some abnormality in rs-EEG activity after a concussion; however, the cortical rhythms that were affected varied. Despite substantial methodological and analytical differences across the 16 studies, the current review suggests that rs-EEG may provide a reliable technique to identify persistent functional changes in athletes after a concussion. Because of the varied approaches, however, considerable work is needed to establish a systematic methodology to assess its efficacy as a marker of return-to-play.

Neural Network Reconfigurations: Changes of the Respiratory Network by Hypoxia as an Example

Neural networks, including the respiratory network, can undergo a reconfiguration process by just changing the number, the connectivity or the activity of their elements. Those elements can be either brain regions or neurons, which constitute the building blocks of macrocircuits and microcircuits, respectively. The reconfiguration processes can also involve changes in the number of connections and/or the strength between the elements of the network. These changes allow neural networks to acquire different topologies to perform a variety of functions or change their responses as a consequence of physiological or pathological conditions. Thus, neural networks are not hardwired entities, but they constitute flexible circuits that can be constantly reconfigured in response to a variety of stimuli. Here, we are going to review several examples of these processes with special emphasis on the reconfiguration of the respiratory rhythm generator in response to different patterns of hypoxia, which can lead to changes in respiratory patterns or lasting changes in frequency and/or amplitude.

Concussion: A Window Into Brain–Movement Relations in Motor Control

Research into sports-related concussion (SRC) or brain injury has vastly expanded our knowledge of the connection between brain activity and behavioral outcomes. Historical examination of concussion reveals components of structural changes in the brain resulting from injury. A constellation of clinical symptoms is typically present following concussion for several days and weeks. However, the intersection of structural changes and clinical examination still remains elusive to medical professionals. With emerging technologies and modalities such as quantitative electroencephalography (EEG), functional magnetic resonance imaging (fMRI), virtual reality (VR), and the study of movement, we can better understand the brain–behavior relationship on clinical findings post-injury. Our advancement in SRC study using athletics provides a unique window into the advances in our ability to study this public health crisis. SRC also allows us to understand how athletics and exercise influence brain health. The evolution of SRC diagnosis, treatment, and management informs our current abilities in the study of the brain.

Correlation between standardized assessment of concussion scores and small-world brain network in mild traumatic brain injury

This study investigated the characteristics of the small-world brain network architecture of patients with mild traumatic brain injury (MTBI), and a correlation between brain functional connectivity network properties in the resting-state fMRI and Standardized Assessment of Concussion (SAC) parameters. The neurological conditions of 22 MTBI patients and 17 normal control individuals were evaluated according to the SAC. Resting-state fMRI was performed in all subjects 3 and 7days after injury respectively. After preprocessing the fMRI data, cortex functional regions were marked using AAL90 and Dosenbach160 templates. The small-world network parameters and areas under the integral curves were computed in the range of sparsity from 0.01 to 0.5. Independent-sample t-tests were used to compare these parameters between the MTBI and control group. Significantly different parameters were investigated for correlations with SAC scores; those that correlated were chosen for further curve fitting. The clustering coefficient, the communication efficiency across in local networks, and the strength of connectivity were all higher in MTBI patients relative to control individuals. Parameters in 160 brain regions of the MTBI group significantly correlated with total SAC score and score for attention; the network parameters may be a quadratic function of attention scores of SAC and a cubic function of SAC scores. MTBI patients were characterized by elevated communication efficiency across global brain regions, and in local networks, and strength of mean connectivity. These features may be associated with brain function compensation. The network parameters significantly correlated with SAC total and attention scores.

Increased gamma connectivity during working memory retention following traumatic brain injury

PRIMARY OBJECTIVE

Alterations to functional connectivity following a traumatic brain injury (TBI) may lead to impaired cognitive performance and major depressive disorder (MDD). In particular, functional gamma band connectivity is thought to reflect information binding important for working memory. The objective of this study was to determine whether altered functional gamma connectivity may be a factor in MDD following TBI (TBI-MDD).

RESEARCH DESIGN

This study assessed individuals with TBI-MDD, as well as individuals with TBI alone and MDD alone using electroencephalographic recordings while participants performed a working memory task to assess differences in functional connectivity between these groups.

METHODS AND PROCEDURES

Functional connectivity was compared using the debiased weighted phase lag index (wPLI). wPLI was measured from a group of healthy controls (n = 31), participants with MDD (n = 17), participants with TBI (n = 20) and participants with TBI-MDD (n = 15).

MAIN OUTCOMES AND RESULTS

Contrary to the predictions, this study found both the groups with TBI and TBI-MDD showed higher gamma connectivity from posterior regions during WM retention.

CONCLUSIONS

This may reflect dysfunctional functional connectivity in these groups, as a result of maladaptive neuroplastic reorganization.

Preliminary investigation of Brain Network Activation (BNA) and its clinical utility in sport-related concussion

Background: The clinical diagnosis and management of patients with sport-related concussion is largely dependent on subjectively reported symptoms, clinical examinations, cognitive, balance, vestibular and oculomotor testing. Consequently, there is an unmet need for objective assessment tools that can identify the injury from a physiological perspective and add an important layer of information to the clinician’s decision-making process.

Objective: The goal of the study was to evaluate the clinical utility of the EEG-based tool named Brain Network Activation (BNA) as a longitudinal assessment method of brain function in the management of young athletes with concussion.

Methods: Athletes with concussion (n = 86) and age-matched controls (n = 81) were evaluated at four time points with symptom questionnaires and BNA. BNA scores were calculated by comparing functional networks to a previously defined normative reference brain network model to the same cognitive task.

Results: Subjects above 16 years of age exhibited a significant decrease in BNA scores immediately following injury, as well as notable changes in functional network activity, relative to the controls. Three representative case studies of the tested population are discussed in detail, to demonstrate the clinical utility of BNA.

Conclusion: The data support the utility of BNA to augment clinical examinations, symptoms and additional tests by providing an effective method for evaluating objective electrophysiological changes associated with sport-related concussions.

Detecting Mild Traumatic Brain Injury Using Resting State Magnetoencephalographic Connectivity

Accurate means to detect mild traumatic brain injury (mTBI) using objective and quantitative measures remain elusive. Conventional imaging typically detects no abnormalities despite post-concussive symptoms. In the present study, we recorded resting state magnetoencephalograms (MEG) from adults with mTBI and controls. Atlas-guided reconstruction of resting state activity was performed for 90 cortical and subcortical regions, and calculation of inter-regional oscillatory phase synchrony at various frequencies was performed. We demonstrate that mTBI is associated with reduced network connectivity in the delta and gamma frequency range (>30 Hz), together with increased connectivity in the slower alpha band (8–12 Hz). A similar temporal pattern was associated with correlations between network connectivity and the length of time between the injury and the MEG scan. Using such resting state MEG network synchrony we were able to detect mTBI with 88% accuracy. Classification confidence was also correlated with clinical symptom severity scores. These results provide the first evidence that imaging of MEG network connectivity, in combination with machine learning, has the potential to accurately detect and determine the severity of mTBI.

Detecting concussion is typically not possible using currently clinically used brain imaging, such as MRI and CT scans. Magnetoencephalographic (MEG) imaging is able to directly measure brain activity at fast time scales, and this can be used to map how various areas of the brain interact. We recorded MEG from individuals who had suffered a concussion, as well as control subjects who had not. We found characteristic alterations of inter-regional interactions associated with concussion. Moreover, using a machine learning approach, we were able to detect concussion with 88% accuracy from MEG connectivity, and confidence of classification correlated with symptom severity. This potentially provides new quantitative and objective methods for detecting and assessing the severity of concussion using neuroimaging.

Long-Term Effects of Concussion on Electrophysiological Indices of Attention in Varsity College Athletes: An Event-Related Potential and Standardized Low-Resolution Brain Electromagnetic Tomography Approach

This study investigated the effects of a past concussion on electrophysiological indices of attention in college athletes. Forty-four varsity football athletes (22 with at least one past concussion) participated in three neuropsychological tests and a two-tone auditory oddball task while undergoing high-density event-related potential (ERP) recording. Athletes previously diagnosed with a concussion experienced their most recent injury approximately 4 years before testing. Previously concussed and control athletes performed equivalently on three neuropsychological tests. Behavioral accuracy and reaction times on the oddball task were also equivalent across groups. However, athletes with a concussion history exhibited significantly larger N2 and P3b amplitudes and longer P3b latencies. Source localization using standardized low-resolution brain electromagnetic tomography indicated that athletes with a history of concussion generated larger electrical current density in the left inferior parietal gyrus compared to control athletes. These findings support the hypothesis that individuals with a past concussion recruit compensatory neural resources in order to meet executive functioning demands. High-density ERP measures combined with source localization provide an important method to detect long-term neural consequences of concussion in the absence of impaired neuropsychological performance.

Small world properties changes in mild traumatic brain injury

PURPOSE

To investigate local and global efficiency changes characterized by small-world properties based on resting-state functional MRI, such as centrality and clustering coefficient, in mild traumatic brain injury (MTBI) patients; and to associate these findings with axonal injury as measured by diffusion tensor imaging (DTI) as well as with post-concussive symptom (PCS).

MATERIALS AND METHODS

Thirty patients (mean age 35 ± 13 years) with clinically defined MTBI and 45 age-matched healthy controls (mean age 37 ± 10 years) participated in the experiments. Resting-state functional MRI was performed using gradient echo planar imaging sequence with 3 Tesla MRI scanner to obtain functional small-world networks. Out of all participants, 20 MTBI patients and 20 controls had available DTI data with three b-values (0, 500, 1000) s/mm² and 30 directions for diffuse axonal injury analyses.

RESULTS

Compared with controls, MTBI patients showed lower relative betweenness centrality (P = 0.01), but significantly higher clustering coefficient (P = 0.04), and these two metrics correlated negatively in patients (r = -0.77; P < 0.001). Regions with lower betweenness centrality (e.g., frontal and occipital) corresponded with the regions of reduced FA in patients, while global FA reduction correlated with betweenness centrality (r = 0.48; P = 0.03) and clustering coefficient (r = -0.46; P = 0.04) in MTBI patients. In addition, there was significantly higher thalamocortical connectivity that correlated with clustering coefficient (r = 0.39; P = 0.03) in patients. Also, patients with higher clustering coefficient tended to have less PCS score with negative correlation (r = -0.4; P = 0.04).

CONCLUSION

Our results demonstrated significant functional small-world properties changes in patients with MTBI, and suggest decreased global efficiency, possibly due to diffuse axonal injury and local network upregulation including increased thalamo-cortical connectivity.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:518-527.

Mapping the Connectome Following Traumatic Brain Injury

There is a paucity of accurate and reliable biomarkers to detect traumatic brain injury, grade its severity, and model post-traumatic brain injury (TBI) recovery. This gap could be addressed via advances in brain mapping which define injury signatures and enable tracking of post-injury trajectories at the individual level. Mapping of molecular and anatomical changes and of modifications in functional activation supports the conceptual paradigm of TBI as a disorder of large-scale neural connectivity. Imaging approaches with particular relevance are magnetic resonance techniques (diffusion weighted imaging, diffusion tensor imaging, susceptibility weighted imaging, magnetic resonance spectroscopy, functional magnetic resonance imaging, and positron emission tomographic methods including molecular neuroimaging). Inferences from mapping represent unique endophenotypes which have the potential to transform classification and treatment of patients with TBI. Limitations of these methods, as well as future research directions, are highlighted.

The Effects of External Jugular Compression Applied during Head Impact Exposure on Longitudinal Changes in Brain Neuroanatomical and Neurophysiological Biomarkers: A Preliminary Investigation

OBJECTIVES

Utilize a prospective in vivo clinical trial to evaluate the potential for mild neck compression applied during head impact exposure to reduce anatomical and physiological biomarkers of brain injury.

METHODS

This project utilized a prospective randomized controlled trial to evaluate effects of mild jugular vein (neck) compression (collar) relative to controls (no collar) during a competitive hockey season (males; 16.3 ± 1.2 years). The collar was designed to mildly compress the jugular vein bilaterally with the goal to increase intracranial blood volume to reduce risk of brain slosh injury during head impact exposure. Helmet sensors were used to collect daily impact data in excess of 20 g (games and practices) and the primary outcome measures, which included changes in white matter (WM) microstructure, were assessed by diffusion tensor imaging (DTI). Specifically, four DTI measures: fractional anisotropy, mean diffusivity (MD), axial diffusivity, and radial diffusivity (RD) were used in the study. These metrics were analyzed using the tract-based Spatial Statistics (TBSS) approach - a voxel-based analysis. In addition, electroencephalography-derived event-related potentials were used to assess changes in brain network activation (BNA) between study groups.

RESULTS

For athletes not wearing the collar, DTI measures corresponding to a disruption of WM microstructure, including MD and RD, increased significantly from pre-season to mid-season (p < 0.05). Athletes wearing the collar did not show a significant change in either MD or RD despite similar accumulated linear accelerations from head impacts (p > 0.05). In addition to these anatomical findings, electrophysiological network analysis of the degree of congruence in the network electrophysiological activation pattern demonstrated concomitant changes in brain network dynamics in the non-collar group only (p < 0.05). Similar to the DTI findings, the increased change in BNA score in the non-collar relative to the collar group was statistically significant (p < 0.01). Changes in DTI outcomes were also directly correlated with altered brain network dynamics (r = 0.76; p < 0.05) as measured by BNA.

CONCLUSION

Group differences in the longitudinal changes in both neuroanatomical and electrophysiological measures, as well as the correlation between the measures, provide initial evidence indicating that mild jugular vein compression may have reduced alterations in the WM response to head impacts during a competitive hockey season. The data indicate sport-related alterations in WM microstructure were ameliorated by application of jugular compression during head impact exposure. These results may lead to a novel line of research inquiry to evaluate the effects of protecting the brain from sports-related head impacts via optimized intracranial fluid dynamics.

Robustness and dynamicity of functional networks in phantom sound

Phantom sound perception is the perception of a sound in the absence of a corresponding external sound source. It is a common symptom for which no treatment exists. Gaining a better understanding of its pathophysiology by applying network science might help in identifying targets in the brain for neuromodulatory approaches to treat this elusive symptom. Brain networks are commonly organized as functional modules which have a densely connected core network coupled to a communally-organized peripheral network. The core network is called the rich club network and the peripheral network is divided into the feeder and local networks. In current study, we investigate the effects of virtual lesions on the endogenous dynamics, complexity and robustness of the remaining brain. It is hypothesized that depending on whether nodes is functionally central to the network or not, the robustness and dynamics of the network change when a lesion in introduced. We therefore investigate the effect of introducing a virtual focal lesion randomly to different nodes is in the tinnitus network and contrast it to the effect of specifically targeting the nodes of the rich-club, feeder and local nodes in patients experiencing a phantom sound (i.e. tinnitus). The tinnitus and control networks were computed from the source-localized EEG of 311 tinnitus patients and 256 control subjects. The results of the current study indicate that both the tinnitus and control networks are robust to the attack on random and rich club nodes, but are drastically modified when attacked from the periphery, especially while targeting the feeder hubs. In both the tinnitus and control networks, feeder nodes were found to have a higher betweenness centrality value than the rich club nodes. This shows that the feeders have a larger influence on the information transmission through the brain than the rich club nodes, by transferring information from the peripheral communities to the core. Further, evidence for the theoretical model of a multimodal tinnitus network is also presented showing that the tinnitus network is divided into individual, separable modules each possibly encoding a different aspect of tinnitus. The current study alludes to the concept that the efficient modification of the tinnitus network is theoretically possible by disconnecting the individual communities from the core of the pathological network.

Reorganization of Motor Execution Networks During Sub-Acute Phase After Stroke

Numerous studies focused on brain reorganization after stroke from aspects of task-related brain activity and resting-state brain networks. However, studies focusing on the longitudinal reorganization of task-state brain networks were scarce. In this study, functional magnetic resonance imaging data were collected from twelve stroke patients during blocked finger-tapping task at four post-stroke time points (less than 10 days, around 2 weeks, 1 month and 3 months), respectively. The dynamic changes and prognostic value of the network parameters (i.e., topological parameters, functional connectivity and nodal parameters) in task-state motor execution networks were thoroughly evaluated. We found that the topological configuration (clustering coefficient and characteristic path length) of task-state motor execution networks underwent significant shift during stroke recovery. Especially, we found the topological configuration of task-state motor execution networks at the early recovery stage were capable of predicting the motor function restoration during sub-acute phase. In addition, we found increasing functional connectivity between ipsilesional cerebellum and motor cortices in task-state motor execution networks. In general, this study demonstrated the reorganization and prognostic value of task-state brain network after stroke, which provides new insights into understanding the brain reorganization and rehabilitation after stroke.

Resting State EEG in Children With Learning Disabilities: An Independent Component Analysis Approach

In this study, the neurophysiological underpinnings of learning disabilities (LD) in children are examined using resting state EEG. We were particularly interested in the neurophysiological differences between children with learning disabilities not otherwise specified (LD-NOS), learning disabilities with verbal disabilities (LD-Verbal), and healthy control (HC) children. We applied 2 different approaches to examine the differences between the different groups. First, we calculated theta/beta and theta/alpha ratios in order to quantify the relationship between slow and fast EEG oscillations. Second, we used a recently developed method for analyzing spectral EEG, namely the group independent component analysis (gICA) model. Using these measures, we identified substantial differences between LD and HC children and between LD-NOS and LD-Verbal children in terms of their spectral EEG profiles. We obtained the following findings: (a) theta/beta and theta/alpha ratios were substantially larger in LD than in HC children, with no difference between LD-NOS and LD-Verbal children; (b) there was substantial slowing of EEG oscillations, especially for gICs located in frontal scalp positions, with LD-NOS children demonstrating the strongest slowing; (c) the estimated intracortical sources of these gICs were mostly located in brain areas involved in the control of executive functions, attention, planning, and language; and (d) the LD-Verbal children demonstrated substantial differences in EEG oscillations compared with LD-NOS children, and these differences were localized in language-related brain areas. The general pattern of atypical neurophysiological activation found in LD children suggests that they suffer from neurophysiological dysfunction in brain areas involved with the control of attention, executive functions, planning, and language functions. LD-Verbal children also demonstrate atypical activation, especially in language-related brain areas. These atypical neurophysiological activation patterns might provide a helpful guide for rehabilitation strategies to treat the deficiencies in these children with LD.

Changes of cortical connectivity during deep anaesthesia

BACKGROUND AND AIMS

The aim of this study was to evaluate the frontal intracortical connectivity during deep anaesthesia (burst-suppression).

METHODS

Experiments were carried out on 5 adult Sprague Dawley rats. The anaesthesia was induced and maintained with isoflurane. Following the induction of anaesthesia, rats were placed in a stereotactic instrument. A hole was drilled in the skull over the frontal cortex and electrodes were inserted in order to record the local field potentials. Rats were maintained in deep level anaesthesia (burst-suppression). The cortical connectivity was assessed by computing the coherence spectra. The frontal intracortical connectivity was calculated during burst, suppression (non-burst) and slow wave anaesthesia periods.

RESULTS

The global cortical connectivity (0.5-100 Hz) was 0.61 ± 0.078 during the burst periods compared to 0.55 ± 0.032 (p < 0.05) during the suppression periods and 0.55 ± 0.015 (p < 0.05) during slow wave anaesthesia.

CONCLUSIONS

The global cortical connectivity increased during the burst periods compared to the suppression periods and slow wave anaesthesia. This increase in the cortical synchronization might be due to the subcortical origin of the bursts.

Cortical Brain Connectivity and B-Type Natriuretic Peptide in Patients With Congestive Heart Failure

The brain has a high level of complexity and needs continuous oxygen supply. So it is clear that any pathological condition, or physiological (aging) change, in the cardiovascular system affects functioning of the central nervous system. We evaluated linear aspects of the relationship between the slowness of cortical rhythms, as revealed by the modulation of a graph connectivity parameter, and congestive heart failure (CHF), as a reflection of neurodegenerative processes. Eyes-closed resting electroencephalographic (EEG) data of 10 patients with CHF were recorded by 19 electrodes positioned according the international 10-20 system. Graph theory function (normalized characteristic path length λ) was applied to the undirected and weighted networks obtained by lagged linear coherence evaluated by eLORETA software, therefore getting rid of volumetric propagation influences. The EEG frequency bands of interest were: delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-40 Hz). The analysis between B-type natriuretic peptide (BNP) values and λ showed positive correlation in delta, associated with a negative correlation in alpha 2 band. Namely, the higher the severity of the disease (as revealed by the BNP vales), the higher the λ in delta, and lower in alpha 2 band. Results suggest that delta and alpha λ indices are good markers of the severity of CHF.

Effects of repetitive sub-concussive brain injury on the functional connectivity of Default Mode Network in high school football athletes

Sub-concussive head impacts are identified as a source of accrued damage. Football athletes experience hundreds of such blows each season. Resting state functional magnetic resonance imaging was used to prospectively study changes in Default Mode Network connectivity for clinically asymptomatic high school football athletes. Athletes exhibited short-term changes relative to baseline and across sessions.

Traumatic brain injury detection using electrophysiological methods

Measuring neuronal activity with electrophysiological methods may be useful in detecting neurological dysfunctions, such as mild traumatic brain injury (mTBI). This approach may be particularly valuable for rapid detection in at-risk populations including military service members and athletes. Electrophysiological methods, such as quantitative electroencephalography (qEEG) and recording event-related potentials (ERPs) may be promising; however, the field is nascent and significant controversy exists on the efficacy and accuracy of the approaches as diagnostic tools. For example, the specific measures derived from an electroencephalogram (EEG) that are most suitable as markers of dysfunction have not been clearly established. A study was conducted to summarize and evaluate the statistical rigor of evidence on the overall utility of qEEG as an mTBI detection tool. The analysis evaluated qEEG measures/parameters that may be most suitable as fieldable diagnostic tools, identified other types of EEG measures and analysis methods of promise, recommended specific measures and analysis methods for further development as mTBI detection tools, identified research gaps in the field, and recommended future research and development thrust areas. The qEEG study group formed the following conclusions: (1) Individual qEEG measures provide limited diagnostic utility for mTBI. However, many measures can be important features of qEEG discriminant functions, which do show significant promise as mTBI detection tools. (2) ERPs offer utility in mTBI detection. In fact, evidence indicates that ERPs can identify abnormalities in cases where EEGs alone are non-disclosing. (3) The standard mathematical procedures used in the characterization of mTBI EEGs should be expanded to incorporate newer methods of analysis including non-linear dynamical analysis, complexity measures, analysis of causal interactions, graph theory, and information dynamics. (4) Reports of high specificity in qEEG evaluations of TBI must be interpreted with care. High specificities have been reported in carefully constructed clinical studies in which healthy controls were compared against a carefully selected TBI population. The published literature indicates, however, that similar abnormalities in qEEG measures are observed in other neuropsychiatric disorders. While it may be possible to distinguish a clinical patient from a healthy control participant with this technology, these measures are unlikely to discriminate between, for example, major depressive disorder, bipolar disorder, or TBI. The specificities observed in these clinical studies may well be lost in real world clinical practice. (5) The absence of specificity does not preclude clinical utility. The possibility of use as a longitudinal measure of treatment response remains. However, efficacy as a longitudinal clinical measure does require acceptable test-retest reliability. To date, very few test-retest reliability studies have been published with qEEG data obtained from TBI patients or from healthy controls. This is a particular concern because high variability is a known characteristic of the injured central nervous system.

EEG-based investigation of brain connectivity changes in psychotic patients undergoing the primitive expression form of dance therapy: a methodological pilot study

Primitive expression (PE) is a form of dance therapy (DT) that involves an interaction of ethologically and socially based forms which are supplied for re-enactment. There exist very few studies of DT applications including in their protocol the measurement of neurophysiological parameters. The present pilot study investigates the use of the correlation coefficient (ρ) and mutual information (MI), and of novel measures extracted from ρ and MI, on electroencephalographic (EEG) data recorded in patients with schizophrenia while they undergo PE DT, in order to expand the set of neurophysiology-based approaches for quantifying possible DT effects, using parameters that might provide insights about any potential brain connectivity changes in these patients during the PE DT process. Indication is provided for an acute potentiation effect, apparent at late-stage PE DT, on the inter-hemispheric connectivity in frontal areas, as well as for attenuation of the inter-hemispheric connectivity of left frontal and right central areas and for potentiation of the intra-hemispheric connectivity of frontal and central areas, bilaterally, in the transition from early to late-stage PE DT. This pilot study indicates that by using EEG connectivity measures based on ρ and MI, the set of useful neurophysiology-based approaches for quantifying possible DT effects is expanded. In the framework of the present study, the causes of the observed connectivity changes cannot be attributed with certainty to PE DT, but indications are provided that these measures may contribute to a detailed assessment of neurophysiological mechanisms possibly being affected by this therapeutic process.

Alteration of default mode network in high school football athletes due to repetitive subconcussive mild traumatic brain injury: a resting-state functional magnetic resonance imaging study

Long-term neurological damage as a result of head trauma while playing sports is a major concern for football athletes today. Repetitive concussions have been linked to many neurological disorders. Recently, it has been reported that repetitive subconcussive events can be a significant source of accrued damage. Since football athletes can experience hundreds of subconcussive hits during a single season, it is of utmost importance to understand their effect on brain health in the short and long term. In this study, resting-state functional magnetic resonance imaging (rs-fMRI) was used to study changes in the default mode network (DMN) after repetitive subconcussive mild traumatic brain injury. Twenty-two high school American football athletes, clinically asymptomatic, were scanned using the rs-fMRI for a single season. Baseline scans were acquired before the start of the season, and follow-up scans were obtained during and after the season to track the potential changes in the DMN as a result of experienced trauma. Ten noncollision-sport athletes were scanned over two sessions as controls. Overall, football athletes had significantly different functional connectivity measures than controls for most of the year. The presence of this deviation of football athletes from their healthy peers even before the start of the season suggests a neurological change that has accumulated over the years of playing the sport. Football athletes also demonstrate short-term changes relative to their own baseline at the start of the season. Football athletes exhibited hyperconnectivity in the DMN compared to controls for most of the sessions, which indicates that, despite the absence of symptoms typically associated with concussion, the repetitive trauma accrued produced long-term brain changes compared to their healthy peers.

Disrupted structural connectome is associated with both psychometric and real-world neuropsychological impairment in diffuse traumatic brain injury

Traumatic brain injury (TBI) is likely to disrupt structural network properties due to diffuse white matter pathology. The present study aimed to detect alterations in structural network topology in TBI and relate them to cognitive and real-world behavioral impairment. Twenty-two people with moderate to severe TBI with mostly diffuse pathology and 18 demographically matched healthy controls were included in the final analysis. Graph theoretical network analysis was applied to diffusion tensor imaging (DTI) data to characterize structural connectivity in both groups. Neuropsychological functions were assessed by a battery of psychometric tests and the Frontal Systems Behavior Scale (FrSBe). Local connection-wise analysis demonstrated reduced structural connectivity in TBI arising from subcortical areas including thalamus, caudate, and hippocampus. Global network metrics revealed that shortest path length in participants with TBI was longer compared to controls, and that this reduced network efficiency was associated with worse performance in executive function and verbal learning. The shortest path length measure was also correlated with family-reported FrSBe scores. These findings support the notion that the diffuse form of neuropathology caused by TBI results in alterations in structural connectivity that contribute to cognitive and real-world behavioral impairment.

Quantification of the effects of volume conduction on the EEG/MEG connectivity estimates: an index of sensitivity to brain interactions

In the context of EEG/MEG, the term 'volume conduction (VC) effects' refers to the recording of an instantaneous linear mixture of multiple brain source activities by each EEG/MEG channel. VC effects may lead to the detection of spurious functional/effective couplings among EEG/MEG channels that are not caused by brain interactions. It is of importance to determine which detected couplings are indicators of brain interactions and which originate from the VC artefacts. In this paper, a quantitative framework is proposed to explore the origin of detected channel couplings by using two types of surrogate datasets. Also, a sensitivity index (called SI) is proposed to compare the power of different connectivity measures to discriminate between the brain interactions and the instantaneous linear mixing effects. We use seven different functional connectivity estimators to evaluate our method on simulation models and resting state EEG data. The error rate of the proposed framework for simulation data by using each of the connectivity estimators is less than 5.2%. Also, SI ranks these connectivity estimators according to their sensitivity to brain interactions in the presence of VC artefacts. As expected, the connectivity measures which are theoretically robust to VC artefacts yield high SI in simulation models and EEG data. In addition, for EEG data in the alpha frequency band the reproducible functional couplings which are indicators of brain interactions are in the back-front directions. This is consistent with the previous studies in this field.