The contribution of EEG to assess and treat motor disorders in multiple sclerosis

OBJECTIVE

Electroencephalography (EEG) can highlight significant changes in spontaneous electrical activity of the brain produced by altered brain network connectivity linked to inflammatory demyelinating lesions and neuronal loss occurring in multiple sclerosis (MS). In this review, we describe the main EEG findings reported in the literature to characterize motor network alteration in term of local activity or functional connectivity changes in patients with MS (pwMS).

METHODS

A comprehensive literature search was conducted to include articles with quantitative analyses of resting-state EEG recordings (spectrograms or advanced methods for assessing spatial and temporal dynamics, such as coherence, theory of graphs, recurrent quantification, microstates) or dynamic EEG recordings during a motor task, with or without connectivity analyses.

RESULTS

In this systematic review, we identified 26 original articles using EEG in the evaluation of MS-related motor disorders. Various resting or dynamic EEG parameters could serve as diagnostic biomarkers of motor control impairment to differentiate pwMS from healthy subjects or be related to a specific clinical condition (fatigue) or neuroradiological aspects (lesion load).

CONCLUSIONS

We highlight some key EEG patterns in pwMS at rest and during movement, both suggesting an alteration or disruption of brain connectivity, more specifically involving sensorimotor networks.

SIGNIFICANCE

Some of these EEG biomarkers of motor disturbance could be used to design future therapeutic strategies in MS based on neuromodulation approaches, or to predict the effects of motor training and rehabilitation in pwMS.

Examining electroencephalogram signatures of people with multiple sclerosis using a nonlinear dynamics approach: a systematic review and bibliographic analysis

Background

Considering that brain activity involves communication between millions of neurons in a complex network, nonlinear analysis is a viable tool for studying electroencephalography (EEG). The main objective of this review was to collate studies that utilized chaotic measures and nonlinear dynamical analysis in EEG of multiple sclerosis (MS) patients and to discuss the contributions of chaos theory techniques to understanding, diagnosing, and treating MS.

Methods

Using the preferred reporting items for systematic reviews and meta-analysis (PRISMA), the databases EbscoHost, IEEE, ProQuest, PubMed, Science Direct, Web of Science, and Google Scholar were searched for publications that applied chaos theory in EEG analysis of MS patients.

Results

A bibliographic analysis was performed using VOSviewer software keyword co-occurrence analysis indicated that MS was the focus of the research and that research on MS diagnosis has shifted from conventional methods, such as magnetic resonance imaging, to EEG techniques in recent years. A total of 17 studies were included in this review. Among the included articles, nine studies examined resting-state, and eight examined task-based conditions.

Conclusion

Although nonlinear EEG analysis of MS is a relatively novel area of research, the findings have been demonstrated to be informative and effective. The most frequently used nonlinear dynamics analyses were fractal dimension, recurrence quantification analysis, mutual information, and coherence. Each analysis selected provided a unique assessment to fulfill the objective of this review. While considering the limitations discussed, there is a promising path forward using nonlinear analyses with MS data.

Automated epilepsy detection techniques from electroencephalogram signals: a review study

Epilepsy is a serious neurological condition which contemplates as top 5 reasons for avoidable mortality from ages 5-29 in the worldwide. The avoidable deaths due to epilepsy can be reduced by developing efficient automated epilepsy detection or prediction machines or software. To develop an automated epilepsy detection framework, it is essential to properly understand the existing techniques and their benefit as well as detriment also. This paper aims to provide insight on the information about the existing epilepsy detection and classification techniques as they are crucial for supporting clinical-decision in the course of epilepsy treatment. This review study accentuate on the existing epilepsy detection approaches and their drawbacks. This information presented in this article will be helpful to the neuroscientist, researchers as well as to technicians for assisting them in selecting the reliable and appropriate techniques for analyzing epilepsy and developing an automated software system of epilepsy identification.

Electrocortical dynamics differentiate athletes exhibiting low- and high- ACL injury risk biomechanics

Anterior cruciate ligament (ACL) injuries are physically and emotionally debilitating for athletes,while motor and biomechanical deficits that contribute to ACL injury have been identified, limited knowledge about the relationship between the central nervous system (CNS) and biomechanical patterns of motion has impeded approaches to optimize ACL injury risk reduction strategies. In the current study it was hypothesized that high-risk athletes would exhibit altered temporal dynamics in their resting state electrocortical activity when compared to low-risk athletes. Thirty-eight female athletes performed a drop vertical jump (DVJ) to assess their biomechanical risk factors related to an ACL injury. The athletes' electrocortical activity was also recorded during resting state in the same visit as the DVJ assessment. Athletes were divided into low- and high-risk groups based on their performance of the DVJ. Recurrence quantification analysis was used to quantify the temporal dynamics of two frequency bands previously shown to relate to sensorimotor and attentional control. Results revealed that high-risk participants showed more deterministic electrocortical behavior than the low-risk group in the frontal theta and central/parietal alpha-2 frequency bands. The more deterministic resting state electrocortical dynamics for the high-risk group may reflect maladaptive neural behavior-excessively stable deterministic patterning that makes transitioning among functional task-specific networks more difficult-related to attentional control and sensorimotor processing neural regions.

Predictive value of electroencephalography connectivity measures for motor training outcome in multiple sclerosis: an observational longitudinal study

BACKGROUND

Neurophysiological investigations represent powerful tools to shed light on brain plasticity in multiple sclerosis (MS) patients.

AIM

We investigated the relationship between electroencephalography (EEG)-based connectivity, the extent of brain lesions and changes in motor performance after an intensive task-oriented circuit training (TOCT).

DESIGN

Observational longitudinal study.

SETTING

Outpatients training program.

POPULATION

Sixteen MS patients (10F; mean age =51.4 years; range: 27-67; mean disease duration =15.1 years; range: 2-26; mean Expanded Disability Status Scale 4.4; range: 3.5-5.5), were included in our study.

METHODS

MS patients with mild gait impairment were evaluated through functional scales and submitted to TOCT. Resting-state EEG was performed before (T0) and after (T1) rehabilitation. Alpha-band weighted Phase Lag Index (wPLI) and broadband weighted Symbolic Mutual Information (wSMI) connectivity analyses were performed. White matter lesion load was measured using MRI prior to the TOCT. Neurophysiological and structural parameters were then related to behavioral changes.

RESULTS

Dynamic Gait Index significantly improved after TOCT (F(1,14) =13.10, P=0.003). Moreover, the interaction between TOCT and age was observed for changes in Timed Up and Go (TUG) performance (F(1,14) = 7.75, P=0.015), indicating that older patients only benefited in this measure. Regarding the relationship between EEG connectivity and TOCT outcome, we observed positive correlations between changes in TUG and strength (P=0.017) and efficiency (Pone-tail =0.029) of alpha-band wPLI connectivity at T0. Such correlation was mainly driven by antero-posterior regional interactions (P=0.038), rather than by inter-hemispheric connectivity (P=0.089). Moreover, we observed a positive correlation between performance improvements and wSMI connectivity at T1 (P=0.001) as well as the difference between T0 and T1 (P=0.005). Lesion load percentage was not related to functional improvement after TOCT (Pone-tail=0.137).

CONCLUSIONS

Results of the current study demonstrated that baseline alpha-band wPLI connectivity predicts TOCT outcome in MS patients. Moreover, broadband wSMI tracks neural changes that accompany treatment-related variations in motor performance.

CLINICAL REHABILITATION IMPACT

Our findings suggest that EEG-based connectivity measures may represent a potential tool for customizing rehabilitative management of the disease.

An original method for staging sleep based on dynamical analysis of a single EEG signal

BACKGROUND

The dynamical complexity of brain electrical activity manifested in the EEG is quantifiable using recurrence analysis (RA). Employing RA, we described and validated an originative method for automatically classifying epochs of sleep that is conceptually and instrumentally distinct from the existing method.

NEW METHOD

Complexity in single overnight EEGs was characterized second-by-second using four RA variables that were each averaged over consecutive 30-sec epochs to form four-component vectors. The vectors were staged using four-component cluster analysis. Method validity and utility were established by showing: (1) inter- and intra-subject consistency of staging results (method insusceptible to nonstationarity of the EEG); (2) use of method to eliminate costly and arduous visual staging in a binary classifications task for detecting a neurogenic disorder; (3) ability of method to provide new physiological insights into brain activity during sleep.

RESULTS

RA of sleep-acquired EEGs yielded four continuous measures of complexity and its change-rate that allowed automatic classification of epochs into four statistically distinct clusters ("stages"). Matched subjects with and without mental distress were accurately classified using biomarkers based on stage designations.

COMPARISON WITH EXISTING METHODS

For binary-classification purposes, the method was cheaper, faster, and at least as accurate as the existing staging method. Epoch-by-epoch comparison of new versus existing methods revealed that the latter assigned epochs having widely different dynamical complexities into the same stage (dynamical incoherence).

CONCLUSIONS

Sleep can be automatically staged using an originative method that is fundamentally different from the existing method.

A jugular vein compression collar prevents alterations of endogenous electrocortical dynamics following blast exposure during special weapons and tactical (SWAT) breacher training

Exposure to explosive blasts places one at risk for traumatic brain injury, especially for special weapons and tactics (SWAT) and military personnel, who may be repeatedly exposed to blasts. In the current study, the effectiveness of a jugular vein compression collar to prevent alterations in resting-state electrocortical activity following a single-SWAT breacher training session was investigated. SWAT team personnel were randomly assigned to wear a compression collar during breacher training and resting state electroencephalography (EEG) was measured within 2 days prior to and two after breacher training. It was hypothesized that significant changes in brain dynamics-indicative of possible underlying neurodegenerative processes-would follow blast exposure for those who did not wear the collar, with ameliorated changes for the collar-wearing group. Using recurrence quantification analysis (RQA) it was found that participants who did not wear the collar displayed longer periods of laminar electrocortical behavior (as indexed by RQA's vertical max line measure) after breacher training. It is proposed that the blast wave exposure for the no-collar group may have reduced the number of pathways, via axonal disruption-for electrical transmission-resulting in the EEG signals becoming trapped in laminar states for longer periods of time. Longer laminar states have been associated with other electrocortical pathologies, such as seizure, and may be important for understanding head trauma and recovery.

Multiplex recurrence networks

We have introduced a multiplex recurrence network approach by combining recurrence networks with the multiplex network approach in order to investigate multivariate time series. The potential use of this approach is demonstrated on coupled map lattices and a typical example from palaeobotany research. In both examples, topological changes in the multiplex recurrence networks allow for the detection of regime changes in their dynamics. The method goes beyond classical interpretation of pollen records by considering the vegetation as a whole and using the intrinsic similarity in the dynamics of the different regional vegetation elements. We find that the different vegetation types behave more similarly when one environmental factor acts as the dominant driving force.

Recurrence analysis of the EEG during sleep accurately identifies subjects with mental health symptoms

Analysis of brain recurrence (ABR) is a novel computational method that uses two variables for sleep depth and two for sleep fragmentation to quantify temporal changes in non-random brain electrical activity. We postulated that ABR of the sleep-staged EEG could identify an EEG signature specific for the presence of mental health symptoms. Using the Mental Health Inventory Questionnaire (MHI-5) as ground truth, psychological distress was assessed in a study cohort obtained from the Sleep Heart Health Study. Subjects with MHI-5 <50 (N=34) were matched for sex, BMI, age, and race with 34 subjects who had MHI-5 scores >50. Sixteen ABR markers derived from the EEG were analyzed using linear discriminant analysis to identify marker combinations that reliably classified individual subjects. A biomarker function computed from 12 of the markers accurately classified the subjects based on their MHI-5 scores (AUROC=82%). Use of additional markers did not improve classification accuracy. Subgroup analysis (20 highest and 20 lowest MHI-5 scores) improved classification accuracy (AUROC=89%). Biomarker values for individual subjects were significantly correlated with MHI-5 score (r=0.36, 0.54 for N=68, 40, respectively). ABR of EEGs obtained during sleep successfully classified subjects with regard to the severity of mental health symptoms, indicating that mood systems were reflected in brain electrical activity.

Two-group classification of patients with obstructive sleep apnea based on analysis of brain recurrence

OBJECTIVE

To demonstrate that the severity of obstructive sleep apnea (OSA) could be predicted algorithmically by means of recurrence analysis of the sleep-staged electroencephalogram (EEG).

METHODS

A randomly selected cohort of 20 sleep-staged patients with OSA (apnea-hypopnea index (AHI) 5-30) was divided into mild and moderate sub-cohorts (AHI 5-15, 16-30, respectively), and the sleep EEG (C3) was analyzed using analysis of brain recurrence (ABR) (LSU cohort). Twenty distinct but related markers for sleep depth and fragmentation were computed from four ABR variables, and a marker function capable of classifying each patient into one of the two sub-cohorts was determined by linear discriminant analysis. Classification accuracy of individual patients was evaluated using area under the receiver operator characteristics curve (AUROC). As a control procedure, 20 additional sleep-staged patients with OSA whose polysomnographic data was obtained from an independent database were also evaluated (SHHS cohort).

RESULTS

On average, markers for sleep depth were reduced and those for sleep fragmentation were increased in the patients with moderate OSA, as expected. All patients in both cohorts were correctly classified using as few as 5-6 markers.

SIGNIFICANCE

The degree of severity of OSA was reflected in objective changes in the sleep EEG. Recurrence analysis of the EEG potentially has uses beyond identification of the degree of OSA.

Autism and EMF? Plausibility of a pathophysiological link part II

Autism spectrum conditions (ASCs) are defined behaviorally, but they also involve multileveled disturbances of underlying biology that find striking parallels in the physiological impacts of electromagnetic frequency and radiofrequency radiation exposures (EMF/RFR). Part I (Vol 776) of this paper reviewed the critical contributions pathophysiology may make to the etiology, pathogenesis and ongoing generation of behaviors currently defined as being core features of ASCs. We reviewed pathophysiological damage to core cellular processes that are associated both with ASCs and with biological effects of EMF/RFR exposures that contribute to chronically disrupted homeostasis. Many studies of people with ASCs have identified oxidative stress and evidence of free radical damage, cellular stress proteins, and deficiencies of antioxidants such as glutathione. Elevated intracellular calcium in ASCs may be due to genetics or may be downstream of inflammation or environmental exposures. Cell membrane lipids may be peroxidized, mitochondria may be dysfunctional, and various kinds of immune system disturbances are common. Brain oxidative stress and inflammation as well as measures consistent with blood-brain barrier and brain perfusion compromise have been documented. Part II of this paper documents how behaviors in ASCs may emerge from alterations of electrophysiological oscillatory synchronization, how EMF/RFR could contribute to these by de-tuning the organism, and policy implications of these vulnerabilities. It details evidence for mitochondrial dysfunction, immune system dysregulation, neuroinflammation and brain blood flow alterations, altered electrophysiology, disruption of electromagnetic signaling, synchrony, and sensory processing, de-tuning of the brain and organism, with autistic behaviors as emergent properties emanating from this pathophysiology. Changes in brain and autonomic nervous system electrophysiological function and sensory processing predominate, seizures are common, and sleep disruption is close to universal. All of these phenomena also occur with EMF/RFR exposure that can add to system overload ('allostatic load') in ASCs by increasing risk, and can worsen challenging biological problems and symptoms; conversely, reducing exposure might ameliorate symptoms of ASCs by reducing obstruction of physiological repair. Various vital but vulnerable mechanisms such as calcium channels may be disrupted by environmental agents, various genes associated with autism or the interaction of both. With dramatic increases in reported ASCs that are coincident in time with the deployment of wireless technologies, we need aggressive investigation of potential ASC-EMF/RFR links. The evidence is sufficient to warrant new public exposure standards benchmarked to low-intensity (non-thermal) exposure levels now known to be biologically disruptive, and strong, interim precautionary practices are advocated.