Frontal brain activity and cognitive processing speed in multiple sclerosis: An exploration of EEG neurofeedback training

Test-Retest Reliability of EEG Aperiodic Components in Resting and Mental Task States

Aperiodic activity is derived from the electroencephalography (EEG) power spectrum and reflects changes in the slope and shifts of the broadband spectrum. Studies have shown inconsistent test-retest reliability of the aperiodic components. This study systematically measured how the test-retest reliability of the aperiodic components was affected by data duration (1, 2, 3, 4, and 5 min), states (resting with eyes closed, resting with eyes open, performing mental arithmetic, recalling the events of the day, and mentally singing songs), and methods (the Fitting Oscillations and One-Over-F (FOOOF) and Linear Mixed-Effects Regression (LMER)) at both short (90-min) and long (one-month) intervals. The results showed that aperiodic components had fair, good, or excellent test-retest reliability (ranging from 0.53 to 0.91) at both short and long intervals. It is recommended that better reliability of the aperiodic components be obtained using data durations longer than 3 min, the resting state with eyes closed, the mental arithmetic task state, and the LMER method.

Down-regulation of theta amplitude through neurofeedback improves executive control network efficiency in healthy children

Despite extensive clinical research on neurofeedback (NF) in attention-deficit/hyperactivity disorder (ADHD), few studies targeted the optimization of attention performance in healthy children. As a crucial component of attention networks, the executive control network, involved in resolving response conflicts and allocating cognitive resources, is closely linked to theta activity. Here, we aimed to answer whether theta down-regulating NF can enhance healthy children's attention performance, especially the executive control network. Sixty children aged 6-12 years were randomly assigned to the NF and waitlist control groups. The NF group received theta down-regulation NF training for five days (a total of 100 mins), and the attention performance of both groups was measured by the attention network test (ANT) in the pre, post-NF, and 7-day follow-up. The electroencephalographic (EEG) results demonstrated a significant decrease in resting-state theta amplitude within sessions. For the behavioral results, the NF group exhibited significant improvements in overall attention performance and the efficiency of the executive control network relative to the control group in the post-NF and follow-up assessment, whereas the alerting and orienting networks remained unchanged. These findings proved the feasibility of theta down-regulating NF and its positive effect on attention in the healthy children population. In particular, the facilitation of the efficiency of the executive control network and the unaltered performance of the other two attention networks in the NF group may support the causality between theta rhythm and the executive control network.

Effects of transcranial alternating current stimulation on cognitive function in people with multiple sclerosis: A randomized controlled trial

BACKGROUND

Cognitive impairment is a core symptom that profoundly impacts the lives of people with multiple sclerosis (PwMS). Since the existing disease modifying therapies can only stabilize, but not actively treat, cognition in PwMS, there is an unmet need to expand approaches to treat these cognitive symptoms. Transcranial alternating current stimulation (tACS) permits frequency-specific entrainment of neural oscillations intrinsic to cognitive activity. However, the effects of the tACS on cognitive function in PwMS have not yet been assessed. We aimed to evaluate the potential efficacy of applying frontal theta-tACS to improve information processing speed in PwMS.

METHODS

60 PwMS with cognitive complaints were enrolled in a double-blinded, randomized, controlled trial with three stimulation groups: 2 mA, 1 mA, or sham control. A single session of theta-tACS was applied while participants were engaged in a cognitive program which has shown to improve processing speed in PwMS. tACS effects were examined by the Symbol Digit Modalities Test (SDMT). Tolerability, side effects and acceptability were measured.

RESULTS

1 mA groups had a significantly higher SDMT score after stimulation compared to their pre-stimulation score, 2 mA group showed a marginally significant improvement of their SDMT score, while the SDMT score in the sham group did not change. Overall, 49% of the stimulation group participants showed a clinically meaningful SDMT improvement (4+-point increase).

CONCLUSION

tACS is a well-tolerated, non-pharmacological intervention. Based on the positive effects observed in the current study of a single session of tACS applied during cognitive engagement, the effects of repeated tACS on cognitive function in PwMS merit further research.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04466228.

Feasibility of Using Source-Level Brain Computer Interface for People with Multiple Sclerosis

This work evaluates the feasibility of using a source level Brain-Computer Interface (BCI) for people with Multiple Sclerosis (MS). Data used was previously collected EEG of eight participants (one participant with MS and seven neurotypical participants) who performed imagined movement of the right and left hand. Equivalent current dipole cluster fitting was used to assess related brain activity at the source level and assessed using dipole location and power spectrum analysis. Dipole clusters were resolved within the motor cortices with some notable spatial difference between the MS and control participants. Neural sources that generate motor imagery originated from similar motor areas in the participant with MS compared to the neurotypical participants. Power spectral analysis indicated a reduced level of alpha power in the participant with MS during imagery tasks compared to neurotypical participants. Power in the beta band may be used to distinguish between left and right imagined movement for users with MS in BCI applications.Clinical Relevance- This paper demonstrates the cortical areas activated during imagined BCI-type tasks in a participant with Multiple Sclerosis (MS), and is a proof of concept for translating BCI research to potential users with MS.

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.

Stability, change, and reliable individual differences in electroencephalography measures: a lifespan perspective on progress and opportunities

Electroencephalographic (EEG) methods have great potential to serve both basic and clinical science approaches to understand individual differences in human neural function. Importantly, the psychometric properties of EEG data, such as internal consistency and test-retest reliability, constrain their ability to differentiate individuals successfully. Rapid and recent technological and computational advancements in EEG research make it timely to revisit the topic of psychometric reliability in the context of individual difference analyses. Moreover, pediatric and clinical samples provide some of the most salient and urgent opportunities to apply individual difference approaches, but the changes these populations experience over time also provide unique challenges from a psychometric perspective. Here we take a developmental neuroscience perspective to consider progress and new opportunities for parsing the reliability and stability of individual differences in EEG measurements across the lifespan. We first conceptually map the different profiles of measurement reliability expected for different types of individual difference analyses over the lifespan. Next, we summarize and evaluate the state of the field's empirical knowledge and need for testing measurement reliability, both internal consistency and test-retest reliability, across EEG measures of power, event-related potentials, nonlinearity, and functional connectivity across ages. Finally, we highlight how standardized pre-processing software for EEG denoising and empirical metrics of individual data quality may be used to further improve EEG-based individual differences research moving forward. We also include recommendations and resources throughout that individual researchers can implement to improve the utility and reproducibility of individual differences analyses with EEG across the lifespan.

Cognitive impairment in multiple sclerosis: Utility of electroencephalography

OBJECTIVE

to evaluate associations between neurocognitive impairment and electroencephalography (EEG) data in Multiple Sclerosis (MS).

METHODS

patients aged between 18 and 65 years, diagnosed with MS accordingly to the McDonald 2017 criteria and who were in remission for at least one month were included. Cognitive functions were evaluated by validated neuropsychological tests for Tunisian population. Electroencephalography data of each patient were analysed, Grand Total EEG (GTE) score was calculated and we evaluated their statistical links with cognitive impairment.

RESULTS

Thirty five patients were included. Slower background activity was associated with presence of: reduced information processing speed (IPS) (p = 0,03), verbal memory impairment (p = 0,04) and executive dysfunction (p = 0,016). The score 3 of GTE (reactivity of background activity) was associated with reduced IPS (p = 0,007) and executive dysfunction (p = 0,014). We found a positive correlation between background activity and Tunisian Verbal Test (TVLT) (ρ =0,46 ; p = 0,005) and Symbol Digit Modalities Test (SDMT) (ρ =0,35 ; p = 0,03). Sensitivity of GTE score was 68,4% for executive dysfunction (cut-off=2,5) and 66,7% for reduced IPS (cut-off=2,5).

CONCLUSIONS

Our results have shown utility of EEG in detecting cortical involvement and its correlation with cognitive impairment in MS patients.

SIGNIFICANCE

EEG could be a tool for monitoring cortical involvement during MS and predict cognitive impairment.

EEG-Neurofeedback as a Potential Therapeutic Approach for Cognitive Deficits in Patients with Dementia, Multiple Sclerosis, Stroke and Traumatic Brain Injury

Memory deficits are common in patients with dementia, such as Alzheimer's disease, but also in patients with other neurological and psychiatric disorders, such as brain injury, multiple sclerosis, ischemic stroke and schizophrenia. Memory loss affects patients' functionality and, by extension, their quality of life. Non-invasive brain training methods, such as EEG neurofeedback, are used to address cognitive deficits and behavioral changes in dementia and other neurological disorders by training patients to alter their brain activity via operant activity. In this review paper, we analyze various protocols of EEG neurofeedback in memory rehabilitation in patients with dementia, multiple sclerosis, strokes and traumatic brain injury. The results from the studies show the effectiveness of the ΕΕG-NFB method in improving at least one cognitive domain, regardless of the number of sessions or the type of protocol applied. In future research, it is important to address methodological weaknesses in the application of the method, its long-term effects as well as ethical issues.

An Efficient Approach for Driver Drowsiness Detection at Moderate Drowsiness Level Based on Electroencephalography Signal and Vehicle Dynamics Data

Background

Drowsy driving is one of the leading causes of severe accidents worldwide. In this study, an analyzing method based on drowsiness level proposed to detect drowsiness through electroencephalography (EEG) measurements and vehicle dynamics data.

Methods

A driving simulator was used to collect brain data in the alert and drowsy states. The tests were conducted on 19 healthy men. Brain signals from the parietal, occipital, and central parts were recorded. Observer Ratings of Drowsiness (ORD) were used for the drowsiness stages assessment. This study used an innovative method, analyzing drowsiness EEG data were in respect to ORD instead of time. Thirteen features of EEG signal were extracted, then through Neighborhood Component Analysis, a feature selection method, 5 features including mean, standard deviation, kurtosis, energy, and entropy are selected. Six classification methods including K-nearest neighbors (KNN), Regression Tree, Classification Tree, Naive Bayes, Support vector machines Regression, and Ensemble Regression are employed. Besides, the lateral position and steering angle as a vehicle dynamic data were used to detect drowsiness, and the results were compared with classification result based on EEG data.

Results

According to the results of classifying EEG data, classification tree and ensemble regression classifiers detected over 87.55% and 87.48% of drowsiness at the moderate level, respectively. Furthermore, the classification results demonstrate that if only the single-channel P4 is used, higher performance can achieve than using data of all the channels (C3, C4, P3, P4, O1, O2). Classification tree classifier and regression classifiers showed 91.31% and 91.12% performance with data from single-channel P4. The best classification results based on vehicle dynamic data were 75.11 through KNN classifier.

Conclusion

According to this study, driver drowsiness could be detected at the moderate drowsiness level based on features extracted from a single-channel P4 data.

Cognitive performance and electroencephalographic variations in air traffic controllers under various mental workload and time of day

The aim of this study was to investigate the effects of mental workload (MWL) and time of day on cognitive performance and electroencephalographic (EEG) parameters of air traffic controllers. EEG signals recorded while 20 professional air traffic controllers performed cognitive tasks [A-X Continuous Performance Test (AX-CPT) and 3-back working memory task] after they were exposed to two levels of task difficulty (high and low MWL) in the morning and afternoon. Significant decreases in cognitive performance were found when the levels of task difficulty increased in both tasks. The results confirmed the sensitivity of the theta and beta activities to levels of task difficulty in the 3-back task, while they were not affected in the AX-CPT. Theta and beta activities were influenced by time of day in the AX-CPT. The findings provide guidance for application of changes in EEG parameters when MWL level is manipulated during the day that could be implemented in future for the development of real-time monitoring systems to improve aviation safety.

Correlation Between Resting Theta Power and Cognitive Performance in Patients With Schizophrenia

Objective

Schizophrenia is a mental disorder that is characterized by progressive cognitive impairment. Objective measures of cognitive function may provide reliable neurobiomarkers for patients with schizophrenia. The goal of the current work is to explore the correlation between resting theta power and cognitive performance in patients with schizophrenia.

Methods

Twenty-two patients with schizophrenia and 23 age-, sex-, and education-matched healthy controls were included in this study. The MATRICS Consensus Cognitive Battery (MCCB) was used for cognitive evaluation and the Positive and Negative Syndrome Scale (PANSS) for evaluation of clinical symptoms. EEGs were acquired in the resting state with closed and opened eyes. Between the two groups, we compared the relative theta power and examined their relationship with cognitive performance.

Results

Compared to healthy controls, patients with schizophrenia showed significantly higher theta power, both with eyes closed and open (P < 0.05). When the eyes were open, negative correlations were found in patients with schizophrenia between theta power in the central and parietal regions with processing speed scores, and between the theta power of the Pz electrode and verbal learning and reasoning and problem-solving scores (r ≥ −0.446). In the control group, theta power over the Fz electrode was negatively correlated with processing speed (r = −0.435).

Conclusions

Our findings showed that theta activity increased in certain brain regions during resting state in schizophrenia. Negative associations between resting theta power (increased) over the parietal-occipital regions with MCCB domains scores (decreased) suggest that altered theta activity can be used as a neurobiological indicator to predict cognitive performance.

Feasibility of using discrete Brain Computer Interface for people with Multiple Sclerosis

AIM

Brain-Computer Interfaces (BCIs) hold promise to provide people with partial or complete paralysis, the ability to control assistive technology. This study reports offline classification of imagined and executed movements of the upper and lower limb in one participant with multiple sclerosis and people with no limb function deficits.

METHODS

We collected neural signals using electroencephalography (EEG) while participants performed executed and imagined motor tasks as directed by prompts shown on a screen.

RESULTS

Participants with no limb function attained >70% decoding accuracy on their best-imagined task compared to rest and on at-least one task comparison. The participant with multiple sclerosis also achieved accuracies within the range of participants with no limb function loss.Clinical Relevance - While only one case study is provided it was promising that the participant with MS was able to achieve comparable classification to that of the seven healthy controls. Further studies are needed to assess whether people suffering from MS may be able to use a BCI to improve their quality of life.

Mindfulness training during brief periods of hospitalization in multiple sclerosis (MS): beneficial alterations in fatigue and the mediating role of depression

OBJECTIVES

Persons with MS (PwMS) are frequently affected by fatigue and depression. Mindfulness-based interventions may reduce these symptoms in PwMS and consequently their application has been extended to various settings. Only few efforts have been made to explore effects of short-term mindfulness training during brief periods of hospitalization. In the current study, the feasibility and potential effects of short-term mindfulness training on depression, fatigue, rumination and cognition were explored in PwMS in an acute-care hospital setting. Based on previous work, it was further examined whether the relation between trait mindfulness and fatigue prior to and following the intervention was mediated by depression and whether a mediation effect was also observable throughout the intervention.

METHODS

A short-term mindfulness training protocol was developed, tailored to the requirements of the acute-care setting. Subsequently, 30 PwMS were recruited sequentially and received mindfulness training during the routine clinical process (median duration in hospital: eight days, number of sessions: four). Participants completed relevant self-report measures (depression, fatigue, rumination) and a neuropsychological assessment before and after training.

RESULTS

Participants reported significantly increased trait mindfulness and decreased depression and fatigue following the intervention. Respective change scores were highly correlated so that increased trait mindfulness was associated with decreased symptoms. In the rumination domain, patients reported a tendency for an increased adaptive ability to engage in distractive behavior during arising negative mood. Other measures of trait rumination and cognition remained relatively stable. Results of the mediation analyses indicated that depression mediated the negative relationship between trait mindfulness and fatigue symptoms at pre and post assessments. With regards to the change scores, an association between mindfulness and cognitive fatigue ceased to be significant when depression was controlled, albeit in this case, the mediation effect did not reach significance.

CONCLUSION

Results of the current study indicate that short-term mindfulness training during brief periods of hospitalization may be beneficial for PwMS. They further complement previous work by identifying depression as a potential mediator of the antagonistic relationship between mindfulness and fatigue. Based on the current exploratory study, future trials are warranted to address this mechanism of mindfulness training in more detail.

MRI correlates of cognitive improvement after home-based EEG neurofeedback training in patients with multiple sclerosis: a pilot study

OBJECTIVE

Neurofeedback training may improve cognitive function in patients with neurological disorders. However, the underlying cerebral mechanisms of such improvements are poorly understood. Therefore, we aimed to investigate MRI correlates of cognitive improvement after EEG-based neurofeedback training in patients with MS (pwMS).

METHODS

Fourteen pwMS underwent ten neurofeedback training sessions within 3-4 weeks at home using a tele-rehabilitation system. Half of the pwMS (N = 7, responders) learned to self-regulate sensorimotor rhythm (SMR, 12-15 Hz) by visual feedback and improved cognitively after training, whereas the remainder (non-responders, n = 7) did not. Diffusion-tensor imaging and resting-state fMRI of the brain was performed before and after training. We analyzed fractional anisotropy (FA) and functional connectivity (FC) of the default-mode, sensorimotor (SMN) and salience network (SAL).

RESULTS

At baseline, responders and non-responders were comparable regarding sex, age, education, disease duration, physical and cognitive impairment, and MRI parameters. After training, compared to non-responders, responders showed increased FA and FC within the SAL and SMN. Cognitive improvement correlated with increased FC in SAL and a correlation trend with increased FA was observed.

CONCLUSIONS

This exploratory study suggests that successful neurofeedback training may not only lead to cognitive improvement, but also to increases in brain microstructure and functional connectivity.

Increased brain atrophy and lesion load is associated with stronger lower alpha MEG power in multiple sclerosis patients

In multiple sclerosis, the interplay of neurodegeneration, demyelination and inflammation leads to changes in neurophysiological functioning. This study aims to characterize the relation between reduced brain volumes and spectral power in multiple sclerosis patients and matched healthy subjects. During resting-state eyes closed, we collected magnetoencephalographic data in 67 multiple sclerosis patients and 47 healthy subjects, matched for age and gender. Additionally, we quantified different brain volumes through magnetic resonance imaging (MRI). First, a principal component analysis of MRI-derived brain volumes demonstrates that atrophy can be largely described by two components: one overall degenerative component that correlates strongly with different cognitive tests, and one component that mainly captures degeneration of the cortical grey matter that strongly correlates with age. A multimodal correlation analysis indicates that increased brain atrophy and lesion load is accompanied by increased spectral power in the lower alpha (8-10 Hz) in the temporoparietal junction (TPJ). Increased lower alpha power in the TPJ was further associated with worse results on verbal and spatial working memory tests, whereas an increased lower/upper alpha power ratio was associated with slower information processing speed. In conclusion, multiple sclerosis patients with increased brain atrophy, lesion and thalamic volumes demonstrated increased lower alpha power in the TPJ and reduced cognitive abilities.

Trait mindfulness is primarily associated with depression and not with fatigue in multiple sclerosis (MS): implications for mindfulness-based interventions

Objectives

Persons with MS (PwMS) often display symptoms of depression and fatigue. Mindfulness-based interventions are known to counteract these symptoms. However, to-date the exact relations between trait mindfulness, depression and fatigue remain to be examined. Fatigue is generally regarded as a symptom immanent to the disease and as a direct neurobiological consequence of increased cytokine levels and cortical atrophy. In depression on the other hand, psychosocial factors in the context of adaptation difficulties are probably of higher relevance. Hence, one may argue that mindfulness, as a trait that promotes successful adaption, may show a strong negative association with depression and a relatively minor negative association with fatigue in PwMS.

Methods

In the current study, the association between self-reported trait mindfulness, fatigue and depression was examined in a sample of 69 PwMS.

Results

Trait mindfulness showed highly significant negative correlations with both, depression and fatigue. Mediation analyses however, revealed that depression mediated the relation between mindfulness and fatigue.

Conclusion

It may be concluded that in PwMS, trait mindfulness shows a genuine negative association with depression, but that it is only secondarily associated with fatigue. Implications for mindfulness-based interventions in MS are discussed. Based on the results of the current study, it may be feasible to promote the acceptance of default fatigue symptoms, instead of an actual reduction of fatigue symptoms.

Altered phase and nonphase EEG activity expose impaired maintenance of a spatial-object attentional focus in multiple sclerosis patients

Some of the anatomical and functional basis of cognitive impairment in multiple sclerosis (MS) currently remains unknown. In particular, there is scarce knowledge about modulations in induced EEG (nonphase activity) for diverse frequency bands related to attentional deficits in this pathology. The present study analyzes phase and nonphase alpha and gamma modulations in 26 remitting-relapsing multiple sclerosis patients during their participation in the attention network test compared with twenty-six healthy controls (HCs) matched in sociodemographic variables. Behavioral results showed that the MS group exhibited general slowing, suggesting impairment in alerting and orienting networks, as has been previously described in other studies. Time–frequency analysis of EEG revealed that the gamma band was related to the spatial translation of the attentional focus, and the alpha band seemed to be related to the expectancy mechanisms and cognitive processing of the target. Moreover, phase and nonphase modulations differed in their psychophysiological roles and were affected differently in the MS and HC groups. In summary, nonphase modulations can unveil hidden cognitive mechanisms for phase analysis and complete our knowledge of the neural basis of cognitive impairment in multiple sclerosis pathology.

Frontal Beta Transcranial Alternating Current Stimulation Improves Reversal Learning

Electroencephalogram (EEG) studies suggest an association between beta (13-30 Hz) power and reversal learning performance. In search for direct evidence concerning the involvement of beta oscillations in reversal learning, transcranial alternating current stimulation (tACS) was applied in a double-blind, sham-controlled and between-subjects design. Exogenous oscillatory currents were administered bilaterally to the frontal cortex at 20 Hz with an intensity of 1 mA peak-to-peak and the effects on reward-punishment based reversal learning were evaluated in hundred-and-eight healthy volunteers. Pre- and post-tACS resting state EEG recordings were analyzed. Results showed that beta-tACS improved rule implementation during reversal learning and decreases left and right resting-state frontal theta/beta EEG ratios following tACS. Our findings provide the first behavioral and electrophysiological evidence for exogenous 20 Hz oscillatory electric field potentials administered over to the frontal cortex to improve reversal learning.