Exploring resting-state EEG brain oscillatory activity in relation to cognitive functioning in multiple sclerosis

Decoupling Alpha Desynchronization from Neural Resource Use: Evidence from Cognitive Load Modulation

In prior studies, desynchronization of the induced alpha band (non-phase-locked but time-locked) has been observed across various cognitive tasks. Proposed hypotheses for the cognitive role of this alpha decrement include neural activation, an inhibition/timing mechanism, or a reduction in "neural noise". This study aimed to examine the effect of cognitive load on induced alpha activity using two versions of a go/no-go visual task: a single-target (ST) version with one target and one distractor, and a double-target (DT) version with two targets and two distractors. EEG was recorded from 58 electrodes, and Temporal Spectral Evolution (TSE) was used for time-frequency analysis. Behavioral results revealed faster reaction times in the ST task compared to the DT task. The P3 component displayed delayed latency and reduced amplitude under increased cognitive load, consistent with prior findings. However, the latencies and amplitudes of evoked and induced alpha responses were unaffected by cognitive load. This suggests that increased alpha desynchronization in subjects with cognitive impairment should not be interpreted as enhanced neural resource recruitment due to task difficulty. Instead, it may reflect other mechanisms unrelated to cognitive load differences in task performance.

Feasibility of source-level motor imagery classification for people with multiple sclerosis

Objective.There is limited work investigating brain-computer interface (BCI) technology in people with multiple sclerosis (pwMS), a neurodegenerative disorder of the central nervous system. Present work is limited to recordings at the scalp, which may be significantly altered by changes within the cortex due to volume conduction. The recordings obtained from the sensors, therefore, combine disease-related alterations and task-relevant neural signals, as well as signals from other regions of the brain that are not relevant. The current study aims to unmix signals affected by multiple sclerosis (MS) progression and BCI task-relevant signals using estimated source activity to improve classification accuracy.Approach.Data was collected from eight participants with a range of MS severity and ten neurotypical participants. This dataset was used to report the classification accuracy of imagined movements of the hands and feet at the sensor-level and the source-level in the current study.K-means clustering of equivalent current dipoles was conducted to unmix temporally independent signals. The location of these dipoles was compared between MS and control groups and used for classification of imagined movement. Linear discriminant analysis classification was performed at each time-frequency point to highlight differences in frequency band delay.Main Results.Source-level signal acquisition significantly improved decoding accuracy of imagined movement vs rest and movement vs movement classification in pwMS and controls. There was no significant difference found in alpha (7-13 Hz) and beta (13-30 Hz) band classification delay between the neurotypical control and MS group, including imagery of limbs with weakness or paralysis.Significance.This study is the first to demonstrate the advantages of source-level analysis for BCI applications in pwMS. The results highlight the potential for enhanced clinical outcomes and emphasize the need for longitudinal studies to assess the impact of MS progression on BCI performance, which is crucial for effective clinical translation of BCI technology.

Cognitive-motor interference in multiple sclerosis revisited: a dual-task paradigm using wearable inertial sensors and the Paced Auditory Serial Addition Test

Introduction

Multiple sclerosis (MS) is a chronic autoimmune disease affecting the central nervous system, leading to motor and cognitive impairment. These impairments become especially evident during dual-tasks, such as walking while performing a cognitive activity. Previous research has highlighted changes in gait-specific parameters during dual-tasks, but the cognitive component remains underexamined in MS. This study aims to expand on prior findings by using wearable inertial sensors and the Paced Auditory Serial Addition Test (PASAT) to evaluate the effects of dual-tasks on gait and cognitive performance in persons with MS (PwMS) compared to healthy controls.

Methods

Eighty-six adults (54 PwMS and 32 healthy controls) participated. PwMS were further divided into groups with lower (MS_LCP) and higher (MS_HCP) cognitive performance based on performance on the Symbol-Digit-Modalities Test (SDMT). Gait parameters were assessed using wearable inertial sensors during single- and dual-task 3-min-walking. Statistical analyses compared gait and cognitive performance across conditions and groups.

Results

Under dual-task conditions, PwMS showed significant changes in all gait parameters, including reduced walking speed, stride length, percentage of swing phase and toe clearance, and increased stride time and percentage of stance phase compared to single-task condition. However, under dual-task condition in PwMS only walking speed, stride length and stride time differed from healthy controls. MS_LCP exhibited greater changes in both gait and PASAT performance than MS_HCP and healthy controls. While MS_HCP showed gait parameters comparable to healthy controls during single-tasks, deficits became apparent during dual-tasks. Correlations revealed strong associations between SDMT and PASAT scores but weak links between cognitive and self-reported measures.

Discussion

The findings confirm that dual-task conditions exacerbate gait impairments in PwMS, particularly in those with lower cognitive performance. The use of PASAT as a dual-task cognitive challenge was feasible and had a considerable influence on gait. Results support the capacity sharing theory, suggesting that limited cognitive resources are redistributed between tasks under dual-task conditions.

Harnessing Brainwave Entrainment: A Non-invasive Strategy To Alleviate Neurological Disorder Symptoms

From 1990-2019, the burden of neurological disorders varied considerably across countries and regions. Psychiatric disorders, often emerging in early to mid-adulthood, are linked to late-life neurodegenerative diseases like Alzheimer's disease and Parkinson's disease. Individuals with conditions such as Major Depressive Disorder, Anxiety Disorder, Schizophrenia, and Bipolar Disorder face up to four times higher risk of developing neurodegenerative disorders. Contrarily, 65 % of those with neurodegenerative conditions experience severe psychiatric symptoms during their illness. Further, the limitation of medical resources continues to make this burden a significant global and local challenge. Therefore, brainwave entrainment provides therapeutic avenues for improving the symptoms of diseases. Brainwaves are rhythmic oscillations produced either spontaneously or in response to stimuli. Key brainwave patterns include gamma, beta, alpha, theta, and delta waves, yet the underlying physiological mechanisms and the brain's ability to shift between these dynamic states remain areas for further exploration. In neurological disorders, brainwaves are often disrupted, a phenomenon termed "oscillopathy". However, distinguishing these impaired oscillations from the natural variability in brainwave activity across different regions and functional states poses significant challenges. Brainwave-mediated therapeutics represents a promising research field aimed at correcting dysfunctional oscillations. Herein, we discuss a range of non-invasive techniques such as non-invasive brain stimulation (NIBS), neurologic music therapy (NMT), gamma stimulation, and somatosensory interventions using light, sound, and visual stimuli. These approaches, with their minimal side effects and cost-effectiveness, offer potential therapeutic benefits. When integrated, they may not only help in delaying disease progression but also contribute to the development of innovative medical devices for neurological care.

Neurophysiological methods for assessing and treating cognitive impairment in multiple sclerosis: A scoping review of the literature

In recent years, there has been a growing interest in exploring the non-classical symptoms of multiple sclerosis (MS), with a particular focus on cognitive impairments associated with the disease's progression. These cognitive symptoms are now recognized as crucial elements in the assessment of disease activity. In this context, neurophysiology has emerged as a valuable and accessible tool for studying and addressing cognitive decline in individuals with MS. This scoping literature review investigates the role of neurophysiology in assessing and treating cognitive impairment in MS patients. The review focuses on Electroencephalography (EEG), Non-Invasive Brain Stimulation (NIBS), and magnetoencephalography (MEG) to assess cognitive decline in MS patients. Moreover, we discuss all the papers that tried to treat this cognitive impairment with NIBS techniques. While several neurophysiological markers show potential, standardization of protocols is essential for enhancing the reliability and consistency of these approaches. Further research is warranted to explore other NIBS techniques and deepen our understanding of the neurophysiological underpinnings of cognitive deficits in MS.

Verbal Support From a Stranger Reduces the Development of Mechanical Hypersensitivity: Behavioral and Neurophysiological Evidence

Hand-holding reduces experimentally induced acute pain and buffers against the development of mechanical secondary hypersensitivity, an indirect proxy of central sensitization. Here, we tested if verbal support from a stranger, a common occurrence in clinical contexts, exerts the same effects. In this preregistered study, 44 healthy female participants were assigned to an alone or support group whereby a supportive female stranger encouraged them through the painful procedure leading to secondary mechanical hypersensitivity. Mechanical hypersensitivity was measured via self-reports and by the size of the anteroposterior and mediolateral spread of mechanical hypersensitivity. We investigated the moderating role of attachment style on self-reports and the effects of support on skin conductance level, salivary cortisol, and pinprick-evoked potentials. We also tested whether theta/beta ratio in the resting-state electroencephalogram predicted mechanical hypersensitivity. Self-reported ratings and the late part of the pinprick-evoked potentials were reduced in the support group, but the spread of mechanical hypersensitivity was not. Attachment anxiety and avoidance moderated the self-reported intensity such that individuals with higher attachment anxiety and avoidance scores reported lower intensity ratings in the support group. No significant effect of the verbal support was observed on skin conductance level and salivary cortisol. The theta/beta ratio did not predict the extent of hypersensitivity. Our data indicate that, in women, verbal support during intense pain leading to hypersensitivity is effective on some behavioral outcomes, but altogether the lack of group differences in cortisol, self-reported stress, and skin conductance does not provide strong support for the stress-buffering hypothesis. PERSPECTIVE: Verbal support by a stranger during a painful procedure leading to secondary mechanical hypersensitivity attenuated the development of some measures of mechanical hypersensitivity and associated neural responses in healthy female participants. No evidence was found for the role of stress. DATA AVAILABILITY: The authors will make all data available upon request.

A simplified method for relapsing-remitting multiple sclerosis detection: Insights from resting EEG signals

BACKGROUND AND OBJECTIVE

Multiple sclerosis (MS) is a neurodegenerative autoimmune disease affecting the central nervous system, leading to various neurological symptoms. Early detection is paramount to prevent enduring damage during MS episodes. Although magnetic resonance imaging (MRI) is a common diagnostic tool, this study aims to explore the feasibility of using electroencephalography (EEG) signals for MS detection, considering their accessibility and ease of application compared to MRI.

METHODS

The study involved the analysis of EEG signals during rest from 17 MS patients and 27 healthy volunteers to investigate MS-healthy patterns. Power spectral density features (PSD) were extracted from the 32-channel EEG signals. The study employed Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Classification and Regression Trees (CART), and k-Nearest Neighbor (kNN) classifiers to identify channels with the highest accuracy. Notably, the study achieved 100% accuracy in MS detection using the "Fp1" and "Pz" channels with the LDA classifier. A statistical analysis, utilizing the independent sample t-test, was conducted to explore whether PSD features of these channels differed significantly between healthy individuals and those with MS.

RESULTS

The results of the study demonstrate that effective detection of MS can be achieved using PSD features from only two channels of the EEG signal. Specifically, the "Fp1" and "Pz" channels exhibited 100% accuracy in MS detection with the LDA classifier. The statistical analysis further explored and confirmed the significant differences in PSD features between healthy individuals and MS patients.

CONCLUSION

The study concludes that the proposed method, utilizing PSD features from specific EEG channels, offers a straightforward and efficient diagnostic approach for the effective detection of MS. The findings suggest the potential utility of EEG signals as a non-invasive and accessible alternative for MS detection, highlighting the importance of further research in this direction.

Examining attentional control deficits in adolescents with test anxiety: An evidential synthesis using self-report, behavioral, and resting-state EEG measures

Attentional control theory suggests that test anxiety hinders individuals' attentional control, aiding our understanding of how test anxiety may impair cognitive function. However, various methods used to assess attentional control have yielded inconsistent findings. Moreover, past studies, especially on adolescents, that examine the distinct impacts of worry and the emotional components of test anxiety on individuals' attentional control capacity are scarce. This study, using self-report, behavioral, and resting-state EEG measures, explores how worry and emotionality, impact attentional control in adolescents. It enhances our understanding of the link between test anxiety and cognitive function. Referring to the effect size from prior studies, a total of 42 adolescents took part in the study. We used the Test Anxiety Inventory, due to it can assess worry and emotionality components. We employed three widely-utilized measures of attentional control: the Attentional Control Scale (ACS), the Go/Nogo task, and resting-state electroencephalography measures (alpha oscillation and the theta/beta power ratio). Both worry and emotionality components were significantly and negatively correlated with the ACS scores. Unlike worry, emotionality demonstrated a significant positive correlation with response times for the Go trials and alpha power in the parietal cortex. These results suggest that, emotionality, but not worry, is highly correlated with attentional control deficits in adolescents. This study underscores the significance of distinguishing between the components of test anxiety, which aids in comprehending the negative impacts of test anxiety on adolescents' academic performance.

A Subject-Specific Attention Index Based on the Weighted Spectral Power

As an essential cognitive function, attention has been widely studied and various indices based on EEG have been proposed for its convenience and easy availability for real-time attention monitoring. Although existing indices based on spectral power of empirical frequency bands are able to describe the attentional state in some way, the reliability still needs to be improved. This paper proposed a subject-specific attention index based on the weighted spectral power. Unlike traditional indices, the ranges of frequency bands are not empirical but obtained from subject-specific change patterns of spectral power of electroencephalograph (EEG) to overcome the great inter-subject variance. In addition, the contribution of each frequency component in the frequency band is considered different. Specifically, the ratio of power spectral density (PSD) function in attentional and inattentional state is utilized to calculate the weight to enhance the effectiveness of the proposed index. The proposed subject-specific attention index based on the weighted spectral power is evaluated on two open datasets including EEG data of a total of 44 subjects. The results of the proposed index are compared with 3 traditional attention indices using various statistical analysis methods including significance tests and distribution variance measurements. According to the experimental results, the proposed index can describe the attentional state more accurately. The proposed index respectively achieves accuracies of 86.21% and 70.00% at the 1% significance level in both the t-test and Wilcoxon rank-sum test for two datasets, which obtains improvements of 41.38% and 20.00% compared to the best result of the traditional indices. These results indicate that the proposed index provides an efficient way to measure attentional state.

Neurophysiological brain function predicts response to cognitive rehabilitation and mindfulness in multiple sclerosis: a randomized trial

BACKGROUND

Cognitive treatment response varies highly in people with multiple sclerosis (PwMS). Identification of mechanisms is essential for predicting response.

OBJECTIVES

This study aimed to investigate whether brain network function predicts response to cognitive rehabilitation therapy (CRT) and mindfulness-based cognitive therapy (MBCT).

METHODS

PwMS with cognitive complaints completed CRT, MBCT, or enhanced treatment as usual (ETAU) and performed three measurements (baseline, post-treatment, 6-month follow-up). Baseline magnetoencephalography (MEG) measures were used to predict treatment effects on cognitive complaints, personalized cognitive goals, and information processing speed (IPS) using mixed models (secondary analysis REMIND-MS study).

RESULTS

We included 105 PwMS (96 included in prediction analyses; 32 CRT, 31 MBCT, 33 ETAU), and 56 healthy controls with baseline MEG. MEG did not predict reductions in complaints. Higher connectivity predicted better goal achievement after MBCT (p = 0.010) and CRT (p = 0.018). Lower gamma power (p = 0.006) and higher connectivity (p = 0.020) predicted larger IPS benefits after MBCT. These MEG predictors indicated worse brain function compared to healthy controls (p < 0.05).

CONCLUSIONS

Brain network function predicted better cognitive goal achievement after MBCT and CRT, and IPS improvements after MBCT. PwMS with neuronal slowing and hyperconnectivity were most prone to show treatment response, making network function a promising tool for personalized treatment recommendations.

TRIAL REGISTRATION

The REMIND-MS study was prospectively registered in the Dutch Trial registry (NL6285; https://trialsearch.who.int/Trial2.aspx?TrialID=NTR6459 ).

Relationship between theta/beta ratio and mind wandering in schizotypy

Negative association was found between the frontal theta/beta ratio and mind wandering in participants with high schizotypal traits, while no such association was found in participants with low schizotypal traits. These findings provide insights into the neural mechanism of mind wandering in individuals with high schizotypal traits.

Exploring resting-state EEG oscillations in patients with Neuromyelitis Optica Spectrum Disorder

BACKGROUND AND OBJECTIVE

Quantitative resting-state electroencephalography (rs-EEG) is a convenient method for characterizing the functional impairments and adaptations of the brain that has been shown to be valuable for assessing many neurological and psychiatric disorders, especially in monitoring disease status and assisting neuromodulation treatment. However, it has not yet been explored in patients with neuromyelitis optica spectrum disorder (NMOSD). This study aimed to investigate the rs-EEG features of NMOSD patients and explore the rs-EEG features related to disease characteristics and complications (such as anxiety, depression, and fatigue).

METHODS

A total of 32 NMOSD patients and 20 healthy controls (HCs) were recruited; their demographic and disease information were collected, and their anxiety, depression, and fatigue symptoms were evaluated. The rs-EEG power spectra of all the participants were obtained. After excluding the participants with low-quality rs-EEG data during processing, statistical analysis was conducted based on the clinical information and rs-EEG data of 29 patients and 19 HCs. The rs-EEG power (the mean spectral energy (MSE) of absolute power and relative power in all frequency bands, as well as the specific power for all electrode sites) of NMOSD patients and HCs was compared. Furthermore, correlation analyses were performed between rs-EEG power and other variables for NMOSD patients (including the disease characteristics and complications).

RESULTS

The distribution of the rs-EEG power spectra in NMOSD patients was similar to that in HCs. The dominant alpha-peaks shifted significantly towards a lower frequency for patients when compared to HCs. The delta and theta power was significantly increased in the NMOSD group compared to that in the HC group. The alpha oscillation power was found to be significantly negatively associated with the degree of anxiety (reflected by the anxiety subscore of hospital anxiety and depression scale (HADS)) and the degree of depression (reflected by the depression subscore of HADS). The gamma oscillation power was revealed to be significantly positively correlated with the fatigue severity scale (FSS) score, while further analysis indicated that the electrode sites of almost the whole brain region showing correlations with fatigue. Regarding the disease variables, no statistically significant rs-EEG features were related to the main disease features in NMOSD patients.

CONCLUSION

The results of this study suggest that the rs-EEG power spectra of NMOSD patients show increased slow oscillations and are potential biomarkers of widespread white matter microstructural damage in NMOSD. Moreover, this study revealed the rs-EEG features associated with anxiety, depression, and fatigue in NMOSD patients, which might help in the evaluation of these complications and the development of neuromodulation treatment. Quantitative rs-EEG analysis may play an important role in the management of NMOSD patients, and future studies are warranted to more comprehensively understand its application value.

The role of leadership level in college students' facial emotion recognition: evidence from event-related potential analysis

While the role of emotion in leadership practice is well-acknowledged, there is still a lack of clarity regarding the behavioral distinctions between individuals with varying levels of leadership and the underlying neurocognitive mechanisms at play. This study utilizes facial emotion recognition in conjunction with electroencephalograms to explore the temporal dynamics of facial emotion recognition processes among college students with high and low levels of leadership. The results showed no significant differences in the amplitude of P1 during the early stage of facial emotion recognition between the two groups. In the middle stage of facial emotion recognition, the main effect of group was significant on the N170 component, with higher N170 amplitude evoked in high-leadership students than low-leadership students. In the late stage of facial emotion recognition, low-leadership students evoked greater LPP amplitude in the temporal-parietal lobe when recognizing happy facial emotions compared to high-leadership students. In addition, time-frequency results revealed a difference in the alpha frequency band, with high-leadership students exhibiting lower alpha power than low-leadership students. The results suggest differences in the brain temporal courses of facial emotion recognition between students with different leadership levels, which are mainly manifested in the middle stage of structural encoding and the late stage of delicate emotional processing during facial emotion recognition.

Attentional State Classification Using Amplitude and Phase Feature Extraction Method Based on Filter Bank and Riemannian Manifold

As a significant aspect of cognition, attention has been extensively studied and numerous measurements have been developed based on brain signal processing. Although existing attentional state classification methods have achieved good accuracy by extracting a variety of handcrafted features, spatial features have not been fully explored. This paper proposes an attentional state classification method based on Riemannian manifold to utilize spatial information. Based on the concept of Riemannian manifold of symmetric positive definite (SPD) matrix, the proposed method exploits the structure of covariance matrix to extract spatial features instead of using spatial filters. Specifically, Riemannian distances from intra-class Riemannian means are extracted as features for their robustness. To fully extend the potential of electroencephalograph (EEG) signal, both amplitude and phase information is utilized. In addition, to solve the variance of frequency bands, a filter bank is employed to process the signal of different frequency bands separately. Finally, features are fed into a support vector machine with a polynomial kernel to obtain classification results. The proposed attentional state classification using amplitude and phase feature extraction method based on filter bank and Riemannian manifold (AP-FBRM) method is evaluated on two open datasets including EEG data of 29 and 26 subjects. According to the experimental results, the optimal set of filter bank and the optimal technique to extract features containing both amplitude and phase information are determined. The proposed method respectively achieves accuracies of 88.06% and 80.00% and outperforms 8 baseline methods, which manifests that the proposed method creates an efficient way to recognize attentional state.

Reduced alpha2 power is associated with slowed information processing speed in multiple sclerosis

OBJECTIVE

Cognitive impairment is common in multiple sclerosis (MS), significantly impacts daily functioning, is time-consuming to assess, and is prone to practice effects. We examined whether the alpha band power measured with magnetoencephalography (MEG) is associated with the different cognitive domains affected by MS.

METHODS

Sixty-eight MS patients and 47 healthy controls underwent MEG, T1- and FLAIR-weighted magnetic resonance imaging (MRI), and neuropsychological testing. Alpha power in the occipital cortex was quantified in the alpha1 (8-10 Hz) and alpha2 (10-12 Hz) bands. Next, we performed best subset regression to assess the added value of neurophysiological measures to commonly available MRI measures.

RESULTS

Alpha2 power significantly correlated with information processing speed (p < 0.001) and was always retained in all multilinear models, whereas thalamic volume was retained in 80% of all models. Alpha1 power was correlated with visual memory (p < 0.001) but only retained in 38% of all models.

CONCLUSIONS

Alpha2 (10-12 Hz) power in rest is associated with IPS, independent of standard MRI parameters. This study stresses that a multimodal assessment, including structural and functional biomarkers, is likely required to characterize cognitive impairment in MS. Resting-state neurophysiology is thus a promising tool to understand and follow up changes in IPS.

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.

Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification Results

Most studies on EEG-based biometry recognition report results based on signal databases, with a limited number of recorded EEG sessions using the same single EEG recording for both training and testing a proposed model. However, the EEG signal is highly vulnerable to interferences, electrode placement, and temporary conditions, which can lead to overestimated assessments of the considered methods. Our study examined how different numbers of distinct recording sessions used as training sessions would affect EEG-based verification. We analyzed the original data from 29 participants with 20 distinct recorded sessions each, as well as 23 additional impostors with only one session each. We applied raw coefficients of power spectral density estimate, and the coefficients of power spectral density estimate converted to the decibel scale, as the input to a shallow neural network. Our study showed that the variance introduced by multiple recording sessions affects sensitivity. We also showed that increasing the number of sessions above eight did not improve the results under our conditions. For 15 training sessions, the achieved accuracy was 96.7 ± 4.2%, and for eight training sessions and 12 test sessions, it was 94.9 ± 4.6%. For 15 training sessions, the rate of successful impostor attacks over all attack attempts was 3.1 ± 2.2%, but this number was not significantly different from using six recording sessions for training. Our findings indicate the need to include data from multiple recording sessions in EEG-based recognition for training, and that increasing the number of test sessions did not significantly affect the obtained results. Although the presented results are for the resting-state, they may serve as a baseline for other paradigms.

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.

The effectiveness of anodal tDCS and cognitive training on cognitive functions in multiple sclerosis; a randomized, double-blind, parallel-group study

BACKGROUND

Forty to 70% of patients with multiple sclerosis (MS) suffer from cognitive impairment during their illness. Only a few studies have examined the effects of anodal transcranial direct current stimulation (a-tDCS) along with cognitive training on cognitive performance in MS patients. This study aims to determine whether multi-session a-tDCS with or without cognitive training impacts cognitive performance in MS.

METHODS

Eighty MS patients received a-tDCS, cognitive training, a-tDCS plus cognitive training, and sham for ten consecutive daily sessions. Cognitive function (including episodic memory, attention, and inhibitory control, working memory, and visuospatial skill) was measured at baseline, week 4, and week 12 after the intervention.

RESULTS

All cognitive functions significantly improved after the intervention compared to the sham condition. This effect also showed persistence during follow-up for some cognitive tasks in the a-tDCS and a-tDCS combined cognitive training groups. Although the cognitive training group experienced an immediate improvement in attention and inhibitory control, the difference was not significant at follow-up. Also, there were no significant differences between these three groups in cognitive scores after the intervention.

CONCLUSION

a-tDCS alone and a-tDCS paired with or without cognitive training as compared to sham appears to be a promising therapeutic option for cognitive performance in MS patients.

Cues to body-related distortions and hallucinations? Spontaneous sensations correlate with EEG oscillatory activity recorded at rest in the somatosensory cortices

Body awareness may arise in the total absence of sensory input, as suggested by the spontaneous occurrence of normal and pathological (i.e., hallucinatory) bodily sensations. These phenomena may arise due to back-projections from higher-order cortical areas to the primary (SI) and secondary (SII) somatosensory cortices, and would appear to be reflected in cortical oscillatory activity in both SI and SII. Here, we set to investigate the relationship of SI and SII in SPS. Healthy participants underwent an EEG recording session at rest, and then completed an experiment on the perception of spontaneous sensations occurring on the hands. Cortical oscillatory activity was extracted from specified ROIs in the somatosensory cortices. The findings showed that (i) SPS perceived in the fingers correlated positively with alpha-band oscillations recorded in SI, and that (ii) SPS perceived in the palm correlated positively with gamma-band oscillations and negatively with beta-band oscillations recorded in SII. Apart from supporting the idea that the somatosensory cortices are involved in bodily awareness even in the absence of sensory input, these findings also suggest that default oscillatory activity in the somatosensory cortices reflects individual differences in bodily awareness. The results are interpreted in terms of neural and cognitive processes that may give rise to bodily awareness and modulate it, and their importance in understanding body perception distortions and bodily delusions and hallucinations is discussed.

Effect of the side of presentation in the visual field on phase-locked and nonphase-locked alpha and gamma responses

Recent studies have suggested that nonphase-locked activity can reveal cognitive mechanisms that cannot be observed in phase-locked activity. In fact, we describe a concomitant decrease in nonphase-locked alpha activity (desynchronization) when stimuli were processed (alpha phase-locked modulation). This desynchronization may represent a reduction in "background activity" in the visual cortex that facilitates stimulus processing. Alternatively, nonphase-locked gamma activity has been hypothesized to be an index of shifts in attentional focus. In this study, our main aim was to confirm these potential roles for nonphase-locked alpha and gamma activities with a lateralized Go/NoGo paradigm. The results showed that nonphase-locked alpha modulation is bilaterally represented in the scalp compared to the contralateral distribution of the phase-locked response. This finding suggests that the decrease in background activity is not limited to neural areas directly involved in the visual processing of stimuli. Additionally, gamma activity showed a higher desynchronization of nonphase-locked activity in the ipsilateral hemisphere, where the phase-locked activity reached the minimum amplitude. This finding suggests that the possible functions of nonphase-locked gamma activity extend beyond shifts in attentional focus and could represent an attentional filter reducing the gamma representation in the visual area irrelevant to the task.

EEG Evidence of Altered Functional Connectivity and Microstate in Children Orphaned by HIV/AIDS

Children orphaned by HIV/AIDS ("AIDS orphans") suffer numerous early-life adverse events which have a long-lasting effect on brain function. Although previous studies found altered electroencephalography (EEG) oscillation during resting state in children orphaned by HIV/AIDS, data are limited regarding the alterations in connectivity and microstate. The current study aimed to investigate the functional connectivity (FC) and microstate in children orphaned by HIV/AIDS with resting-state EEG data. Data were recorded from 63 children orphaned by HIV/AIDS and 65 non-orphan controls during a close-eyes resting state. The differences in phase-locking value (PLV) of global average FC and temporal dynamics of microstate were compared between groups. For functional connectivity, children orphaned by HIV/AIDS showed decreased connectivity in alpha, beta, theta, and delta band compared with non-orphan controls. For microstate, EEG results demonstrated that children orphaned by HIV/AIDS show increased duration and coverage of microstate C, decreased occurrence and coverage of microstate B, and decreased occurrence of microstate D than non-orphan controls. These findings suggest that the microstate and functional connectivity has altered in children orphaned by HIV/AIDS compared with non-orphan controls and provide additional evidence that early life stress (ELS) would alter the structure and function of the brain and increase the risk of psychiatric disorders.

A Study Protocol for Occupational Rehabilitation in Multiple Sclerosis

Digital medical solutions can be very helpful in restorative neurology, as they allow the patients to practice their rehabilitation activities remotely. This work discloses ReMoVES, an IoMT system providing telemedicine services, in the context of Multiple Sclerosis rehabilitation, within the frame of the project STORMS. A rehabilitative protocol of exercises can be provided as ReMoVES services and integrated into the Individual Rehabilitation Project as designed by a remote multidimensional medical team. In the present manuscript, the first phase of the study is described, including the definition of the needs to be addressed, the employed technology, the design and the development of the exergames, and the possible practical/professional and academic consequences. The STORMS project has been implemented with the aim to act as a starting point for the development of digital telerehabilitation solutions that support Multiple Sclerosis patients, improving their living conditions. This paper introduces a study protocol and it addresses pre-clinical research needs, where system issues can be studied and better understood how they might be addressed. It also includes tools to favor remote patient monitoring and to support the clinical staff.

The utility of Magnetoencephalography in multiple sclerosis - A systematic review

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We conducted a Systematic Review of studies, looking at 30 studies from 13 centres.

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MS patients had reduced power in some induced responses (motor beta, visual gamma).

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Increased latency and reduced connectivity were seen for somatosensory evoked fields.

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There was an association between upper alpha connectivity and cognitive function.

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MEG shows promise, although work is too preliminary to recommend current clinical use.

Introduction

Magnetoencephalography (MEG), allows for a high degree temporal and spatial accuracy in recording cortical oscillatory activity and evoked fields. To date, no review has been undertaken to synthesise all MEG studies in Multiple Sclerosis (MS). We undertook a Systematic Review of the utility of MEG in MS.

Methods

We identified MEG studies carried out in MS using EMBASE, Medline, Cochrane, TRIP and Psychinfo databases. We included original research articles with a cohort of minimum of five multiple sclerosis patients and quantifying of at least one MEG parameter. We used a modified version of the JBI (mJBI) for case-control studies to assess for risk of bias.

Results

We identified 30 studies from 13 centres involving at least 433 MS patients and 347 controls. We found evidence that MEG shows perturbed activity (most commonly reduced power modulations), reduced connectivity and association with altered clinical function in Multiple Sclerosis. Specific replicated findings were decreased motor induced responses in the beta band, diminished increase of gamma power after visual stimulation, increased latency and reduced connectivity for somatosensory evoked fields. There was an association between upper alpha connectivity and cognitive measures in people with MS. Overall studies were of moderate quality (mean mJBI score 6.7).

Discussion

We find evidence for the utility of MEG in Multiple Sclerosis. Event-related designs are of particular value and show replicability between centres. At this stage, it is not clear whether these changes are specific to Multiple Sclerosis or are also observable in other diseases. Further studies should look to explore cognitive control in more depth using in-task designs and undertake longitudinal studies to determine whether these changes have prognostic value.

Multiple sclerosis reduces synchrony of the magnocellular pathway

Multiple Sclerosis (MS) is an autoimmune demyelinating disease that damages the insulation of nerve cell fibers in the brain and spinal cord. In the visual system, this demyelination results in a robust delay of visually evoked potentials (VEPs), even in the absence of overt clinical symptoms such as blurred vision. VEPs, therefore, offer an avenue for early diagnosis, monitoring disease progression, and, potentially, insight into the differential impairment of specific pathways. A primary hypothesis has been that visual stimuli driving the magno-, parvo-, and konio-cellular pathways should lead to differential effects because these pathways differ considerably in terms of myelination. Experimental tests of this hypothesis, however, have led to conflicting results. Some groups reported larger latency effects for chromatic stimuli, while others found equivalent effects across stimulus types. We reasoned that this lack of pathway specificity could, at least in part, be attributed to the relatively coarse measure of pathway impairment afforded by the latency of a VEP. We hypothesized that network synchrony could offer a more sensitive test of pathway impairments. To test this hypothesis, we analyzed the synchrony of occipital electroencephalography (EEG) signals during the presentation of visual stimuli designed to bias activity to one of the three pathways. Specifically, we quantified synchrony in the occipital EEG using two graph-theoretic measures of functional connectivity: the characteristic path length (L; a measure of long-range connectivity) and the clustering coefficient (CC; a measure of short-range connectivity). Our main finding was that L and CC were both smaller in the MS group than in controls. Notably, this change in functional connectivity was limited to the magnocellular pathway. The effect sizes (Hedge's g) were 0.89 (L) and 1.26 (CC) measured with magno stimuli. Together, L and CC define the small-world nature of a network, and our finding can be summarized as a reduction in the small-worldness of the magnocellular network. We speculate that the reduced efficiency of information transfer associated with a reduction in small-worldness could underlie visual deficits in MS. Relating these measures to differential diagnoses and disease progression is an important avenue for future work.

Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review

Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. In order to diagnose MS, multiple screening methods have been proposed so far; among them, magnetic resonance imaging (MRI) has received considerable attention among physicians. MRI modalities provide physicians with fundamental information about the structure and function of the brain, which is crucial for the rapid diagnosis of MS lesions. Diagnosing MS using MRI is time-consuming, tedious, and prone to manual errors. Research on the implementation of computer aided diagnosis system (CADS) based on artificial intelligence (AI) to diagnose MS involves conventional machine learning and deep learning (DL) methods. In conventional machine learning, feature extraction, feature selection, and classification steps are carried out by using trial and error; on the contrary, these steps in DL are based on deep layers whose values are automatically learn. In this paper, a complete review of automated MS diagnosis methods performed using DL techniques with MRI neuroimaging modalities is provided. Initially, the steps involved in various CADS proposed using MRI modalities and DL techniques for MS diagnosis are investigated. The important preprocessing techniques employed in various works are analyzed. Most of the published papers on MS diagnosis using MRI modalities and DL are presented. The most significant challenges facing and future direction of automated diagnosis of MS using MRI modalities and DL techniques are also provided.

Effects of transcranial direct current stimulation on cognitive dysfunction in multiple sclerosis

BACKGROUND

Around 40%-70% of patients with multiple sclerosis (MS) may experience cognitive impairments during the course of their disease with detrimental effects on social and occupational activities. Transcranial direct current stimulation (tDCS has been investigated in pain, fatigue, and mood disorders related to MS, but to date, few studies have examined effects of tDCS on cognitive performance in MS.

OBJECTIVE

The current study aimed to investigate the effects of a multi-session tDCS protocol on cognitive performance and resting-state brain electrical activities in patients with MS.

METHODS

Twenty-four eligible MS patients were randomly assigned to real (anodal) or sham tDCS groups. Before and after 8 consecutive daily tDCS sessions over the left dorsolateral prefrontal cortex (DLPFC), patients' cognitive performance was assessed using the Cambridge Brain Sciences-Cognitive Platform (CBS-CP). Cortical electrical activity was also evaluated using quantitative electroencephalography (QEEG) analysis at baseline and after the intervention.

RESULTS

Compared to the sham condition, significant improvement in reasoning and executive functions of the patients in the real tDCS group was observed. Attention was also improved considerably but not statistically significantly following real tDCS. However, no significant changes in resting-state brain activities were observed after stimulation in either group.

CONCLUSION

Anodal tDCS over the left DLPFC appears to be a promising therapeutic option for cognitive dysfunction in patients with MS. Larger studies are required to confirm these findings and to investigate underlying neuronal mechanisms.

Deficits in Early Sensory and Cognitive Processing Are Related to Phase and Nonphase EEG Activity in Multiple Sclerosis Patients

Currently, there is scarce knowledge about the relation between spectral bands modulations and the basis of cognitive impairment in multiple sclerosis (MS). In this sense, analyzing the evoked or phase activity can confirm results from traditional event-related potential (ERP) studies. However, studying the induced or nonphase activity may be necessary to elucidate hidden compensatory or affected cognitive mechanisms. In this study, 30 remitting-relapsing multiple sclerosis patients and 30 healthy controls (HCs) matched in sociodemographic variables performed a visual oddball task. The main goal was to analyze phase and nonphase alpha and gamma bands by applying temporal spectral evolution (TSE) and its potential relation with cognitive impairment in these patients. The behavioural results showed slower reaction time and poorer accuracy in MS patients compared to controls. In contrast, the time-frequency analysis of electroencephalography (EEG) revealed a delay in latency and lower amplitude in MS patients in evoked and induced alpha compared to controls. With respect to the gamma band, there were no differences between the groups. In summary, MS patients showed deficits in early sensorial (evoked alpha activity) and cognitive processing (induced alpha activity in longer latencies), whereas the induced gamma band supported the hypothesis of its role in translation of attentional focus (induced activity) and did not show strong activity in this paradigm (visual oddball).

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.

Electrophysiological Frequency Band Ratio Measures Conflate Periodic and Aperiodic Neural Activity

Band ratio measures, computed as the ratio of power between two frequency bands, are a common analysis measure in neuroelectrophysiological recordings. Band ratio measures are typically interpreted as reflecting quantitative measures of periodic, or oscillatory, activity. This assumes that the measure reflects relative powers of distinct periodic components that are well captured by predefined frequency ranges. However, electrophysiological signals contain periodic components and a 1/f-like aperiodic component, the latter of which contributes power across all frequencies. Here, we investigate whether band ratio measures truly reflect oscillatory power differences, and/or to what extent ratios may instead reflect other periodic changes, such as in center frequency or bandwidth, and/or aperiodic activity. In simulation, we investigate how band ratio measures relate to changes in multiple spectral features, and show how multiple periodic and aperiodic features influence band ratio measures. We validate these findings in human electroencephalography (EEG) data, comparing band ratio measures to parameterizations of power spectral features and find that multiple disparate features influence ratio measures. For example, the commonly applied θ/β ratio is most reflective of differences in aperiodic activity, and not oscillatory θ or β power. Collectively, we show that periodic and aperiodic features can create the same observed changes in band ratio measures, and that this is inconsistent with their typical interpretations as measures of periodic power. We conclude that band ratio measures are a non-specific measure, conflating multiple possible underlying spectral changes, and recommend explicit parameterization of neural power spectra as a more specific approach.

Individual Differences in Negative Emotion Differentiation Predict Resting-State Spontaneous Emotional Regulatory Processes

Negative emotion differentiation facilitates emotion regulation. However, whether individual differences in negative emotion differentiation is associated with resting-state spontaneous emotion regulation remains unclear. This study aimed to explore the effect of individual differences in negative emotion differentiation on spontaneous emotional regulatory processes as indexed by resting electroencephalogram (EEG) indicators (e.g., frontal alpha asymmetry and theta/beta ratio). Participants (n = 40, M_age = 21.74 years, 62% women) completed a negative emotion differentiation task. Afterward, 4 min of resting EEG data were recorded. Multiple regression results showed that negative emotion differentiation significantly predicted the alpha asymmetry at electrode pairs (F4–F3 and FP2–FP1) and the theta/beta ratio at the F3 and FZ electrode sites. Individuals with high negative emotion differentiation presented more left-lateralized activations and a lower theta/beta ratio. Taken together, these results suggest that individuals with high negative emotion differentiation show enhanced spontaneous emotional regulatory functioning. Thus, we provided the first resting-state neural evidence on emotion differentiation of spontaneous emotional regulatory functioning.

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.

To the self and beyond: Arousal and functional connectivity of the temporo-parietal junction contributes to spontaneous sensations perception

The temporoparietal junction (TPJ), along with the anterior insula (AI) and the extrastriate body area (EBA), play a major part in embodiment and self-awareness. However, these connections also appear to be frequently engaged in arousal and attentional processing of external events. Considering that these networks may focus attention both toward and away from the self, we set to investigate how they contribute to the perception of spontaneous sensations (SPS), a common phenomenon related to self-awareness and mediated by both interoceptive and attentional processes. In Experiment 1, resting-state EEG was recorded, as well as arousal reported via a questionnaire, followed by a SPS task. Functional TPJ-AI and TPJ-EBA connectivity were computed using eLORETA. Spatial correlational analyses showed that less frequent SPS coincided with greater TPJ-AI and TPJ-EBA functional connectivity, especially in the theta and alpha frequency bands. High self-reported arousal predicted low intensity and low confidence in the location of SPS. Resting-state skin conductance level (SCL) was recorded in Experiment 2, followed by the SPS task. Less frequent SPS coincided with greater SCL. Findings are interpreted in terms of attention and self-related processes, and a discussion of the TPJ participation in self-awareness through SPS is presented.

High working memory load impairs selective attention: EEG signatures

According to the load theory of attention, increased working memory load impairs selective attention, resulting in greater distractor interference during inhibitory control processing. However, the EEG signatures correlated with this modulation effect of working memory on inhibitory control remain unclear. In present study, 25 healthy human participants performed a flanker task in a low and high working memory load conditions, while behavioral and electroencephalography (EEG) data were recorded. The results showed a larger reaction time interference effect while increasing working memory load, and this was accompanied by a larger N2 amplitude and a smaller P3 amplitude for incongruent trials. Time-frequency analysis revealed that, increased working memory load had no significant modulation effect on flanker related theta-ERS magnitude. Incongruent trials evoked smaller alpha-ERD magnitude than congruent trials in both low and high working memory load conditions. Increased working memory load was associated with larger flanker related alpha-ERD magnitude. Taken together, these results suggested that increased working memory load can impair top-down cognitive control processes, impairing inhibitory control processes during performance of the flanker task.

Towards a Pragmatic Approach to a Psychophysiological Unit of Analysis for Mental and Brain Disorders: An EEG-Copeia for Neurofeedback

This article proposes what we call an "EEG-Copeia" for neurofeedback, like the "Pharmacopeia" for psychopharmacology. This paper proposes to define an "EEG-Copeia" as an organized list of scientifically validated EEG markers, characterized by a specific association with an identified cognitive process, that define a psychophysiological unit of analysis useful for mental or brain disorder evaluation and treatment. A characteristic of EEG neurofeedback for mental and brain disorders is that it targets a EEG markers related to a supposed cognitive process, whereas conventional treatments target clinical manifestations. This could explain why EEG neurofeedback studies encounter difficulty in achieving reproducibility and validation. The present paper suggests that a first step to optimize EEG neurofeedback protocols and future research is to target a valid EEG marker. The specificity of the cognitive skills trained and learned during real time feedback of the EEG marker could be enhanced and both the reliability of neurofeedback training and the therapeutic impact optimized. However, several of the most well-known EEG markers have seldom been applied for neurofeedback. Moreover, we lack a reliable and valid EEG targets library for further RCT to evaluate the efficacy of neurofeedback in mental and brain disorders. With the present manuscript, our aim is to foster dialogues between cognitive neuroscience and EEG neurofeedback according to a psychophysiological perspective. The primary objective of this review was to identify the most robust EEG target. EEG markers linked with one or several clearly identified cognitive-related processes will be identified. The secondary objective was to organize these EEG markers and related cognitive process in a psychophysiological unit of analysis matrix inspired by the Research Domain Criteria (RDoC) project.

Frontoparietal connectivity correlates with working memory performance in multiple sclerosis

Working Memory (WM) impairment is the most common cognitive deficit of patients with Multiple Sclerosis (MS). However, evidence of its neurobiological mechanisms is scarce. Here we recorded electroencephalographic activity of twenty patients with relapsing-remitting MS and minimal cognitive deficit, and 20 healthy control (HC) subjects while they solved a WM task. In spite of similar performance, the HC group demonstrated both a correlation between temporoparietal theta activity and memory load, and a correlation between medial frontal theta activity and successful memory performances. MS patients did not show theses correlations leading significant differences between groups. Moreover, cortical connectivity analyses using granger causality and phase-amplitude coupling between theta and gamma revealed that HC group, but not MS group, presented a load-modulated progression of the frontal-to-parietal connectivity. This connectivity correlated with working memory capacity in MS groups. This early alterations in the oscillatory dynamics underlaying working memory could be useful for plan therapeutic interventions.

A controlled clinical crossover trial of exercise training to improve cognition and neural communication in pediatric brain tumor survivors

OBJECTIVE

To assess the efficacy of aerobic exercise training to improve controlled attention, information processing speed and neural communication during increasing task load and rest in pediatric brain tumor survivors (PBTS) treated with cranial radiation.

METHODS

Participants completed visual-motor Go and Go/No-Go tasks during magnetoencephalography recording prior to and following the completion of 12-weeks of exercise training. Exercise-related changes in response accuracy and visual-motor latency were evaluated with Linear Mixed models. The Phase Lag Index (PLI) was used to estimate functional connectivity during task performance and rest. Changes in PLI values after exercise training were assessed using Partial Least Squares analysis.

RESULTS

Exercise training predicted sustained (12-weeks) improvement in response accuracy (p<0.05) during No-Go trials. Altered functional connectivity was detected in theta (4-7Hz) alpha (8-12Hz) and high gamma (60-100Hz) frequency bands (p<0.001) during Go and Go/No-Go trials. Significant changes in response latency and resting state connectivity were not detected.

CONCLUSION

These findings support the efficacy of aerobic exercise to improve controlled attention and enhance functional mechanisms under increasing task load in participants.

SIGNIFICANCE

It may be possible to harness the beneficial effects of exercise as therapy to promote cognitive recovery and enhance brain function in PBTS.

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.

Phase-synchrony evaluation of EEG signals for Multiple Sclerosis diagnosis based on bivariate empirical mode decomposition during a visual task

BACKGROUND AND OBJECTIVE

Despite the widespread prevalence of Multiple Sclerosis (MS), the study of brain interactions is still poorly understood. Moreover, there has always been a great need to automate the MS diagnosis procedure to eliminate the evaluation errors thereby improving its consistency and reliability. To address these issues, in this work, we proposed a robust pattern recognition algorithm as a computer-aided diagnosis system. This method is based on calculating the pairwise phase-synchrony of EEG recordings during a visual task. Initially, the bivariate empirical mode decomposition (BEMD) was applied to extract the intrinsic mode functions (IMFs). The phases of these IMFs were then obtained using the Hilbert transform to be utilized in the mean phase coherence (MPC), a measure for phase-synchrony calculation. After the construction of the feature space using MPC values, the ReliefF algorithm was applied for dimension reduction. Finally, the best distinguishing features were input to a k-nearest neighbor (KNN) classifier. The results revealed a higher level of network synchronization in the posterior regions of the brain and desynchronization in the anterior regions among the MS group as compared with the normal subjects. In the validation phase, the leave-one-subject-out cross-validation (LOOCV) method was used to assess the validity of the proposed algorithm. We achieved an accuracy, sensitivity, and specificity of 93.09%, 91.07%, and 95.24% for red-green, 90.44%, 88.39%, and 92.62% for luminance, and 87.44%, 87.05%, and 87.86% for blue-yellow tasks, respectively. The experimental results demonstrated the reliability of the presented method to be generalized in the field of automated MS diagnosis systems.

Resting-state MEG measurement of functional activation as a biomarker for cognitive decline in MS

BACKGROUND

Neurophysiological measures of brain function, such as magnetoencephalography (MEG), are widely used in clinical neurology and have strong relations with cognitive impairment and dementia but are still underdeveloped in multiple sclerosis (MS).

OBJECTIVES

To demonstrate the value of clinically applicable MEG-measures in evaluating cognitive impairment in MS.

METHODS

In eyes-closed resting-state, MEG data of 83 MS patients and 34 healthy controls (HCs) peak frequencies and relative power of six canonical frequency bands for 78 cortical and 10 deep gray matter (DGM) areas were calculated. Linear regression models, correcting for age, gender, and education, assessed the relation between cognitive performance and MEG biomarkers.

RESULTS

Increased alpha1 and theta power was strongly associated with impaired cognition in patients, which differed between cognitively impaired (CI) patients and HCs in bilateral parietotemporal cortices. CI patients had a lower peak frequency than HCs. Oscillatory slowing was also widespread in the DGM, most pronounced in the thalamus.

CONCLUSION

There is a clinically relevant slowing of neuronal activity in MS patients in parietotemporal cortical areas and the thalamus, strongly related to cognitive impairment. These measures hold promise for the application of resting-state MEG as a biomarker for cognitive disturbances in MS in a clinical setting.

Individualized pattern recognition for detecting mind wandering from EEG during live lectures

Neural correlates of mind wandering

The ability to detect mind wandering as it occurs is an important step towards improving our understanding of this phenomenon and studying its effects on learning and performance. Current detection methods typically rely on observable behaviour in laboratory settings, which do not capture the underlying neural processes and may not translate well into real-world settings. We address both of these issues by recording electroencephalography (EEG) simultaneously from 15 participants during live lectures on research in orthopedic surgery. We performed traditional group-level analysis and found neural correlates of mind wandering during live lectures that are similar to those found in some laboratory studies, including a decrease in occipitoparietal alpha power and frontal, temporal, and occipital beta power. However, individual-level analysis of these same data revealed that patterns of brain activity associated with mind wandering were more broadly distributed and highly individualized than revealed in the group-level analysis.

Mind wandering detection

To apply these findings to mind wandering detection, we used a data-driven method known as common spatial patterns to discover scalp topologies for each individual that reflects their differences in brain activity when mind wandering versus attending to lectures. This approach avoids reliance on known neural correlates primarily established through group-level statistics. Using this method for individual-level machine learning of mind wandering from EEG, we were able to achieve an average detection accuracy of 80–83%.

Conclusions

Modelling mind wandering at the individual level may reveal important details about its neural correlates that are not reflected when using traditional observational and statistical methods. Using machine learning techniques for this purpose can provide new insight into the varieties of neural activity involved in mind wandering, while also enabling real-time detection of mind wandering in naturalistic settings.

Electroencephalography theta/beta ratio covaries with mind wandering and functional connectivity in the executive control network

The ratio between frontal resting‐state electroencephalography (EEG) theta and beta frequency power (theta/beta ratio, TBR) is negatively related to cognitive control. It is unknown which psychological processes during resting state account for this. Increased theta and reduced beta power are observed during mind wandering (MW), and MW is related to decreased connectivity in the executive control network (ECN) and increased connectivity in the default mode network (DMN). The goal of this study was to test if MW‐related fluctuations in TBR covary with such functional variation in ECN and DMN connectivity and if this functional variation is related to resting‐state TBR. Data were analyzed for 26 participants who performed a 40‐min breath‐counting task and reported the occurrence of MW episodes while EEG was measured and again during magnetic resonance imaging. Frontal TBR was higher during MW than controlled thought and this was marginally related to resting‐state TBR. DMN connectivity was higher and ECN connectivity was lower during MW. Greater ECN connectivity during focus than MW was correlated to lower TBR during focus than MW. These results provide the first evidence of the neural correlates of TBR and its functional dynamics and further establish TBR's usefulness for the study of executive control, in normal and potentially abnormal psychology.

Hyperexcitability of Cortical Oscillations in Patients with Somatoform Pain Disorder: A Resting-State EEG Study

Patients with somatoform pain disorder (SPD) suffer from somatic pain that cannot be fully explained by specific somatic pathology. While the pain experience requires the integration of sensory and contextual processes, the cortical oscillations have been suggested to play a crucial role in pain processing and integration. The present study is aimed at identifying the abnormalities of spontaneous cortical oscillations among patients with SPD, thus for a better understanding of the ongoing brain states in these patients. Spontaneous electroencephalography data during a resting state with eyes open were recorded from SPD patients and healthy controls, and their cortical oscillations as well as functional connectivity were compared using both electrode-level and source-level analysis. Compared with healthy controls, SPD patients exhibited greater resting-state alpha oscillations (8.5-12.5 Hz) at the parietal region, as reflected by both electrode-level spectral power density and exact low-resolution brain electromagnetic tomography (eLORETA) cortical current density. A significant correlation between parietal alpha oscillation and somatization severity was observed in SPD patients, after accounting for the influence of anxiety and depression. Functional connectivity analysis further revealed a greater frontoparietal connectivity of the resting-state alpha oscillations in SPD patients, which was indexed by the coherence between pairs of electrodes and the linear connectivity between pairs of eLORETA cortical sources. The enhanced resting-state alpha oscillation in SPD patients could be relevant with attenuated sensory information gating and excessive integration of pain-related information, while the enhanced frontoparietal connectivity could be reflecting their sustained attention to bodily sensations and hypervigilance to somatic sensations.

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

BACKGROUND

Cognitive deficits including impaired information processing speed as assessed by the Symbol Digit Modalities Test (SDMT) are common in multiple sclerosis (MS). Oscillatory markers of processing speed may be extracted from magnetoencephalographic (MEG) and electroencephalographic (EEG) resting-state recordings. In this context, an increased proportion of frontal slow-wave (theta, 4-8 Hz) to fast-wave (beta, 13-30 Hz) EEG activity was indicative of impaired SDMT performance. Such an increased theta/beta ratio may reflect oscillatory slowing associated with deficits in attention control. Therapeutic approaches that consider atypical oscillatory activity in MS remain sparse.

OBJECTIVES

In a cross-sectional design, we examined the relation between SDMT performance, the EEG theta/beta ratio and its components. We also explored longitudinally, whether EEG neurofeedback could be used to induce a putatively adaptive alteration in these EEG parameters, toward a pattern indicative of improved processing speed.

METHODS

N = 58 MS patients (RRMS/SPMS/PPMS N: 18/35/3, 2 cases excluded) participated in a neuropsychological examination and a resting-state EEG recording. Subsequently, N = 10 patients received neurofeedback training for two weeks in a hospitalized setting. The purpose was to reduce the frontal theta/beta ratio through operant conditioning.

RESULTS

In the cross-sectional examination, patients with slow SDMT speed displayed an increased theta/beta ratio, relative to those with normal speed. This involved increased frontal theta power, whereas beta power was equal across groups. The theta/beta ratio remained stable during neurofeedback across sessions of the two-week training period. In an exploratory secondary analysis, within sessions a reduction in the theta/beta ratio during active training blocks relative pre/post session resting-states was observed, driven by reduced theta power.

CONCLUSIONS

These findings provide support for utilizing frontal EEG theta activity as an inverse marker of processing speed in MS. Across sessions, there was no support for successful operant conditioning of the theta/beta ratio during the two-week training period. The observed state-specific shift within sessions, involving a transient reduction in theta activity, nevertheless may provide a rationale for a further investigation of neurofeedback as a treatment approach in MS.

Early and late dot-probe attentional bias to mild and high threat pictures: Relations with EEG theta/beta ratio, self-reported trait attentional control, and trait anxiety

Frontal EEG theta/beta ratio (TBR; negatively associated with attentional control, or AC) was previously reported to moderate threat-level dependent attentional bias in a pictorial dot-probe task, interacting with trait anxiety. Unexpectedly, this was independent from processing stage (using cue-target delays of 200 and 500 ms) and also not observed for self-reported trait AC. We therefore aimed to replicate these effects of TBR and trait anxiety and to test if effects of early versus late processing stages are evident for shorter cue-target delays. This study also revisited the hypothesis that TBR and self-reported trait AC show similar effects. Fifty-three participants provided measurements of frontal TBR, self-reported trait AC, trait anxiety, and dot-probe task bias for mild and high threat pictures using the same dot-probe task, but this time with 80- and 200-ms cue-target delays. Results indicated that higher TBR predicted more attention to mild than high threat, but this was independent from trait anxiety or delay. Lower self-reported trait AC predicted more attention to mild than high threat, only after 200 ms (also independent of trait anxiety). We conclude that the moderating effect of TBR on threat-level dependent dot-probe task bias was replicated, but not the role of trait anxiety, and this study partially confirms that effects of trait AC are more dominant in later processing.

Acute effects of caffeine on threat-selective attention: moderation by anxiety and EEG theta/beta ratio

BACKGROUND

Spontaneous EEG theta/beta ratio (TBR) probably marks prefrontal cortical (PFC) executive control, and its regulation of attentional threat-bias. Caffeine at moderate doses may strengthen executive control through increased PFC catecholamine action, dependent on basal PFC function.

GOAL

To test if caffeine affects threat-bias, moderated by baseline frontal TBR and trait-anxiety.

METHODS

A pictorial emotional Stroop task was used to assess threat-bias in forty female participants in a cross-over, double-blind study after placebo and 200 mg caffeine.

RESULTS

At baseline and after placebo, comparable relations were observed for negative pictures: high TBR was related to low threat-bias in low trait-anxious people. Caffeine had opposite effects on threat-bias in low trait-anxious people with low and high TBR.

CONCLUSIONS

This further supports TBR as a marker of executive control and highlights the importance of taking baseline executive function into consideration when studying effects of caffeine on executive functions.

Do not look away! Spontaneous frontal EEG theta/beta ratio as a marker for cognitive control over attention to mild and high threat

BACKGROUND

Low spontaneous EEG theta/beta ratio (TBR) is associated with greater executive control. Their role in regulation of attentional bias for stimuli of different threat-levels is unknown.

OBJECTIVES

To provide the first relations between frontal TBR, trait anxiety and attentional bias to mildly and highly threatening stimuli at different processing-stages.

METHODS

Seventy-four healthy volunteers completed spontaneous EEG measurement, a self-report trait anxiety questionnaire and a dot-probe task with stimuli of different threat-level and 200 and 500 ms cue-target delays.

RESULTS

Participants with high TBR directed attention towards mildly threatening and avoided highly threatening pictures. Moreover, the most resilient participants, (low TBR and low trait anxiety) showed attention towards highly threatening stimuli. There were no effects of delay.

CONCLUSIONS

These data confirm that executive control is crucial for the study of threat-related attentional bias and further support the notion that TBR is a marker of cognitive control over emotional information.