Negative emotions mediate the association between the topology of the complex brain network and smartphone use disorder: A resting-state EEG study

Background

Increasing research has examined the factors related to smartphone use disorder. However, limited research has explored its neural basis.

Aims

We aimed to examine the relationship between the topology of the resting-state electroencephalography (rs-EEG) brain network and smartphone use disorder using minimum spanning tree analysis. Furthermore, we examined how negative emotions mediate this relationship.

Methods

This study included 113 young, healthy adults (mean age = 20.87 years, 46.9% males).

Results

The results showed that the alpha- and delta-band kappas and delta-band leaf fraction were positively correlated with smartphone use disorder. In contrast, the alpha-band diameter was negatively correlated with smartphone use disorder. Negative emotions fully mediated the relationship between alpha-band kappa and alpha-band diameter and smartphone use disorder. Furthermore, negative emotions partially mediated the relationship between delta-band kappa and smartphone use disorder. The findings suggest that excessive scale-free alpha- and delta-band brain networks contribute to the emergence of smartphone use disorder. In addition, the findings also demonstrate that negative emotions and smartphone use disorder share the same neural basis. Negative emotions play a mediating role in the association between topological deviations and smartphone use disorder.

Discussion

To the best of our knowledge, this is the first study to examine the neural basis of smartphone use disorder from the perspective of the topology of the rs-EEG brain network. Therefore, neuromodulation may be a potential intervention for smartphone use disorder.

EEG rhythmic and arrhythmic spectral components and functional connectivity at resting state may predict the development of synucleinopathies in idiopathic REM sleep behavior disorder

STUDY OBJECTIVES

Idiopathic/isolated REM-sleep behavior disorder (iRBD) often precedes the onset of synucleinopathies. Here, we investigated whether baseline resting-state EEG advanced spectral power and functional connectivity differ between iRBD patients who converted towards a synucleinopathy at follow-up and those who did not.

METHODS

Eighty-one participants with iRBD (66.89±6.91 years) underwent a baseline resting-state EEG recording, a neuropsychological assessment and a neurological examination. We estimated EEG power spectral density using standard analyses and derived spectral estimates of rhythmic and arrhythmic components. Global and pairwise EEG functional connectivity analyses were computed using the weighted phase-lag index (wPLI). Pixel-based permutation tests were used to compare groups.

RESULTS

After a mean follow-up of 5.01±2.76 years, 34 patients were diagnosed with a synucleinopathy (67.81±7.34 years) and 47 remained disease-free (65.53±7.09 years). Among patients who converted, 22 were diagnosed with Parkinson's disease and 12 with dementia with Lewy bodies. As compared to patients who did not convert, patients who converted exhibited at baseline higher relative theta standard power, steeper slopes of the arrhythmic component and higher theta rhythmic power mostly in occipital regions. Furthermore, patients who converted showed higher beta global wPLI but lower alpha wPLI between left temporal and occipital regions.

CONCLUSION

Analyses of resting-state EEG rhythmic and arrhythmic components and functional connectivity suggest an imbalanced excitatory-to-inhibitory activity within large-scale networks, which is associated with later development of a synucleinopathy in iRBD patients.

How well do neural signatures of resting-state EEG detect consciousness? A large-scale clinical study

The assessment of consciousness states, especially distinguishing minimally conscious states (MCS) from unresponsive wakefulness states (UWS), constitutes a pivotal role in clinical therapies. Despite that numerous neural signatures of consciousness have been proposed, the effectiveness and reliability of such signatures for clinical consciousness assessment still remains an intense debate. Through a comprehensive review of the literature, inconsistent findings are observed about the effectiveness of diverse neural signatures. Notably, the majority of existing studies have evaluated neural signatures on a limited number of subjects (usually below 30), which may result in uncertain conclusions due to small data bias. This study presents a systematic evaluation of neural signatures with large-scale clinical resting-state electroencephalography (EEG) signals containing 99 UWS, 129 MCS, 36 emergence from the minimally conscious state, and 32 healthy subjects (296 total) collected over 3 years. A total of 380 EEG-based metrics for consciousness detection, including spectrum features, nonlinear measures, functional connectivity, and graph-based measures, are summarized and evaluated. To further mitigate the effect of data bias, the evaluation is performed with bootstrap sampling so that reliable measures can be obtained. The results of this study suggest that relative power in alpha and delta serve as dependable indicators of consciousness. With the MCS group, there is a notable increase in the phase lag index-related connectivity measures and enhanced functional connectivity between brain regions in comparison to the UWS group. A combination of features enables the development of an automatic detector of conscious states.

Variational mode decomposition-based EEG analysis for the classification of disorders of consciousness

Aberrant alterations in any of the two dimensions of consciousness, namely awareness and arousal, can lead to the emergence of disorders of consciousness (DOC). The development of DOC may arise from more severe or targeted lesions in the brain, resulting in widespread functional abnormalities. However, when it comes to classifying patients with disorders of consciousness, particularly utilizing resting-state electroencephalogram (EEG) signals through machine learning methods, several challenges surface. The non-stationarity and intricacy of EEG data present obstacles in understanding neuronal activities and achieving precise classification. To address these challenges, this study proposes variational mode decomposition (VMD) of EEG before feature extraction along with machine learning models. By decomposing preprocessed EEG signals into specified modes using VMD, features such as sample entropy, spectral entropy, kurtosis, and skewness are extracted across these modes. The study compares the performance of the features extracted from VMD-based approach with the frequency band-based approach and also the approach with features extracted from raw-EEG. The classification process involves binary classification between unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS), as well as multi-class classification (coma vs. UWS vs. MCS). Kruskal-Wallis test was applied to determine the statistical significance of the features and features with a significance of p < 0.05 were chosen for a second round of classification experiments. Results indicate that the VMD-based features outperform the features of other two approaches, with the ensemble bagged tree (EBT) achieving the highest accuracy of 80.5% for multi-class classification (the best in the literature) and 86.7% for binary classification. This approach underscores the potential of integrating advanced signal processing techniques and machine learning in improving the classification of patients with disorders of consciousness, thereby enhancing patient care and facilitating informed treatment decision-making.

An electroencephalographic signature predicts craving for methamphetamine

Craving is central to methamphetamine use disorder (MUD) and both characterizes the disease and predicts relapse. However, there is currently a lack of robust and reliable biomarkers for monitoring craving and diagnosing MUD. Here, we seek to identify a neurobiological signature of craving based on individual-level functional connectivity pattern differences between healthy control and MUD subjects. We train high-density electroencephalography (EEG)-based models using data recorded during the resting state and then calculate imaginary coherence features between the band-limited time series across different brain regions of interest. Our prediction model demonstrates that eyes-open beta functional connectivity networks have significant predictive value for craving at the individual level and can also identify individuals with MUD. These findings advance the neurobiological understanding of craving through an EEG-tailored computational model of the brain connectome. Dissecting neurophysiological features provides a clinical avenue for personalized treatment of MUD.

Functional network dynamics revealed by EEG microstates reflect cognitive decline in amyotrophic lateral sclerosis

Recent electroencephalography (EEG) studies have shown that patterns of brain activity can be used to differentiate amyotrophic lateral sclerosis (ALS) and control groups. These differences can be interrogated by examining EEG microstates, which are distinct, reoccurring topographies of the scalp's electrical potentials. Quantifying the temporal properties of the four canonical microstates can elucidate how the dynamics of functional brain networks are altered in neurological conditions. Here we have analysed the properties of microstates to detect and quantify signal-based abnormality in ALS. High-density resting-state EEG data from 129 people with ALS and 78 HC were recorded longitudinally over a 24-month period. EEG topographies were extracted at instances of peak global field power to identify four microstate classes (labelled A-D) using K-means clustering. Each EEG topography was retrospectively associated with a microstate class based on global map dissimilarity. Changes in microstate properties over the course of the disease were assessed in people with ALS and compared with changes in clinical scores. The topographies of microstate classes remained consistent across participants and conditions. Differences were observed in coverage, occurrence, duration, and transition probabilities between ALS and control groups. The duration of microstate class B and coverage of microstate class C correlated with lower limb functional decline. The transition probabilities A to D, C to B and C to B also correlated with cognitive decline (total ECAS) in those with cognitive and behavioural impairments. Microstate characteristics also significantly changed over the course of the disease. Examining the temporal dependencies in the sequences of microstates revealed that the symmetry and stationarity of transition matrices were increased in people with late-stage ALS. These alterations in the properties of EEG microstates in ALS may reflect abnormalities within the sensory network and higher-order networks. Microstate properties could also prospectively predict symptom progression in those with cognitive impairments.

Five-week of solution-focused group counseling successfully reduces internet addiction among college students: A pilot study

Background and Aims

In the digital age, Internet addiction (IA) was deemed an epidemic and few treatments had been effectively developed for it. Here, we proposed a solution-focused group counseling (SFGC) as a potentially solution to reduce Internet addiction among college students. The present study examined the short- and long-term effect of a five-week solution-focused group counseling intervention on Internet addiction.

Methods

Thirty-two participants were recruited and randomly assigned to either the experimental or control group, and twenty-six participants completed the whole intervention. The experimental group (n = 14) received the intervention, while control group (n = 12) did not. The revised version of the Chinese Internet Addiction Scale (CIAS-R), the Future Time Perspective, and resting-state EEG were administered pre-intervention, post-intervention, and at two follow-up tests (one month and six months after intervention).

Results

The results showed that the scores of the CIAS-R in the experimental group were significantly decreased after intervention, and these effects could be sustained for one month and six months follow-ups. Additionally, the intervention conducted an increase in future time perspective. EEG results further suggested that the alpha, beta, and gamma absolute power decreased after the intervention.

Conclusion

These results from the pilot-study primarily suggested that solution-focused group counseling could be an effective intervention for Internet addiction.

Fronto-parietal theta high-definition transcranial alternating current stimulation may modulate working memory under postural control conditions in young healthy adults

Objects

This study aimed to investigate the immediate effects of fronto-parietal θ HD-tACS on a dual task of working memory-postural control.

Methods

In this within-subject cross-over pilot study, we assessed the effects of 20 min of 6 Hz-tACS targeting both the left dorsolateral prefrontal cortex (lDLPFC) and posterior parietal cortex (PPC) in 20 healthy adults (age: 21.6 ± 1.3 years). During each session, single- and dual-task behavioral tests (working memory single-task, static tandem standing, and a dual-task of working memory-postural control) and closed-eye resting-state EEG were assessed before and immediately after stimulation.

Results

Within the tACS group, we found a 5.3% significant decrease in working memory response time under the dual-task following tACS (t = -3.157, p = 0.005, Cohen's d = 0.742); phase synchronization analysis revealed a significant increase in the phase locking value (PLV) of θ band between F3 and P3 after tACS (p = 0.010, Cohen's d = 0.637). Correlation analyses revealed a significant correlation between increased rs-EEG θ power in the F3 and P3 channels and faster reaction time (r = -0.515, p = 0.02; r = -0.483, p = 0.031, respectively) in the dual-task working memory task after tACS. However, no differences were observed on either upright postural control performance or rs-EEG results (p-values <0.05).

Conclusion

Fronto-parietal θ HD-tACS has the potential of being a neuromodulatory tool for improving working memory performance in dual-task situations, but its effect on the modulation of concurrently performed postural control tasks requires further investigation.

Machine learning-based classifying of risk-takers and risk-aversive individuals using resting-state EEG data: A pilot feasibility study

BACKGROUND

Decision-making is vital in interpersonal interactions and a country's economic and political conditions. People, especially managers, have to make decisions in different risky situations. There has been a growing interest in identifying managers' personality traits (i.e., risk-taking or risk-averse) in recent years. Although there are findings of signal decision-making and brain activity, the implementation of an intelligent brain-based technique to predict risk-averse and risk-taking managers is still in doubt.

METHODS

This study proposes an electroencephalogram (EEG)-based intelligent system to distinguish risk-taking managers from risk-averse ones by recording the EEG signals from 30 managers. In particular, wavelet transform, a time-frequency domain analysis method, was used on resting-state EEG data to extract statistical features. Then, a two-step statistical wrapper algorithm was used to select the appropriate features. The support vector machine classifier, a supervised learning method, was used to classify two groups of managers using chosen features.

RESULTS

Intersubject predictive performance could classify two groups of managers with 74.42% accuracy, 76.16% sensitivity, 72.32% specificity, and 75% F1-measure, indicating that machine learning (ML) models can distinguish between risk-taking and risk-averse managers using the features extracted from the alpha frequency band in 10 s analysis window size.

CONCLUSIONS

The findings of this study demonstrate the potential of using intelligent (ML-based) systems in distinguish between risk-taking and risk-averse managers using biological signals.

There is a party in my head and no one is invited: Resting-state electrocortical activity and solitude

OBJECTIVE

What are the motivational underpinnings of solitude? We know from self-report studies that increases in solitude are associated with drops in approach motivation and rises in avoidance motivation, but only when solitude is experienced as non-self-determined (i.e., non-autonomous). However, the extent to which individual differences in solitude relate to neurophysiological markers of approach-avoidance motivation derived from resting-state electroencephalogram (EEG) is unknown. These markers are Frontal Alpha Asymmetry, beta suppression, and midline Posterior versus Frontal EEG Theta Activity.

METHOD

We assessed the relation among individual differences in the reasons for solitude (i.e., preference for solitude, motivation for solitude), approach-avoidance motivation, and resting-state EEG markers of approach-avoidance motivation (N = 115).

RESULTS

General preference for solitude was negatively related to approach motivation, observed in both self-reported measures and EEG markers of approach motivation. Self-determined solitude was positively related to both self-reported approach motivation and avoidance motivation in the social domain (i.e., friendship). Non-self-determined solitude was negatively associated with self-reported avoidance motivation.

CONCLUSION

This research was a preliminary attempt to address the neurophysiological underpinnings of solitude in the context of motivation.

Greater resting frontal alpha asymmetry associated with higher emotional expressive flexibility

Emotional expressive flexibility (EEF) is an important social ability that has prompted scholars to examine its benefits to human mental health. However, the neural underpinnings of individual differences in the EEF remain unclear. In neuroscience, frontal alpha asymmetry (FAA) is regarded as a sensitive indicator of certain emotional modalities and affective styles. To the best of our knowledge, no study has linked FAA with EEF to examine whether FAA could be a potential neural indicator of EEF. In the present study, 47 participants (Mage = 22.38 years, 55.3% women) underwent a resting electroencephalogram and completed the flexible regulation of emotional expression scale (FREE). The results revealed that after controlling for gender, resting FAA scores positively predicted EEF, with relative left frontal activity associated with higher EEF. Additionally, this prediction was reflected in both the enhancement and suppression dimensions of EEF. Furthermore, individuals with relative left frontal activity reported greater enhancement and EEF than individuals with relative right frontal activity. The present study indicated that FAA may be a neural marker of EEF. In the future, more empirical studies are needed to provide causal evidence that the improvement in FAA can enhance EEF.

Increased neurocardiological interplay after mindfulness meditation: a brain oscillation-based approach

Background

Brain oscillations facilitate interaction within the brain network and between the brain and heart activities, and the alpha wave, as a prominent brain oscillation, plays a major role in these coherent activities. We hypothesize that mindfully breathing can make the brain and heart activities more coherent in terms of increased connectivity between the electroencephalogram (EEG) and electrocardiogram (ECG) signals.

Methods

Eleven participants (28-52 years) attended 8 weeks of Mindfulness Based Stress Reduction (MBSR) training. EEG and ECG data of two states of mindful breathing and rest, both eye-closed, were recorded before and after the training. EEGLAB was used to analyze the alpha band (8-12 Hz) power, alpha peak frequency (APF), peak power and coherence. FMRIB toolbox was used to extract the ECG data. Heart coherence (HC) and heartbeat evoked potential (HEP) were calculated for further correlation analysis.

Results

After 8 weeks of MBSR training, the correlation between APF and HC increased significantly in the middle frontal region and bilateral temporal regions. The correlation between alpha coherence and heart coherence had similar changes, while alpha peak power did not reflect such changes. In contrast, spectrum analysis alone did not show difference before and after MBSR training.

Conclusion

The brain works in rhythmic oscillation, and this rhythmic connection becomes more coherent with cardiac activity after 8 weeks of MBSR training. Individual APF is relatively stable and its interplay with cardiac activity may be a more sensitive index than power spectrum by monitoring the brain-heart connection. This preliminary study has important implications for the neuroscientific measurement of meditative practice.

Comparing executive functions in children with attention deficit hyperactivity disorder with or without reading disability: A resting-state EEG study

INTRODUCTION

As numerous studies have shown, executive dysfunction is the main impairment in attention-deficit/hyperactivity disorder. According to recent neuroimaging studies, the frontoparietal coherence plays a key role in overall cognitive functions. Therefore, the aim of this study was to compare executive functions during resting-state EEG by monitoring brain connectivity (coherence) patterns in children with attention deficit hyperactivity disorder (ADHD) with or without reading disability (RD).

METHODS

The statistical sample of the study consisted of 32 children with ADHD aged between 8 and 12 years old with or without specific RD. Each group consisted of 11 boys and 5 girls that were matched on chronological age and gender. EEG was recorded during eyes-opened condition and brain connectivity within and between frontal and parietal regions was analyzed within theta, alpha, and beta bands.

RESULTS

The results revealed that across the frontal regions, the comorbid group showed a significant reduction in the left intrahemispheric coherence in the alpha and beta bands. The ADHD-alone group exhibited increased theta and decreased alpha and beta coherence in frontal regions. In the frontoparietal regions, children in the comorbid group showed lower coherence between frontal and parietal networks compared to children without comorbid RD.

CONCLUSION

The findings indicate that brain connectivity (coherence) patterns of children with ADHD with comorbid RD were more abnormal and lend support to more disrupted cortical connectivity in the comorbid group. Thus, these findings can be a useful marker for better recognizing ADHD and comorbid disabilities.

Differential associations between scale-free neural dynamics and different levels of cognitive ability

As indicators of cognitive function, scale-free neural dynamics are gaining increasing attention in cognitive neuroscience. Although the functional relevance of scale-free dynamics has been extensively reported, one fundamental question about its association with cognitive ability remains unanswered: is the association universal across a wide spectrum of cognitive abilities or confined to specific domains? Based on dual-process theory, we designed two categories of tasks to analyze two types of cognitive processes-automatic and controlled-and examined their associations with scale-free neural dynamics characterized from resting-state electroencephalography (EEG) recordings obtained from a large sample of human adults (N = 102). Our results showed that resting-state scale-free neural dynamics did not predict individuals' behavioral performance in tasks that primarily engaged the automatic process but did so in tasks that primarily engaged the controlled process. In addition, by fitting the scale-free parameters separately in different frequency bands, we found that the cognitive association of scale-free dynamics was more strongly manifested in higher-band EEG spectrum. Our findings indicate that resting-state scale-free dynamics are not universal neural indicators for all cognitive abilities but are mainly associated with high-level cognition that entails controlled processes. This finding is compatible with the widely claimed role of scale-free dynamics in reflecting properties of complex dynamic systems.

Effects of acute exercise fatigue on the spatiotemporal dynamics of resting-state large-scale brain networks

Introduction

Various approaches have been used to explore different aspects of the regulation of brain activity by acute exercise, but few studies have been conducted on the effects of acute exercise fatigue on large-scale brain functional networks. Therefore, the present study aimed to explore the effects of acute exercise fatigue on resting-state electroencephalogram (EEG) microstates and large-scale brain network rhythm energy.

Methods

The Bruce protocol was used as the experimental exercise model with a self-controlled experimental design. Thirty males performed incremental load exercise tests on treadmill until exhaustion. EEG signal acquisition was completed before and after exercise. EEG microstates and resting-state cortical rhythm techniques were used to analyze the EEG signal.

Results

The microstate results showed that the duration, occurrence, and contribution of Microstate C were significantly higher after exhaustive exercise (p's < 0.01). There was a significantly lower contribution of Microstate D (p < 0.05), a significant increase in transition probabilities between Microstate A and C (p < 0.05), and a significant decrease in transition probabilities between Microstate B and D (p < 0.05). The results of EEG rhythm energy on the large-scale brain network showed that the energy in the high-frequency β band was significantly higher in the visual network (p < 0.05).

Discussion

Our results suggest that frequently Microstate C associated with the convexity network are important for the organism to respond to internal and external information stimuli and thus regulate motor behavior in time to protect organism integrity. The decreases in Microstate D parameters, associated with the attentional network, are an important neural mechanism explaining the decrease in attention-related cognitive or behavioral performance due to acute exercise fatigue. The high energy in the high-frequency β band on the visual network can be explained in the sense of the neural efficiency hypothesis, which indicates a decrease in neural efficiency.

Alpha peak activity in resting-state EEG is associated with depressive score

Introduction

Depression is a serious psychiatric disorder characterized by prolonged sadness, loss of interest or pleasure. The dominant alpha peak activity in resting-state EEG is suggested to be an intrinsic neural marker for diagnosis of mental disorders.

Methods

To investigate an association between alpha peak activity and depression severity, the present study recorded resting-state EEG (EGI 128 channels, off-line average reference, source reconstruction by a distributed inverse method with the sLORETA normalization, parcellation of 68 Desikan-Killiany regions) from 155 patients with depression (42 males, mean age 35 years) and acquired patients' scores of Self-Rating Depression Scales. We measured both the alpha peak amplitude that is more related to synchronous neural discharging and the alpha peak frequency that is more associated with brain metabolism.

Results

The results showed that over widely distributed brain regions, individual patients' alpha peak amplitudes were negatively correlated with their depressive scores, and individual patients' alpha peak frequencies were positively correlated with their depressive scores.

Discussion

These results reveal that alpha peak amplitude and frequency are associated with self-rating depressive score in different manners, and the finding suggests the potential of alpha peak activity in resting-state EEG acting as an important neural factor in evaluation of depression severity in supplement to diagnosis.

Predicting age from resting-state scalp EEG signals with deep convolutional neural networks on TD-brain dataset

Introduction

Brain age prediction has been shown to be clinically relevant, with errors in its prediction associated with various psychiatric and neurological conditions. While the prediction from structural and functional magnetic resonance imaging data has been feasible with high accuracy, whether the same results can be achieved with electroencephalography is unclear.

Methods

The current study aimed to create a new deep learning solution for brain age prediction using raw resting-state scalp EEG. To this end, we utilized the TD-BRAIN dataset, including 1,274 subjects (both healthy controls and individuals with various psychiatric disorders, with a total of 1,335 recording sessions). To achieve the best age prediction, we used data augmentation techniques to increase the diversity of the training set and developed a deep convolutional neural network model.

Results

The model's training was done with 10-fold cross-subject cross-validation, with the EEG recordings of the subjects used for training not considered to test the model. In training, using the relative rather than the absolute loss function led to a better mean absolute error of 5.96 years in cross-validation. We found that the best performance could be achieved when both eyes-open and eyes-closed states are used simultaneously. The frontocentral electrodes played the most important role in age prediction.

Discussion

The architecture and training method of the proposed deep convolutional neural networks (DCNN) improve state-of-the-art metrics in the age prediction task using raw resting-state EEG data by 13%. Given that brain age prediction might be a potential biomarker of numerous brain diseases, inexpensive and precise EEG-based estimation of brain age will be in demand for clinical practice.

Resting-state electroencephalogram microstate to evaluate post-stroke rehabilitation and associate with clinical scales

INTRODUCTION

Stroke is usually accompanied by a range of complications, like post-stroke motor disorders. So far, its evaluation of motor function is developed on clinical scales, such as Fugl-Meyer Assessment (FMA), Instrumental Activities of Daily Living (IADL), etc. These scale results from behavior and kinematic assessment are inevitably influenced by subjective factors, like the experience of patients and doctors, lacking neurological correlations and evidence.

METHODS

This paper applied a microstate model based on modified k-means clustering to analyze 64-channel electroencephalogram (EEG) from 12 stroke patients and 12 healthy volunteers, respectively, to explore the feasibility of applying microstate analysis to stroke patients. We aimed at finding some possible differences between stroke and healthy individuals in resting-state EEG microstate features. We further explored the correlations between EEG microstate features and scales within the stroke group.

RESULTS AND DISCUSSION

By statistical analysis, we obtained significant differences in EEG microstate features between the stroke and healthy groups and significant correlations between microstate features and scales within the stroke group. These results might provide some neurological evidence and correlations in the perspective of EEG microstate analysis for post-stroke rehabilitation and evaluation of motor disorders. Our work suggests that microstate analysis of resting-state EEG is a promising method to assist clinical and assessment applications.

Resting-state EEG functional connectivity predicts post-traumatic stress disorder subtypes in veterans

Objective. Post-traumatic stress disorder (PTSD) is highly heterogeneous, and identification of quantifiable biomarkers that could pave the way for targeted treatment remains a challenge. Most previous electroencephalography (EEG) studies on PTSD have been limited to specific handpicked features, and their findings have been highly variable and inconsistent. Therefore, to disentangle the role of promising EEG biomarkers, we developed a machine learning framework to investigate a wide range of commonly used EEG biomarkers in order to identify which features or combinations of features are capable of characterizing PTSD and potential subtypes.Approach. We recorded 5 min of eyes-closed and 5 min of eyes-open resting-state EEG from 202 combat-exposed veterans (53% with probable PTSD and 47% combat-exposed controls). Multiple spectral, temporal, and connectivity features were computed and logistic regression, random forest, and support vector machines with feature selection methods were employed to classify PTSD. To obtain robust results, we performed repeated two-layer cross-validation to test on an entirely unseen test set.Main results. Our classifiers obtained a balanced test accuracy of up to 62.9% for predicting PTSD patients. In addition, we identified two subtypes within PTSD: one where EEG patterns were similar to those of the combat-exposed controls, and another that were characterized by increased global functional connectivity. Our classifier obtained a balanced test accuracy of 79.4% when classifying this PTSD subtype from controls, a clear improvement compared to predicting the whole PTSD group. Interestingly, alpha connectivity in the dorsal and ventral attention network was particularly important for the prediction, and these connections were positively correlated with arousal symptom scores, a central symptom cluster of PTSD.Significance. Taken together, the novel framework presented here demonstrates how unsupervised subtyping can delineate heterogeneity and improve machine learning prediction of PTSD, and may pave the way for better identification of quantifiable biomarkers.

Bayesian Optimization of Machine Learning Classification of Resting-State EEG Microstates in Schizophrenia: A Proof-of-Concept Preliminary Study Based on Secondary Analysis

Resting-state electroencephalography (EEG) microstates reflect sub-second, quasi-stable states of brain activity. Several studies have reported alterations of microstate features in patients with schizophrenia (SZ). Based on these findings, it has been suggested that microstates may represent neurophysiological biomarkers for the classification of SZ. To explore this possibility, machine learning approaches can be employed. Bayesian optimization is a machine learning approach that selects the best-fitted machine learning model with tuned hyperparameters from existing models to improve the classification. In this proof-of-concept preliminary study based on secondary analysis, 20 microstate features were extracted from 14 SZ patients and 14 healthy controls' EEG signals. These parameters were then ranked as predictors based on their importance, and an optimized machine learning approach was applied to evaluate the performance of the classification. SZ patients had altered microstate features compared to healthy controls. Furthermore, Bayesian optimization outperformed conventional multivariate analyses and showed the highest accuracy (90.93%), AUC (0.90), sensitivity (91.37%), and specificity (90.48%), with reliable results using just six microstate predictors. Altogether, in this proof-of-concept study, we showed that machine learning with Bayesian optimization can be utilized to characterize EEG microstate alterations and contribute to the classification of SZ patients.

Predicting the long-term after-effects of rTMS in autism spectrum disorder using temporal variability analysis of scalp EEG

Objective.Repetitive transcranial magnetic stimulation (rTMS) emerges as a useful therapy for autism spectrum disorder (ASD) clinically. Whereas the mechanisms of action of rTMS on ASD are not fully understood, and no biomarkers until now are available to reliably predict the follow-up rTMS efficacy in clinical practice.Approach.In the current work, the temporal variability was investigated in resting-state electroencephalogram of ASD patients, and the nonlinear complexity of related time-varying networks was accordingly evaluated by fuzzy entropy.Main results.The results showed the hyper-variability in the resting-state networks of ASD patients, while three week rTMS treatment alleviates the hyper fluctuations occurring in the frontal-parietal and frontal-occipital connectivity and further contributes to the ameliorative ASD symptoms. In addition, the changes in variability network properties are closely correlated with clinical scores, which further serve as potential predictors to reliably track the long-term rTMS efficacy for ASD.Significance.The findings consistently demonstrated that the temporal variability of time-varying networks of ASD patients could be modulated by rTMS, and related variability properties also help predict follow-up rTMS efficacy, which provides the potential for formulating individualized treatment strategies for ASD (ChiCTR2000033586).

Effects of the Duration of a Resting-State EEG Recording in Healthy Aging and Mild Cognitive Impairment

INTRODUCTION

The recording of resting-state EEG may provide a means to predict early cognitive decline associated with mild cognitive impairment (MCI). Previous studies have typically used very short recording times to avoid a confound with drowsiness that may occur in longer recordings. The effects of a longer recording have not however been systematically examined.

METHODS

Eyes-closed resting-state EEG activity was recorded in 40 older adult participants (20 healthy older adults and 20 people with MCI). The recording period was a relatively long 6 minutes, divided into two equal 3-minute halves to determine if drowsiness will be more apparent as the recording progresses. The participants also completed standardized neuropsychological tasks that assessed global cognition (Montreal Cognitive Assessment) and memory (California Verbal Learning Test, Second Edition). A spectral analysis was performed on both short (2 seconds) and long (8 seconds) segments in both 3-minute halves.

RESULTS

No differences in power density for any of the EEG frequency bands were found between the 2 halves of the study for either group. There was little evidence of increased drowsiness in the second half of the study even when frequency resolution was increased with the 8-second segmentation. Theta power density was overall larger for people with MCI compared to healthy older adults. A negative correlation was also observed between theta power and global cognition in healthy older adults.

CONCLUSIONS

The present results indicate that longer resting-state EEG recording can be reliably employed without increased risk of drowsiness.

Built to last: Theta and delta changes in resting-state EEG activity after regulating emotions

BACKGROUND

Over the past years, electroencephalography (EEG) studies focused on task-related activity to characterize cortical responses associated with emotion regulation (ER), without exploring the possibility that regulating emotions can leave a trace in the brain by affecting its oscillatory activity. Demonstrating whether the effect of regulation alters the brain activity after the session and whether this reflects an increased cognitive regulatory ability has great relevance.

METHODS

To address this issue, 5 min of electrical brain activity at rest were recorded before and after (1) one session in which participants perceived and regulated (through distancing) their emotions (regulation session, ReS), and (2) another session in which they only perceived emotions (attend session, AtS). One hundred and sixty visual stimuli were presented, and subjective ratings of valence and arousal of stimuli were recorded.

RESULTS

Behavioral results showed the efficacy of the regulation strategy in modulating both arousal and valence. A cluster-based permutation test on EEG data at rest revealed a significant increase in theta and delta activity after the ReS compared to the AtS, suggesting that regulating emotions can alter brain activity after the session.

CONCLUSIONS

These results allowed us to outline a comprehensive view of the neurophysiological mechanisms associated with ER, as well as some possible implications in psychotherapy.

Open Eyes Increase Neural Oscillation and Enhance Effective Brain Connectivity of the Default Mode Network: Resting-State Electroencephalogram Research

The default mode network (DMN) has a unique activity pattern in the resting brain. Studies on resting-state brain activity are helpful to identify various brain dynamic characteristics of patients with mental diseases and those of healthy people. The brain produces a series of changes in different eye states. However, the relationship between eye states and the DMN, which is closely related to the resting state, has not been widely examined. This study recruited 42 healthy students aged 17–22. Participants completed the Profile of Mood States questionnaire. Thereafter, the electroencephalogram data was collected with the patients’ eyes open and closed. Changes in neural oscillation and the DMN’s information transmission during different eye openness states were compared. The results showed that the neural oscillation activities of the parietal-occipital network such as the superior parietal lobule and precuneus were significantly enhanced in the eyes open state. In addition, the effective connectivity within the DMN was enhanced during opened eyes, especially from the left precuneus to the left posterior cingulate cortex, and this connectivity was negatively correlated with the Vigor-Activity mood state in the eyes open state. The activity of the DMN in the resting-state is regulated by eye states, which may relate to mood and emotional perception.

The Acceptability, Feasibility, and Utility of Portable Electroencephalography to Study Resting-State Neurophysiology in Rural Communities

Electroencephalography (EEG) provides a non-invasive means to advancing our understanding of the development and function of the brain. However, the majority of the world’s population residing in low and middle income countries has historically been limited from contributing to, and thereby benefiting from, such neurophysiological research, due to lack of scalable validated methods of EEG data collection. In this study, we establish a standard operating protocol to collect approximately 3 min each of eyes-open and eyes-closed resting-state EEG data using a low-cost portable EEG device in rural households through formative work in the community. We then evaluate the acceptability of these EEG assessments to young children and feasibility of administering them through non-specialist workers. Finally, we describe properties of the EEG recordings obtained using this novel approach to EEG data collection. The formative phase was conducted with 9 families which informed protocols for consenting, child engagement strategies and data collection. The protocol was then implemented on 1265 families. 977 children (Mean age = 38.8 months, SD = 0.9) and 1199 adults (Mean age = 27.0 years, SD = 4) provided resting-state data for this study. 259 children refused to wear the EEG cap or removed it, and 58 children refused the eyes-closed recording session. Hardware or software issues were experienced during 30 and 25 recordings in eyes-open and eyes-closed conditions respectively. Disturbances during the recording sessions were rare and included participants moving their heads, touching the EEG headset with their hands, opening their eyes within the eyes-closed recording session, and presence of loud sounds in the testing environment. Similar to findings in laboratory-based studies from high-income settings, the percentage of recordings which showed an alpha peak was higher in eyes-closed than eyes-open condition, with the peak occurring most frequently in electrodes at O1 and O2 positions, and the mean frequency of the alpha peak was found to be lower in children (8.43 Hz, SD = 1.73) as compared to adults (10.71 Hz, SD = 3.96). We observed a deterioration in the EEG signal with prolonged device usage. This study demonstrates the acceptability, feasibility and utility of conducting EEG research at scale in a rural low-resource community, while highlighting its potential limitations, and offers the impetus needed to further refine the methods and devices and validate such scalable methods to overcome existing research inequity.

Temporal and Spatial Dynamics of EEG Features in Female College Students with Subclinical Depression

Synchronization of the dynamic processes in structural networks connect the brain across a wide range of temporal and spatial scales, creating a dynamic and complex functional network. Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data. Few studies have focused on potential brain spatiotemporal dynamics in the early stages of depression to use as an early screening feature for depression. Thus, this study aimed to explore large-scale brain network dynamics of individuals both with and without subclinical depression, from the perspective of temporal and spatial dimensions and to input them as features into a machine learning framework for the automatic diagnosis of early-stage depression. To achieve this, spatio–temporal dynamics of rest-state EEG signals in female college students (n = 40) with and without (n = 38) subclinical depression were analyzed using EEG microstate and omega complexity analysis. Then, based on differential features of EEGs between the two groups, a support vector machine was utilized to compare performances of spatio–temporal features and single features in the classification of early depression. Microstate results showed that the occurrence rate of microstate class B was significantly higher in the group with subclinical depression when compared with the group without. Moreover, the duration and contribution of microstate class C in the subclinical group were both significantly lower than in the group without subclinical depression. Omega complexity results showed that the global omega complexity of β-2 and γ band was significantly lower for the subclinical depression group compared with the other group (p < 0.05). In addition, the anterior and posterior regional omega complexities were lower for the subclinical depression group compared to the comparison group in α-1, β-2 and γ bands. It was found that AUC of 81% for the differential indicators of EEG microstates and omega complexity was deemed better than a single index for predicting subclinical depression. Thus, since temporal and spatial complexity of EEG signals were manifestly altered in female college students with subclinical depression, it is possible that this characteristic could be adopted as an early auxiliary diagnostic indicator of depression.

Fully automated discrimination of Alzheimer's disease using resting-state electroencephalography signals

Background

The Alzheimer's disease (AD) population increases worldwide, placing a heavy burden on the economy and society. Presently, there is no cure for AD. Developing a convenient method of screening for AD and mild cognitive impairment (MCI) could enable early intervention, thus slowing down the progress of the disease and enabling better overall disease management.

Methods

In the current study, resting-state electroencephalography (EEG) data were acquired from 113 normal cognition (NC) subjects, 116 amnestic MCI patients, and 72 probable AD patients. After preprocessing by an automatic algorithm, features including spectral power, complexity, and functional connectivity were extracted, and machine-learning classifiers were built to differentiate among the 3 groups. The classification performance was evaluated from multiple perspectives, including accuracy, specificity, sensitivity, area under the curve (AUC) with 95% confidence intervals, and compared to the empirical chance level by permutation tests.

Results

The analysis of variance results (P<0.05 with false discovery rate correction) confirmed the tendency to slow brain activity, reduced complexity, and connectivity with AD progress. By combining the features, the ability of the machine-learning classifiers, especially the ensemble trees, to differentiate among the 3 groups, was significantly better than that of the empirical chance level of the permutation test. The AUC of the classifier with the best performance was 80.08% for AD vs. NC, 70.82% for AD vs. MCI, and 63.95% for MCI vs. NC.

Conclusions

The current study presented a fully automatic procedure that could significantly distinguish NC, MCI, and AD subjects via resting-state EEG signals. The study was based on a large data set with evidence-based medical diagnosis and provided further evidence that resting-state EEG data could assist in the discrimination of AD patients.

Objective Recognition of Tinnitus Location Using Electroencephalography Connectivity Features

Purpose: Tinnitus is a common but obscure auditory disease to be studied. This study will determine whether the connectivity features in electroencephalography (EEG) signals can be used as the biomarkers for an efficient and fast diagnosis method for chronic tinnitus.

Methods: In this study, the resting-state EEG signals of tinnitus patients with different tinnitus locations were recorded. Four connectivity features [including the Phase-locking value (PLV), Phase lag index (PLI), Pearson correlation coefficient (PCC), and Transfer entropy (TE)] and two time-frequency domain features in the EEG signals were extracted, and four machine learning algorithms, included two support vector machine models (SVM), a multi-layer perception network (MLP) and a convolutional neural network (CNN), were used based on the selected features to classify different possible tinnitus sources.

Results: Classification accuracy was highest when the SVM algorithm or the MLP algorithm was applied to the PCC feature sets, achieving final average classification accuracies of 99.42 or 99.1%, respectively. And based on the PLV feature, the classification result was also particularly good. And MLP ran the fastest, with an average computing time of only 4.2 s, which was more suitable than other methods when a real-time diagnosis was required.

Conclusion: Connectivity features of the resting-state EEG signals could characterize the differentiation of tinnitus location. The connectivity features (PCC and PLV) were more suitable as the biomarkers for the objective diagnosing of tinnitus. And the results were helpful for clinicians in the initial diagnosis of tinnitus.

Acute alcohol intoxication modulates the temporal dynamics of resting electroencephalography networks

This study aimed to provide a currently missing link between general intoxication-induced changes in overall brain activity and the multiple cognitive control deficits typically observed during acute alcohol intoxication. For that purpose, we analyzed the effects of acute alcohol intoxication (1.1‰) on the four archetypal electroencephalography (EEG) resting networks (i.e., microstates A-D) and their temporal dynamics (e.g., coverage and transitions from one microstate to another), as well as on self-reported resting-state cognition in n = 22 healthy young males using a counterbalanced within-subject design. Our microstate analyses indicated that alcohol increased the coverage of the visual processing-related microstate B at the expense of the autonomic processing-related microstate C. Add-on exploratory analyses revealed that alcohol increased transitions from microstate C to microstate B and decreased bidirectional transitions between microstate C and the attention-related microstate D. In line with the observed alcohol-induced decrease of the autonomic processing-related microstate C, participants reported decreases of their somatic awareness during intoxication, which were positively associated with more transitions from microstate C to microstate B. In sum, the observed effects provide mechanistic insights into how alcohol might hamper cognitive processing by generally prioritizing the bottom-up processing of visual stimuli over top-down internal information processing. The fact that this was found during the resting state further proves that alcohol-induced changes in brain activity are continuously present and do not only emerge during demanding situations or tasks.

Simultaneously stimulating both brain hemispheres by rTMS in patients with unilateral brain lesions decreases interhemispheric asymmetry

BACKGROUND

Interhemispheric asymmetry caused by brain lesions is an adverse factor in the recovery of patients with neurological deficits. Repetitive transcranial magnetic stimulation (rTMS) has been shown to modulate cortical oscillation and proposed as an approach to rebalance the symmetry, which has not been documented well.

OBJECTIVE

In this study, we investigated the influence of repetitive transcranial magnetic stimulation (rTMS) on EEG power in patients with unilateral brain lesions by simultaneously stimulating both brain hemispheres and to elucidate asymmetrical changes in rTMS-induced neurophysiological activity.

METHODS

Fourteen patients with unilateral brain lesions were treated with one active and one sham session of 10 Hz rTMS over the vertex (Cz position). Resting-state EEGs were recorded before and immediately after rTMS. The brain symmetry index (BSI), calculated from a fast Fourier transform, was employed to quantify the power asymmetry in both hemispheres and paired channels over the entire range and five frequency bands (delta, theta, alpha, beta and gamma bands).

RESULTS

Comparison between active and sham sessions demonstrated rTMS-induced EEG after-effects. rTMS in the active session significantly reduced the BSI in patients with unilateral brain lesions over the entire frequency range (t = 2.767, P = 0.016). Among the five frequency bands, rTMS only induced a noticeable decrease in the BSI in the delta band (t = 2.254, P = 0.042). Furthermore, analysis of different brain regions showed that significant changes in the BSI of the alpha band were only demonstrated in the posterior parietal lobe. In addition, EEG topographic mapping showed a decreased power of delta oscillations in the ipsilesional hemisphere, whereas distinct cortical oscillations were observed in the alpha band around the parietal-occipital lobe in the contralesional hemisphere.

CONCLUSIONS

When both brain hemispheres were simultaneously activated, rTMS decreased interhemispheric asymmetry primarily via reducing the delta band in the lesioned hemisphere.

Brain variability in dynamic resting-state networks identified by fuzzy entropy: a scalp EEG study

Objective.Exploring the temporal variability in spatial topology during the resting state attracts growing interest and becomes increasingly useful to tackle the cognitive process of brain networks. In particular, the temporal brain dynamics during the resting state may be delineated and quantified aligning with cognitive performance, but few studies investigated the temporal variability in the electroencephalogram (EEG) network as well as its relationship with cognitive performance.Approach.In this study, we proposed an EEG-based protocol to measure the nonlinear complexity of the dynamic resting-state network by applying the fuzzy entropy. To further validate its applicability, the fuzzy entropy was applied into simulated and two independent datasets (i.e. decision-making and P300).Main results.The simulation study first proved that compared to the existing methods, this approach could not only exactly capture the pattern dynamics in time series but also overcame the magnitude effect of time series. Concerning the two EEG datasets, the flexible and robust network architectures of the brain cortex at rest were identified and distributed at the bilateral temporal lobe and frontal/occipital lobe, respectively, whose variability metrics were found to accurately classify different groups. Moreover, the temporal variability of resting-state network property was also either positively or negatively related to individual cognitive performance.Significance.This outcome suggested the potential of fuzzy entropy for evaluating the temporal variability of the dynamic resting-state brain networks, and the fuzzy entropy is also helpful for uncovering the fluctuating network variability that accounts for the individual decision differences.

The Resting-State Electroencephalogram Microstate Correlations With Empathy and Their Moderating Effect on the Relationship Between Empathy and Disgust

Humans have a natural ability to understand the emotions and feelings of others, whether one actually witnesses the situation of another, perceives it from a photograph, reads about it in a fiction book, or merely imagines it. This is the phenomenon of empathy, which requires us to mentally represent external information to experience the emotions of others. Studies have shown that individuals with high empathy have high anterior insula and adjacent frontal operculum activation when they are aware of negative emotions in others. As a negative emotion, disgust processing involves insula coupling. What are the neurophysiological characteristics for regulating the levels of empathy and disgust? To answer this question, we collected electroencephalogram microstates (EEG-ms) of 196 college students at rest and used the Disgust Scale and Interpersonal Reactivity Index. The results showed that: (1) there was a significant positive correlation between empathy and disgust sensitivity; (2) the empathy score and the intensity of transition possibility between EEG-ms C and D were significantly positively correlated; and (3) the connection strength between the transition possibility of EEG-ms C and D could adjust the relationship between the disgust sensitivity score and the empathy score. This study provides new neurophysiological characteristics for an understanding of the regulate relationship between empathy and disgust and provides a new perspective on emotion and attention.

Negative Effects of Mobile Phone Addiction Tendency on Spontaneous Brain Microstates: Evidence From Resting-State EEG

The prevalence of mobile phone addiction (MPA) has increased rapidly in recent years, and it has had a certain negative impact on emotions (e.g., anxiety and depression) and cognitive capacities (e.g., executive control and working memory). At the level of neural circuits, the continued increase in activity in the brain regions associated with addiction leads to neural adaptations and structural changes. At present, the spontaneous brain microstates that could be negatively influenced by MPA are unclear. In this study, the temporal characteristics of four resting-state electroencephalogram (RS-EEG) microstates (MS1, MS2, MS3, and MS4) related to mobile phone addiction tendency (MPAT) were investigated using the Mobile Phone Addiction Tendency Scale (MPATS). We attempted to analyze the correlation between MPAT and corresponding microstates and provide evidence to explain the brain and behavioral changes caused by MPA. The results showed that the total score of the MPATS was positively correlated with the duration of MS1, related to phonological processing and negatively correlated with the duration of MS2, related to visual or imagery processing, and MS4, related to the attentional network; the score of the withdrawal symptoms subscale was additionally associated with duration of MS3, related to the cingulo-opercular emotional network. Based on these results, we believe that MPAT may have some negative effects on attentional networks and sensory brain networks; moreover, withdrawal symptoms may induce some negative emotions.

Critical Dynamics in Spontaneous Resting-State Oscillations Are Associated With the Attention-Related P300 ERP in a Go/Nogo Task

Sustained attention is the ability to continually concentrate on task-relevant information, even in the presence of distraction. Understanding the neural mechanisms underlying this ability is critical for comprehending attentional processes as well as neuropsychiatric disorders characterized by attentional deficits, such as attention deficit hyperactivity disorder (ADHD). In this study, we aimed to investigate how trait-like critical oscillations during rest relate to the P300 evoked potential-a biomarker commonly used to assess attentional deficits. We measured long-range temporal correlations (LRTC) in resting-state EEG oscillations as index for criticality of the signal. In addition, the attentional performance of the subjects was assessed as reaction time variability (RTV) in a continuous performance task following an oddball paradigm. P300 amplitude and latencies were obtained from EEG recordings during this task. We found that, after controlling for individual variability in task performance, LRTC were positively associated with P300 amplitudes but not latencies. In line with previous findings, good performance in the sustained attention task was related to higher P300 amplitudes and earlier peak latencies. Unexpectedly, we observed a positive relationship between LRTC in ongoing oscillations during rest and RTV, indicating that greater criticality in brain oscillations during rest relates to worse task performance. In summary, our results show that resting-state neuronal activity, which operates near a critical state, relates to the generation of higher P300 amplitudes. Brain dynamics close to criticality potentially foster a computationally advantageous state which promotes the ability to generate higher event-related potential (ERP) amplitudes.

Neuromodulatory Effect of Transcranial Direct Current Stimulation on Resting-State EEG Activity in Internet Gaming Disorder: A Randomized, Double-Blind, Sham-Controlled Parallel Group Trial

Transcranial direct current stimulation (tDCS) has been used as an adjunct therapy for psychiatric disorders; however, little is known about the underlying neurophysiological effects of tDCS in Internet gaming disorder (IGD). We investigated the effects of tDCS on cortical activity using resting-state electroencephalography (EEG) in patients with IGD. This randomized, double-blind, sham-controlled parallel group study of tDCS (ClinicalTrials.gov NCT03347643) included 31 IGD patients. Participants received 10 sessions (2 sessions per day for 5 consecutive days) of active repetitive tDCS (2 mA for 20 min per session) or sham stimulation. Anode/cathode electrodes were placed over the left and right dorsolateral prefrontal cortex, respectively. In total, 26 participants (active group n = 14; sham group n = 12) completed the trial. Resting-state EEG spectral activity (absolute power) and functional connectivity (coherence) were used to assess the effects of tDCS on cortical activity before stimulation and 1 month after the intervention. Active stimulation of tDCS suppressed increase of intra-hemispheric beta coherence after 1 month, which was observed in the sham group. The 1-month follow-up assessment revealed that absolute gamma power in the left parietal region was decreased in the active group relative to the sham group. Our findings suggest that repetitive tDCS stabilizes fast-wave activity in IGD.

Resting-State Electroencephalography for Prognosis in Disorders of Consciousness Following Traumatic Brain Injury

Although the majority of patients recover consciousness after a traumatic brain injury (TBI), a minority develop a prolonged disorder of consciousness, which may never fully resolve. For these patients, accurate prognostication is essential to treatment decisions and long-term care planning. In this review, we evaluate the use of resting-state electroencephalography (EEG) as a prognostic measure in disorders of consciousness following TBI. We highlight that routine clinical EEG recordings have prognostic utility in the short to medium term. In particular, measures of alpha power and variability are indicative of relatively better functional outcomes within the first year post-TBI. This is hypothesized to reflect intact thalamocortical loops, and thus the potential for recovery of consciousness even in the apparent absence of current consciousness. However, there is a lack of research into the use of resting-state EEG for predicting longer-term recovery following TBI. We conclude that, given the potential for patients to demonstrate improvements in consciousness and functional capacity even years after TBI, a research focus on EEG-augmented prognostication in very long-term disorders of consciousness is now required.

The Relationships Between Trait Creativity and Resting-State EEG Microstates Were Modulated by Self-Esteem

Numerous studies find that creativity is not only associated with low effort and flexible processes but also associated with high effort and persistent processes especially when defensive behavior is induced by negative emotions. The important role of self-esteem is to buffer negative emotions, and individuals with low self-esteem are prone to instigating various forms of defensive behaviors. Thus, we thought that the relationships between trait creativity and executive control brain networks might be modulated by self-esteem. The resting-state electroencephalogram (RS-EEG) microstates can be divided into four classical types (MS1, MS2, MS3, and MS4), which can reflect the brain networks as well as their dynamic characteristic. Thus, the Williams Creative Tendency Scale (WCTS) and Rosenberg Self-esteem Scale (RSES) were used to investigate the modulating role of self-esteem on the relationships between trait creativity and the RS-EEG microstates. As our results showed, self-esteem consistently modulated the relationships between creativity and the duration and contribution of MS2 related to visual or imagery processing, the occurrence of MS3 related to cingulo-opercular networks, and transitions between MS2 and MS4, which were related to frontoparietal control networks. Based on these results, we thought that trait creativity was related to the executive control of bottom-up processing for individuals with low self-esteem, while the bottom-up information from vision or visual imagery might be related to trait creativity for individuals with high self-esteem.

Classification of Depression Through Resting-State Electroencephalogram as a Novel Practice in Psychiatry: Review

Background

Machine learning applications in health care have increased considerably in the recent past, and this review focuses on an important application in psychiatry related to the detection of depression. Since the advent of computational psychiatry, research based on functional magnetic resonance imaging has yielded remarkable results, but these tools tend to be too expensive for everyday clinical use.

Objective

This review focuses on an affordable data-driven approach based on electroencephalographic recordings. Web-based applications via public or private cloud-based platforms would be a logical next step. We aim to compare several different approaches to the detection of depression from electroencephalographic recordings using various features and machine learning models.

Methods

To detect depression, we reviewed published detection studies based on resting-state electroencephalogram with final machine learning, and to predict therapy outcomes, we reviewed a set of interventional studies using some form of stimulation in their methodology.

Results

We reviewed 14 detection studies and 12 interventional studies published between 2008 and 2019. As direct comparison was not possible due to the large diversity of theoretical approaches and methods used, we compared them based on the steps in analysis and accuracies yielded. In addition, we compared possible drawbacks in terms of sample size, feature extraction, feature selection, classification, internal and external validation, and possible unwarranted optimism and reproducibility. In addition, we suggested desirable practices to avoid misinterpretation of results and optimism.

Conclusions

This review shows the need for larger data sets and more systematic procedures to improve the use of the solution for clinical diagnostics. Therefore, regulation of the pipeline and standard requirements for methodology used should become mandatory to increase the reliability and accuracy of the complete methodology for it to be translated to modern psychiatry.

Role of EEG in Measuring Cognitive Reserve: A Rapid Review

This review aimed to systematically summarize the possible neural correlates of cognitive reserve thus giving an insight into prospective biomarkers for the concept. A total of 44 studies were analyzed following PRISMA guidelines and four studies were included in the further analysis. The results indicated a relationship between P3b waveform and cognitive reserve, while more ambiguous outcomes were found when conducting resting-state EEG. This review indicates the first steps into assessing CR using physiological measures; however, more research is needed for deeper understanding of its underlying mechanisms.

Phase-Amplitude Markers of Synchrony and Noise: A Resting-State and TMS-EEG Study of Schizophrenia

The electroencephalogram (EEG) of schizophrenia patients is known to exhibit a reduction of signal-to-noise ratio and of phase locking, as well as a facilitation of excitability, in response to a variety of external stimuli. Here, we demonstrate these effects in transcranial magnetic stimulation (TMS)-evoked potentials and in the resting-state EEG. To ensure veracity, we used 3 weekly sessions and analyzed both resting-state and TMS-EEG data. For the TMS responses, our analysis verifies known results. For the resting state, we introduce the methodology of mean-normalized variation to the EEG analysis (quartile-based coefficient of variation), which allows for a comparison of narrow-band EEG amplitude fluctuations to narrow-band Gaussian noise. This reveals that amplitude fluctuations in the delta, alpha, and beta bands of healthy controls are different from those in schizophrenia patients, on time scales of tens of seconds. We conclude that the EEG-measured cortical activity patterns of schizophrenia patients are more similar to noise, both in alpha- and beta-resting state and in TMS responses. Our results suggest that the ability of neuronal populations to form stable, locally, and temporally correlated activity is reduced in schizophrenia, a conclusion, that is, in accord with previous experiments on TMS-EEG and on resting-state EEG.

EEG spectral power, but not theta/beta ratio, is a neuromarker for adult ADHD

Adults with attention-deficit/hyperactivity disorder (ADHD) have been described as having altered resting-state electroencephalographic (EEG) spectral power and theta/beta ratio (TBR). However, a recent review (Pulini et al. 2018) identified methodological errors in neuroimaging, including EEG, ADHD classification studies. Therefore, the specific EEG neuromarkers of adult ADHD remain to be identified, as do the EEG characteristics that mediate between genes and behaviour (mediational endophenotypes). Resting-state eyes-open and eyes-closed EEG was measured from 38 adults with ADHD, 45 first-degree relatives of people with ADHD and 51 unrelated controls. A machine learning classification analysis using penalized logistic regression (Elastic Net) examined if EEG spectral power (1-45 Hz) and TBR could classify participants into ADHD, first-degree relatives and/or control groups. Random-label permutation was used to quantify any bias in the analysis. Eyes-open absolute and relative EEG power distinguished ADHD from control participants (area under receiver operating characteristic = 0.71-0.77). The best predictors of ADHD status were increased power in delta, theta and low-alpha over centro-parietal regions, and in frontal low-beta and parietal mid-beta. TBR did not successfully classify ADHD status. Elevated eyes-open power in delta, theta, low-alpha and low-beta distinguished first-degree relatives from controls (area under receiver operating characteristic = 0.68-0.72), suggesting that these features may be a mediational endophenotype for adult ADHD. Resting-state EEG spectral power may be a neuromarker and mediational endophenotype of adult ADHD. These results did not support TBR as a diagnostic neuromarker for ADHD. It is possible that TBR is a characteristic of childhood ADHD.

Altered Cortical Functional Networks in Patients With Schizophrenia and Bipolar Disorder: A Resting-State Electroencephalographic Study

BACKGROUND

Pathologies of schizophrenia and bipolar disorder have been poorly understood. Brain network analysis could help understand brain mechanisms of schizophrenia and bipolar disorder. This study investigates the source-level brain cortical networks using resting-state electroencephalography (EEG) in patients with schizophrenia and bipolar disorder.

METHODS

Resting-state EEG was measured in 38 patients with schizophrenia, 34 patients with bipolar disorder type I, and 30 healthy controls. Graph theory based source-level weighted functional networks were evaluated: strength, clustering coefficient (CC), path length (PL), and efficiency in six frequency bands.

RESULTS

At the global level, patients with schizophrenia or bipolar disorder showed higher strength, CC, and efficiency, and lower PL in the theta band, compared to healthy controls. At the nodal level, patients with schizophrenia or bipolar disorder showed higher CCs, mostly in the frontal lobe for the theta band. Particularly, patients with schizophrenia showed higher nodal CCs in the left inferior frontal cortex and the left ascending ramus of the lateral sulcus compared to patients with bipolar disorder. In addition, the nodal-level theta band CC of the superior frontal gyrus and sulcus (cognition-related region) correlated with positive symptoms and social and occupational functioning scale (SOFAS) scores in the schizophrenia group, while that of the middle frontal gyrus (emotion-related region) correlated with SOFAS scores in the bipolar disorder group.

CONCLUSIONS

Altered cortical networks were revealed and these alterations were significantly correlated with core pathological symptoms of schizophrenia and bipolar disorder. These source-level cortical network indices could be promising biomarkers to evaluate patients with schizophrenia and bipolar disorder.

Impaired Coupling of the Brain's Default Network During Sleep Deprivation: A Resting-State EEG Study

INTRODUCTION

Sleep deprivation (SD) has a negative influence on mood and emotion processing, and previous studies have elucidated the impaired coupling within the default network (DN) after SD. However, the dynamic characteristic with high temporal precision was rarely investigated in the DN after SD.

METHODS

Here, the resting-state EEG after nocturnal sleep (NS) and SD was collected from 31 participants. The cortical electrical activities of the posterior cingulate cortex (PCC) and the anterior medial prefrontal cortex (aMPFC) were reconstructed applying the eLORETA, and the functional connectivity (FC) of PCC-aMPFC was calculated using the power envelope connectivity (PEC).

RESULTS

Compared with NS, the power spectrums of the PCC and the FC of PCC-aMPFC were significantly reduced in the α band after SD. Interestingly, the impaired PCC-aMPFC integration was positively correlated with the decreased positive affect, implying that the DN plays a critical role in the subjective mood state. Our moderation analysis further revealed that the intensity of the DN posterior-anterior interaction moderated sleep loss and positive affect.

DISCUSSION

Overall, the results reveal the strong relationship between the uncoupling of DN and the feeling down of mood. Our research may contribute towards a better understanding of the mood and cognition processing after sleep loss.

Distinct Functional Connectivity Patterns Are Associated With Social and Cognitive Lifestyle Factors: Pathways to Cognitive Reserve

The importance of diverse lifestyle factors in sustaining cognition during aging and delaying the onset of decline in Alzheimer's disease and related dementias cannot be overstated. We explored the influence of cognitive, social, and physical lifestyle factors on resting-state lagged linear connectivity (LLC) in high-density electroencephalography (EEG) in adults, ages 35-75 years. Diverse lifestyle factors build cognitive reserve (CR), protecting cognition in the presence of physical brain decline. Differences in LLC were examined between high- and low-CR groups formed using cognitive, social, and exercise lifestyle factors. LLC is a measure of lagged coherence that excludes zero phase contributions and limits the effects of volume conduction on connectivity estimates. Significant differences in LLC were identified for cognitive and social factors, but not exercise. Participants high in social CR possessed greater local and long-range connectivity in theta and low alpha for eyes-open and eyes-closed recording conditions. In contrast, participants high in cognitive CR exhibited greater eyes-closed long-range connectivity between the occipital lobe and other cortical regions in low alpha. Greater eyes-closed local LLC in delta was also present in men high in cognitive CR. Cognitive factor scores correlated with sustained attention, whereas social factors scores correlated with spatial working memory. Gender was a significant covariate in our analyses, with women displaying higher local and long-range LLC in low beta. Our findings support distinct relationships between CR and LLC, as well as CR and cognitive function for cognitive and social subcomponents. These patterns reflect the importance of diverse lifestyle factors in building CR.

Unsuccessful reduction of high-frequency alpha activity during cognitive activation in schizophrenia

AIMS

Electroencephalogram (EEG) alpha activity during resting state reflects the 'readiness' of an individual to respond to the environment; this includes the performance of cognitive processes. Alpha activity is reported to be attenuated in schizophrenia (SCZ). Understanding the interaction between alpha activity during rest and when cognitively engaged may provide insights into the neural circuitry, which is dysfunctional in SCZ. This study investigated the changes of alpha activity between resting state and cognitive engagement in SCZ patients.

METHODS

Thirty-four SCZ patients and 29 healthy controls (HC) were recruited. EEG was performed in the resting state and during an auditory P300 task. All experimental procedures followed the relevant institutional guidelines and regulations.

RESULTS

In SCZ, high-frequency alpha activity was reduced in the resting state. High-frequency alpha source density was decreased in both the resting-state and a P300 task condition in patients, compared to HC. HC, but not SCZ patients, showed a reduction in high-frequency alpha source density during the P300 task compared to the resting state. The negative correlation between high-frequency alpha source density in the resting state and positive symptoms was significant.

CONCLUSIONS

High-frequency alpha activity in SCZ patients and its unsuccessful reduction during cognitive processing may be biological markers of SCZ.

Long-Range Temporal Correlations in Resting State Beta Oscillations are Reduced in Schizophrenia

Symptoms of schizophrenia (SCZ) are likely to be generated by genetically mediated synaptic dysfunction, which contribute to large-scale functional neural dysconnectivity. Recent electrophysiological studies suggest that this dysconnectivity is present not only at a spatial level but also at a temporal level, operationalized as long-range temporal correlations (LRTCs). Previous research suggests that alpha and beta frequency bands have weaker temporal stability in people with SCZ. This study sought to replicate these findings with high-density electroencephalography (EEG), enabling a spatially more accurate analysis of LRTC differences, and to test associations with characteristic SCZ symptoms and cognitive deficits. A 128-channel EEG was used to record eyes-open resting state brain activity of 23 people with SCZ and 24 matched healthy controls (HCs). LRTCs were derived for alpha (8-12 Hz) and beta (13-25 Hz) frequency bands. As an exploratory analysis, LRTC was source projected using sLoreta. People with SCZ showed an area of significantly reduced beta-band LRTC compared with HCs over bilateral posterior regions. There were no between-group differences in alpha-band activity. Individual symptoms of SCZ were not related to LRTC values nor were cognitive deficits. The study confirms that people with SCZ have reduced temporal stability in the beta frequency band. The absence of group differences in the alpha band may be attributed to the fact that people had, in contrast to previous studies, their eyes open in the current study. Taken together, our study confirms the utility of LRTC as a marker of network instability in people with SCZ and provides a novel empirical perspective for future examinations of network dysfunction salience in SCZ research.

Long-Range Temporal Correlations in Alpha Oscillations Stabilize Perception of Ambiguous Visual Stimuli

Ongoing brain dynamics have been proposed as a type of “neuronal noise” that can trigger perceptual switches when viewing an ambiguous, bistable stimulus. However, no prior study has directly quantified how such neuronal noise relates to the rate of percept reversals. Specifically, it has remained unknown whether individual differences in complexity of resting-state oscillations—as reflected in long-range temporal correlations (LRTC)—are associated with perceptual stability. We hypothesized that participants with stronger resting-state LRTC in the alpha band experience more stable percepts, and thereby fewer perceptual switches. Furthermore, we expected that participants who report less discontinuous thoughts during rest, experience less switches. To test this, we recorded electroencephalography (EEG) in 65 healthy volunteers during 5 min Eyes-Closed Rest (ECR), after which they filled in the Amsterdam Resting-State Questionnaire (ARSQ). This was followed by three conditions where participants attended an ambiguous structure-from-motion stimulus—Neutral (passively observe the stimulus), Hold (the percept for as long as possible), and Switch (as often as possible). LRTC of resting-state alpha oscillations predicted the number of switches only in the Hold condition, with stronger LRTC associated with less switches. Contrary to our expectations, there was no association between resting-state Discontinuity of Mind and percept stability. Participants were capable of controlling switching according to task goals, and this was accompanied by increased alpha power during Hold and decreased power during Switch. Fewer switches were associated with stronger task-related alpha LRTC in all conditions. Together, our data suggest that bistable visual perception is to some extent under voluntary control and influenced by LRTC of alpha oscillations.

The Impact of Age and Cognitive Reserve on Resting-State Brain Connectivity

Cognitive reserve (CR) is a protective mechanism that supports sustained cognitive function following damage to the physical brain associated with age, injury, or disease. The goal of the research was to identify relationships between age, CR, and brain connectivity. A sample of 90 cognitively normal adults, ages 45–64 years, had their resting-state brain activity recorded with electroencephalography (EEG) and completed a series of memory and executive function assessments. CR was estimated using years of education and verbal IQ scores. Participants were divided into younger and older age groups and low- and high-CR groups. We observed greater left- than right-hemisphere coherence in younger participants, and greater right- than left-hemisphere coherence in older participants. In addition, greater coherence was observed under eyes-closed than eyes-open recording conditions for both low-CR and high-CR participants, with a more substantial difference between recording conditions in individuals high in CR regardless of age. Finally, younger participants low in CR exhibited greater mean coherence than younger participants high in CR, whereas the opposite pattern was observed in older participants, with greater coherence in older participants high in CR. Together, these findings suggest the possibility of a shift in the relationship between CR and brain connectivity during aging.

Bridging the gap between perceptual and cognitive perspectives on absolute pitch

Absolute pitch (AP) refers to the rare ability to identify the chroma of a tone or to produce a specific pitch without reference to keyality (e.g., G or C). Previously, AP has been proposed to rely on the distinctive functional-anatomical architecture of the left auditory-related cortex (ARC), this specific trait possibly enabling an optimized early "categorical perception". In contrast, currently prevailing models of AP postulate that cognitive rather than perceptual processes, namely "pitch labeling" mechanisms, more likely constitute the bearing skeleton of AP. This associative memory component has previously been proposed to be dependent, among other mechanisms, on the recruitment of the left dorsolateral prefrontal cortex (DLPFC) as well as on the integrity of the left arcuate fasciculus, a fiber bundle linking the posterior supratemporal plane with the DLPFC. Here, we attempted to integrate these two apparently conflicting perspectives on AP, namely early "categorical perception" and "pitch labeling". We used electroencephalography and evaluated resting-state intracranial functional connectivity between the left ARC and DLPFC in a sample of musicians with and without AP. Results demonstrate significantly increased left-hemispheric theta phase synchronization in AP compared with non-AP musicians. Within the AP group, this specific electrophysiological marker was predictive of absolute-hearing behavior and explained ∼30% of variance. Thus, we propose that in AP subjects the tonal inputs and the corresponding mnemonic representations are tightly coupled in such a manner that the distinctive electrophysiological signature of AP can saliently be detected in only 3 min of resting-state measurements.

Changes in functional brain networks following sports-related concussion in adolescents

Sports-related concussion is a major public health issue; however, little is known about the underlying changes in functional brain networks in adolescents following injury. Our aim was to use the tools from graph theory to evaluate the changes in brain network properties following concussion in adolescent athletes. We recorded resting state electroencephalography (EEG) in 33 healthy adolescent athletes and 9 adolescent athletes with a clinical diagnosis of subacute concussion. Graph theory analysis was applied to these data to evaluate changes in brain networks. Global and local metrics of the structural properties of the graph were calculated for each group and correlated with Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) scores. Brain networks of both groups showed small-world topology with no statistically significant differences in the global metrics; however, significant differences were found in the local metrics. Specifically, in the concussed group, we noted: 1) increased values of betweenness and degree in frontal electrode sites corresponding to the (R) dorsolateral prefrontal cortex and the (R) inferior frontal gyrus and 2) decreased values of degree in the region corresponding to the (R) frontopolar prefrontal cortex. In addition, there was significant negative correlation between degree and hub value, with total symptom score at the electrode site corresponding to the (R) prefrontal cortex. This preliminary report in adolescent athletes shows for the first time that resting-state EEG combined with graph theoretical analysis may provide an objective method of evaluating changes in brain networks following concussion. This approach may be useful in identifying individuals at risk for future injury.