Reliability of quantitative EEG (qEEG) measures and LORETA current source density at 30 days

Microstate Analysis of Continuous Infant EEG: Tutorial and Reliability

Microstate analysis of resting-state EEG is a unique data-driven method for identifying patterns of scalp potential topographies, or microstates, that reflect stable but transient periods of synchronized neural activity evolving dynamically over time. During infancy - a critical period of rapid brain development and plasticity - microstate analysis offers a unique opportunity for characterizing the spatial and temporal dynamics of brain activity. However, whether measurements derived from this approach (e.g., temporal properties, transition probabilities, neural sources) show strong psychometric properties (i.e., reliability) during infancy is unknown and key information for advancing our understanding of how microstates are shaped by early life experiences and whether they relate to individual differences in infant abilities. A lack of methodological resources for performing microstate analysis of infant EEG has further hindered adoption of this cutting-edge approach by infant researchers. As a result, in the current study, we systematically addressed these knowledge gaps and report that most microstate-based measurements of brain organization and functioning except for transition probabilities were stable with four minutes of video-watching resting-state data and highly internally consistent with just one minute. In addition to these results, we provide a step-by-step tutorial, accompanying website, and open-access data for performing microstate analysis using a free, user-friendly software called Cartool. Taken together, the current study supports the reliability and feasibility of using EEG microstate analysis to study infant brain development and increases the accessibility of this approach for the field of developmental neuroscience.

EEG-Based Cortical Alterations in Individuals With Chronic Knee Pain Secondary to Osteoarthritis: A Cross-sectional Investigation

Chronic painful knee osteoarthritis (OA) is a disabling physical health condition. Alterations in brain responses to arthritic changes in the knee may explain persistent pain. This study investigated source localized, resting-state electroencephalography activity and functional connectivity in people with knee OA, compared to healthy controls. Adults aged 44 to 85 years with knee OA (n = 37) and healthy control (n = 39) were recruited. Resting-state electroencephalography was collected for 10 minutes and decomposed into infraslow frequency (ISF) to gamma frequency bands. Standard low-resolution electromagnetic brain tomography statistical nonparametric maps were conducted, current densities of regions of interest were compared between groups and correlation analyses were performed between electroencephalography (EEG) measures and clinical pain and functional outcomes in the knee OA group. Standard low-resolution electromagnetic brain tomography nonparametric maps revealed higher (P = .006) gamma band activity over the right insula (RIns) in the knee OA group. A significant (P < .0001) reduction in ISF band activity at the pregenual anterior cingulate cortex, whereas higher theta, alpha, beta, and gamma band activity at the dorsal anterior cingulate cortex, pregenual anterior cingulate cortex, the somatosensory cortex, and RIns in the knee OA group were identified. ISF activity of the dorsal anterior cingulate cortex was positively correlated with pain measures and psychological distress scores. Theta and alpha activity of RIns were negatively correlated with pain interference. In conclusion, aberrations in infraslow and faster frequency EEG oscillations at sensory discriminative, motivational-affective, and descending inhibitory cortical regions were demonstrated in people with chronic painful knee OA. Moreover, EEG oscillations were correlated with pain and functional outcome measures. PERSPECTIVE: This study confirms alterations in the rsEEG oscillations and its relationship with pain experience in people with knee OA. The study provides potential cortical targets and the EEG frequency bands for neuromodulatory interventions for managing chronic pain experience in knee OA.

Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach

Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95-100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.

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

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

Saccades and presaccadic stimulus repetition alter cortical network topology and dynamics: evidence from EEG and graph theoretical analysis

Parietal and frontal cortex are involved in saccade generation, and their output signals modify visual signals throughout cortex. Local signals associated with these interactions are well described, but their large-scale progression and network dynamics are unknown. Here, we combined source localized electroencephalography (EEG) and graph theory analysis (GTA) to understand how saccades and presaccadic visual stimuli interactively alter cortical network dynamics in humans. Twenty-one participants viewed 1-3 vertical/horizontal grids, followed by grid with the opposite orientation just before a horizontal saccade or continued fixation. EEG signals from the presaccadic interval (or equivalent fixation period) were used for analysis. Source localization-through-time revealed a rapid frontoparietal progression of presaccadic motor signals and stimulus-motor interactions, with additional band-specific modulations in several frontoparietal regions. GTA analysis revealed a saccade-specific functional network with major hubs in inferior parietal cortex (alpha) and the frontal eye fields (beta), and major saccade-repetition interactions in left prefrontal (theta) and supramarginal gyrus (gamma). This network showed enhanced segregation, integration, synchronization, and complexity (compared with fixation), whereas stimulus repetition interactions reduced synchronization and complexity. These cortical results demonstrate a widespread influence of saccades on both regional and network dynamics, likely responsible for both the motor and perceptual aspects of saccades.

Interindividual differences in intergenerational sustainable behavior are associated with cortical thickness of the dorsomedial and dorsolateral prefrontal cortex

Intergenerational sustainability requires people of the present generation to make sacrifices today to benefit others of future generations (e.g. mitigating climate change, reducing public debt). Individuals vary greatly in their intergenerational sustainability, and the cognitive and neural sources of these interindividual differences are not yet well understood. We here combined neuroscientific and behavioral methods by assessing interindividual differences in cortical thickness and by using a common-pool resource paradigm with intergenerational contingencies. This enabled us to look for objective, stable, and trait-like neural markers of interindividual differences in consequential intergenerational behavior. We found that individuals behaving sustainably (vs. unsustainably) were marked by greater cortical thickness of the dorsomedial and dorsolateral prefrontal cortex. Given that these brain areas are involved in perspective-taking and self-control and supported by mediation analyses, we speculate that greater cortical thickness of these brain areas better enable individuals to take the perspective of future generations and to resist temptations to maximize personal benefits that incur costs for future generations. By meeting recent calls for the contribution of neuroscience to sustainability research, it is our hope that the present study advances the transdisciplinary understanding of interindividual differences in intergenerational sustainability.

Treatment biomarkers for ADHD: Taking stock and moving forward

The development of treatment biomarkers for psychiatric disorders has been challenging, particularly for heterogeneous neurodevelopmental conditions such as attention-deficit/hyperactivity disorder (ADHD). Promising findings are also rarely translated into clinical practice, especially with regard to treatment decisions and development of novel treatments. Despite this slow progress, the available neuroimaging, electrophysiological (EEG) and genetic literature provides a solid foundation for biomarker discovery. This article gives an updated review of promising treatment biomarkers for ADHD which may enhance personalized medicine and novel treatment development. The available literature points to promising pre-treatment profiles predicting efficacy of various pharmacological and non-pharmacological treatments for ADHD. These candidate predictive biomarkers, particularly those based on low-cost and non-invasive EEG assessments, show promise for the future stratification of patients to specific treatments. Studies with repeated biomarker assessments further show that different treatments produce distinct changes in brain profiles, which track treatment-related clinical improvements. These candidate monitoring/response biomarkers may aid future monitoring of treatment effects and point to mechanistic targets for novel treatments, such as neurotherapies. Nevertheless, existing research does not support any immediate clinical applications of treatment biomarkers for ADHD. Key barriers are the paucity of replications and external validations, the use of small and homogeneous samples of predominantly White children, and practical limitations, including the cost and technical requirements of biomarker assessments and their unknown feasibility and acceptability for people with ADHD. We conclude with a discussion of future directions and methodological changes to promote clinical translation and enhance personalized treatment decisions for diverse groups of individuals with ADHD.

Enhanced effective connectivity from the middle frontal gyrus to the parietal lobe is associated with impaired mental rotation after total sleep deprivation: An electroencephalogram study

Sleep deprivation impairs cognitive functions, including attention, memory, and decision-making. Studies on the neuro-electro-physiological mechanisms underlying total sleep deprivation (TSD) that impairs spatial cognition are limited. Based on electroencephalogram (EEG) and Exact Low Resolution Brain Electromagnetic Tomography (eLORETA), this study focused on the effects of TSD on mental rotation and the cognitive neural mechanisms underlying its damage. Twenty-four healthy college students completed mental rotation tasks while resting and after 36 h of TSD; their EEG data were simultaneously recorded. The amplitude of P300 component associated with mental rotation was observed and localized through source reconstruction, while changes in effective connectivity between multiple brain regions associated with mental rotation cognitive processing were calculated using isolated effective coherence (iCoh) of eLORETA. Compared with the baseline before TSD, the amplitude of the P300 component related to mental rotation decreased. The task-state data of P300 were localized to the source of the difference in ERP current density, and it was found that the brain regions related to the difference in the decrease in P300 amplitude included the superior parietal lobule, precuneus, prefrontal lobe, and other related regions. Effective connectivity analysis found that TSD enhanced the effective connectivity from the left middle frontal gyrus to the left superior parietal lobule, left inferior parietal lobule, and left precuneus under the identical condition. Pearson correlation analysis showed a positive correlation between the decrease in accuracy of mental rotation and increase in effective connectivity. Thus, our study suggests that TSD impairs the ability of the mental rotation, showing a decrease in P300 amplitude and an enhanced effective connectivity between the middle frontal gyrus and the parietal lobe in the task state.

Quantitative Electroencephalography and Surface Electromyography Correlations upon Predictable and Unpredictable Perturbation in Older Adults

BACKGROUND

Quantitative Electroencephalography (qEEG) is a non-invasive method used to quantify electrical activity over the cortex. QEEG provides an accurate temporal resolution of the brain activity, making it a useful tool for assessing cortical function during challenging tasks.

OBJECTIVE

This study aimed to investigate postural adjustments in older adults in response to an external perturbation.

MATERIAL AND METHODS

In this observational study, nineteen healthy older adults were involved. A 32-channel qEEG was employed to track alterations in beta power on the electrodes over the two sensory-motor areas. Integrated electromyographic activity (IntEMG) of the leg muscles was evaluated in response to perturbations under predictable and unpredictable conditions.

RESULTS

The results indicated higher beta power during late-phase in the Cz electrode in both conditions. IntEMG was significantly greater in the tibialis anterior muscle during both conditions in the CPA epoch. In predictable condition, a positive correlation was found between the beta power over C₄ (r = 0.560, p = 0.013) and C₃ (r = 0.458, p = 0.048) electrodes and tibialis anterior muscle amplitude, and between beta power in C₄ and gastrocnemius amplitude (r = 0.525, p = 0.021). In unpredictable condition, there was a positive correlation between beta power over the C₄ and the tibialis anterior amplitude (r = 0.580, p = 0.009) and also it over the C₃ and the tibialis anterior amplitude (r = 0.452, p = 0.049).

CONCLUSION

Our findings demonstrate that sensorimotor processing occurs in the brain during response to perturbation. Furthermore, cortical activity appeared to be greatest during the recruitment of the muscles upon late-phase in older adults.

Human resting-state EEG and radiofrequency GSM mobile phone exposure: the impact of the individual alpha frequency

PURPOSE

With the extensive use of mobile phone (MP), several studies have been realized to investigate the effects of radiofrequency electromagnetic fields (RF-EMF) exposure on brain activity at rest via electroencephalography (EEG), and the most consistent effect has been seen on the alpha band power spectral density (PSD). However, some studies reported an increase or a decrease of the PSD, while others showed no effect. It has been suggested that these differences might partly be due to a variability of the physiological state of the brain between subjects. So, the aim of this study was to investigate the alpha band modulation, exploring the impact of the alpha band frequency ranges applied in the PSD analysis.

MATERIALS AND METHODS

Twenty-one healthy volunteers took part to the study with a double-blind, randomized and counterbalanced crossover design, during which eyes-open (EO) and eyes-closed (EC) resting-state EEG was recorded. The exposure system was a sham or a real GSM (global system for mobile) 900 MHz MP (pulse modulated at 217 Hz, mean power of 250 mW and 2 W peak, with a maximum specific absorption rate of 0.70 W/kg on 1 g tissue). The experimental protocol presented a baseline recording phase without MP exposure, an exposure phase during which the exposure system was placed against the left ear, and the post-exposure phase without MP. EEG data from baseline and exposure phases were analyzed and PSD was computed for the alpha band in the fixed range of 8-12 Hz and for the individual alpha band frequency range (IAF).

RESULTS

Results showed a trend in decrease or increase of EEG power of both alpha oscillations during exposure in relation to EC and EO recording conditions, respectively, but not reaching statistical significance. Findings did not provide evidence for a different sensitivity to RF-EMF MP related to individual variability in the frequency of the alpha band.

CONCLUSION

In conclusion, these results did not show alpha band activity modulation during resting-state under RF-EMF. It might be argued the need of a delay after the exposure in order to appreciate an EEG spectral power modulation related to RF-EMF exposure.

Modulation of magnetoencephalography alpha band activity by radiofrequency electromagnetic field depicted in sensor and source space

Several studies reported changes in spontaneous electroencephalogram alpha band activity related to radiofrequency electromagnetic fields, but findings showed both an increase and a decrease of its spectral power or no effect. Here, we studied the alpha band modulation after 900 MHz mobile phone radiofrequency exposure and localized cortical regions involved in these changes, via a magnetoencephalography (MEG) protocol with healthy volunteers in a double-blind, randomized, counterbalanced crossover design. MEG was recorded during eyes open and eyes closed resting-state before and after radiofrequency exposure. Potential confounding factors, known to affect alpha band activity, were assessed as control parameters to limit bias. Entire alpha band, lower and upper alpha sub-bands MEG power spectral densities were estimated in sensor and source space. Biochemistry assays for salivary biomarkers of stress (cortisol, chromogranin-A, alpha amylase), heart rate variability analysis and high-performance liquid chromatography for salivary caffeine concentration were realized. Results in sensor and source space showed a significant modulation of MEG alpha band activity after the radiofrequency exposure, with different involved cortical regions in relation to the eyes condition, probably because of different attention level with open or closed eyes. None of the control parameters reported a statistically significant difference between experimental sessions.

Neural signatures of heterogeneity in risk-taking and strategic consistency

People display a high degree of heterogeneity in risk-taking behaviour, but this heterogeneity remains poorly understood. Here, we use a neural trait approach to examine if task-independent, brain-based differences can help uncover the sources of heterogeneity in risky decision-making. We extend prior research in two key ways. First, we disentangled risk-taking and strategic consistency using novel measures afforded by the Balloon Analogue Risk Task. Second, we applied a personality neuroscience framework to explore why personality traits are typically only weakly related to risk-taking behaviour. We regressed participants' (N = 104) source localized resting-state electroencephalographic activity on risk-taking and strategic consistency. Results revealed that higher levels of resting-state delta-band current density (reflecting reduced cortical activation) in the left dorsal anterior cingulate cortex and the left dorsolateral prefrontal cortex were associated with increased risk-taking and decreased strategic consistency, respectively. These results suggest that heterogeneity in risk-taking behaviour is associated with neural dispositions related to sensitivity to the risk of loss, whereas heterogeneity in strategic consistency is associated with neural dispositions related to strategic decision-making. Finally, extraversion, neuroticism, openness, and self-control were broadly associated with both of the identified neural traits, which in turn mediated indirect associations between personality traits and behavioural measures. These results provide an explanation for the weak direct relationships between personality traits and risk-taking behaviour, supporting a personality neuroscience framework of traits and decision-making.

Development of Frontal EEG Differences Between Eyes-Closed and Eyes-Open Resting Conditions in Children: Data From a Single-Channel Dry-Sensor Portable Device

Changes in EEG when moving from an eyes-closed to an eyes-open resting condition result from bottom-up sensory processing and have been referred to as activation. In children, activation is characterized by a global reduction in alpha, frontally present reductions for delta and theta, and a frontal increase for beta. The present study aimed to replicate frontal EEG activation effects using single-channel, dry-sensor EEG, and to extend current understanding by examining developmental change in children. Frontal EEG was recorded using a single-channel, dry-sensor EEG device while 182 children aged 7 to 12 years completed eyes-closed resting (EC), eyes-open resting (EO), and focus (FO) tasks. Results indicated that frontal delta, theta, and alpha power were reduced, and frontal beta power was increased, in the EO compared with the EC condition. Exploratory analysis of a form of top-down activation showed that frontal beta power was increased in the FO compared with to the EO condition, with no differences for other bands. The activation effects were robust at the individual level. The bottom-up activation effects reduced with age for frontal delta and theta, increased for frontal alpha, with no developmental change for top-down or bottom-up frontal beta activation. These findings contribute further to validation of the single-channel, dry-sensor, frontal EEG and provide support for use in a range of medical, therapeutic, and clinical domains.

Aiding diagnosis of childhood attention-deficit/hyperactivity disorder of the inattentive presentation: Discriminant function analysis of multi-domain measures including EEG

INTRODUCTION

We developed a neurocognitive assessment tool (NCAT) in consultation with mental health professionals working with children with AD/HD as a diagnostic aid and screening tool. This study examines the predictive utility of NCAT in the classification of children with AD/HD Inattentive presentation.

METHOD

Fifty three children with AD/HD Inattentive presentation and 161 typically-developing children completed an NCAT assessment. Discriminant function analyses examined group membership prediction for separate components of NCAT and for the components combined.

RESULTS

The combined model correctly classified 93.4 % of participants, with 91.4 % sensitivity and 93.9 % specificity. Contributions to classification were from SNAP-IV, psychological needs satisfaction, self-regulation, executive function performance, and EEG. The combined model resulted in a 9.3 % increase in specificity and 5.9 % increase in sensitivity compared to SNAP-IV alone.

CONCLUSIONS

NCAT provides good discrimination between children with and without AD/HD of the Inattentive presentation, and further investigation including other subtypes and comorbidities is warranted.

Exploration of illusory visual motion stimuli: An EEG-based brain-computer interface for practical assistive communication systems

This paper presents an illusory visual motion stimulus-based brain-computer interface (BCI). We aim to use the proposed system to enhance the motor imagery (MI) modality. Since motor imagery requires a long time for training, a stimulation method with external stimuli through the sensory system is an alternative method for increasing efficiency. The research is divided into two parts. First, we observed the visual motion illusion pattern based on brain topographic maps for the novel BCI modality. Second, we implemented the illusory visual motion stimulus-based BCI system. Arrow and moving-arrow patterns were used to modulate alpha rhythms at the visual and motor cortex. The arrow pattern had an average classification accuracy of approximately 78.5%. Additionally, illusory visual motion stimulus-based BCI systems are proposed using the proposed feature extraction and decision-making algorithm. This proposed BCI system can control the cursor moving in the left or right direction with the designed algorithm to create five commands for assistive communication. Ten volunteers participated in the experiment, and a brain-computer interface system with motor imagery and an illusory visual motion stimulus were used to compare efficiencies. The results showed that the proposed method achieved approximately 4% higher accuracy than motor imagery. The accuracy of the proposed illusory visual motion stimulus and algorithm was approximately 80.3%. Therefore, an illusory visual motion stimulus hybrid BCI system can be incorporated into the MI-based BCI system for beginner motor imagery. Based on the results, the proposed assistive communication system can be used to enhance communication in people with severe disabilities.

Clinical Application of Combined EEG-qEEG Functional Neuroimaging in the Practice of Pediatric Neuroscience: A Personal Perspective

This brief article is an overview of my personal experience over the past almost 10 years of the clinical use of EEG and quantitative EEG (qEEG) functional neuroimaging in a busy pediatric neurology practice. The concomitant use of surface EEG and functional electromagnetic EEG neuroimaging/qEEG in clinical practice provides significant additional clinical and neurophysiologic information. The qEEG is a noninvasive, inexpensive, portable technique with high temporal resolution (milliseconds) and improving spatial resolution (down to 3 mm³) and is an appropriate and validated tool for investigation of abnormal brain dynamics and connectivity of neuronal networks in clinical disorders of the brain. This article describes the daily applicability and utility of this modality in assisting diagnosis and clinical management of patients with a wide variety of presenting symptoms, including headaches, tics, autism spectrum disorder, inattention, sleep dysregulation, anxiety, and depression. The ease of data acquisition and analysis in clinical practices, coupled with skilled interpretation and clinical application, makes this tool one of the most valuable clinical tools to complement a thorough history and examination process.

Variability and effect sizes of intracranial current source density estimations during pain: Systematic review, experimental findings, and future perspectives

Pain arises from the integration of sensory and cognitive processes in the brain, resulting in specific patterns of neural oscillations that can be characterized by measuring electrical brain activity. Current source density (CSD) estimation from low-resolution brain electromagnetic tomography (LORETA) and its standardized (sLORETA) and exact (eLORETA) variants, is a common approach to identify the spatiotemporal dynamics of the brain sources in physiological and pathological pain-related conditions. However, there is no consensus on the magnitude and variability of clinically or experimentally relevant effects for CSD estimations. Here, we systematically examined reports of sample size calculations and effect size estimations in all studies that included the keywords pain, and LORETA, sLORETA, or eLORETA in Scopus and PubMed. We also assessed the reliability of LORETA CSD estimations during non-painful and painful conditions to estimate hypothetical sample sizes for future experiments using CSD estimations. We found that none of the studies included in the systematic review reported sample size calculations, and less than 20% reported measures of central tendency and dispersion, which are necessary to estimate effect sizes. Based on these data and our experimental results, we determined that sample sizes commonly used in pain studies using CSD estimations are suitable to detect medium and large effect sizes in crossover designs and only large effects in parallel designs. These results provide a comprehensive summary of the effect sizes observed using LORETA in pain research, and this information can be used by clinicians and researchers to improve settings and designs of future pain studies.

Long-term stability of resting state EEG-based linear and nonlinear measures

This preliminary study is aimed to evaluate the stability of various linear and nonlinear EEG measures over three years on healthy adults. The linear measures, relative powers of EEG frequency bands, interhemispheric (IHAS) and spectral (SASI) asymmetries plus nonlinear Higuchi's fractal dimension (HFD) and detrended fluctuation analyses (DFA), have been calculated from the resting state eyes closed EEG of 17 participants during two sessions separated over three years. Our results indicate that the stability is highest for the nonlinear (HFD and DFA) and the linear (relative powers of EEG frequency bands) EEG measures that use the signal from a single EEG channel and frequency band, followed by the SASI employing signals from a single channel and two frequency bands and lowest for the IHAS employing signals from two channels. The result support the prospect of using EEG-based measures in clinical practice.

Reliability and Individual Specificity of EEG Microstate Characteristics

Electroencephalography (EEG) microstates (MSs) are defined as quasi-stable topographies that represent global coherent activation. Alternations in EEG MSs have been reported in numerous neuropsychiatric disorders. Transferring the results of these studies into clinical practice requires not only high reliability but also sufficient individual specificity. Nevertheless, whether the amount of data used in microstate analysis influences reliability and how much individual information is provided by EEG MSs are unclear. In the current study, we aimed to assess the within-subject consistency and between-subject differences in the characteristics of EEG MSs. Two sets of eyes-closed resting-state EEG recordings were collected from 54 young, healthy participants on two consecutive days. The Raven Advanced Progressive Matrices test was conducted to assess general fluid intelligence (gF). We obtained four MSs (labeled A, B, C and D) through EEG microstate analysis. EEG MS characteristics including traditional features (the global explained variances, mean durations, coverages, occurrences and transition probabilities), the Hurst exponents and temporal dynamic features (the autocorrelation functions and the partial autocorrelation functions) were calculated and evaluated. The data with a duration greater than 2 min showed moderate to high reliability and individual specificity. The mean duration and coverage of MS C were significantly correlated with the gF score. The dynamic features showed a higher identification accuracy and were more significantly correlated with gF than the traditional MS features. These findings reveal that EEG microstate characteristics are reliably unique in single subjects and possess abundant inter-individual variability.

Theta resting EEG in the right TPJ is associated with individual differences in implicit intergroup bias

Why are some people more biased than others in their implicit evaluations during social interaction? The dispositional determinants of individual differences in implicit intergroup bias are poorly understood. Here, we explored whether such variability might be explained by stable neural traits. For that purpose, we used the source-localized resting electroencephalograms of 83 members of naturalistic social groups to explain their bias in an in-/outgroup implicit association test. Lower levels of resting theta current density in the right temporo-parietal junction (TPJ) were associated with stronger implicit intergroup bias and explained unique variability in bias beyond relevant personality questionnaires. These findings demonstrate the added value of the neural trait approach in predicting inter-individual differences in implicit social cognition. Given that low levels of resting theta current density during wakefulness likely reflect increased cortical activation, our results suggest that individuals with an efficiently working right TPJ possess capacities to mediate specific cognitive processes that predispose them towards stronger implicit intergroup bias. As the human species has evolved living in distinct social groups, the capacity to quickly differentiate friend from foe became highly adaptive and might thus constitute an essential part of human nature.

Neural traits characterize unconditional cooperators, conditional cooperators, and noncooperators in group-based cooperation

Contributing to and maintaining public goods are important for a functioning society. In reality, however, we see large variations in contribution behavior. While some individuals are not cooperative, others are highly so. Still others cooperate only to the extent they believe others will. Although these distinct behavioral types clearly have a divergent social impact, the sources of heterogeneity are poorly understood. We used source‐localized resting electroencephalography in combination with a model‐free clustering approach to participants' behavior in the Public Goods Game to explain heterogeneity. Findings revealed that compared to noncooperators, both conditional cooperators and unconditional cooperators are characterized by higher baseline activation in the right temporo‐parietal junction, an area involved in social cognition. Interestingly, conditional cooperators were further characterized by higher baseline activation in the left lateral prefrontal cortex, an area involved in behavioral control. Our findings suggest that conditional cooperators' better capacities for behavioral control enable them to control their propensity to cooperate and thus to minimize the risk of exploitation by noncooperators.

Influence of depressive feelings in the brain processing of women with fibromyalgia: An EEG study

Depression is one of the most common mental health problems which affects more than 10% of the global population. The prevalence of this disorder is higher in fibromyalgia patients. However, the influence of the combination of depression and fibromyalgia in the brain processing is poorly understood.To explore the modifications of EEG power spectrum in women with fibromyalgia when depressive feelings are elicited.Twenty eight women with fibromyalgia participated in this cross-sectional study. They were classified as women with depression or women without depression according to the score in the Geriatric Depression Scale. This questionnaire was used to elicit depression symptoms during the EEG recording. Analyses were performed with the standardized LOw Resolution Electric Tomography (sLORETA) software. Power spectrum were compared in the following frequency bands: delta, theta, alpha-1, alpha-2, beta-1, beta-2, and beta-3.Fibromyalgia patients with untreated depression showed a hypoactivation of the left hemisphere when compared with fibromyalgia patients without depression. In addition, when compared fibromyalgia patients without depression and women with both fibromyalgia and depression who were taking antidepressant medications, differences in EEG power spectrum in the studied frequency bands were not found.The current study contributes to the understanding on the influence of the combination of fibromyalgia and depression in the brain activity patterns. Patients with untreated depression showed a hypoactivation of the left hemisphere while eliciting depression symptoms. However, further research is needed, antidepressant medication might reduce the differences between patients with depression and those who do not suffer from depression symptoms.

Automated Source Estimation of Scalp EEG Epileptic Activity Using eLORETA Kurtosis Analysis

OBJECTIVES

eLORETA (exact low-resolution brain electromagnetic tomography) is a technique created by Pascual-Marqui et al. [Int J Psychophysiol. 1994 Oct; 18(1): 49-65] for the 3-dimensional representation of current source density in the brain by electroencephalography (EEG) data. Kurtosis analysis allows for the identification of spiky activity in the brain. In this study, we focused on the evaluation of the reliability of eLORETA kurtosis analysis. For this purpose, the results of eLORETA kurtosis source localization of paroxysmal activity in EEG were compared with those of eLORETA current source density (CSD) analysis of EEG data in 3 epilepsy patients with partial seizures.

METHODS

EEG was measured using a digital EEG system with 19 channels. We set the bandpass filter at traditional frequency band settings (1-4, 4-8, 8-15, 15-30, and 30-60 Hz) and 5-10 and 20-70 Hz and performed eLORETA kurtosis to compare the source localization of paroxysmal activity with that of visual interpretation of EEG data and CSD analysis of eLORETA in focal epilepsy patients.

RESULTS

The eLORETA kurtosis analysis of EEG data preprocessed by bandpass filtering from 20 to 70 Hz and traditional frequency band settings did not show any discrete paroxysmal source activity compatible with the results of CSD analysis of eLORETA. In all 3 cases, eLORETA kurtosis analysis filtered at 5-10 Hz showed paroxysmal activities in the theta band, which were all consistent with the visual inspection results and the CSD analysis results.

DISCUSSION

Our findings suggested that eLORETA kurtosis analysis of EEG data might be useful for the identification of spiky paroxysmal activity sources in epilepsy patients. Since EEG is widely used in the clinical practice of epilepsy, eLORETA kurtosis analysis is a promising method that can be applied to epileptic activity mapping.

Theta resting EEG in TPJ/pSTS is associated with individual differences in the feeling of being looked at

Direct eye gaze is a powerful stimulus in social interactions, yet people vary considerably in the range of gaze lines that they accept as being direct (cone of direct gaze, CoDG). Here, we searched for a possible neural trait marker of these individual differences. We measured the width of the CoDG in 137 healthy participants and related their individual CoDG to their neural baseline activation as measured with resting electroencephalogram. Using a source-localization technique, we found that resting theta current density in the left temporo-parietal junction (TPJ) and adjacent posterior superior temporal sulcus (pSTS) was associated with the width of CoDG. Our findings suggest that the higher the baseline cortical activation in the left TPJ/pSTS, the wider the CoDG and thus the more liberal the individuals’ judgments were in deciding whether a looker stimulus was making eye contact or not. This is a first demonstration of the neural signatures underlying individual differences in the feeling of being looked at.

Understanding Individual Differences in Domain-General Prosociality: A Resting EEG Study

Prosocial behavior is of vital importance for the smooth functioning of society. However, the propensity to behave in a prosocial manner is characterized by vast individual differences. In order to reveal the sources of these differences, some studies have used objective, task-independent neural traits, for instance resting electroencephalography (EEG). Despite providing valuable insights into the neural signatures of several domains of prosociality, each of these studies has only focused on one single domain. Here, we exposed 137 participants to different social dilemma situations in order to obtain a measure of the individuals’ domain-general prosociality and recorded multi-channel task-independent, resting EEG. Using a source-localization technique, we found that resting current density within the temporo-parietal junction in two beta bands (beta2 and beta3) was positively associated with domain-general prosociality. This is the first demonstration of neural signatures underlying individual differences in the propensity to behave in a prosocial manner across different social situations.

An fMRI investigation of the neural correlates underlying the autonomous sensory meridian response (ASMR)

Introduction : The "autonomous sensory meridian response" (ASMR) is a neologism used to describe an internal sensation of deep relaxation and pleasant head tingling which is often stimulated by gentle sounds, light touch, and personal attention. Methods : An fMRI-based methodology was employed to examine the brain activation of subjects prescreened for ASMR-receptivity (n=10) as they watched ASMR videos and identified specific moments of relaxation and tingling. Results : Subjects who experienced ASMR showed significant activation in regions associated with both reward (NAcc) and emotional arousal (dACC and Insula/IFG). Brain activation during ASMR showed similarities to patterns previously observed in musical frisson as well as affiliative behaviors. Conclusion : This is the first study to measure the activation of various brain regions during ASMR and these results may help to reveal the mechanistic underpinnings of this sensation.

Neural signatures of different behavioral types in fairness norm compliance

Fairness norm compliance is critical in any society. However, norm compliant behavior is very heterogeneous. Some people are reliably fair (voluntary compliers). Some are fair to avoid sanctions (sanction-based compliers), and some are reliably unfair (non-compliers). These types play divergent roles in society. However, they remain poorly understood. Here, we combined neural measures (resting electroencephalography and event-related potentials) and economic paradigms to better understand these types. We found that voluntary compliers are characterized by higher baseline activation in the right temporo-parietal junction, suggesting better social cognition capacity compared to sanction-based compliers and non-compliers. The latter two types are differentiated by (a) baseline activation in the dorso-lateral prefrontal cortex, a brain area known to be involved in self-control processes, and (b) event-related potentials in a classic self-control task. Both results suggest that sanction-based compliers have better self-control capacity than non-compliers. These findings improve our understanding of fairness norm compliance. Broadly, our findings suggest that established training techniques that boost self-control might help non-compliers adhere to fairness norms.

Surface EEG-Transcranial Direct Current Stimulation (tDCS) Closed-Loop System

Conventional transcranial direct current stimulation (tDCS) protocols rely on applying electrical current at a fixed intensity and duration without using surrogate markers to direct the interventions. This has led to some mixed results; especially because tDCS induced effects may vary depending on the ongoing level of brain activity. Therefore, the objective of this preliminary study was to assess the feasibility of an EEG-triggered tDCS system based on EEG online analysis of its frequency bands. Six healthy volunteers were randomized to participate in a double-blind sham-controlled crossover design to receive a single session of 10[Formula: see text]min 2[Formula: see text]mA cathodal and sham tDCS. tDCS trigger controller was based upon an algorithm designed to detect an increase in the relative beta power of more than 200%, accompanied by a decrease of 50% or more in the relative alpha power, based on baseline EEG recordings. EEG-tDCS closed-loop-system was able to detect the predefined EEG magnitude deviation and successfully triggered the stimulation in all participants. This preliminary study represents a proof-of-concept for the development of an EEG-tDCS closed-loop system in humans. We discuss and review here different methods of closed loop system that can be considered and potential clinical applications of such system.

Reliability of EEG Measures of Interaction: A Paradigm Shift Is Needed to Fight the Reproducibility Crisis

Measures of interaction (connectivity) of the EEG are at the forefront of current neuroscientific research. Unfortunately, test-retest reliability can be very low, depending on the measure and its estimation, the EEG-frequency of interest, the length of the signal, and the population under investigation. In addition, artifacts can hamper the continuity of the EEG signal, and in some clinical situations it is impractical to exclude artifacts. We aimed to examine factors that moderate test-retest reliability of measures of interaction. The study involved 40 patients with a range of neurological diseases and memory impairments (age median: 60; range 21–76; 40% female; 22 mild cognitive impairment, 5 subjective cognitive complaints, 13 temporal lobe epilepsy), and 20 healthy controls (age median: 61.5; range 23–74; 70% female). We calculated 14 measures of interaction based on the multivariate autoregressive model from two EEG-recordings separated by 2 weeks. We characterized test-retest reliability by correlating the measures between the two EEG-recordings for variations of data length, data discontinuity, artifact exclusion, model order, and frequency over all combinations of channels and all frequencies, individually for each subject, yielding a correlation coefficient for each participant. Excluding artifacts had strong effects on reliability of some measures, such as classical, real valued coherence (~0.1 before, ~0.9 after artifact exclusion). Full frequency directed transfer function was highly reliable and robust against artifacts. Variation of data length decreased reliability in relation to poor adjustment of model order and signal length. Variation of discontinuity had no effect, but reliabilities were different between model orders, frequency ranges, and patient groups depending on the measure. Pathology did not interact with variation of signal length or discontinuity. Our results emphasize the importance of documenting reliability, which may vary considerably between measures of interaction. We recommend careful selection of measures of interaction in accordance with the properties of the data. When only short data segments are available and when the signal length varies strongly across subjects after exclusion of artifacts, reliability becomes an issue. Finally, measures which show high reliability irrespective of the presence of artifacts could be extremely useful in clinical situations when exclusion of artifacts is impractical.

Reliability of EEG Interactions Differs between Measures and Is Specific for Neurological Diseases

Alterations of interaction (connectivity) of the EEG reflect pathological processes in patients with neurologic disorders. Nevertheless, it is questionable whether these patterns are reliable over time in different measures of interaction and whether this reliability of the measures is the same across different patient populations. In order to address this topic we examined 22 patients with mild cognitive impairment, five patients with subjective cognitive complaints, six patients with right-lateralized temporal lobe epilepsy, seven patients with left lateralized temporal lobe epilepsy, and 20 healthy controls. We calculated 14 measures of interaction from two EEG-recordings separated by 2 weeks. In order to characterize test-retest reliability, we correlated these measures for each group and compared the correlations between measures and between groups. We found that both measures of interaction as well as groups differed from each other in terms of reliability. The strongest correlation coefficients were found for spectrum, coherence, and full frequency directed transfer function (average rho > 0.9). In the delta (2–4 Hz) range, reliability was lower for mild cognitive impairment compared to healthy controls and left lateralized temporal lobe epilepsy. In the beta (13–30 Hz), gamma (31–80 Hz), and high gamma (81–125 Hz) frequency ranges we found decreased reliability in subjective cognitive complaints compared to mild cognitive impairment. In the gamma and high gamma range we found increased reliability in left lateralized temporal lobe epilepsy patients compared to healthy controls. Our results emphasize the importance of documenting reliability of measures of interaction, which may vary considerably between measures, but also between patient populations. We suggest that studies claiming clinical usefulness of measures of interaction should provide information on the reliability of the results. In addition, differences between patient groups in reliability of interactions in the EEG indicate the potential of reliability to serve as a new biomarker for pathological memory decline as well as for epilepsy. While the brain concert of information flow is generally variable, high reliability, and thus, low variability may reflect abnormal firing patterns.

Demonstrating test-retest reliability of electrophysiological measures for healthy adults in a multisite study of biomarkers of antidepressant treatment response

Growing evidence suggests that loudness dependency of auditory evoked potentials (LDAEP) and resting EEG alpha and theta may be biological markers for predicting response to antidepressants. In spite of this promise, little is known about the joint reliability of these markers, and thus their clinical applicability. New standardized procedures were developed to improve the compatibility of data acquired with different EEG platforms, and used to examine test-retest reliability for the three electrophysiological measures selected for a multisite project-Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC). Thirty-nine healthy controls across four clinical research sites were tested in two sessions separated by about 1 week. Resting EEG (eyes-open and eyes-closed conditions) was recorded and LDAEP measured using binaural tones (1000 Hz, 40 ms) at five intensities (60-100 dB SPL). Principal components analysis of current source density waveforms reduced volume conduction and provided reference-free measures of resting EEG alpha and N1 dipole activity to tones from auditory cortex. Low-resolution electromagnetic tomography (LORETA) extracted resting theta current density measures corresponding to rostral anterior cingulate (rACC), which has been implicated in treatment response. There were no significant differences in posterior alpha, N1 dipole, or rACC theta across sessions. Test-retest reliability was .84 for alpha, .87 for N1 dipole, and .70 for theta rACC current density. The demonstration of good-to-excellent reliability for these measures provides a template for future EEG/ERP studies from multiple testing sites, and an important step for evaluating them as biomarkers for predicting treatment response.

Unravelling the effect of experimental pain on the corticomotor system using transcranial magnetic stimulation and electroencephalography

The interaction between pain and the motor system is well-known, with past studies showing that pain can alter corticomotor excitability and have deleterious effects on motor learning. The aim of this study was to better understand the cortical mechanisms underlying the interaction between pain and the motor system. Experimental pain was induced on 19 young and healthy participants using capsaicin cream, applied on the middle volar part of the left forearm. The effect of pain on brain activity and on the corticomotor system was assessed with electroencephalography (EEG) and transcranial magnetic stimulation (TMS), respectively. Compared to baseline, resting state brain activity significantly increased after capsaicin application in the central cuneus (theta frequency), left dorsolateral prefrontal cortex (alpha frequency), and left cuneus and right insula (beta frequency). A pain-evoked increase in the right primary motor cortex (M1) activity was also observed (beta frequency), but only among participants who showed a reduction in corticospinal output (as depicted by TMS recruitment curves). These participants further showed greater beta M1-cuneus connectivity than the other participants. These findings indicate that pain-evoked increases in M1 beta power are intimately tied to changes in the corticospinal system, and provide evidence that beta M1-cuneus connectivity is related to the corticomotor alterations induced by pain. The differential pattern of response observed in our participants suggest that the effect of pain on the motor system is variable from on individual to another; an observation that could have important clinical implications for rehabilitation professionals working with pain patients.

Neuroimaging Measures to Assess the Effectiveness of a Two-Day Marathon Group of Individuals with Early Developmental Trauma: A Pilot Study

Group therapy can be effective with individuals with developmental trauma who re-experience intense physiological traumatic distress and struggle with terror and despair. This modality can address the long-term ramifications of developmental trauma, including problems with perceptions, trust, emotional regulation, and loss of sense of self. Neuroimaging techniques can be combined with group therapy interventions as a way to empirically validate the effectiveness of group psychotherapy on brain structures and networks impacted by trauma. The neuroscience explaining overpowering traumatic responses and related emotions will be reviewed. Results of a pilot study combining group therapy with neuroimaging are presented.

Improving test-retest reliability of quantitative electroencephalography using different preprocessing approaches

This work aims to assess the effect of preprocessing approaches over test-retest reliability of quantitative electroencephalography measurements. Two electroencephalography sessions were recorded during an eyes-closed resting state condition in 15 young healthy individuals. The second session was 4 to 6 weeks after the first one. Clean recordings were obtained from the implementation of different preprocessing approaches commonly used in the literature. We then estimated the power spectrum density, for each individual and preprocessing approach, in six frequency bands: delta, theta, alpha1, alpha2, beta, and gamma. Test-retest reliability using the intraclass correlation coefficient was calculated for power spectrum in each methodology and frequency band. We found that the test-retest reliability varied considerably across frequency bands and preprocessing approaches. Reliability was higher for theta, alpha1, and alpha2 frequency bands. Also, the use of preprocessing approach that includes a robust reference to average and independent component analysis, can improve test-retest reliability in other bands such as beta and gamma. Results suggest that quantitative electroencephalography are test-retest reliable and can be used in longitudinal studies.

Prediction of postoperative opioid analgesia using clinical-experimental parameters and electroencephalography

BACKGROUND

Opioids are often used for pain treatment, but the response is often insufficient and dependent on e.g. the pain condition, genetic factors and drug class. Thus, there is an urgent need to identify biomarkers to enable selection of the appropriate drug for the individual patient, a concept known as personalized medicine. Quantitative sensory testing (QST) and clinical parameters can provide some guidance for response, but better and more objective biomarkers are urgently warranted. Electroencephalography (EEG) may be suitable since it assesses the central nervous system where opioids mediate their effects.

METHODS

Clinical parameters, QST and EEG (during rest and tonic pain) was recorded from patients the day prior to total hip replacement surgery. Postoperative pain treatment was performed using oxycodone and piritramide as patient-controlled analgesia. Patients were stratified into responders and non-responders based on pain ratings 24 h post-surgery. Parameters were analysed using conventional group-wise statistical methods. Furthermore, EEG was analysed by machine learning to predict individual response.

RESULTS

Eighty-one patients were included, of which 51 responded to postoperative opioid treatment (30 non-responders). Conventional statistics showed that more severe pre-existing chronic pain was prevalent among non-responders to opioid treatment (p = 0.04). Preoperative EEG analysis was able to predict responders with an accuracy of 65% (p = 0.009), but only during tonic pain.

CONCLUSIONS

Chronic pain grade before surgery is associated with the outcome of postoperative pain treatment. Furthermore, EEG shows potential as an objective biomarker and might be used to predict postoperative opioid analgesia.

SIGNIFICANCE

The current clinical study demonstrates the viability of EEG as a biomarker and with results consistent with previous experimental results. The combined method of machine learning and electroencephalography offers promising results for future developments of personalized pain treatment.

A Methodology of Analysis for Monitoring Treatment Progression with 19-Channel Z-Score Neurofeedback (19ZNF) in a Single-Subject Design

19-Channel Z-Score Neurofeedback (19ZNF) is a modality using 19-electrodes with real-time normative database z-scores, suggesting effective clinical outcomes in fewer sessions than traditional neurofeedback. Thus, monitoring treatment progression and clinical outcome is necessary. The area of focus in this study was a methodology of quantitative analysis for monitoring treatment progression and clinical outcome with 19ZNF. This methodology is noted as the Sites-of-Interest, which included repeated measures analyses of variance (rANOVA) and t-tests for z-scores; it was conducted on 10 cases in a single subject design. To avoid selection bias, the 10 sample cases were randomly selected from a pool of 17 cases that met the inclusion criteria. Available client outcome measures (including self-report) are briefly discussed. The results showed 90% of the pre-post comparisons moved in the targeted direction (z = 0) and of those, 96% (80% Bonferroni corrected) of the t-tests and 96% (91% Bonferroni corrected) of the rANOVAs were statistically significant; thus indicating a progression towards the mean in 15 or fewer 19ZNF sessions. All cases showed and reported improvement in all outcome measures (including quantitative electroencephalography assessment) at case termination.

Test-retest reliability of resting-state magnetoencephalography power in sensor and source space

Several studies have reported changes in spontaneous brain rhythms that could be used as clinical biomarkers or in the evaluation of neuropsychological and drug treatments in longitudinal studies using magnetoencephalography (MEG). There is an increasing necessity to use these measures in early diagnosis and pathology progression; however, there is a lack of studies addressing how reliable they are. Here, we provide the first test-retest reliability estimate of MEG power in resting-state at sensor and source space. In this study, we recorded 3 sessions of resting-state MEG activity from 24 healthy subjects with an interval of a week between each session. Power values were estimated at sensor and source space with beamforming for classical frequency bands: delta (2-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), low beta (13-20 Hz), high beta (20-30 Hz), and gamma (30-45 Hz). Then, test-retest reliability was evaluated using the intraclass correlation coefficient (ICC). We also evaluated the relation between source power and the within-subject variability. In general, ICC of theta, alpha, and low beta power was fairly high (ICC > 0.6) while in delta and gamma power was lower. In source space, fronto-posterior alpha, frontal beta, and medial temporal theta showed the most reliable profiles. Signal-to-noise ratio could be partially responsible for reliability as low signal intensity resulted in high within-subject variability, but also the inherent nature of some brain rhythms in resting-state might be driving these reliability patterns. In conclusion, our results described the reliability of MEG power estimates in each frequency band, which could be considered in disease characterization or clinical trials.

Emotion Regulation of Neuroticism: Emotional Information Processing Related to Psychosomatic State Evaluated by Electroencephalography and Exact Low-Resolution Brain Electromagnetic Tomography

Emotion regulation is the process that adjusts the type or amount of emotion when we experience an emotional situation. The aim of this study was to reveal quantitative changes in brain activity during emotional information processing related to psychosomatic states and to determine electrophysiological features of neuroticism. Twenty-two healthy subjects (mean age 25 years, 14 males and 8 females) were registered. Electroencephalography (EEG) was measured during an emotional audiovisual memory task under three conditions (neutral, pleasant and unpleasant sessions). We divided the subjects into two groups using the Cornell Medical Index (CMI): (CMI-I: control group, n = 10: CMI-II, III or IV: neuroticism group, n = 12). We analyzed the digital EEG data using exact low-resolution brain electromagnetic tomography (eLORETA) current source density (CSD) and functional connectivity analysis in several frequency bands (δ, θ, α, β, γ and whole band). In all subjects, bilateral frontal α CSD in the unpleasant session increased compared to the pleasant session, especially in the control group (p < 0.05). CSD of the neuroticism group was significantly higher than that of the control group in the full band at the amygdala and inferior temporal gyrus, and in the α band at the right temporal lobe (p < 0.05). Additionally, we found an increase in functional connectivity between the left insular cortex and right superior temporal gyrus in all subjects during the unpleasant session compared to the pleasant session (p < 0.05). In this study, using EEG analysis, we could find a novel cortical network related to brain mechanisms underlying emotion regulation. Overall findings indicate that it is possible to characterize neuroticism electrophysiologically, which may serve as a neurophysiological marker of this personality trait. © 2015 S. Karger AG, Basel.

Reliability of event-related EEG functional connectivity during visual entrainment: magnitude squared coherence and phase synchrony estimates

There is an increasing trend towards using noninvasive electroencephalography (EEG) to quantify functional brain connectivity. However, little is known about the psychometrics of commonly used functional connectivity indices. We examined the internal consistency of two different connectivity metrics: magnitude squared coherence and phase synchrony. EEG was recorded during visual entrainment to elicit a strong oscillatory component of known frequency. We found acceptable to good split-half reliability for the connectivity metrics when computing all possible pairwise interactions and after selecting an a priori seed reference. We also compared reliability estimates when using average referenced sensor versus reference independent current source density EEG data. Additional considerations were given to determining how reliability was influenced by factors including trial number, signal-to-noise ratio, and frequency content.

Central neural mechanisms of interindividual difference in discomfort during sensorimotor incongruence in healthy volunteers: an experimental study

OBJECTIVES

It has been reported that disturbance in sensory and motor function may induce sensorimotor incongruence and produce pain, discomfort and other sensations in healthy volunteers. One study suggested that sensorimotor incongruent information to healthy subjects results in increased neuronal activity in the posterior parietal cortex (PPC) and dorsolateral prefrontal cortex; however, this study did not take into consideration the discomfort induced by sensorimotor incongruence. The present study attempted to characterize intracortical electrical activities for sensorimotor incongruence in the frequency domain. In our study, electroencephalogram (EEG) cortical sources were compared between sensorimotor congruence and sensorimotor incongruence. In addition, high and no discomfort subgroups were compared during sensorimotor incongruence.

METHODS

Eighteen healthy female subjects participated in this study. Subjects were then asked to flex/extend both arms in a congruent/incongruent manner while viewing a whiteboard/mirror. EEG was performed to determine the cortical activation during sensorimotor congruence and incongruence.

RESULTS

Alpha band activity in the right posterior parietal cortex during sensorimotor incongruence was significantly lower than that of sensorimotor congruence. The source activities induced in the anterior cingulate cortex (ACC) beta band activity and the posterior cingulate cortex (PCC) alpha band activity significantly decreased in the high-discomfort vs the no-discomfort subgroup.

CONCLUSION

The present findings suggest that the ACC and PCC are more activated in the high-discomfort subgroup than in the no-discomfort subgroup during sensorimotor incongruence. This method may evaluate the effectiveness of new medication therapy and/or rehabilitation by assessing the difference in the neuronal activity of chronic patients before and after treatment.

Residual alterations of brain electrical activity in clinically asymptomatic concussed individuals: an EEG study

OBJECTIVE

To examine the neural substrates underlying performance on Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) and HeadRehab Virtual Reality (VR) balance and spatial modules in a concussed and control group.

METHODS

Thirteen controls and seven concussed participants were fitted with a Geodesic 128-channel EEG cap and completed three assessments: EEG baseline, ImPACT testing, and VR balance and spatial modules. Concussed participants completed were tested within 8 (5 ± 1) days after injury.

RESULTS

EEG power was significantly (p < .05) decreased in the concussed group over all testing modalities. EEG coherence was significantly (p < .05) increased in the concussed group during EEG baseline and ImPACT. For VR testing, two conditions showed significant (p < .05) increases in EEG coherence between ROIs, while two different conditions showed significant (p < .05) decreases in coherence levels.

CONCLUSIONS

Concussed participants passed all clinical concussion testing tools, but showed pathophysiological dysfunction when evaluating EEG variables.

SIGNIFICANCE

Concussed participants are able to compensate and achieve normal functioning due to recruiting additional brain networks. This allows concussed participants to pass clinical tests while still displaying electrophysiological deficits and clinicians must consider this information when making return-to-play decisions.

Reversing pathologically increased EEG power by acoustic coordinated reset neuromodulation

Acoustic Coordinated Reset (CR) neuromodulation is a patterned stimulation with tones adjusted to the patient's dominant tinnitus frequency, which aims at desynchronizing pathological neuronal synchronization. In a recent proof-of-concept study, CR therapy, delivered 4-6 h/day more than 12 weeks, induced a significant clinical improvement along with a significant long-lasting decrease of pathological oscillatory power in the low frequency as well as γ band and an increase of the α power in a network of tinnitus-related brain areas. As yet, it remains unclear whether CR shifts the brain activity toward physiological levels or whether it induces clinically beneficial, but nonetheless abnormal electroencephalographic (EEG) patterns, for example excessively decreased δ and/or γ. Here, we compared the patients' spontaneous EEG data at baseline as well as after 12 weeks of CR therapy with the spontaneous EEG of healthy controls by means of Brain Electrical Source Analysis source montage and standardized low-resolution brain electromagnetic tomography techniques. The relationship between changes in EEG power and clinical scores was investigated using a partial least squares approach. In this way, we show that acoustic CR neuromodulation leads to a normalization of the oscillatory power in the tinnitus-related network of brain areas, most prominently in temporal regions. A positive association was found between the changes in tinnitus severity and the normalization of δ and γ power in the temporal, parietal, and cingulate cortical regions. Our findings demonstrate a widespread CR-induced normalization of EEG power, significantly associated with a reduction of tinnitus severity.

Who is honest and why: baseline activation in anterior insula predicts inter-individual differences in deceptive behavior

Humans engage in deceptive behavior that negatively affects others. The propensity to deceive is, however, characterized by vast inter-individual heterogeneity that is poorly understood. Attempts to investigate the origins of this heterogeneity have so far mainly relied on subjective measures and have shown little predictive power. Here, we used resting electroencephalography to measure objective and stable individual differences in neural baseline activation in combination with an ecologically valid deception paradigm. Results showed that task-independent baseline activation in the anterior insula, a brain area implicated in mapping internal bodily states and in representing emotional arousal and conscious feelings, predicts individuals' propensity for deceptive behavior. The higher the neural baseline activation in this area is, the lower individuals' propensity to deceive. Moreover, results provide evidence that high baseline activation in the anterior insula is associated with negative affect and dispositional tendencies to avoid aversive emotional situations. These results provide converging neural and psychological evidence that individuals might avoid a deceptive act due to a highly active negative emotional system which would make a deceptive act too stressful and bothersome.

Remission of benign epilepsy with rolandic spikes: an EEG-based connectivity study at the onset of the disease and at remission

PURPOSE

The neuronal mechanisms of remission of epilepsy are not known. Based on the principles of the "network theory of epilepsy" we postulated the existence of abnormal cortico-cortical interactions at the onset of epilepsy (Hypothesis-1), and postulated that remission is associated with the decrease or disappearance of the abnormal quantitative EEG findings (Hypothesis-2).

METHODS

Four children with benign epilepsy with rolandic sharp waves (BERS) were investigated. 21-channel EEG was recorded at the onset of the disease (Setting No. 1) and in remission (Setting No. 2). Local EEG synchronization was estimated by LORETA (low resolution electromagnetic tomography). Remote EEG synchronization (intra-hemispheric, cortico-cortical EEG functional connectivity, EEGfC) was computed by the LSC (LORETA Source Correlation) method, among 23 regions of interest (ROI) in both hemispheres. Both local and remote EEG synchronization were evaluated in very narrow frequency bands of 1Hz bandwidth (VNB), from 1 to 25Hz.

RESULTS

Individual results were presented. Abnormal but topographically very dissimilar LORETA and LSC findings were found at the onset of the disease. The disappearance of the initial abnormalities was found in Setting No. 2. An unforeseen finding was the presence of abnormal EEGfC results in Setting No. 2.

DISCUSSION

The authors confirmed both hypotheses. The dissimilarity of the initial abnormalities is in accord with the network concept of epilepsy and the etiology of BERS. The disappearance of the initial abnormalities reflects "normalization" of network dynamics while the emergence of new EEGfC abnormalities is interpreted as "compensation".

CONCLUSION

EEG-based local and remote connectivity (EEGfC) are appropriate tools to describe network dynamics in the active state of BERS and in remission.