Beta power in the EEG of alcoholics

Neural oscillation in bipolar disorder: a systematic review of resting-state electroencephalography studies

Bipolar disorder (BD) is a severe psychiatric disease with high rates of misdiagnosis and underdiagnosis, resulting in a significant disease burden on both individuals and society. Abnormal neural oscillations have garnered significant attention as potential neurobiological markers of BD. However, untangling the mechanisms that subserve these baseline alternations requires measurement of their electrophysiological underpinnings. This systematic review investigates consistent abnormal resting-state EEG power of BD and conducted an initial exploration into how methodological approaches might impact the study outcomes. This review was conducted in Pubmed-Medline and Web-of-Science in March 2024 to summarize the oscillation changes in resting-state EEG (rsEEG) of BD. We focusing on rsEEG to report spectral power in different frequency bands. We identified 10 studies, in which neural oscillations was compared with healthy individuals (HCs). We found that BD patients had abnormal oscillations in delta, theta, beta, and gamma bands, predominantly characterized by increased power, indicating potential widespread neural dysfunction, involving multiple neural networks and cognitive processes. However, the outcomes regarding alpha oscillation in BD were more heterogeneous, which is thought to be potentially influenced by the disease severity and the diversity of samples. Furthermore, we conducted an initial exploration into how demographic and methodological elements might impact the study outcomes, underlining the importance of implementing standardized data collection methods. Key aspects we took into account included gender, age, medication usage, medical history, the method of frequency band segmentation, and situation of eye open/eye close during the recordings. Therefore, in the face of abnormal multiple oscillations in BD, we need to adopt a comprehensive research approach, consider the multidimensional attributes of the disease and the heterogeneity of samples, and pay attention to the standardized experimental design to improve the reliability and reproducibility of the research results.

Brain regulation training improves emotional competences in patients with alcohol use disorder

Alcohol use disorder (AUD) is defined as the impaired ability to stop or control alcohol use despite adverse social, occupational, or health consequences and still represents one of the biggest challenges for society regarding health conditions, social consequences, and financial costs, including the high relapse rates after traditional alcohol rehabilitation treatment. Especially, the deficient emotional competence in AUD is said to play a key role in the development of AUD and hinders the interruption of substance compulsion, often leading to a viscous circle of relapse. Although the empirical evidence of a neurophysiological basis of AUD is solid and increases even further, clinical interventions based on neurophysiology are still rare for individuals with AUD. This randomized controlled trial investigates changes in emotional competences, alcohol-related cognitions, and drinking behavior before and after an established alcohol rehabilitation treatment (control group: nCG = 29) compared to before and after an optimized, add-on neurofeedback (NF) training (experimental group: nEG = 27). Improvements on the clinical-psychological level, i.e. increases in emotional competences as well as life satisfaction, were found after the experimental electroencephalography (EEG) NF training. Neurophysiological measurements via resting-state EEG indicate decreases in low beta frequency band, while alpha and theta bands remained unaffected.

Fatigue in Parkinson's Disease Is Due to Decreased Efficiency of the Frontal Network: Quantitative EEG Analysis

OBJECTIVE

Fatigue is a common, debilitating nonmotor symptom of Parkinson's disease (PD), but its mechanism is poorly understood. We aimed to determine whether electroencephalography (EEG) could objectively measure fatigue and to explore the pathophysiology of fatigue in PD.

METHODS

We studied 32 de novo PD patients who underwent EEG. We compared brain activity between 19 PD patients without fatigue and 13 PD patients with fatigue via EEG power spectra and graphs, including the global efficiency, characteristic path length, clustering coefficient, small-worldness, local efficiency, degree centrality, closeness centrality, and betweenness centrality.

RESULTS

No significant differences in absolute or relative power were detected between PD patients without or with fatigue (all p > 0.02, Bonferroni-corrected). According to our network analysis, brain network efficiency differed by frequency band. Generally, the brain network in the frontal area for theta and delta bands showed greater efficiency, and in the temporal area, the alpha1 band was less efficient in PD patients without fatigue (p < 0.0001, p = 0.0011, and p = 0.0007, respectively, Bonferroni-corrected).

CONCLUSION

Our study suggests that PD patients with fatigue have less efficient networks in the frontal area than PD patients without fatigue. These findings may explain why fatigue is common in PD, a frontostriatal disorder. Increased efficiency in the temporal area in PD patients with fatigue is assumed to be compensatory. Brain network analysis using graph theory is more valuable than power spectrum analysis in revealing the brain mechanism related to fatigue.

Multimodal Assessment of Smoking cue Reactivity During a Smoking Cue Exposure Task

Background. Cue-reactivity as a characteristic symptom of substance use disorders (SUD) is highly context dependent. Paradigms with high context validity need to be established for the investigation of underlying neurobiological mechanisms. While craving can be assessed by self-report as one aspect of cue-reactivity (CR), the assessment of biological measures such as the autonomous response and EEG promises a holistic perspective including CR at an automatized level. In a multimodal approach, smoking cue exposure (CE) effects on heart rate variability (HRV), EEG frequency power, and craving as well as their interrelation were assessed. This pilot study focused on the validity of CR measurements in a naturalistic CE paradigm. Methods. EEG frequency power, HRV, and craving were assessed during resting state (RS) and smoking CE in smokers (n = 14) and nonsmoking controls (n = 10) to investigate the psychophysiological and subjective reactions to CE. Results. Increased beta power was found only in smokers during CE compared to the control condition. There was an inverse correlation of beta power and maximum craving. Likewise, HRV correlated negatively with maximum smoking urges in smokers immediately after the measurements, without differentiation between CE and control condition. Conclusion. The increased beta power in smokers during CE is discussed as increased inhibitory control related to reduced craving in smokers. Furthermore, increased craving during CE seems to be associated to decreased vagal activity. The multimodal measurements during the CE showed ecological validity to be fundamental for CE assessment in clinical populations to evaluate its predictive value.

Neurotoxicology of alcohol: a bibliometric and science mapping analysis

Alcohol consumption is common in many societies and has increased considerably, resulting in many socioeconomic and public health problems. In this sense, studies have been carried out in order to understand the mechanisms involved in alcohol consumption and related harmful effects. This study aimed to identify and map the knowledge and to perform bibliometric analysis of the neurotoxicology of alcohol based on the 100 most cited articles. A search was carried out in the Web of Science Core Collection database and information was extracted regarding the journal, authors, keywords, year of publication, number of citations, country and continent of the corresponding author. For each selected manuscript, the study design, alcohol exposure model, dose, period of exposure, and effect on the central nervous system and research hotspots were mapped. The journal with the highest number of publications was Alcoholism: Clinical and Experimental Research (n = 11 papers), the author who contributed the most was Crews FT (n = 8 papers), the studies had a total of 288 keywords and 75% of the publications were from the United States of America. The experimental studies evaluated the effects of prenatal and postnatal exposure and were conducted in rats and mice using doses ranging from 2.5 to 14 g/kg/day, with administration by subcutaneous, intraperitoneal, intragastric, or inhalation route or with free access through drinking bottles. Among the studies mapped, the oldest one (1989) aimed to understand the systemic damage and mechanisms of action involved, while the most recent focused on understanding the receptors and mechanisms involved in addiction, as well as genetic factors. Our results show the panorama of the most widespread scientific production in the scientific community on the neurotoxicology of ethanol, a high prevalence was observed in studies that addressed fetal alcohol syndrome and/or the effects of ethanol on neurodevelopment.

Genetic factors linked to aberrant neural activity of individuals with substance use disorder phenotypes: A systematic review and meta-analysis of EEG studies

Background: Alterations in EEG activity have been considered valid endophenotypes of substance use disorders (SUDs). Empirical evidence has supported the association between genetic factors (e.g., genes, single nucleotide polymorphisms [SNPs]) and SUDs, considering both clinical samples and individuals with a positive family history of SUDs [F⁺SUD]). Nevertheless, the relationship between genetic factors and intermediate phenotypes (i.e., altered EEG activity) among individuals with SUD phenotypes remains unclear.Objective(s): The current study aims at summarizing genetic factors linked to aberrant EEG activity among individuals with SUDs and those with F⁺SUD.Methods: Sixteen studies (5 [N = 986] + 11 from the Collaborative Studies On Genetics of Alcoholism [COGA] sample [432 ≤ N ≤ 8810]) were included for a qualitative systematic review. Thirteen studies (5 + 8 studies from the COGA sample) were used for multi-level meta-analytic procedures.Results: Qualitative analyses highlighted a multivariate genetic architecture linked to alterations in EEG waves among individuals with SUD phenotypes (i.e., augmented resting-state beta waves; reduced resting-state alpha waves; reduced resting-state and task-dependent theta waves). The most recurrent genetic factors were involved in cellular energy homeostasis, modulation of inhibitory and excitatory neural activity together with neural cell growth. Meta-analytic results showed a moderate association between genetic factors and altered resting-state and task-dependent EEG activity. Meta-analytic results also suggested non-additive genetic effects on altered EEG activity.Conclusions: Complex genetic interactions mediating neural activity and brain development might constitute a causal pathway toward intermediate phenotypes associated with phenotypic features, which in turn are linked to SUDs.

Problematic social media use is associated with depression and similar to behavioral addictions: Physiological and behavioral evidence

While many studies have examined the relationship between problematic social media use (PSMU) and mental health disorders, little is known about reward responsiveness mechanisms that might be driving this relationship and the neurophysiological characteristics of PSMU. We surveyed 96 undergraduate students at a private liberal arts college in upstate NY. PSMU was assessed using the Social Media Disorder Scale. Fourteen Individuals endorsing in five or more and three or less categories on the Social Media Disorder Scale were offered and underwent resting state QEEG. Mental health was assessed with the Center for Epidemiological Studies Depression Scale Short Form, Social Interaction Anxiety Scale, Penn State Worry Questionnaire, the 10-item Perceived Stress Scale, and a locally developed measure of Substance Use Disorder. Reward and motivational systems were studied using the Brief Sensation Seeking Scale, Behavioral Inhibition/Behavioral Activation Scale, and Temporal Experience of Pleasure Scale. SMDS scores were associated with poorer mental health on all measures except substance use. SMDS scores were positively associated with the behavioral inhibition scale, and the anticipatory pleasure scale. QEEG results revealed a negative association of high PSMU and right central and frontal lobeta, right central beta, and a positive association with frontal alpha asymmetry. The study replicates findings that PSMU is associated with mental health issues. Further the pattern of reward response is different compared with other addictive behaviors. QEEG results are consistent with previous work in substance use and depression.

WOMEN'S USE OF ALCOHOL: NEUROBIOBEHAVIORAL CONCOMITANTS AND CONSEQUENCES

In this narrative review, we draw from historical and contemporary literature to explore the impact of alcohol consumption on brain and behavior among women. We examine three domains: 1) the impact of alcohol use disorder (AUD) on neurobiobehavioral outcomes, 2) its impact on social cognition/emotion processing, and 3) alcohol's acute effects in older women. There is compelling evidence of alcohol-related compromise in neuropsychological function, neural activation, and brain structure. Investigations of social cognition and alcohol effects in older women represent emerging areas of study. Initial analyses suggest that women with AUD show significant deficits in emotion processing, a finding also observed in older women who have consumed a moderate dose of alcohol. Critically, despite the long-recognized need for programmatic interrogation of alcohol's effect in women, studies with sufficient numbers of women for meaningful analysis represent a small proportion of the literature, constraining interpretation and generalization.

Increased EEG gamma power under exposure to drug-related cues: a translational index for cue-elicited craving in METH-dependent individuals

BACKGROUND

This study explored the feasibility of using EEG gamma-band (30-49 Hz) power as an index of cue-elicited craving in METH-dependent individuals.

METHODS

Twenty-nine participants dependent on methamphetamine (METH) and 30 healthy participants were instructed to experience a METH-related virtual reality (VR) social environment.

RESULTS

Individuals with METH dependence showed significantly stronger self-reported craving and higher gamma power in a VR environment than healthy individuals. In the METH group, the VR environment elicited a significant increase in gamma power compared with the resting state. The METH group then received a VR counterconditioning procedure (VRCP), which was deemed useful in suppressing cue-induced reactivity. After VRCP, participants showed significantly lower self-reported craving scores and gamma power when exposed to drug-related cues than the first time.

CONCLUSIONS

These findings suggest that the EEG gamma-band power may be a marker of cue-induced reactivity in patients with METH dependence.

Predicting Alcohol-Related Memory Problems in Older Adults: A Machine Learning Study with Multi-Domain Features

Memory problems are common among older adults with a history of alcohol use disorder (AUD). Employing a machine learning framework, the current study investigates the use of multi-domain features to classify individuals with and without alcohol-induced memory problems. A group of 94 individuals (ages 50-81 years) with alcohol-induced memory problems (the memory group) were compared with a matched control group who did not have memory problems. The random forests model identified specific features from each domain that contributed to the classification of the memory group vs. the control group (AUC = 88.29%). Specifically, individuals from the memory group manifested a predominant pattern of hyperconnectivity across the default mode network regions except for some connections involving the anterior cingulate cortex, which were predominantly hypoconnected. Other significant contributing features were: (i) polygenic risk scores for AUD, (ii) alcohol consumption and related health consequences during the past five years, such as health problems, past negative experiences, withdrawal symptoms, and the largest number of drinks in a day during the past twelve months, and (iii) elevated neuroticism and increased harm avoidance, and fewer positive "uplift" life events. At the neural systems level, hyperconnectivity across the default mode network regions, including the connections across the hippocampal hub regions, in individuals with memory problems may indicate dysregulation in neural information processing. Overall, the study outlines the importance of utilizing multidomain features, consisting of resting-state brain connectivity data collected ~18 years ago, together with personality, life experiences, polygenic risk, and alcohol consumption and related consequences, to predict the alcohol-related memory problems that arise in later life.

Targeting electroencephalography for alcohol dependence: A narrative review

BACKGROUND

Electroencephalography (EEG)-based electrophysiological techniques have made progress in diagnosing and treating alcohol dependence in recent years.

AIMS

The article reviews the latest literature in this field.

MATERIALS AND METHODS

Alcohol dependence, which is common and prone to relapsing, poses a serious threat to individuals, families, and society. At present, the objective detection methods for alcohol dependence in clinic are not enough. As electrophysiological techniques developed in psychiatry, some researches on EEG-based monitoring methods are of great significance in the diagnosis and treatment of alcohol dependence.

DISCUSSION

As electrophysiological techniques developed in psychiatry, some researches on EEG-based monitoring methods such as resting electroencephalography (REEG), event-related potentials (ERP), event-related oscillations (ERO), and polysomnography (PSG), was reported.

CONCLUSION

In this paper, the status of electrophysiological researches on EEG in alcoholics are reviewed in detail.

A pilot randomized clinical trial of biomedical link with mental health in art therapy intervention programs for alcohol use disorder: Changes in NK cells, addiction biomarkers, electroencephalography, and MMPI-2 profiles

OBJECTIVE

Alcohol intake is a major risk factor for various diseases. Elucidating alcohol use disorder (AUD) is important in preventing diseases and promoting health. We aimed to investigate the effect of art therapy on emotional (Minnesota Multiphasic Personality Inventory-2 [MMPI-2]) and physical (natural killer [NK] cell count, expression of stress-associated proteins [SAP], and electroencephalography) changes in patients with AUD.

METHODS

Participants were randomly divided into two groups (n = 35), with the experimental group undergoing art therapy involving weekly 60-min group therapy sessions for 10 weeks. Statistical analysis was performed using Ranked ANCOVA and Wilcoxon's signed rank test. Western blotting was performed to analyze serum SAP levels.

RESULTS

We observed an association between psychological mechanisms and stress proteins. There was an increased number of NK cells in the experimental group after the program. Moreover, compared with the control group, the experimental group showed significant changes in SAP expression. Further, the experimental group showed a positive change in the MMPI-2 profile, as well as a decrease in depression, anxiety, impulsivity, and alcohol dependence.

CONCLUSIONS

Continuous psychological support could be applied as a stress-control program for preventing stress recurrence and post-discharge relapse. Our findings strengthen the link between biomedical science and mental health in rehabilitation treatment for AUD.

Memristive Circuit Design of Brain-Like Emotional Learning and Generation

In this work, a bionic memristive circuit with the functions of emotional learning and generation is proposed, which can perform brain-like emotional learning and generation based on various types of input information. The proposed circuit is designed based on the brain emotional learning theory in the limbic system, which mainly includes three layers of design: 1) the bottom layer is the design of the basic unit modules, such as neuron and synapse; 2) the middle layer is the design of the functional modules related to emotional learning in the limbic system, such as the amygdala, thalamus, and so on; and 3) the top layer is the design of the overall circuit, which is used to realize the function of the emotional generation. A 2-D emotional space composed of valence and arousal signals is adopted. According to the above bottom-up circuit design method, the valence and arousal signals can be generated, respectively, by designing corresponding emotional learning circuits, so as to form continuous emotions. The volatile and nonvolatile memristors are mainly used to mimic the functions of the neuron and synapse at the bottom layer of the circuit to achieve the core emotional learning function of the middle layer, thereby constructing a brain-like information processing architecture to realize the function of the emotional generation in the top layer. The simulation results in PSPICE show that the proposed circuit can learn and generate emotions like humans. If the proposed circuit is applied to a humanoid robot platform through further research, the robot may have the ability of personalized emotional interaction with humans, so that it can be effectively used in emotional companionship and other aspects.

Identifying Complex Emotions in Alexithymia Affected Adolescents Using Machine Learning Techniques

Many scientific researchers' study focuses on enhancing automated systems to identify emotions and thus relies on brain signals. This study focuses on how brain wave signals can be used to classify many emotional states of humans. Electroencephalography (EEG)-based affective computing predominantly focuses on emotion classification based on facial expression, speech recognition, and text-based recognition through multimodality stimuli. The proposed work aims to implement a methodology to identify and codify discrete complex emotions such as pleasure and grief in a rare psychological disorder known as alexithymia. This type of disorder is highly elicited in unstable, fragile countries such as South Sudan, Lebanon, and Mauritius. These countries are continuously affected by civil wars and disaster and politically unstable, leading to a very poor economy and education system. This study focuses on an adolescent age group dataset by recording physiological data when emotion is exhibited in a multimodal virtual environment. We decocted time frequency analysis and amplitude time series correlates including frontal alpha symmetry using a complex Morlet wavelet. For data visualization, we used the UMAP technique to obtain a clear district view of emotions. We performed 5-fold cross validation along with 1 s window subjective classification on the dataset. We opted for traditional machine learning techniques to identify complex emotion labeling.

Quantitative electroencephalographic analysis of delirium tremens development following alcohol-withdrawal seizure based on a small number of male cases

INTRODUCTION

Seizures and delirium tremens (DTs) are recognized as severe alcohol-withdrawal symptoms. Prolonged admission and serious complications associated with alcohol withdrawal are responsible for increased costs and use of medical and social resources. This study investigated the predictive value of quantitative electroencephalography (QEEG) for developing alcohol-related DTs after alcohol-withdrawal seizure (AWS).

METHODS

We compared differences in QEEG in patients after AWS (n = 13). QEEG was performed in the intensive care unit within 48 h of admission, including in age- and sex-matched healthy controls. We also investigated the prognostic value of QEEG for the development of alcohol DTs after AWS in a retrospective, case-control study. The spectral power of each band frequency and the ratio of the theta to alpha band (TAR) in the electroencephalogram were analyzed using iSyncBrain^® (iMediSync, Inc., Korea).

RESULTS

The beta frequency and the alpha frequency band power were significantly higher and lower, respectively, in patients than in age- and sex-matched healthy controls. In AWS patients with DTs, the relative beta-3 power was lower, particularly in the left frontal area, and the TAR was significantly higher in the central channel than in those without DTs.

CONCLUSION

Quantitative EEG showed neuronal excitability and decreased cognitive activities characteristic of AWS associated with alcohol-withdrawal state, and we demonstrated that quantitative EEG might be a helpful tool for detecting patients at a high risk of developing DTs during an alcohol-dependence period.

Sensation Seeking and Adolescent Drinking: Do Protective Behavioral Strategies Lower Risk?

Using a cross-sectional design, we examined protective behavioral strategies (PBS) as a moderator of the relationship between sensation seeking and hazardous drinking and alcohol-related consequences among high school seniors (N = 212). Hierarchical regression analyses indicated sensation seeking was a significant predictor of binge drinking (β = .65, p < .001), pre-partying (β = .71, p < .001), gaming (β = .75, p < .001), and alcohol-related consequences (β = .69, p < .001). Further, PBS moderated these relationships such that among high sensation seeking adolescents, PBS use was associated with better outcomes, including lower levels of binge drinking (β = -.37, p < .01), pre-partying (β = -.44, p < .01), gaming (β = -.31, p < .05), and alcohol-related consequences (β = -.53, p < .001). We discuss counseling implications, including assessment and harm reduction strategies focusing on PBS to reduce hazardous drinking among high sensation seeking adolescents.

EEG study on implicit beliefs regarding sexuality: Psychophysiological measures in relation to self-report measures

In this exploratory, correlational study, several psychophysiological measures were assessed and the relation between these measures and an experimental self-report questionnaire to measure the seven implicit beliefs of sexual offenders (the Questionnaire for Implicit Theories of Sexual Offenders (QITSO)) was established in a sample of Dutch participants recruited from the healthy population (N = 28) using correlational analyses. After analyzing task performance, electroencephalogram (EEG) data and electrocardiogram (ECG) data, the psychophysiological variables were correlated with the experimental QITSO subscales. The subscale “children as sexual beings” correlated positively with the P300 amplitude at electrode Pz. The subscale “women are unknowable” correlated positively with resting-state beta activity during eyes closed and eyes open, and with alpha activity during eyes open. Additionally, the subscale “entitlement’ correlated positively with low frequency heart rate variability power during eyes closed and eyes open, and with high frequency power during eyes closed. This study is a first exploratory step towards establishing a psychophysiological profile underlying the self-report questionnaire QITSO.

Targeting prefrontal cortex GABAergic microcircuits for the treatment of alcohol use disorder

Developing novel treatments for alcohol use disorders (AUDs) is of paramount importance for improving patient outcomes and alleviating the suffering related to the disease. A better understanding of the molecular and neurocircuit mechanisms through which alcohol alters brain function will be instrumental in the rational development of new efficacious treatments. Clinical studies have consistently associated the prefrontal cortex (PFC) function with symptoms of AUDs. Population-level analyses have linked the PFC structure and function with heavy drinking and/or AUD diagnosis. Thus, targeting specific PFC cell types and neural circuits holds promise for the development of new treatments. Here, we overview the tremendous diversity in the form and function of inhibitory neuron subtypes within PFC and describe their therapeutic potential. We then summarize AUD population genetics studies, clinical neurophysiology findings, and translational neuroscience discoveries. This study collectively suggests that changes in fast transmission through PFC inhibitory microcircuits are a central component of the neurobiological effects of ethanol and the core symptoms of AUDs. Finally, we submit that there is a significant and timely need to examine sex as a biological variable and human postmortem brain tissue to maximize the efforts in translating findings to new clinical treatments.

Emotional Activity Is Negatively Associated With Cognitive Load in Multimedia Learning: A Case Study With EEG Signals

We aimed to investigate the relationship between emotional activity and cognitive load during multimedia learning from an emotion dynamics perspective using electroencephalography (EEG) signals. Using a between-subjects design, 42 university students were randomly assigned to two video lecture conditions (color-coded vs. grayscale). While the participants watched the assigned video, their EEG signals were recorded. After processing the EEG signals, we employed the correlation-based feature selector (CFS) method to identify emotion-related subject-independent features. We then put these features into the Isomap model to obtain a one-dimensional trajectory of emotional changes. Next, we used the zero-crossing rate (ZCR) as the quantitative characterization of emotional changes ZCR EC . Meanwhile, we extracted cognitive load-related features to analyze the degree of cognitive load (CLI). We employed a linear regression fitting method to study the relationship between ZCR EC and CLI. We conducted this study from two perspectives. One is the frequency domain method (wavelet feature), and the other is the non-linear dynamic method (entropy features). The results indicate that emotional activity is negatively associated with cognitive load. These findings have practical implications for designing video lectures for multimedia learning. Learning material should reduce learners' cognitive load to keep their emotional experience at optimal levels to enhance learning.

Brain oscillatory functioning after long-term alcohol abstinence

The heterogeneity of the population with alcohol use disorder (AUD) sometimes makes the knowledge generated in areas such as neuroscience appear to be contradictory. One aspect that may help elucidate this apparent contradiction is controlling for certain variables that are not usually controlled, such as the abstinence time in people with AUD. This research aims to study the neuroelectrical oscillations in people with AUD with longer and shorter abstinence time in comparison with healthy individuals. We recruited twenty-nine individuals with AUD with abstinence time longer than fifteen days and shorter than six months (STA), twenty-six individuals with AUD with abstinence time longer than six months and shorter than thirteen months (LTA), and sixteen healthy individuals (HC). All participants underwent electroencephalographic recording in resting-state with eyes closed. The oscillatory activity obtained was analyzed to obtain the spectral power and phase synchronization level. Regarding the obtained spectral power results, these revealed that the STA group showed higher theta band power and lower alpha band power than the LTA and HC groups. The obtained results at the phase synchronization level also show two main results. On the one hand, the STA group showed lower alpha band phase synchronization than the LTA and HC groups. On the other hand, the HC group showed higher beta band phase synchronization than the STA and LTA groups. In conclusion, the obtained results reflect that abstinence maintenance for six or more months appears to produce an important oscillatory brain functioning normalization in people with AUD.

Resting State EEG Activity Related to Impulsivity in People with Prescription Opioid Use Disorder

Previous studies on EEG activity in prescription opioid use disorder (OUD) have reported neuronal dysfunction related to heroin use, most consistently reflected by increases in β-brain oscillations. As similar research has yet to examine EEG associated with non-medical use of prescription opioid and as inhibitory deficits are associated with OUD, this pilot study compared quantitative EEGs of 18 patients with prescription OUD and 18 healthy volunteers and assessed relationships between oscillatory activity and impulsivity with the Barratt Impulsiveness Scale (BIS-11). Spectral EEGs showed greater amplitude density in β₁, β₂, and β₃ frequencies across frontal, temporal-central and posterior recording areas in patients. Similar abnormal amplitude density increases were seen in δ but not in θ or α frequency bands. Patients exhibited greater scores (impaired impulse control) on BIS-11 subscales (attention, motor, self-control) and impairment of these impulsive subtypes was associated with increases in β and δ oscillations. In patients, β1, β2, and δ activity was positively associated with disorder severity. Taken together, the results suggest that altered brain oscillations in persons with prescription OUD show some similarities with reported oscillatory changes in heroin use and may indicate a chronic state of imbalance in neuronal networks regulating impulsive and inhibitory control systems.

EEG Spectral Power Analysis: A Comparison Between Heroin Dependent and Control Groups

Previous studies indicated that heroin abuse would result in abnormal functional organization of the brain. However, studies of heroin abuse- related brain dysfunction are scarce. The purpose of the present study was to investigate heroin effects on brain function by studying relationships between Electroencephalograph (EEG) spectral power and heroin abuse. The resting EEG signals were acquired from 15 male heroin dependent group and 15 male control group. The differences in the EEG components of each group were evaluated using the statistical Mann-Whitney examination and Davis Bouldin Index. The results show that heroin dependent group has an attenuated relative beta-2 power compared with other EEG frequency sub bands. Nevertheless, the results indicate heroin dependent group have an increase of power spectrum density for theta at all locations, as well as delta in the temporal, frontal and central areas compared with control group. Compared to control group, the heroin dependent group decreased its spectral power more than the control group in all three alpha bands. The present findings using the Davis Bouldin Index provide evidence that alpha-3 band in the FZ channel is more affected by heroin abuse than other frequency sub bands.

Identification of Lower-Limb Motor Tasks via Brain-Computer Interfaces: A Topical Overview

Recent engineering and neuroscience applications have led to the development of brain-computer interface (BCI) systems that improve the quality of life of people with motor disabilities. In the same area, a significant number of studies have been conducted in identifying or classifying upper-limb movement intentions. On the contrary, few works have been concerned with movement intention identification for lower limbs. Notwithstanding, lower-limb neurorehabilitation is a major topic in medical settings, as some people suffer from mobility problems in their lower limbs, such as those diagnosed with neurodegenerative disorders, such as multiple sclerosis, and people with hemiplegia or quadriplegia. Particularly, the conventional pattern recognition (PR) systems are one of the most suitable computational tools for electroencephalography (EEG) signal analysis as the explicit knowledge of the features involved in the PR process itself is crucial for both improving signal classification performance and providing more interpretability. In this regard, there is a real need for outline and comparative studies gathering benchmark and state-of-art PR techniques that allow for a deeper understanding thereof and a proper selection of a specific technique. This study conducted a topical overview of specialized papers covering lower-limb motor task identification through PR-based BCI/EEG signal analysis systems. To do so, we first established search terms and inclusion and exclusion criteria to find the most relevant papers on the subject. As a result, we identified the 22 most relevant papers. Next, we reviewed their experimental methodologies for recording EEG signals during the execution of lower limb tasks. In addition, we review the algorithms used in the preprocessing, feature extraction, and classification stages. Finally, we compared all the algorithms and determined which of them are the most suitable in terms of accuracy.

EEG Neurofeedback in the Treatment of Adults with Binge-Eating Disorder: a Randomized Controlled Pilot Study

Specific alterations in electroencephalography (EEG)-based brain activity have recently been linked to binge-eating disorder (BED), generating interest in treatment options targeting these neuronal processes. This randomized-controlled pilot study examined the effectiveness and feasibility of two EEG neurofeedback paradigms in the reduction of binge eating, eating disorder and general psychopathology, executive functioning, and EEG activity. Adults with BED and overweight (N = 39) were randomly assigned to either a food-specific EEG neurofeedback paradigm, aiming at reducing fronto-central beta activity and enhancing theta activity after viewing highly palatable food pictures, or a general EEG neurofeedback paradigm training the regulation of slow cortical potentials. In both conditions, the study design included a waiting period of 6 weeks, followed by 6 weeks EEG neurofeedback (10 sessions à 30 min) and a 3-month follow-up period. Both EEG neurofeedback paradigms significantly reduced objective binge-eating episodes, global eating disorder psychopathology, and food craving. Approximately one third of participants achieved abstinence from objective binge-eating episodes after treatment without any differences between treatments. These results were stable at 3-month follow-up. Among six measured executive functions, only decision making improved at posttreatment in both paradigms, and cognitive flexibility was significantly improved after food-specific neurofeedback only. Both EEG neurofeedback paradigms were equally successful in reducing relative beta and enhancing relative theta power over fronto-central regions. The results highlight EEG neurofeedback as a promising treatment option for individuals with BED. Future studies in larger samples are needed to determine efficacy and treatment mechanisms.

Prediction model for potential depression using sex and age-reflected quantitative EEG biomarkers

Depression is a prevalent mental disorder in modern society, causing many people to suffer or even commit suicide. Psychiatrists and psychologists typically diagnose depression using representative tests, such as the Beck's Depression Inventory (BDI) and the Hamilton Depression Rating Scale (HDRS), in conjunction with patient consultations. Traditional tests, however, are time-consuming, can be trained on patients, and entailed a lot of clinician subjectivity. In the present study, we trained the machine learning models using sex and age-reflected z-score values of quantitative EEG (QEEG) indicators based on data from the National Standard Reference Data Center for Korean EEG, with 116 potential depression subjects and 80 healthy controls. The classification model has distinguished potential depression groups and normal groups, with a test accuracy of up to 92.31% and a 10-cross-validation loss of 0.13. This performance proposes a model with z-score QEEG metrics, considering sex and age as objective and reliable biomarkers for early screening for the potential depression.

Memristive Circuit Design of Brain-Inspired Emotional Evolution Based on Theories of Internal Regulation and External Stimulation

In this work, a bionic memristive circuit with functions of emotional evolution is proposed by mimicking the emotional circuit in limbic system, which can perform unconscious and conscious emotional evolutions by using theories of internal regulation and external stimulation respectively. Two kinds of memristive models, volatile and non-volatile, play key roles in the process of emotional evolution. That is, the internal regulation is mainly responsible for simulating the unconscious evolution process over time by using the forgetting effect of the volatile memristor. The external stimulation is mainly responsible for using the memristance plasticity of the non-volatile memristor to simulate the evolutionary learning behavior under the action of multi-modal inputs (such as visual, speech and text signals), so as to realize the conscious emotional evolution. A two-dimensional (2D) emotional state space consisted of valence and arousal signals is adopted, the evolution behaviors are performed on the basis of valence and arousal signals in the space, in order to achieve continuous emotional evolution and express the evolved emotions intuitively. Due to the use of memristors, the proposed circuit can realize in-memory computing, which fundamentally avoids the problem of storage wall and constructs a brain-inspired information processing architecture. The simulation results in PSPICE show that a nonlinear mapping relationship between inputs and outputs is constructed through the proposed circuit, which can carry out diversified emotional evolution based on the designed internal regulation and external stimulation evolution circuits.

Neurophysiological correlate of incubation of craving in individuals with methamphetamine use disorder

Previous studies both in laboratory animals and humans have reported that abstinence induces incubation of cue-induced drug craving for nicotine, alcohol, cocaine, and methamphetamine. However, current experimental procedures utilized to study incubation of methamphetamine craving do not incorporate the temporal dynamics of neuropsychological measures and electrophysiological activities associated with this incubation process. This study utilized the high-density electroencephalogram (EEG) signals as a rapid, inexpensive, and noninvasive measure of cue-induced craving potential. A total of 156 male individuals with methamphetamine use disorder (MUD) enrolled in this multisite, cross-sectional study. Structured clinical interview data, self-report questionnaires (cued craving, quality of sleep, impulsivity, anxiety, and depression) and resting-state, eye-closed 128 high-density channel EEG signals were collected at 5 abstinence duration time points (<1, 1-3, 3-6, 6-12, and 12-24 months) to track the neuropsychological and neurophysiological signatures. Cue-induced craving was higher after 1-3 months than after the other time points. This incubation effect was also observed for sleep quality but not for anxiety, depression, and impulsivity symptoms, along with exhibited decreased power spectrum for theta (5.5-8 Hz) and alpha (8-13 Hz), and increased in beta (16.5-26.5 Hz) frequency band. Source reconstructed resting-state EEG analysis showed increased synchronization of medial prefrontal cortex (MPFC) for the beta frequency band in 1-3 months abstinent MUD group, and associated with the incubation of craving. Remarkably, the robust incubation-related abnormalities may be driven by beta-band source space connectivity between MPFC and bilateral orbital gyrus (ORB). Our findings suggest the enhancement of beta activity in the incubation period most likely originates from a dysfunction involving frontal brain regions. This neurophysiological signature of incubation of craving can be used to identify individuals who might be most susceptible to relapse, providing a potential insight into future therapeutic interventions for MUD via neuromodulation of beta activity.

Mindfulness Training Associated With Resting-State Electroencephalograms Dynamics in Novice Practitioners via Mindful Breathing and Body-Scan

Objectives: Mindfulness-based stress reduction has been proven to improve mental health and quality of life. This study examined how mindfulness training and various types of mindfulness practices altered brain activity. Methods: Specifically, the spectral powers of scalp electroencephalography of the mindfulness-based stress reduction (MBSR) group (n=17) who underwent an 8-week MBSR training-including mindful breathing and body-scan-were evaluated and compared with those of the waitlist controls (n=14). Results: Empirical results indicated that the post-intervention effect of MBSR significantly elevated the resting-state beta powers and reduced resting-state delta powers in both practices; such changes were not observed in the waitlist control. Compared with mindful breathing, body-scanning resulted in an overall decline in electroencephalograms (EEG) spectral powers at both delta and low-gamma bands among trained participants. Conclusion: Together with our preliminary data of expert mediators, the aforementioned spectral changes were salient after intervention, but mitigated along with expertise. Additionally, after receiving training, the MBSR group's mindfulness and emotion regulation levels improved significantly, which were correlated with the EEG spectral changes in the theta, alpha, and low-beta bands. The results supported that MBSR might function as a unique internal processing tool that involves increased vigilant capability and induces alterations similar to other cognitive training.

Electroencephalography as a Non-Invasive Biomarker of Alzheimer's Disease: A Forgotten Candidate to Substitute CSF Molecules?

Biomarkers for disease diagnosis and prognosis are crucial in clinical practice. They should be objective and quantifiable and respond to specific therapeutic interventions. Optimal biomarkers should reflect the underlying process (pathological or not), be reproducible, widely available, and allow measurements repeatedly over time. Ideally, biomarkers should also be non-invasive and cost-effective. This review aims to focus on the usefulness and limitations of electroencephalography (EEG) in the search for Alzheimer's disease (AD) biomarkers. The main aim of this article is to review the evolution of the most used biomarkers in AD and the need for new peripheral and, ideally, non-invasive biomarkers. The characteristics of the EEG as a possible source for biomarkers will be revised, highlighting its advantages compared to the molecular markers available so far.

Large-scale collaboration in ENIGMA-EEG: A perspective on the meta-analytic approach to link neurological and psychiatric liability genes to electrophysiological brain activity

BACKGROUND AND PURPOSE

The ENIGMA-EEG working group was established to enable large-scale international collaborations among cohorts that investigate the genetics of brain function measured with electroencephalography (EEG). In this perspective, we will discuss why analyzing the genetics of functional brain activity may be crucial for understanding how neurological and psychiatric liability genes affect the brain.

METHODS

We summarize how we have performed our currently largest genome-wide association study of oscillatory brain activity in EEG recordings by meta-analyzing the results across five participating cohorts, resulting in the first genome-wide significant hits for oscillatory brain function located in/near genes that were previously associated with psychiatric disorders. We describe how we have tackled methodological issues surrounding genetic meta-analysis of EEG features. We discuss the importance of harmonizing EEG signal processing, cleaning, and feature extraction. Finally, we explain our selection of EEG features currently being investigated, including the temporal dynamics of oscillations and the connectivity network based on synchronization of oscillations.

RESULTS

We present data that show how to perform systematic quality control and evaluate how choices in reference electrode and montage affect individual differences in EEG parameters.

CONCLUSION

The long list of potential challenges to our large-scale meta-analytic approach requires extensive effort and organization between participating cohorts; however, our perspective shows that these challenges are surmountable. Our perspective argues that elucidating the genetic of EEG oscillatory activity is a worthwhile effort in order to elucidate the pathway from gene to disease liability.

Effectiveness of an Integrated Intervention Program for Alcoholism: Electrophysiological Findings

BACKGROUND

Neuroelectrophysiological measures such as electroencephalograms (EEGs) in resting state and event-related potentials (ERPs) provide valuable information about the vulnerability and treatment-related changes in persons with alcoholism. This study examined the effectiveness of an Integrated Intervention Program for Alcoholism (IIPA) using electrophysiological measures.

METHODS

Fifty individuals with early onset of alcohol dependence participated. They were grouped randomly into two: the treatment as usual (TAU) group and the treatment group, matched on age (±1 year) and education (±1 year). eyes closed and resting state EEGs and ERPs on cognitive tasks (flanker task, alcohol Go/No-Go task, and single outcome gambling task) were recorded before and after treatment. The TAU group received pharmacotherapy, six days/week yoga sessions, and three sessions/week group therapy on relapse prevention while the treatment group received IIPA along with usual treatment (except yoga) for 18 days.

RESULTS

There was no significant difference between the groups pre-treatment. RM-ANOVA for pre- and post-treatment stages showed a significant difference between the two groups in the absolute power of alpha, beta, theta, and delta, during eye closure, in the resting-state EEGs. The treatment group showed significantly larger N200/N2 amplitude in congruent and incongruent conditions (flanker task), N200/N2 amplitude for alcohol No-Go, P300/P3 amplitude for neutral No-Go on alcohol Go/No-Go task, and outcome-related positivity (ORP) amplitude on single outcome gambling task.

CONCLUSION

This exploratory study suggests that IIPA is effective for enhancing relaxation state and attentiveness, decreasing hyperarousal, and ameliorating neurocognitive dysfunctions of conflict-monitoring, response inhibition, and reward processing.

Effective Connectivity in Default Mode Network for Alcoholism Diagnosis

Alcohol Use Disorder (AUD) is a chronic relapsing brain disease characterized by excessive alcohol use, loss of control over alcohol intake, and negative emotional states under no alcohol consumption. The key factor in successful treatment of AUD is the accurate diagnosis for better medical and therapy management. Conventionally, for individuals to be diagnosed with AUD, certain criteria as outlined in the Diagnostic and Statistical Manual of Mental Disorders (DSM) should be met. However, this process is subjective in nature and could be misleading due to memory problems and dishonesty of some AUD patients. In this paper, an assessment scheme for objective diagnosis of AUD is proposed. For this purpose, EEG recording of 31 healthy controls and 31 AUD patients are used for the calculation of effective connectivity (EC) between the various regions of the brain Default Mode Network (DMN). The EC is estimated using partial directed coherence (PDC) which are then used as input to a 3D Convolutional Neural Network (CNN) for binary classification of AUD cases. Using 5-fold cross validation, the classification of AUD vs. HC effective connectivity matrices using the proposed 3D-CNN gives an accuracy of 87.85 ± 4.64 %. For further validation, 32 and 30 subjects are randomly selected for training and testing, respectively, giving 100% correct classification of all the testing subjects.

Use of common average reference and large-Laplacian spatial-filters enhances EEG signal-to-noise ratios in intrinsic sensorimotor activity

BACKGROUND

Oscillations in the resting-state scalp electroencephalogram (EEG) represent various intrinsic brain activities. One of the characteristic EEG oscillations is the sensorimotor rhythm (SMR)-with its arch-shaped waveform in alpha- and betabands-that reflect sensorimotor activity. The representation of sensorimotor activity by the SMR depends on the signal-to-noise ratio achieved by EEG spatial filters.

NEW METHOD

We employed simultaneous recording of EEG and functional magnetic resonance imaging, and 10-min resting-state brain activities were recorded in 19 healthy volunteers. To compare the EEG spatial-filtering methods commonly used for extracting sensorimotor cortical activities, we assessed nine different spatial-filters: a default reference of EEG amplifier system, a common average reference (CAR), small-, middle- and large-Laplacian filters, and four types of bipolar manners (C3-Cz, C3-F3, C3-P3, and C3-T7). We identified the brain region that correlated with the EEG-SMR power obtained after each spatial-filtering method was applied. Subsequently, we calculated the proportion of the significant voxels in the sensorimotor cortex as well as the sensorimotor occupancy in all significant regions to examine the sensitivity and specificity of each spatial-filter.

RESULTS

The CAR and large-Laplacian spatial-filters were superior at improving the signal-to-noise ratios for extracting sensorimotor activity from the EEG-SMR signal.

COMPARISON WITH EXISTING METHODS

Our results are consistent with the spatial-filter selection to extract task-dependent activation for better control of EEG-SMR-based interventions. Our approach has the potential to identify the optimal spatial-filter for EEG-SMR.

CONCLUSIONS

Evaluating spatial-filters for extracting spontaneous sensorimotor activity from the EEG is a useful procedure for constructing more effective EEG-SMR-based interventions.

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.

Electroencephalographic signatures of the binge drinking pattern during adolescence and young adulthood: A PRISMA-driven systematic review

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Controls and binge drinkers (BDs) do not differ in their behavioral performance.

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BDs show increased neural activity during attention, working memory and inhibition.

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Augmented P3 amplitude in BDs was the most solid electrophysiological finding.

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Evidence does not support specific gender vulnerabilities to the effects of BD.

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Memory, emotional processing and decision-making processes need further exploration.

Research on neurophysiological impairments associated with binge drinking (BD), an excessive but episodic alcohol use pattern, has significantly increased over the last decade. This work is the first to systematically review –following PRISMA guidelines- the empirical evidence regarding the effects of BD on neural activity –assessed by electroencephalography- of adolescents and young adults. A systematic review was conducted in 34 studies (N = 1723). Results indicated that binge drinkers (BDs) showed similar behavioral performance as non/low drinkers. The most solid electrophysiological finding was an augmented P3 amplitude during attention, working memory and inhibition tasks. This increased neural activity suggests the recruitment of additional resources to perform the task at adequate/successful levels, which supports the neurocompensation hypothesis. Similar to alcoholics, BDs also displayed increased reactivity to alcohol-related cues, augmented resting-state electrophysiological signal and reduced activity during error detection –which gives support to the continuum hypothesis. Evidence does not seem to support greater vulnerability to BD in females. Replication and longitudinal studies are required to account for mixed results and to elucidate the extent/direction of the neural impairments associated with BD.

Scalp electroencephalograms over ipsilateral sensorimotor cortex reflect contraction patterns of unilateral finger muscles

A variety of neural substrates are implicated in the initiation, coordination, and stabilization of voluntary movements underpinned by adaptive contraction and relaxation of agonist and antagonist muscles. To achieve such flexible and purposeful control of the human body, brain systems exhibit extensive modulation during the transition from resting state to motor execution and to maintain proper joint impedance. However, the neural structures contributing to such sensorimotor control under unconstrained and naturalistic conditions are not fully characterized. To elucidate which brain regions are implicated in generating and coordinating voluntary movements, we employed a physiologically inspired, two-stage method to decode relaxation and three patterns of contraction in unilateral finger muscles (i.e., extension, flexion, and co-contraction) from high-density scalp electroencephalograms (EEG). The decoder consisted of two parts employed in series. The first discriminated between relaxation and contraction. If the EEG data were discriminated as contraction, the second stage then discriminated among the three contraction patterns. Despite the difficulty in dissociating detailed contraction patterns of muscles within a limb from scalp EEG signals, the decoder performance was higher than chance-level by 2-fold in the four-class classification. Moreover, weighted features in the trained decoders revealed EEG features differentially contributing to decoding performance. During the first stage, consistent with previous reports, weighted features were localized around sensorimotor cortex (SM1) contralateral to the activated fingers, while those during the second stage were localized around ipsilateral SM1. The loci of these weighted features suggested that the coordination of unilateral finger muscles induced different signaling patterns in ipsilateral SM1 contributing to motor control. Weighted EEG features enabled a deeper understanding of human sensorimotor processing as well as of a more naturalistic control of brain-computer interfaces.

High-dose ethanol intoxication decreases 1/f neural noise or scale-free neural activity in the resting state

Binge drinking is a frequent phenomenon in many western societies and has been associated with an increased risk of developing alcohol use disorder later in life. Yet, the effects of high-dose alcohol intoxication on neurophysiological processes are still quite poorly understood. This is particularly the case given that neurophysiological brain activity not only contains recurring (oscillatory) patterns of activity, but also a significant fraction of "scale-free" or arrhythmic dynamics referred to as 1/f type activity, pink noise, or 1/f neural noise. Neurobiological considerations suggest that it should be modulated by alcohol intoxication. To investigate this assumption, we collected resting state EEG data from n = 23 healthy young male subjects in a crossover design, where each subject was once tested sober and once tested while intoxicated (mean breath alcohol concentration of 1.1 permille ±0.2). Analyses of the 1/f neural dynamics showed that ethanol intoxication decreased resting state 1/f neural noise, as compared with a sober state. The effects were strongest when the eyes were closed and particularly reliable in the beta frequency band. Given that the dynamics of the beta band have been shown to strongly depend on GABA_A receptor neural transmission, this finding nicely aligns with the fact that ethanol increases GABAergic signaling. The study reveals a currently unreported effect of binge drinking on neurophysiological dynamics, which likely revealed a higher sensitivity for ethanol effects than most commonly considered measures of power in neural oscillations. Implications and applicability of these findings are discussed.

The Theta/Beta Ratio as an Index of Cognitive Processing in Adults With the Combined Type of Attention Deficit Hyperactivity Disorder

An elevated theta/beta ratio in the EEG has long been observed among individuals with Attention Deficit Hyperactivity Disorder (ADHD). The theta/beta ratio was previously hypothesised to be an index of arousal, but a number of studies failed to find any association between the ratio and indices of arousal, instead proposing that the theta/beta ratio may actually be indicative of cognitive processing. This hypothesis was tested by Clarke et al using a sample of healthy adults, with results indicating that the theta/beta ratio correlated with a marker of cognitive processing (P300 latency in an auditory oddball task), while P300 amplitude correlated with an arousal marker (alpha power). The aim of this study was to test whether similar results could be found in a sample of 41 adults with the combined type of ADHD. EEGs were recorded during an eyes-closed resting condition and an auditory oddball task. Results demonstrated that the theta/beta ratio correlated significantly with P300 latency. Absolute alpha power did not correlate significantly with P300 amplitude or P300 latency. These results support the hypotheses that the theta/beta ratio is a marker of cognitive processing capacity in both the general population and in participants with ADHD, and that the alpha/arousal linkage is anomalous in ADHD.

Lifespan associated global patterns of coherent neural communication

Healthy ageing is accompanied by changes to spontaneous electromagnetic oscillations. At the macroscopic scale, previous studies have quantified the basic features, e.g., power and frequencies in rhythms of interest from the perspective of attention, perception, learning and memory. On the other hand, signatures and modes of neural communication have recently been argued to be identifiable from global measures applied on neuro-electromagnetic data such as global coherence that quantifies the degree of togetherness of distributed neural oscillations and metastability that parametrizes the transient dynamics of the network switching between successive stable states. Here, we demonstrate that global coherence and metastability can be informative measures to track healthy ageing dynamics over lifespan and together with the traditional spectral measures provides an attractive explanation of neuronal information processing. Finding normative patterns of brain rhythms in resting state MEG would naturally pave the way for tracking task relevant metrics that could crucially determine cognitive flexibility and performance. While previously reported observations of a reduction in peak alpha frequency and increased beta power in older adults are reflective of changes at individual sensors (during rest and task), global coherence and metastability pinpoint the underlying coordination dynamics over multiple brain areas across the entire lifespan. In addition to replication of the previous observations in a substantially larger lifespan cohort than what was previously reported, we also demonstrate, for the first time to the best of our knowledge, age related changes in coherence and metastability in signals over time scales of neuronal processing. Furthermore, we observed a marked frequency dependence in changes in global coordination dynamics, which, coupled with the long-held view of specific frequency bands subserving different aspects of cognition, hints at differential functional processing roles for slower and faster brain dynamics.

Higher hypnotic suggestibility is associated with the lower EEG signal variability in theta, alpha, and beta frequency bands

Variation of information in the firing rate of neural population, as reflected in different frequency bands of electroencephalographic (EEG) time series, provides direct evidence for change in neural responses of the brain to hypnotic suggestibility. However, realization of an effective biomarker for spiking behaviour of neural population proves to be an elusive subject matter with its impact evident in highly contrasting results in the literature. In this article, we took an information-theoretic stance on analysis of the EEG time series of the brain activity during hypnotic suggestions, thereby capturing the variability in pattern of brain neural activity in terms of its information content. For this purpose, we utilized differential entropy (DE, i.e., the average information content in a continuous time series) of theta, alpha, and beta frequency bands of fourteen-channel EEG time series recordings that pertain to the brain neural responses of twelve carefully selected high and low hypnotically suggestible individuals. Our results show that the higher hypnotic suggestibility is associated with a significantly lower variability in information content of theta, alpha, and beta frequencies. Moreover, they indicate that such a lower variability is accompanied by a significantly higher functional connectivity (FC, a measure of spatiotemporal synchronization) in the parietal and the parieto-occipital regions in the case of theta and alpha frequency bands and a non-significantly lower FC in the central region's beta frequency band. Our results contribute to the field in two ways. First, they identify the applicability of DE as a unifying measure to reproduce the similar observations that are separately reported through adaptation of different hypnotic biomarkers in the literature. Second, they extend these previous findings that were based on neutral hypnosis (i.e., a hypnotic procedure that involves no specific suggestions other than those for becoming hypnotized) to the case of hypnotic suggestions, thereby identifying their presence as a potential signature of hypnotic experience.

Effect of suvorexant on event-related oscillations and EEG sleep in rats exposed to chronic intermittent ethanol vapor and protracted withdrawal

STUDY OBJECTIVES

Insomnia is a prominent complaint in patients with alcohol use disorders (AUD). However, despite the importance of sleep in the maintenance of sobriety, treatment options for sleep disturbance associated with a history of AUD are currently limited. Recent clinical trials have demonstrated that suvorexant, a dual Hct/OX receptor antagonist, normalizes sleep in patients with primary insomnia; yet, its potential for the treatment of sleep pathology associated with AUD has not been investigated in either preclinical or clinical studies.

METHODS

This study employed a model whereby ethanol vapor exposure or control conditions were administered for 8 weeks to adult rats. Waking event-related oscillations (EROs) and EEG sleep were evaluated at baseline before exposure and again following 24 hr of withdrawal from the exposure. Subsequently, the ability of vehicle (VEH) and two doses (10, 30 mg/kg IP) of suvorexant to modify EROs, sleep, and the sleep EEG was investigated.

RESULTS

After 24 hr following EtOH withdrawal, the ethanol-treated group had increases in waking ERO θ and β activity, more fragmented sleep (shorter duration and increased frequency of slow wave (SW) and rapid eye movement [REM] sleep episodes), and increased θ and β power in REM and SW sleep. Suvorexant induced a dose-dependent decrease in the latency to REM and SW sleep onsets but also produced REM and SW sleep fragmentation and increased β energy in waking EROs when compared with VEH.

CONCLUSIONS

Taken together, these studies suggest that suvorexant has overall sleep-promoting effects, but it may exacerbate some aspects of sleep and EEG pathology.

Mu-Beta event-related (de)synchronization and EEG classification of left-right foot dorsiflexion kinaesthetic motor imagery for BCI

The left and right foot representation area is located within the interhemispheric fissure of the sensorimotor cortex and share spatial proximity. This makes it difficult to visualize the cortical lateralization of event-related (de)synchronization (ERD/ERS) during left and right foot motor imageries. The aim of this study is to investigate the possibility of using ERD/ERS in the mu, low beta, and high beta bandwidth, during left and right foot dorsiflexion kinaesthetic motor imageries (KMI), as unilateral control commands for a brain-computer interface (BCI). EEG was recorded from nine healthy participants during cue-based left-right foot dorsiflexion KMI tasks. The features were analysed for common average and bipolar references. With each reference, mu and beta band-power features were analysed using time–frequency (TF) maps, scalp topographies, and average time course for ERD/ERS. The cortical lateralization of ERD/ERS, during left and right foot KMI, was confirmed. Statistically significant features were classified using LDA, SVM, and KNN model, and evaluated using the area under ROC curves. An increase in high beta power following the end of KMI for both tasks was recorded, from right and left hemispheres, respectively, at the vertex. The single trial analysis and classification models resulted in high discrimination accuracies, i.e. maximum 83.4% for beta ERS, 79.1% for beta ERD, and 74.0% for mu ERD. With each model the features performed above the statistical chance level of 2-class discrimination for a BCI. Our findings indicate these features can evoke left-right differences in single EEG trials. This suggests that any BCI employing unilateral foot KMI can attain classification accuracy suitable for practical implementation. Given results stipulate the novel utilization of mu and beta as independent control features for discrimination of bilateral foot KMI in a BCI.

Random Forest Classification of Alcohol Use Disorder Using EEG Source Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures

: Individuals with alcohol use disorder (AUD) manifest a variety of impairments that can be attributed to alterations in specific brain networks. The current study aims to identify features of EEG-based functional connectivity, neuropsychological performance, and impulsivity that can classify individuals with AUD (N = 30) from unaffected controls (CTL, N = 30) using random forest classification. The features included were: (i) EEG source functional connectivity (FC) of the default mode network (DMN) derived using eLORETA algorithm, (ii) neuropsychological scores from the Tower of London test (TOLT) and the visual span test (VST), and (iii) impulsivity factors from the Barratt impulsiveness scale (BIS). The random forest model achieved a classification accuracy of 80% and identified 29 FC connections (among 66 connections per frequency band), 3 neuropsychological variables from VST (total number of correctly performed trials in forward and backward sequences and average time for correct trials in forward sequence) and all four impulsivity scores (motor, non-planning, attentional, and total) as significantly contributing to classifying individuals as either AUD or CTL. Although there was a significant age difference between the groups, most of the top variables that contributed to the classification were not significantly correlated with age. The AUD group showed a predominant pattern of hyperconnectivity among 25 of 29 significant connections, indicating aberrant network functioning during resting state suggestive of neural hyperexcitability and impulsivity. Further, parahippocampal hyperconnectivity with other DMN regions was identified as a major hub region dysregulated in AUD (13 connections overall), possibly due to neural damage from chronic drinking, which may give rise to cognitive impairments, including memory deficits and blackouts. Furthermore, hypoconnectivity observed in four connections (prefrontal nodes connecting posterior right-hemispheric regions) may indicate a weaker or fractured prefrontal connectivity with other regions, which may be related to impaired higher cognitive functions. The AUD group also showed poorer memory performance on the VST task and increased impulsivity in all factors compared to controls. Features from all three domains had significant associations with one another. These results indicate that dysregulated neural connectivity across the DMN regions, especially relating to hyperconnected parahippocampal hub as well as hypoconnected prefrontal hub, may potentially represent neurophysiological biomarkers of AUD, while poor visual memory performance and heightened impulsivity may serve as cognitive-behavioral indices of AUD.

Neuropsychophysiological Measures of Alcohol Dependence: Can We Use EEG in the Clinical Assessment?

Addiction management is complex, and it requires a bio-psycho-social perspective, that ought to consider the multiple etiological and developmental factors. Because of this, a large amount of resources has been allocated to assess the vulnerability to dependence, i.e., to identify the processes underlying the transition from substance use to dependence, as well as its course, in order to determine the key points in its prevention, treatment, and recovery. Consequently, knowledge \from neuroscience must be taken into account, which is why different initiatives have emerged with this objective, such as the "Research Domain Criteria" (RDoC), and the "Addiction Neuroclinical Assessment" (ANA). Particularly, neuropsychophysiological measures could be used as markers of cognitive and behavioral attributes or traits in alcohol dependence, and even trace clinical change. In this way, the aim of this narrative review is to provide an overview following ANA clinical framework, to the most robust findings in neuropsychophysiological changes in alcohol dependence, that underlie the main cognitive domains implicated in addiction: incentive salience, negative emotionality, and executive functioning. The most consistent results have been found in event-related potential (ERP) analysis, especially in the P3 component, that could show a wide clinical utility, mainly for the executive functions. The review also shows the usefulness of other components, implicated in affective and substance-related processing (P1, N1, or the late positive potential LPP), as well as event-related oscillations, such as theta power, with a possible use as vulnerability or clinical marker in alcohol dependence. Finally, new tools emerging from psychophysiology research, based on functional connectivity or brain graph analysis could help toward a better understanding of altered circuits in alcohol dependence, as well as communication efficiency and effort during mental operations. This review concludes with an examination of these tools as possible markers in the clinical field and discusses methodological differences, the need for more replicability studies and incipient lines of work. It also uses consistent findings in psychophysiology to draw possible treatment targets and cognitive profiles in alcohol dependence.

Music Improvisation Is Characterized by Increase EEG Spectral Power in Prefrontal and Perceptual Motor Cortical Sources and Can be Reliably Classified From Non-improvisatory Performance

This study expores neural activity underlying creative processes through the investigation of music improvisation. Fourteen guitar players with a high level of improvisation skill participated in this experiment. The experimental task involved playing 32-s alternating blocks of improvisation and scales on guitar. electroencephalography (EEG) data was measured continuously throughout the experiment. In order to remove potential artifacts and extract brain-related activity the following signal processing techniques were employed: bandpass filtering, Artifact Subspace Reconstruction, and Independent Component Analysis (ICA). For each participant, artifact related independent components (ICs) were removed from the EEG data and only ICs found to be from brain activity were retained. Source localization using this brain-related activity was carried out using sLORETA. Greater activity for improvisation over scale was found in multiple frequency bands (theta, alpha, and beta) localized primarily in the medial frontal cortex (MFC), Middle frontal gyrus (MFG), anterior cingulate, polar medial prefrontal cortex (MPFC), premotor cortex (PMC), pre and postcentral gyrus (PreCG and PostCG), superior temporal gyrus (STG), inferior parietal lobule (IPL), and the temporal-parietal junction. Together this collection of brain regions suggests that improvisation was mediated by processes involved in coordinating planned sequences of movement that are modulated in response to ongoing environmental context through monitoring and feedback of sensory states in relation to internal plans and goals. Machine-learning using Common Spatial Patterns (CSP) for EEG feature extraction attained a mean of over 75% classification performance for improvisation vs. scale conditions across participants. These machine-learning results are a step towards the development of a brain-computer interface that could be used for neurofeedback training to improve creativity.

Computer-aided classifying and characterizing of methamphetamine use disorder using resting-state EEG

Methamphetamine (meth) is potently addictive and is closely linked to high crime rates in the world. Since meth withdrawal is very painful and difficult, most abusers relapse to abuse in traditional treatments. Therefore, developing accurate data-driven methods based on brain functional connectivity could be helpful in classifying and characterizing the neural features of meth dependence to optimize the treatments. Accordingly, in this study, computation of functional connectivity using resting-state EEG was used to classify meth dependence. Firstly, brain functional connectivity networks (FCNs) of 36 meth dependent individuals and 24 normal controls were constructed by weighted phase lag index, in six frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-15 Hz), beta (15-30 Hz), gamma (30-45 Hz) and wideband (1-45 Hz).Then, significant differences in graph metrics and connectivity values of the FCNs were used to distinguish the two groups. Support vector machine classifier had the best performance with 93% accuracy, 100% sensitivity, 83% specificity and 0.94 F-score for differentiating between MDIs and NCs. The best performance yielded when selected features were the combination of connectivity values and graph metrics in the beta frequency band.