A systematic review of quantitative EEG as a possible biomarker in child psychiatric disorders

The utility of wearable electroencephalography combined with behavioral measures to establish a practical multi-domain model for facilitating the diagnosis of young children with attention-deficit/hyperactivity disorder

BACKGROUND

A multi-method, multi-informant approach is crucial for evaluating attention-deficit/hyperactivity disorders (ADHD) in preschool children due to the diagnostic complexities and challenges at this developmental stage. However, most artificial intelligence (AI) studies on the automated detection of ADHD have relied on using a single datatype. This study aims to develop a reliable multimodal AI-detection system to facilitate the diagnosis of ADHD in young children.

METHODS

78 young children were recruited, including 43 diagnosed with ADHD (mean age: 68.07 ± 6.19 months) and 35 with typical development (mean age: 67.40 ± 5.44 months). Machine learning and deep learning methods were adopted to develop three individual predictive models using electroencephalography (EEG) data recorded with a wearable wireless device, scores from the computerized attention assessment via Conners' Kiddie Continuous Performance Test Second Edition (K-CPT-2), and ratings from ADHD-related symptom scales. Finally, these models were combined to form a single ensemble model.

RESULTS

The ensemble model achieved an accuracy of 0.974. While individual modality provided the optimal classification with an accuracy rate of 0.909, 0.922, and 0.950 using the ADHD-related symptom rating scale, the K-CPT-2 score, and the EEG measure, respectively. Moreover, the findings suggest that teacher ratings, K-CPT-2 reaction time, and occipital high-frequency EEG band power values are significant features in identifying young children with ADHD.

CONCLUSIONS

This study addresses three common issues in ADHD-related AI research: the utility of wearable technologies, integrating databases from diverse ADHD diagnostic instruments, and appropriately interpreting the models. This established multimodal system is potentially reliable and practical for distinguishing ADHD from TD, thus further facilitating the clinical diagnosis of ADHD in preschool young children.

EEG activation in preschool children: Characteristics and predictive value for current and future mental health status

BACKGROUND

Previous research has characterised EEG changes associated with resting activation in primary school children and adults, while task-related activation has only been considered in adults. The current study characterises physiological activation in preschool children and examines the potential value of activation indices for predicting mental health status at two time points.

AIMS

To investigate how resting activation and task-related activation are represented in 4- to 5-year-old preschool children and examine if these activation indices can predict current and future mental health status.

METHODS AND PROCEDURES

Frontal EEG was recorded from 81 preschool children during eyes-closed resting, eyes-open resting, and an inhibitory control task to allow calculation of activation indices. The Child Behaviour Checklist was completed by the child's parent at this time, and again 6-8 months later after the child's transition to kindergarten.

OUTCOMES AND RESULTS

Resting activation was represented by reductions in frontal delta, theta, and alpha power in the eyes-open compared to eyes-closed condition, and an increase in frontal beta power. Task-related activation was represented by increases in frontal delta, theta, and alpha power and a decrease in beta power. Frontal delta and theta task-related activation significantly predicted externalising behaviours in both preschool and kindergarten, with stronger prediction in kindergarten.

CONCLUSIONS AND IMPLICATIONS

This study characterised resting and task-related activation in preschool children, and reported similar effects to those found in older children and adults for resting activation, with novel effects for task-related activation. As task-related activation indices were predictive of externalising behaviours in both preschool and kindergarten, these results have implications for early identification of children who experience externalising behavioural problems across the transition to school period. WHAT DOES THIS STUDY ADD?: This study provides new data on how the fundamental physiological processes of resting and task-related activation, both of which are theorised to contribute to "upstream" processes such as executive functions and broader behaviour, are represented in the frontal EEG of preschool aged children. We also learn that the top-down task-related activation indices for delta and theta activity were predictive of current mental health status and future status after the transition to kindergarten, while the bottom-up resting activation indices were not.

Electrophysiological functional connectivity and complexity reflecting cognitive processing speed heterogeneity in young children with ADHD

Early intervention is imperative for young children with attention-deficit/hyperactivity disorder (ADHD) who manifest heterogeneous neurocognitive deficits. The study investigated the functional connectivity and complexity of brain activity among young children with ADHD exhibiting a fast cognitive processing speed (ADHD-F, n = 26), with ADHD exhibiting a slow cognitive processing speed (ADHD-S, n = 17), and typically developing children (n = 35) using wireless electroencephalography (EEG) during rest and task conditions. During rest, compared with the typically developing group, the ADHD-F group displayed lower long-range intra-hemispheric connectivity, while the ADHD-S group had lower frontal beta inter-hemispheric connectivity. During task performance, the ADHD-S group displayed lower frontal beta inter-hemispheric connectivity than the typically developing group. The ADHD-S group had lower frontal inter-hemispheric connectivity in broader frequency bands than the ADHD-F group, indicating ADHD heterogeneity in mental processing speed. Regarding complexity, the ADHD-S group tended to show lower frontal entropy estimators than the typically developing group during the task condition. These findings suggest that the EEG profile of brain connectivity and complexity can aid the early clinical diagnosis of ADHD, support subgrouping young children with ADHD based on cognitive processing speed heterogeneity, and may contain specific novel neural biomarkers for early intervention planning.

Advances, challenges, and prospects of electroencephalography-based biomarkers for psychiatric disorders: a narrative review

Owing to a lack of appropriate biomarkers for accurate diagnosis and treatment, psychiatric disorders cause significant distress and functional impairment, leading to social and economic losses. Biomarkers are essential for diagnosing, predicting, treating, and monitoring various diseases. However, their absence in psychiatry is linked to the complex structure of the brain and the lack of direct monitoring modalities. This review examines the potential of electroencephalography (EEG) as a neurophysiological tool for identifying psychiatric biomarkers. EEG noninvasively measures brain electrophysiological activity and is used to diagnose neurological disorders, such as depression, bipolar disorder (BD), and schizophrenia, and identify psychiatric biomarkers. Despite extensive research, EEG-based biomarkers have not been clinically utilized owing to measurement and analysis constraints. EEG studies have revealed spectral and complexity measures for depression, brainwave abnormalities in BD, and power spectral abnormalities in schizophrenia. However, no EEG-based biomarkers are currently used clinically for the treatment of psychiatric disorders. The advantages of EEG include real-time data acquisition, noninvasiveness, cost-effectiveness, and high temporal resolution. Challenges such as low spatial resolution, susceptibility to interference, and complexity of data interpretation limit its clinical application. Integrating EEG with other neuroimaging techniques, advanced signal processing, and standardized protocols is essential to overcome these limitations. Artificial intelligence may enhance EEG analysis and biomarker discovery, potentially transforming psychiatric care by providing early diagnosis, personalized treatment, and improved disease progression monitoring.

Timing and cortical region matter: theta power differences between teenagers affected by Major Depression and healthy controls

In adults affected by Major Depressive Disorder (MDD), most findings point to higher electroencephalographic (EEG) theta power during wake compared to healthy controls (HC) as a potential biomarker aiding the diagnostic process or subgrouping for stratified treatment. Besides these group differences, theta power is modulated by time of day, sleep/wake history, and age. Thus, we aimed at assessing if the time of recording alters theta power in teenagers affected by MDD or HC. Standardized wake EEG power was assessed with high-density EEG in 15 children and adolescents with MDD and in 15 age- and sex-matched HC in the evening and morning. Using a two-way ANOVA, group, time, and their interaction were tested. In patients, the current severity of depression was rated using the Children's Depression Rating Scale. Broadband EEG power was lower in the morning after sleep, with a significant interaction (group x time) in central regions in the 4-6 Hz range. In MDD relative to HC, theta power was decreased over occipital areas in the evening and increased over frontal areas in the morning. A higher frontal theta power was correlated with more severe depressive mood in the morning but not in the evening. This was a cross-sectional study design, including patients on antidepressant medication. In conclusion, depending on time of recording, region-specific opposite differences of theta power were found between teenagers with MDD and HC. These findings stress the importance of the time of the recording when investigating theta power's relationship to psychopathology.

Correlation between autistic traits and brain functional connectivity in preschoolers with autism spectrum disorder: a resting state MEG study

BACKGROUND

Neurophysiological studies recognized that Autism Spectrum Disorder (ASD) is associated with altered patterns of over- and under-connectivity. However, little is known about network organization in children with ASD in the early phases of development and its correlation with the severity of core autistic features.

METHODS

The present study aimed at investigating the association between brain connectivity derived from MEG signals and severity of ASD traits measured with different diagnostic clinical scales, in a sample of 16 children with ASD aged 2 to 6 years.

RESULTS

A significant correlation emerged between connectivity strength in cortical brain areas implicated in several resting state networks (Default mode, Central executive, Salience, Visual and Sensorimotor) and the severity of communication anomalies, social interaction problems, social affect problems, and repetitive behaviors. Seed analysis revealed that this pattern of correlation was mainly caused by global rather than local effects.

CONCLUSIONS

The present evidence suggests that altered connectivity strength in several resting state networks is related to clinical features and may contribute to neurofunctional correlates of ASD. Future studies implementing the same method on a wider and stratified sample may further support functional connectivity as a possible biomarker of the condition.

Mapping the evolution of neurofeedback research: a bibliometric analysis of trends and future directions

Introduction

This study conducts a bibliometric analysis on neurofeedback research to assess its current state and potential future developments.

Methods

It examined 3,626 journal articles from the Web of Science (WoS) using co-citation and co-word methods.

Results

The co-citation analysis identified three major clusters: "Real-Time fMRI Neurofeedback and Self-Regulation of Brain Activity," "EEG Neurofeedback and Cognitive Performance Enhancement," and "Treatment of ADHD Using Neurofeedback." The co-word analysis highlighted four key clusters: "Neurofeedback in Mental Health Research," "Brain-Computer Interfaces for Stroke Rehabilitation," "Neurofeedback for ADHD in Youth," and "Neural Mechanisms of Emotion and Self-Regulation with Advanced Neuroimaging.

Discussion

This in-depth bibliometric study significantly enhances our understanding of the dynamic field of neurofeedback, indicating its potential in treating ADHD and improving performance. It offers non-invasive, ethical alternatives to conventional psychopharmacology and aligns with the trend toward personalized medicine, suggesting specialized solutions for mental health and rehabilitation as a growing focus in medical practice.

The Effect of Accelerated Continuous Theta Burst Stimulation on Weight Loss in Overweight Individuals With Schizophrenia: A Double-Blind, Randomized, Sham-Controlled Clinical Trial

BACKGROUND AND HYPOTHESIS

Obesity is a common comorbidity in individuals with schizophrenia and is associated with poor clinical outcomes. At present, there are limited effective approaches for addressing this issue. We conducted a double-blind, randomized, sham-controlled clinical trial to investigate the efficacy of noninvasive magnetic stimulation techniques in reducing obesity in individuals with schizophrenia.

STUDY DESIGN

Forty overweight individuals with schizophrenia were recruited and randomly assigned to receive either the active or sham intervention. The active group received 50 accelerated continuous theta burst stimulation (cTBS) sessions over the left primary motor area (M1), while the sham group received sham stimulation. The primary outcomes were the change in body weight and body mass index (BMI), and the secondary outcomes were the psychiatric symptoms, eating behavior scales, metabolic measures, and electrophysiological to food picture stimuli.

STUDY RESULTS

The study demonstrated a significant decrease in body weight and BMI after the intervention selectively in the active group (mean = -1.33 kg, P = .002), and this improvement remained at the 1-month follow-up (mean = -2.02 kg, P = .008). The score on the Barratt Impulsivity Scale (mean = -1.78, P = 0.036) decreased in the active group and mediated the effect of accelerated cTBS on body weight. In the food picture cue electroencephalograph task, the late positive potential component, which is related to motivated attention and emotional processing, decreased in frontal brain regions and increased in posterior regions after the active intervention.

CONCLUSIONS

The accelerated cTBS may offer a promising approach for treating obesity in individuals with schizophrenia. Further research with a larger sample size or individualized stimulation protocol should be promising.

TRIAL REGISTRATION

Clinical trial registered with clinicaltrials.gov (NCT05086133).

Construction of machine learning models for recognizing comorbid anxiety in epilepsy patients based on their clinical and quantitative EEG features

BACKGROUND

This study aimed to construct prediction models for the recognizing of anxiety disorders (AD) in patients with epilepsy (PWEs) by combining clinical features with quantitative electroencephalogram (qEEG) features and using machine learning (ML).

METHODS

Nineteen clinical features and 20-min resting-state EEG were collected from 71 PWEs comorbid with AD and another 60 PWEs without AD who met the inclusion-exclusion criteria of this study. The EEG were preprocessed and 684 Phase Locking Value (PLV) and 76 Lempel-Ziv Complexity (LZC) features on four bands were extracted. The Fisher score method was used to rank all the derived features. We constructed four models for recognizing AD in PWEs, whether PWEs based on different combinations of features using eXtreme gradient boosting (XGboost) and evaluated these models using the five-fold cross-validation method.

RESULTS

The prediction model constructed by combining the clinical, PLV, and LZC features showed the best performance, with an accuracy of 96.18%, precision of 94.29%, sensitivity of 98.33%, F1-score of 96.06%, and Area Under the Curve (AUC) of 0.96. The Fisher score ranking results displayed that the top ten features were depression, educational attainment, α_P3LZC, α_T6-PzPLV, α_F7LZC, β_Fp2-O1PLV, θ_T4-CzPLV, θ_F7-PzPLV, α_Fp2LZC, and θ_T4-PzPLV.

CONCLUSIONS

The model, constructed by combining the clinical and qEEG features PLV and LZC, efficiently identified the presence of AD comorbidity in PWEs and might have the potential to complement the clinical diagnosis. Our findings suggest that LZC features in the α band and PLV features in Fp2-O1 may be potential biomarkers for diagnosing AD in PWEs.

Characteristics of ADHD Symptoms and EEG Theta/Beta Ratio in Children With Sleep Disordered Breathing

Objectives. This study aimed to explore parent-reported symptoms of attention deficit-hyperactivity disorder (ADHD) and sleep electroencephalogram (EEG) theta/beta ratio (TBR) characteristics in children with sleep disordered breathing (SDB). Methods. The parents of children (aged 6-11 years) with SDB (n = 103) and healthy controls (n = 28) completed the SNAP-IV questionnaire, and children underwent overnight polysomnography. Children with SDB were grouped according to obstructive apnea/hypopnea index: primary snoring, mild, and moderate-severe obstructive sleep apnea (OSA) groups. The TBR in non-rapid eye movement (NREM) periods in three sleep cycles was analyzed. Results. Children with SDB showed worse ADHD symptoms compared with the healthy control. There was no intergroup difference in TBR. The time-related decline in TBR observed in the control, primary snoring and mild OSA groups, which was not observed in the moderate-severe OSA group. Overnight transcutaneous oxygen saturation was negatively associated with the hyperactivity/impulsivity score of ADHD symptom. The global TBR during the NREM period in the first sleep cycle was positively correlated with inattention score. Conclusion. Children with SDB showed more ADHD inattention symptoms than the healthy control. Although we found no difference in TBR among groups, we found significant main effect for NREM period. There existed a relationship between hypoxia, TBR, and scores of ADHD symptoms. Hence, it was speculated that TBR can reflect the nocturnal electrophysiological manifestations in children with SDB, which may be related to daytime ADHD symptoms.

Resting-state EEG power differences in autism spectrum disorder: a systematic review and meta-analysis

Narrative reviews have described various resting-state EEG power differences in autism across all five canonical frequency bands, with increased power for low and high frequencies and reduced power for middle frequencies. However, these differences have yet to be quantified using effect sizes and probed robustly for consistency, which are critical next steps for clinical translation. Following PRISMA guidelines, we conducted a systematic review of published and gray literature on resting-state EEG power in autism. We performed 10 meta-analyses to synthesize and quantify differences in absolute and relative resting-state delta, theta, alpha, beta, and gamma EEG power in autism. We also conducted moderator analyses to determine whether demographic characteristics, methodological details, and risk-of-bias indicators might account for heterogeneous study effect sizes. Our literature search and study selection processes yielded 41 studies involving 1,246 autistic and 1,455 neurotypical individuals. Meta-analytic models of 135 effect sizes demonstrated that autistic individuals exhibited reduced relative alpha (g = -0.35) and increased gamma (absolute: g = 0.37, relative: g = 1.06) power, but similar delta (absolute: g = 0.06, relative: g = 0.10), theta (absolute: g = -0.03, relative: g = -0.15), absolute alpha (g = -0.17), and beta (absolute: g = 0.01, relative: g = 0.08) power. Substantial heterogeneity in effect sizes was observed across all absolute (I2: 36.1-81.9%) and relative (I2: 64.6-84.4%) frequency bands. Moderator analyses revealed that age, biological sex, IQ, referencing scheme, epoch duration, and use of gold-standard autism diagnostic instruments did not moderate study effect sizes. In contrast, resting-state paradigm type (eyes-closed versus eyes-open) moderated absolute beta, relative delta, and relative alpha power effect sizes, and resting-state recording duration moderated relative alpha power effect sizes. These findings support further investigation of resting-state alpha and gamma power as potential biomarkers for autism.

Investigating Sex-Based Neural Differences in Autism and Their Extended Reality Intervention Implications

Autism Spectrum Disorder (ASD) affects millions of individuals worldwide, and there is growing interest in the use of extended reality (XR) technologies for intervention. Despite the promising potential of XR interventions, there remain gaps in our understanding of the neurobiological mechanisms underlying ASD, particularly in relation to sex-based differences. This scoping review synthesizes the current research on brain activity patterns in ASD, emphasizing the implications for XR interventions and neurofeedback therapy. We examine the brain regions commonly affected by ASD, the potential benefits and drawbacks of XR technologies, and the implications of sex-specific differences for designing effective interventions. Our findings underscore the need for ongoing research into the neurobiological underpinnings of ASD and sex-based differences, as well as the importance of developing tailored interventions that consider the unique needs and experiences of autistic individuals.

Sleep Spindle-Related EEG Connectivity in Children with Attention-Deficit/Hyperactivity Disorder: An Exploratory Study

Attention-deficit/hyperactivity disorder (ADHD) is a neurobehavioral disorder with known brain abnormalities but no biomarkers to support clinical diagnosis. Recently, EEG analysis methods such as functional connectivity have rekindled interest in using EEG for ADHD diagnosis. Most studies have focused on resting-state EEG, while connectivity during sleep and spindle activity has been underexplored. Here we present the results of a preliminary study exploring spindle-related connectivity as a possible biomarker for ADHD. We compared sensor-space connectivity parameters in eight children with ADHD and nine age/sex-matched healthy controls during sleep, before, during, and after spindle activity in various frequency bands. All connectivity parameters were significantly different between the two groups in the delta and gamma bands, and Principal Component Analysis (PCA) in the gamma band distinguished ADHD from healthy subjects. Cluster coefficient and path length values in the sigma band were also significantly different between epochs, indicating different spindle-related brain activity in ADHD.

Electroencephalographic (EEG) Biomarkers in Genetic Neurodevelopmental Disorders

Collectively, neurodevelopmental disorders are highly prevalent, but more than a third of neurodevelopmental disorders have an identifiable genetic etiology, each of which is individually rare. The genes associated with neurodevelopmental disorders are often involved in early brain development, neuronal signaling, or synaptic plasticity. Novel treatments for many genetic neurodevelopmental disorders are being developed, but disease-relevant clinical outcome assessments and biomarkers are limited. Electroencephalography (EEG) is a promising noninvasive potential biomarker of brain function. It has been used extensively in epileptic disorders, but its application in neurodevelopmental disorders needs further investigation. In this review, we explore the use of EEG in 3 of the most prevalent genetic neurodevelopmental disorders-Angelman syndrome, Rett syndrome, and fragile X syndrome. Quantitative analyses of EEGs, such as power spectral analysis or measures of connectivity, can quantify EEG signatures seen on qualitative review and potentially correlate with phenotypes. In both Angelman syndrome and Rett syndrome, increased delta power on spectral analysis has correlated with clinical markers of disease severity including developmental disability and seizure burden, whereas spectral power analysis on EEG in fragile X syndrome tends to demonstrate abnormalities in gamma power. Further studies are needed to establish reliable relationships between quantitative EEG biomarkers and clinical phenotypes in rare genetic neurodevelopmental disorders.

Time for Brain Medicine

Unprecedented knowledge of the brain is inevitably contributing to the convergence of neurology and psychiatry. However, clinical training continues to follow a divergent approach established in the 19th century. An etiological approach will continue to shift more psychiatric patients to the care of neurologists who are untrained in psychiatric management. At the same time, this new era of diagnostic biomarkers and neuroscience-based precision treatments requires skills not readily available to those trained in psychiatry. The challenges in training the next generation of doctors include establishing competence involving aspects of the whole brain, fostering the subspecialized expertise needed to remain current, and developing programs that are feasible in duration and practical in implementation. A new 4-year residency training program proposed in this article could replace existing residency programs. The program includes 2 years of common and urgent training in various aspects of neurology and psychiatry followed by 2 years of elective subspecialty tracks. The concept is similar to internal medicine residencies and fellowships. No changes to existing departmental structures are necessary. In concert with the emerging biological approach to the brain, "brain medicine" is proposed as a new name to denote this practice in the simplest terms: a focus on all aspects of the brain.

Initial study on quantitative electroencephalographic analysis of bioelectrical activity of the brain of children with fetal alcohol spectrum disorders (FASD) without epilepsy

Fetal alcohol spectrum disorders (FASD) are spectrum of neurodevelopmental conditions associated with prenatal alcohol exposure. The FASD manifests mostly with facial dysmorphism, prenatal and postnatal growth retardation, and selected birth defects (including central nervous system defects). Unrecognized and untreated FASD leads to severe disability in adulthood. The diagnosis of FASD is based on clinical criteria and neither biomarkers nor imaging tests can be used in order to confirm the diagnosis. The quantitative electroencephalography (QEEG) is a type of EEG analysis, which involves the use of mathematical algorithms, and which has brought new possibilities of EEG signal evaluation, among the other things-the analysis of a specific frequency band. The main objective of this study was to identify characteristic patterns in QEEG among individuals affected with FASD. This study was of a pilot prospective study character with experimental group consisting of patients with newly diagnosed FASD and of the control group consisting of children with gastroenterological issues. The EEG recordings of both groups were obtained, than analyzed using a commercial QEEG module. As a results we were able to establish the dominance of the alpha rhythm over the beta rhythm in FASD-participants compared to those from the control group, mostly in frontal and temporal regions. Second important finding is an increased theta/beta ratio among patients with FASD. These findings are consistent with the current knowledge on the pathological processes resulting from the prenatal alcohol exposure. The obtained results and conclusions were promising, however, further research is necessary (and planned) in order to validate the use of QEEG tools in FASD diagnostics.

Electroencephalogram utilization and psychiatric comorbidities among children and adolescents with epilepsy in rural Southwestern Uganda

OBJECTIVE

This study aimed at describing routine electroencephalogram (EEG) findings among children and adolescents with a clinical diagnosis of epilepsy and determines how interictal EEG abnormalities vary with the psychiatric comorbidities.

METHODS

We conducted a cross-sectional study among children and adolescents with epilepsy aged 5-18 years receiving care from a regional referral hospital in Southwestern Uganda. Psychiatric comorbidities were assessed using an adapted parent version of Child and Adolescent Symptom Inventory-5. Thirty-minute EEG samples were taken from routine EEG recordings that were locally performed and remotely interpreted for all participants.

RESULTS

Of the 140 participants, 71 (50.7%) had normal EEG findings and 51 (36.4%) had epileptiform abnormalities while 18 (12.9%) had non-epileptiform. Of those who had epileptiform abnormalities on EEG, 23 (45.1%) were focal, 26 (51.0%) were generalized, and 2 (3.9%) were focal with bilateral spread. There was no significant association between the different psychiatric comorbidities and the interictal EEG abnormalities.

CONCLUSIONS

Among children and adolescents with a clinical diagnosis of epilepsy in Southwestern Uganda, only 36% showed epileptiform abnormalities on their EEG recordings. There was no association between the interictal EEG abnormalities and psychiatric comorbidities.

Incremental Validity of Multi-Method and Multi-Informant Evaluations in the Clinical Diagnosis of Preschool ADHD

OBJECTIVES

This study investigated the discriminative validity of various single or combined measurements of electroencephalogram (EEG) data, Conners' Kiddie Continuous Performance Test (K-CPT), and Disruptive Behavior Disorder Rating Scale (DBDRS) to differentiate preschool children with ADHD from those with typical development (TD).

METHOD

We recruited 70 preschoolers, of whom 38 were diagnosed with ADHD and 32 exhibited TD; all participants underwent the K-CPT and wireless EEG recording in different conditions (rest, slow-rate, and fast-rate task).

RESULTS

Slow-rate task-related central parietal delta (1-4 Hz) and central alpha (8-13 Hz) and beta (13-30 Hz) powers between groups with ADHD and TD were significantly distinct (p < .05). A combination of DBDRS, K-CPT, and specific EEG data provided the best probability scores (area under curve = 0.926, p < .001) and discriminative validity to identify preschool children with ADHD (overall correct classification rate = 85.71%).

CONCLUSIONS

Multi-method and multi-informant evaluations should be emphasized in clinical diagnosis of preschool ADHD.

State and Trait Anxiety Related Gamma Oscillations in Patients With Anxiety Within the Research Domain Criteria Framework

OBJECTIVE

Diagnosis of anxiety has relied primarily on self-report. This study aimed to investigate the neural correlates of anxiety with quantitative electroencephalography (qEEG) focusing on the state and trait anxiety defined according to the Research Domain Criteria framework existing across the differential diagnosis, rather than focusing on the diagnosis.

METHODS

A total of 41 participants who visited a psychiatric clinic underwent resting state EEG and completed the State-Trait Anxiety Inventory. The absolute power of six frequency bands were analyzed: delta (1-4 Hz), theta (4-8 Hz), alpha (8-10 Hz), fast alpha (10-13.5 Hz), beta (13.5-30 Hz), and gamma (30-80 Hz).

RESULTS

State anxiety scores were significantly negatively correlated with absolute gamma power in frontal (Fz, r=-0.484) and central (Cz, r=-0.523) regions, while trait anxiety scores were significantly negatively correlated with absolute gamma power in frontal (Fz, r= -0.523), central (Cz, r=-0.568), parietal (P7, r=-0.500; P8, r=-0.541), and occipital (O1, r=-0.510; O2, r=-0.480) regions.

CONCLUSION

The present study identified the significantly negative correlations between the anxiety level and gamma band power in fronto-central and posterior regions assessed at resting status. Further studies to confirm our findings and identify the neural correlates of anxiety are needed.

Atypical functional connectivity during rest and task-related dynamic alteration in young children with attention deficit hyperactivity disorder: An analysis using the phase-locking value

AIM

The study investigated the electroencephalography (EEG) functional connectivity (FC) profiles during rest and tasks of young children with attention deficit hyperactivity disorder (ADHD) and typical development (TD).

METHODS

In total, 78 children (aged 5-7 years) were enrolled in this study; 43 of them were diagnosed with ADHD and 35 exhibited TD. Four FC metrics, coherence, phase-locking value (PLV), pairwise phase consistency, and phase lag index, were computed for feature selection to discriminate ADHD from TD.

RESULTS

The support vector machine classifier trained by phase-locking value (PLV) features yielded the best performance to differentiate the ADHD from the TD group and was used for further analysis. In comparing PLVs with the TD group at rest, the ADHD group exhibited significantly lower values on left intrahemispheric long interelectrode lower-alpha and beta as well as frontal interhemispheric beta frequency bands. However, the ADHD group showed higher values of central interhemispheric PLVs on the theta, higher-alpha, and beta bands. Regarding PLV alterations within resting and task conditions, left intrahemispheric long interelectrode beta PLVs declined from rest to task in the TD group, but the alterations did not differ in the ADHD group. Negative correlations were observed between frontal interhemispheric beta PLVs and the Disruptive Behavior Disorder Rating Scale as rated by teachers.

CONCLUSIONS

These results, which complement the findings of other sparse studies that have investigated task-related brain FC dynamics, particularly in young children with ADHD, can provide clinicians with significant and interpretable neural biomarkers for facilitating the diagnosis of ADHD.

The effect of transcranial direct current stimulation (tDCS) combined with cognitive training on EEG spectral power in adolescent boys with ADHD: A double-blind, randomized, sham-controlled trial

Transcranial direct current stimulation (tDCS) is a possible alternative to psychostimulants in Attention-Deficit/Hyperactivity Disorder (ADHD), but its mechanisms of action in children and adolescents with ADHD are poorly understood. We conducted the first 15-session, sham-controlled study of anodal tDCS over right inferior frontal cortex (rIFC) combined with cognitive training (CT) in 50 children/adolescents with ADHD. We investigated the mechanisms of action on resting and Go/No-Go Task-based QEEG measures in a subgroup of 23 participants with ADHD (n, sham = 10; anodal tDCS = 13). We failed to find a significant sham versus anodal tDCS group differences in QEEG spectral power during rest and Go/No-Go Task performance, a correlation between QEEG and Go/No-Go Task performance, and changes in clinical and cognitive measures. These findings extend the non-significant clinical and cognitive effects in our sample of 50 children/adolescents with ADHD. Given that the subgroup of 23 participants would have been underpowered, the interpretation of our findings is limited and should be used as a foundation for future investigations. Larger, adequately powered randomized controlled trials should explore different protocols titrated to the individual and using comprehensive measures to assess cognitive, clinical, and neural effects of tDCS and its underlying mechanisms of action in ADHD.

Neuromorphological and Neurofunctional Correlates of ADHD and ADD in the Auditory Cortex of Adults

Attention deficit (hyperactivity) disorder (AD(H)D) is one of the most common neurodevelopmental disorders in children with up to 60% probability of prevailing into adulthood. AD(H)D has far-fetching negative impacts on various areas of life. Until today, no observer-independent diagnostic biomarker is available for AD(H)D, however recent research found evidence that AD(H)D is reflected in auditory dysfunctions. Furthermore, the official diagnostic classification systems, being mainly the ICD-10 in Europe and the DSM-5 in the United States, are not entirely consistent. The neuro-auditory profiles of 82 adults (27 ADHD, 30 ADD, 25 controls) were measured via structural magnetic resonance imaging (MRI) and magnetoencephalography (MEG) to determine gray matter volumes and activity of auditory subareas [Heschl’s gyrus (HG) and planum temporale (PT)]. All three groups (ADHD, ADD, and controls) revealed distinct neuro-auditory profiles. In the left hemisphere, both ADHD and ADD showed reduced gray matter volumes of the left HG, resulting in diminished left HG/PT ratios. In the right hemisphere, subjects with ADHD were characterized by lower right HG/PT ratios and ADD by a similar right HG/PT ratio compared to controls. Controls and ADD had well-balanced hemispheric response patterns, ADHD a left-right asynchrony. With this study, we present the structural and functional differences in the auditory cortex of adult patients with AD(H)D.

Task-Rate-Related Neural Dynamics Using Wireless EEG to Assist Diagnosis and Intervention Planning for Preschoolers with ADHD Exhibiting Heterogeneous Cognitive Proficiency

This study used a wireless EEG system to investigate neural dynamics in preschoolers with ADHD who exhibited varying cognitive proficiency pertaining to working memory and processing speed abilities. Preschoolers with ADHD exhibiting high cognitive proficiency (ADHD-H, n = 24), those with ADHD exhibiting low cognitive proficiency (ADHD-L, n = 18), and preschoolers with typical development (TD, n = 31) underwent the Conners’ Kiddie Continuous Performance Test and wireless EEG recording under different conditions (rest, slow-rate, and fast-rate task). In the slow-rate task condition, compared with the TD group, the ADHD-H group manifested higher delta and lower beta power in the central region, while the ADHD-L group manifested higher parietal delta power. In the fast-rate task condition, in the parietal region, ADHD-L manifested higher delta power than those in the other two groups (ADHD-H and TD); additionally, ADHD-L manifested higher theta as well as lower alpha and beta power than those with ADHD-H. Unlike those in the TD group, the delta power of both ADHD groups was enhanced in shifting from rest to task conditions. These findings suggest that task-rate-related neural dynamics contain specific neural biomarkers to assist clinical planning for ADHD in preschoolers with heterogeneous cognitive proficiency. The novel wireless EEG system used was convenient and highly suitable for clinical application.

Utility of Cognitive Neural Features for Predicting Mental Health Behaviors

Cognitive dysfunction underlies common mental health behavioral symptoms including depression, anxiety, inattention, and hyperactivity. In this study of 97 healthy adults, we aimed to classify healthy vs. mild-to-moderate self-reported symptoms of each disorder using cognitive neural markers measured with an electroencephalography (EEG). We analyzed source-reconstructed EEG data for event-related spectral perturbations in the theta, alpha, and beta frequency bands in five tasks, a selective attention and response inhibition task, a visuospatial working memory task, a Flanker interference processing task, and an emotion interference task. From the cortical source activation features, we derived augmented features involving co-activations between any two sources. Logistic regression on the augmented feature set, but not the original feature set, predicted the presence of psychiatric symptoms, particularly for anxiety and inattention with >80% sensitivity and specificity. We also computed current flow closeness and betweenness centralities to identify the “hub” source signal predictors. We found that the Flanker interference processing task was the most useful for assessing the connectivity hubs in general, followed by the inhibitory control go-nogo paradigm. Overall, these interpretable machine learning analyses suggest that EEG biomarkers collected on a rapid suite of cognitive assessments may have utility in classifying diverse self-reported mental health symptoms.

Automatic Classification of Subjects of the PSEN1-E280A Family at Risk of Developing Alzheimer’s Disease Using Machine Learning and Resting State Electroencephalography

Background: The study of genetic variant carriers provides an opportunity to identify neurophysiological changes in preclinical stages. Electroencephalography (EEG) is a low-cost and minimally invasive technique which, together with machine learning, provide the possibility to construct systems that classify subjects that might develop Alzheimer’s disease (AD). Objective: The aim of this paper is to evaluate the capacity of the machine learning techniques to classify healthy Non-Carriers (NonCr) from Asymptomatic Carriers (ACr) of PSEN1-E280A variant for autosomal dominant Alzheimer’s disease (ADAD), using spectral features from EEG channels and brain-related independent components (ICs) obtained using independent component analysis (ICA). Methods: EEG was recorded in 27 ACr and 33 NonCr. Statistical significance analysis was applied to spectral information from channels and group ICA (gICA), standardized low-resolution tomography (sLORETA) analysis was applied over the IC as well. Strategies for feature selection and classification like Chi-square, mutual informationm and support vector machines (SVM) were evaluated over the dataset. Results: A test accuracy up to 83% was obtained by implementing a SVM with spectral features derived from gICA. The main findings are related to theta and beta rhythms, generated in the parietal and occipital regions, like the precuneus and superior parietal lobule. Conclusion: Promising models for classification of preclinical AD due to PSEN-1-E280A variant can be trained using spectral features, and the importance of the beta band and precuneus region is highlighted in asymptomatic stages, opening up the possibility of its use as a screening methodology.

Quantitative Electroencephalography (QEEG) as an Innovative Diagnostic Tool in Mental Disorders

Quantitative electroencephalography (QEEG) is becoming an increasingly common method of diagnosing neurological disorders and, following the recommendations of The American Academy of Neurology (AAN) and the American Clinical Neurophysiology Society (ACNS), it can be used as a complementary method in the diagnosis of epilepsy, vascular diseases, dementia, and encephalopathy. However, few studies are confirming the importance of QEEG in the diagnosis of mental disorders and changes occurring as a result of therapy; hence, there is a need for analyses in this area. The aim of the study is analysis of the usefulness of QEEG in the diagnosis of people with generalized anxiety disorders. Our research takes the form of case studies. The paper presents an in-depth analysis of the QEEG results of five recently studied people with a psychiatric diagnosis: generalized anxiety disorder. The results show specific pattern amplitudes at C3 and C4. In all of the examined patients, two dependencies are repeated: low contribution of the sensorimotor rhythm (SMR) wave amplitudes and high beta2 wave amplitudes, higher or equal to the alpha amplitudes. The QEEG study provides important information about the specificity of brain waves of people with generalized anxiety disorder; therefore, it enables the preliminary and quick diagnosis of dysfunction. It is also possible to monitor changes due to QEEG, occurring as a result of psychotherapy, pharmacological therapy and EEG-biofeedback.

Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021

BACKGROUND

With the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis.

METHODS

QEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform (bibliometric.com) were employed to perform bibliographic and visualized analysis.

RESULTS

A total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends.

CONCLUSION

The present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.

Auditory event-related electroencephalographic potentials in borderline personality disorder

BACKGROUND

Borderline Personality Disorder (BPD) is characterized by mood dysregulation, impulsivity, identity disturbances, and a higher risk for suicide. Currently, the diagnosis is solely based on clinical observation of overt symptoms, and this can delay the detection of the disease and the timely start of appropriate treatment. Several candidate clinical tools have been studied to better characterize BPD, including event-related potentials (ERP). This review aimed at summarizing the results of the available ERP studies on BPD to clarify the possible application of this technique in the early diagnosis of BPD.

METHODS

A bibliographic search on PubMed and PsycInfo was performed in order to identify studies comprising individuals with BPD diagnosis and a control group that evaluated the ERP elicited by auditory stimuli.

RESULTS

Ten studies that explored various ERP components associated with auditory stimuli in BPD were included. Overall, the results showed that positive ERP (P50, P100, and P300) amplitude and latencies as well as loudness dependance were altered in BPD patients compared to controls, possibly reflecting deficits involving attention, mainly at its early stage, and executive functions.

LIMITATIONS

The reviewed studies used different ERP approaches and non-homogeneous BPD diagnostic criteria.

CONCLUSIONS

Auditory ERP appear to be a promising tool for the assessment of BPD patients, especially for early diagnosis and evaluation of cognitive symptoms.

Associations of Hyperactivity and Inattention Scores with Theta and Beta Oscillatory Dynamics of EEG in Stop-Signal Task in Healthy Children 7-10 Years Old

Simple Summary

Most studies on ADHD have been focused on the comparisons between healthy subjects and clinical patients. The dimensional approaches propose that the main pathological behavioral domains are distributed in the normal population and not only in individual categories of people (as assumed in traditional schemes of comparisons between patients and controls). In the current study, we used a similar approach to identify potential markers of ADHD by studying the EEG dynamics of healthy children with a natural variability in hyperactivity and inattention scores during performance of the Stop-Signal task. We found that hyperactivity/inattention scores were positively associated with RT variability. Hyperactivity/inattention scores were negatively associated with an increase in beta spectral power in the first 200 ms and positively associated with an increase in theta rhythm at about 300 ms after presentation of the Go stimulus. It has been hypothesized that such results imply insufficient vigilance in the early stages of perception and subsequent compensatory enhancing of attention to the stimulus in children with higher hyperactivity and inattention scores.

Abstract

In the current study, we aimed to investigate the associations between the natural variability in hyperactivity and inattention scores, as well as their combination with EEG oscillatory responses in the Stop-Signal task in a sample of healthy children. During performance, the Stop-Signal task EEGs were recorded in 94 Caucasian children (40 girls) from 7 to 10 years. Hyperactivity/inattention and inattention scores positively correlated with RT variability. Hyperactivity/inattention and inattention scores negatively correlated with an increase in beta spectral power in the first 200 ms after presentation of the Go stimulus. Such results are in line with the lack of arousal model in ADHD children and can be associated with less sensory arousal in the early stages of perception in children with symptoms of inattention. The subsequent greater increase in theta rhythm at about 300 ms after presentation of the Go stimulus in children with higher inattention scores may be associated with increased attention processes and compensation for insufficient vigilance in the early stages of perception.

Resting state EEG in youth with ASD: age, sex, and relation to phenotype

BACKGROUND

Identification of ASD biomarkers is a key priority for understanding etiology, facilitating early diagnosis, monitoring developmental trajectories, and targeting treatment efforts. Efforts have included exploration of resting state encephalography (EEG), which has a variety of relevant neurodevelopmental correlates and can be collected with minimal burden. However, EEG biomarkers may not be equally valid across the autism spectrum, as ASD is strikingly heterogeneous and individual differences may moderate EEG-behavior associations. Biological sex is a particularly important potential moderator, as females with ASD appear to differ from males with ASD in important ways that may influence biomarker accuracy.

METHODS

We examined effects of biological sex, age, and ASD diagnosis on resting state EEG among a large, sex-balanced sample of youth with (N = 142, 43% female) and without (N = 138, 49% female) ASD collected across four research sites. Absolute power was extracted across five frequency bands and nine brain regions, and effects of sex, age, and diagnosis were analyzed using mixed-effects linear regression models. Exploratory partial correlations were computed to examine EEG-behavior associations in ASD, with emphasis on possible sex differences in associations.

RESULTS

Decreased EEG power across multiple frequencies was associated with female sex and older age. Youth with ASD displayed decreased alpha power relative to peers without ASD, suggesting increased neural activation during rest. Associations between EEG and behavior varied by sex. Whereas power across various frequencies correlated with social skills, nonverbal IQ, and repetitive behavior for males with ASD, no such associations were observed for females with ASD.

CONCLUSIONS

Research using EEG as a possible ASD biomarker must consider individual differences among participants, as these features influence baseline EEG measures and moderate associations between EEG and important behavioral outcomes. Failure to consider factors such as biological sex in such research risks defining biomarkers that misrepresent females with ASD, hindering understanding of the neurobiology, development, and intervention response of this important population.

Neural Dynamics for Facilitating ADHD Diagnosis in Preschoolers: Central and Parietal Delta Synchronization in the Kiddie Continuous Performance Test

The present study aimed to characterize children at risk of attention-deficit/hyperactivity disorder (ADHD) during preschool age and provide early intervention. The continuous performance test (CPT) and electroencephalography (EEG) can contribute additional valuable information to facilitate diagnosis. This study measured brain dynamics at slow and fast task rates in the CPT using a wireless wearable EEG and identified correlations between the EEG and CPT data in preschool children with ADHD. Forty-nine preschool children participated in this study, of which 29 were diagnosed with ADHD and 20 exhibited typical development (TD). The Conners Kiddie Continuous Performance Test (K-CPT) and wireless wearable EEG recordings were employed simultaneously. Significant differences were observed between the groups with ADHD and TD in task-related EEG spectral powers (central as well as parietal delta, P < 0.01), which were distinct only in the slow-rate task condition. A shift from resting to the CPT task condition induced overall alpha powers decrease in the ADHD group. In the task condition, the delta powers were positively correlated with the CPT perseveration scores, whereas the alpha powers were negatively correlated with specific CPT scores mainly on perseveration and detectability (P < 0.05). These results, which complement the findings of other sparse studies that have investigated within-task-related brain dynamics, particularly in preschool children, can assist specialists working in early intervention to plan training and educational programs for preschoolers with ADHD.

Atypical resting-state gamma band trajectory in adult attention deficit/hyperactivity disorder

Decreased gamma activity has been reported both in children and adults with attention deficit/hyperactivity disorder (ADHD). However, while ADHD is a lifelong neurodevelopmental disorder, our insight into the associations of spontaneous gamma band activity with age is limited, especially in adults. Therefore, we conducted an explorative study to investigate trajectories of resting gamma activity in adult ADHD patients (N = 42) versus matched healthy controls (N = 59). We investigated the relationship of resting gamma activity (30–48 Hz) with age in four right hemispheric electrode clusters where diminished gamma power in ADHD had previously been demonstrated by our group. We found significant non-linear association between resting gamma power and age in the lower frequency gamma₁ range (30–39 Hz) in ADHD as compared to controls in all investigated locations. Resting gamma₁ increased with age and was significantly lower in ADHD than in control subjects from early adulthood. We found no significant association between gamma activity and age in the gamma₂ range (39–48 Hz). Alterations of gamma band activity might reflect altered cortical network functioning in adult ADHD relative to controls. Our results reveal that abnormal gamma power is present at all ages, highlighting the lifelong nature of ADHD. Nonetheless, longitudinal studies are needed to confirm our results.

Peripheral biomarkers of attention-deficit hyperactivity disorder: Current status and future perspective

Attention-deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, characterized by a persistent pattern of inattention, hyperactivity, and impulsivity. Since the diagnosis of ADHD is defined by operational diagnostic criteria consisting of several clinical symptoms, a number of heterogeneous mechanisms have been considered to be implicated in its pathophysiology. Although no clinically reliable biomarkers are available for the diagnosis of ADHD, several plausible candidate biomarkers have been proposed based on recent advances in biochemistry and molecular biology. This review article summarizes potential peripheral biomarkers associated with ADHD, mainly from recently published case-control studies. These include 1) biochemical markers: neurotransmitters and their receptors, neurotrophic factors, serum electrolytes, and inflammation markers; 2) genetic and epigenetic markers: microRNA, mRNA expression, and peripheral DNA methylation; 3) physiological markers: eye movement and electroencephalography. It also discusses the limitations and future directions of these potential biomarkers for application in clinical practice.

Changes in EEG Recordings in COVID-19 Patients as a Basis for More Accurate QEEG Diagnostics and EEG Neurofeedback Therapy: A Systematic Review

INTRODUCTION AND PURPOSE

The SARS-CoV-2 virus is able to cause abnormalities in the functioning of the nervous system and induce neurological symptoms with the features of encephalopathy, disturbances of consciousness and concentration and a reduced ability to sense taste and smell as well as headaches. One of the methods of detecting these types of changes in COVID-19 patients is an electroencephalogram (EEG) test, which allows information to be obtained about the functioning of the brain as well as diagnosing diseases and predicting their consequences. The aim of the study was to review the latest research on changes in EEG in patients with COVID-19 as a basis for further quantitative electroencephalogram (QEEG) diagnostics and EEG neurofeedback training. Description of the state of knowledge: Based on the available scientific literature using the PubMed database from 2020 and early 2021 regarding changes in the EEG records in patients with COVID-19, 17 publications were included in the analysis. In patients who underwent an EEG test, changes in the frontal area were observed. A few patients were not found to be responsive to external stimuli. Additionally, a previously non-emerging, uncommon pattern in the form of continuous, slightly asymmetric, monomorphic, biphasic and slow delta waves occurred.

CONCLUSION

The results of this analysis clearly indicate that the SARS-CoV-2 virus causes changes in the nervous system that can be manifested and detected in the EEG record. The small number of available articles, the small number of research groups and the lack of control groups suggest the need for further research regarding the short and long term neurological effects of the SARS-CoV-2 virus and the need for unquestionable confirmation that observed changes were caused by the virus per se and did not occur before. The presented studies described non-specific patterns appearing in encephalograms in patients with COVID-19. These observations are the basis for more accurate QEEG diagnostics and EEG neurofeedback training.

Electroencephalographic Parameters Differentiating Melancholic Depression, Non-melancholic Depression, and Healthy Controls. A Systematic Review

Introduction: The objective of this systematic review was to investigate whether electroencephalographic parameters can serve as a tool to distinguish between melancholic depression, non-melancholic depression, and healthy controls in adults. Methods: A systematic review comprising an extensive literature search conducted in PubMed, Embase, Google Scholar, and PsycINFO in August 2020 with monthly updates until November 1st, 2020. In addition, we performed a citation search and scanned reference lists. Clinical trials that performed an EEG-based examination on an adult patient group diagnosed with melancholic unipolar depression and compared with a control group of non-melancholic unipolar depression and/or healthy controls were eligible. Risk of bias was assessed by the Strengthening of Reporting of Observational Studies in Epidemiology (STROBE) checklist. Results: A total of 24 studies, all case-control design, met the inclusion criteria and could be divided into three subgroups: Resting state studies (n = 5), sleep EEG studies (n = 10), and event-related potentials (ERP) studies (n = 9). Within each subgroup, studies were characterized by marked variability on almost all levels, preventing pooling of data, and many studies were subject to weighty methodological problems. However, the main part of the studies identified one or several EEG parameters that differentiated the groups. Conclusions: Multiple EEG modalities showed an ability to distinguish melancholic patients from non-melancholic patients and/or healthy controls. The considerable heterogeneity across studies and the frequent methodological difficulties at the individual study level were the main limitations to this work. Also, the underlying premise of shifting diagnostic paradigms may have resulted in an inhomogeneous patient population. Systematic Review Registration: Registered in the PROSPERO registry on August 8th, 2020, registration number CRD42020197472.

Brain Arousal as Measured by EEG-Assessment Differs Between Children and Adolescents With Attention-Deficit/Hyperactivity Disorder (ADHD) and Depression

Objective: Disturbed regulation of vigilance in the wake state seems to play a key role in the development of mental disorders. It is assumed that hyperactivity in adult ADHD is an attempt to increase a general low vigilance level via external stimulation in order to avoid drowsiness. For depression, the avoidance of stimulation is interpreted as a reaction to a tonic increased vigilance state. Although ADHD is assumed to start during childhood, this vigilance model has been barely tested with children diagnosed for ADHD so far. Methods: Resting-state EEG (8 min) measures from two groups of children diagnosed with either ADHD [N = 76 (16 female, 60 male), age: (mean/SD) 118/33 months] or depression [N = 94 (73 female, 21 male), age: 184/23 months] were analyzed. Using the VIGALL toolbox, EEG patterns of vigilance level, and regulation were derived and compared between both groups. In correlation analysis, the relations between vigilance measures, attentional test performance (alertness and inhibition), and mental health symptoms were analyzed. Results: Children with ADHD differed from children with most prominent depressive symptoms in brain arousal regulation and level, but EEG vigilance was not related to behavior problems and not related to the attentional test performance. Brain arousal was dependent on the age of the participant in the whole sample; younger children showed lower vigilance stages than teenagers; this effect was not present when analyzed separately for each diagnostic group. EEG assessment time and received medication had no effect on the EEG vigilance. Discussion: Although based on a small sample, this explorative research revealed that EEG vigilance level is different between children with ADHD and with depression. Moreover, even the standard procedure of the clinical routine EEG (resting state) can be used to differentiate brain arousal states between participants with ADHD and depression. Because routine EEG is not specialized to vigilance assessment, it may not be sufficiently sensitive to find vigilance-symptomatology associations. Further research should address developmental changes in EEG measurements in children and use bigger samples of participants within the same age range.

A novel quantitative electroencephalography subtype with high alpha power in ADHD: ADHD or misdiagnosed ADHD?

This study investigated quantitative electroencephalography (QEEG) subtypes as auxiliary tools to assess Attention Deficit Hyperactivity Disorder (ADHD). A total of 74 subjects (58 male and 16 female) were assessed using the Korean version of the Diagnostic Interview Schedule for Children Version IV and were assigned to one of three groups: ADHD, ADHD-Not Otherwise specified (NOS), and Neurotypical (NT). We measured absolute and relative EEG power in 19 channels and conducted an auditory continuous performance test. We analyzed QEEG according to the frequency range: delta (1–4 Hz), theta (4–8 Hz), slow alpha (8–10 Hz), fast alpha (10–13.5 Hz), and beta (13.5–30 Hz). The subjects were then grouped by Ward’s method of cluster analysis using the squared Euclidian distance to measure dissimilarities. We discovered four QEEG clusters, which were characterized by: (a) elevated delta power with less theta activity, (b) elevated slow alpha relative power, (c) elevated theta with deficiencies of alpha and beta relative power, and (d) elevated fast alpha and beta absolute power. The largest proportion of participants in clusters (a) and (c) were from the ADHD group (48% and 47%, respectively). Conversely, group (b) mostly consisted of the participants from the NOS group (59%), while group (d) had the largest proportion of participants from the NT group (62%). These results indicate that children with ADHD does not neurophysiologically constitute a homogenous group. We also identified a new subtype with increased alpha power in addition to those commonly reported in ADHD. Given the QEEG characteristics with increased alpha power, we should consider the possibility that this subtype may be caused by childhood depression. In conclusion, we believe that these QEEG subtypes of ADHD are expected to provide valuable information for accurately diagnosing ADHD.

The Changes of qEEG Approximate Entropy during Test of Variables of Attention as a Predictor of Major Depressive Disorder

Evaluating brain function through biosignals remains challenging. Quantitative electroencephalography (qEEG) outcomes have emerged as a potential intermediate biomarker for diagnostic clarification in psychological disorders. The Test of Variables of Attention (TOVA) was combined with qEEG to evaluate biomarkers such as absolute power, relative power, cordance, and approximate entropy from covariance matrix images to predict major depressive disorder (MDD). EEG data from 18 healthy control and 18 MDD patients were monitored during the resting state and TOVA. TOVA was found to provide aspects for the evaluation of MDD beyond resting electroencephalography. The results showed that the prefrontal qEEG theta cordance of the control and MDD groups were significantly different. For comparison, the changes in qEEG approximate entropy (ApEn) patterns observed during TOVA provided features to distinguish between participants with or without MDD. Moreover, ApEn scores during TOVA were a strong predictor of MDD, and the ApEn scores correlated with the Beck Depression Inventory (BDI) scores. Between-group differences in ApEn were more significant for the testing state than for the resting state. Our results provide further understanding for MDD treatment selection and response prediction during TOVA.

Correlation between executive function and quantitative EEG in patients with anxiety by the Research Domain Criteria (RDoC) framework

The Research Domain Criteria (RDoC) project was proposed by the National Institute of Mental Health in 2010 to create a new diagnostic system including symptoms and data from genetics, neuroscience, physiology, and self-reports. The purpose of this study was to determine the link between anxiety and executive functions through quantitative electroencephalography (qEEG) based on the RDoC system. Nineteen-channel EEGs were recorded at the psychiatric clinic from 41 patients with symptoms of anxiety. The EEG power spectra were analysed. The Executive Intelligence Test (EXIT) including the K-WAIS-IV, Stroop, controlled oral word association, and the design fluency tests were performed. A partial, inversed, and significant association was observed between executive intelligence quotient (EIQ) and the absolute delta power in the central region. Similarly, a partial, inversed, and significant association was observed between design fluency and the absolute delta power in the left parietal area. Our findings suggest that the increase in delta power in the central region and left P3 was negatively correlated with the decrease in executive function. It is expected that the absolute delta power plays a specific role in the task-negative default mode network in the relationship between anxiety and executive function.

Towards a Personalized Multi-Domain Digital Neurophenotyping Model for the Detection and Treatment of Mood Trajectories

The commercial availability of many real-life smart sensors, wearables, and mobile apps provides a valuable source of information about a wide range of human behavioral, physiological, and social markers that can be used to infer the user's mental state and mood. However, there are currently no commercial digital products that integrate these psychosocial metrics with the real-time measurement of neural activity. In particular, electroencephalography (EEG) is a well-validated and highly sensitive neuroimaging method that yields robust markers of mood and affective processing, and has been widely used in mental health research for decades. The integration of wearable neuro-sensors into existing multimodal sensor arrays could hold great promise for deep digital neurophenotyping in the detection and personalized treatment of mood disorders. In this paper, we propose a multi-domain digital neurophenotyping model based on the socioecological model of health. The proposed model presents a holistic approach to digital mental health, leveraging recent neuroscientific advances, and could deliver highly personalized diagnoses and treatments. The technological and ethical challenges of this model are discussed.

Inhibition of RhoA/ROCK Pathway in the Early Stage of Hypoxia Ameliorates Depression in Mice via Protecting Myelin Sheath

Neuroplasticity and connectivity in the central nervous system (CNS) are easily damaged after hypoxia. Long-term exposure to an anoxic environment can lead to neuropsychiatric symptoms and increases the likelihood of depression. Demyelination is an important lesion of CNS injury that may occur in depression. Previous studies have found that the RhoA/ROCK pathway is upregulated in neuropsychiatric disorders such as multiple sclerosis, stroke, and neurodegenerative diseases. Therefore, the chief aim of this study is to explore the regulatory role of the RhoA/ROCK pathway in the development of depression after hypoxia by behavioral tests, Western blotting, immunostaining as well as electron microscopy. Results showed that HIF-1α, S100β, RhoA/ROCK, and immobility time in FST were increased, sucrose water preference ratio in SPT was decreased, and the aberrant activity of neurocyte and demyelination occurred after hypoxia. After the administration of Y-27632 and fluoxetine in hypoxia, these alterations were improved. Lingo1, a negative regulatory factor, was also overexpressed after hypoxia and its expression was decreased when the pathway blocked. However, fluoxetine had no effect on the expression of Lingo1. Then, we demonstrated that demyelination was associated with failures of oligodendrocyte precursor cell proliferation and differentiation and increased apoptosis of oligodendrocytes. Collectively, our data indicate that the RhoA/ROCK pathway plays a vital role in the initial depression during hypoxia. Blocking this pathway in the early stage of hypoxia can enhance the effectiveness of antidepressants, rescue myelin damage, and reduce the expression of the negative regulatory protein of myelination. The findings provide new insight into the prophylaxis and treatment of depression.

Electroencephalographic Abnormalities in Autism Spectrum Disorder: Characteristics and Therapeutic Implications

A large body of literature reports the higher prevalence of epilepsy in subjects with Autism Spectrum Disorder (ASD) compared to the general population. Similarly, several studies report an increased rate of Subclinical Electroencephalographic Abnormalities (SEAs) in seizure-free patients with ASD rather than healthy controls, although with varying percentages. SEAs include both several epileptiform discharges and different non-epileptiform electroencephalographic abnormalities. They are more frequently associated with lower intellectual functioning, more serious dysfunctional behaviors, and they are often sign of severer forms of autism. However, SEAs clinical implications remain controversial, and they could represent an epiphenomenon of the neurochemical alterations of autism etiology. This paper provides an overview of the major research findings with two main purposes: to better delineate the state-of-the-art about EEG abnormalities in ASD and to find evidence for or against appropriateness of SEAs pharmacological treatment in ASD.

Environmental Factors Shape Sleep EEG Connectivity During Early Adolescence

Quantifying the degree to which genetic and environmental factors shape brain network connectivity is critical to furthering our understanding of the developing human brain. Sleep, a state of sensory disengagement, provides a unique opportunity to study brain network activity noninvasively by means of sleep electroencephalography (EEG) coherence. We conducted a high-density sleep EEG study in monozygotic (MZ; n = 38; mean age = 12.46; 20 females) and dizygotic (DZ; n = 24; mean age = 12.50; 12 females) twins to assess the heritability of sleep EEG coherence in early adolescence—a period of significant brain rewiring. Structural equation modeling was used to estimate three latent factors: genes, environmental factors shared between twins and environmental factors unique to each twin. We found a strong contribution of unique environmental factors (66% of the variance) and moderate genetic influence (19% of the variance) on sleep EEG coherence across frequencies and sleep states. An exception to this was sleep spindle activity, an index of the thalamocortical network, which showed on average a genetic contribution of 48% across connections. Furthermore, we observed high intraindividual stability of coherence across two consecutive nights suggesting that despite only a modest genetic contribution, sleep EEG coherence is like a trait. Our findings in adolescent humans are in line with earlier findings in animals that show the primordial cerebral map and its connections are plastic and it is through interaction with the environment that the pattern of brain network connectivity is shaped. Therefore, even in twins living together, small differences in the environment may cascade into meaningful differences in brain connectivity.

A systematic review evaluating the implementation of technologies to assess, monitor and treat neurodevelopmental disorders: A map of the current evidence

Technology-based interventions provide an attractive option for improving service provision for neurodevelopmental disorders (NDD), for example, widening access to interventions, objective assessment, and monitoring; however, it is unclear whether there is sufficient evidence to support their use in clinical settings. This review provides an evidence map describing how technology is implemented in the assessment/diagnosis and monitoring/ treatment of NDD (Prospero CRD42018091156). Using predefined search terms in six databases, 7982 articles were identified, 808 full-texts were screened, resulting in 47 included papers. These studies were appraised and synthesised according to the following outcomes of interest: effectiveness (clinical effectiveness/ service delivery efficiencies), economic impact, and user impact (acceptability/ feasibility). The findings describe how technology is currently being utilised clinically, highlights gaps in knowledge, and discusses future research needs. Technology has been used to facilitate assessment and treatment across multiple NDD, especially Autism Spectrum (ASD) and attention-deficit/hyperactivity (ADHD) disorders. Technologies include mobile apps/tablets, robots, gaming, computerised tests, videos, and virtual reality. The outcomes presented largely focus on the clinical effectiveness of the technology, with approximately half the papers demonstrating some degree of effectiveness, however, the methodological quality of many studies is limited. Further research should focus on randomised controlled trial designs with longer follow-up periods, incorporating an economic evaluation, as well as qualitative studies including process evaluations and user impact.