Research Review: Use of EEG biomarkers in child psychiatry research - current state and future directions

Early brain cognitive development in late preterm infants: an event-related potential and resting EEG study

BACKGROUND

Late preterm infants (LPIs) are at risk of neurodevelopmental delay. Research on their cognitive development is helpful for early intervention and follow-up.

METHODS

Event-related potential (ERP) and resting electroencephalography (RS-EEG) were used to study the brain cognitive function of LPIs in the early stage of life. The Gesell Developmental Scale (GDS) was used to track the neurodevelopmental status at the age of 1 year after correction, and to explore the neurophysiological indicators that could predict the outcome of cognitive development in the early stage.

RESULTS

The results showed that mismatch response (MMR) amplitude, RS-EEG power spectrum and functional connectivity all suggested that LPIs were lagging behind. At the age of 1 year after correction, high-risk LPIs showed no significant delay in gross motor function, but lagged behind in fine motor function, language, personal social interaction and adaptability. The ROC curve was used to evaluate the predictive role of MMR amplitude in the brain cognitive development prognosis at 1 year, showing a sensitivity of 80.00% and a specificity of 90.57%. The area under the curve (AUC) was 0.788, with a P-value of 0.007.

CONCLUSIONS

Based on our findings we supposed that the cognitive function of LPI lags behind that of full-term infants in early life. Preterm birth and perinatal diseases or high risk factors affected brain cognitive function in LPIs. MMR amplitude can be used as an early predictor of brain cognitive development in LPIs.

TRIAL REGISTRATION

This clinical trial is registered with the Chinese Clinical Trial Registry (ChiCTR).

TRIAL REGISTRATION NUMBER

ChiCTR2100041929. Date of registration: 2021-01-10. URL of the trial registry record: https://www.chictr.org.cn/ .

Resting EEG correlates of neurodevelopment in a socioeconomically and linguistically diverse sample of toddlers: Wave 1 of the Kia Tīmata Pai best start New Zealand study

Development of communication and self-regulation skills is fundamental to psychosocial maturation in childhood. The Kia Tīmata Pai Best Start (KTP) longitudinal study aims to promote these skills through interventions delivered at early childcare centers across New Zealand. In addition to evaluating effects of the interventions on behavioral and cognitive outcomes, the study utilizes electroencephalography (EEG) to characterize cortical development in a subsample of participating children. Here, we present results of the baseline resting EEG assessment with 193 children aged 15 to 33 months. We identified EEG correlates of individual differences in demographics, communication abilities, and temperament. We obtained communication and behavior ratings from multiple informants, and we applied contemporary analytic methods to the EEG data. Periodic spectral power adjusted for aperiodic activity was most closely associated with demographic, language, and behavioral measures. As in previous studies, gamma power was positively associated with verbal language. Alpha power was positively associated with effortful control. Nonverbal and verbal language measures showed distinct associations with EEG indices, as did the three temperament domains. Our results identified a number of candidate EEG measurements for use as longitudinal markers of optimal cortical development and response to interventions in the KTP cohort.

Maternal Adversity and Epigenetic Age Acceleration Predict Heightened Emotional Reactivity in Offspring: Implications for Intergenerational Transmission of Risk

Black American women are disproportionately exposed to adversities that may have an intergenerational impact on mental health. The present study examined whether maternal exposure to adversity and epigenetic age acceleration (EAA; a biomarker of stress exposure) predicts the socioemotional health of her offspring. During pregnancy, 180 Black American women self-reported experiences of childhood adversity and marginalization-related adversity (i.e., racial discrimination and gendered racial stress) and provided a blood sample for epigenetic assessment. At a three-year follow-up visit, women reported their offspring's emotional reactivity (an early indicator of psychopathology) via the CBCL/1.5-5. After adjusting for maternal education and offspring sex, results indicated that greater maternal experiences of childhood trauma (β = 0.21, SE(β) = 0.01; p = 0.01) and racial discrimination (β = 0.14, SE(β) = 0.07; p = 0.049) predicted greater offspring emotional reactivity, as did maternal EAA (β = 0.17, SE(β) = 0.09, p = 0.046). Our findings suggest that maternal EAA could serve as an early biomarker for intergenerational risk conferred by maternal adversity, and that 'maternal adversity' must be defined more broadly to include social marginalization, particularly for Black Americans.

Retinal electrophysiology in central nervous system disorders. A review of human and mouse studies

The retina and brain share similar neurochemistry and neurodevelopmental origins, with the retina, often viewed as a "window to the brain." With retinal measures of structure and function becoming easier to obtain in clinical populations there is a growing interest in using retinal findings as potential biomarkers for disorders affecting the central nervous system. Functional retinal biomarkers, such as the electroretinogram, show promise in neurological disorders, despite having limitations imposed by the existence of overlapping genetic markers, clinical traits or the effects of medications that may reduce their specificity in some conditions. This narrative review summarizes the principal functional retinal findings in central nervous system disorders and related mouse models and provides a background to the main excitatory and inhibitory retinal neurotransmitters that have been implicated to explain the visual electrophysiological findings. These changes in retinal neurochemistry may contribute to our understanding of these conditions based on the findings of retinal electrophysiological tests such as the flash, pattern, multifocal electroretinograms, and electro-oculogram. It is likely that future applications of signal analysis and machine learning algorithms will offer new insights into the pathophysiology, classification, and progression of these clinical disorders including autism, attention deficit/hyperactivity disorder, bipolar disorder, schizophrenia, depression, Parkinson's, and Alzheimer's disease. New clinical applications of visual electrophysiology to this field may lead to earlier, more accurate diagnoses and better targeted therapeutic interventions benefiting individual patients and clinicians managing these individuals and their families.

Methylphenidate, Guanfacine, and Combined Treatment Effects on Electroencephalography Correlates of Spatial Working Memory in Attention-Deficit/Hyperactivity Disorder

OBJECTIVE

The combination of d-methylphenidate and guanfacine (an α-2A adrenergic agonist) may be an effective alternative to either agent as monotherapy in children with attention-deficit/hyperactivity disorder (ADHD). This study investigated the neural mechanisms underlying medication effects using cortical source analysis of electroencephalography (EEG) data.

METHOD

A total of 172 children with ADHD (aged 7-14; 118 boys) completed an 8-week randomized, double-blind, comparative study with 3 treatment arms: d-methylphenidate, guanfacine, or their combination. EEG modulations of brain oscillations at baseline and end point were measured during a spatial working memory task from cortical sources localized within the anterior cingulate (midfrontal) and primary visual cortex (midoccipital), based on previously reported ADHD and control differences. Linear mixed models examined treatment effects on EEG and performance measures.

RESULTS

Combined treatment decreased midoccipital EEG power across most frequency bands and task phases. Several midoccipital EEG measures also showed significantly greater changes with combined treatment than with monotherapies. D-methylphenidate significantly increased midoccipital theta during retrieval, while guanfacine produced only trend-level reductions in midoccipital alpha during maintenance and retrieval. Task accuracy improved with combined treatment, was unchanged with d-methylphenidate, and worsened with guanfacine. Treatment-related changes in midoccipital power correlated with improvement in ADHD severity.

CONCLUSION

These findings show that combined treatment ameliorates midoccipital neural activity associated with treatment-related behavioral improvements and previously implicated in visuo-attentional deficits in ADHD. Both monotherapies had limited effects on EEG measures, with guanfacine further showing detrimental effects on performance. The identified midoccipital EEG profile may aid future treatment monitoring for children with ADHD.

CLINICAL TRIAL REGISTRATION INFORMATION

Single Versus Combination Medication Treatment for Children With Attention Deficit Hyperactivity Disorder (Project1); https://clinicaltrials.gov/; NCT00429273.

DIVERSITY & INCLUSION STATEMENT

We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure sex and gender balance in the recruitment of human participants. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. We actively worked to promote sex and gender balance in our author group.

Identifying depression disorder using multi-view high-order brain function network derived from electroencephalography signal

Brain function networks (BFN) are widely used in the diagnosis of electroencephalography (EEG)-based major depressive disorder (MDD). Typically, a BFN is constructed by calculating the functional connectivity (FC) between each pair of channels. However, it ignores high-order relationships (e.g., relationships among multiple channels), making it a low-order network. To address this issue, a novel classification framework, based on matrix variate normal distribution (MVND), is proposed in this study. The framework can simultaneously generate high-and low-order BFN and has a distinct mathematical interpretation. Specifically, the entire time series is first divided into multiple epochs. For each epoch, a BFN is constructed by calculating the phase lag index (PLI) between different EEG channels. The BFNs are then used as samples, maximizing the likelihood of MVND to simultaneously estimate its low-order BFN (Lo-BFN) and high-order BFN (Ho-BFN). In addition, to solve the problem of the excessively high dimensionality of Ho-BFN, Kronecker product decomposition is used for dimensionality reduction while retaining the original high-order information. The experimental results verified the effectiveness of Ho-BFN for MDD diagnosis in 24 patients and 24 normal controls. We further investigated the selected discriminative Lo-BFN and Ho-BFN features and revealed that those extracted from different networks can provide complementary information, which is beneficial for MDD diagnosis.

Treatment biomarkers for ADHD: Taking stock and moving forward

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

Machine Learning Techniques for the Diagnosis of Schizophrenia Based on Event-Related Potentials

Antecedent

The event-related potential (ERP) components P300 and mismatch negativity (MMN) have been linked to cognitive deficits in patients with schizophrenia. The diagnosis of schizophrenia could be improved by applying machine learning procedures to these objective neurophysiological biomarkers. Several studies have attempted to achieve this goal, but no study has examined Multiple Kernel Learning (MKL) classifiers. This algorithm finds optimally a combination of kernel functions, integrating them in a meaningful manner, and thus could improve diagnosis.

Objective

This study aimed to examine the efficacy of the MKL classifier and the Boruta feature selection method for schizophrenia patients (SZ) and healthy controls (HC) single-subject classification.

Methods

A cohort of 54 SZ and 54 HC participants were studied. Three sets of features related to ERP signals were calculated as follows: peak related features, peak to peak related features, and signal related features. The Boruta algorithm was used to evaluate the impact of feature selection on classification performance. An MKL algorithm was applied to address schizophrenia detection.

Results

A classification accuracy of 83% using the whole dataset, and 86% after applying Boruta feature selection was obtained. The variables that contributed most to the classification were mainly related to the latency and amplitude of the auditory P300 paradigm.

Conclusion

This study showed that MKL can be useful in distinguishing between schizophrenic patients and controls when using ERP measures. Moreover, the use of the Boruta algorithm provides an improvement in classification accuracy and computational cost.

A benchmark for prediction of psychiatric multimorbidity from resting EEG data in a large pediatric sample

Psychiatric disorders are among the most common and debilitating illnesses across the lifespan and begin usually during childhood and adolescence, which emphasizes the importance of studying the developing brain. Most of the previous pediatric neuroimaging studies employed traditional univariate statistics on relatively small samples. Multivariate machine learning approaches have a great potential to overcome the limitations of these approaches. On the other hand, the vast majority of existing multivariate machine learning studies have focused on differentiating between children with an isolated psychiatric disorder and typically developing children. However, this line of research does not reflect the real-life situation as the majority of children with a clinical diagnosis have multiple psychiatric disorders (multimorbidity), and consequently, a clinician has the task to choose between different diagnoses and/or the combination of multiple diagnoses. Thus, the goal of the present benchmark is to predict psychiatric multimorbidity in children and adolescents. For this purpose, we implemented two kinds of machine learning benchmark challenges: The first challenge targets the prediction of the seven most prevalent DSM-V psychiatric diagnoses for the available data set, of which each individual can exhibit multiple ones concurrently (i.e. multi-task multi-label classification). Based on behavioral and cognitive measures, a second challenge focuses on predicting psychiatric symptom severity on a dimensional level (i.e. multiple regression task). For the present benchmark challenges, we will leverage existing and future data from the biobank of the Healthy Brain Network (HBN) initiative, which offers a unique large-sample dataset (N = 2042) that provides a wide array of different psychiatric developmental disorders and true hidden data sets. Due to limited real-world practicability and economic viability of MRI measurements, the present challenge will permit only resting state EEG data and demographic information to derive predictive models. We believe that a community driven effort to derive predictive markers from these data using advanced machine learning algorithms can help to improve the diagnosis of psychiatric developmental disorders.

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

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

Application of Machine Learning Techniques to Detect the Children with Autism Spectrum Disorder

Early detection of autism spectrum disorder (ASD) is highly beneficial to the health sustainability of children. Existing detection methods depend on the assessment of experts, which are subjective and costly. In this study, we proposed a machine learning approach that fuses physiological data (electroencephalography, EEG) and behavioral data (eye fixation and facial expression) to detect children with ASD. Its implementation can improve detection efficiency and reduce costs. First, we used an innovative approach to extract features of eye fixation, facial expression, and EEG data. Then, a hybrid fusion approach based on a weighted naive Bayes algorithm was presented for multimodal data fusion with a classification accuracy of 87.50%. Results suggest that the machine learning classification approach in this study is effective for the early detection of ASD. Confusion matrices and graphs demonstrate that eye fixation, facial expression, and EEG have different discriminative powers for the detection of ASD and typically developing children, and EEG may be the most discriminative information. The physiological and behavioral data have important complementary characteristics. Thus, the machine learning approach proposed in this study, which combines the complementary information, can significantly improve classification accuracy.

Diminished error-related negativity and error positivity in children and adults with externalizing problems and disorders: a meta-analysis on error processing

BACKGROUND

Deficits in error processing are reflected in an inability of people with externalizing problems to adjust their problem behaviour. The present study contains 2 meta-analyses, testing whether error processing - indexed by the event-related potentials error-related negativity (ERN) and error positivity (Pe) - is reduced in children and adults with externalizing problems and disorders compared to healthy controls.

METHODS

We conducted a systematic search in PubMed (1980 to December 2018), PsycInfo (1980 to December 2018) and Scopus (1970 to December 2018), identifying 328 studies. We included studies that measured error processing using the Eriksen flanker task, the go/no-go task or the stop-signal task in healthy controls and in adults or children with clearly described externalizing behavioural problems (e.g., aggression) or a clinical diagnosis on the externalizing spectrum (e.g., addiction).

RESULTS

Random-effect models (ERN: 23 studies, 1739 participants; Pe: 27 studies, 1456 participants) revealed a reduced ERN amplitude (Hedges' g = 0.44, 95% confidence interval [CI] 0.29 to 0.58) and a reduced Pe amplitude (Hedges' g = -0.27, 95% CI -0.44 to -0.09) during error processing in people with externalizing problems or disorders compared to healthy controls. Type of diagnosis, age and the presence of performance feedback or comorbidity did not moderate the results. The employed cognitive task was a moderator for Pe but not for ERN. The go/no-go task generated a greater amplitude difference in Pe than the Eriksen flanker task. Small-sample assessment revealed evidence of publication bias for both event-related potentials. However, a p curve analysis for ERN showed that evidential value was present; for Pe, the p curve analysis was inconclusive.

LIMITATIONS

The moderators did not explain the potential heterogeneity in most of the analysis, suggesting that other disorder- and patient-related factors affect error processing.

CONCLUSION

Our findings indicate the presence of compromised error processing in externalizing psychopathology, suggesting diminished activation of the prefrontal cortex during performance monitoring.

Instantaneous radiated power of brain activity: application to prepulse inhibition and facilitation for body dysmorphic disorder

Background

Global measures of neuronal activity embrace the advantage of a univariate, holistic and unique description of brain activity, reducing the spatial dimensions of electroencephalography (EEG) analysis at the cost of lower precision in localizing effects. In this work, the instantaneous radiated power (IRP) is proposed as a new whole-brain descriptor, reflecting the cortical activity from an exclusively electromagnetic perspective. Considering that the brain consists of multiple elementary dipoles, the whole-brain IRP takes into account the radiational contribution of all cortical sources. Unlike conventional EEG analyses that evaluate a large number of scalp or source locations, IRP reflects a whole-brain, event-related measure and forces the analysis to focus on a single time-series, thus efficiently reducing the EEG spatial dimensions and multiple comparisons.

Results

To apply the developed methodology in real EEG data, two groups (25 controls vs 30 body dysmorphic disorder, BDD, patients) were matched for age and sex and tested in a prepulse inhibition (PPI) and facilitation (PPF) paradigm. Two global brain descriptors were extracted for between-groups and between-conditions comparison purposes, namely the global field power (GFP) and the whole-brain IRP. Results showed that IRP can replicate the expected condition differences (with PPF being greater than PPI responses), exhibiting also reduced levels in BDD compared to control group overall. There were also similar outcomes using GFP and IRP, suggesting consistency between the two measures. Finally, regression analysis showed that the PPI-related IRP (during N100 time-window) is negatively correlated with BDD psychometric scores.

Conclusions

Investigating the brain activity with IRP significantly reduces the data dimensionality, giving insights about global brain synchronization and strength. We conclude that IRP can replicate the existing evidence regarding sensorimotor gating effects, revealing also group electrophysiological alterations. Finally, electrophysiological IRP responses exhibited correlations with BDD psychometrics, potentially useful as supplementary tool in BDD symptomatology.

Emerging biomarkers for child & family intervention studies: A review of EEG studies of parenting

Most interventions for childhood mental health problems require significant parental involvement, and treatment programs are increasingly incorporating components aimed at enhancing parents' own self-regulation in the context of potentially stressful parent-child interactions. This paper discusses the promise of EEG in examining the rapidly unfolding perceptual, cognitive, emotional, and regulatory processes that occur in parenting, in hopes of ultimately informing child and family interventions. First, we review two separate bodies of work that have used EEG with parents: one examining event-related potential (ERP) measures, and the other examining frontal alpha asymmetry (FAA). We discuss benefits of each within the study of parenting, and also suggest other EEG metrics (such as event-related time-frequency analyses) that can be leveraged to fill current gaps in our knowledge. Finally, we discuss the potential for these findings to inform clinical work with children and families, such as identifying biomarkers that could aid in assessment, treatment recommendations, and monitoring response to interventions.

EEG/ERP as a pragmatic method to expand the reach of infant-toddler neuroimaging in HBCD: Promises and challenges

Though electrophysiological measures (EEG and ERP) offer complementary information to MRI and a variety of advantages for studying infants and young children, these measures have not yet been included in large cohort studies of neurodevelopment. This review summarizes the types of EEG and ERP measures that could be used in the HEALthy Brain and Cognitive Development (HBCD) study, and the promises and challenges in doing so. First, we provide brief overview of the use of EEG/ERP for studying the developing brain and discuss exemplar findings, using resting or baseline EEG measures as well as the ERP mismatch negativity (MMN) as exemplars. We then discuss the promises of EEG/ERP such as feasibility, while balancing challenges such as ensuring good signal quality in diverse children with different hair types. We then describe an ongoing multi-site EEG data harmonization from our groups. We discuss the process of alignment and provide preliminary usability data for both resting state EEG data and auditory ERP MMN in diverse samples including over 300 infants and toddlers. Finally, we provide recommendations and considerations for the HBCD study and other studies of neurodevelopment.

Resting State EEG Related to Mathematical Improvement After Spatial Training in Children

Spatial cognitive abilities, including mental rotation (MR) and visuo-spatial working memory (vsWM) are correlated with mathematical performance, and several studies have shown that training of these abilities can enhance mathematical performance. Here, we investigated the behavioral and neural correlates of MR and vsWM training combined with number line (NL) training. Fifty-seven children, aged 6–7, performed 25 days of NL training combined with either vsWM or MR and participated in an Electroencephalography (EEG)-session in school to measure resting state activity and steady-state visual evoked potentials during a vsWM task before and after training. Fifty children, aged 6–7, received usual teaching and acted as a control group. Compared to the control group, both training groups improved on a combined measure of mathematics. Cognitive improvement was specific to the training. Significant pre-post changes in resting state-EEG (rs-EEG), common to both training groups, were found for power as well as for coherence, with no significant differences in rs-EEG-changes between the vsWM and MR groups. Two of the common rs-EEG changes were correlated with mathematical improvement: (1) an increase in coherence between the central frontal lobe and the right parietal lobe in frequencies ranging from 16 to 25 Hz, and (2) an increase in coherence between the left frontal lobe and the right parietal lobe ranging from 23 to 25 Hz. These results indicate that changes in fronto-parietal coherence are related to an increase in mathematical performance, which thus might be a useful measure in further investigations of mathematical interventions in children.

What Event-Related Potential Tells Us about Brain Function: Child-Adolescent Psychiatric Perspectives

Electroencephalography (EEG) measures neural activation due to various cognitive processes. EEG and event-related potentials (ERPs) are widely used in studies investigating psychopathology and neural substrates of psychiatric diseases in children and adolescents. The present study aimed to review recent ERP studies in child and adolescent psychiatry. ERPs are non-invasive methods for studying synaptic functions in the brain. ERP might be a candidate biomarker in child-adolescent psychiatry, considering its ability to reflect cognitive and behavioral functions in humans. For the EEG study of psychiatric diseases in children and adolescents, several ERP components have been used, such as mismatch negativity, P300, error-related negativity (ERN), and reward positivity (RewP). Regarding executive functions and inhibition in patients with attention-deficit/hyperactivity disorder (ADHD), P300 latency, and ERN were significantly different in patients with ADHD compared to those in the healthy population. ERN showed meaningful changes in patients with anxiety disorders, such as generalized anxiety disorder, separation anxiety disorder, and obsessive-compulsive disorder. Patients with depression showed significantly attenuated RewP compared to the healthy population, which was related to the symptoms of anhedonia.

Feasibility of assessing brain activity using mobile, in-home collection of electroencephalography: methods and analysis

The last decade has seen increased availability of mobile electroencephalography (EEG). These mobile systems enable researchers to conduct data collection "in-context," reducing participant burden and potentially increasing diversity and representation of research samples. Our research team completed in-home data collection from more than 400 twelve-month-old infants from low-income backgrounds using a mobile EEG system. In this paper, we provide methodological and analytic guidance for collecting high-quality, mobile EEG in infants. Specifically, we offer insights and recommendations for equipment selection, data collection, and data analysis, highlighting important considerations for selecting a mobile EEG system. Examples include the size of the recording equipment, electrode type, reference types, and available montages. We also highlight important recommendations surrounding preparing a nonstandardized recording environment for EEG collection, obtaining informed consent from parents, instructions for parents during capping and recording, stimuli and task design, training researchers, and monitoring data as it comes in. Additionally, we provide access to the analysis code and demonstrate the robustness of the data from a recent study using this approach, in which 20 artifact-free epochs achieve good internal consistency reliability. Finally, we provide recommendations and publicly available resources for future studies aiming to collect mobile EEG.

Shared and unique neural mechanisms underlying pediatric trichotillomania and obsessive compulsive disorder

BACKGROUND

Little is known about the neural underpinnings of pediatric trichotillomania (TTM). We examined error-related negativity (ERN)-amplitude and theta-EEG power differences among youth with TTM, OCD, and healthy controls (HC).

METHODS

Forty channel EEG was recorded from 63 pediatric participants (22 with TTM, 22 with OCD, and 19 HC) during the Eriksen Flanker Task. EEG data from inhibitory control were used to derive estimates of ERN amplitude and event-related spectral power associated with motor inhibition.

RESULTS

TTM and HC were similar in brain activity patterns in frontal and central regions and TTM and OCD were similar in the parietal region. Frontal ERN-amplitude was significantly larger in OCD relative to TTM and HC, who did not differ from each other. The TTM group had higher theta power compared to OCD in frontal and central regions, and higher theta than both comparison groups in right motor cortex and superior parietal regions. Within TTM, flanker task performance was correlated with EEG activity in frontal, central, and motor cortices whereas global functioning and impairment were associated with EEG power in bilateral motor and parietal cortices.

CONCLUSIONS

Findings are discussed in terms of shared and unique neural mechanisms in TTM and OCD and treatment implications.

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

INTRODUCTION

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

METHOD

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

RESULTS

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

CONCLUSIONS

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

Genome-Wide Scan for Five Brain Oscillatory Phenotypes Identifies a New QTL Associated with Theta EEG Band

Brain waves, measured by electroencephalography (EEG), are a powerful tool in the investigation of neurophysiological traits and a noninvasive and cost-effective alternative in the diagnostic of some neurological diseases. In order to identify novel Quantitative Trait Loci (QTLs) for brain wave relative power (RP), we collected resting state EEG data in five frequency bands (δ, θ, α, β1, and β2) and genome-wide data in a cohort of 105 patients with late onset Alzheimer’s disease (LOAD), 41 individuals with mild cognitive impairment and 45 controls from Iberia, correcting for disease status. One novel association was found with an interesting candidate for a role in brain wave biology, CLEC16A (C-type lectin domain family 16), with a variant at this locus passing the adjusted genome-wide significance threshold after Bonferroni correction. This finding reinforces the importance of immune regulation in brain function. Additionally, at a significance cutoff value of 5 × 10⁻⁶, 18 independent association signals were detected. These signals comprise brain expression Quantitative Loci (eQTLs) in caudate basal ganglia, spinal cord, anterior cingulate cortex and hypothalamus, as well as chromatin interactions in adult and fetal cortex, neural progenitor cells and hippocampus. Moreover, in the set of genes showing signals of association with brain wave RP in our dataset, there is an overrepresentation of loci previously associated with neurological traits and pathologies, evidencing the pleiotropy of the genetic variation modulating brain function.

Electrophysiological correlates of spontaneous mind wandering in attention-deficit/hyperactivity disorder

We recently hypothesised that increased spontaneous mind wandering (MW-S) reflects a core process underlying attention-deficit/hyperactivity disorder (ADHD). Previous studies show that individuals with ADHD and neurotypical individuals with increased MW-S display similar cognitive-performance and electrophysiological (EEG) impairments in attentional processes. However, the cognitive-EEG markers associated with increased MW-S in ADHD remain poorly understood. We therefore investigated such markers in a sample of 69 sex- and age-matched adults with ADHD and 29 controls during the Sustained Attention to Response Task. We compared task performance and EEG measures (P3, time-frequency brain-oscillations) of attentional processes between groups, and examined their association with a validated self-report questionnaire of MW-S. Finally, we tested the hypothesis that MW-S and ADHD diagnosis relate to the same cognitive-EEG impairments using a hierarchical regression model. Compared to controls, adults with ADHD showed attenuations in P3, event-related alpha and beta suppression during response inhibition (No-Go trials), and theta power activations during response execution (Go trials), as well as increased reaction time variability and more commission/omission errors. MW-S was also continuously associated with most cognitive-EEG measures related to ADHD. The hierarchical regressions on measures associated with both ADHD diagnosis and MW-S showed that MW-S did not explain additional variance in the cognitive-EEG markers (except for beta suppression) beyond ADHD diagnosis, and vice versa. These findings are consistent with our hypothesis that ADHD diagnosis and MW-S share common neural deficits, and that MW-S may reflect a core symptom of the disorder.

Integrated Development Environment for EEG-Driven Cognitive-Neuropsychological Research

Background: EEG-driven research is paramount in cognitive-neuropsychological studies, as it provides a non-invasive window to the underlying neural mechanisms of cognition and behavior. A myriad collection of software and hardware frameworks has been developed to alleviate some of the technical barriers involved in EEG-driven research. Methods: we propose an integrated development environment which encompasses the entire technical “data-collection pipeline” of cognitive-neuropsychological research, including experiment design, data acquisition, data exploration and analysis in a state-of-the-art user interface. Our framework is based on a unique integration between a python-based web framework, time-oriented databases and object-based data schemes. Results: we demonstrated our framework with the recording and analysis of an n-Back task completed by 15 elderly (ages 50 to 80) participants. This case study demonstrates the highly utilized nature of our integrated framework with a challenging target population. Furthermore, our results may provide new insights into the correlation between brain activity and working memory performance in elderly people, who are prone to experience accelerated decline in executive prefrontal cortex functioning. Conclusion: our framework extends the range of EEG-driven experimental methods for assessing cognition available for cognitive-neuroscientists, allowing them to concentrate on the creative part of their work instead of technical aspects.

Electrophysiological correlates of action observation treatment in children with cerebral palsy: A pilot study

Action Observation Treatment (AOT) has been shown to be effective in the functional recovery of several clinical populations. However, little is known about the neural underpinnings of the clinical efficacy of AOT in children with Cerebral Palsy (CP). Using electroencephalography (EEG), we recorded µ rhythm desynchronization as an index of sensorimotor cortex modulation during a passive action observation task before and after AOT. The relationship between sensorimotor modulation and clinical outcomes was also assessed. Eight children with CP entered the present randomized controlled crossover pilot study in which the experimental AOT preceded or followed a control Videogame Observation Treatment (VOT). Results provide further evidence of the clinical efficacy of AOT for improving hand motor function in CP, as assessed with the Assisting Hand Assessment (AHA) and Melbourne Assessment of Unilateral Upper Limb Function Scale (MUUL). The novel finding is that AOT increases µ rhythm desynchronization at scalp locations corresponding to the hand representation areas. This effect is associated to functional improvement assessed with the MUUL. These preliminary findings, although referred to as a small sample, suggest that AOT may affect upper limb motor recovery in children with CP and modulate the activation of sensorimotor areas, offering a potential neurophysiological correlate to support the clinical utility of AOT.

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

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

No evidence of associations between ADHD and event-related brain potentials from a continuous performance task in a population-based sample of adolescent twins

We investigated key event-related brain potential markers (ERPs) derived from a flanked continuous performance task (CPT) and whether these would show phenotypic associations with ADHD (attention-deficit/hyperactivity disorder) in a population-based sample. We further explored whether there was preliminary evidence that such ERPs could also index genetic risk for ADHD (depending on finding phenotypic associations). Sixty-seven male-only twin pairs (N = 134; aged 12–15) from a subsample of the Twins’ Early Development Study, concordant and discordant for ADHD symptoms, performed the flanked CPT (or CPT-OX) while electroencephalography (EEG) was recorded. ERPs were obtained for cue (P3, CNV or contingency negative variation), go (P3, N2) and nogo trials (P3, N2). We found no phenotypic associations between CPT-derived ERPs and ADHD—the sizes of the estimated phenotypic correlations were nonsignificant and very small (r’s = -.11 to .04). Twin-model fitting analyses using structural equation modelling provided preliminary evidence that some of the ERPs were heritable (with the most robust effect for go-P3 latency), but there was limited evidence of any genetic associations between ERPs and ADHD, although with the caveat that our sample was small and hence had limited power. Overall, unlike in previous research, there was no evidence of phenotypic (nor preliminary evidence for genetic) associations between ADHD and CPT-derived ERPs in this study. Hence, it may be currently premature for genetic analyses of ADHD to be guided by CPT-derived ERP parameters (unlike alternative cognitive-neurophysiological approaches which may be more promising). Further research with better-powered, population-based, genetically-informative and cross-disorder samples are required, which could be facilitated by emerging mobile EEG technologies.

Reduced premovement positivity during the stimulus-response interval precedes errors: Using single-trial and regression ERPs to understand performance deficits in ADHD

Brain mechanisms linked to incorrect response selections made under time pressure during cognitive task performance are poorly understood, particularly in adolescents with attention-deficit hyperactivity disorder (ADHD). Using subject-specific multimodal imaging (electroencephalogram, magnetic resonance imaging, behavior) during flanker task performance by a sample of 94 human adolescents (mean age = 15.5 years, 50% female) with varying degrees of ADHD symptomatology, we examined the degree to which amplitude features of source-resolved event-related potentials (ERPs) from brain-independent component processes within a critical (but often ignored) period in the action selection process, the stimulus-response interval, were associated with motor response errors (across trials) and error rates (across individuals). Response errors were typically preceded by two smaller peaks in both trial-level and trial-averaged ERP projections from posterior medial frontal cortex (pMFC): a frontocentral P3 peaking about 390 ms after stimulus onset, and a premovement positivity (PMP) peaking about 110 ms before the motor response. Separating overlapping stimulus-locked and response-locked ERP contributions using a "regression ERP" approach showed that trial errors and participant error rates were primarily associated with smaller PMP, and not with frontocentral P3. Moreover, smaller PMP mediated the association between larger numbers of errors and ADHD symptoms, suggesting the possible value of using PMP as an intervention target to remediate performance deficits in ADHD.

Atypical resting state neuromagnetic connectivity and spectral power in very preterm children

BACKGROUND

Children born very preterm often display selective cognitive difficulties at school age even in the absence of major brain injury. Alterations in neurophysiological activity underpinning such difficulties, as well as their relation to specific aspects of adverse neonatal experience, remain poorly understood. In the present study, we examined interregional connectivity and spectral power in very preterm children at school age, and their relationship with clinical neonatal variables and long-term outcomes (IQ, executive functions, externalizing/internalizing behavior, visual-motor integration).

METHODS

We collected resting state magnetoencephalographic (MEG) and psychometric data from a cohort at the age of 8 years followed prospectively since birth, which included three groups: Extremely Low Gestational Age (ELGA, 24-28 weeks GA n = 24, age 7.7 ± 0.38, 10 girls), Very Low Gestational Age (VLGA, 29-32 weeks GA n = 37, age 7.7 ± 0.39, 24 girls), and full-term children (38-41 weeks GA n = 39, age 7.9 ± 1.02, 24 girls). Interregional phase synchrony and spectral power were tested for group differences, and associations with neonatal and outcome variables were examined using mean-centered and behavioral Partial Least Squares (PLS) analyses, respectively.

RESULTS

We found greater connectivity in the theta band in the ELGA group compared to VLGA and full-term groups, primarily involving frontal connections. Spectral power analysis demonstrated overall lower power in the ELGA and VLGA compared to full-term group. PLS indicated strong associations between neurophysiological connectivity at school age, adverse neonatal experience and cognitive performance, and behavior. Resting spectral power was associated only with behavioral scores.

CONCLUSIONS

Our findings indicate significant atypicalities of neuromagnetic brain activity and connectivity in very preterm children at school age, with alterations in connectivity mainly observed only in the ELGA group. We demonstrate a significant relationship between connectivity, adverse neonatal experience, and long-term outcome, indicating that the disruption of developing neurophysiological networks may mediate relationships between neonatal events and cognitive and behavioral difficulties at school age.

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

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

Unmixing Oscillatory Brain Activity by EEG Source Localization and Empirical Mode Decomposition

Neuronal activity is composed of synchronous and asynchronous oscillatory activity at different frequencies. The neuronal oscillations occur at time scales well matched to the temporal resolution of electroencephalography (EEG); however, to derive meaning from the electrical brain activity as measured from the scalp, it is useful to decompose the EEG signal in space and time. In this study, we elaborate on the investigations into source-based signal decomposition of EEG. Using source localization, the electrical brain signal is spatially unmixed and the neuronal dynamics from a region of interest are analyzed using empirical mode decomposition (EMD), a technique aimed at detecting periodic signals. We demonstrate, first in simulations, that the EMD is more accurate when applied to the spatially unmixed signal compared to the scalp-level signal. Furthermore, on EEG data recorded simultaneously with transcranial magnetic stimulation (TMS) over the hand area of the primary motor cortex, we observe a link between the peak to peak amplitude of the motor-evoked potential (MEP) and the phase of the decomposed localized electrical activity before TMS onset. The results thus encourage combination of source localization and EMD in the pursuit of further insight into the mechanisms of the brain with respect to the phase and frequency of the electrical oscillations and their cortical origin.

Reduced left-lateralized pattern of event-related EEG oscillations in infants at familial risk for language and learning impairment

The ability to rapidly discriminate successive auditory stimuli within tens-of-milliseconds is crucial for speech and language development, particularly in the first year of life. This skill, called Rapid Auditory Processing (RAP), is altered in infants at familial risk for language and learning impairment (LLI) and is a robust predictor of later language outcomes. In the present study, we investigate the neural substrates of RAP, i.e., the underlying neural oscillatory patterns, in a group of Italian 6-month-old infants at risk for LLI (FH+, n = 24), compared to control infants with no known family history of LLI (FH−, n = 32). Brain responses to rapid changes in fundamental frequency and duration were recorded via high-density electroencephalogram during a non-speech double oddball paradigm. Sources of event-related potential generators were localized to right and left auditory regions in both FH+ and FH− groups. Time-frequency analyses showed variations in both theta (Ɵ) and gamma (ɣ) ranges across groups. Our results showed that overall RAP stimuli elicited a more left-lateralized pattern of oscillations in FH− infants, whereas FH+ infants demonstrated a more right-lateralized pattern, in both the theta and gamma frequency bands. Interestingly, FH+ infants showed reduced early left gamma power (starting at 50 ms after stimulus onset) during deviant discrimination. Perturbed oscillatory dynamics may well constitute a candidate neural mechanism to explain group differences in RAP. Additional group differences in source location suggest that anatomical variations may underlie differences in oscillatory activity. Regarding the predictive value of early oscillatory measures, we found that the amplitude of the source response and the magnitude of oscillatory power and phase synchrony were predictive of expressive vocabulary at 20 months of age. These results further our understanding of the interplay among neural mechanisms that support typical and atypical rapid auditory processing in infancy.

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Neural sources of RAP in infancy were identified at right/left auditory regions.

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FH− infants demonstrated a mature left-lateralized pattern of neural oscillations.

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FH+ infants demonstrated a more right-lateralized pattern of neural oscillations.

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FH+ infants showed reduced left gamma power during rapid auditory discrimination.

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Source and oscillatory measures are both associated with later language skills.

Mobile EEG in research on neurodevelopmental disorders: Opportunities and challenges

Mobile electroencephalography (mobile EEG) represents a next-generation neuroscientific technology – to study real-time brain activity – that is relatively inexpensive, non-invasive and portable. Mobile EEG leverages state-of-the-art hardware alongside established advantages of traditional EEG and recent advances in signal processing. In this review, we propose that mobile EEG could open unprecedented possibilities for studying neurodevelopmental disorders. We first present a brief overview of recent developments in mobile EEG technologies, emphasising the proliferation of studies in several neuroscientific domains. As these developments have yet to be exploited by neurodevelopmentalists, we then identify three research opportunities: 1) increase in the ease and flexibility of brain data acquisition in neurodevelopmental populations; 2) integration into powerful developmentally-informative research designs; 3) development of innovative non-stationary EEG-based paradigms. Critically, we address key challenges that should be considered to fully realise the potential of mobile EEG for neurodevelopmental research and for understanding developmental psychopathology more broadly, and suggest future research directions.

Neuropsychological and Neurophysiological Indicators of General and Food-Specific Impulsivity in Children with Overweight and Obesity: A Pilot Study

Impulsivity, particularly towards food, is a potential risk factor for increased energy intake and the development and maintenance of obesity in children. However, neuropsychological and neurophysiological indicators of general and food-specific impulsivity and their association with children's weight status are poorly understood. This pilot study examined electroencephalography (EEG) frequency band profiles during eyes-closed and eyes-open resting state in n = 12 children with overweight or obesity versus n = 22 normal-weight controls and their link to child- and parent-reported and experimentally assessed impulsivity of children (e.g., risk-taking behavior, approach-avoidance behavior towards food). The main results indicated that children with overweight/obesity versus normal weight showed significantly increased delta and decreased alpha band activity during eyes-closed resting state. Across the total sample, EEG slow-wave band activity was particularly linked to self- and parent-reported impulsivity and greater risk-taking behavior, but not to approach behavior towards food, after controlling for children's age and weight status. The identification of specific EEG patterns in children with excess weight may provide a new basis for developing neurophysiological diagnostic and treatment approaches for childhood obesity. Future studies with larger samples and longitudinal designs are needed to replicate the present findings and test their stability over time.

Genome-wide association analysis links multiple psychiatric liability genes to oscillatory brain activity

Oscillatory activity is crucial for information processing in the brain, and has a long history as a biomarker for psychopathology. Variation in oscillatory activity is highly heritable, but current understanding of specific genetic influences remains limited. We performed the largest genome-wide association study to date of oscillatory power during eyes-closed resting electroencephalogram (EEG) across a range of frequencies (delta 1-3.75 Hz, theta 4-7.75 Hz, alpha 8-12.75 Hz, and beta 13-30 Hz) in 8,425 subjects. Additionally, we performed KGG positional gene-based analysis and brain-expression analyses. GABRA2-a known genetic marker for alcohol use disorder and epilepsy-significantly affected beta power, consistent with the known relation between GABAA interneuron activity and beta oscillations. Tissue-specific SNP-based imputation of gene-expression levels based on the GTEx database revealed that hippocampal GABRA2 expression may mediate this effect. Twenty-four genes at 3p21.1 were significant for alpha power (FDR q < .05). SNPs in this region were linked to expression of GLYCTK in hippocampal tissue, and GNL3 and ITIH4 in the frontal cortex-genes that were previously implicated in schizophrenia and bipolar disorder. In sum, we identified several novel genetic variants associated with oscillatory brain activity; furthermore, we replicated and advanced understanding of previously known genes associated with psychopathology (i.e., schizophrenia and alcohol use disorders). Importantly, these psychopathological liability genes affect brain functioning, linking the genes' expression to specific cortical/subcortical brain regions.

Frontal Alpha Asymmetry and Theta Oscillations Associated With Information Sharing Intention

Social media has gained increasing importance in many aspects of everyday life, from building relationships to establishing collaborative networks between individuals worldwide. Sharing behavior is an essential part of maintaining these dynamic networks. However, the precise neural factors that could be related to sharing behavior in online communities remain unclear. In this study, we recorded electroencephalographic (EEG) oscillations of human subjects while they were watching short videos. The subjects were later asked to evaluate the videos based on how much they liked them and whether they would share them. We found that, at the population level, subjects watching videos that would not be shared had higher power spectral density (PSD) amplitudes in the theta band (4-8 Hz), primarily over the frontal and parietal sites of the right hemisphere, than subjects watching videos that would be shared. Previous studies have associated task disengagement with an increase in scalp-wide theta activation, which can be interpreted as a mind-wandering effect. This might suggest that the decision to not share the video may lead to a more automatic/effortless neural pattern. We also found that watching videos that would be shared was associated with lower PSD amplitudes in the alpha band (8-12 Hz) over the central and right frontal sites, and with more negative scores of frontal alpha asymmetry (FAA) index scores. These results may be related to previous work linking right-sided frontal EEG asymmetry to the pursuit of social conformity and avoidance of negative outcomes, such as social isolation. Finally, using support vector machine (SVM) algorithms, we show that these EEG parameters and preference rating scores can be used to improve the predictability of sharing information behavior. The information sharing-related EEG pattern described here could therefore improve our understanding of the neural markers associated with sharing behavior and contribute to studies about stimuli propagation.

Comparing Self-Regulation-Associated Event Related Potentials in Preschool Children with and without High Levels of Disruptive Behavior

Deficient self-regulation plays a key role in the etiology of early onset disruptive behavior disorders and signals risk for chronic psychopathology. However, to date, there has been no research comparing preschool children with and without high levels of disruptive behavior using Event Related Potentials (ERPs) associated with specific self-regulation sub-processes. We examined 15 preschool children with high levels of disruptive behavior (35 % female) and 20 peers with low disruptive behavior (50 % female) who completed a Go/No-go task that provided emotionally valenced feedback. We tested whether 4 ERP components: the Error Related Negativity, the Error Positivity, the Feedback Related Negativity, and the No-go N2, differed in preschool children with and without high levels of disruptive behavior. Preschoolers with high levels of disruptive behavior showed less differentiation between the Error Positivity and corresponding waveforms following correct responses at posterior sites. Preschoolers with high and low disruptive behavior also showed differences in Go/No-go N2 waveform amplitudes across electrodes. These findings suggest that preschool children with high levels of disruptive behavior may show abnormal brain activity during certain self-regulation sub-processes, informing potential advances in conceptualizing and treating early disruptive behavior.

A Pilot Study of Neurofeedback for Chronic PTSD

EEG Biofeedback (also known as neurofeedback) has been in use as a clinical intervention for well over 30 years; however, it has made very little impact on clinical care. One reason for this has been the difficulty in designing research to measure clinical change in the real world. While substantial evidence exists for its efficacy in treating attention deficit/hyperactivity disorder, relatively little evidence exists for its utility in other disorders including posttraumatic stress disorder (PTSD). The current study represents a "proof-of-concept" pilot for the use of neurofeedback with multiply-traumatized individuals with treatment-resistant PTSD. Participants completed 40 sessions of neurofeedback training two times per week with sensors randomly assigned (by the study coordinator, who was not blind to condition) to sensor placements of either T4-P4 or T3-T4. We found that neurofeedback significantly reduced PTSD symptoms (Davidson Trauma Scale scores averaged 69.14 at baseline to 49.26 at termination), and preceded gains in affect regulation (Inventory of Altered Self-Capacities-Affect Dysregulation scores averaged 23.63 at baseline to 17.20 at termination). We discuss a roadmap for future research.

Shared and Disorder-Specific Event-Related Brain Oscillatory Markers of Attentional Dysfunction in ADHD and Bipolar Disorder

Attention-deficit/hyperactivity disorder (ADHD) and bipolar disorder (BD) often present with overlapping symptoms and cognitive impairments, such as increased fluctuations in attentional performance measured by increased reaction-time variability (RTV). We previously provided initial evidence of shared and distinct event-related potential (ERP) impairments in ADHD and BD in a direct electrophysiological comparison, but no study to date has compared neural mechanisms underlying attentional impairments with finer-grained brain oscillatory markers. Here, we aimed to compare the neural underpinnings of impaired attentional processes in ADHD and BD, by examining event-related brain oscillations during a reaction-time task under slow-unrewarded baseline and fast-incentive conditions. We measured cognitive performance, ERPs and brain-oscillatory modulations of power and phase variability in 20 women with ADHD, 20 women with BD (currently euthymic) and 20 control women. Compared to controls, both ADHD and BD groups showed increased RTV in the baseline condition and increased RTV, theta phase variability and lower contingent negative variation in the fast-incentive condition. Unlike controls, neither clinical group showed an improvement from the slow-unrewarded baseline to the fast-incentive condition in attentional P3 amplitude or alpha power suppression. Most impairments did not differ between the disorders, as only an adjustment in beta suppression between conditions (lower in the ADHD group) distinguished between the clinical groups. These findings suggest shared impairments in women with ADHD and BD in cognitive and neural variability, preparatory activity and inability to adjust attention allocation and activation. These overlapping impairments may represent shared neurobiological mechanisms of attentional dysfunction in ADHD and BD, and potentially underlie common symptoms in both disorders.

Frontal alpha asymmetry predicts inhibitory processing in youth with attention deficit/hyperactivity disorder

INTRODUCTION

Atypical asymmetry in brain activity has been implicated in the behavioral and attentional dysregulation observed in ADHD. Specifically, asymmetry in neural activity in the right versus left frontal regions has been linked to ADHD, as well as to symptoms often associated with ADHD such as heightened approach behaviors, impulsivity and difficulties with inhibition. Clarifying the role of frontal asymmetry in ADHD-like traits, such as disinhibition, may provide information on the neurophysiological processes underlying these behaviors.

METHOD

ADHD youth (ADHD: n = 25) and healthy, typically developing controls (TD: n = 25) underwent an electroencephalography (EEG) recording while completing a go/no-go task-a commonly used test measuring behavioral inhibition. In addition, advanced signal processing for source localization estimated the location of signal generators underlying frontal alpha asymmetry (FA) during correct and incorrect trials.

RESULTS

This is the first study in ADHD to demonstrate that the dorsal-lateral prefrontal cortex (DLPFC) may be responsible for generating frontal alpha. During failed inhibition trials, ADHD youth displayed greater FA than TD youth. In addition, within the ADHD group, frontal asymmetry during later processing stages (i.e., 400-800ms after stimulus) predicted a higher number of commission errors throughout the task.

CONCLUSIONS

These results suggest that frontal alpha asymmetry may be a specific biomarker of cognitive disinhibition among youth with ADHD.

Age-Related Changes in Resting-State EEG Activity in Attention Deficit/Hyperactivity Disorder: A Cross-Sectional Study

Numerous studies indicate that attention deficit/hyperactivity disorder (ADHD) is related to some developmental trends, as its symptoms change widely over time. Nevertheless, the etiology of this phenomenon remains ambiguous. There is a disagreement whether ADHD is related to deviations in brain development or to a delay in brain maturation. The model of deviated brain development suggests that the ADHD brain matures in a fundamentally different way, and does not reach normal maturity at any developmental stage. On the contrary, the delayed brain maturation model assumes that the ADHD brain indeed matures in a different, delayed way in comparison to healthy age-matched controls, yet eventually reaches proper maturation. We investigated age-related changes in resting-state EEG activity to find evidence to support one of the alternative models. A total of 141 children and teenagers participated in the study; 67 diagnosed with ADHD and 74 healthy controls. The absolute power of delta, theta, alpha, and beta frequency bands was analyzed. We observed a significant developmental pattern of decreasing absolute EEG power in both groups. Nonetheless, ADHD was characterized by consistently lower absolute EGG power, mostly in the theta frequency band, in comparison to healthy controls. Our results are in line with the deviant brain maturation theory of ADHD, as the observed effects of age-related changes in EEG power are parallel but different in the two groups.

Attentional selection predicts rapid automatized naming ability in Chinese-speaking children with ADHD

Children with attention-deficit/hyperactivity disorder (ADHD) are reported to have a significantly higher risk of showing reading difficulties or disorders. Here, we aimed to identify the relationship between electroencephalographic (EEG) marker of spatial attention and reading ability in Chinese children with ADHD. First, we demonstrated that rapid automatized naming (RAN) is a strong predictor of reading ability in Chinese-speaking children. Then, EEG data of 9-to 15-year-old children with ADHD (n = 38) and typically developing (TD) controls (n = 36) were collected while the children performed a classical visual search task. Children with ADHD showed slower RAN speed than TD children. For event-related potentials (ERPs), children with ADHD showed a reduced target-evoked N2pc component, which predicted their poorer RAN performance. However, in TD children the early occipital P1 amplitude was negatively correlated with their RAN performance. The correlation between decreased N2pc and poor RAN performance in children with ADHD suggests that their reading problems may in part be due to impaired attentional selection. In contrast, in TD children, development in early visual processing co-occurs with improvements in reading ability.

Shared genetic influences among childhood shyness, social competences, and cortical responses to emotions

Visual event-related potentials (ERPs) evoked by facial expressions are useful to map socioemotional responses among shy children and to predict transition into social phobia. We investigated the sources of covariation among childhood shyness, social competences, and ERPs to other children's happy, neutral, and angry expressions. Electrophysiological and twin analyses examined the phenotypic and etiological association among an index of childhood shyness, an index of social competences, and ERP responses to facial expressions in 200 twins (mean age=9.23years). Multivariate twin analyses showed that the covariation among shyness, social competences, and a composite of a frontal late negative component occurring around 200-400ms in response to happy, neutral, and angry expressions could be entirely explained by shared genetic factors. A coherent causal structure links childhood shyness, social competences, and the cortical responses to facial emotions. A common genetic substrate can explain the interrelatedness of individual differences for childhood shyness, social competences, and some associated electrophysiological responses to socioemotional signals.

A resource for assessing information processing in the developing brain using EEG and eye tracking

We present a dataset combining electrophysiology and eye tracking intended as a resource for the investigation of information processing in the developing brain. The dataset includes high-density task-based and task-free EEG, eye tracking, and cognitive and behavioral data collected from 126 individuals (ages: 6–44). The task battery spans both the simple/complex and passive/active dimensions to cover a range of approaches prevalent in modern cognitive neuroscience. The active task paradigms facilitate principled deconstruction of core components of task performance in the developing brain, whereas the passive paradigms permit the examination of intrinsic functional network activity during varying amounts of external stimulation. Alongside these neurophysiological data, we include an abbreviated cognitive test battery and questionnaire-based measures of psychiatric functioning. We hope that this dataset will lead to the development of novel assays of neural processes fundamental to information processing, which can be used to index healthy brain development as well as detect pathologic processes.

Endophenotype best practices

This review examines the current state of electrophysiological endophenotype research and recommends best practices that are based on knowledge gleaned from the last decade of molecular genetic research with complex traits. Endophenotype research is being oversold for its potential to help discover psychopathology relevant genes using the types of small samples feasible for electrophysiological research. This is largely because the genetic architecture of endophenotypes appears to be very much like that of behavioral traits and disorders: they are complex, influenced by many variants (e.g., tens of thousands) within many genes, each contributing a very small effect. Out of over 40 electrophysiological endophenotypes covered by our review, only resting heart, a measure that has received scant advocacy as an endophenotype, emerges as an electrophysiological variable with verified associations with molecular genetic variants. To move the field forward, investigations designed to discover novel variants associated with endophenotypes will need extremely large samples best obtained by forming consortia and sharing data obtained from genome wide arrays. In addition, endophenotype research can benefit from successful molecular genetic studies of psychopathology by examining the degree to which these verified psychopathology-relevant variants are also associated with an endophenotype, and by using knowledge about the functional significance of these variants to generate new endophenotypes. Even without molecular genetic associations, endophenotypes still have value in studying the development of disorders in unaffected individuals at high genetic risk, constructing animal models, and gaining insight into neural mechanisms that are relevant to clinical disorder.

ICA-derived cortical responses indexing rapid multi-feature auditory processing in six-month-old infants

The abilities of infants to perceive basic acoustic differences, essential for language development, can be studied using auditory event-related potentials (ERPs). However, scalp-channel averaged ERPs sum volume-conducted contributions from many cortical areas, reducing the functional specificity and interpretability of channel-based ERP measures. This study represents the first attempt to investigate rapid auditory processing in infancy using independent component analysis (ICA), allowing exploration of source-resolved ERP dynamics and identification of ERP cortical generators. Here, we recorded 60-channel EEG data in 34 typically developing 6-month-old infants during a passive acoustic oddball paradigm presenting 'standard' tones interspersed with frequency- or duration-deviant tones. ICA decomposition was applied to single-subject EEG data. The best-fitting equivalent dipole or bilaterally symmetric dipole pair was then estimated for each resulting independent component (IC) process using a four-layer infant head model. Similar brain-source ICs were clustered across subjects. Results showed ERP contributions from auditory cortex and multiple extra-auditory cortical areas (often, bilaterally paired). Different cortical source combinations contributed to the frequency- and duration-deviant ERP peak sequences. For ICs in an ERP-dominant source cluster located in or near the mid-cingulate cortex, source-resolved frequency-deviant response N2 latency and P3 amplitude at 6 months-of-age predicted vocabulary size at 20 months-of-age. The same measures for scalp channel F6 (though not for other frontal channels) showed similar but weaker correlations. These results demonstrate the significant potential of ICA analyses to facilitate a deeper understanding of the neural substrates of infant sensory processing.

Performance Monitoring in Medication-Naïve Children with Tourette Syndrome

Background: Tourette syndrome (TS) is a childhood-onset neurodevelopmental disorder and its impact on cognitive development needs further study. Evidence from neuropsychological, neuroimaging and electrophysiological studies suggests that the decline in tic severity and the ability to suppress tics relate to the development of self-regulatory functions in late childhood and adolescence. Hence, tasks measuring performance monitoring might provide insight into the regulation of tics in children with TS.

Method: Twenty-five children with TS, including 14 with comorbid Attention-deficit/ hyperactivity disorder (ADHD), 39 children with ADHD and 35 typically developing children aged 8–12 years were tested with a modified Eriksen-Flanker task during a 34-channel electroencephalography (EEG) recording. Task performance, as well as stimulus-locked and response-locked event-related potentials (ERP) were analyzed and compared across groups.

Results: Participants did not differ in their behavioral performance. Children with TS showed higher amplitudes of an early P3 component of the stimulus-locked ERPs in ensemble averages and in separate trial outcomes, suggesting heightened orienting and/or attention during stimulus evaluation. In response-locked averages, children with TS had a slightly higher positive complex before the motor response, likely also reflecting a late P3. Groups did not differ in post-response components, particularly in the error-related negativity (ERN) and error-related positivity (Pe).

Conclusions: These findings suggest that children with TS may employ additional attentional resources as a compensatory mechanism to maintain equal behavioral performance.