Electrophysiological biomarkers of diagnosis and outcome in neurodevelopmental disorders

Antisocial personality disorder:Failure to balance excitation/inhibition?

While healthy brain function relies on a dynamic but tightly regulated interaction between excitation (E) and inhibition (I), a spectrum of social cognition disorders, including antisocial behavior and antisocial personality disorder (ASPD), frequently ensuing from irregular neurodevelopment, may be associated with E/I imbalance and concomitant alterations in neural connectivity. Technological advances in the evaluation of structural and functional E/I balance proxies in clinical settings and in human cell culture models provide a general basis for identification of biomarkers providing a powerful concept for prevention and intervention across different dimensions of mental health and disease. In this perspective we outline a framework for research to characterize neurodevelopmental pathways to antisocial behavior and ASPD driven by (epi)genetic factors across life, and to identify molecular targets for preventing the detrimental effects of cognitive dysfunction and maladaptive social behavior, considering psychosocial experience; to validate signatures of E/I imbalance and altered myelination proxies as biomarkers of pathogenic neural circuitry mechanisms to determine etiological processes in the transition from mental health to antisocial behavior and ASPD and in the switch from prevention to treatment; to develop a neurobiologically-grounded integrative model of antisocial behavior and ASPD resultant of disrupted E/I balance, allowing to establish objective diagnoses and monitoring tools, to personalize prevention and therapeutic decisions, to predict treatment response, and thus counteract relapse; and finally, to promote transformation of dimensional disorder taxonomy and to enhance societal awareness and reception of the neurobiological basis of antisocial behavior and ASPD.

Do Influential Articles on the Genetics of Autism Show Evidence of Engagement With the Autistic Community?

Investigations into the etiology and genetic basis of autism continue to drive much autism research, yet reports are emerging of this research not aligning with priorities of autistic people. Engagement of autistic people in the research process is a key way to take their perspectives on board. We investigated whether influential genetic autism research shows evidence of engagement with the autistic community via indicators in published article texts. Through text mining of the abstracts of articles mentioning the words "autism" or "autistic," we found minimal prevalence of progressive terminology associated with autism. We also devised a novel rating system to assess three hallmarks of autistic community engagement: presence of non-stigmatizing language, referencing community priorities, and the use of participatory methods. We reviewed 149 articles within leading autism and genetic journals. Minimal evidence of engagement with the autistic community was found within all three hallmarks. Genetics researchers focused on autism should embrace opportunities to engage with the autistic community to bring their work into closer alignment with their priorities, yielding scientific and moral benefits.

Interbrain synchrony attenuation during a peer cooperative task in young children with autistic traits -an EEG hyperscanning study

Young children with autism spectrum disorder (ASD) traits frequently encounter difficulties in peer interaction. Assessing peer interaction performance is crucial but challenging within the clinical diagnostic paradigm of ASD. Hyperscanning, which simultaneously monitors brain activity in multiple individuals, has become a popular tool for assessing social interaction's neural features. The present study aims to investigate the brain-to-brain connectivity between child-dyads engaged in a game-like collaborative peer interaction task via the hyperscanning electroencephalogram (EEG) approach. The final sample comprised 66 young children: 18 child dyads with typical development (TD), TD-TD, and 15 with ASD traits matched to TD, TD-ASD. The study indicated a depressed level of connectivity in the dyad group with ASD as the responder, with a notable decrease observed in the beta oscillation over the right parietal to left temporal coupling between subjects. A pattern that differed from that observed in the TD-TD group was identified with regard to full-band connectivity over the right-to-right temporal region. It was observed that the TD-TD group exhibited enhanced connectivity following the completion of the task, which was not the case for the TD-ASD group. Significant correlations were observed between scores on the ASD symptom rating scale and the selected significant interbrain connectivity index. The application of a hyperscanning EEG paradigm demonstrated that the participating children with autistic traits exhibited an attenuated and apparently distinct alteration pattern of interbrain connectivity in comparison to a control group. These findings highlight the value of physiologically based measures in informing etiological and interventional studies in neuropsychiatry.

A human electrophysiological biomarker of Fragile X Syndrome is shared in V1 of Fmr1 KO mice and caused by loss of FMRP in cortical excitatory neurons

Predicting clinical therapeutic outcomes from preclinical animal studies remains an obstacle to developing treatments for neuropsychiatric disorders. Electrophysiological biomarkers analyzed consistently across species could bridge this divide. In humans, alpha oscillations in the resting state electroencephalogram (rsEEG) are altered in many disorders, but these disruptions have not yet been characterized in animal models. Here, we employ a uniform analytical method to show in males with fragile X syndrome (FXS) that the slowed alpha oscillations observed in adults are also present in children and in visual cortex of adult and juvenile Fmr1 -/y mice. We find that alpha-like oscillations in mice reflect the differential activity of two classes of inhibitory interneurons, but the phenotype is caused by deletion of Fmr1 specifically in cortical excitatory neurons. These results provide a framework for studying alpha oscillation disruptions across species, advance understanding of a critical rsEEG signature in the human brain and inform the cellular basis for a putative biomarker of FXS.

Exploring EEG resting state differences in autism: sparse findings from a large cohort

BACKGROUND

Autism is a complex neurodevelopmental condition, the precise neurobiological underpinnings of which remain elusive. Here, we focus on group differences in resting state EEG (rsEEG). Although many previous reports have pointed to differences between autistic and neurotypical participants in rsEEG, results have failed to replicate, sample sizes have typically been small, and only a small number of variables are reported in each study.

METHODS

Here, we combined five datasets to create a large sample of autistic and neurotypical individuals (n = 776) and extracted 726 variables from each participant's data. We computed effect sizes and split-half replication rate for group differences between autistic and neurotypical individuals for each EEG variable while accounting for age, sex and IQ. Bootstrapping analysis with different sample sizes was done to establish how effect size and replicability varied with sample size.

RESULTS

Despite the broad and exploratory approach, very few EEG measures varied with autism diagnosis, and when larger effects were found, the majority were not replicable under split-half testing. In the bootstrap analysis, smaller sample sizes were associated with larger effect sizes but lower replication rates.

LIMITATIONS

Although we extracted a comprehensive set of EEG signal components from the data, there is the possibility that measures more sensitive to group differences may exist outside the set that we tested. The combination of data from different laboratories may have obscured group differences. However, our harmonisation process was sufficient to reveal several expected maturational changes in the EEG (e.g. delta power reduction with age), providing reassurance regarding both the integrity of the data and the validity of our data-handling and analysis approaches.

CONCLUSIONS

Taken together, these data do not produce compelling evidence for a clear neurobiological signature that can be identified in autism. Instead, our results are consistent with heterogeneity in autism, and caution against studies that use autism diagnosis alone as a method to categorise complex and varied neurobiological profiles.

Therapeutic effects of CGS21680, a selective A2A receptor agonist, via BDNF-related pathways in R106W mutation Rett syndrome model

Rett syndrome (RTT) is a neurological disorder caused by a mutation in the X-linked methyl-CpG binding protein 2 (MECP2), leading to cognitive and motor skill regression. Therapeutic strategies aimed at increasing brain-derived neurotrophic factor (BDNF) levels have been reported; however, BDNF treatment has limitations, including the inability to penetrate the blood-brain barrier, a short half-life, and potential for adverse effects when administered via intrathecal injection, necessitating novel therapeutic approaches. In this study, we focused on the adenosine A2A receptor (A2AR), which modulates BDNF and its downstream pathways, and investigated the therapeutic potential of CGS21680, an A2AR agonist, through in vitro and in vivo studies using R106W RTT model. CGS21680 restored neurite outgrowth, the number of SYN1+/MAP2+ puncta pairs, genes related to the BDNF-TrkB signaling pathway (Bdnf, TrkB, and Mtor) and neural development (Tuj1 and Syn1), and electrophysiological functions in in vitro RTT primary neurons. Additionally, CGS21680 alleviated neurobehavioral impairments and modulated gene expression in an RTT in vivo model. Our findings suggest that activation of A2AR via CGS21680 enhances BDNF-TrkB signaling, which in turn activates downstream pathways, ultimately increasing neurite outgrowth and synaptic plasticity, and restoring neurobehavioral clinical symptoms. This is the first study to report the therapeutic effect of CGS21680 in R106W point mutation RTT models, both in vitro and in vivo. These research results suggest that CGS21680 could be a promising therapeutic candidate for the treatment of RTT.

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.

Electrophysiological indices of reward anticipation as ADHD risk and prognostic biomarkers

The attention-deficit/hyperactivity disorder (ADHD) clinical phenotype has limitations for deciphering ADHD etiology and predicting prognosis. Although relative to the clinical phenotype, intermediate phenotypes may have better explanatory and prognostic power, the extent to which ADHD intermediate phenotypes are associated with ADHD risk and prognosis is unknown. The aim of this study was to evaluate evidence for event-related potential (ERP) measures of reward anticipation as ADHD risk and prognostic biomarkers. We examined, whether (1) in a sample of adolescents (N = 304; Mage = 15.78 years, SD = 1.08; 39.5% female), accounting for the effects of age, sex, ADHD severity and depression, ERPs are associated with ADHD polygenic risk scores (PRSs) (ADHD risk) and (2) in a sample of adolescents at-risk for ADHD (n = 99; Mage = 15.78 years, SD = 1.08; 39.5% female), accounting for the effects of age, sex, ADHD severity, depression, and baseline outcome values, ERPs are associated, prospectively, with alcohol misuse (ADHD prognosis). In adolescents, greater ADHD PRSs were associated with lower electrophysiological anticipatory attention to motivationally relevant feedback (b = -0.115, p = .046 95%CI [-0.228; -0.002]). The predictors accounted for 5% of the variance in the outcome. In adolescents at-risk for ADHD, at 18-month follow-up, lower electrophysiological anticipatory attention to motivationally relevant feedback was associated with greater alcohol consumption (b = -7.454, p = .007, 95%CI [-12.873; -2.034]). The predictors accounted for 31% of the variance in this outcome. These findings were replicated in sensitivity analyses with behavioral performance variables added as covariates. The current findings support the hypothesis that ERP amplitudes of reward anticipation may be ADHD risk and prognostic biomarkers and suggest that intermediate phenotypes may confer advantages over the ADHD clinical phenotype in delineating etiology and predicting prognosis.

Electroencephalography-Based Brain-Computer Interfaces in Rehabilitation: A Bibliometric Analysis (2013-2023)

EEG-based Brain-Computer Interfaces (BCIs) have gained significant attention in rehabilitation due to their non-invasive, accessible ability to capture brain activity and restore neurological functions in patients with conditions such as stroke and spinal cord injuries. This study offers a comprehensive bibliometric analysis of global EEG-based BCI research in rehabilitation from 2013 to 2023. It focuses on primary research and review articles addressing technological innovations, effectiveness, and system advancements in clinical rehabilitation. Data were sourced from databases like Web of Science, and bibliometric tools (bibliometrix R) were used to analyze publication trends, geographic distribution, keyword co-occurrences, and collaboration networks. The results reveal a rapid increase in EEG-BCI research, peaking in 2022, with a primary focus on motor and sensory rehabilitation. EEG remains the most commonly used method, with significant contributions from Asia, Europe, and North America. Additionally, there is growing interest in applying BCIs to mental health, as well as integrating artificial intelligence (AI), particularly machine learning, to enhance system accuracy and adaptability. However, challenges remain, such as system inefficiencies and slow learning curves. These could be addressed by incorporating multi-modal approaches and advanced neuroimaging technologies. Further research is needed to validate the applicability of EEG-BCI advancements in both cognitive and motor rehabilitation, especially considering the high global prevalence of cerebrovascular diseases. To advance the field, expanding global participation, particularly in underrepresented regions like Latin America, is essential. Improving system efficiency through multi-modal approaches and AI integration is also critical. Ethical considerations, including data privacy, transparency, and equitable access to BCI technologies, must be prioritized to ensure the inclusive development and use of these technologies across diverse socioeconomic groups.

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.

Mechanisms Linking Physical Activity With Mental Health in Children and Adolescents With Neurodevelopmental Disorders: A Systematic Review

INTRODUCTION

Physical activity (PA) is a promising way to improve mental health in children and adolescents with neurodevelopmental disorders (NDDs). However, the underlying mechanisms remain unclear. The current review aimed to explore the potential neurobiological, psychosocial, and behavioral mechanisms between PA interventions and mental health in children and adolescents with NDDs.

METHODS

Web of Science, PsycINFO, SPORTDiscus, MEDLINE, CINAHL, and ERIC were searched from inception to June 2023. Randomized controlled trials/quasi-experimental designs applying PA interventions and reporting at least one mental health outcome and at least one potential mechanism in children and adolescents with NDDs were included. The best evidence synthesis rating system was adopted to determine the strength and consistency of potential mechanisms and was performed in 2024.

RESULTS

In total, 45 studies were included, 29 of which were randomized controlled trials and 16 were quasi-experimental, with a total of 1,751 participants. According to the best evidence synthesis rating system, neurobiological (theta activity and P3 amplitude), psychosocial (social skills and social participation), and behavioral (motor skills and sleep) mechanisms were the frequently examined and consistent mechanisms through which PA affected mental health in children and adolescents with NDDs. However, evidence regarding P3 latency, beta activity, and physical self-concept was insufficient.

DISCUSSION

Future PA interventions could consider neurobiological (theta activity and P3 amplitude), psychosocial (social skills and social participation), and behavioral (motor skills and sleep) mechanisms. Alternatively, PA can be developed as an adjunctive approach with interventions that specifically focus on these mechanisms to enhance mental health in children and adolescents with NDDs.

Vocal tasks-based EEG and speech signal analysis in children with neurodevelopmental disorders: a multimodal investigation

Neurodevelopmental disorders (NDs) often hamper multiple functional prints of a child brain. Despite several studies on their neural and speech responses, multimodal researches on NDs are extremely rare. The present work examined the electroencephalography (EEG) and speech signals of the ND and control children, who performed "Hindi language" vocal tasks (V) of seven different categories, viz. 'vowel', 'consonant', 'one syllable', 'multi-syllable', 'compound', 'complex', and 'sentence' (V1-V7). Statistical testing of EEG parameters showed substantially high beta and gamma band energies in frontal, central, and temporal head sites of NDs for tasks V1-V5 and in parietal too for V6. For the 'sentence' task (V7), the NDs yielded significantly high theta and low alpha energies in the parietal area. These findings imply that even performing a general context-based task exerts a heavy cognitive loading in neurodevelopmental subjects. They also exhibited poor auditory comprehension while executing a long phrasing. Further, the speech signal analysis manifested significantly high amplitude (for V1-V7) and frequency (for V3-V7) perturbations in the voices of ND children. Moreover, the classification of subjects as ND or control was done via EEG and speech features. We attained 100% accuracy, precision, and F-measure using EEG features of all tasks, and using speech features of the 'complex' task. Jointly, the 'complex' task transpired as the best vocal stimuli among V1-V7 for characterizing ND brains. Meanwhile, we also inspected inter-relations between EEG energies and speech attributes of the ND group. Our work, thus, represents a unique multimodal layout to explore the distinctiveness of neuro-impaired children.

Multi-site EEG studies in early infancy: Methods to enhance data quality

Brain differences linked to autism spectrum disorder (ASD) can manifest before observable symptoms. Studying these early neural precursors in larger and more diverse cohorts is crucial for advancing our understanding of developmental pathways and potentially facilitating earlier identification. EEG is an ideal tool for investigating early neural differences in ASD, given its scalability and high tolerability in infant populations. In this context, we integrated EEG into an existing multi-site MRI study of infants with a higher familial likelihood of developing ASD. This paper describes the comprehensive protocol established to collect longitudinal, high-density EEG data from infants across five sites as part of the Infant Brain Imaging Study (IBIS) Network and reports interim feasibility and data quality results. We evaluated feasibility by measuring the percentage of infants from whom we successfully collected each EEG paradigm. The quality of task-free data was assessed based on the duration of EEG recordings remaining after artifact removal. Preliminary analyses revealed low data loss, with average in-session loss rates at 4.16 % and quality control loss rates at 11.66 %. Overall, the task-free data retention rate, accounting for both in-session issues and quality control, was 84.16 %, with high consistency across sites. The insights gained from this preliminary analysis highlight key sources of data attrition and provide practical considerations to guide similar research endeavors.

Parsing evoked and induced gamma response differences in Autism: A visual evoked potential study

OBJECTIVE

Electroencephalography (EEG) measures of visual evoked potentials (VEPs) provide a targeted approach for investigating neural circuit dynamics. This study separately analyses phase-locked (evoked) and non-phase-locked (induced) gamma responses within the VEP to comprehensively investigate circuit differences in autism.

METHODS

We analyzed VEP data from 237 autistic and 114 typically developing (TD) children aged 6-11, collected through the Autism Biomarkers Consortium for Clinical Trials (ABC-CT). Evoked and induced gamma (30-90 Hz) responses were separately quantified using a wavelet-based time-frequency analysis, and group differences were evaluated using a permutation-based clustering procedure.

RESULTS

Autistic children exhibited reduced evoked gamma power but increased induced gamma power compared to TD peers. Group differences in induced responses showed the most prominent effect size and remained statistically significant after excluding outliers.

CONCLUSIONS

Our study corroborates recent research indicating diminished evoked gamma responses in children with autism. Additionally, we observed a pronounced increase in induced power. Building upon existing ABC-CT findings, these results highlight the potential to detect variations in gamma-related neural activity, despite the absence of significant group differences in time-domain VEP components.

SIGNIFICANCE

The contrasting patterns of decreased evoked and increased induced gamma activity in autistic children suggest that a combination of different EEG metrics may provide a clearer characterization of autism-related circuitry than individual markers alone.

Reduced Visual Evoked Potential Amplitude in Autistic Children with Co-Occurring Features of Attention-Deficit/Hyperactivity Disorder

Provided the significant overlap in features of autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), there is a critical need to identify transdiagnostic markers that could meaningfully stratify subgroups. The objective of this study was to compare the visual evoked potential (VEP) between 30 autistic children, 17 autistic children with co-occurring ADHD presentation (ASD + ADHD), and 21 neurotypical children (NTC). Electroencephalography was recorded while children passively viewed a pattern-reversal stimulus. Mean amplitude of the P1 event-related potential was extracted from a midline occipital channel and compared between groups. P1 mean amplitude was reduced in the ASD + ADHD group compared to the ASD and NTC groups, indicating a distinct pattern of brain activity in autistic children with co-occurring ADHD features.

Longitudinal Stability of Neural Correlates of Pediatric Attention Deficit Hyperactivity Disorder: A Pilot Study of Event Related Potentials and Electroencephalography

OBJECTIVE

Stability and developmental effects of electroencephalography (EEG) and event related potential (ERP) correlates of ADHD are understudied. This pilot study examined stability and developmental changes in ERP and EEG metrics of interest.

METHODS

Thirty-seven 7 to 11-year-old children with ADHD and 15 typically developing (TD) children completed EEG twice, 11 to 36 months apart. A series of mixed effects linear models were run to examine stability and developmental effects of EEG and ERP metrics.

RESULTS

Stability and developmental effects of EEG and ERP correlates of ADHD varied considerably across metrics. P3 amplitude was stable over time and showed diverging developmental trajectories across groups. Developmental differences were apparent in error related ERPs and resting aperiodic exponent. Theta-beta ratio was stable over time among all children.

CONCLUSIONS

Developmental trajectories of EEG and ERP correlates of ADHD are candidate diagnostic markers. Replication with larger samples is needed.

Beyond Diagnosis: Preliminary Study of Impact on Children and Parents in Neurodevelopmental Disorders and Juvenile Idiopathic Arthritis-Associated Uveitis

Chronic diseases are a growing problem for global health due to the large number of people they involve, the repercussions they have on the mental and physical well-being of those affected, and the costs to society. Particularly, chronic illnesses of childhood have important psychological implications, not only for affected children but also for their parents. Among these pathologies, neurodevelopmental disorders (NDDs) and uveitis associated with juvenile idiopathic arthritis (JIA-U) may affect mental and physical health, emotions, memory, learning, and socializing. This study evaluates the psychological and behavioral/emotional impact of NDDs and JIA-U on children and parents. Specifically, 30 children with active JIA-U and 30 children with NDDs and their parents completed the Child Behavior Checklist (CBCL) and Parent Stress Index-Short Form (PSI) questionnaires. Children with NDDs have statistically significant differences in all the emotional and behavioral variables compared to JIA-U children, and parents of children with NDDs experience an increased stress load compared to parents of children with JIA-U. This study emphasizes the wide range of emotional and behavioral challenges that parents face with NDDs. This study emphasizes that parents of children with NDDs not only experience higher levels of stress compared to parents of normally developing children but also experience higher levels of stress compared to parents of children with potentially debilitating chronic diseases such as JIA-U.

An integrated action plan to fund and support drug development for Dup15q syndrome: a patient organization perspective

Maternal 15q11.2-13.1 duplication syndrome, or Dup15q syndrome (Dup15q), is a rare neurodevelopmental disorder affecting as many as 1 in 5000 to 1 in 20,000 children worldwide. Autism and seizures are two of the most commonly observed phenotypes in Dup15q, with intellectual disability, hypotonia, gastrointestinal distress, and substantial fine and gross motor deficits also commonly reported. The community that is now known as the Dup15q Alliance started in 1994 as a small group of families raising children with chromosome 15q duplications. Originally named IsoDicentric 15 Exchange, Advocacy and Support (IDEAS), the group received official nonprofit organization status 10 years later and rebranded to its current name, Dup15q Alliance, shortly thereafter. Today, there are over 2200 families affiliated with Dup15q Alliance, with an average intake of 10 new families each month. Historically, Dup15q Alliance has provided the community with access to family and caregiver resources in addition to serving as a repository for basic educational information about Dup15q and research developments. The recent installation of a dedicated director of scientific and clinical initiatives alongside other infrastructural changes has now primed the Dup15q Alliance to expand its scientific footprint by funding cutting-edge research, supporting clinical sites and trials, and investing in novel therapeutics that have the potential to change the reality of a Dup15q syndrome diagnosis. To do this, we have developed the LEARN. TREAT. CURE. program to align initiatives, fast-track progress, and bring hope and reality into coexistence. Briefly, we seek to learn as much as we can about the syndrome through cutting-edge research, natural history studies, and patient registry utilization, identify and develop methods to treat the symptoms of our patient community, with the ultimate goal of developing a cure for the disease-causing symptoms of the syndrome.

Mapping neurodevelopment with sleep macro- and micro-architecture across multiple pediatric populations

Profiles of sleep duration and timing and corresponding electroencephalographic activity reflect brain changes that support cognitive and behavioral maturation and may provide practical markers for tracking typical and atypical neurodevelopment. To build and evaluate a sleep-based, quantitative metric of brain maturation, we used whole-night polysomnography data, initially from two large National Sleep Research Resource samples, spanning childhood and adolescence (total N = 4,013, aged 2.5 to 17.5 years): the Childhood Adenotonsillectomy Trial (CHAT), a research study of children with snoring without neurodevelopmental delay, and Nationwide Children's Hospital (NCH) Sleep Databank, a pediatric sleep clinic cohort. Among children without neurodevelopmental disorders (NDD), sleep metrics derived from the electroencephalogram (EEG) displayed robust age-related changes consistently across datasets. During non-rapid eye movement (NREM) sleep, spindles and slow oscillations further exhibited characteristic developmental patterns, with respect to their rate of occurrence, temporal coupling and morphology. Based on these metrics in NCH, we constructed a model to predict an individual's chronological age. The model performed with high accuracy (r = 0.93 in the held-out NCH sample and r = 0.85 in a second independent replication sample - the Pediatric Adenotonsillectomy Trial for Snoring (PATS)). EEG-based age predictions reflected clinically meaningful neurodevelopmental differences; for example, children with NDD showed greater variability in predicted age, and children with Down syndrome or intellectual disability had significantly younger brain age predictions (respectively, 2.1 and 0.8 years less than their chronological age) compared to age-matched non-NDD children. Overall, our results indicate that sleep architectureoffers a sensitive window for characterizing brain maturation, suggesting the potential for scalable, objective sleep-based biomarkers to measure neurodevelopment.

Self-Regulation of Attention in Children in a Virtual Classroom Environment: A Feasibility Study

Attention is a crucial cognitive function that enables us to selectively focus on relevant information from the surrounding world to achieve our goals. Impairments in sustained attention pose challenges, particularly in children with attention deficit hyperactivity disorder, a neurodevelopmental disorder characterized by impulsive and inattentive behavior. While psychostimulant medications are the most effective ADHD treatment, they often yield unwanted side effects, making it crucial to explore non-pharmacological treatments. We propose a groundbreaking protocol that combines electroencephalography-based neurofeedback with virtual reality (VR) as an innovative approach to address attention deficits. By integrating a virtual classroom environment, we aim to enhance the transferability of attentional control skills while simultaneously increasing motivation and interest among children. The present study demonstrates the feasibility of this approach through an initial assessment involving a small group of healthy children, showcasing its potential for future evaluation in ADHD children. Preliminary results indicate high engagement and positive feedback. Pre- and post-protocol assessments via EEG and fMRI recordings suggest changes in attentional function. Further validation is required, but this protocol is a significant advancement in neurofeedback therapy for ADHD. The integration of EEG-NFB and VR presents a novel avenue for enhancing attentional control and addressing behavioral challenges in children with ADHD.

Pubertal maturation and timing effects on resting state electroencephalography in autistic and comparison youth

Autistic and comparison individuals differ in resting-state electroencephalography (EEG), such that sex and age explain variability within and between groups. Pubertal maturation and timing may further explain variation, as previous work has suggested alterations in pubertal timing in autistic youth. In a sample from two studies of 181 autistic and 94 comparison youth (8 years to 17 years and 11 months), mixed-effects linear regressions were conducted to assess differences in EEG (midline power for theta, alpha, and beta frequency bands). Alpha power was analyzed as a mediator in the relation between pubertal maturation and timing with autistic traits in the autistic groups to understand the role of puberty in brain-based changes that contribute to functional outcomes. Individuals advanced in puberty exhibited decreased power in all bands. Those who experienced puberty relatively early showed decreased power in theta and beta bands, controlling for age, sex, and diagnosis. Autistic individuals further along in pubertal development exhibited lower social skills. Alpha mediated the relation between puberty and repetitive behaviors. Pubertal maturation and timing appear to play unique roles in the development of cognitive processes for autistic and comparison youth and should be considered in research on developmental variation in resting-state EEG.

Region-Wise Brain Response Classification of ASD Children Using EEG and BiLSTM RNN

AbstractAutism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairment in sensory modulation. These sensory modulation deficits would ultimately lead them to difficulties in adaptive behavior and intellectual functioning. The purpose of this study was to observe changes in the nervous system with responses to auditory/visual and only audio stimuli in children with autism and typically developing (TD) through electroencephalography (EEG). In this study, 20 children with ASD and 20 children with TD were considered to investigate the difference in the neural dynamics. The neural dynamics could be understood by non-linear analysis of the EEG signal. In this research to reveal the underlying nonlinear EEG dynamics, recurrence quantification analysis (RQA) is applied. RQA measures were analyzed using various parameter changes in RQA computations. In this research, the cosine distance metric was considered due to its capability of information retrieval and the other distance metrics parameters are compared for identifying the best biomarker. Each computational combination of the RQA measure and the responding channel was analyzed and discussed. To classify ASD and TD, the resulting features from RQA were fed to the designed BiLSTM (bi-long short-term memory) network. The classification accuracy was tested channel-wise for each combination. T3 and T5 channels with neighborhood selection as FAN (fixed amount of nearest neighbors) and distance metric as cosine is considered as the best-suited combination to discriminate between ASD and TD with the classification accuracy of 91.86%, respectively.

EEG repetition and change detection responses in infancy predict adaptive functioning in preschool age: a longitudinal study

Neurodevelopmental disorders (NDDs) are mostly diagnosed around the age of 4-5 years, which is too late considering that the brain is most susceptive to interventions during the first two years of life. Currently, diagnosis of NDDs is based on observed behaviors and symptoms, but identification of objective biomarkers would allow for earlier screening. In this longitudinal study, we investigated the relationship between repetition and change detection responses measured using an EEG oddball task during the first year of life and at two years of age, and cognitive abilities and adaptive functioning during preschool years (4 years old). Identification of early biomarkers is challenging given that there is a lot of variability in developmental courses among young infants. Therefore, the second aim of this study is to assess whether brain growth is a factor of interindividual variability that influences repetition and change detection responses. To obtain variability in brain growth beyond the normative range, infants with macrocephaly were included in our sample. Thus, 43 normocephalic children and 20 macrocephalic children were tested. Cognitive abilities at preschool age were assessed with the WPPSI-IV and adaptive functioning was measured with the ABAS-II. Time-frequency analyses were conducted on the EEG data. Results indicated that repetition and change detection responses in the first year of life predict adaptive functioning at 4 years of age, independently of head circumference. Moreover, our findings suggested that brain growth explains variability in neural responses mostly in the first years of life, so that macrocephalic children did not display repetition suppression responses, while normocephalic children did. This longitudinal study demonstrates that the first year of life is an important period for the early screening of children at risk of developing NDDs.

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.

Prediction of Behavioral Improvement Through Resting-State Electroencephalography and Clinical Severity in a Randomized Controlled Trial Testing Bumetanide in Autism Spectrum Disorder

BACKGROUND

Mechanism-based treatments such as bumetanide are being repurposed for autism spectrum disorder. We recently reported beneficial effects on repetitive behavioral symptoms that might be related to regulating excitation-inhibition (E/I) balance in the brain. Here, we tested the neurophysiological effects of bumetanide and the relationship to clinical outcome variability and investigated the potential for machine learning-based predictions of meaningful clinical improvement.

METHODS

Using modified linear mixed models applied to intention-to-treat population, we analyzed E/I-sensitive electroencephalography (EEG) measures before and after 91 days of treatment in the double-blind, randomized, placebo-controlled Bumetanide in Autism Medication and Biomarker study. Resting-state EEG of 82 subjects out of 92 participants (7-15 years) were available. Alpha frequency band absolute and relative power, central frequency, long-range temporal correlations, and functional E/I ratio treatment effects were related to the Repetitive Behavior Scale-Revised (RBS-R) and the Social Responsiveness Scale 2 as clinical outcomes.

RESULTS

We observed superior bumetanide effects on EEG, reflected in increased absolute and relative alpha power and functional E/I ratio and in decreased central frequency. Associations between EEG and clinical outcome change were restricted to subgroups with medium to high RBS-R improvement. Using machine learning, medium and high RBS-R improvement could be predicted by baseline RBS-R score and EEG measures with 80% and 92% accuracy, respectively.

CONCLUSIONS

Bumetanide exerts neurophysiological effects related to clinical changes in more responsive subsets, in whom prediction of improvement was feasible through EEG and clinical measures.

An integrated perspective for the diagnosis and therapy of neurodevelopmental disorders - From an engineering point of view

Neurodevelopmental disorders (NDDs) are complex conditions with largely unknown pathophysiology. While many NDD symptoms are familiar, the cause of these disorders remains unclear and may involve a combination of genetic, biological, psychosocial, and environmental risk factors. Current diagnosis relies heavily on behaviorally defined criteria, which may be biased by the clinical team's professional and cultural expectations, thus a push for new biological-based biomarkers for NDDs diagnosis is underway. Emerging new research technologies offer an unprecedented view into the electrical, chemical, and physiological activity in the brain and with further development in humans may provide clinically relevant diagnoses. These could also be extended to new treatment options, which can start to address the underlying physiological issues. When combined with current speech, language, occupational therapy, and pharmacological treatment these could greatly improve patient outcomes. The current review will discuss the latest technologies that are being used or may be used for NDDs diagnosis and treatment. The aim is to provide an inspiring and forward-looking view for future research in the field.

Deficiency in Re-Orienting of Attention in Adults with Attention-Deficit Hyperactivity Disorder

Objective: To characterize potential brain indexes of attention deficit hyperactivity disorder (ADHD) in adults. Methods: In an effort to develop objective, laboratory-based tests that can help to establish ADHD diagnosis, the brain indexes of distractibility was investigated in a group of adults. We used event-related brain potentials (ERPs) and performance measures in a forced-choice visual task. Results: Behaviorally aberrant distractibility in the ADHD group was significantly higher. Across three ERP components of distraction: N1 enhancement, P300 (P3a), and Reorienting Negativity (RON) the significant difference between ADHD and matched controls was found in the amplitude of the RON. We used non-parametric randomization tests, enabling us to statistically validated this difference between-group. Conclusions: Our main results of this feasibility study suggest that among other ERP components associated with auditory distraction, the RON response is promising index for a potential biomarker of deficient re-orienting of attention in adults s with ADHD.

Energy-Efficient EEG-Based Scheme for Autism Spectrum Disorder Detection Using Wearable Sensors

The deployment of wearable wireless systems that collect physiological indicators to aid in diagnosing neurological disorders represents a potential solution for the new generation of e-health systems. Electroencephalography (EEG), a recording of the brain's electrical activity, is a promising physiological test for the diagnosis of autism spectrum disorders. It can identify the abnormalities of the neural system that are associated with autism spectrum disorders. However, streaming EEG samples remotely for classification can reduce the wireless sensor's lifespan and creates doubt regarding the application's feasibility. Therefore, decreasing data transmission may conserve sensor energy and extend the lifespan of wireless sensor networks. This paper suggests the development of a sensor-based scheme for early age autism detection. The proposed scheme implements an energy-efficient method for signal transformation allowing relevant feature extraction for accurate classification using machine learning algorithms. The experimental results indicate an accuracy of 96%, a sensitivity of 100%, and around 95% of F1 score for all used machine learning models. The results also show that our scheme energy consumption is 97% lower than streaming the raw EEG samples.

[Recent research on neurodevelopmental disorders in children]

Neurodevelopmental disorders (NDDs) in children are a group of chronic developmental brain disorders caused by multiple genetic or acquired causes, including disorders of intellectual development, developmental speech or language disorders, autism spectrum disorders, developmental learning disorders, attention deficit hyperactivity disorder, tic disorders, and other neurodevelopmental disorders. With the improvement in the research level and the diagnosis and treatment techniques of NDDs, great progress has been made in the research on NDDs in children. This article reviews the research advances in NDDs, in order to further improve the breadth and depth of the understanding of NDDs in children among pediatricians.

The Autism Biomarkers Consortium for Clinical Trials: Initial Evaluation of a Battery of Candidate EEG Biomarkers

OBJECTIVE

Numerous candidate EEG biomarkers have been put forward for use in clinical research on autism spectrum disorder (ASD), but biomarker development has been hindered by limited attention to the psychometric properties of derived variables, inconsistent results across small studies, and variable methodology. The authors evaluated the basic psychometric properties of a battery of EEG assays for their potential suitability as biomarkers in clinical trials.

METHODS

This was a large, multisite, naturalistic study in 6- to 11-year-old children who either had an ASD diagnosis (N=280) or were typically developing (N=119). The authors evaluated an EEG battery composed of well-studied assays of resting-state activity, face perception (faces task), biological motion perception, and visual evoked potentials (VEPs). Biomarker psychometrics were evaluated in terms of acquisition rates, construct performance, and 6-week stability. Preliminary evaluation of use was explored through group discrimination and phenotypic correlations.

RESULTS

Three assays (resting state, faces task, and VEP) show promise in terms of acquisition rates and construct performance. Six-week stability values in the ASD group were moderate (intraclass correlations ≥0.66) for the faces task latency of the P1 and N170, the VEP amplitude of N1 and P1, and resting alpha power. Group discrimination and phenotype correlations were primarily observed for the faces task P1 and N170.

CONCLUSIONS

In the context of a large-scale, rigorous evaluation of candidate EEG biomarkers for use in ASD clinical trials, neural response to faces emerged as a promising biomarker for continued evaluation. Resting-state activity and VEP yielded mixed results. The study's biological motion perception assay failed to display construct performance. The results provide information about EEG biomarker performance that is relevant for the next stage of biomarker development efforts focused on context of use.

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.

Simplified Attachable EEG Revealed Child Development Dependent Neurofeedback Brain Acute Activities in Comparison with Visual Numerical Discrimination Task and Resting

The development of an easy-to-attach electroencephalograph (EEG) would enable its frequent use for the assessment of neurodevelopment and clinical monitoring. In this study, we designed a two-channel EEG headband measurement device that could be used safely and was easily attachable and removable without the need for restraint or electrode paste or gel. Next, we explored the use of this device for neurofeedback applications relevant to education or neurocognitive development. We developed a prototype visual neurofeedback game in which the size of a familiar local mascot changes in the PC display depending on the user's brain wave activity. We tested this application at a local children's play event. Children at the event were invited to experience the game and, upon agreement, were provided with an explanation of the game and support in attaching the EEG device. The game began with a consecutive number visual discrimination task which was followed by an open-eye resting condition and then a neurofeedback task. Preliminary linear regression analyses by the least-squares method of the acquired EEG and age data in 30 participants from 5 to 20 years old suggested an age-dependent left brain lateralization of beta waves at the neurofeedback stage (p = 0.052) and of alpha waves at the open-eye resting stage (p = 0.044) with potential involvement of other wave bands. These results require further validation.

Frequency-Tagging EEG of Superimposed Social and Non-Social Visual Stimulation Streams Provides No Support for Social Salience Enhancement after Intranasal Oxytocin Administration

The social salience hypothesis proposes that the neuropeptide oxytocin (OT) can impact human social behavior by modulating the salience of social cues. Here, frequency-tagging EEG was used to quantify the neural responses to social versus non-social stimuli while administering a single dose of OT (24 IU) versus placebo treatment. Specifically, two streams of faces and houses were superimposed on one another, with each stream of stimuli tagged with a particular presentation rate (i.e., 6 and 7.5 Hz or vice versa). These distinctive frequency tags allowed unambiguously disentangling and objectively quantifying the respective neural responses elicited by the different streams of stimuli. This study involved a double-blind, placebo-controlled, cross-over trial with 31 healthy adult men. Based on four trials of 60 s, we detected robust frequency-tagged neural responses in each individual, with entrainment to faces being more pronounced in lateral occipito-temporal regions and entrainment to houses being focused in medial occipital regions. However, contrary to our expectation, a single dose of OT did not modulate these stimulus-driven neural responses, not in terms of enhanced social processing nor in terms of generally enhanced information salience. Bayesian analyses formally confirmed these null findings. Possibly, the baseline ceiling level performance of these neurotypical adult participants as well as the personal irrelevance of the applied stimulation streams might have hindered the observation of any OT effect.

Auditory processing in rodent models of autism: a systematic review

Autism is a complex condition with many traits, including differences in auditory sensitivity. Studies in human autism are plagued by the difficulty of controlling for aetiology, whereas studies in individual rodent models cannot represent the full spectrum of human autism. This systematic review compares results in auditory studies across a wide range of established rodent models of autism to mimic the wide range of aetiologies in the human population. A search was conducted in the PubMed and Web of Science databases to find primary research articles in mouse or rat models of autism which investigate central auditory processing. A total of 88 studies were included. These used non-invasive measures of auditory function, such as auditory brainstem response recordings, cortical event-related potentials, electroencephalography, and behavioural tests, which are translatable to human studies. They also included invasive measures, such as electrophysiology and histology, which shed insight on the origins of the phenotypes found in the non-invasive studies. The most consistent results across these studies were increased latency of the N1 peak of event-related potentials, decreased power and coherence of gamma activity in the auditory cortex, and increased auditory startle responses to high sound levels. Invasive studies indicated loss of subcortical inhibitory neurons, hyperactivity in the lateral superior olive and auditory thalamus, and reduced specificity of responses in the auditory cortex. This review compares the auditory phenotypes across rodent models and highlights those that mimic findings in human studies, providing a framework and avenues for future studies to inform understanding of the auditory system in autism.

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.

Alpha oscillatory activity during attentional control in children with Autism Spectrum Disorder (ASD), Attention-Deficit/Hyperactivity Disorder (ADHD), and ASD+ADHD

BACKGROUND

Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) share impairments in top-down and bottom-up modulation of attention. However, it is not yet well understood if co-occurrence of ASD and ADHD reflects a distinct or additive profile of attention deficits. We aimed to characterise alpha oscillatory activity (stimulus-locked alpha desynchronisation and prestimulus alpha) as an index of integration of top-down and bottom-up attentional processes in ASD and ADHD.

METHODS

Children with ASD, ADHD, comorbid ASD+ADHD, and typically-developing children completed a fixed-choice reaction-time task ('Fast task') while neurophysiological activity was recorded. Outcome measures were derived from source-decomposed neurophysiological data. Main measures of interest were prestimulus alpha power and alpha desynchronisation (difference between poststimulus and prestimulus alpha). Poststimulus activity linked to attention allocation (P1, P3), attentional control (N2), and cognitive control (theta synchronisation, 100-600 ms) was also examined. ANOVA was used to test differences across diagnostics groups on these measures. Spearman's correlations were used to investigate the relationship between attentional control processes (alpha oscillations), central executive functions (theta synchronisation), early visual processing (P1), and behavioural performance.

RESULTS

Children with ADHD (ADHD and ASD+ADHD) showed attenuated alpha desynchronisation, indicating poor integration of top-down and bottom-up attentional processes. Children with ADHD showed reduced N2 and P3 amplitudes, while children with ASD (ASD and ASD+ADHD) showed greater N2 amplitude, indicating atypical attentional control and attention allocation across ASD and ADHD. In the ASD group, prestimulus alpha and theta synchronisation were negatively correlated, and alpha desynchronisation and theta synchronisation were positively correlated, suggesting an atypical association between attentional control processes and executive functions.

CONCLUSIONS

ASD and ADHD are associated with disorder-specific impairments, while children with ASD+ADHD overall presented an additive profile with attentional deficits of both disorders. Importantly, these findings may inform the improvement of transdiagnostic procedures and optimisation of personalised intervention approaches.

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.

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.

Bumetanide Effects on Resting-State EEG in Tuberous Sclerosis Complex in Relation to Clinical Outcome: An Open-Label Study

Neuronal excitation-inhibition (E/I) imbalances are considered an important pathophysiological mechanism in neurodevelopmental disorders. Preclinical studies on tuberous sclerosis complex (TSC), suggest that altered chloride homeostasis may impair GABAergic inhibition and thereby E/I-balance regulation. Correction of chloride homeostasis may thus constitute a treatment target to alleviate behavioral symptoms. Recently, we showed that bumetanide-a chloride-regulating agent-improved behavioral symptoms in the open-label study Bumetanide to Ameliorate Tuberous Sclerosis Complex Hyperexcitable Behaviors trial (BATSCH trial; Eudra-CT: 2016-002408-13). Here, we present resting-state EEG as secondary analysis of BATSCH to investigate associations between EEG measures sensitive to network-level changes in E/I balance and clinical response to bumetanide. EEGs of 10 participants with TSC (aged 8-21 years) were available. Spectral power, long-range temporal correlations (LRTC), and functional E/I ratio (fE/I) in the alpha-frequency band were compared before and after 91 days of treatment. Pre-treatment measures were compared against 29 typically developing children (TDC). EEG measures were correlated with the Aberrant Behavioral Checklist-Irritability subscale (ABC-I), the Social Responsiveness Scale-2 (SRS-2), and the Repetitive Behavior Scale-Revised (RBS-R). At baseline, TSC showed lower alpha-band absolute power and fE/I than TDC. Absolute power increased through bumetanide treatment, which showed a moderate, albeit non-significant, correlation with improvement in RBS-R. Interestingly, correlations between baseline EEG measures and clinical outcomes suggest that most responsiveness might be expected in children with network characteristics around the E/I balance point. In sum, E/I imbalances pointing toward an inhibition-dominated network are present in TSC. We established neurophysiological effects of bumetanide although with an inconclusive relationship with clinical improvement. Nonetheless, our results further indicate that baseline network characteristics might influence treatment response. These findings highlight the possible utility of E/I-sensitive EEG measures to accompany new treatment interventions for TSC.

Clinical Trial Registration

EU Clinical Trial Register, EudraCT 2016-002408-13 (www.clinicaltrialsregister.eu/ctr-search/trial/2016-002408-13/NL). Registered 25 July 2016.

Timing of adoption is associated with electrophysiological brain activity and externalizing problems among children adopted internationally

This study investigated middle childhood resting electroencephalography (EEG) and behavioral adjustment in 35 internationally adopted children removed from early caregiving adversity between 6 and 29 months of age. Older age of adoption was associated with more immature or atypical profiles of middle childhood cortical function, based on higher relative theta power (4-6 Hz), lower relative alpha power (7-12 Hz), lower peak alpha frequency, and lower absolute beta (13-20 Hz) and gamma (21-50 Hz) power. More immature or atypical EEG spectral power indirectly linked older age of adoption with increased risk for externalizing problems in middle childhood. The findings add to existing evidence linking duration of early adverse exposures with lasting effects on brain function and behavioral regulation even years after living in a stable adoptive family setting. Findings underscore the need to minimize and prevent children's exposures to early caregiving adversity, especially in the first years of life. They call for innovative interventions to support neurotypical development in internationally adopted children at elevated risk.

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.

Evaluation of electroencephalography biomarkers for Angelman syndrome during overnight sleep

Angelman syndrome (AS) is a neurodevelopmental disorder caused by loss‐of‐function mutations in the maternal copy of the UBE3A gene. AS is characterized by intellectual disability, impaired speech and motor skills, epilepsy, and sleep disruptions. Multiple treatment strategies to re‐express functional neuronal UBE3A from the dormant paternal allele were successful in rodent models of AS and have now moved to early phase clinical trials in children. Developing reliable and objective AS biomarkers is essential to guide the design and execution of current and future clinical trials. Our prior work quantified short daytime electroencephalograms (EEGs) to define promising biomarkers for AS. Here, we asked whether overnight sleep is better suited to detect AS EEG biomarkers. We retrospectively analyzed EEGs from 12 overnight sleep studies from individuals with AS with age and sex‐matched Down syndrome and neurotypical controls, focusing on low frequency (2–4 Hz) delta rhythms and sleep spindles. Delta EEG rhythms were increased in individuals with AS during all stages of overnight sleep, but overnight sleep did not provide additional benefit over wake in the ability to detect increased delta. Abnormal sleep spindles were not reliably detected in EEGs from individuals with AS during overnight sleep, suggesting that delta rhythms represent a more reliable biomarker. Overall, we conclude that periods of wakefulness are sufficient, and perhaps ideal, to quantify delta EEG rhythms for use as AS biomarkers.

Social EEG: A novel neurodevelopmental approach to studying brain-behavior links and brain-to-brain synchrony during naturalistic toddler-parent interactions

Despite increasing emphasis on emergent brain-behavior patterns supporting language, cognitive, and socioemotional development in toddlerhood, methodologic challenges impede their characterization. Toddlers are notoriously difficult to engage in brain research, leaving a developmental window in which neural processes are understudied. Further, electroencephalography (EEG) and event-related potential paradigms at this age typically employ structured, experimental tasks that rarely reflect formative naturalistic interactions with caregivers. Here, we introduce and provide proof of concept for a new "Social EEG" paradigm, in which parent-toddler dyads interact naturally during EEG recording. Parents and toddlers sit at a table together and engage in different activities, such as book sharing or watching a movie. EEG is time locked to the video recording of their interaction. Offline, behavioral data are microcoded with mutually exclusive engagement state codes. From 216 sessions to date with 2- and 3-year-old toddlers and their parents, 72% of dyads successfully completed the full Social EEG paradigm, suggesting that it is possible to collect dual EEG from parents and toddlers during naturalistic interactions. In addition to providing naturalistic information about child neural development within the caregiving context, this paradigm holds promise for examination of emerging constructs such as brain-to-brain synchrony in parents and children.

Stretchable Mesh Nanoelectronics for 3D Single-Cell Chronic Electrophysiology from Developing Brain Organoids

Human induced pluripotent stem cell derived brain organoids have shown great potential for studies of human brain development and neurological disorders. However, quantifying the evolution of the electrical properties of brain organoids during development is currently limited by the measurement techniques, which cannot provide long-term stable 3D bioelectrical interfaces with developing brain organoids. Here, a cyborg brain organoid platform is reported, in which "tissue-like" stretchable mesh nanoelectronics are designed to match the mechanical properties of brain organoids and to be folded by the organogenetic process of progenitor or stem cells, distributing stretchable electrode arrays across the 3D organoids. The tissue-wide integrated stretchable electrode arrays show no interruption to brain organoid development, adapt to the volume and morphological changes during brain organoid organogenesis, and provide long-term stable electrical contacts with neurons within brain organoids during development. The seamless and noninvasive coupling of electrodes to neurons enables long-term stable, continuous recording and captures the emergence of single-cell action potentials from early-stage brain organoid development.

Following Excitation/Inhibition Ratio Homeostasis from Synapse to EEG in Monogenetic Neurodevelopmental Disorders

Pharmacological options for neurodevelopmental disorders are limited to symptom suppressing agents that do not target underlying pathophysiological mechanisms. Studies on specific genetic disorders causing neurodevelopmental disorders have elucidated pathophysiological mechanisms to develop more rational treatments. Here, we present our concerted multi-level strategy ‘BRAINMODEL’, focusing on excitation/inhibition ratio homeostasis across different levels of neuroscientific interrogation. The aim is to develop personalized treatment strategies by linking iPSC-based models and novel EEG measurements to patient report outcome measures in individual patients. We focus our strategy on chromatin- and SNAREopathies as examples of severe genetic neurodevelopmental disorders with an unmet need for rational interventions.

Management of Autism Spectrum Disorder in Italian Units of Child and Adolescent Mental Health: Diagnostic and Referral Pathways

Overall, the present pilot study provides detailed information on clinical management for Autism Spectrum Disorder (ASD) referral and diagnosis processes that are mandatory for child and adolescent mental health management. The analysis of ASD management, even if carried out on a selected sample of Child and Adolescent Mental Health (CAMH) units, represents a good approximation of how, in Italian outpatient settings, children and adolescents with ASD are recognised and eventually diagnosed. One of the aims of the study was to verify the adherence of Italian CAMH units to international recommendations for ASD referral and diagnosis and whether these processes can be traced using individual chart reports. Overall, the analysis evidenced that Italian CAMH units adopt an acceptable standard for ASD diagnosis, although the reporting of the ASD managing process in the individual chart is not always accurate. Furthermore, data collected suggest some improvements that CAMH units should implement to fill the gap with international recommendations, namely, establishing a multidisciplinary team for diagnosis, improving the assessment of physical and mental conditions by the use of standardised tools, implementing a specific assessment for challenging behaviours that could allow timely and specific planning of intervention.

Searching for a "Brain Signature" of Neurodevelopmental Disorders: Event-Related Potentials and the Quest for Biomarkers of Cognition

SUMMARY

This review summarizes main applications of event-related potentials (ERPs) to the study of cognitive processes in persons with neurodevelopmental disorders, for whom traditional behavioral assessments may not be suitable. A brief introduction to the ERPs is followed by a review of empirical studies using passive ERP paradigms to address three main questions: characterizing individual differences, predicting risk for poor developmental outcomes, and documenting treatment effects in persons with neurodevelopmental disorders. Evidence across studies reveals feasibility of ERP methodology in a wide range of clinical populations and notes consistently stronger brain-behavior associations involving ERP measures of higher-order cognition compared with sensory-perceptual processes. The final section describes the current limitations of ERP methodology that need to be addressed before it could be used as a clinical tool and highlights the needed steps toward translating ERPs from group-level research applications to individually interpretable clinical use.

Bumetanide for Irritability in Children With Sensory Processing Problems Across Neurodevelopmental Disorders: A Pilot Randomized Controlled Trial

BACKGROUND

Treatment development for neurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) is impeded by heterogeneity in clinical manifestation and underlying etiologies. Symptom traits such as aberrant sensory reactivity are present across NDDs and might reflect common mechanistic pathways. Here, we test the effectiveness of repurposing a drug candidate, bumetanide, on irritable behavior in a cross-disorder neurodevelopmental cohort defined by the presence of sensory reactivity problems.

METHODS

Participants, aged 5-15 years and IQ ≥ 55, with ASD, ADHD, and/or epilepsy and proven aberrant sensory reactivity according to deviant Sensory Profile scores were included. Participants were randomly allocated (1:1) to bumetanide (max 1 mg twice daily) or placebo tablets for 91 days followed by a 28-day wash-out period using permuted block design and minimization. Participants, parents, healthcare providers, and outcome assessors were blinded for treatment allocation. Primary outcome was the differences in ABC-irritability at day 91. Secondary outcomes were differences in SRS-2, RBS-R, SP-NL, BRIEF parent, BRIEF teacher at D91. Differences were analyzed in a modified intention-to-treat sample with linear mixed models and side effects in the intention-to-treat population.

RESULTS

A total of 38 participants (10.1 [SD 3.1] years) were enrolled between June 2017 and June 2019 in the Netherlands. Nineteen children were allocated to bumetanide and nineteen to placebo. Five patients discontinued (n = 3 bumetanide). Bumetanide was superior to placebo on the ABC-irritability [mean difference (MD) -4.78, 95%CI: -8.43 to -1.13, p = 0.0125]. No effects were found on secondary endpoints. No wash-out effects were found. Side effects were as expected: hypokalemia (p = 0.046) and increased diuresis (p = 0.020).

CONCLUSION

Despite the results being underpowered, this study raises important recommendations for future cross-diagnostic trial designs.

Electrophysiological biomarkers of brain function in CDKL5 deficiency disorder

CDKL5 deficiency disorder is a debilitating developmental and epileptic encephalopathy for which no targeted treatment exists. A number of promising therapeutics are under development for CDKL5 deficiency disorder but a lack of validated biomarkers of brain function and clinical severity may limit the ability to objectively assess the efficacy of new treatments as they become available. To address this need, the current study quantified electrophysiological measures in individuals with CDKL5 deficiency disorder and the association between these parameters and clinical severity. Visual and auditory evoked potentials, as well as resting EEG, were acquired across 5 clinical sites from 26 individuals with CDKL5 deficiency disorder. Evoked potential and quantitative EEG features were calculated and compared with typically developing individuals in an age- and sex-matched cohort. Baseline and Year 1 data, when available, were analysed and the repeatability of the results was tested. Two clinician-completed severity scales were used for evaluating the clinical relevance of the electrophysiological parameters. Group-level comparisons revealed reduced visual evoked potential amplitude in CDKL5 deficiency disorder individuals versus typically developing individuals. There were no group differences in the latency of the visual evoked potentials or in the latency or amplitude of the auditory evoked potentials. Within the CDKL5 deficiency disorder group, auditory evoked potential amplitude correlated with disease severity at baseline as well as Year 1. Multiple quantitative EEG features differed between CDKL5 deficiency disorder and typically developing participants, including amplitude standard deviation, 1/f slope and global delta, theta, alpha and beta power. Several quantitative EEG features correlated with clinical severity, including amplitude skewness, theta/delta ratio and alpha/delta ratio. The theta/delta ratio was the overall strongest predictor of severity and also among the most repeatable qEEG measures from baseline to Year 1. Together, the present findings point to the utility of evoked potentials and quantitative EEG parameters as objective measures of brain function and disease severity in future clinical trials for CDKL5 deficiency disorder. The results also underscore the utility of the current methods, which could be similarly applied to the identification and validation of electrophysiological biomarkers of brain function for other developmental encephalopathies.