Guidelines and best practices for electrophysiological data collection, analysis and reporting in autism

It's all in the timing: delayed feedback in autism may weaken predictive mechanisms during contour integration

Humans rely on predictive and integrative mechanisms during visual processing to efficiently resolve incomplete or ambiguous sensory signals. Although initial low-level sensory data are conveyed by feedforward connections, feedback connections are believed to shape sensory processing through automatic conveyance of statistical probabilities based on prior exposure to stimulus configurations. Individuals with autism spectrum disorder (ASD) show biases in stimulus processing toward parts rather than wholes, suggesting their sensory processing may be less shaped by statistical predictions acquired through prior exposure to global stimulus properties. Investigations of illusory contour (IC) processing in neurotypical (NT) adults have established a well-tested marker of contour integration characterized by a robust modulation of the visually evoked potential (VEP)-the IC-effect-that occurs over lateral occipital scalp during the timeframe of the visual N1 component. Converging evidence strongly supports the notion that this IC-effect indexes a signal with significant feedback contributions. Using high-density VEPs, we compared the IC-effect in 6- to 17-yr-old children with ASD (n = 32) or NT development (n = 53). Both groups of children generated an IC-effect that was equivalent in amplitude. However, the IC-effect notably onset 21 ms later in ASD, even though initial VEP afference was identical across groups. This suggests that feedforward information predominated during perceptual processing for 15% longer in ASD compared with NT children. This delay in the feedback-dependent IC-effect, in the context of known developmental differences between feedforward and feedback fibers, suggests a potential pathophysiological mechanism of visual processing in ASD, whereby ongoing stimulus processing is less shaped by visual feedback.NEW & NOTEWORTHY Children with autism often present with an atypical visual perceptual style that emphasizes parts or details over the whole. Using electroencephalography (EEG), this study identifies delays in the visual feedback from higher-order sensory brain areas to primary sensory regions. Because this type of visual feedback is thought to carry information about prior sensory experiences, individuals with autism may have difficulty efficiently using prior experience or putting together parts into a whole to help make sense of incoming new visual information. This provides empirical neural evidence to support theories of disrupted sensory perception mechanisms in autism.

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.

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.

Conducting head-mounted eye-tracking research with young children with autism and children with increased likelihood of later autism diagnosis

BACKGROUND

Over the past years, researchers have been using head-mounted eye-tracking systems to study young children's gaze behaviors in everyday activities through which children learn about the world. This method has great potential to further our understanding of how millisecond-level gaze behaviors create multisensory experiences and fluctuate around social environments. While this line of work can yield insight into early perceptual experiences and potential learning mechanisms, the majority of the work is exclusively conducted with typically-developing children. Sensory sensitivities, social-communication difficulties, and challenging behaviors (e.g., disruption, elopement) are common among children with developmental disorders, and they may represent potential methodological challenges for collecting high-quality data.

RESULTS

In this paper, we describe our research practices of using head-mounted eye trackers with 41 autistic children and 17 children with increased likelihood of later autism diagnosis without auditory or visual impairments, including those who are minimally or nonspeaking and/or have intellectual disabilities. The success rate in gathering data among children with autism was 92.68%. 3 of 41 children failed to complete the play-session, resulting in an 86.36% success rate among 1-4-year-olds and a 100.00% success rate among 5-8-year-olds. 1 of 17 children with increased likelihood of later autism diagnosis failed to complete the play-session, resulting in a success rate of 94.11%. There were numerous "challenging" behaviors relevant to the method. The most common challenging behaviors included taking the eye-tracking device off, elopement, and becoming distressed. Overall, among children with autism, 88.8% of 1-4-year-olds and 29.4% of 5-8-year-olds exhibited at least one challenging behavior.

CONCLUSIONS

Research capitalizing on this methodology has the potential to reveal early, socially-mediated gaze behaviors that are relevant for autism screening, diagnosis, and intervention purposes. We hope that our efforts in documenting our study methodology will help researchers and clinicians effectively study early naturally-occuring gaze behaviors of children during non-experimental contexts across the spectrum and other developmental disabilities using head-mounted eye-tracking. Ultimately, such applications may increase the generalizability of results, better reflect the diversity of individual characteristics, and offer new ways in which this method can contribute to the field.

Preliminary observations on the associations between sensory processing abnormalities and event-related potentials in adults with autism spectrum disorder

Aim

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is thought to involve a variety of neurophysiological characteristics. Event-related potentials (ERPs) reflect cognitive functions in the brain's cognitive processing. In this study, we investigated differences in P300 and N100 of ERPs between ASD and typically developing groups and focused on the relationship between the components of ERPs and measures of autistic traits and sensory processing characteristics.

Methods

ERPs were measured in 96 subjects in the ASD group and 62 subjects in the age- and sex-adjusted typically developing group. Correlations between each component and the scores of the Autism-Spectrum Quotient Japanese version (AQ-J) and the Adolescent and Adult Sensory Profile (AASP) were also evaluated.

Results

The ASD group showed a significant decrease in the amplitude of N100 at C3. Furthermore, a negative correlation was found between lower amplitude at C3 of N100 and low registered sensory scores in both groups.

Conclusion

Our findings imply that the N100 amplitude at C3 could be a potential indicator for examining the neurophysiological traits of ASD; however, these results should be interpreted with caution due to their preliminary nature. These tentative insights into sensory processing anomalies may be discernible in specific subsets of the ASD population, providing a foundation for future investigative pathways.

It's all in the timing: Delayed feedback in autism may weaken predictive mechanisms during contour integration

Humans rely on predictive mechanisms during visual processing to efficiently resolve incomplete or ambiguous sensory signals. While initial low-level sensory data are conveyed by feedforward connections, feedback connections are believed to shape sensory processing through conveyance of statistical predictions based on prior exposure to stimulus configurations. Individuals with autism spectrum disorder (ASD) show biases in stimulus processing toward parts rather than wholes, suggesting their sensory processing may be less shaped by statistical predictions acquired through prior exposure to global stimulus properties. Investigations of illusory contour (IC) processing in neurotypical (NT) adults have established a well-tested marker of contour integration characterized by a robust modulation of the visually evoked potential (VEP) - the IC-effect - that occurs over lateral occipital scalp during the timeframe of the N1 component. Converging evidence strongly supports the notion that this IC-effect indexes a signal with significant feedback contributions. Using high-density VEPs, we compared the IC-effect in 6-17-year-old children with ASD (n=32) or NT development (n=53). Both groups of children generated an IC-effect that was equivalent in amplitude. However, the IC-effect notably onset 21ms later in ASD, even though timing of initial VEP afference was identical across groups. This suggests that feedforward information predominated during perceptual processing for 15% longer in ASD compared to NT children. This delay in the feedback dependent IC-effect, in the context of known developmental differences between feedforward and feedback fibers, suggests a potential pathophysiological mechanism of visual processing in ASD, whereby ongoing stimulus processing is less shaped by statistical prediction mechanisms.

A Preliminary Study Characterizing Subcortical and Cortical Auditory Processing and Their Relation to Autistic Traits and Sensory Features

This study characterizes the subcortical auditory brainstem response (speech-ABR) and cortical auditory processing (P1 and Mismatch Negativity; MMN) to speech sounds and their relationship to autistic traits and sensory features within the same group of autistic children (n = 10) matched on age and non-verbal IQ to their typically developing (TD) peers (n = 21). No speech-ABR differences were noted, but autistic individuals had larger P1 and faster MMN responses. Correlations revealed that larger P1 amplitudes and MMN responses were associated with greater autistic traits and more sensory features. These findings highlight the complexity of the auditory system and its relationships to behaviours in autism, while also emphasizing the importance of measurement and developmental matching.

Investigating Social Competence in a Pilot Randomized Clinical Trial of a Theatre-Based Intervention Enhanced for Adults with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is characterized by challenges in social competence that persist in adulthood, yet few treatment options exist. A pilot randomized clinical trial (RCT) of a peer-mediated, theatre-based intervention with established efficacy in youth with ASD was examined in autistic adults. The final sample consisted of forty-seven 18-to-40-year-old participants randomized to the experimental (EXP N = 23) or waitlist control (WLC N = 24) condition. A multimodal, social interdependent model was employed to examine social competence changes in brain (incidental face memory (IFM) using event-related potentials), cognition (Wechsler Memory Scale-III), behavior (Contextual Assessment of Social Skills) and function (Social Responsiveness Scale (SRS); Adaptive Behavior Assessment Scale (ABAS) Social Composite). Using analysis of covariance in which pretest was controlled in the model, posttest between-group differences were observed on IFM (p = 0.016, η2 = 0.139, d = 0.79) and several social and adaptive functional (SRS, ABAS) outcomes in social communication and interaction (SCI) (p = 0.019, η2 = 0.121, d = -00.45), communication (p = 0.044 η2 = 0.09, d = -00.31), and motivation (p = 0.001, η2 = 0.229, d = -0.79) domains. At two-month follow-up, gains in social motivation remained (p = 0.041, η2 = 0.100, d = -0.77). The results offer preliminary support for a unique theatre-based social skills intervention for autistic adults who have few treatment options to enhance social competence. The trial was pre-registered with ClinicalTrials.gov (Identifier: NCT04349644).

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

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

Modulation of Resting-State Brain Complexity After Bilateral Cerebellar Anodal Transcranial Direct Current Stimulation in Children with Autism Spectrum Disorders: a Randomized Controlled Trial Study

BACKGROUND

Autism spectrum disorders (ASD) are heterogeneous neurodevelopmental disorders characterized by aberrant neural networks. Cerebellum is best known for its role in controlling motor behaviors; however, recently, there have been significant reports showed that dysfunction in cerebellar-cerebral networks contributes significantly to many of the clinical features of ASD. Hereby, this is a randomized controlled trial (RCT) study examining the potential modulating effects of bilateral anodal tDCS stimulation over cerebellar hemispheres on the resting-state brain complexity in children with ASD.

METHODS

Thirty-six children with ASD (aged 4-14) years old were divided equally and randomly into a tDCS treatment group, which underwent 10 sessions (20-min duration, five sessions/per week) of bilateral anodal tDCS stimulation applied over left and right cerebellar hemispheres, and control group underwent the same procedures, but with sham tDCS stimulation. Resting-state brain complexity was evaluated through recording and calculating the approximate entropy (ApxEnt) values of the resting-state electroencephalograph (EEG) data obtained from a 64-channel EEG system before and after the interventions.

RESULTS

Repeated measures of ANOVA showed that tDCS had significant effects on the treatment group (Wilks' Lambda = 0.29, F (15, 16) = 2.67, p = 0.03) compared with the control group. Analyzed data showed a significant increase in the averaged ApxEnt values in the right frontal cortical region (F (1, 16) = 10.46, p = 0.005) after the bilateral cerebellar anodal tDCS stimulation. Besides, the Cohen's d effect size showed a large effect size (0.70-0.92) of bilateral cerebellar anodal tDCS on the ApxEnt values increases in the left and right frontal cortical regions, the right central cortical region, and left parietal cortical region. However, there were no any significant differences or increases in the brain complexity before and after the sham tDCS stimulation of the control group.

CONCLUSION

Bilateral cerebellar anodal tDCS modulated and increased the brain complexity in children with ASD with no any reported adverse effect. Hereby, cerebellum and cerebellar-cerebral circuitry would serve as a promising target for non-invasive brain stimulation and neuro-modulation as a therapeutic intervention.

Evaluation of interpretability for deep learning algorithms in EEG emotion recognition: A case study in autism

Current models on Explainable Artificial Intelligence (XAI) have shown a lack of reliability when evaluating feature-relevance for deep neural biomarker classifiers. The inclusion of reliable saliency-maps for obtaining trustworthy and interpretable neural activity is still insufficiently mature for practical applications. These limitations impede the development of clinical applications of Deep Learning. To address, these limitations we propose the RemOve-And-Retrain (ROAR) algorithm which supports the recovery of highly relevant features from any pre-trained deep neural network. In this study we evaluated the ROAR methodology and algorithm for the Face Emotion Recognition (FER) task, which is clinically applicable in the study of Autism Spectrum Disorder (ASD). We trained a Convolutional Neural Network (CNN) from electroencephalography (EEG) signals and assessed the relevance of FER-elicited EEG features from individuals diagnosed with and without ASD. Specifically, we compared the ROAR reliability from well-known relevance maps such as Layer-Wise Relevance Propagation, PatternNet, Pattern-Attribution, and Smooth-Grad Squared. This study is the first to bridge previous neuroscience and ASD research findings to feature-relevance calculation for EEG-based emotion recognition with CNN in typically-development (TD) and in ASD individuals.

A new acquisition protocol for conducting studies with children: The science camp research experience

In the last 50 years, the study of brain development has brought major discoveries to education and medicine, changing the lives of millions of children and families. However, collecting behavioral and neurophysiological data from children has specific challenges, such as high rates of data loss and participant dropout. We have developed a science camp method to collect data from children using the benefits of positive peer interactions and interactive and engaging activities, to allow researchers to better collect data repeatedly and reliably from groups of children. A key advantage of this approach is that by increasing participant engagement, attention is also increased, thereby increasing data quality, reducing data loss, and lowering attrition rates. This protocol describes the step-by-step procedure for facilitation of a science camp, including behavioral, electrophysiological, and participatory engagement activities. As this method is robust but also flexible, we anticipate that it can also be applied to different demographics and research needs.

Bridging the translational gap: what can synaptopathies tell us about autism?

Multiple molecular pathways and cellular processes have been implicated in the neurobiology of autism and other neurodevelopmental conditions. There is a current focus on synaptic gene conditions, or synaptopathies, which refer to clinical conditions associated with rare genetic variants disrupting genes involved in synaptic biology. Synaptopathies are commonly associated with autism and developmental delay and may be associated with a range of other neuropsychiatric outcomes. Altered synaptic biology is suggested by both preclinical and clinical studies in autism based on evidence of differences in early brain structural development and altered glutamatergic and GABAergic neurotransmission potentially perturbing excitatory and inhibitory balance. This review focusses on the NRXN-NLGN-SHANK pathway, which is implicated in the synaptic assembly, trans-synaptic signalling, and synaptic functioning. We provide an overview of the insights from preclinical molecular studies of the pathway. Concentrating on NRXN1 deletion and SHANK3 mutations, we discuss emerging understanding of cellular processes and electrophysiology from induced pluripotent stem cells (iPSC) models derived from individuals with synaptopathies, neuroimaging and behavioural findings in animal models of Nrxn1 and Shank3 synaptic gene conditions, and key findings regarding autism features, brain and behavioural phenotypes from human clinical studies of synaptopathies. The identification of molecular-based biomarkers from preclinical models aims to advance the development of targeted therapeutic treatments. However, it remains challenging to translate preclinical animal models and iPSC studies to interpret human brain development and autism features. We discuss the existing challenges in preclinical and clinical synaptopathy research, and potential solutions to align methodologies across preclinical and clinical research. Bridging the translational gap between preclinical and clinical studies will be necessary to understand biological mechanisms, to identify targeted therapies, and ultimately to progress towards personalised approaches for complex neurodevelopmental conditions such as autism.

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.

Autism with Epilepsy: A Neuropsychopharmacology Update

The association between autism spectrum disorders (ASD) and epilepsy has been extensively documented, and the estimated prevalence varies depending upon the selected population and the clinical characteristics. Currently, there are a lack of studies assessing the patient care pathways in ASD, particularly for comorbidity with epilepsy, despite its personal, familial, and economic impacts. Genetic abnormalities are likely implicated in the association of ASD and epilepsy, although they are currently detectable in only a small percentage of patients, and some known genetic and medical conditions are associated with ASD and epilepsy. There is no specificity of seizure type to be expected in children and adolescents with ASD compared with other neurodevelopmental disorders or epileptic syndromes. Treatment options include antiepileptic drugs (AED) and developmentally-based early interventions for ASD. Carbamazepine and lamotrigine are the most used AED, but further studies are needed to more precisely define the most suitable medications for this specific group of children with ASD.

Stratifying the autistic phenotype using electrophysiological indices of social perception

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties in social communication, but also great heterogeneity. To offer individualized medicine approaches, we need to better target interventions by stratifying autistic people into subgroups with different biological profiles and/or prognoses. We sought to validate neural responses to faces as a potential stratification factor in ASD by measuring neural (electroencephalography) responses to faces (critical in social interaction) in N = 436 children and adults with and without ASD. The speed of early-stage face processing (N170 latency) was on average slower in ASD than in age-matched controls. In addition, N170 latency was associated with responses to faces in the fusiform gyrus, measured with functional magnetic resonance imaging, and polygenic scores for ASD. Within the ASD group, N170 latency predicted change in adaptive socialization skills over an 18-month follow-up period; data-driven clustering identified a subgroup with slower brain responses and poor social prognosis. Use of a distributional data-driven cutoff was associated with predicted improvements of power in simulated clinical trials targeting social functioning. Together, the data provide converging evidence for the utility of the N170 as a stratification factor to identify biologically and prognostically defined subgroups in ASD.

Independent evaluation of the harvard automated processing pipeline for Electroencephalography 1.0 using multi-site EEG data from children with Fragile X Syndrome

BACKGROUND

The Harvard Automatic Processing Pipeline for Electroencephalography (HAPPE) is a computerized EEG data processing pipeline designed for multiple site analysis of populations with neurodevelopmental disorders. This pipeline has been validated in-house by the developers but external testing using real-world datasets remains to be done.

NEW METHOD

Resting and auditory event-related EEG data from 29 children ages 3-6 years with Fragile X Syndrome as well as simulated EEG data was used to evaluate HAPPE's noise reduction techniques, data standardization features, and data integration compared to traditional manualized processing.

RESULTS

For the real EEG data, HAPPE pipeline showed greater trials retained, greater variance retained through independent component analysis (ICA) component removal, and smaller kurtosis than the manual pipeline; the manual pipeline had a significantly larger signal-to-noise ratio (SNR). For simulated EEG data, correlation between the pure signal and processed data was significantly higher for manually-processed data compared to HAPPE-processed data. Hierarchical linear modeling showed greater signal recovery in the manual pipeline with the exception of the gamma band signal which showed mixed results.

COMPARISON WITH EXISTING METHODS

SNR and simulated signal retention was significantly greater in the manually-processed data than the HAPPE-processed data. Signal reduction may negatively affect outcome measures.

CONCLUSIONS

The HAPPE pipeline benefits from less active processing time and artifact reduction without removing segments. However, HAPPE may bias toward elimination of noise at the cost of signal. Recommended implementation of the HAPPE pipeline for neurodevelopmental populations depends on the goals and priorities of the research.

An Electrocortical Measure Associated with Metarepresentation Mediates the Relationship between Autism Symptoms and Theory of Mind

Impairments in theory of mind (ToM) - long considered common among individuals with autism spectrum disorder (ASD) - are in fact highly heterogeneous across this population. While such heterogeneity should be reflected in differential recruitment of neural mechanisms during ToM reasoning, no research has yet uncovered a mechanism that explains these individual differences. In this study, 78 (48 ASD) adolescents viewed ToM vignettes and made mental state inferences about characters' behavior while participant electrophysiology was concurrently recorded. Two candidate event-related potentials (ERPs) - the Late Positive Complex (LPC) and the Late Slow Wave (LSW) - were successfully elicited. LPC scores correlated positively with ToM accuracy and negatively with ASD symptom severity. Notably, the LPC partially mediated the relationship between ASD symptoms and ToM accuracy, suggesting this ERP component, thought to represent cognitive metarepresentation, may help explain differences in ToM performance in some individuals with ASD.

The Synaptic Gene Study: Design and Methodology to Identify Neurocognitive Markers in Phelan-McDermid Syndrome and NRXN1 Deletions

Synaptic gene conditions, i.e., “synaptopathies,” involve disruption to genes expressed at the synapse and account for between 0.5 and 2% of autism cases. They provide a unique entry point to understanding the molecular and biological mechanisms underpinning autism-related phenotypes. Phelan-McDermid Syndrome (PMS, also known as 22q13 deletion syndrome) and NRXN1 deletions (NRXN1ds) are two synaptopathies associated with autism and related neurodevelopmental disorders (NDDs). PMS often incorporates disruption to the SHANK3 gene, implicated in excitatory postsynaptic scaffolding, whereas the NRXN1 gene encodes neurexin-1, a presynaptic cell adhesion protein; both are implicated in trans-synaptic signaling in the brain. Around 70% of individuals with PMS and 43–70% of those with NRXN1ds receive a diagnosis of autism, suggesting that alterations in synaptic development may play a crucial role in explaining the aetiology of autism. However, a substantial amount of heterogeneity exists between conditions. Most individuals with PMS have moderate to profound intellectual disability (ID), while those with NRXN1ds have no ID to severe ID. Speech abnormalities are common to both, although appear more severe in PMS. Very little is currently known about the neurocognitive underpinnings of phenotypic presentations in PMS and NRXN1ds. The Synaptic Gene (SynaG) study adopts a gene-first approach and comprehensively assesses these two syndromic forms of autism. The study compliments preclinical efforts within AIMS-2-TRIALS focused on SHANK3 and NRXN1. The aims of the study are to (1) establish the frequency of autism diagnosis and features in individuals with PMS and NRXN1ds, (2) to compare the clinical profile of PMS, NRXN1ds, and individuals with ‘idiopathic’ autism (iASD), (3) to identify mechanistic biomarkers that may account for autistic features and/or heterogeneity in clinical profiles, and (4) investigate the impact of second or multiple genetic hits on heterogeneity in clinical profiles. In the current paper we describe our methodology for phenotyping the sample and our planned comparisons, with information on the necessary adaptations made during the global COVID-19 pandemic. We also describe the demographics of the data collected thus far, including 25 PMS, 36 NRXN1ds, 33 iASD, and 52 NTD participants, and present an interim analysis of autistic features and adaptive functioning.

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.

Neurobehavioral Biomarkers: An EEG Family Reunion

SUMMARY

The field of clinical EEG has had an uneasy relationship with the use of this technology for clinical cognitive applications and often for good reason. However, apart from its clinical use, EEG has had a tradition as a major tool in cognitive psychology and cognitive neuroscience dating back at least to the 1960s. Based on accumulated knowledge from its research application, EEG-based biomarkers are beginning to see applications in clinical trials and may eventually enter clinical care. We address concerns surrounding quality control, the treatment of artifact, and normal variants and how developments in engineering, biomarker validation, and implementation science rigorously applied to these tools can lead to well-justified approaches.

Investigating neural dynamics in autism spectrum conditions outside of the laboratory using mobile electroencephalography

There is currently a paucity of neuroscientific data recorded from more severely affected individuals with autism spectrum conditions (ASC). Enabling data collection to take place in a more familiar environment, that is, at home, may increase access to research participation in this group. Here, we present a new accessible method of studying brain activity of autistic individuals outside the laboratory in their home environment, using mobile electroencephalography (EEG) technology. The primary aim of the present study was to test the feasibility of acquiring good quality EEG data from autistic children at home, assessed via a set of objective data quality metrics, and to develop a list of practical guidelines on how to successfully conduct an EEG experiment in such a naturalistic setting based directly upon participants' views. To demonstrate the utility of this method, we evaluated the EEG signal quality recorded from 69 children with ASC at home using a gel-based Eego Sports mobile EEG system. Five key indicators of data quality were assessed. Our results demonstrate that it is possible to record high quality EEG signal from children with ASC at home, generating data that could address a number of research questions. A user experience survey identified areas of good practice, which researchers should take into consideration when designing mobile EEG studies aiming to acquire data from children with ASC at a home environment.

Cognitive assessment using ERP in child and adolescent psychiatry: Difficulties and opportunities

Event related potentials (ERPs) represent powerful tools to investigate cognitive functioning in child and adolescent psychiatry. So far, the available body of research has largely focused on advancements in analysis methods, with little attention given to the perspective of assessment. The aim of this brief report is to provide recommendations for cognitive ERPs assessment that can be applied across diagnostic categories in child and adolescent psychiatry research. First, we discuss major issues for ERPs testing using examples from common psychiatric disorders. We conclude by summing up our recommendations for methodological standards and highlighting the potential role of ERPs in the field.

A single-session behavioral protocol for successful event-related potential recording in children with neurodevelopmental disorders

Event-related potentials (ERPs) are an ideal tool for measuring neural responses in a wide range of participants, including children diagnosed with neurodevelopmental disorders (NDDs). However, due to perceived barriers regarding participant compliance, much of this work has excluded children with low IQ and/or reduced adaptive functioning, significant anxiety symptoms, and/or sensory processing difficulties, including heterogeneous samples of children with autism spectrum disorder (ASD) and children with fragile X syndrome (FXS). We have developed a behavioral support protocol designed to obtain high-quality ERP data from children in a single session. Using this approach, ERP data were successfully collected from participants with ASD, FXS, and typical development (TD). Higher success rates were observed for children with ASD and TD than children with FXS. Unique clinical-behavioral characteristics were associated with successful data collection across these groups. Higher chronological age, nonverbal mental age, and receptive language skills were associated with a greater number of valid trials completed in children with ASD. In contrast, higher language ability, lower autism severity, increased anxiety, and increased sensory hyperresponsivity were associated with a greater number of valid trials completed in children with FXS. This work indicates that a "one-size-fits-all" approach cannot be taken to ERP research on children with NDDs, but that a single-session paradigm is feasible and is intended to promote increased representation of children with NDDs in neuroscience research through development of ERP methods that support inclusion of diverse and representative samples.

Automatic classification of ICA components from infant EEG using MARA

Automated systems for identifying and removing non-neural ICA components are growing in popularity among EEG researchers of adult populations. Infant EEG data differs in many ways from adult EEG data, but there exists almost no specific system for automated classification of source components from paediatric populations. Here, we adapt one of the most popular systems for adult ICA component classification for use with infant EEG data. Our adapted classifier significantly outperformed the original adult classifier on samples of naturalistic free play EEG data recorded from 10 to 12-month-old infants, achieving agreement rates with the manual classification of over 75% across two validation studies (n = 44, n = 25). Additionally, we examined both classifiers’ ability to remove stereotyped ocular artifact from a basic visual processing ERP dataset compared to manual ICA data cleaning. Here, the new classifier performed on level with expert manual cleaning and was again significantly better than the adult classifier at removing artifact whilst retaining a greater amount of genuine neural signal operationalised through comparing ERP activations in time and space. Our new system (iMARA) offers developmental EEG researchers a flexible tool for automatic identification and removal of artifactual ICA components.

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Currently, few tools are available for ICA based data correction that are designed around infant EEG.

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ICA correction is necessary for naturalistic paradigms when movement artifacts covary with cognitive processes.

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We present a system for automated ICA classification designed around infant EEG data.

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This system offers increased performance compared to systems of automatic ICA classification designed around adult EEG.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Cortical Auditory Processing of Simple Stimuli Is Altered in Autism: A Meta-analysis of Auditory Evoked Responses

BACKGROUND

Auditory perceptual abnormalities are common in persons on the autism spectrum. The neurophysiologic underpinnings of these differences have frequently been studied using auditory event-related potentials (ERPs) and event-related magnetic fields (ERFs). However, no study to date has quantitatively synthesized this literature to determine whether early auditory ERP/ERF latencies or amplitudes in autistic persons differ from those of typically developing control subjects.

METHODS

We searched PubMed and ProQuest for studies comparing 1) latencies/amplitudes of P1/M50, N1b, N1c, M100, P2/M200, and/or N2 ERP/ERF components evoked by pure tones and 2) paired-click sensory gating (P1/N1b amplitude suppression) in autistic individuals and typically developing control subjects. Effects were synthesized using Bayesian 3-level meta-analysis.

RESULTS

In response to pure tones, autistic individuals exhibited prolonged P1/M50 latencies (g = 0.341 [95% credible interval = 0.166, 0.546]), prolonged M100 latencies (g = 0.319 [0.093, 0.550]), reduced N1c amplitudes (g = -0.812 [-1.278, -0.187]), and reduced N2 amplitudes (g = -0.374 [-0.633, -0.179]). There were no practically significant group differences in P2/M200 latencies, N2 latencies, P1/M50 amplitudes, N1b amplitudes, M100 amplitudes, or P2/M200 amplitudes. Paired-click sensory gating was also reduced in autistic individuals (g = -0.389 [-0.619, -0.112]), although this effect was primarily driven by smaller responses to the first click stimulus.

CONCLUSIONS

Relative to typically developing control subjects, autistic individuals demonstrate multiple alterations in early cortical auditory processing of simple stimuli. However, most group differences were modest in size and based on small numbers of heterogeneous studies with variable quality. Future work is necessary to understand whether these neurophysiologic measures can predict clinically meaningful outcomes or serve as stratification biomarkers for the autistic population.

Modeling temporal dynamics of face processing in youth and adults

A hierarchical model of temporal dynamics was examined in adults (n = 34) and youth (n = 46) across the stages of face processing during the perception of static and dynamic faces. Three ERP components (P100, N170, N250) and spectral power in the mu range were extracted, corresponding to cognitive stages of face processing: low-level vision processing, structural encoding, higher-order processing, and action understanding. Youth and adults exhibited similar yet distinct patterns of hierarchical temporal dynamics such that earlier cognitive stages predicted later stages, directly and indirectly. However, latent factors indicated unique profiles related to behavioral performance for adults and youth and age as a continuous factor. The application of path analysis to electrophysiological data can yield novel insights into the cortical dynamics of social information processing.

Association between spectral electroencephalography power and autism risk and diagnosis in early development

Autism spectrum disorder (ASD) has its origins in the atypical development of brain networks. Infants who are at high familial risk for, and later diagnosed with ASD, show atypical activity in multiple electroencephalography (EEG) oscillatory measures. However, infant-sibling studies are often constrained by small sample sizes. We used the International Infant EEG Data Integration Platform, a multi-site dataset with 432 participants, including 222 at high-risk for ASD, from whom repeated measurements of EEG were collected between the ages of 3-36 months. We applied a latent growth curve model to test whether familial risk status predicts developmental trajectories of spectral power across the first 3 years of life, and whether these trajectories predict ASD outcome. Change in spectral EEG power in all frequency bands occurred during the first 3 years of life. Familial risk, but not a later diagnosis of ASD, was associated with reduced power at 3 months, and a steeper developmental change between 3 and 36 months in nearly all absolute power bands. ASD outcome was not associated with absolute power intercept or slope. No associations were found between risk or outcome and relative power. This study applied an analytic approach not used in previous prospective biomarker studies of ASD, which was modeled to reflect the temporal relationship between genetic susceptibility, brain development, and ASD diagnosis. Trajectories of spectral power appear to be predicted by familial risk; however, spectral power does not predict diagnostic outcome above and beyond familial risk status. Discrepancies between current results and previous studies are discussed. LAY SUMMARY: Infants with an older sibling who is diagnosed with ASD are at increased risk of developing ASD themselves. This article tested whether EEG spectral power in the first year of life can predict whether these infants did or did not develop ASD.

Facial Emotions Are Accurately Encoded in the Neural Signal of Those With Autism Spectrum Disorder: A Deep Learning Approach

BACKGROUND

Individuals with autism spectrum disorder (ASD) exhibit frequent behavioral deficits in facial emotion recognition (FER). It remains unknown whether these deficits arise because facial emotion information is not encoded in their neural signal or because it is encodes but fails to translate to FER behavior (deployment). This distinction has functional implications, including constraining when differences in social information processing occur in ASD, and guiding interventions (i.e., developing prosthetic FER vs. reinforcing existing skills).

METHODS

We utilized a discriminative and contemporary machine learning approach-deep convolutional neural networks-to classify facial emotions viewed by individuals with and without ASD (N = 88) from concurrently recorded electroencephalography signals.

RESULTS

The convolutional neural network classified facial emotions with high accuracy for both ASD and non-ASD groups, even though individuals with ASD performed more poorly on the concurrent FER task. In fact, convolutional neural network accuracy was greater in the ASD group and was not related to behavioral performance. This pattern of results replicated across three independent participant samples. Moreover, feature importance analyses suggested that a late temporal window of neural activity (1000-1500 ms) may be uniquely important in facial emotion classification for individuals with ASD.

CONCLUSIONS

Our results reveal for the first time that facial emotion information is encoded in the neural signal of individuals with (and without) ASD. Thus, observed difficulties in behavioral FER associated with ASD likely arise from difficulties in decoding or deployment of facial emotion information within the neural signal. Interventions should focus on capitalizing on this intact encoding rather than promoting compensation or FER prostheses.

MEG-PLAN: a clinical and technical protocol for obtaining magnetoencephalography data in minimally verbal or nonverbal children who have autism spectrum disorder

BACKGROUND

Neuroimaging research on individuals who have autism spectrum disorder (ASD) has historically been limited primarily to those with age-appropriate cognitive and language performance. Children with limited abilities are frequently excluded from such neuroscience research given anticipated barriers like tolerating the loud sounds associated with magnetic resonance imaging and remaining still during data collection. To better understand brain function across the full range of ASD there is a need to (1) include individuals with limited cognitive and language performance in neuroimaging research (non-sedated, awake) and (2) improve data quality across the performance range. The purpose of this study was to develop, implement, and test the feasibility of a clinical/behavioral and technical protocol for obtaining magnetoencephalography (MEG) data. Participants were 38 children with ASD (8-12 years) meeting the study definition of minimally verbal/nonverbal language. MEG data were obtained during a passive pure-tone auditory task.

RESULTS

Based on stakeholder feedback, the MEG Protocol for Low-language/cognitive Ability Neuroimaging (MEG-PLAN) was developed, integrating clinical/behavioral and technical components to be implemented by an interdisciplinary team (clinicians, behavior specialists, scientists, and technologists). Using MEG-PLAN, a 74% success rate was achieved for acquiring MEG data, with a 71% success rate for evaluable and analyzable data. Exploratory analyses suggested nonverbal IQ and adaptive skills were related to reaching the point of acquirable data. No differences in group characteristics were observed between those with acquirable versus evaluable/analyzable data. Examination of data quality (evaluable trial count) was acceptable. Moreover, results were reproducible, with high intraclass correlation coefficients for pure-tone auditory latency.

CONCLUSIONS

Children who have ASD who are minimally verbal/nonverbal, and often have co-occurring cognitive impairments, can be effectively and comfortably supported to complete an electrophysiological exam that yields valid and reproducible results. MEG-PLAN is a protocol that can be disseminated and implemented across research teams and adapted across technologies and neurodevelopmental disorders to collect electrophysiology and neuroimaging data in previously understudied groups of individuals.

Early screening of autism spectrum disorder using cry features

The increase in the number of children with autism and the importance of early autism intervention has prompted researchers to perform automatic and early autism screening. Consequently, in the present paper, a cry-based screening approach for children with Autism Spectrum Disorder (ASD) is introduced which would provide both early and automatic screening. During the study, we realized that ASD specific features are not necessarily observable in all children with ASD and in all instances collected from each child. Therefore, we proposed a new classification approach to be able to determine such features and their corresponding instances. To test the proposed approach a set of data relating to children between 18 to 53 months which had been recorded using high-quality voice recording devices and typical smartphones at various locations such as homes and daycares was studied. Then, after preprocessing, the approach was used to train a classifier, using data for 10 boys with ASD and 10 Typically Developed (TD) boys. The trained classifier was tested on the data of 14 boys and 7 girls with ASD and 14 TD boys and 7 TD girls. The sensitivity, specificity, and precision of the proposed approach for boys were 85.71%, 100%, and 92.85%, respectively. These measures were 71.42%, 100%, and 85.71% for girls, respectively. It was shown that the proposed approach outperforms the common classification methods. Furthermore, it demonstrated better results than the studies which used voice features for screening ASD. To pilot the practicality of the proposed approach for early autism screening, the trained classifier was tested on 57 participants between 10 to 18 months. These 57 participants consisted of 28 boys and 29 girls and the results were very encouraging for the use of the approach in early ASD screening.

Facial Expression Processing Across the Autism-Psychosis Spectra: A Review of Neural Findings and Associations With Adverse Childhood Events

Autism spectrum disorder (ASD) and primary psychosis are classified as distinct neurodevelopmental disorders, yet they display overlapping epidemiological, environmental, and genetic components as well as endophenotypic similarities. For instance, both disorders are characterized by impairments in facial expression processing, a crucial skill for effective social communication, and both disorders display an increased prevalence of adverse childhood events (ACE). This narrative review provides a brief summary of findings from neuroimaging studies investigating facial expression processing in ASD and primary psychosis with a focus on the commonalities and differences between these disorders. Individuals with ASD and primary psychosis activate the same brain regions as healthy controls during facial expression processing, albeit to a different extent. Overall, both groups display altered activation in the fusiform gyrus and amygdala as well as altered connectivity among the broader face processing network, probably indicating reduced facial expression processing abilities. Furthermore, delayed or reduced N170 responses have been reported in ASD and primary psychosis, but the significance of these findings is questioned, and alternative frequency-tagging electroencephalography (EEG) measures are currently explored to capture facial expression processing impairments more selectively. Face perception is an innate process, but it is also guided by visual learning and social experiences. Extreme environmental factors, such as adverse childhood events, can disrupt normative development and alter facial expression processing. ACE are hypothesized to induce altered neural facial expression processing, in particular a hyperactive amygdala response toward negative expressions. Future studies should account for the comorbidity among ASD, primary psychosis, and ACE when assessing facial expression processing in these clinical groups, as it may explain some of the inconsistencies and confound reported in the field.

Psychophysiological and Eye-Tracking Markers of Speech and Language Processing in Neurodevelopmental Disorders: New Options for Difficult-to-Test Populations

It can be challenging to accurately assess speech and language processing in preverbal or minimally verbal individuals with neurodevelopmental disabilities (NDD) using standardized behavioral tools. Event-related potential and eye tracking methods offer novel means to objectively document receptive language processing without requiring purposeful behavioral responses. Working around many of the cognitive, motor, or social difficulties in NDDs, these tools allow for minimally invasive, passive assessment of language processing and generate continuous scores that may have utility as biomarkers of individual differences and indicators of treatment effectiveness. Researchers should consider including physiological measures in assessment batteries to allow for more precise capture of language processing in individuals for whom it may not behaviorally apparent.

Functional connectivity during language processing in 3-month-old infants at familial risk for autism spectrum disorder

Auditory statistical learning (ASL) plays a role in language development and may lay a foundation for later social communication impairment. As part of a longitudinal study of infant siblings, we asked whether electroencephalography (EEG) measures of connectivity during ASL at 3 months of age-differentiated infants who showed signs of autism spectrum disorder (ASD) at age 18 months. We measured spectral power and phase coherence in the theta (4-6 Hz) and alpha (6-12 Hz) frequency bands within putative language networks. Infants were divided into ASD-concern (n = 14) and No-ASD-concern (n = 49) outcome groups based on their ASD symptoms at 18 months, measured using the Autism Diagnostic Observation Scale Toddler Module. Using permutation testing, we identified a trend toward reduced left fronto-central phase coherence at the electrode pair F9-C3 in both theta and alpha frequency bands in infants who later showed ASD symptoms at 18 months. Across outcome groups, alpha coherence at 3 months correlated with greater word production at 18 months on the MacArthur-Bates Communicative Development Inventory. This study introduces signal processing and analytic tools that account for the challenges inherent in infant EEG studies, such as short duration of recordings, considerable movement artifact, and variable volume conduction. Our results indicate that connectivity, as measured by phase coherence during 2.5 min of ASL, can be quantified as early as 3 months and suggest that early alternations in connectivity may serve as markers of resilience for neurodevelopmental impairments.

Infant EEG theta modulation predicts childhood intelligence

Intellectual functioning is a critical determinant of economic and personal productivity. Identifying early neural predictors of cognitive function in infancy will allow us to map the neurodevelopmental pathways that underpin individual differences in intellect. Here, in three different cohorts we investigate the association between a putative neurophysiological indicator of information encoding (change in frontal theta during a novel video) in infancy and later general cognitive outcome. In a discovery cohort of 12-month-old typically developing infants, we recorded EEG during presentation of dynamic movies of people and objects. Frontal theta power (3-6 Hz) significantly increased during the course of viewing each video. Critically, increase in frontal theta during viewing of a video was associated with a differential response to repetition of that specific video, confirming relation to learning. Further, individual differences in the magnitude of change in frontal theta power were related to concurrent nonverbal cognitive level. We then sought to extend this association in two independent samples enriched for variation in cognitive outcome due to the inclusion of infants at familial risk for autism. We observed similar patterns of theta EEG change at 12 months, and found a predictive relation to verbal and nonverbal cognitive skills measured at 2, 3 and 7 years of age. For the subset of high-risk infants later diagnosed with autism, infant theta EEG explained over 80% of the variance in nonverbal skills at age 3 years. We suggest that EEG theta change in infancy is an excellent candidate predictive biomarker that could yield substantial insight into the mechanisms that underlie individual differences in childhood intelligence, particularly in high risk populations.

EEG-IP: an international infant EEG data integration platform for the study of risk and resilience in autism and related conditions

BACKGROUND

Establishing reliable predictive and diganostic biomarkers of autism would enhance early identification and facilitate targeted intervention during periods of greatest plasticity in early brain development. High impact research on biomarkers is currently limited by relatively small sample sizes and the complexity of the autism phenotype.

METHODS

EEG-IP is an International Infant EEG Data Integration Platform developed to advance biomarker discovery by enhancing the large scale integration of multi-site data. Currently, this is the largest multi-site standardized dataset of infant EEG data.

RESULTS

First, multi-site data from longitudinal cohort studies of infants at risk for autism was pooled in a common repository with 1382 EEG longitudinal recordings, linked behavioral data, from 432 infants between 3- to 36-months of age. Second, to address challenges of limited comparability across independent recordings, EEG-IP applied the Brain Imaging Data Structure (BIDS)-EEG standard, resulting in a harmonized, extendable, and integrated data state. Finally, the pooled and harmonized raw data was preprocessed using a common signal processing pipeline that maximizes signal isolation and minimizes data reduction. With EEG-IP, we produced a fully standardized data set, of the pooled, harmonized, and pre-processed EEG data from multiple sites.

CONCLUSIONS

Implementing these integrated solutions for the first time with infant data has demonstrated success and challenges in generating a standardized multi-site data state. The challenges relate to annotation of signal sources, time, and ICA analysis during pre-processing. A number of future opportunities also emerge, including validation of analytic pipelines that can replicate existing findings and/or test novel hypotheses.

A review of the use of EEG connectivity to measure the neurological characteristics of the sensory features in young people with autism

Autism spectrum disorder (ASD) is a neurodevelopmental condition affecting about 1 in 100 children and is currently incurable. ASD represents a challenge to traditional methods of assessment and diagnosis, and it has been suggested that direct measures of brain activity and connectivity between brain regions during demanding tasks represents a potential pathway to building more accurate models of underlying brain function and ASD. One of the key behavioural diagnostic indicators of ASD consists of sensory features (SF), often characterised by over- or under-reactivity to environmental stimuli. SF are associated with behavioural difficulties that impede social and education success in these children as well as anxiety and depression. This review examines the previous literature on the measurement of EEG connectivity and SF observed in individuals with ASD.

Biomarker Acquisition and Quality Control for Multi-Site Studies: The Autism Biomarkers Consortium for Clinical Trials

The objective of the Autism Biomarkers Consortium for Clinical Trials (ABC-CT) is to evaluate a set of lab-based behavioral video tracking (VT), electroencephalography (EEG), and eye tracking (ET) measures for use in clinical trials with children with autism spectrum disorder (ASD). Within the larger organizational structure of the ABC-CT, the Data Acquisition and Analytic Core (DAAC) oversees the standardization of VT, EEG, and ET data acquisition, data processing, and data analysis. This includes designing and documenting data acquisition and analytic protocols and manuals; facilitating site training in acquisition; data acquisition quality control (QC); derivation and validation of dependent variables (DVs); and analytic deliverables including preparation of data for submission to the National Database for Autism Research (NDAR). To oversee consistent application of scientific standards and methodological rigor for data acquisition, processing, and analytics, we developed standard operating procedures that reflect the logistical needs of multi-site research, and the need for well-articulated, transparent processes that can be implemented in future clinical trials. This report details the methodology of the ABC-CT related to acquisition and QC in our Feasibility and Main Study phases. Based on our acquisition metrics from a preplanned interim analysis, we report high levels of acquisition success utilizing VT, EEG, and ET experiments in a relatively large sample of children with ASD and typical development (TD), with data acquired across multiple sites and use of a manualized training and acquisition protocol.

Relative Average Look Duration and its Association with Neurophysiological Activity in Young Children with Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is characterized by early attentional differences that often precede the hallmark symptoms of social communication impairments. Development of novel measures of attentional behaviors may lead to earlier identification of children at risk for ASD. In this work, we first introduce a behavioral measure, Relative Average Look Duration (RALD), indicating attentional preference to different stimuli, such as social versus nonsocial stimuli; and then study its association with neurophysiological activity. We show that (1) ASD and typically developing (TD) children differ in both (absolute) Average Look Duration (ALD) and RALD to stimuli during an EEG experiment, with the most pronounced differences in looking at social stimuli; and (2) associations between looking behaviors and neurophysiological activity, as measured by EEG, are different for children with ASD versus TD. Even when ASD children show attentional engagement to social content, our results suggest that their underlying brain activity is different than TD children. This study therefore introduces a new measure of social/nonsocial attentional preference in ASD and demonstrates the value of incorporating attentional variables measured simultaneously with EEG into the analysis pipeline.

Early patterns of functional brain development associated with autism spectrum disorder in tuberous sclerosis complex

Tuberous sclerosis complex (TSC) is a rare genetic disorder that confers a high risk for autism spectrum disorders (ASD), with behavioral predictors of ASD emerging early in life. Deviations in structural and functional neural connectivity are highly implicated in both TSC and ASD. For the first time, we explore whether electroencephalographic (EEG) measures of neural network function precede or predict the emergence of ASD in TSC. We determine whether altered brain function (a) is present in infancy in TSC, (b) differentiates infants with TSC based on ASD diagnostic status, and (c) is associated with later cognitive function. We studied 35 infants with TSC (N = 35), and a group of typically developing infants (N = 20) at 12 and 24 months of age. Infants with TSC were later subdivided into ASD and non-ASD groups based on clinical evaluation. We measured features of spontaneous alpha oscillations (6-12 Hz) that are closely associated with neural network development: alpha power, alpha phase coherence (APC), and peak alpha frequency (PAF). Infants with TSC demonstrated reduced interhemispheric APC compared to controls at 12 months of age, and these differences were found to be most pronounced at 24 months in the infants who later developed ASD. Across all infants, PAF at 24 months was associated with verbal and nonverbal cognition at 36 months. Associations between early network function and later neurodevelopmental and cognitive outcomes highlight the potential utility of early scalable EEG markers to identify infants with TSC requiring additional targeted intervention initiated very early in life. Autism Res 2019, 12: 1758-1773. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Approximately half of infants with tuberous sclerosis complex (TSC) develop autism. Here, using EEG, we find that there is a reduction in communication between brain regions during infancy in TSC, and that the infants who show the largest reductions are those who later develop autism. Being able to identify infants who show early signs of disrupted brain development may improve the timing of early prediction and interventions in TSC, and also help us to understand how early brain changes lead to autism.

Test-retest reliability of the N2 event-related potential in school-aged children with autism spectrum disorder (ASD)

OBJECTIVE

The N2 ERP component is used as a biomeasure of executive function in children with autism spectrum disorder (ASD). The aim of the current study was to evaluate the test-retest reliability of N2 amplitude in this population.

METHODS

ERPs were recorded from 7 to 11-year-old children with ASD during Flanker (n = 21) and Go/Nogo tasks (n = 14) administered at two time points separated by approximately three months. Reliability of the N2 component was examined using intraclass correlation coefficients (ICCs).

RESULTS

Reliability for mean N2 amplitude obtained during the Flanker task was moderate (congruent: ICC = 0.542, 95% CI [0.173, 0.782]; incongruent: ICC = 0.629, 95% CI [0.276, 0.831]). Similarly, reliability for the Go/Nogo task ranged from moderate to good ('go': ICC = 0.817, 95% CI [0.535, 0.937]; 'nogo': ICC = 0.578, 95% CI [0.075, 0.843]).

CONCLUSIONS

These findings support the use of N2 amplitude as a biomeasure of executive function in school-aged children with ASD.

SIGNIFICANCE

This research addresses a critical gap in clinical neurophysiology, as an understanding of the stability and reliability of the N2 component is needed in order to differentiate variance explained by repeated measurement versus targeted treatments and interventions.

Conceptual, Regulatory and Strategic Imperatives in the Early Days of EEG-Based Biomarker Validation for Neurodevelopmental Disabilities

Biological treatment development for syndromal neuropsychiatric conditions such as autism has seen slow progress for decades. Speeding drug discovery may result from the judicious development and application of biomarker measures of brain function to select patients for clinical trials, to confirm target engagement and to optimize drug dose. For neurodevelopmental disorders, electrophysiology (EEG) offers considerable promise because of its ability to monitor brain activity with high temporal resolution and its more ready application for pediatric populations relative to MRI. Here, we discuss conceptual/definitional issues related to biomarker development, discuss practical implementation issues, and suggest preliminary guidelines for validating EEG approaches as biomarkers with a context of use in neurodevelopmental disorder drug development.

Eurosibs: Towards robust measurement of infant neurocognitive predictors of autism across Europe

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The Eurosibs consortium is a nine-site European neurocognitive study of infants with an older sibling with ASD.

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Data quality assessments show that that neurocognitive measures hold promise for cross-site consistency in diverse populations.

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We present robust data analysis pipelines and highlight challenges and opportunities for future multisite research efforts.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects social communication skills and flexible behaviour. Developing new treatment approaches for ASD requires early identification of the factors that influence later behavioural outcomes. One fruitful research paradigm has been the prospective study of infants with a first degree relative with ASD, who have around a 20% likelihood of developing ASD themselves. Early findings have identified a range of candidate neurocognitive markers for later ASD such as delayed attention shifting or neural responses to faces, but given the early stage of the field most sample sizes are small and replication attempts remain rare. The Eurosibs consortium is a European multisite neurocognitive study of infants with an older sibling with ASD conducted across nine sites in five European countries. In this manuscript, we describe the selection and standardization of our common neurocognitive testing protocol. We report data quality assessments across sites, showing that neurocognitive measures hold great promise for cross-site consistency in diverse populations. We discuss our approach to ensuring robust data analysis pipelines and boosting future reproducibility. Finally, we summarise challenges and opportunities for future multi-site research efforts.

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.

An Observational Study With the Janssen Autism Knowledge Engine (JAKE®) in Individuals With Autism Spectrum Disorder

Objective: The Janssen Autism Knowledge Engine (JAKE®) is a clinical research outcomes assessment system developed to more sensitively measure treatment outcomes and identify subpopulations in autism spectrum disorder (ASD). Here we describe JAKE and present results from its digital phenotyping (My JAKE) and biosensor (JAKE Sense) components. Methods: An observational, non-interventional, prospective study of JAKE in children and adults with ASD was conducted at nine sites in the United States. Feedback on JAKE usability was obtained from caregivers. JAKE Sense included electroencephalography, eye tracking, electrocardiography, electrodermal activity, facial affect analysis, and actigraphy. Caregivers of individuals with ASD reported behaviors using My JAKE. Results from My JAKE and JAKE Sense were compared to traditional ASD symptom measures. Results: Individuals with ASD (N = 144) and a cohort of typically developing (TD) individuals (N = 41) participated in JAKE Sense. Most caregivers reported that overall use and utility of My JAKE was "easy" (69%, 74/108) or "very easy" (74%, 80/108). My JAKE could detect differences in ASD symptoms as measured by traditional methods. The majority of biosensors included in JAKE Sense captured sizable amounts of quality data (i.e., 93-100% of eye tracker, facial affect analysis, and electrocardiogram data was of good quality), demonstrated differences between TD and ASD individuals, and correlated with ASD symptom scales. No significant safety events were reported. Conclusions: My JAKE was viewed as easy or very easy to use by caregivers participating in research outside of a clinical study. My JAKE sensitively measured a broad range of ASD symptoms. JAKE Sense biosensors were well-tolerated. JAKE functioned well when used at clinical sites previously inexperienced with some of the technologies. Lessons from the study will optimize JAKE for use in clinical trials to assess ASD interventions. Additionally, because biosensors were able to detect features differentiating TD and ASD individuals, and also were correlated with standardized symptom scales, these measures could be explored as potential biomarkers for ASD and as endpoints in future clinical studies. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT02668991 identifier: NCT02668991.

Increased cortical reactivity to repeated tones at 8 months in infants with later ASD

Dysregulation of cortical excitation/inhibition (E/I) has been proposed as a neuropathological mechanism underlying core symptoms of autism spectrum disorder (ASD). Determining whether dysregulated E/I could contribute to the emergence of behavioural symptoms of ASD requires evidence from human infants prior to diagnosis. In this prospective longitudinal study, we examine differences in neural responses to auditory repetition in infants later diagnosed with ASD. Eight-month-old infants with (high-risk: n = 116) and without (low-risk: n = 27) an older sibling with ASD were tested in a non-linguistic auditory oddball paradigm. Relative to high-risk infants with typical development (n = 44), infants with later ASD (n = 14) showed reduced repetition suppression of 40-60 Hz evoked gamma and significantly greater 10-20 Hz inter-trial coherence (ITC) for repeated tones. Reduced repetition suppression of cortical gamma and increased phase-locking to repeated tones are consistent with cortical hyper-reactivity, which could in turn reflect disturbed E/I balance. Across the whole high-risk sample, a combined index of cortical reactivity (cortical gamma amplitude and ITC) was dimensionally associated with reduced growth in language skills between 8 months and 3 years, as well as elevated levels of parent-rated social communication symptoms at 3 years. Our data show that cortical 'hyper-reactivity' may precede the onset of behavioural traits of ASD in development, potentially affecting experience-dependent specialisation of the developing brain.

EEG Frequency Bands in Psychiatric Disorders: A Review of Resting State Studies

A significant proportion of the electroencephalography (EEG) literature focuses on differences in historically pre-defined frequency bands in the power spectrum that are typically referred to as alpha, beta, gamma, theta and delta waves. Here, we review 184 EEG studies that report differences in frequency bands in the resting state condition (eyes open and closed) across a spectrum of psychiatric disorders including depression, attention deficit-hyperactivity disorder (ADHD), autism, addiction, bipolar disorder, anxiety, panic disorder, post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD) and schizophrenia to determine patterns across disorders. Aggregating across all reported results we demonstrate that characteristic patterns of power change within specific frequency bands are not necessarily unique to any one disorder but show substantial overlap across disorders as well as variability within disorders. In particular, we show that the most dominant pattern of change, across several disorder types including ADHD, schizophrenia and OCD, is power increases across lower frequencies (delta and theta) and decreases across higher frequencies (alpha, beta and gamma). However, a considerable number of disorders, such as PTSD, addiction and autism show no dominant trend for spectral change in any direction. We report consistency and validation scores across the disorders and conditions showing that the dominant result across all disorders is typically only 2.2 times as likely to occur in the literature as alternate results, and typically with less than 250 study participants when summed across all studies reporting this result. Furthermore, the magnitudes of the results were infrequently reported and were typically small at between 20% and 30% and correlated weakly with symptom severity scores. Finally, we discuss the many methodological challenges and limitations relating to such frequency band analysis across the literature. These results caution any interpretation of results from studies that consider only one disorder in isolation, and for the overall potential of this approach for delivering valuable insights in the field of mental health.