Search

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.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.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.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.

When designing and interpreting results from clinical trials evaluating treatments for children on the autism spectrum, a complicating factor is that most children receive a range of concurrent treatments. Thus, it is important to better understand the types and hours of interventions that participants typically receive as part of standard of care, as well as to understand the child, family, and geographic factors that are associated with different patterns of service utilization. In this multi-site study, we interviewed 280 caregivers of 6-to-11-year-old school-aged children on the autism spectrum about the types and amounts of interventions their children received in the prior 6 weeks. Reported interventions were coded as “evidence-based practice” or “other interventions,” reflecting the level of empirical support. Results indicated that children received a variety of interventions with varying levels of empirical evidence and a wide range of hours (0 to 79.3 hours/week). Children with higher autism symptom levels, living in particular states, and who identified as non-Hispanic received more evidence-based intervention hours. Higher parental education level related to more hours of other interventions. Children who were younger, had lower cognitive ability, and with higher autism symptom levels received a greater variety of interventions overall. Thus, based on our findings, it would seem prudent when designing clinical trials to take into consideration a variety of factors including autism symptom levels, age, cognitive ability, ethnicity, parent education and geographic location. Future research should continue to investigate the ethnic, racial, and socioeconomic influences on school-aged intervention services.

Early differences in sensorimotor functioning have been documented in young autistic children and infants who are later diagnosed with autism. Previous research has demonstrated that autistic toddlers exhibit more frequent head movement when viewing dynamic audiovisual stimuli, compared to neurotypical toddlers. To further explore this behavioral characteristic, in this study, computer vision (CV) analysis was used to measure several aspects of head movement dynamics of autistic and neurotypical toddlers while they watched a set of brief movies with social and nonsocial content presented on a tablet.Data were collected from 457 toddlers, 17-36 months old, during their well-child visit to four pediatric primary care clinics. Forty-one toddlers were subsequently diagnosed with autism. An application (app) displayed several brief movies on a tablet, and the toddlers watched these movies while sitting on their caregiver's lap. The front-facing camera in the tablet recorded the toddlers' behavioral responses. CV was used to measure the participants' head movement rate, movement acceleration, and complexity using multiscale entropy.Autistic toddlers exhibited significantly higher rate, acceleration, and complexity in their head movements while watching the movies compared to neurotypical toddlers, regardless of the type of movie content (social vs. nonsocial). The combined features of head movement acceleration and complexity reliably distinguished the autistic and neurotypical toddlers.Autistic toddlers exhibit differences in their head movement dynamics when viewing audiovisual stimuli. Higher complexity of their head movements suggests that their movements were less predictable and less stable compared to neurotypical toddlers. CV offers a scalable means of detecting subtle differences in head movement dynamics, which may be helpful in identifying early behaviors associated with autism and providing insight into the nature of sensorimotor differences associated with autism.

Purpose: Visual face recognition - the ability to encode, discriminate, and recognize the faces of others - is fundamentally supported by eye movements and is a common source of difficulty for autistic individuals. We aimed to evaluate how visual processing strategies (i.e., eye movement patterns) directly support encoding and recognition of faces in autistic and neurotypical (NT) individuals. Methods: We used a hidden Markov modeling approach to evaluate the spatiotemporal dynamics of eye movements in autistic (n = 15) and neurotypical (NT) adolescents (n = 17) during a face identity recognition task.Results: We discovered distinct eye movement patterns among all participants, which included a focused and exploratory strategy. When evaluating change in visual processing strategy across encoding and recognition phases, autistic individuals did not shift their eye movement patterns like their NT peers, who shifted to a more exploratory visual processing strategy during recognition. Conclusions: These findings suggest that autistic individuals do not modulate their visual processing strategy across encoding and recognition of faces, which may be an indicator of less efficient face processing.

One of the main drivers of autism spectrum disorder (ASD) are risk alleles within hundreds of genes, which may interact within shared biological processes through as-yet unclear mechanisms. Here we develop a high-throughput genome-editing-mediated approach to target 14 high-confidence ASD genes within the mouse brain for proximity-based proteomics of endogenous interactomes. The resulting interactomes are enriched for human genes dysregulated in the brain of ASD patients and reveal unexpected, but highly significant, interactions with other lower confidence ASD-risk gene products, positing new avenues to prioritize genetic risk. Importantly, the datasets are enriched for shared cellular functions and genetic interactions that may underlie the disorder. We test this notion by spatial proteomics and CRISPR-based regulation of expression in two ASD models, demonstrating new functional interactions that modulate mechanisms of their dysregulation. Together, our results reveal native protein-interaction networks in ASD, providing new inroads for understanding its cellular neurobiology.

Children with autism spectrum disorder are prescribed various medications to address behavior and mood. In clinical trials, individuals taking concomitant psychotropic medications often are excluded to maintain homogeneity and prevent contamination of clinical endpoints. However, this choice may compromise the representativeness of the sample. In a recent study designed to identify biomarkers and endpoints for clinical trials (the Autism Biomarkers Consortium for Clinical Trials), school-age children with autism spectrum disorder were enrolled without excluding for medications, providing the opportunity to examine characteristics of psychotropic medication use and guide future decisions on medication-related inclusion criteria. The aims of the current analysis were (1) to quantify the frequency and type of psychotropic medications reported in school-age children enrolled in the study and (2) to examine behavioral features of children with autism spectrum disorder based on medication classes. Of the 280 children with autism spectrum disorder in the cohort, 42.5% were taking psychotropic medications, with polypharmacy in half. The most commonly reported psychotropic medications included melatonin, stimulants, selective serotonin reuptake inhibitors, alpha agonists, and antipsychotics. Our findings suggest that exclusion of children taking concomitant psychotropic medications could limit the representativeness of the study population, perhaps even excluding children who may most benefit from new treatment options. Lay abstract Children with autism spectrum disorder are prescribed a variety of medications that affect the central nervous system (psychotropic medications) to address behavior and mood. In clinical trials, individuals taking concomitant psychotropic medications often are excluded to maintain homogeneity of the sample and prevent contamination of biomarkers or clinical endpoints. However, this choice may significantly diminish the clinical representativeness of the sample. In a recent multisite study designed to identify biomarkers and behavioral endpoints for clinical trials (the Autism Biomarkers Consortium for Clinical Trials), school-age children with autism spectrum disorder were enrolled without excluding for medications, thus providing a unique opportunity to examine characteristics of psychotropic medication use in a research cohort and to guide future decisions on medication-related inclusion criteria. The aims of the current analysis were (1) to quantify the frequency and type of psychotropic medications reported in school-age children enrolled in the ABC-CT and (2) to examine behavioral features of children with autism spectrum disorder based on medication classes. Of the 280 children with autism spectrum disorder in the cohort, 42.5% were taking psychotropic medications, with polypharmacy in half of these children. The most commonly reported psychotropic medications included melatonin, stimulants, selective serotonin reuptake inhibitors, alpha agonists, and antipsychotics. Descriptive analysis showed that children taking antipsychotics displayed a trend toward greater overall impairment. Our findings suggest that exclusion of children taking concomitant psychotropic medications in trials could limit the clinical representativeness of the study population, perhaps even excluding children who may most benefit from new treatment options.

Attention-deficit/hyperactivity disorder (ADHD) symptoms affect 40-60% of autistic children and have been linked to differences in adaptive behavior. It is unclear whether adaptive behavior in autistic youth is directly impacted by co-occurring ADHD symptoms or by another associated feature of both autism and ADHD, such as increased irritability. The current study examined relationships between irritability, ADHD symptoms, and adaptive behavior in 3- to 7-year-old autistic children. Results suggest that, after adjusting for co-occurring ADHD symptoms, higher levels of irritability are associated with differences in social adaptive behavior specifically. Understanding relationships between irritability, ADHD, and adaptive behavior in autistic children is critical because measures of adaptive behavior, such as the Vineland Scales of Adaptive Functioning, are often used as a proxy for global functioning, as well as for developing intervention plans and measuring outcomes as primary endpoints in clinical trials.

Although the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) diagnostic criteria for autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) show very little symptom-level overlap between the conditions, similar behavioral challenges are evident in individuals with each of these disorders. Clinical questions often arise about whether presenting concerns should be conceptualized as reflecting a primary diagnosis of ASD or ADHD. The current chapter provides guidelines for the differential diagnosis of these disorders. Using clinical case studies, the chapter describes behaviors that may be understood as part of the core symptom domains of ASD and ADHD. Specific suggestions are provided for disentangling the underlying nosology of common behavioral challenges. The chapter ends with recommendations to support differential diagnosis in clinical practice.

Visual exploration paradigms involving object arrays have been used to examine salience of social stimuli such as faces in ASD. Recent work suggests performance on these paradigms may associate with clinical features of ASD. We evaluate metrics from a visual exploration paradigm in 4-to-11-year-old children with ASD (n = 23; 18 males) and typical development (TD; n = 23; 13 males). Presented with arrays containing faces and nonsocial stimuli, children with ASD looked less at (p = 0.002) and showed fewer fixations to (p = 0.022) faces than TD children, and spent less time looking at each object on average (p = 0.004). Attention to the screen and faces correlated positively with social and cognitive skills in the ASD group (ps .05). This work furthers our understanding of objective measures of visual exploration in ASD and its potential for quantifying features of ASD.

Use of artificial intelligence (AI) is a burgeoning field in otolaryngology and the communication sciences. A virtual symposium on the topic was convened from Duke University on October 26, 2020, and was attended by more than 170 participants worldwide. This review presents summaries of all but one of the talks presented during the symposium; recordings of all the talks, along with the discussions for the talks, are available at https://www.youtube.com/watch?v=ktfewrXvEFg and https://www.youtube.com/watch?v=-gQ5qX2v3rg . Each of the summaries is about 2500 words in length and each summary includes two figures. This level of detail far exceeds the brief summaries presented in traditional reviews and thus provides a more-informed glimpse into the power and diversity of current AI applications in otolaryngology and the communication sciences and how to harness that power for future applications.

Autism spectrum disorder (henceforth autism) is a neurodevelopmental condition that can be reliably diagnosed in children by age 18-24 months. Prospective longitudinal studies of infants aged 1 year and younger who are later diagnosed with autism are elucidating the early developmental course of autism and identifying ways of predicting autism before diagnosis is possible. Studies that use MRI, EEG, and near-infrared spectroscopy have identified differences in brain development in infants later diagnosed with autism compared with infants without autism. Retrospective studies of infants younger than 1 year who received a later diagnosis of autism have also showed an increased prevalence of health conditions, such as sleep disorders, gastrointestinal disorders, and vision problems. Behavioural features of infants later diagnosed with autism include differences in attention, vocalisations, gestures, affect, temperament, social engagement, sensory processing, and motor abilities. Although research findings offer insight on promising screening approaches for predicting autism in infants, individual-level predictions remain a future goal. Multiple scientific challenges and ethical questions remain to be addressed to translate research on early brain-based and behavioural predictors of autism into feasible and reliable screening tools for clinical practice.

Autism spectrum disorder (ASD) is associated with deficits in the processing of social information and difficulties in social interaction, and individuals with ASD exhibit atypical attention and gaze. Traditionally, gaze studies have relied upon precise and constrained means of monitoring attention using expensive equipment in laboratories. In this work we develop a low-cost off-the-shelf alternative for measuring attention that can be used in natural settings. The head and iris positions of 104 16-31 months children, an age range appropriate for ASD screening and diagnosis, 22 of them diagnosed with ASD, were recorded using the front facing camera in an iPad while they watched on the device screen a movie displaying dynamic stimuli, social stimuli on the left and non-social stimuli on the right. The head and iris position were then automatically analyzed via computer vision algorithms to detect the direction of attention. We validate the proposed framework and computational tool showing that children in the ASD group paid less attention to the movie, showed less attention to the social as compared to the non-social stimuli, and often fixated their attention to one side of the screen. These results are expected from the ASD literature, here obtained with significantly simpler and less expensive attention tracking methods. The proposed method provides a low-cost means of monitoring attention to properly designed stimuli, demonstrating that the integration of stimuli design and automatic response analysis results in the opportunity to use off-the-shelf cameras to assess behavioral biomarkers.

Observational behavior analysis plays a key role for the discovery and evaluation of risk markers for many neurodevelopmental disorders. Research on autism spectrum disorder (ASD) suggests that behavioral risk markers can be observed at 12 months of age or earlier, with diagnosis possible at 18 months. To date, these studies and evaluations involving observational analysis tend to rely heavily on clinical practitioners and specialists who have undergone intensive training to be able to reliably administer carefully designed behavioural-eliciting tasks, code the resulting behaviors, and interpret such behaviors. These methods are therefore extremely expensive, time-intensive, and are not easily scalable for large population or longitudinal observational analysis. We developed a self-contained, closed-loop, mobile application with movie stimuli designed to engage the child’s attention and elicit specific behavioral and social responses, which are recorded with a mobile device camera and then analyzed via computer vision algorithms. Here, in addition to presenting this paradigm, we validate the system to measure engagement, name-call responses, and emotional responses of toddlers with and without ASD who were presented with the application. Additionally, we show examples of how the proposed framework can further risk marker research with fine-grained quantification of behaviors. The results suggest these objective and automatic methods can be considered to aid behavioral analysis, and can be suited for objective automatic analysis for future studies.

Electroencephalography experiments produce region-referenced functional data representing brain signals in the time or the frequency domain collected across the scalp. The data typically also have a multilevel structure with high-dimensional observations collected across multiple experimental conditions or visits. Common analysis approaches reduce the data complexity by collapsing the functional and regional dimensions, where event-related potential (ERP) features or band power are targeted in a pre-specified scalp region. This practice can fail to portray more comprehensive differences in the entire ERP signal or the power spectral density (PSD) across the scalp. Building on the weak separability of the high-dimensional covariance process, the proposed multilevel hybrid principal components analysis (M-HPCA) utilizes dimension reduction tools from both vector and functional principal components analysis to decompose the total variation into between- and within-subject variance. The resulting model components are estimated in a mixed effects modeling framework via a computationally efficient minorization-maximization algorithm coupled with bootstrap. The diverse array of applications of M-HPCA is showcased with two studies of individuals with autism. While ERP responses to match vs mismatch conditions are compared in an audio odd-ball paradigm in the first study, short-term reliability of the PSD across visits is compared in the second. Finite sample properties of the proposed methodology are studied in extensive simulations.

Autistic children’s play provides insights into social, communication, and other skills; however, methods for measuring these observations can be labor-intensive and rely on subjective judgment. This study explored whether children’s movement and location during play measured via automated video tracking correlates with clinical features. Movement tracking metrics of 164 autistic children (27–96 months old) during free play were analyzed in relation to standard assessments of cognitive, language, social-communication, and adaptive skills. Children with higher cognitive and language abilities were more likely to spend time in the center toy region and more slowly or never approached the region without toys. Children with higher autism-related features spent less time in the center. Children with lower daily living skills spent more time near the caregiver and those with lower overall adaptive and language skills approached the caregiver more quickly. Over 90% of autistic children representing a range of ages and skills provided analyzable movement data during play and those with higher cognitive, language, and adaptive skills displayed movement tracking patterns that reflect more sustained focus on toy play and independence from the caregiver. Results suggest that automated movement tracking is a promising complementary, objective method for assessing clinical variation during autistic children’s play. Lay Abstract Play-based observations allow researchers to observe autistic children across a wide range of ages and skills. We recorded autistic children playing with toys in the center of a room and at a corner table while a caregiver remained seated off to the side and used video tracking technology to track children’s movement and location. We examined how time children spent in room regions and whether or not they approached each region during play related to their cognitive, social, communication, and adaptive skills to determine if tracking child movement and location can meaningfully demonstrate clinical variation among autistic children representing a range of ages and skills. One significant finding was that autistic children who spent more time in the toy-containing center of the room had higher cognitive and language abilities, whereas those who spent less time in the center had higher levels of autism-related behaviors. In contrast, children who spent more time in the caregiver region had lower daily living skills and those who were quicker to approach the caregiver had lower adaptive behavior and language skills. These findings support the use of movement tracking as a complementary method of measuring clinical differences among autistic children. Furthermore, over 90% of autistic children representing a range of ages and skills in this study provided analyzable play observation data, demonstrating that this method allows autistic children of all levels of support needs to participate in research and demonstrate their social, communication, and attention skills without wearing any devices.

The American Academy of Pediatrics recommends that children be screened for autism at 18 and 24 months. It is unknown whether a positive screen usually represents the first developmental concern or is typically preceded by other developmental and/or medical concerns. This study evaluated the proportions of toddlers (M = 18.69 months) who screened positive for autism for whom caregivers or providers had prior concerns, the nature and timing of prior concerns, and their relationship with family characteristics. The electronic health records of 242 children who screened positive using the Modified Checklist for Autism in Toddlers–Revised with Follow-up during routine well-child visits were reviewed. Among toddlers who screened positive for autism, the majority (79.34%) had a documented prior motor, language, and/or autism concern. The presence of specific concerns was associated with earlier timing and higher Modified Checklist for Autism in Toddlers–Revised with Follow-up scores. Prior medical concerns were common, such as sleep (23.14%) and gastrointestinal (66.12%) problems. A positive autism screen often occurs in the context of pre-existing concerns. Thus, screening can provide providers with an opportunity to contextualize feedback about autism in the context of prior developmental and medical concerns. Lay abstract The American Academy of Pediatrics recommends that all children be screened for autism at their 18- and 24-month well-child visit. For children who screen positive for autism, it is unknown whether this usually represents the first time a developmental concern has been raised or if other developmental concerns typically precede a positive autism screen. Such knowledge could help guide providers in how to appropriately convey feedback regarding autism screening. This study found that, for close to 80% of children with a positive autism screen, caregivers or providers had a prior autism, language, motor, or other developmental concern documented in the electronic health record. Many also had other prior concerns frequently linked to autism, such as sleep and gastrointestinal problems, and received physical or speech therapy. On average, prior to screening children who received a positive Modified-Checklist for Autism in Toddlers had two documented concerns by at 1 year of age and three concerns by 2 years of age. These findings imply that screening for autism as a part of routine pediatric care likely takes place in the context of larger conversations regarding existing developmental concerns, allowing for a less stigmatizing discussion of autism. Framing the presence of prior concerns in the setting of a positive screen in this context may create a reaffirming space for existing caregiver concerns and a lessened emotional burden on caregivers.

Although it has been found that autistic children exhibit delays in executive function abilities and atypical patterns of attention, less is known about the relationship between executive function and attention abilities and social and language skills in early childhood. In this study, 180 autistic children, age 2–8 years, participated in a study examining the relationship between executive function abilities, measured by the Behavior Rating Inventory of Executive Function, and assessments of sustained attention measured via eye-tracking and several language and social communication measures. Results revealed that children with higher caregiver-reported executive function skills, specifically, working memory and planning/organization abilities, demonstrated higher levels of caregiver-reported receptive-expressive social communication abilities measured via the Pervasive Developmental Disorder Behavior Inventory. Higher executive function abilities across all domains were associated with lower levels of social pragmatic problems. Children who were able to sustain their attention for a longer duration demonstrated higher expressive language abilities. These results suggest that executive function and attention skills may play an important role in multiple domains of functioning in autistic children. It will be useful to determine whether therapies that seek to improve executive function skills in autistic individuals also positively influence their social/communication and language abilities. Lay Abstract Executive functioning describes a set of cognitive processes that affect thinking and behavior. Past research has shown that autistic individuals often have delays in the acquisition of executive function abilities. Our study explored how differences in executive function and attention abilities relate to social abilities and communication/language in 180 young autistic children. Data were gathered via caregiver report (questionnaires/interviews) and an assessment of vocabulary skills. The ability to sustain attention to a dynamic video was measured via eye tracking. We found that children with higher levels of executive function skills demonstrated lower levels of social pragmatic problems, a measure of having difficulties in social contexts. Furthermore, children who were able to sustain their attention longer to the video displayed higher levels of expressive language. Our results emphasize the importance of executive function and attention skills across multiple areas of functioning in autistic children, in particular those that involve language and social communication.

ImportanceAutism detection early in childhood is critical to ensure that autistic children and their families have access to early behavioral support. Early correlates of autism documented in electronic health records (EHRs) during routine care could allow passive, predictive model-based monitoring to improve the accuracy of early detection.ObjectiveTo quantify the predictive value of early autism detection models based on EHR data collected before age 1 year.Design, Setting, and ParticipantsThis retrospective diagnostic study used EHR data from children seen within the Duke University Health System before age 30 days between January 2006 and December 2020. These data were used to train and evaluate L2-regularized Cox proportional hazards models predicting later autism diagnosis based on data collected from birth up to the time of prediction (ages 30-360 days). Statistical analyses were performed between August 1, 2020, and April 1, 2022.Main Outcomes and MeasuresPrediction performance was quantified in terms of sensitivity, specificity, and positive predictive value (PPV) at clinically relevant model operating thresholds.ResultsData from 45 080 children, including 924 (1.5%) meeting autism criteria, were included in this study. Model-based autism detection at age 30 days achieved 45.5% sensitivity and 23.0% PPV at 90.0% specificity. Detection by age 360 days achieved 59.8% sensitivity and 17.6% PPV at 81.5% specificity and 38.8% sensitivity and 31.0% PPV at 94.3% specificity.Conclusions and RelevanceIn this diagnostic study of an autism screening test, EHR-based autism detection achieved clinically meaningful accuracy by age 30 days, improving by age 1 year. This automated approach could be integrated with caregiver surveys to improve the accuracy of early autism screening.

Introduction Sleep problems are common, impairing correlates of ADHD. These associations may emerge as early as toddlerhood, suggesting sleep-focused interventions in early development may improve ADHD-related outcomes. However, previous research has been limited by cross-sectional methodology and small, clinically-referred samples. Thus, relationships between sleep and ADHD symptoms over time and in non-clinical samples remain unknown. The current study evaluated concurrent and longitudinal relationships between sleep problems and ADHD symptoms in early childhood in a large, non-clinical sample. Methods Children were recruited from primary care clinics at an academic medical center and assessed at two time points: T1 (n=1806, mean age: 20 months) and T2 (n=646, mean age: 37 months). Sleep problems and ADHD symptoms were evaluated via the caregiver-reported Child Behavior Checklist (CBCL) Sleep and DSM-Oriented ADHD scales, respectively. At T2, caregivers also completed the ADHD Rating Scale (ADHD-RS), which includes inattention and hyperactive/impulsive subscales. Associations between sleep and ADHD were examined using Pearson correlations at each time point. Stability of and longitudinal relationships between sleep and ADHD symptoms were also examined. Results Greater sleep problems were correlated with elevated CBCL ADHD at T1 (r=.40, p Conclusion Sleep disturbances and ADHD symptoms were bi-directionally associated in a non-clinical sample of young children recruited in primary care. Results are consistent with conceptualization of ADHD as a 24-hour disorder and suggest that incorporating behavioral sleep techniques into empirically-based ADHD treatments may improve clinical outcomes for young children displaying ADHD symptoms. Future research may focus on 1) optimizing identification of toddlers and preschoolers at risk for sleep problems and ADHD and 2) developing interventions that can be delivered in primary care settings. Support (If Any) P50HD093074 , K23MH108704