Early detection of autism using digital behavioral phenotyping.

Early detection of autism, a neurodevelopmental condition associated with challenges in social communication, ensures timely access to intervention. Autism screening questionnaires have been shown to have lower accuracy when used in real-world settings, such as primary care, as compared to research studies, particularly for children of color and girls. Here we report findings from a multiclinic, prospective study assessing the accuracy of an autism screening digital application (app) administered during a pediatric well-child visit to 475 (17-36 months old) children (269 boys and 206 girls), of which 49 were diagnosed with autism and 98 were diagnosed with developmental delay without autism. The app displayed stimuli that elicited behavioral signs of autism, quantified using computer vision and machine learning. An algorithm combining multiple digital phenotypes showed high diagnostic accuracy with the area under the receiver operating characteristic curve = 0.90, sensitivity = 87.8%, specificity = 80.8%, negative predictive value = 97.8% and positive predictive value = 40.6%. The algorithm had similar sensitivity performance across subgroups as defined by sex, race and ethnicity. These results demonstrate the potential for digital phenotyping to provide an objective, scalable approach to autism screening in real-world settings. Moreover, combining results from digital phenotyping and caregiver questionnaires may increase autism screening accuracy and help reduce disparities in access to diagnosis and intervention.

Early life adversity and psychopathology in preschoolers: mechanisms and moderators.

Developmental theories suggest that exposure to early life adversity (ELA) alters developing emotional response systems, predicting risk for psychopathology across the life span. The present study examines whether negative emotionality (NE), a trait-like measure of emotionality that develops during early childhood, mediates the association between ELA and psychopathology in a representative sample of 917 preschoolers (Mage = 3.84). Additionally, we explored whether cognitive control, which supports attentional focusing and inhibition and has been identified as a transdiagnostic protective factor, moderates the impact of heightened emotionality following adversity on psychopathology risk. We utilized parent report of adversity, psychopathology, and NE and parent report and task-based measures of cognitive control. Structural equation modeling of cross-sectional data revealed that NE partially mediated the link between ELA and psychopathology symptoms. Moreover, parent-reported cognitive control buffered this link such that the effect of ELA on psychopathology through NE was stronger in children with low versus high cognitive control. These results identify elevated NE as one mechanism linking ELA and psychopathology, specifically among children with poorer top-down control, informing our understanding of key risk and protective factors among adversity-exposed children.

Automated Video Tracking of Autistic Children's Movement During Caregiver-Child Interaction: An Exploratory Study.

Objective, quantitative measures of caregiver-child interaction during play are needed to complement caregiver or examiner ratings for clinical assessment and tracking intervention responses. In this exploratory study, we examined the feasibility of using automated video tracking, Noldus EthoVision XT, to measure 159 2-to-7-year-old autistic children's patterns of movement during play-based, caregiver-child interactions and examined their associations with standard clinical measures and human observational coding of caregiver-child joint engagement. Results revealed that autistic children who exhibited higher durations and velocity of movement were, on average, younger, had lower cognitive abilities, greater autism-related features, spent less time attending to the caregiver, and showed lower levels of joint engagement. After adjusting for age and nonverbal cognitive abilities, we found that children who remained in close proximity to their caregiver were more likely to engage in joint engagement that required support from the caregiver. These findings suggest that video tracking offers promise as a scalable, quantitative, and relevant measure of autism-related behaviors.

Relationship between quantitative digital behavioral features and clinical profiles in young autistic children.

Early behavioral markers for autism include differences in social attention and orienting in response to one's name when called, and differences in body movements and motor abilities. More efficient, scalable, objective, and reliable measures of these behaviors could improve early screening for autism. This study evaluated whether objective and quantitative measures of autism-related behaviors elicited from an app (SenseToKnow) administered on a smartphone or tablet and measured via computer vision analysis (CVA) are correlated with standardized caregiver-report and clinician administered measures of autism-related behaviors and cognitive, language, and motor abilities. This is an essential step in establishing the concurrent validity of a digital phenotyping approach. In a sample of 485 toddlers, 43 of whom were diagnosed with autism, we found that CVA-based gaze variables related to social attention were associated with the level of autism-related behaviors. Two language-related behaviors measured via the app, attention to people during a conversation and responding to one's name being called, were associated with children's language skills. Finally, performance during a bubble popping game was associated with fine motor skills. These findings provide initial support for the concurrent validity of the SenseToKnow app and its potential utility in identifying clinical profiles associated with autism. Future research is needed to determine whether the app can be used as an autism screening tool, can reliably stratify autism-related behaviors, and measure changes in autism-related behaviors over time.

Exploring Complexity of Facial Dynamics in Autism Spectrum Disorder.

Atypical facial expression is one of the early symptoms of autism spectrum disorder (ASD) characterized by reduced regularity and lack of coordination of facial movements. Automatic quantification of these behaviors can offer novel biomarkers for screening, diagnosis, and treatment monitoring of ASD. In this work, 40 toddlers with ASD and 396 typically developing toddlers were shown developmentally-appropriate and engaging movies presented on a smart tablet during a well-child pediatric visit. The movies consisted of social and non-social dynamic scenes designed to evoke certain behavioral and affective responses. The front-facing camera of the tablet was used to capture the toddlers' face. Facial landmarks' dynamics were then automatically computed using computer vision algorithms. Subsequently, the complexity of the landmarks' dynamics was estimated for the eyebrows and mouth regions using multiscale entropy. Compared to typically developing toddlers, toddlers with ASD showed higher complexity (i.e., less predictability) in these landmarks' dynamics. This complexity in facial dynamics contained novel information not captured by traditional facial affect analyses. These results suggest that computer vision analysis of facial landmark movements is a promising approach for detecting and quantifying early behavioral symptoms associated with ASD.

Associations of preschool reactive bed-sharing with sociodemographic factors, sleep disturbance, and psychopathology.

OBJECTIVE: To advance understanding of early childhood bed-sharing and its clinical significance, we examined reactive bed-sharing rates, sociodemographic correlates, persistence, and concurrent and longitudinal associations with sleep disturbances and psychopathology. METHODS: Data from a representative cohort of 917 children (mean age 3.8 years) recruited from primary pediatric clinics in a Southeastern city for a preschool anxiety study were used. Sociodemographics and diagnostic classifications for sleep disturbances and psychopathology were obtained using the Preschool Age Psychiatric Assessment (PAPA), a structured diagnostic interview administered to caregivers. A subsample of 187 children was re-assessed approximately 24.7 months after the initial PAPA interview. RESULTS: Reactive bed-sharing was reported by 38.4% of parents, 22.9% nightly and 15.5% weekly, and declined with age. At follow-up, 48.9% of nightly bed-sharers and 88.7% of weekly bed-sharers were no longer bed-sharing. Sociodemographics associated with nightly bed-sharing were Black and (combined) American Indian, Alaska Native and Asian race and ethnicity, low income and parent education less than high school. Concurrently, bed-sharing nightly was associated with separation anxiety and sleep terrors; bed-sharing weekly was associated with sleep terrors and difficulty staying asleep. No longitudinal associations were found between reactive bed-sharing and sleep disturbances or psychopathology after controlling for sociodemographics, baseline status of the outcome and time between interviews. CONCLUSIONS: Reactive bed-sharing is relatively common among preschoolers, varies significantly by sociodemographic factors, declines during the preschool years and is more persistent among nightly than weekly bed-sharers. Reactive bed-sharing may be an indicator of sleep disturbances and/or anxiety but there is no evidence that bed-sharing is an antecedent or consequence of sleep disturbances or psychopathology.

Distinct Associations of Deprivation and Threat With Alterations in Brain Structure in Early Childhood.

OBJECTIVE: The dimensional model of adversity and psychopathology hypothesizes deprivation and threat impact distinct neurobiological pathways, such as brain structure. This hypothesis has not been examined longitudinally or in young children. This study tested longitudinal associations between threat and deprivation measured in preschool and brain structure in childhood. It was hypothesized that threat would be associated with amygdala and hippocampal subcortical volume and deprivation would be associated with cortical thickness in association cortex. METHOD: The study included T1-weighted scans from 72 children (5-10 years old, 54.2% female participants). Threat was measured by the presence of domestic violence, sexual abuse, physical abuse, or neighborhood violence. Deprivation was measured by the presence of neglect. Associations of deprivation or threat with brain structure were examined controlling for other dimension (deprivation or threat) and nuisance covariates using whole-brain vertex-wise analyses. Subcortical volume was extracted, and the same associations were examined using multiple regression. RESULTS: Threat was associated with widespread decreases in cortical surface area across the prefrontal cortex and other regions. Threat was not associated with amygdala or hippocampal volume. Deprivation was associated with increased thickness in occipital cortex, insula, and cingulate. CONCLUSION: Results suggest distinct associations of deprivation and threat on brain structure in early childhood. Threat is associated with widespread differences in surface area, and deprivation is associated with differences in cortical thickness. These observations are consistent with work in adolescence and adulthood and reflect how dimensions of adversity differentially impact neural structure.

A tablet-based game for the assessment of visual motor skills in autistic children.

Increasing evidence suggests that early motor impairments are a common feature of autism. Thus, scalable, quantitative methods for measuring motor behavior in young autistic children are needed. This work presents an engaging and scalable assessment of visual-motor abilities based on a bubble-popping game administered on a tablet. Participants are 233 children ranging from 1.5 to 10 years of age (147 neurotypical children and 86 children diagnosed with autism spectrum disorder [autistic], of which 32 are also diagnosed with co-occurring attention-deficit/hyperactivity disorder [autistic+ADHD]). Computer vision analyses are used to extract several game-based touch features, which are compared across autistic, autistic+ADHD, and neurotypical participants. Results show that younger (1.5-3 years) autistic children pop the bubbles at a lower rate, and their ability to touch the bubble's center is less accurate compared to neurotypical children. When they pop a bubble, their finger lingers for a longer period, and they show more variability in their performance. In older children (3-10-years), consistent with previous research, the presence of co-occurring ADHD is associated with greater motor impairment, reflected in lower accuracy and more variable performance. Several motor features are correlated with standardized assessments of fine motor and cognitive abilities, as evaluated by an independent clinical assessment. These results highlight the potential of touch-based games as an efficient and scalable approach for assessing children's visual-motor skills, which can be part of a broader screening tool for identifying early signs associated with autism.

Complexity analysis of head movements in autistic toddlers.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Changes in the geometry and robustness of diffusion tensor imaging networks: Secondary analysis from a randomized controlled trial of young autistic children receiving an umbilical cord blood infusion.

Diffusion tensor imaging (DTI) has been used as an outcome measure in clinical trials for several psychiatric disorders but has rarely been explored in autism clinical trials. This is despite a large body of research suggesting altered white matter structure in autistic individuals. The current study is a secondary analysis of changes in white matter connectivity from a double-blind placebo-control trial of a single intravenous cord blood infusion in 2-7-year-old autistic children (1). Both clinical assessments and DTI were collected at baseline and 6 months after infusion. This study used two measures of white matter connectivity: change in node-to-node connectivity as measured through DTI streamlines and a novel measure of feedback network connectivity, Ollivier-Ricci curvature (ORC). ORC is a network measure which considers both local and global connectivity to assess the robustness of any given pathway. Using both the streamline and ORC analyses, we found reorganization of white matter pathways in predominantly frontal and temporal brain networks in autistic children who received umbilical cord blood treatment versus those who received a placebo. By looking at changes in network robustness, this study examined not only the direct, physical changes in connectivity, but changes with respect to the whole brain network. Together, these results suggest the use of DTI and ORC should be further explored as a potential biomarker in future autism clinical trials. These results, however, should not be interpreted as evidence for the efficacy of cord blood for improving clinical outcomes in autism. This paper presents a secondary analysis using data from a clinical trial that was prospectively registered with ClinicalTrials.gov(NCT02847182).

Adaptive Behavior in Young Autistic Children: Associations with Irritability and ADHD Symptoms.

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.

Association of adversity with psychopathology in early childhood: Dimensional and cumulative approaches.

BACKGROUND: The association between adversity and psychopathology in adolescents and adults is characterized by equifinality. These associations, however, have not been assessed during early childhood when psychopathology first emerges. Defining adversity using both dimensional and cumulative risk approaches, we examined whether specific types of adversity are differentially associated with psychopathology in preschool-aged children. METHODS: Measures of threat, deprivation, and total adversities (i.e., cumulative risk) were calculated based on parent-reported information for 755 2- to 5-year old children recruited from pediatric primary care clinics. Logistic regression was used to estimate cross-sectional associations between type of adversity and anxiety, depression, ADHD, and behavioral disorder diagnoses. RESULTS: Threat and cumulative risk exhibited independent associations with psychopathology. Threat was strongly related to behavioral disorders. Cumulative risk was consistently related to all psychopathologies. CONCLUSIONS: Using mutually adjusted models, we identified differential associations between threat and psychopathology outcomes in preschool-aged children. This selectivity may reflect different pathways through which adversity increases the risk for psychopathology during this developmentally important period. As has been observed at other ages, a cumulative risk approach also effectively identified the cumulative impact of all forms of adversity on most forms of psychopathology during early childhood.

Distance from Typical Scan Path When Viewing Complex Stimuli in Children with Autism Spectrum Disorder and its Association with Behavior.

Eye-tracking is often used to study attention in children with autism spectrum disorder (ASD). Previous research has identified multiple atypical patterns of attention in children with ASD based on areas-of-interest analysis. Fewer studies have investigated gaze path, a measure which is dependent on the dynamic content of the stimulus presented. Here, rather than looking at proportions of looking time to areas of interest, we calculated mean fixations frame-by-frame in a group of typically developing children (36 to 72 months) and determined the distance from those typical fixations for 155 children with ASD (27-95 months). Findings revealed that distance from the typical scan path among the children with ASD was associated with lower communication abilities and greater ASD symptomatology.

A Review of the Default Mode Network in Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder.

Functional magnetic resonance imaging (fMRI) has been widely used to examine the relationships between brain function and phenotypic features in neurodevelopmental disorders. Techniques such as resting-state functional connectivity (FC) have enabled the identification of the primary networks of the brain. One fMRI network, in particular, the default mode network (DMN), has been implicated in social-cognitive deficits in autism spectrum disorders (ASD) and attentional deficits in attention deficit hyperactivity disorder (ADHD). Given the significant clinical and genetic overlap between ASD and ADHD, surprisingly, no reviews have compared the clinical, developmental, and genetic correlates of DMN in ASD and ADHD and here we address this knowledge gap. We find that, compared with matched controls, ASD studies show a mixed pattern of both stronger and weaker FC in the DMN and ADHD studies mostly show stronger FC. Factors such as age, intelligence quotient, medication status, and heredity affect DMN FC in both ASD and ADHD. We also note that most DMN studies make ASD versus ADHD group comparisons and fail to consider ASD+ADHD comorbidity. We conclude, by identifying areas for improvement and by discussing the importance of using transdiagnostic approaches such as the Research Domain Criteria (RDoC) to fully account for the phenotypic and genotypic heterogeneity and overlap of ASD and ADHD.

Computer Vision Analysis for Quantification of Autism Risk Behaviors.

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.

Gastrointestinal problems are associated with increased repetitive behaviors but not social communication difficulties in young children with autism spectrum disorders.

LAY ABSTRACT: Individuals with autism spectrum disorder are more likely than typically developing individuals to experience a range of gastrointestinal abnormalities, including chronic diarrhea, constipation, food sensitivities, and abdominal pain. These gastrointestinal symptoms have been associated with higher levels of irritability and aggressive behavior, but less is known about their relationship with core autism spectrum disorder symptoms. We investigated the relationship between autism spectrum disorder symptom severity and gastrointestinal symptoms while accounting for three associated behavioral symptom domains (Irritability, Aggressiveness, and Specific Fears), in a sample of 176 children (140 males and 36 females) ages 2-7 years old with autism spectrum disorder. A large majority (93.2%) of the sample had at least one reported gastrointestinal symptom, and most (88.1%) participants had more than one gastrointestinal symptom. Various types of gastrointestinal symptoms were reported; the most common symptoms reported were constipation, food limits, gas/bloating, and stomach pain. After accounting for each associated behavioral symptom domain, repetitive behaviors and stereotypies were significantly associated with gastrointestinal symptom severity. Increased severity of autism spectrum disorder symptoms was correlated with increased gastrointestinal symptom severity. Social and communication difficulties were not significantly associated with gastrointestinal symptom severity after accounting for associated behavioral symptoms. Our findings replicate a previously described association between irritability and aggression and gastrointestinal symptoms. Furthermore, we found that repetitive behaviors, but not social or communication symptoms, are associated with gastrointestinal symptom severity, even after accounting for associated behavioral symptoms. This suggests that gastrointestinal symptoms may exacerbate repetitive behaviors, or vice versa, independent from other associated behavioral symptoms.

Digital Behavioral Phenotyping Detects Atypical Pattern of Facial Expression in Toddlers with Autism.

Commonly used screening tools for autism spectrum disorder (ASD) generally rely on subjective caregiver questionnaires. While behavioral observation is more objective, it is also expensive, time-consuming, and requires significant expertise to perform. As such, there remains a critical need to develop feasible, scalable, and reliable tools that can characterize ASD risk behaviors. This study assessed the utility of a tablet-based behavioral assessment for eliciting and detecting one type of risk behavior, namely, patterns of facial expression, in 104 toddlers (ASD N = 22) and evaluated whether such patterns differentiated toddlers with and without ASD. The assessment consisted of the child sitting on his/her caregiver's lap and watching brief movies shown on a smart tablet while the embedded camera recorded the child's facial expressions. Computer vision analysis (CVA) automatically detected and tracked facial landmarks, which were used to estimate head position and facial expressions (Positive, Neutral, All Other). Using CVA, specific points throughout the movies were identified that reliably differentiate between children with and without ASD based on their patterns of facial movement and expressions (area under the curves for individual movies ranging from 0.62 to 0.73). During these instances, children with ASD more frequently displayed Neutral expressions compared to children without ASD, who had more All Other expressions. The frequency of All Other expressions was driven by non-ASD children more often displaying raised eyebrows and an open mouth, characteristic of engagement/interest. Preliminary results suggest computational coding of facial movements and expressions via a tablet-based assessment can detect differences in affective expression, one of the early, core features of ASD. LAY SUMMARY: This study tested the use of a tablet in the behavioral assessment of young children with autism. Children watched a series of developmentally appropriate movies and their facial expressions were recorded using the camera embedded in the tablet. Results suggest that computational assessments of facial expressions may be useful in early detection of symptoms of autism.

Measuring robustness of brain networks in autism spectrum disorder with Ricci curvature.

Ollivier-Ricci curvature is a method for measuring the robustness of connections in a network. In this work, we use curvature to measure changes in robustness of brain networks in children with autism spectrum disorder (ASD). In an open label clinical trials, participants with ASD were administered a single infusion of autologous umbilical cord blood and, as part of their clinical outcome measures, were imaged with diffusion MRI before and after the infusion. By using Ricci curvature to measure changes in robustness, we quantified both local and global changes in the brain networks and their potential relationship with the infusion. Our results find changes in the curvature of the connections between regions associated with ASD that were not detected via traditional brain network analysis.

A Six-Minute Measure of Vocalizations in Toddlers with Autism Spectrum Disorder.

To improve early identification of autism spectrum disorder (ASD), we need objective, reliable, and accessible measures. To that end, a previous study demonstrated that a tablet-based application (app) that assessed several autism risk behaviors distinguished between toddlers with ASD and non-ASD toddlers. Using vocal data collected during this study, we investigated whether vocalizations uttered during administration of this app can distinguish among toddlers aged 16-31 months with typical development (TD), language or developmental delay (DLD), and ASD. Participant's visual and vocal responses were recorded using the camera and microphone in a tablet while toddlers watched movies designed to elicit behaviors associated with risk for ASD. Vocalizations were then coded offline. Results showed that (a) children with ASD and DLD were less likely to produce words during app administration than TD participants; (b) the ratio of syllabic vocalizations to all vocalizations was higher among TD than ASD or DLD participants; and (c) the rates of nonsyllabic vocalizations were higher in the ASD group than in either the TD or DLD groups. Those producing more nonsyllabic vocalizations were 24 times more likely to be diagnosed with ASD. These results lend support to previous findings that early vocalizations might be useful in identifying risk for ASD in toddlers and demonstrate the feasibility of using a scalable tablet-based app for assessing vocalizations in the context of a routine pediatric visit. LAY SUMMARY: Although parents often report symptoms of autism spectrum disorder (ASD) in infancy, we are not yet reliably diagnosing ASD until much later in development. A previous study tested a tablet-based application (app) that recorded behaviors we know are associated with ASD to help identify children at risk for the disorder. Here we measured how children vocalize while they watched the movies presented on the tablet. Children with ASD were less likely to produce words, less likely to produce speechlike sounds, and more likely to produce atypical sounds while watching these movies. These measures, combined with other behaviors measured by the app, might help identify which children should be evaluated for ASD. Autism Res 2020, 13: 1373-1382. © 2020 International Society for Autism Research, Wiley Periodicals, Inc.

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.