Factors Associated with Caregivers' Decisions to Pursue a Diagnostic Evaluation After a Positive Autism Screen in Primary Care.

PURPOSE: We sought to understand whether a child's sex, age, race, ethnicity, caregiver education, family income, and/or number of endorsed autism signs are associated with a caregiver's decision to pursue an autism diagnostic evaluation after their child received a positive autism screen. METHODS: 129 children, 17-30 months, received a positive autism screen on the Modified Checklist for Autism in Toddlers-Revised with Follow-Up, and all caregivers were offered ready access to a diagnostic evaluation by a trained professional in English or Spanish at no cost. RESULTS: 88 children received an evaluation and 41 did not. The likelihood of receiving an evaluation was associated with the child's race. Only 58.1% of Black children were evaluated, compared to 80% of Hispanic/Latino and 88.5% of White children. Children of Spanish-speaking caregivers showed high rates of evaluation completion (85.7%). Children who were evaluated versus were not evaluated did not significantly differ in terms of child's sex, number of autism signs endorsed by the caregiver, caregiver's education and preferred language (English versus Spanish), or household income. CONCLUSION: Even though the present study removed many common barriers to receiving a timely diagnostic evaluation, caregivers of Black children were less likely to pursue an autism diagnostic evaluation for their child. Future research is needed to understand the needs and perspectives of Black families to promote engagement in clinical care and reduce disparities in receiving a timely autism diagnosis which is important for accessing supports and services that can improve children's outcomes.

Integrating tailored approaches in perioperative care strategies for neurodivergent individuals.

Neurodivergent (ND) individuals exhibit variations in communication, behaviors, and cognition, which present both opportunities and challenges in healthcare settings. Anesthesiologists can offer personalized and compassionate care to ND patients throughout the surgical process. Yet, often, there is limited knowledge of the specific actions that anesthesiologists can take to build a healthcare environment that fully recognizes and meets the unique needs of ND patients. This document highlights the importance of integrating tailored communication and supportive strategies throughout the distinct stages of perioperative and intraoperative care.

Clinician-caregiver informant discrepancy is associated with sex, diagnosis age, and intervention use among autistic children.

LAY ABSTRACT: In some cases, a clinician's perceptions of a child's autism-related behaviors are not the same as the child's caregiver's perceptions. Identifying how these discrepancies relate to the characteristics of the child is critical for ensuring that diagnosis procedures are unbiased and suitable for all children. This study examined whether discrepancies between clinician and caregiver reports of autism features related to the child's sex at birth. We also explored how the discrepancies related to the age at which the child received their autism diagnosis and how much intervention they received. We found that clinicians rated autism features higher than caregivers for boys and rated autism features lower than caregivers for girls. In addition, lower clinician relative to parent ratings was related to being diagnosed at an older age and receiving less intervention. These findings suggest that there is more to learn about the presentation of autism-related behaviors in girls. When caregiver and clinician ratings of autism features do not align, it may be important to consider caregivers' ratings to obtain a more accurate picture of the child's autism features and the support they may need.

Behavioral characteristics of toddlers later identified with an autism diagnosis, ADHD symptoms, or combined autism and ADHD symptoms.

BACKGROUND: Autism commonly co-occurs with attention-deficit/hyperactivity disorder (ADHD), but less is known regarding how ADHD symptoms impact the early presentation of autism. This study examined early behavioral characteristics of a community sample of toddlers later identified with autism diagnosis, ADHD symptoms, combined autism and ADHD symptoms, or neither condition. METHODS: Participants were 506 toddlers who were part of a longitudinal study of children's behavioral development. Parents completed questionnaires about their children's behavior at two time points. Four groups were identified based on study measures or medical record: autism diagnosis (n = 45), elevated ADHD symptoms (n = 70), autism and ADHD symptoms (n = 30), or neurotypical development (n = 361). Relationships between early parent report of autism- and ADHD-related behaviors, social-emotional and behavioral functioning, and caregiver experience and subsequent group designation were evaluated with adjusted linear regression models controlling for sex. RESULTS: Significant group differences were found in measures of autism-related behaviors, ADHD-related behaviors, externalizing and internalizing behaviors, and parent support needs (p < .0001). Pairwise comparisons indicated toddlers later identified with combined autism diagnosis and ADHD symptoms had higher levels of autism-related behaviors, externalizing and internalizing behaviors, and autism-related parent support needs compared to the other groups. Toddlers with subsequent elevated ADHD symptoms or combined autism diagnosis and ADHD symptoms exhibited similar levels of ADHD-related behaviors, while both groups displayed more ADHD-related behaviors than toddlers subsequently identified with autism or those with neither condition. CONCLUSIONS: In this community sample, toddlers for whom combined autism diagnosis and ADHD symptoms were subsequently identified showed a distinct presentation characterized by higher early autism-related behaviors, broader behavioral concerns, and higher parent support needs. Presence of ADHD symptoms (alone or in combination with autism) was associated with higher parent-reported ADHD-related behaviors during toddlerhood. Results indicate that ADHD-related behaviors are manifest by toddlerhood, supporting screening for both autism and ADHD during early childhood.

Spatiotemporal Eye Movement Dynamics Reveal Altered Face Prioritization in Early Visual Processing Among Autistic Children.

BACKGROUND: Reduced social attention - looking at faces - is one of the most common manifestations of social difficulty in autism central to social development. Although reduced social attention is well-characterized in autism, qualitative differences in how social attention unfolds across time remains unknown. METHODS: We used a computational modeling (i.e., hidden Markov modeling) approach to assess and compare the spatiotemporal dynamics of social attention in a large, well-characterized sample of autistic (n = 280) and neurotypical (n = 120) children (ages 6-11) that completed three social eye-tracking assays across three longitudinal time points (Baseline, 6 weeks, 24 weeks). RESULTS: Our analysis supported the existence of two common eye movement patterns that emerged across three ET assays. A focused pattern was characterized by small face regions of interest, which had high probability of capturing fixations early in visual processing. In contrast, an exploratory pattern was characterized by larger face regions of interest, with lower initial probability of fixation, and more non-social regions of interest. In the context of social perception, autistic children showed significantly more exploratory eye movement patterns than neurotypical children across all social perception assays and all three longitudinal time points. Eye movement patterns were associated with clinical features of autism, including adaptive function, face recognition, and autism symptom severity. CONCLUSIONS: Decreased likelihood of precisely looking to faces early in social visual processing may be an important feature of autism that was associated with autism-related symptomology and may reflect less visual sensitivity to face information.

Proximity analysis of native proteomes reveals phenotypic modifiers in a mouse model of autism and related neurodevelopmental conditions.

One of the main drivers of autism spectrum disorder is risk alleles within hundreds of genes, which may interact within shared but unknown protein complexes. Here we develop a scalable genome-editing-mediated approach to target 14 high-confidence autism risk genes within the mouse brain for proximity-based endogenous proteomics, achieving the identification of high-specificity spatial proteomes. The resulting native proximity proteomes are enriched for human genes dysregulated in the brain of autistic individuals, and reveal proximity interactions between proteins from high-confidence risk genes with those of lower-confidence that may provide new avenues to prioritize genetic risk. Importantly, the datasets are enriched for shared cellular functions and genetic interactions that may underlie the condition. We test this notion by spatial proteomics and CRISPR-based regulation of expression in two autism models, demonstrating functional interactions that modulate mechanisms of their dysregulation. Together, these results reveal native proteome networks in vivo relevant to autism, providing new inroads for understanding and manipulating the cellular drivers underpinning its etiology.

A conditional multi-label model to improve prediction of a rare outcome: An illustration predicting autism diagnosis.

OBJECTIVE: This study aimed to develop a novel approach using routinely collected electronic health records (EHRs) data to improve the prediction of a rare event. We illustrated this using an example of improving early prediction of an autism diagnosis, given its low prevalence, by leveraging correlations between autism and other neurodevelopmental conditions (NDCs). METHODS: To achieve this, we introduced a conditional multi-label model by merging conditional learning and multi-label methodologies. The conditional learning approach breaks a hard task into more manageable pieces in each stage, and the multi-label approach utilizes information from related neurodevelopmental conditions to learn predictive latent features. The study involved forecasting autism diagnosis by age 5.5 years, utilizing data from the first 18 months of life, and the analysis of feature importance correlations to explore the alignment within the feature space across different conditions. RESULTS: Upon analysis of health records from 18,156 children, we are able to generate a model that predicts a future autism diagnosis with moderate performance (AUROC=0.76). The proposed conditional multi-label method significantly improves predictive performance with an AUROC of 0.80 (p < 0.001). Further examination shows that both the conditional and multi-label approach alone provided marginal lift to the model performance compared to a one-stage one-label approach. We also demonstrated the generalizability and applicability of this method using simulated data with high correlation between feature vectors for different labels. CONCLUSION: Our findings underscore the effectiveness of the developed conditional multi-label model for early prediction of an autism diagnosis. The study introduces a versatile strategy applicable to prediction tasks involving limited target populations but sharing underlying features or etiology among related groups.

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

OBJECTIVE: Electroencephalography (EEG) measures of visual evoked potentials (VEPs) provide a targeted approach for investigating neural circuit dynamics. This study separately analyses phase-locked (evoked) and non-phase-locked (induced) gamma responses within the VEP to comprehensively investigate circuit differences in autism. METHODS: We analyzed VEP data from 237 autistic and 114 typically developing (TD) children aged 6-11, collected through the Autism Biomarkers Consortium for Clinical Trials (ABC-CT). Evoked and induced gamma (30-90 Hz) responses were separately quantified using a wavelet-based time-frequency analysis, and group differences were evaluated using a permutation-based clustering procedure. RESULTS: Autistic children exhibited reduced evoked gamma power but increased induced gamma power compared to TD peers. Group differences in induced responses showed the most prominent effect size and remained statistically significant after excluding outliers. CONCLUSIONS: Our study corroborates recent research indicating diminished evoked gamma responses in children with autism. Additionally, we observed a pronounced increase in induced power. Building upon existing ABC-CT findings, these results highlight the potential to detect variations in gamma-related neural activity, despite the absence of significant group differences in time-domain VEP components. SIGNIFICANCE: The contrasting patterns of decreased evoked and increased induced gamma activity in autistic children suggest that a combination of different EEG metrics may provide a clearer characterization of autism-related circuitry than individual markers alone.

Brief Report: Relationships Between Caregiver-Reported Externalizing and Internalizing Behaviors and the Modified Checklist for Autism in Toddlers.

PURPOSE: Early detection and intervention are associated with improved outcomes for autistic children. Thus, it is important to understand factors influencing early screening tools designed to detect autism. This study examined the relationship between caregiver-reported emotional and behavioral symptoms and children's scores on a commonly used autism screening questionnaire, the Modified Checklist for Autism in Toddlers-Revised with Follow-Up (M-CHAT-R/F). METHODS: Toddlers were recruited from four primary care clinics between 2018 and 2021. Their caregivers completed the M-CHAT-R/F as well as the Child Behavior Checklist (CBCL), a well-validated, normed measure of emotional and behavioral functioning. Correlational and group analyses were evaluated to examine relationships between CBCL scales and M-CHAT-R/F scores. RESULTS: 1765 toddlers were recruited for the study. CBCL scores for the internalizing, externalizing, autism, ADHD, and anxiety scales were all modestly positively correlated with M-CHAT-R/F scores. Compared to toddlers with elevated autism scale scores only, toddlers with elevations in both autism and ADHD/externalizing scales had higher M-CHAT-R/F scores. In contrast, no significant difference in scores were found between toddlers with elevated autism scale scores only compared to those with elevated scores on both autism and internalizing scales. CONCLUSION: Findings suggest that, for children with elevated autism behaviors, the presence of externalizing symptoms, including ADHD-related concerns, is associated with elevated scores on the M-CHAT-R/F. In contrast, internalizing symptoms did not show an association with elevated M-CHAT-R/F scores among toddlers with elevated autism-related behaviors. Interpretation of the M-CHAT-R/F should include consideration of co-occurring psychiatric conditions, especially externalizing conditions such as ADHD.

Modeling intra-individual inter-trial EEG response variability in autism.

Autism spectrum disorder (autism) is a prevalent neurodevelopmental condition characterized by early emerging impairments in social behavior and communication. EEG represents a powerful and non-invasive tool for examining functional brain differences in autism. Recent EEG evidence suggests that greater intra-individual trial-to-trial variability across EEG responses in stimulus-related tasks may characterize brain differences in autism. Traditional analysis of EEG data largely focuses on mean trends of the trial-averaged data, where trial-level analysis is rarely performed due to low neural signal to noise ratio. We propose to use nonlinear (shape-invariant) mixed effects (NLME) models to study intra-individual inter-trial EEG response variability using trial-level EEG data. By providing more precise metrics of response variability, this approach could enrich our understanding of neural disparities in autism and potentially aid the identification of objective markers. The proposed multilevel NLME models quantify variability in the signal's interpretable and widely recognized features (e.g., latency and amplitude) while also regularizing estimation based on noisy trial-level data. Even though NLME models have been studied for more than three decades, existing methods cannot scale up to large data sets. We propose computationally feasible estimation and inference methods via the use of a novel minorization-maximization (MM) algorithm. Extensive simulations are conducted to show the efficacy of the proposed procedures. Applications to data from a large national consortium find that children with autism have larger intra-individual inter-trial variability in P1 latency in a visual evoked potential (VEP) task, compared to their neurotypical peers.

Cognitive Disengagement Syndrome in Young Autistic Children, Children with ADHD, and Autistic Children with ADHD.

OBJECTIVE: Cognitive Disengagement Syndrome (CDS; previously called Sluggish Cognitive Tempo) refers to a constellation of cognitive and motor behaviors characterized by a predisposition toward mind wandering (cognitive subdomain) and slowed motor behavior (hypoactive). While there are a number of studies linking CDS traits to greater global impairment in children with attention-deficit/hyperactivity disorder (ADHD) and autistic children, there are few studies examining the prevalence and impact of CDS traits in autistic children with co-occurring ADHD (Autistic+ADHD). The current study explored CDS traits in autistic children with and without co-occurring ADHD, children with ADHD, and neurotypical children. METHODS: Participants were 196 children between 3- and 7-years-of-age comprising four groups: Neurotypical (N = 44), ADHD (N = 51), Autistic (N = 55), and Autistic+ADHD (N = 46). CDS traits, social and communication skills, repetitive behaviors, and sensory processing were all assessed via parent report. RESULTS: Children diagnosed with ADHD, autistic children, and Autistic+ADHD children exhibited similar levels of overall CDS traits. However, when explored separately, Autistic+ADHD children had higher cognitive CDS trait scores compared to children with ADHD alone. Both overall CDS traits and the cognitive subdomain were associated with greater social difficulties, particularly social withdrawal, higher levels of repetitive behaviors, and more sensory sensitivities, regardless of diagnosis. CONCLUSIONS: Findings suggest that CDS traits may be an additional factor directly impact functional outcomes in both autistic and ADHD children. As such, clinicians should be assessing CDS traits in addition to other clinical domains associated with ADHD and autism when developing intervention plans for young neurodiverse children.

Autistic Individuals Do Not Alter Visual Processing Strategy During Encoding Versus Recognition of Faces: A Hidden Markov Modeling Approach.

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. CONCLUSION: 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.

Autism Caregiver Coaching in Africa (ACACIA): Protocol for a type 1-hybrid effectiveness-implementation trial.

BACKGROUND: While early autism intervention can significantly improve outcomes, gaps in implementation exist globally. These gaps are clearest in Africa, where forty percent of the world's children will live by 2050. Task-sharing early intervention to non-specialists is a key implementation strategy, given the lack of specialists in Africa. Naturalistic Developmental Behavioral Interventions (NDBI) are a class of early autism intervention that can be delivered by caregivers. As a foundational step to address the early autism intervention gap, we adapted a non-specialist delivered caregiver coaching NDBI for the South African context, and pre-piloted this cascaded task-sharing approach in an existing system of care. OBJECTIVES: First, we will test the effectiveness of the caregiver coaching NDBI compared to usual care. Second, we will describe coaching implementation factors within the Western Cape Department of Education in South Africa. METHODS: This is a type 1 effectiveness-implementation hybrid design; assessor-blinded, group randomized controlled trial. Participants include 150 autistic children (18-72 months) and their caregivers who live in Cape Town, South Africa, and those involved in intervention implementation. Early Childhood Development practitioners, employed by the Department of Education, will deliver 12, one hour, coaching sessions to the intervention group. The control group will receive usual care. Distal co-primary outcomes include the Communication Domain Standard Score (Vineland Adaptive Behavior Scales, Third Edition) and the Language and Communication Developmental Quotient (Griffiths Scales of Child Development, Third Edition). Proximal secondary outcome include caregiver strategies measured by the sum of five items from the Joint Engagement Rating Inventory. We will describe key implementation determinants. RESULTS: Participant enrolment started in April 2023. Estimated primary completion date is March 2027. CONCLUSION: The ACACIA trial will determine whether a cascaded task-sharing intervention delivered in an educational setting leads to meaningful improvements in communication abilities of autistic children, and identify implementation barriers and facilitators. TRIAL REGISTRATION: NCT05551728 in Clinical Trial Registry (https://clinicaltrials.gov).

Prevalence of bias against neurodivergence-related terms in artificial intelligence language models.

Given the increasing role of artificial intelligence (AI) in many decision-making processes, we investigate the presence of AI bias towards terms related to a range of neurodivergent conditions, including autism, ADHD, schizophrenia, and obsessive-compulsive disorder (OCD). We use 11 different language model encoders to test the degree to which words related to neurodiversity are associated with groups of words related to danger, disease, badness, and other negative concepts. For each group of words tested, we report the mean strength of association (Word Embedding Association Test [WEAT] score) averaged over all encoders and find generally high levels of bias. Additionally, we show that bias occurs even when testing words associated with autistic or neurodivergent strengths. For example, embedders had a negative average association between words related to autism and words related to honesty, despite honesty being considered a common strength of autistic individuals. Finally, we introduce a sentence similarity ratio test and demonstrate that many sentences describing types of disabilities, for example, "I have autism" or "I have epilepsy," have even stronger negative associations than control sentences such as "I am a bank robber."

Addressing the Gap in Research Training in Child Psychiatry and Neurodevelopment.

Psychiatric and neurodevelopmental conditions in children are common, often co-occur, and can be highly impairing. Moreover, psychiatric disorders that typically do not fully manifest until adulthood, such as schizophrenia, have their roots in early development, with atypical brain and behavioral patterns arising well before a clinical diagnosis is made. The relevance of brain development to improving outcomes of psychiatric and neurodevelopmental conditions underscores the need to cultivate a pipeline of investigators with the necessary training to conduct rigorous, developmentally focused research.

Sex differences in the age of childhood autism diagnosis and the impact of co-occurring conditions.

Sex differences in the age of autism diagnosis during childhood have been documented consistently but remain poorly understood. In this study, we used electronic health records data from a diverse, academic medical center to quantify differences in the age of autism diagnosis between boys and girls and identify associations between the age of diagnosis and co-occurring neurodevelopmental, psychiatric, and medical conditions. An established computable phenotype was used to identify all autism diagnoses within the Duke University Health System between 2014 and 2021. Co-occurring neurodevelopmental and psychiatric diagnoses as well as visits to specific medical and supportive services were identified in the 2 years prior to the autism diagnosis. Cox proportional hazards models were fitted to quantify associations between diagnosis age and sex with and without controlling for the presence of each co-occurring diagnosis and visit type. Records from 1438 individuals (1142 boys and 296 girls) were included. Girls were more likely to be diagnosed either before age 3 ( χ 2 = 497.720, p < 0.001) or after age 11 ( χ 2 = 4.014, p = 0.047), whereas boys were more likely to be diagnosed between ages 3 and 11 ( χ 2 = 5.532, p = 0.019). Visits for anxiety ( χ 2 = 4.200, p = 0.040) and mood disorders ( χ 2 = 7.033, p = 0.008) were more common in girls and associated with later autism diagnosis (HR = 0.615, p < 0.001; and HR = 0.493, p < 0.001). Visits for otolaryngology were more common in boys and associated with an earlier autism diagnosis (HR = 1.691, p < 0.001). After controlling for these conditions, associations between sex and diagnosis age were reduced and not statistically significant. These results show that the age of autism diagnosis differs in girls compared to boys, but these differences were neutralized when controlling for co-occurring neurodevelopmental and psychiatric conditions prior to autism diagnosis. Understanding sex differences and the possible mediating role of other diagnoses may suggest targets for intervention to promote earlier and more equitable diagnosis.

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.

Prevalence and nature of prior developmental and medical concerns in toddlers who screen positive for autism in primary care.

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.

The Selective Social Attention task in children with autism spectrum disorder: Results from the Autism Biomarkers Consortium for Clinical Trials (ABC-CT) feasibility study.

The Selective Social Attention (SSA) task is a brief eye-tracking task involving experimental conditions varying along socio-communicative axes. Traditionally the SSA has been used to probe socially-specific attentional patterns in infants and toddlers who develop autism spectrum disorder (ASD). This current work extends these findings to preschool and school-age children. Children 4- to 12-years-old with ASD (N = 23) and a typically-developing comparison group (TD; N = 25) completed the SSA task as well as standardized clinical assessments. Linear mixed models examined group and condition effects on two outcome variables: percent of time spent looking at the scene relative to scene presentation time (%Valid), and percent of time looking at the face relative to time spent looking at the scene (%Face). Age and IQ were included as covariates. Outcome variables' relationships to clinical data were assessed via correlation analysis. The ASD group, compared to the TD group, looked less at the scene and focused less on the actress' face during the most socially-engaging experimental conditions. Additionally, within the ASD group, %Face negatively correlated with SRS total T-scores with a particularly strong negative correlation with the Autistic Mannerism subscale T-score. These results highlight the extensibility of the SSA to older children with ASD, including replication of between-group differences previously seen in infants and toddlers, as well as its ability to capture meaningful clinical variation within the autism spectrum across a wide developmental span inclusive of preschool and school-aged children. The properties suggest that the SSA may have broad potential as a biomarker for ASD.