Early developmental milestone clusters of autistic children based on electronic health records

Autistic children vary in symptoms, co-morbidities, and response to interventions. This study aimed to identify clusters of autistic children with a distinct pattern of attaining early developmental milestones (EDMs). The clustering of 5836 autistic children was based on the attainment of 43 gross motor, fine motor, language, and social developmental milestones during the first 3 years of life as recorded in baby wellness visits. K-means cluster analysis detected four EDM clusters: mild (n = 1686); moderate (n = 1691); severe (n = 2265); and global (n = 194). The most prominent cluster differences were in the language domain. The global cluster showed earlier and greater developmental delay across domains, unique early gross motor delays, and more were born preterm via cesarean section. The severe cluster had poor language development prominently in the second year of life, and later fine motor delays. Moderate cluster had mainly language delays in the third year of life. The mild cluster mostly passed milestones. EDM clusters differed demographically, with higher socioeconomic status in mild cluster and lowest in global cluster. However, the severe cluster had more immigrant and non-Jewish mothers followed by the moderate cluster. The rates of parental concerns and provider developmental referrals were significantly higher in the global, followed by the severe, moderate, and mild EDM clusters. Autistic children's language and motor delay in the first 3 years can be grouped by common magnitude and onset profiles as distinct groups that may link to specific etiologies (like prematurity or genetics) and specific intervention programs. Early autism screening should be tailored to these different developmental profiles.

Utilising Behavioural and Sensory Profiles and Associated Perinatal Factors to Identify Meaningful Subgroups in Autism Spectrum Disorder

The heterogeneity of autism spectrum disorder (ASD) clinically and aetiologically hinders intervention matching and prediction of outcomes. This study investigated if the behavioural, sensory, and perinatal factor profiles of autistic children could be used to identify distinct subgroups. Participants on the autism spectrum aged 2 to 17 years and their families were sourced via the Australian Autism Biobank (AAB). Latent class analysis was used to identify subgroups within this cohort, utilising twenty-six latent variables representing child's behavioural and sensory features and perinatal factors. Four distinct subgroups within the sample (n = 1168) distinguished by sensory and behavioural autism traits and exposure to perinatal determinants were identified. Class 2 and Class 4, which displayed the greatest behavioural and sensory impairment respectively, were associated with the highest perinatal factor exposure. Class 1, labelled "Most behavioural concerns and moderate sensory and behavioural skills concerns" had mixed exposure to perinatal determinants while Class 3, named "Least sensory and behavioural skills concerns" had the least perinatal determinant exposure, indicating a directly proportional correlation between severity of clinical features and perinatal factor exposure. Additionally, association between specific exposures such as maternal mental illness in Class 1 and significant behavioural concerns was recognised. Identifying distinct subgroups among autistic children can lead to development of targeted interventions and supports. Close monitoring of children exposed to specific perinatal determinants for developmental differences could assist early intervention and supports.

Brief Report: Atypical Temporal Sensitivity in Coarticulation in Autism: Evidence from Sibilant-Vowel Interaction in Cantonese

PURPOSE

Atypicalities in the prosodic aspects of speech are commonly considered in clinical assessments of autism. While there is an increasing number of studies using objective measures to assess prosodic deficits, such studies have primarily focused on the intonational and rhythmic aspects of prosody. Little is known about prosodic deficits that are reflected at the segmental level, despite the strong connection between prosody and segmental realization. This study examines the nature of sibilant-vowel coarticulation among male adult native speakers of Cantonese with autism and those without.

METHODS

Fifteen Cantonese-speaking autistic (ASD) adults (mean age = 25 years) and 23 neuro-typical (NT) adults (mean age = 20 years) participated. Each participant read aloud 42 syllables with a sibilant onset in carrier phrase. Spectral means and variance, skewness and kurtosis were measured, and regressed by vocalic rounding (rounded vs. unrounded), cohort (ASD vs. NT), sibilant duration, and articulation rate.

RESULTS

While neurotypical participants exhibit sibilant-vowel coarticulation that are sensitive to variation in sibilant duration, autistic participants show no sensitivity to segmental temporal changes.

CONCLUSION

These findings point to the potential for atypicalities in prosody-segment interaction as an important characteristic of autistic speech.

A meta-analysis of cognitive flexibility in autism spectrum disorder

Cognitive flexibility is a fundamental process that underlies adaptive behaviour in response to environmental change. Studies examining the profile of cognitive flexibility in autism spectrum disorder (ASD) have reported inconsistent findings. To address whether difficulties with cognitive flexibility are characteristic of autism, we conducted a random-effects meta-analysis and employed subgroup analyses and meta-regression to assess the impact of relevant moderator variables such as task, outcomes, and age. Fifty-nine studies were included and comprised of 2122 autistic individuals without intellectual disabilities and 2036 neurotypical controls, with an age range of 4 to 85 years. The results showed that autistic individuals have greater difficulties with cognitive flexibility, with an overall statistically significant small to moderate effect size. Subgroup analyses revealed a significant difference between task outcomes, with perseverative errors obtaining the largest effect size. In summary, the present meta-analysis highlights the existence of cognitive flexibility difficulties in autistic people, in the absence of learning disabilities, but also that this profile is characterised by substantial heterogeneity. Potential contributing factors are discussed.

Examining Phenotypical Heterogeneity and its Underlying Factors in Gesture Skills of Chinese Autistic Children: Clustering Analysis

PURPOSE

The heterogeneity of autism is well documented, but few studies have studied the heterogeneity of gesture production ability in autistic children. The present study aimed to identify subgroups of autistic children who displayed heterogeneous gesture production abilities and explore the underlying factors, including autism characteristics, intellectual ability, and language ability, that were associated with the heterogeneity.

METHODS

A total of 65 Chinese autistic children (mean age = 5;3) participated. Their autism characteristics and intellectual ability were assessed by standardized measurements. Language output and gesture production were captured from a parent-child interaction task.

RESULTS

We conducted a hierarchical cluster analysis and identified four distinct clusters. Cluster 1 and Cluster 2 both had low gesture production whereas Cluster 3 and Cluster 4 had high gesture production. Both Clusters 1 and 2 had relatively strong autism characteristics, in comparison to Clusters 3 and 4.

CONCLUSIONS

Our findings revealed that children with stronger autism characteristics may gesture less often than those with weaker characteristics. However, the relationship between language ability and intellectual ability and gesture production was not clear. These findings shed light on the directions of intervention on gesture production for autistic children, especially those with stronger autism characteristics.

Dissecting Symptom-linked Dimensions of Resting-State Electroencephalographic Functional Connectivity in Autism with Contrastive Learning

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by social interaction deficits, communication difficulties, and restricted/repetitive behaviors or fixated interests. Despite its high prevalence, development of effective therapy for ASD is hindered by its symptomatic and neurophysiological heterogeneities. To collectively dissect the ASD heterogeneity in neurophysiology and symptoms, we develop a new analytical framework combining contrastive learning and sparse canonical correlation analysis to identify resting-state EEG connectivity dimensions linked to ASD behavioral symptoms within 392 ASD samples. Two dimensions are successfully identified, showing significant correlations with social/communication deficits (r = 0.70) and restricted/repetitive behaviors (r = 0.45), respectively. We confirm the robustness of these dimensions through cross-validation and further demonstrate their generalizability using an independent dataset of 223 ASD samples. Our results reveal that the right inferior parietal lobe is the core region displaying EEG activity associated with restricted/repetitive behaviors, and functional connectivity between the left angular gyrus and the right middle temporal gyrus is a promising biomarker of social/communication deficits. Overall, these findings provide a promising avenue to parse ASD heterogeneity with high clinical translatability, paving the way for treatment development and precision medicine for ASD.

Comparing the effectiveness of robot-based to human-based intervention in improving joint attention in autistic children

Background

Children with autism have impairments in initiation of joint attention (IJA) and response to joint attention (RJA).

Aims

The present study compared the learning effectiveness of robot-based intervention (RBI) with that of content-matched human-based intervention (HBI) in improving joint attention (JA). We examined whether RBI would enhance RJA, in comparison to HBI. We also examined whether RBI would increase IJA, in comparison to HBI.

Methods and procedures

Thirty-eight Chinese-speaking children with autism aged 6 to 9 years were randomly assigned to RBI and HBI groups. Before intervention, their autism severity, cognitive abilities, and language skills were assessed. Each child received six 30-min training sessions over 3 weeks. During training, he/she watched one or two robot/human dramas twice where two robot/human actors demonstrated eye contact and RJA.

Outcomes and results

Children in the RBI (but not HBI) group produced more RJA and IJA behaviors in the delayed post-test than in the pre-test. Parents of the RBI children rated the program more positively than those of the HBI children.

Conclusions and implications

RBI may be more effective than HBI in promoting JA in autistic children with high support needs. Our findings shed light on the application of robot dramas in enhancing social communication skills.

Developmental and behavioral groupings can predict changes in adaptive behavior over time in young children with neurodevelopmental disorders

The heterogeneity within, and the overlap between, diagnostic categories for neurodevelopmental disorders (NDDs) remain poorly understood. Developmental trajectories may diverge among children with the same diagnosis, who may also respond very differently to treatment. In a previous study, we used statistical clustering methods in a sample of 194 preschoolers who were referred for NDD assessment. We identified three distinct subgroups based on multiple developmental and behavioral variables. The present study aimed to identify: (1) early developmental markers at the surveillance and screening period that are predictive of subgroup membership at the diagnostic period (i.e., around age 5), (2) associations between subgroups and the evolution of adaptive behavior over the course of two years, and (3) predictors of adaptive behavior change. Subgroup membership was the only significant predictor of adaptive behavior change over time, which suggests that a clustering method based on developmental and behavioral profiles may be useful in treatment planning.

Characterizing the ASD-ADHD phenotype: measurement structure and invariance in a clinical sample

BACKGROUND

Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) have considerable overlap, supporting the need for a dimensional framework that examines neurodevelopmental domains which cross traditional diagnostic boundaries. In the following study, we use factor analysis to deconstruct the ASD-ADHD phenotype into its underlying phenotypic domains and test for measurement invariance across adaptive functioning, age, gender and ASD/ADHD clinical diagnoses.

METHODS

Participants included children and youth (aged 3-20 years) with a clinical diagnosis of ASD (n = 727) or ADHD (n = 770) for a total of 1,497 participants. Parents of these children completed the Social Communication Questionnaire (SCQ), a measure of autism symptoms, and the Strengths and Weaknesses of ADHD and Normal Behaviour (SWAN) questionnaire, a measure of ADHD symptoms. An exploratory factor analysis (EFA) was performed on combined SCQ and SWAN items. This was followed by a confirmatory factor analysis (CFA) and tests of measurement invariance.

RESULTS

EFA revealed a four-factor solution (inattention, hyperactivity/impulsivity, social-communication, and restricted, repetitive, behaviours and interests (RRBI)) and a CFA confirmed good model fit. This solution also showed good model fit across subgroups of interest.

CONCLUSIONS

Our study shows that a combined ASD-ADHD phenotype is characterized by two latent ASD domains (social communication and RRBIs) and two latent ADHD domains (inattention and hyperactivity/impulsivity). We established measurement invariance of the derived measurement model across adaptive functioning, age, gender and ASD/ADHD diagnoses.

Contrastive machine learning reveals the structure of neuroanatomical variation within autism

Autism spectrum disorder (ASD) is highly heterogeneous. Identifying systematic individual differences in neuroanatomy could inform diagnosis and personalized interventions. The challenge is that these differences are entangled with variation because of other causes: individual differences unrelated to ASD and measurement artifacts. We used contrastive deep learning to disentangle ASD-specific neuroanatomical variation from variation shared with typical control participants. ASD-specific variation correlated with individual differences in symptoms. The structure of this ASD-specific variation also addresses a long-standing debate about the nature of ASD: At least in terms of neuroanatomy, individuals do not cluster into distinct subtypes; instead, they are organized along continuous dimensions that affect distinct sets of regions.

Mapping the Heterogeneous Brain Structural Phenotype of Autism Spectrum Disorder Using the Normative Model

BACKGROUND

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by substantial clinical and biological heterogeneity. Quantitative and individualized metrics for delineating the heterogeneity of brain structure in ASD are still lacking. Likewise, the extent to which brain structural metrics of ASD deviate from typical development (TD) and whether deviations can be used for parsing brain structural phenotypes of ASD is unclear.

METHODS

T1-weighted magnetic resonance imaging data from the Autism Brain Imaging Data Exchange (ABIDE) II (n_TD = 564) were used to generate a normative model to map brain structure deviations of ABIDE I subjects (n_TD = 560, n_ASD = 496). Voxel-based morphometry was used to compute gray matter volume. Non-negative matrix factorization was employed to decompose the gray matter matrix into 6 factors and weights. These weights were used for normative modeling to estimate the factor deviations. Then, clustering analysis was used to identify ASD subtypes.

RESULTS

Compared with TD, ASD showed increased weights and deviations in 5 factors. Three subtypes with distinct neuroanatomical deviation patterns were identified. ASD subtype 1 and subtype 3 showed positive deviations, whereas ASD subtype 2 showed negative deviations. Distinct clinical manifestations in social communication deficits were identified among the three subtypes.

CONCLUSIONS

Our findings suggest that individuals with ASD have heterogeneous deviation patterns in brain structure. The results highlight the need to test for subtypes in neuroimaging studies of ASD. This study also presents a framework for understanding neuroanatomical heterogeneity in this increasingly prevalent neurodevelopmental disorder.

Heterogeneity of social cognitive performance in autism and schizophrenia

Autistic adults and those with schizophrenia (SCZ) demonstrate similar levels of reduced social cognitive performance at the group level, but it is unclear whether these patterns are relatively consistent or highly variable within and between the two conditions. Seventy-two adults with SCZ (52 male, M_age  = 28.2 years) and 94 with diagnoses on the autism spectrum (83 male, M_age  = 24.2 years) without intellectual disability completed a comprehensive social cognitive battery. Latent profile analysis identified four homogeneous subgroups that were compared on their diagnosis, independent living skills, neurocognition, and symptomatology. Two groups showed normative performance across most social cognitive tasks but were differentiated by one having significantly higher hostility and blaming biases. Autistic participants were more likely to demonstrate fully normative performance (46.8%) than participants with SCZ, whereas normative performance in SCZ was more likely to co-occur with increased hostility and blaming biases (36.1%). Approximately 43% of participants in the full sample were classified into the remaining two groups showing low or very low performance. These participants tended to perform worse on neurocognitive tests and have lower IQ and fewer independent living skills. The prevalence of low performance on social cognitive tasks was comparable across clinical groups. However, nearly half of autistic participants demonstrated normative social cognitive performance, challenging assumptions that reduced social cognitive performance is inherent to the condition. Subgrouping also revealed a meaningful distinction between the clinical groups: participants with SCZ were more likely to demonstrate hostility biases than autistic participants, even when social cognitive performance was otherwise in the typical range. LAY SUMMARY: Social cognition refers to the perception and interpretation of social information. Previous research has shown that both autistic people and those with schizophrenia demonstrate reduced performance on traditional social cognitive tasks, which we replicate here at the group level. However, we also found that almost half of autistic participants performed in the normal range. Over a third of participants with schizophrenia did as well, but for them this performance was accompanied by a hostility bias not commonly found in the autistic sample. Taken together, findings challenge assumptions that difficulties in social cognition are a uniform characteristic of these clinical conditions in those without intellectual disability.

Features that best define the heterogeneity and homogeneity of autism in preschool-age children: A multisite case-control analysis replicated across two independent samples

The heterogeneous nature of children with symptoms of autism spectrum disorder (ASD) makes it difficult to identify risk factors and effective treatment options. We sought to identify behavioral and developmental features that best define the heterogeneity and homogeneity in 2-5-year-old children classified with ASD and subthreshold ASD characteristics. Children were enrolled in a multisite case-control study of ASD. Detailed behavioral and developmental data were gathered by maternal telephone interview, parent-administered questionnaires, child cognitive evaluation, and ASD diagnostic measures. Participants with a positive ASD screen score or prior ASD diagnosis were referred for comprehensive evaluation. Children in the ASD group met study criteria based on this evaluation; children who did not meet study criteria were categorized as having subthreshold ASD characteristics. There were 1480 children classified as ASD (81.6% boys) and 594 children classified as having subthreshold ASD characteristics (70.2% boys) in the sample. Factors associated with dysregulation (e.g., aggression, anxiety/depression, sleep problems) followed by developmental abilities (e.g., expressive and receptive language skills) most contributed to heterogeneity in both groups of children. Atypical sensory response contributed to homogeneity in children classified as ASD but not those with subthreshold characteristics. These findings suggest that dysregulation and developmental abilities are clinical features that can impact functioning in children with ASD and other DD, and that documenting these features in pediatric records may help meet the needs of the individual child. Sensory dysfunction could be considered a core feature of ASD and thus used to inform more targeted screening, evaluation, treatment, and research efforts. LAY SUMMARY: The diverse nature of autism spectrum disorder (ASD) makes it difficult to find risk factors and treatment options. We identified the most dissimilar and most similar symptom(s) in children classified as ASD and as having subthreshold ASD characteristics. Factors associated with dysregulation and developmental abilities contributed to diversity in both groups of children. Sensory dysfunction was the most common symptom in children with ASD but not those with subthreshold characteristics. Findings can inform clinical practice and research.

Applications of Unsupervised Machine Learning in Autism Spectrum Disorder Research: a Review

Large amounts of autism spectrum disorder (ASD) data is created through hospitals, therapy centers, and mobile applications; however, much of this rich data does not have pre-existing classes or labels. Large amounts of data—both genetic and behavioral—that are collected as part of scientific studies or a part of treatment can provide a deeper, more nuanced insight into both diagnosis and treatment of ASD. This paper reviews 43 papers using unsupervised machine learning in ASD, including k-means clustering, hierarchical clustering, model-based clustering, and self-organizing maps. The aim of this review is to provide a survey of the current uses of unsupervised machine learning in ASD research and provide insight into the types of questions being answered with these methods.

Distinct Patterns of Cognitive Outcome in Young Children With Autism Spectrum Disorder Receiving the Early Start Denver Model

Evidence-based, early intervention significantly improves developmental outcome in young children with autism. Nonetheless, there is high interindividual heterogeneity in developmental trajectories during the therapy. It is established that starting intervention as early as possible results in better developmental outcomes. But except for younger age at start, there is no clear consensus about behavioral characteristics that could provide a reliable individual prediction of a child's developmental outcome after receiving an early intervention. In this study, we analyze developmental trajectories of preschoolers with autism who received 2 years of intervention using the Early Start Denver Model (ESDM) approach in Geneva, Switzerland in an individual setting (n = 55, aged 28.7 ± 5.1 months with a range of 15-42). Our aim was to identify early predictors of response to intervention. We applied a cluster analysis to distinguish between 3 groups based on their cognitive level at intake, and rates of cognitive change over the course of intervention. The first group of children only had a mild cognitive delay at intake and nearly no cognitive delay by the end of intervention (Higher Cognitive at baseline: HC). The children in the two other groups all presented with severe cognitive delay at baseline. However, they had two very different patterns of response to intervention. The majority significantly improved developmental scores over the course of intervention (Optimal Responders: OptR) whereas a minority of children showed only modest improvement (Minimal Responders: MinR). Further analyses showed that children who ended up having an optimal 2-year intervention outcome (OptR) were characterized by higher adaptive functioning at baseline combined with rapid developmental improvement during the first 6 months of intervention. Inversely, less significant progress by the sixth month of intervention was associated with a less optimal response to treatment (MinR).

Extracting Latent Subdimensions of Social Communication: A Cross-Measure Factor Analysis

OBJECTIVE

Social communication deficits associated with autism spectrum disorder (ASD) are commonly represented as a single behavioral domain. However, increased precision of measurement of social communication is needed to promote more nuanced phenotyping, both within the autism spectrum and across diagnostic boundaries.

METHOD

A large sample (N = 1,470) of 4- to 10-year-old children was aggregated from across 4 data sources, and then randomly split into testing and validation samples. A total of 57 selected social communication items from 3 widely used autism symptom measures (the Autism Diagnostic Observation Scale [ADOS], Autism Diagnostic Interview-Revised [ADI-R], and Social Responsiveness Scale [SRS]) were analyzed in the multi-trait/multi-method factor analysis framework. The selected model was then confirmed with the validation sample.

RESULTS

The 4-substantive factor model, with 3 orthogonal method factors, was selected using the testing sample based on fit indices and then confirmed with the validation sample. Two of the factors, "Basic Social Communication Skills" and "Interaction Quality," were similar to those identified in a previous analysis of the ADOS, Module 3. Two additional factors, "Peer Interaction and Modification of Behavior" and "Social Initiation and Affiliation," also emerged. Factor scores showed nominal correlations with age and verbal IQ.

CONCLUSION

Identification of subdimensions could inform the creation of better conceptual models of social communication impairments, including mapping of how the cascading effects of social communication deficits unfold in ASD versus other disorders. Especially if extended to include both older and younger age cohorts and individuals with more varying developmental levels, these efforts could inform phenotype-based exploration for biological and genetic mechanisms by pinpointing specific mechanisms that contribute to various types of social communication deficits.

Examining Phenotypical Heterogeneity in Language Abilities in Chinese-Speaking Children with Autism: A Naturalistic Sampling Approach

Phenotypical heterogeneity in language abilities is a hallmark of autism but remains poorly understood. The present study collected naturalistic language samples from parent-child interactions. We quantified verbal abilities (mean length of utterance, tokens, types) of 50 Chinese-speaking children (M = 5; 6) and stratified subgroups based on their autism traits, IQ, and language abilities. Using hierarchical cluster analysis, four groups were identified. Group 1, the least affected group, had mild autism, the highest IQ, and the strongest verbal abilities. Group 2, the severely affected group, had the lowest IQ, most severe autism symptoms, and weakest verbal abilities. Group 3 and Group 4 displayed average levels of verbal abilities and IQ. These findings may characterize the heterogeneous profiles of verbal abilities in Chinese-speaking children.

Subgroups of Children with Autism Spectrum Disorder without Intellectual Disability: A Longitudinal Examination of Executive and Socio-Adaptive Behaviors in Adolescence

Within the autistic spectrum, there is remarkable variability in the etiology, presentation, and treatment response. This prospective study was designed to identify, through cluster analysis, subgroups of individuals with ASD without intellectual disability (ID) based on the severity of the core symptoms in childhood. The secondary aim was to explore whether these subgroups and a group with typical development (TD) differ in cognitive, adaptive, and social aspects measured in adolescence. The sample at baseline was comprised of 52 children with ASD without ID and 37 children with TD, aged 7-11. Among the ASD group, three clusters were identified. Cluster 1 (40%), 'high severity', presented high symptom severity on the DSM-5 criteria and the Social Communication Questionnaire. Cluster 2 (34%) showed 'moderate severity' on most of the scores. Cluster 3 (25%) corresponded to 'low severity', showing moderate social impairment and low restrictive, repetitive patterns of behavior, interests and activities. At 5-year follow-up, 45 adolescents with ASD without ID and 27 adolescents with TD were assessed. All clusters had significantly more difficulties in EF, ToM, socialization and adaptive behavior compared to TD. Social and adaptive trajectories between the ASD subgroups were relatively different; Cluster 3 showed poorer socialization and daily living skills than the other two subgroups. These findings highlight the importance of fully assessing social, cognitive, and adaptive profiles to develop care plans tailored to specific needs.

Validation strategies for subtypes in psychiatry: A systematic review of research on autism spectrum disorder

Heterogeneity within autism spectrum disorder (ASD) is recognized as a challenge to both biological and psychological research, as well as clinical practice. To reduce unexplained heterogeneity, subtyping techniques are often used to establish more homogeneous subtypes based on metrics of similarity and dissimilarity between people. We review the ASD literature to create a systematic overview of the subtyping procedures and subtype validation techniques that are used in this field. We conducted a systematic review of 156 articles (2001-June 2020) that subtyped participants (range N of studies = 17-20,658), of which some or all had an ASD diagnosis. We found a large diversity in (parametric and non-parametric) methods and (biological, psychological, demographic) variables used to establish subtypes. The majority of studies validated their subtype results using variables that were measured concurrently, but were not included in the subtyping procedure. Other investigations into subtypes' validity were rarer. In order to advance clinical research and the theoretical and clinical usefulness of identified subtypes, we propose a structured approach and present the SUbtyping VAlidation Checklist (SUVAC), a checklist for validating subtyping results.

Latent Structure of Combined Autistic and ADHD Symptoms in Clinical and General Population Samples: A Scoping Review

Background: Many phenotypic studies have estimated the degree of comorbidity between Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD), but few have examined the latent, or unobserved, structure of combined ASD and ADHD symptoms. This is an important perquisite toward better understanding the overlap between ASD and ADHD. Methods: We conducted a scoping review of studies that examined the factor or latent class structure of ASD and ADHD symptoms within the same clinical or general population sample. Results: Eight studies met final inclusion criteria. Four factor analysis studies found that ASD and ADHD domains loaded separately and one found that some ASD and ADHD domains loaded together. In the three latent class studies, there were evidence of profiles with high levels of co-occurring ASD and ADHD symptoms. Conclusions: Our scoping review provides some evidence of phenotypic overlap between ASD and ADHD at the latent, or unobserved, level, particularly when using a "person-centered" (latent class analysis) vs. a "variable-centered" (factor analysis) approach.