Trajectories of Autism Symptom Severity Change During Early Childhood

Waizbard-Bartov, Einat; Ferrer, Emilio; Young, Gregory S.; Heath, Brianna; Rogers, Sally J.; Nordahl, Christine Wu; Solomon, Marjorie; Amaral, David G.

doi:10.1007/s10803-020-04526-z

Cited by 53 publications

(70 citation statements)

References 84 publications

(96 reference statements)

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“…Of the studies that reported participants' IQ metrics, all mean FSIQ and VIQ scores were within the average to above average range of intelligence. A minority of studies included in the current review included autistic participants with higher support needs, and/or lower verbal or intellectual ability (Damico & Nelson, 2005;Frith, Happe, Siddons, Happé, & Siddons, 1994;Livingston et al, 2018;Sutherland, Hodge, Bruck, Costley, & Klieve, 2017;Waizbard-Bartov et al, 2020). These findings are consistent with recent evidence showing a selection bias skewed toward inclusion of more cognitively able research participants across ASD research, a trend which has been increasing in specific domains of ASD research in recent years (Stedman, Taylor, Erard, Peura, & Siegel, 2018).…”

Section: Participant Characteristicssupporting

confidence: 84%

A Systematic Review of Passing as Non-autistic in Autism Spectrum Disorder

Libsack

Keenan

Freden

et al. 2021

Clin Child Fam Psychol Rev

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While long described in anecdotal accounts of the lived experiences of autistic individuals, the phenomenon of behaving in ways that appear inconsistent with the presence of autism (or passing as non-autistic; PAN) has recently seen a dramatic increase in scrutiny in the published scientific literature. Increased research attention has coincided with a proliferation of methods, definitions, measures, and population assumptions associated with PAN. To date, however, no review has sought to systematically identify and synthesize the literature on PAN. This systematic review reflects the state of the PAN literature as of May 2020. Ninety articles were screened, 66 were identified for evaluation, and 46 met inclusion criteria and were reliably coded for study characteristics and participant characteristics. Results reveal that the PAN literature includes a relatively even mix of qualitative, quantitative, and mixed-method studies, and that a variety of terms are used for PAN (with masking and camouflage being the most frequent). Sample sizes varied widely (from one to 832 participants), with 63.06% of participants being categorized as autistic. Nearly all studies reported methods for confirming autism diagnoses, with community and clinical diagnoses being most common. The majority of studies reported participant gender, with slightly more females included than males on average, but fewer than half of all studies compared PAN across genders. Nearly all studies reported participant age, demonstrating a wide range of 2 to 79 years, with a mean age of 23.85. Conversely, only 23.91% of studies provided participant race or ethnicity data. Nearly all studies formally or informally excluded participants with intellectual disability. Finally, measures of internalizing symptoms, which are often thought to be linked to PAN, were reported in only 17.4% of studies. Implications for gaps in understanding of PAN and future directions for the field are discussed.

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Section: Participant Characteristicssupporting

confidence: 84%

A Systematic Review of Passing as Non-autistic in Autism Spectrum Disorder

Libsack

Keenan

Freden

et al. 2021

Clin Child Fam Psychol Rev

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“…It is also important to investigate the stability of these sensory phenotypes over time, as the current study identified age differences across the sensory phenotypes. Further, given the utility of clustering techniques for parsing the heterogeneity in traits and behaviours that has already been demonstrated [ 63 – 65 ], future work should apply these clustering techniques to traits such as anxiety, restricted and repetitive behaviours, and social communication difficulties to determine whether discrete phenotypes can also be identified. In addition, while these sensory phenotypes were related to current behavioural differences, future work should aim to determine whether these phenotypes are also predictive of future behaviours.…”

Section: Discussionmentioning

confidence: 99%

Exploring sensory phenotypes in autism spectrum disorder

et al. 2021

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Background Atypical reactions to the sensory environment are often reported in autistic individuals, with a high degree of variability across the sensory modalities. These sensory differences have been shown to promote challenging behaviours and distress in autistic individuals and are predictive of other functions including motor, social, and cognitive abilities. Preliminary research suggests that specific sensory differences may cluster together within individuals creating discrete sensory phenotypes. However, the manner in which these sensory differences cluster, and whether the resulting phenotypes are associated with specific cognitive and social challenges is unclear. Methods Short sensory profile data from 599 autistic children and adults between the ages of 1 and 21 years were subjected to a K-means cluster analysis. Analysis of variances compared age, adaptive behaviour, and traits associated with autism, attention-deficit and hyperactivity disorder, and obsessive and compulsive disorder across the resultant clusters. Results A five-cluster model was found to minimize error variance and produce five sensory phenotypes: (1) sensory adaptive, (2) generalized sensory differences, (3) taste and smell sensitivity, (4) under-responsive and sensation seeking, and (5) movement difficulties with low energy. Age, adaptive behaviour, and traits associated with autism, attention-deficit and hyperactivity disorder, and obsessive and compulsive disorder were found to differ significantly across the five phenotypes. Limitations The results were based on parent-report measures of sensory processing, adaptive behaviour, traits associated with autism, attention-deficit and hyperactivity disorder, and obsessive and compulsive disorder, which may limit the generalizability of the findings. Further, not all measures are standardized, or psychometrically validated with an autism population. Autistic individuals with an intellectual disability were underrepresented in this sample. Further, as these data were obtained from established records from a large provincial database, not all measures were completed for all individuals. Conclusions These findings suggest that sensory difficulties in autistic individuals can be clustered into sensory phenotypes, and that these phenotypes are associated with behavioural differences. Given the large degree of heterogeneity in sensory difficulties seen in the autistic population, these sensory phenotypes represent an effective way to parse that heterogeneity and create phenotypes that may aid in the development of effective treatments and interventions for sensory difficulties.

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“…Despite this, the average age of diagnosis in the United States remains around 4 years of age [Christensen et al, 2016]. While there is mixed evidence for the stability of autism traits over early childhood [Bieleninik et al, 2017; Waizbard‐Bartov et al, 2020], the delay in diagnosis can still impact timely intervention during a critical window of development. In response to this, in 2007 the American Academy of Pediatrics published guidelines supporting the need for all children to be screened for ASD between 18‐ and 24‐months of age as part of their well‐child visits [Myers, Johnson, & Council on Children With Disabilities, 2007].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Trajectories of Autism Symptom Severity Change During Early Childhood

Cited by 53 publications

References 84 publications

A Systematic Review of Passing as Non-autistic in Autism Spectrum Disorder

A Systematic Review of Passing as Non-autistic in Autism Spectrum Disorder

Exploring sensory phenotypes in autism spectrum disorder

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

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