Factors Associated with Restricted, Repetitive Behaviors and Interests and Diagnostic Severity Level Ratings in Young Children with Autism Spectrum Disorder

Intellectual, Adaptive, and Behavioral Functioning Associated with Designated Levels of Support in a Sample of Autistic Children Referred for Tertiary Assessment

The diagnostic criteria for autism spectrum disorder in the DSM-5-TR features the option to designate levels of support for social communication (SC) and restricted, repetitive behaviors (RRB). These levels are conceptual in nature, but research indicates standardized assessment outcomes correspond with clinician-assigned levels of support. The purpose of the present study was to identify factors that influence designated levels of support for SC and RRBs when diagnosing autism. Standardized assessment scores across intellectual functioning, adaptive skills, and ASD symptomology were analyzed to determine corresponding levels of support in SC and RRBs assigned by clinicians for 136 autistic children following a comprehensive diagnostic evaluation. At diagnosis, approximately 46% of participants were described as needing substantial support (Level 2) for SC and 49% were described as needing substantial support (Level 2) for RRB. There was a consistent pattern of higher to lower intellectual and adaptive functioning needing Level 1-Level 3 support. Autism assessment results followed a gradient of fewer to greater autism symptoms from Level 1 to Level 3 support. Findings indicated clinician-assigned levels of support for SC and RRB were associated with intellectual functioning, adaptive functioning, autism symptomology, and age, but not sex.

Toward a functional classification for autism in adulthood

Autism spectrum disorder (ASD) is a heterogeneous condition that affects development and functioning from infancy through adulthood. Efforts to parse the heterogeneity of the autism spectrum through subgroups such as Asperger's and Profound Autism have been controversial, and have consistently struggled with issues of reliability, validity, and interpretability. Nonetheless, methods for successfully identifying clinically meaningful subgroups within autism are needed to ensure that research, interventions, and services address the range of needs experienced by autistic individuals. The purpose of this study was to generate and test whether a simple set of questions, organized in a flowchart, could be used in clinical practice and research to differentiate meaningful subgroups based on individuals' level of functioning. Once generated, subgroups could also be compared to the recently proposed administrative category of Profound Autism and to groupings based on standardized adaptive measures. Ninety-seven adults with autism or related neurodevelopmental disorders participating in a longstanding longitudinal study, or their caregivers if they could not answer for themselves, completed phone interviews when the participants were ~30 years old. Information from these phone interviews was used to generate vignettes summarizing characteristics and aspects of the daily lives of each participant (e.g., language level, vocational activities, and social relationships). Three expert clinicians then used these vignettes to classify each participant based on their level of support needs. Meaningfully distinct subgroups within the sample were identified which could be reliably distinguished from one another. Implications of such categorizations and future directions are discussed.

Repetitive and restricted behaviors and interests in autism spectrum disorder: relation to individual characteristics and mental health problems

BACKGROUND

Although repetitive and restricted behaviors and interests (RRBIs) may interfere with well-being and functioning in autistic individuals, research on their relation to sex, age, cognitive level, and mental health problems remains unclear. Much of the research to date has used broad categorizations rather than specific categorizations of RRBIs to examine the difference in RRBIs between individuals. The purpose of this study was to explore, in different groups of individuals, the presence of specific RRBI subtypes, and to examine the association of specific RRBI subtypes with symptoms of internalizing and externalizing behaviors.

METHODS

Secondary data analyses were conducted using the Simons Simplex Collection dataset, which included 2,758 participants (aged 4 to 18). Families of autistic children completed the Repetitive Behavior Scale-Revised (RBS-R) and the Child Behavior Checklist.

RESULTS

Across all RBS-R subtypes, results revealed no sex differences. Older children showed higher rates of Ritualistic/Sameness behaviors than younger children and adolescents, whereas younger and older children showed more Stereotypy than adolescents. Additionally, lower cognitive level groups showed higher rates of RBS-R subtypes except for Ritualistic/Sameness. After controlling for age and cognitive level, RBS-R subtypes accounted for a substantial amount of variance in internalizing and externalizing behaviors (23% and 25%, respectively). Specifically, Ritualistic/Sameness and Self-Injurious Behavior both predicted internalizing and externalizing behaviors, whereas Stereotypy only predicted internalizing behavior.

CONCLUSIONS

These findings have key clinical implications that emphasize not only the consideration of sex, age, and cognitive level, but also specific RRBIs and co-occurring mental health problems, when assessing for ASD and designing individualized interventions.

A machine learning-based diagnostic model for children with autism spectrum disorders complicated with intellectual disability

Background

Early detection of children with autism spectrum disorder (ASD) and comorbid intellectual disability (ID) can help in individualized intervention. Appropriate assessment and diagnostic tools are lacking in primary care. This study aims to explore the applicability of machine learning (ML) methods in diagnosing ASD comorbid ID compared with traditional regression models.

Method

From January 2017 to December 2021, 241 children with ASD, with an average age of 6.41 ± 1.96, diagnosed in the Developmental Behavior Department of the Children's Hospital Affiliated with the Medical College of Zhejiang University were included in the analysis. This study trained the traditional diagnostic models of Logistic regression (LR), Support Vector Machine (SVM), and two ensemble learning algorithms [Random Forest (RF) and XGBoost]. Socio-demographic and behavioral observation data were used to distinguish whether autistic children had combined ID. The hyperparameters adjustment uses grid search and 10-fold validation. The Boruta method is used to select variables. The model's performance was evaluated using discrimination, calibration, and decision curve analysis (DCA).

Result

Among 241 autistic children, 98 (40.66%) were ASD comorbid ID. The four diagnostic models can better distinguish whether autistic children are complicated with ID, and the accuracy of SVM is the highest (0.836); SVM and XGBoost have better accuracy (0.800, 0.838); LR has the best sensitivity (0.939), followed by SVM (0.952). Regarding specificity, SVM, RF, and XGBoost performed significantly higher than LR (0.355). The AUC of ML (SVM, 0.835 [95% CI: 0.747-0.944]; RF, 0.829 [95% CI: 0.738-0.920]; XGBoost, 0.845 [95% CI: 0.734-0.937]) is not different from traditional LR (0.858 [95% CI: 0.770-0.944]). Only SVM observed a good calibration degree. Regarding DCA, LR, and SVM have higher benefits in a wider threshold range.

Conclusion

Compared to the traditional regression model, ML model based on socio-demographic and behavioral observation data, especially SVM, has a better ability to distinguish whether autistic children are combined with ID.

Biopsychological correlates of repetitive and restricted behaviors in autism spectrum disorders

BACKGROUND

Autism Spectrum Disorder (ASD) is considered a neurodevelopmental condition that is characterized by alterations in social interaction and communication, as well as patterns of restrictive and repetitive behaviors (RRBs). RRBs are defined as broad behaviors that comprise stereotypies, insistence on sameness, and attachment to objects or routines. RRBs can be divided into lower-level behaviors (motor, sensory, and object-manipulation behaviors) and higher-level behaviors (restrictive interests, insistence on sameness, and repetitive language). According to the DSM-5, the grade of severity in ASD partially depends on the frequency of RRBs and their consequences for disrupting the life of patients, affecting their adaptive skills, and increasing the need for parental support.

METHODS

We conducted a systematic review to examine the biopsychological correlates of the symptomatic domains of RRBs according to the type of RRBs (lower- or higher-level). We searched for articles from the National Library of Medicine (PubMed) using the terms: autism spectrum disorders, ASD, and autism-related to executive functions, inhibitory control, inflexibility, cognitive flexibility, hyper or hypo connectivity, and behavioral approaches. For describing the pathophysiological mechanism of ASD, we also included animal models and followed PRISMA guidelines.

RESULTS

One hundred and thirty-one articles were analyzed to explain the etiology, continuance, and clinical evolution of these behaviors observed in ASD patients throughout life.

CONCLUSIONS

Biopsychological correlates involved in the origin of RRBs include alterations in a) neurotransmission system, b) brain volume, c) inadequate levels of growth factors, d) hypo- or hyper-neural connectivity, e) impairments in behavioral inhibition, cognitive flexibility, and monitoring and f) non-stimulating environments. Understanding these lower- and higher-level of RRBs can help professionals to improve or design novel therapeutic strategies.