Restricted, Repetitive, and Reciprocal Social Behavior in Toddlers Born Small for Gestation Duration

Genetic architecture of reciprocal social behavior in toddlers: Implications for heterogeneity in the early origins of autism spectrum disorder

Impairment in reciprocal social behavior (RSB), an essential component of early social competence, clinically defines autism spectrum disorder (ASD). However, the behavioral and genetic architecture of RSB in toddlerhood, when ASD first emerges, has not been fully characterized. We analyzed data from a quantitative video-referenced rating of RSB (vrRSB) in two toddler samples: a community-based volunteer research registry (n = 1,563) and an ethnically diverse, longitudinal twin sample ascertained from two state birth registries (n = 714). Variation in RSB was continuously distributed, temporally stable, significantly associated with ASD risk at age 18 months, and only modestly explained by sociodemographic and medical factors (r2 = 9.4%). Five latent RSB factors were identified and corresponded to aspects of social communication or restricted repetitive behaviors, the two core ASD symptom domains. Quantitative genetic analyses indicated substantial heritability for all factors at age 24 months (h2 ≥ .61). Genetic influences strongly overlapped across all factors, with a social motivation factor showing evidence of newly-emerging genetic influences between the ages of 18 and 24 months. RSB constitutes a heritable, trait-like competency whose factorial and genetic structure is generalized across diverse populations, demonstrating its role as an early, enduring dimension of inherited variation in human social behavior. Substantially overlapping RSB domains, measurable when core ASD features arise and consolidate, may serve as markers of specific pathways to autism and anchors to inform determinants of autism's heterogeneity.

Longitudinal change in restricted and repetitive behaviors from 8-36 months

BACKGROUND

Restricted and repetitive behaviors (RRBs) are core features of autism spectrum disorder (ASD) and one of the earliest behavioral signs of ASD. However, RRBs are also present in typically developing (TD) infants, toddlers, and preschool-aged children. Past work suggests that examining change in these behaviors over time is essential to distinguish between normative manifestations of these behaviors and behaviors that denote risk for a neurodevelopmental disorder. One challenge in examining changes in these behaviors over time is that most measures of RRBs have not established longitudinal measurement invariance. The aims of this study were to (1) establish measurement invariance in the Repetitive Behavior Scales for Early Childhood (RBS-EC), a parent-report questionnaire of RRBs, and (2) model developmental change in RRBs from 8 to 36 months.

METHODS

We collected RBS-EC responses from parents of TD infants (n = 180) from 8 to 36 months (n = 606 responses, with participants contributing an average of 3-time points). We leverage a novel methodological approach to measurement invariance testing (Bauer, Psychological Models, 22(3), 507-526, 2017), moderated nonlinear factor analysis (MNLFA), to determine whether the RBS-EC was invariant across age and sex. We then generated adjusted factor score estimates for each subscale of the RBS-EC (repetitive motor, self-directed, and higher-order behaviors), and used linear mixed effects models to estimate between- and within-person changes in the RBS-EC over time.

RESULTS

The RBS-EC showed some non-invariance as a function of age. We were able to adjust for this non-invariance in order to more accurately model changes in the RBS-EC over time. Repetitive motor and self-directed behaviors showed a linear decline from 8 to 36 months, while higher-order behaviors showed a quadratic trajectory such that they began to decline later in development at around 18 months. Using adjusted factor scores as opposed to unadjusted raw mean scores provided a number of benefits, including increased within-person variability and precision.

CONCLUSIONS

The RBS-EC is sensitive enough to measure the presence of RRBs in a TD sample, as well as their decline with age. Using factor score estimates of each subscale adjusted for non-invariance allowed us to more precisely estimate change in these behaviors over time.

Examining criterion-oriented validity of the Repetitive Behavior Scales for Early Childhood (RBS-EC) and the Video-Referenced Rating of Reciprocal Social Behavior (vrRSB)

Improved characterization of quantitative traits and dimensionally distributed complex behaviors during toddlerhood may improve early identification of autism spectrum disorder and related neurodevelopmental disorders. Parents of 205 community-ascertained toddlers (age: mean = 20.2, SD = 2.6 months) completed the Repetitive Behavior Scales for Early Childhood (RBS-EC) and the Video-Referenced Rating of Reciprocal Social Behavior (vrRSB), with longitudinal follow-up of behavioral assessments and/or another round of parent-report questionnaires. Criterion validity was examined both concurrently and longitudinally using the Infant Toddler Social Emotional Assessment (ITSEA) as a criterion anchor. Reciprocal social behavior as measured by the vrRSB was significantly associated with social competence as measured by the ITSEA, longitudinally and concurrently. Reciprocal social behavior was not associated with the externalizing subscale on the ITSEA, providing evidence of discriminant validity. Higher-order repetitive behaviors (restricted interests; rituals and routines) as measured by RBS-EC subscales were associated with the dysregulation and internalizing subscales of the ITSEA, longitudinally and concurrently. All RBS-EC subscales (excepting repetitive motor) were associated concurrently and longitudinally with the dysregulation subscale of the ITSEA. We report evidence of criterion-oriented and discriminant validity for the constructs/domains captured by the RBS-EC and vrRSB. These instruments may be particularly useful in characterizing dimensional variability across the typical-to-atypical continuum.

Neurodevelopmental heterogeneity and computational approaches for understanding autism

In recent years, the emerging field of computational psychiatry has impelled the use of machine learning models as a means to further understand the pathogenesis of multiple clinical disorders. In this paper, we discuss how autism spectrum disorder (ASD) was and continues to be diagnosed in the context of its complex neurodevelopmental heterogeneity. We review machine learning approaches to streamline ASD's diagnostic methods, to discern similarities and differences from comorbid diagnoses, and to follow developmentally variable outcomes. Both supervised machine learning models for classification outcome and unsupervised approaches to identify new dimensions and subgroups are discussed. We provide an illustrative example of how computational analytic methods and a longitudinal design can improve our inferential ability to detect early dysfunctional behaviors that may or may not reach threshold levels for formal diagnoses. Specifically, an unsupervised machine learning approach of anomaly detection is used to illustrate how community samples may be utilized to investigate early autism risk, multidimensional features, and outcome variables. Because ASD symptoms and challenges are not static within individuals across development, computational approaches present a promising method to elucidate subgroups of etiological contributions to phenotype, alternative developmental courses, interactions with biomedical comorbidities, and to predict potential responses to therapeutic interventions.

Neuroinflammation in preterm babies and autism spectrum disorders

Genetic anomalies have a role in autism spectrum disorders (ASD). Each genetic factor is responsible for a small fraction of cases. Environment factors, like preterm delivery, have an important role in ASD. Preterm infants have a 10-fold higher risk of developing ASD. Preterm birth is often associated with maternal/fetal inflammation, leading to a fetal/neonatal inflammatory syndrome. There are demonstrated experimental links between fetal inflammation and the later development of behavioral symptoms consistent with ASD. Preterm infants have deficits in connectivity. Most ASD genes encode synaptic proteins, suggesting that ASD are connectivity pathologies. Microglia are essential for normal synaptogenesis. Microglia are diverted from homeostatic functions towards inflammatory phenotypes during perinatal inflammation, impairing synaptogenesis. Preterm infants with ASD have a different phenotype from term born peers. Our original hypothesis is that exposure to inflammation in preterm infants, combined with at risk genetic background, deregulates brain development leading to ASD.