Exploring the quantitative nature of empathy, systemising and autistic traits using factor mixture modelling

Exploring Emotional Dysregulation and Avoidance with Caregivers as the Mechanisms Linking Social Communication Understanding and Aggressive Behaviours

Many autistic adolescents and young adults present with aggressive behaviours, which can be challenging for caregivers. The present study aimed to explore the underlying mechanisms between social communication understanding and aggressive behaviours in autistic and non-autistic adolescents, specifically the role of emotional dysregulation and its impact on avoidance with caregivers. Caregivers of autistic (n = 275) and non-autistic adolescents (n = 123) completed standardised caregiver-report questionnaires measuring social communication understanding, emotional dysregulation, avoidance between the adolescent and caregiver and aggressive behaviours. A serial mediation analysis indicated that levels of social communication understanding were indirectly associated with aggressive behaviours. This occurred through increased emotional dysregulation, which may have led to increased avoidance between the autistic and non-autistic adolescents and their caregivers. These findings support a sequential process by which adolescents with low social communication understanding are more likely to behave aggressively through being emotionally dysregulated and the impact of this on the increased avoidance within the caregiver-adolescent dyad. This process was found within autistic and non-autistic adolescents, suggesting a mechanism across individuals with aggression. These findings indicate that interventions based on improving emotion regulation ability and responses between adolescents and their caregivers may aid in reducing aggressive behaviours in adolescents and young adults with lower social communication understanding.

Re-Examining Labels in Neurocognitive Research: Evidence from Bilingualism and Autism as Spectrum-Trait Cases

Despite the fact that the urge to investigate bilingualism and neurodevelopmental disorders as continuous indices rather than categorical ones has been well-voiced among researchers with respect to research methodological approaches, in the recent literature, when it comes to examining language, cognitive skills and neurodivergent characteristics, it is still the case that the most prevalent view is the categorisation of adults or children into groups. In other words, there is a categorisation of individuals, e.g., monolingual vs. bilingual children or children with typical and atypical/non-typical/non-neurotypical development. We believe that this labelling is responsible for the conflicting results that we often come across in studies. The aim of this review is to bring to the surface the importance of individual differences through the study of relevant articles conducted in bilingual children and children with autism, who are ideal for this study. We concur with researchers who already do so, and we further suggest moving away from labels and instead shift towards the view that not everything is either white or black. We provide suggestions as to how this shift could be implemented in research, while mostly aiming at starting a discourse rather than offering a definite path.

Robustness of Latent Profile Analysis to Measurement Noninvariance Between Profiles

Latent profile analysis (LPA) identifies heterogeneous subgroups based on continuous indicators that represent different dimensions. It is a common practice to measure each dimension using items, create composite or factor scores for each dimension, and use these scores as indicators of profiles in LPA. In this case, measurement models for dimensions are not included and potential noninvariance across latent profiles is not modeled in LPA. This simulation study examined the robustness of LPA in terms of class enumeration and parameter recovery when the noninvariance was unmodeled by using composite or factor scores as profile indicators. Results showed that correct class enumeration rates of LPA were relatively high with small degree of noninvariance, large class separation, large sample size, and equal proportions. Severe bias in profile indicator mean difference was observed with intercept and loading noninvariance, respectively. Implications for applied researchers are discussed.

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.

Testing the Latent Structure of the Autism Spectrum Quotient in a Sub-clinical Sample of University Students Using Factor Mixture Modelling

In the present study, factor mixture models (FMMs) were used to examine the latent structure underlying the Autism-Spectrum Quotient (AQ) among a sample of 633 undergraduate students. FMM represents a combination of latent-class, person-centered approaches and common-factor, variable-centered approaches to modeling population heterogeneity. Findings suggest the presence of either two or six latent classes with distinct profiles across the set of 50 AQ items. In addition, within each class, individuals can be further differentiated according to their scores on five latent factors. These results suggest the presence of phenotypical heterogeneity at the sub-clinical level in addition to that which is known to exist at the clinical level.

The broader autism phenotype constellations-disability matrix paradigm: Theoretical model for autism and the broader autism phenotype

The prevalence of autism has increased dramatically over the last 60 years, and the cause of this increase is unclear. In this paradigm-shift paper, I propose an explanatory paradigm for the cause of autism and its increased prevalence in the general population. I also discuss how social and historical contexts may have influenced the evolution and manifestation of specific traits in the autism population. These traits expand the characterization of the broader autism phenotype to include a constellation of socially valued traits, termed Broader Autism Phenotype Constellations (BAPCO). The frequency of these traits may have increased due to assortative mating opportunities that occurred alongside social changes in education and occupational opportunities over the last 100 years. I propose that assortative mating can lead to both positive and negative developmental consequences affecting social and language development. I also propose that BAPCO traits, which are not intrinsically disabilities, could interact with co-occurring conditions in a new model called the BAPCO-Disability Matrix Paradigm (BAPCO-DMAP). In this paradigm, autism is located at the intersection of BAPCO traits and at least one co-occurring condition. These proposed models support the need to create a more comprehensive definition of autism that includes constellations of BAPCO traits. The BAPCO-DMAP paves the way to testable predictions of autism prevalence and provides a framework to better understand the foundational traits of autism. Finally, this paradigm radically redefines the broader autism phenotype with characteristics that can inform therapy and child development.

Neurological disorders in autism: A systematic review and meta-analysis

LAY ABSTRACT

Neurological disorders, such as epilepsy and cerebral palsy, have been reported to occur among individuals with autism beyond chance and may have an impact on daily living across the lifespan. Although there has been research investigating neurological disorders in autism, the findings are not always conclusive. Previous summaries of existing studies have not evaluated the full range of neurological disorders. This study aimed to comprehensively explore the neurological problems appearing in autism to provide updated information that is needed for better healthcare and support in this population. We looked at already published studies focusing on risk or frequency of neurological disorders in autism. Our results suggest that individuals with autism are more likely than the general population to have a range of neurological disorders, including epilepsy, macrocephaly, hydrocephalus, cerebral palsy, migraine/headache, and inborn abnormalities of the nervous system. In order to provide individualized healthcare and support of high quality to individuals diagnosed with autism, health care professionals and other support providers need to be attentive to neurological complications. To further improve our understanding about the link between autism and neurological disorders, future research should follow the neurological health of children who are diagnosed with or are at increased likelihood of autism.

Dissecting the phenotypic heterogeneity in sensory features in autism spectrum disorder: a factor mixture modelling approach

BACKGROUND

Heterogeneity in the phenotypic presentation of autism spectrum disorder (ASD) is apparent in the profile and the severity of sensory features. Here, we applied factor mixture modelling (FMM) to test a multidimensional factor model of sensory processing in ASD. We aimed to identify homogeneous sensory subgroups in ASD that differ intrinsically in their severity along continuous factor scores. We also investigated sensory subgroups in relation to clinical variables: sex, age, IQ, social-communication symptoms, restricted and repetitive behaviours, adaptive functioning and symptoms of anxiety and attention-deficit/hyperactivity disorder.

METHODS

Three hundred thirty-two children and adults with ASD between the ages of 6 and 30 years with IQs varying between 40 and 148 were included. First, three different confirmatory factor models were fit to the 38 items of the Short Sensory Profile (SSP). Then, latent class models (with two-to-six subgroups) were evaluated. The best performing factor model, the 7-factor structure, was subsequently used in two FMMs that varied in the number of subgroups: a two-subgroup, seven-factor model and a three-subgroup and seven-factor model.

RESULTS

The 'three-subgroup/seven-factor' FMM was superior to all other models based on different fit criteria. Identified subgroups differed in sensory severity from severe, moderate to low. Accounting for the potential confounding effects of age and IQ, participants in these sensory subgroups had different levels of social-communicative symptoms, restricted and repetitive behaviours, adaptive functioning skills and symptoms of inattention and anxiety.

LIMITATIONS

Results were derived using a single parent-report measure of sensory features, the SSP, which limits the generalisability of findings.

CONCLUSION

Sensory features can be best described by three homogeneous sensory subgroups that differ in sensory severity gradients along seven continuous factor scores. Identified sensory subgroups were further differentiated by the severity of core and co-occurring symptoms, and level of adaptive functioning, providing novel evidence on the associated clinical correlates of sensory subgroups. These sensory subgroups provide a platform to further interrogate the neurobiological and genetic correlates of altered sensory processing in ASD.

The distribution of autistic traits across the autism spectrum: evidence for discontinuous dimensional subpopulations underlying the autism continuum

Background

A considerable amount of research has discussed whether autism and psychiatric/neurodevelopmental conditions in general are best described categorically or dimensionally. In recent years, finite mixture models have been increasingly applied to mixed populations of autistic and non-autistic individuals to answer this question. However, the use of such methods with mixed populations may not be appropriate for two reasons: First, subgroups within mixed populations are often skewed and thus violate mixture models assumptions, which are based on weighted sum of Gaussian distributions. Second, these analyses have, to our knowledge, been solely applied to enriched samples, where the prevalence of the clinical condition within the study sample far exceeds epidemiological estimates.

Method

We employed a dual Weibull mixture model to examine the distribution of the Autism Spectrum Quotient scores of a mixed sample of autistic and non-autistic adults (N = 4717; autism = 811), as well as of a derived sample (from the enriched sample; N = 3973; autism = 67) that reflects the current prevalence of autism within the general population.

Results

In a mixed autistic and non-autistic population, our model provided a better description of the underlying structure of autistic traits than traditional finite Gaussian mixture models and performed well when applied to a sample that reflected the prevalence of autism in the general population. The model yielded results, which are consistent with predictions of current theories advocating for the co-existence of a mixed categorical and dimensional architecture within the autism spectrum.

Conclusion

The results provide insight into the continuum nature of the distribution of autistic traits, support the complementary role of both categorical and dimensional approaches to autism spectrum condition, and underscore the importance of analyzing samples that reflect the epidemiological prevalence of the condition. Owing to its flexibility to represent a wide variety of distributions, the Weibull distribution might be better suited for latent structure studies, within enriched and prevalence-true samples.

Empathizing and systemizing are differentially related to dimensions of autistic traits in the general population

Empathizing-Systemizing Theory suggests that low empathizing and high systemizing are linked to autistic traits in the general population. Evidence from autistic individuals is convincing, but more research in the normal population is needed. Method: We conducted two surveys (N = 3,345) investigating the relationships between empathizing, systemizing and autistic traits in the general population, using a large variety of self-report instruments and direct performance tests. Results: Strong connections between autistic symptoms, empathizing, and systemizing were found using commonly used measures (Autism Quotient, Systemizing Quotient and Empathizing Quotient). Other measures on empathizing and systemizing found the connections that E-S-theory predicts, but the correlations were a lot more modest. Weak empathizing was related to autism's social difficulties, while systemizing was linked to non-social aspects of autism. Conclusions: The present results support the main tenets of empathizing-systemizing theory, but suggest that earlier findings might be inflated due to overlapping items in the most common assessment instruments.

Disentangling Heterogeneity of Childhood Disruptive Behavior Problems Into Dimensions and Subgroups

OBJECTIVE

Irritable and oppositional behaviors are increasingly considered as distinct dimensions of oppositional defiant disorder. However, few studies have explored this multidimensionality across the broader spectrum of disruptive behavior problems (DBPs). This study examined the presence of dimensions and distinct subgroups of childhood DBPs, and the cross-sectional and longitudinal associations between these dimensions.

METHOD

Using factor mixture models (FMMs), the presence of dimensions and subgroups of DBPs was assessed in the Generation R Study at ages 6 (n = 6,209) and 10 (n = 4,724) years. Replications were performed in two population-based cohorts (Netherlands Twin Registry, n = 4,402, and Swedish Twin Study of Child and Adolescent Development, n = 1,089) and a clinical sample (n = 1,933). We used cross-lagged modeling in the Generation R Study to assess cross-sectional and longitudinal associations between dimensions. DBPs were assessed using mother-reported responses to the Child Behavior Checklist.

RESULTS

Empirically obtained dimensions of DBPs were oppositional behavior (age 6 years), disobedient behavior, rule-breaking behavior (age 10 years), physical aggression, and irritability (both ages). FMMs suggested that one-class solutions had the best model fit for all dimensions in all three population-based cohorts. Similar results were obtained in the clinical sample. All three dimensions, including irritability, predicted subsequent physical aggression (range, 0.08-0.16).

CONCLUSION

This study showed that childhood DBPs should be regarded as a multidimensional phenotype rather than comprising distinct subgroups. Incorporating multidimensionality will improve diagnostic accuracy and refine treatment. Future studies need to address the biological validity of the DBP dimensions observed in this study; herein lies an important opportunity for neuroimaging and genetic measures.