Impaired eye region search accuracy in children with autistic spectrum disorders

Visual search and childhood vision impairment: A GAMLSS-oriented multiverse analysis approach

The aim of this report was to analyze reaction times and accuracy in children with a vision impairment performing a feature-based visual search task using a multiverse statistical approach. The search task consisted of set sizes 4, 16, and 24, consisting of distractors (circle) and a target (ellipse) that were presented randomly to school-aged individuals with or without a vision impairment. Interactions and main effects of key variables relating to reaction times and accuracy were analyzed via a novel statistical method blending GAMLSS (generalized additive models for location, scale, and shape) and distributional regression trees. Reaction times for the target-present and target-absent conditions were significantly slower in the vision impairment group with increasing set sizes (p < .001). Female participants were significantly slower than were males for set sizes 16 and 24 in the target-absent condition (p < .001), with male participants being significantly slower than females in the target-present condition (p < .001). Accuracy was only significantly worse (p = .03) for participants less than 14 years of age for the target-absent condition with set sizes 16 and 24. There was a positive association between binocular visual acuity and search time (p < .001). The application of GAMLSS with distributional regression trees to the analysis of visual search data may provide further insights into underlying factors affecting search performance in case-control studies where psychological or physical differences may influence visual search outcomes.

Visual Autism

Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disorder characterized by deficits in social communication and restricted, repetitive behaviors. It affects approximately 2.2% of children. Both genetic and environmental risk factors have been identified for ASD. Visual comorbidities are relatively common among children with ASD. Between 20 and 44% of ASD children have visually significant refractive error, on-third have strabismus, and one-fifth have amblyopia. In addition, ASD is 30 times more common in children with congenital blindness. It is unknown whether the association of ASD with visual morbidity is causal, comorbid, or contributing. Structural and functional abnormalities have been identified in MRIs of ASD children, and ASD children have been noted to have aberrant eye tracking. ASD children with visually significant refractive errors and poor spectacle compliance (present in 30% of ASD children) offer the opportunity for investigation into how improved visual acuity influences ASD behaviors. In this review, we focus on what is known of the visual system, refractive surgery, and ASD.

Social orienting and social seeking behaviors in ASD. A meta analytic investigation

Social motivation accounts of autism spectrum disorder (ASD) posit that individuals with ASD find social stimuli less rewarding than neurotypical (NT) individuals. Behaviorally, this is proposed to manifest in reduced social orienting (individuals with ASD direct less attention towards social stimuli) and reduced social seeking (individuals with ASD invest less effort to receive social stimuli). In two meta-analyses, involving data from over 6000 participants, we review the available behavioral studies that assess social orienting and social seeking behaviors in ASD. We found robust evidence for reduced social orienting in ASD, across a range of paradigms, demographic variables and stimulus contexts. The most robust predictor of this effect was interactive content - effects were larger when the stimulus involved an interaction between people. By contrast, the evidence for reduced social seeking indicated weaker evidence for group differences, observed only under specific experimental conditions. The insights gained from this meta-analysis can inform design of relevant task measures for social reward responsivity and promote directions for further study on the ASD phenotype.

Early behavioral indices of inherited liability to autism

OBJECTIVE

The observed heterogeneity of autism spectrum disorder (ASD)-and the diversity of rare germline mutations with which it has been associated-has been difficult to reconcile with knowledge of its pronounced heritability in the population.

METHODS

This article reviews and synthesizes recent family and developmental studies incorporating behavioral indices of inherited risk for ASD.

RESULTS

Autism may arise from critical combinations of early inherited neurobehavioral susceptibilities-some specific to autism, some not-each of which may be traceable to partially-independent sets of genetic variation. These susceptibilities and their respective genetic origins may not relate to the characterizing symptoms of autism (after it develops) in a straightforward way, and may account for "missing heritability" in molecular genetic studies.

CONCLUSIONS

Within-individual aggregations of a finite set of early inherited neurobehavioral susceptibilities-each individually common in the population-may account for a significant share of the heritability of ASD. Comprehensive identification of these underlying traits my help elucidate specific early intervention targets in individual patients, especially if autism represents a developmental consequence of earlier-interacting susceptibilities. Scientific understanding of the early ontogeny of autism will benefit from epidemiologically-rigorous, genetically-informative studies of robust endophenotypic candidates whose inter-relationships in infancy are mapped and normed.

Subtyping cognitive profiles in Autism Spectrum Disorder using a Functional Random Forest algorithm

DSM-5 Autism Spectrum Disorder (ASD) comprises a set of neurodevelopmental disorders characterized by deficits in social communication and interaction and repetitive behaviors or restricted interests, and may both affect and be affected by multiple cognitive mechanisms. This study attempts to identify and characterize cognitive subtypes within the ASD population using our Functional Random Forest (FRF) machine learning classification model. This model trained a traditional random forest model on measures from seven tasks that reflect multiple levels of information processing. 47 ASD diagnosed and 58 typically developing (TD) children between the ages of 9 and 13 participated in this study. Our RF model was 72.7% accurate, with 80.7% specificity and 63.1% sensitivity. Using the random forest model, the FRF then measures the proximity of each subject to every other subject, generating a distance matrix between participants. This matrix is then used in a community detection algorithm to identify subgroups within the ASD and TD groups, and revealed 3 ASD and 4 TD putative subgroups with unique behavioral profiles. We then examined differences in functional brain systems between diagnostic groups and putative subgroups using resting-state functional connectivity magnetic resonance imaging (rsfcMRI). Chi-square tests revealed a significantly greater number of between group differences (p < .05) within the cingulo-opercular, visual, and default systems as well as differences in inter-system connections in the somato-motor, dorsal attention, and subcortical systems. Many of these differences were primarily driven by specific subgroups suggesting that our method could potentially parse the variation in brain mechanisms affected by ASD.

Analysis of cortical shape in children with simplex autism

We used surface-based morphometry to test for differences in cortical shape between children with simplex autism (n = 34, mean age 11.4 years) and typical children (n = 32, mean age 11.3 years). This entailed testing for group differences in sulcal depth and in 3D coordinates after registering cortical midthickness surfaces to an atlas target using 2 independent registration methods. We identified bilateral differences in sulcal depth in restricted portions of the anterior-insula and frontal-operculum (aI/fO) and in the temporoparietal junction (TPJ). The aI/fO depth differences are associated with and likely to be caused by a shape difference in the inferior frontal gyrus in children with simplex autism. Comparisons of average midthickness surfaces of children with simplex autism and those of typical children suggest that the significant sulcal depth differences represent local peaks in a larger pattern of regional differences that are below statistical significance when using coordinate-based analysis methods. Cortical regions that are statistically significant before correction for multiple measures are peaks of more extended, albeit subtle regional differences that may guide hypothesis generation for studies using other imaging modalities.