Specificities of auditory behavior in children with autism spectrum disorder.

Auditory Statistical Learning Deficits in Children with Autism Spectrum Disorder: A Comparison of Visual and Auditory Segmentation Paradigms

This study involved 30 children with Autism Spectrum Disorder (ASD) aged 7–10 years and 31 typically developing (TD) children who were matched for age and IQ. The participants’ performance on visual and auditory statistical learning tasks within a segmentation paradigm was assessed. Generalised linear mixed models (GLMMs) and linear mixed models (LMMs) were used to analyse response accuracy and reaction times, respectively. The aim was to explore the characteristics of statistical learning in children with ASD. The results showed that the children with ASD failed to perform above chance level on the auditory statistical learning task (p = 0.156). Only 40% of ASD participants performed better than expected by chance. Their performance was significantly lower than that of the TD children. Conversely, children with ASD performed significantly above chance level in the visual statistical learning task (p < 0.001). GLMM analysis revealed a significant interaction between group and task modality (p = 0.023). Specifically, children with ASD demonstrated significantly lower accuracy in auditory statistical learning than TD children (p < 0.001). In contrast, no significant difference was observed between groups in the visual task (p = 0.468). LMM analysis showed a significant interaction between group and task modality regarding reaction times (p = 0.020). Children with ASD exhibited significantly longer reaction times than TD children in both the visual and auditory tasks (ps < 0.001). These findings suggest that children with ASD have difficulty with auditory statistical learning, but perform relatively well in visual tasks within the segmentation paradigm.

Association Between Autism Severity and Feeding Behaviors in Children: A Cross-Sectional Study.

[Quality of life of parents of children with autism spectrum disorders].

OBJECTIVE To establish a correlation between the severity of clinical symptoms in children diagnosed with autism spectrum disorders (ASDs) and the quality of life experienced by their parents, and identify potential targets for effective interventions in care. MATERIAL AND METHODS The study included a sample of 40 parents of children diagnosed with ASD and 37 parents of neurotypical children. The clinical and psychometric evaluation of the children with ASD was conducted using the Childhood Autism Rating Scale (CARS). Additionally, the Quality of Life in Autism Questionnaire (QoLA) was used to assess parents' quality of life. Statistical analyses were performed using MS Excel and STATISTICA software, employing nonparametric methods. RESULTS The psychometric assessment using the CARS scale yielded an average score of 22.6±5.8 for the examined children, indicating a mild autism classification. The total QoLA score for parents of children with ASD was 132.6±25.8 points, compared with 171.8±23.6 points for parents of neurotypical children (p<0.001). The quality of life of parents, as assessed by Part B of the QoLA scale, was found to be correlated with the severity of ASD as measured by the CARS scale (p<0.05). The primary symptoms identified that negatively impacted the quality of life of parents included the social impairments associated with autism, challenges in collaborative engagement, a lack of comprehension regarding social rules and roles, alongside communication disorders (p<0.05). CONCLUSIONS The findings highlight potential targets for social and therapeutic interventions with families, which may alleviate parental distress and enhance the quality of life for parents of children diagnosed with ASD.

Neurocognitive Functions and Their Associations With Autistic Symptoms, Emotion, and Behavior in High-Functioning Children and Adolescents With Autism Spectrum Disorder

Objective Studies have revealed the prognostic significance of intelligence in children with autism spectrum disorder (ASD), but the correlations of non-social cognitive functions and clinical characteristics of high-functioning children and adolescents with ASD remain unclear. To identify individual needs, this study aimed to investigate their correlations in high-functioning children and adolescents with ASD and conduct an exploratory comparison between subgroups. Methods We recruited children and adolescents who met the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria for ASD, excluding those with intellectual disability. The Wechsler Intelligence Scale for Children, Fourth Edition and Conners’ Continuous Performance Test, Second-Edition were administered to assess intelligence and attention. Their parents completed questionnaires, including the Child Behaviour Checklist, the Social Responsiveness Scale, and the Aberrant Behaviour Checklist, to report their clinical characteristics. Group differences were analyzed using t-tests and chi-square tests. Partial correlation was used to measure correlations between variables of cognitive tests and questionnaires, while adjusting for age, sex, and, where appropriate, the presence of attention-deficit/hyperactivity disorder. Results A total of 98 high-functioning participants with ASD were recruited (mean age, 11.44±3.13 years; 75.5% male). Significant correlations were found between their non-social cognitive functions and clinical characteristics. Specifically, Full Scale Intelligence Quotient (FSIQ), working memory, processing speed, attention, and impulse control were negatively associated with the severity of autistic, emotional, and behavioral symptoms. In addition, the Asperger’s disorder group demonstrated significantly better FSIQ, perceptual reasoning, working memory, processing speed, attention, and impulse control than the high-functioning autism group. Conclusion Significant correlations between non-social cognitive functions and autistic, emotional, and behavioral symptoms underscore the increased needs of high-functioning individuals with relatively poorer non-social cognitive abilities. Individualized support and management strategies can be developed and provided accordingly.

Sleep in Young Children With Autism, ADHD and Combined Presentations.

Autism and attention deficit hyperactivity disorder (ADHD) are often accompanied by sleep problems. Longitudinal studies hint towards a greater proportion of persisting sleep problems across early development in neurodivergent compared to neurotypical individuals. Within the context of a prospective infant sibling study of autism and ADHD (n = 209), we compared early caregiver-rated sleep trajectories of individuals diagnosed with autism, ADHD or cooccurring autism and ADHD (AuDHD) at 36 months with two groups that did not meet diagnostic criteria-one group of individuals with and one without elevated likelihood of autism and/or ADHD. Sleep behaviors assessed at 10 and 14 months (i.e., number of night awakenings and settle durations) did not differ between individuals with and without a diagnosis of autism and/or ADHD. At 24 and 36 months, individuals diagnosed with autism and/or ADHD took longer to fall asleep, had less sufficient sleep, more parasomnia-related behaviors and had greater overall sleep disturbance than those without a diagnosis, suggesting that their sleep problems are more circumscribed than often reported for older age ranges. In autism specifically, bedtime resistance improved from 24 to 36 months. While additional studies incorporating objective sleep measures are needed, the current results indicate that sleep involvement in neurodevelopmental conditions may mainly reflect transdiagnostic rather than diagnosis-specific processes in early childhood.

Digital phenotyping captures autism-associated behaviors in preschool- and school-age autistic children with and without co-occurring ADHD

There is a need for scalable, objective assessment tools to quantify autism-related behaviors in preschool- and school-age children. A significant challenge is the heterogeneous presentation of autism, driven in part by co-occurring conditions such as Attention-Deficit/Hyperactivity Disorder (ADHD). Tools intended for autism must therefore be tested in samples that include ADHD and other comorbidities, not only in autism-versus-neurotypical comparisons. SenseToKnow, a digital phenotyping app, quantifies autism-related behaviors using computer vision, tactile sensors, and machine learning, distinguishing autistic and neurotypical toddlers. We administered SenseToKnow to 183 children aged 40–100 months (3.3–8.3 years): 41 neurotypical, 48 ADHD, 53 autism, and 41 co-occurring autism and ADHD. Two complementary analyses converged. In age-adjusted group comparisons, autistic children, with and without ADHD, exhibited different SenseToKnow features compared to neurotypical and ADHD children, while autistic children with and without ADHD did not differ; children with ADHD alone differed from neurotypical children, particularly during nonsocial stimuli. Furthermore, in regression modeling, autism status was associated with 21 of 23 SenseToKnow features and ADHD status with none. Across both analysis, SenseToKnow features tracked autism status rather than ADHD status, even when the two co-occurred. These results demonstrate that SenseToKnow captures autism-associated behaviors even in the presence of co-occurring ADHD.