Growth and weight status of Brazilian children with autism spectrum disorders: A mixed longitudinal study

Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder

The individual differences among children with autism spectrum disorder (ASD) may make it challenging to achieve comparable benefits from a specific exercise intervention program. A new method for predicting the possible outcomes and maximizing the benefits of exercise intervention for children with ASD needs further exploration. Using the mini-basketball training program (MBTP) studies to improve the symptom performance of children with ASD as an example, we used the supervised machine learning method to predict the possible intervention outcomes based on the individual differences of children with ASD, investigated and validated the efficacy of this method. In a long-term study, we included 41 ASD children who received the MBTP. Before the intervention, we collected their clinical information, behavioral factors, and brain structural indicators as candidate factors. To perform the regression and classification tasks, the random forest algorithm from the supervised machine learning method was selected, and the cross validation method was used to determine the reliability of the prediction results. The regression task was used to predict the social communication impairment outcome following the MBTP in children with ASD, and explainable variance was used to evaluate the predictive performance. The classification task was used to distinguish the core symptom outcome groups of ASD children, and predictive performance was assessed based on accuracy. We discovered that random forest models could predict the outcome of social communication impairment (average explained variance was 30.58%) and core symptom (average accuracy was 66.12%) following the MBTP, confirming that the supervised machine learning method can predict exercise intervention outcomes for children with ASD. Our findings provide a novel and reliable method for identifying ASD children most likely to benefit from a specific exercise intervention program in advance and a solid foundation for establishing a personalized exercise intervention program recommendation system for ASD children.

Exercise improves the social and behavioral skills of children and adolescent with autism spectrum disorders

Background

Currently, there is no standard treatment for Autism Spectrum Disorders (ASD), but there are many ways to minimize the symptoms and maximize abilities. Some studies suggest that exercise and other physical activities with children with ASD may be beneficial. In this study, we hypothesized that a physical exercise program (48-week exercise-intervention) could improve symptomatology dyad among children and adolescents with ASD. Our main aim was to examine the effects of physical activity on the primary clinical symptoms and associated comorbidities in children and adolescents with ASD.

Methods

We allocated 229 children with ASD, ranging in age from 2.3-17.3 years (M = 7.8, SD = 3.2), into three groups: (a) exercise- intervention group, (b) control group from the same institution, and (c) control group from another institution. The exercise program was performed at moderate intensity in a 30 min section twice a week for 48 weeks. We used Bayesian multilevel regression modeling to examine participant outcomes and responses to the exercise-intervention.

Results

Our results showed that a 48-week exercise-intervention substantially decreased ASD social interaction problems, attention deficit, emotional reactivity, stereotypical verbal and motor behavior, and sleep disturbances. However, physical exercise did not affect eye contact and food selectivity. We also observed that ASD severity and socioeconomic status influence eye contact, attention deficit, and sleep disturbance responses.

Conclusion

In conclusion, children and adolescents with ASD exposed to a 48-week physical exercise-intervention program had important improvements in ASD symptoms. This study highlights that structured exercise programs can be a powerful complementary therapy for the ASD population.

Imbalanced Gamma-band Functional Brain Networks of Autism Spectrum Disorders

Resting gamma-band brain networks are known as an inhibitory component in functional brain networks. Although autism spectrum disorder (ASD) is considered as with imbalanced brain networks, the inhibitory component remains not fully explored. The study reported 10 children with ASD and 10 typically-developing (TD) controls. The power spectral density analysis of the gamma-band signal in the cerebral cortex was performed at the source level. The normalized phase transfer entropy values (nPTEs) were calculated to construct brain connectivity. Gamma-band activity of the ASD group was lower than the TD children. The significantly inhibited brain regions were mainly distributed in the bilateral frontal and temporal lobes. Connectivity analysis showed alterations in the connections from key nodes of the social brain network. The behavior assessments in the ASD group revealed a significantly positive correlation between the total score of Childhood Autism Rating Scale and the regional nPTEs of the right transverse temporal gyrus. Our results provide strong evidence that the gamma-band brain networks of ASD children have a lower level of brain activities and different distribution of information flows. Clinical meanings of such imbalances of both activity and connectivity were also worthy of further explorations.

Differences in Body Mass Index (BMI) in Early Adolescents with Autism Spectrum Disorder Compared to Youth with Typical Development

Adolescence is a time of exceptional physical health juxtaposed against significant psychosocial and weight-related problems. The study included 241, 10-to-13-year-old youth with autism spectrum disorder (ASD, N = 138) or typical development (TD, N = 103). Standardized exams measured pubertal development, height (HT), weight (WT), heart rate (HR), blood pressure (BP) and Body Mass Index (BMI). Analysis of Variance showed no significant between-group differences for HT, WT, HR, or BP (all p > 0.05). There was a significant difference in BMI-percentile between the groups (F(1,234) = 6.05, p = 0.01). Using hierarchical linear regression, significant predictors of BMI-percentile included diagnosis, pubertal stage and socioeconomic status. Pre-to-early pubescent children with ASD evidence higher BMI percentiles compared to youth with TD suggesting they may be at heightened risk for weight-related health concerns.