Psychometric Properties of the Chinese Version of the Gilliam Autism Rating Scale-Third Edition (CV-GARS-3).

Gilliam Autism Rating Scale-Third Edition (GARS-3) serves as an effective screening tool for autism spectrum disorder (ASD) that is based on the latest and authoritative diagnostic criteria, however, there is a deficiency in adaptive research in China. We aimed to revise the Chinese version of GARS-3 (CV-GARS-3) and evaluate its psychometric characteristics, providing a theoretical basis for the improvement of ASD screening tools in China. This study developed CV-GARS-3 through translation and cultural adaptation of GARS-3. 362 ASD individuals, 126 typical development individuals, and 103 individuals with other disorders were recruited to analyze the psychometric characteristics of CV-GARS-3. The results showed that exploratory structural equation model demonstrated satisfactory goodness-of-fit. Within the non-verbal ASD samples, all items loaded on anticipated factors. Regarding verbal ASD samples, 3 items exhibited considerable cross-loadings and were categorized under unexpected factors. Meanwhile, acceptable criterion validity was reflected in the four subscales (r = 0.71) and the six subscales (r = 0.74). Satisfactory reliability was observed in the four subscales (Cronbach's α = 0.96, inter-rater consistency = 0.86, test-retest consistency = 0.87) and the six subscales (Cronbach's α = 0.94, inter-rater consistency = 0.81, test-retest consistency = 0.81). In addition, receiver operating characteristic analysis demonstrated that both the four subscales (sensitivity = 89%, specificity = 85%, accuracy = 88%) and the six subscales (sensitivity = 86%, specificity = 88%, accuracy = 86%) had outstanding screening effects. Therefore, the results suggested that the CV-GARS-3 is considered as a useful tool for the screening and auxiliary diagnosis of ASD. Notably, the expression of scale should be further improved to adapt the context of Chinese culture and achieve more precise diagnostic results.

Physical activity promotes brain development through serotonin during early childhood.

Early childhood serves as a critical period for neural development and skill acquisition when children are extremely susceptible to the external environment and experience. As a crucial experiential stimulus, physical activity is believed to produce a series of positive effects on brain development, such as cognitive function, social-emotional abilities, and psychological well-being. The World Health Organization recommends that children engage in sufficient daily physical activity, which has already been strongly advocated in the practice of preschool education. However, the mechanisms by which physical activity promotes brain development are still unclear. The role of neurotransmitters, especially serotonin, in promoting brain development through physical activity has received increasing attention. Physical activity has been shown to stimulate the secretion of serotonin by increasing the bioavailability of free tryptophan and enriching the diversity of gut microbiota. Due to its important role in modulating neuronal proliferation, differentiation, synaptic morphogenesis, and synaptic transmission, serotonin can regulate children's explicit cognitive and social interaction behavior in the early stages of life. Therefore, we hypothesized that serotonin emerges as a pivotal transmitter that mediates the relationship between physical activity and brain development during early childhood. Further systematic reviews and meta-analyses are needed to specifically explore whether the type, intensity, dosage, duration, and degree of voluntariness of PA may affect the role of serotonin in the relationship between physical activity and brain function. This review not only helps us understand the impact of exercise on development but also provides a solid theoretical basis for increasing physical activity during early childhood.

A Review on Autism Spectrum Disorder Screening by Artificial Intelligence Methods.

PURPOSE: With the increasing prevalence of autism spectrum disorders (ASD), the importance of early screening and diagnosis has been subject to considerable discussion. Given the subtle differences between ASD children and typically developing children during the early stages of development, it is imperative to investigate the utilization of automatic recognition methods powered by artificial intelligence. We aim to summarize the research work on this topic and sort out the markers that can be used for identification. METHODS: We searched the papers published in the Web of Science, PubMed, Scopus, Medline, SpringerLink, Wiley Online Library, and EBSCO databases from 1st January 2013 to 13th November 2023, and 43 articles were included. RESULTS: These articles mainly divided recognition markers into five categories: gaze behaviors, facial expressions, motor movements, voice features, and task performance. Based on the above markers, the accuracy of artificial intelligence screening ranged from 62.13 to 100%, the sensitivity ranged from 69.67 to 100%, the specificity ranged from 54 to 100%. CONCLUSION: Therefore, artificial intelligence recognition holds promise as a tool for identifying children with ASD. However, it still needs to continually enhance the screening model and improve accuracy through multimodal screening, thereby facilitating timely intervention and treatment.