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.