Background: Reduced or absence of the response to name (RTN) has been widely reported as an early specific indicator for autism spectrum disorder (ASD), while few studies have quantified the RTN of toddlers with ASD in an automatic way. The present study aims to apply a multimodal machine learning system (MMLS) in early screening for toddlers with ASD based on the RTN.Methods: A total of 125 toddlers were recruited, including ASD (n = 61), developmental delay (DD, n = 31), and typical developmental (TD, n = 33). Procedures of RTN were, respectively, performed by the evaluator and caregiver. Behavioral data were collected by eight-definition tripod-mounted cameras and coded by the MMLS. Response score, response time, and response duration time were accurately calculated to evaluate RTN.Results: Total accuracy of RTN scores rated by computers was 0.92. In both evaluator and caregiver procedures, toddlers with ASD had significant differences in response score, response time, and response duration time, compared to toddlers with DD and TD (all P-values < 0.05). The area under the curve (AUC) was 0.81 for the computer-rated results, and the AUC was 0.91 for the human-rated results. The accuracy in the identification of ASD based on the computer- and human-rated results was, respectively, 74.8 and 82.9%. There was a significant difference between the AUC of the human-rated results and computer-rated results (Z = 2.71, P-value = 0.007).Conclusion: The multimodal machine learning system can accurately quantify behaviors in RTN procedures and may effectively distinguish toddlers with ASD from the non-ASD group. This novel system may provide a low-cost approach to early screening and identifying toddlers with ASD. However, machine learning is not as accurate as a human observer, and the detection of a single symptom like RTN is not sufficient enough to detect ASD.

Behavioral observation plays an essential role in the diagnosis of Autism Spectrum Disorder (ASD) by analyzing children’s atypical patterns in social activities (e.g., impaired social interaction, restricted interests, and repetitive behavior). To date, this process still heavily relies on the questionnaire survey, clinical observation, or retrospective video analysis, leading to high demand for professionals with massive labor costs. This paper proposes a standardized platform for stimulating, gathering, analyzing, modeling, and interpreting human behavioral data in the application of computer-aided ASD diagnosis. By a structured assessment process, the proposed system can automatically evaluate children’s multiple social interaction skills using the captured audio-visual data and provide the final diagnostic suggestions. We collect a multimodal behavioral database of 95 participants (71 children with ASD and 24 age-matched typical controls) in a real clinic environment, the Third Affiliated Hospital of Sun Yat-sen University, China. On the clinical database, our proposed computer-aided ASD diagnosis system obtains an accuracy of 88.42% for identifying ASD children with an average age of 24 months, representing a performance comparable to top-level human experts. As a unified and replicable solution, it has good potential to be promoted to less developed areas with limited high-quality medical resources.

Original Article Parents’ knowledge and attitude and behavior toward autism: a survey of Chinese families having children with autism spectrum disorder Fang Wang, U-Chong Lao, Yi-Pei Xing, Ping Zhou, Wen-Lin Deng, Yu Wang, Yue Ji, Miao-Ying Chen, Hai Li, Xiao-Bing Zou

The prevalence of autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) is increasing, with a tendency for co-occurrence. Some studies indicate a connection between atypical sensory processing and executive function. This study aims to explore the distinctive etiology of executive function deficits in children with ASD+ADHD by investigating the relationship between sensory processing and executive function, comparing children with ASD, ASD+ADHD, ADHD, and typically developing children (TD). Method: Sensory Profile 2 (SP-2) and Behavior Rating Inventory of Executive Function 2 (BRIEF-2) were measured in 120 school-aged children. The results of the above scales were compared across these four groups, and correlation and regression analyses between BRIEF2 and SP2 were conducted. Results: Our research revealed varying levels of atypical sensory processing and executive function anomalies across the three neurodevelopmental disorder groups compared to the TD group. The ASD+ADHD group showed particularly significant differences. The heightened emotional problems observed in ASD+ADHD children may be associated with more prominent atypical sensory processing. Variance analysis of inhibitory function revealed differences between ASD+ADHD and ADHD children, suggesting distinct etiological mechanisms for attention issues between ASD+ADHD and ADHD. Conclusions: ASD+ADHD represents a phenotype distinct from both ASD and ADHD. Special consideration should be given to interventions for children with ASD+ADHD. The results of this study may offer a new perspective on understanding the occurrence of ASD+ADHD and potential individualized intervention methods.

In the current study, functional near-infrared spectroscopy (fNIRS) was used to collect resting-state signals from 77 males with autism spectrum disorders (ASD, age: 6~16.25) and 40 typically developing (TD) males (age: 6~16.58) in the theory-of-mind (ToM) network. The graph theory analysis was used to obtain the brain network properties in ToM network, and the multiple regression analysis demonstrated that males with ASD showed a comparable global network topology, and a similar age-related decrease in the medial prefrontal cortex area (mPFC) compared to TD individuals. Nevertheless, participants with ASD showed U-shaped trajectories of nodal metrics of right temporo-parietal junction (TPJ), and an age-related decrease in the left middle frontal gyrus (MFG), while trajectories of TD participants were opposite. The nodal metrics of the right TPJ was negatively associated with the social deficits of ASD, while the nodal metrics of the left MFG was negatively associated with the communication deficits of ASD. Current findings suggested a distinct developmental trajectory of the ToM network in males with ASD from childhood to adolescence.