Predictability modulates neural response to eye contact in ASD

A multimodal interface for speech perception: the role of the left superior temporal sulcus in social cognition and autism

Multimodal integration is crucial for human interaction, in particular for social communication, which relies on integrating information from various sensory modalities. Recently a third visual pathway specialized in social perception was proposed, which includes the right superior temporal sulcus (STS) playing a key role in processing socially relevant cues and high-level social perception. Importantly, it has also recently been proposed that the left STS contributes to audiovisual integration of speech processing. In this article, we propose that brain areas along the right STS that support multimodal integration for social perception and cognition can be considered homologs to those in the left, language-dominant hemisphere, sustaining multimodal integration of speech and semantic concepts fundamental for social communication. Emphasizing the significance of the left STS in multimodal integration and associated processes such as multimodal attention to socially relevant stimuli, we underscore its potential relevance in comprehending neurodevelopmental conditions characterized by challenges in social communication such as autism spectrum disorder (ASD). Further research into this left lateral processing stream holds the promise of enhancing our understanding of social communication in both typical development and ASD, which may lead to more effective interventions that could improve the quality of life for individuals with atypical neurodevelopment.

Autism Spectrum Disorder detection framework for children based on federated learning integrated CNN-LSTM

The incidence of Autism Spectrum Disorder (ASD) among children, attributed to genetics and environmental factors, has been increasing daily. ASD is a non-curable neurodevelopmental disorder that affects children's communication, behavior, social interaction, and learning skills. While machine learning has been employed for ASD detection in children, existing ASD frameworks offer limited services to monitor and improve the health of ASD patients. This paper presents a complex and efficient ASD framework with comprehensive services to enhance the results of existing ASD frameworks. Our proposed approach is the Federated Learning-enabled CNN-LSTM (FCNN-LSTM) scheme, designed for ASD detection in children using multimodal datasets. The ASD framework is built in a distributed computing environment where different ASD laboratories are connected to the central hospital. The FCNN-LSTM scheme enables local laboratories to train and validate different datasets, including Ages and Stages Questionnaires (ASQ), Facial Communication and Symbolic Behavior Scales (CSBS) Dataset, Parents Evaluate Developmental Status (PEDS), Modified Checklist for Autism in Toddlers (M-CHAT), and Screening Tool for Autism in Toddlers and Children (STAT) datasets, on different computing laboratories. To ensure the security of patient data, we have implemented a security mechanism based on advanced standard encryption (AES) within the federated learning environment. This mechanism allows all laboratories to offload and download data securely. We integrate all trained datasets into the aggregated nodes and make the final decision for ASD patients based on the decision process tree. Additionally, we have designed various Internet of Things (IoT) applications to improve the efficiency of ASD patients and achieve more optimal learning results. Simulation results demonstrate that our proposed framework achieves an ASD detection accuracy of approximately 99% compared to all existing ASD frameworks.