Dynamic and Systemic Perspective in Autism Spectrum Disorders: A Change of Gaze in Research Opens to A New Landscape of Needs and Solutions

An in-depth analysis of the polyvagal theory in light of current findings in neuroscience and clinical research

The polyvagal theory has led to the understanding of the functions of the autonomic nervous system in biological development in humans, since the vagal system, a key structure within the polyvagal theory, plays a significant role in addressing challenges of the mother-child dyad. This article aims to summarize the neurobiological aspects of the polyvagal theory, highlighting some of its strengths and limitations through the lens of new evidence emerging in several research fields-including comparative anatomy, embryology, epigenetics, psychology, and neuroscience-in the 25 years since the theory's inception. Rereading and incorporating the polyvagal idea in light of modern scientific findings helps to interpret the role of the vagus nerve through the temporal dimension (beginning with intrauterine life) and spatial dimension (due to the numerous connections of the vagus with various structures and systems) in the achievement and maintenance of biopsychosocial well-being, from the uterus to adulthood.

Intellectually able adults with autism spectrum disorder show typical resting-state EEG activity

There is broad interest in discovering quantifiable physiological biomarkers for psychiatric disorders to aid diagnostic assessment. However, finding biomarkers for autism spectrum disorder (ASD) has proven particularly difficult, partly due to high heterogeneity. Here, we recorded five minutes eyes-closed rest electroencephalography (EEG) from 186 adults (51% with ASD and 49% without ASD) and investigated the potential of EEG biomarkers to classify ASD using three conventional machine learning models with two-layer cross-validation. Comprehensive characterization of spectral, temporal and spatial dimensions of source-modelled EEG resulted in 3443 biomarkers per recording. We found no significant group-mean or group-variance differences for any of the EEG features. Interestingly, we obtained validation accuracies above 80%; however, the best machine learning model merely distinguished ASD from the non-autistic comparison group with a mean balanced test accuracy of 56% on the entirely unseen test set. The large drop in model performance between validation and testing, stress the importance of rigorous model evaluation, and further highlights the high heterogeneity in ASD. Overall, the lack of significant differences and weak classification indicates that, at the group level, intellectually able adults with ASD show remarkably typical resting-state EEG.