EEG Connectivity Diversity Differences between Children with Autism and Typically Developing Children: A Comparative Study

Synapse and primary cilia dysfunctions in Autism Spectrum Disorders. Avenues to normalize these functions

AIM

An update on the plasticity of the brain networks involved in autism (autism spectrum disorders [ASD]), and the increasing role of their synapses and primary non-motile cilia.

METHODS

Data from PubMed and Google on this subject, published until February 2024, were analyzed.

RESULTS

Structural and functional brain characteristics and genetic particularities involving synapses and cilia that modify neuronal circuits are observed in ASD, such as reduced pruning of dendrites, minicolumnar pathology, or persistence of connections usually doomed to disappear. Proteins involved in synapse functions (such as neuroligins and neurexins), in the postsynaptic architectural scaffolding (such as Shank proteins) or in cilia functions (such as IFT-independent kinesins) are often abnormal. There is an increase in glutaminergic transmission and a decrease in GABA inhibition. ASD may occur in genetic ciliopathies. The means of modulating these specificities, when deemed useful, are described.

INTERPRETATION

The wide range of clinical manifestations of ASD is strongly associated with abnormalities in the morphology, functions, and plasticity of brain networks, involving their synapses and non-motile cilia. Their modulation offers important research perspectives on treatments when needed, especially since brain plasticity persists much later than previously thought. Improved early detection of ASD and additional studies on synapses and primary cilia are needed.

Enhancing autism spectrum disorder classification in children through the integration of traditional statistics and classical machine learning techniques in EEG analysis

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder hallmarked by challenges in social communication, limited interests, and repetitive, stereotyped movements and behaviors. Numerous research efforts have indicated that individuals with ASD exhibit distinct brain connectivity patterns compared to control groups. However, these investigations, often constrained by small sample sizes, have led to inconsistent results, suggesting both heightened and diminished long-range connectivity within ASD populations. To bolster our analysis and enhance their reliability, we conducted a retrospective study using two different connectivity metrics and employed both traditional statistical methods and machine learning techniques. The concurrent use of statistical analysis and classical machine learning techniques advanced our understanding of model predictions derived from the spectral or connectivity attributes of a subject's EEG signal, while also verifying these predictions. Significantly, the utilization of machine learning methodologies empowered us to identify a unique subgroup of correctly classified children with ASD, defined by the analyzed EEG features. This improved approach is expected to contribute significantly to the existing body of knowledge on ASD and potentially guide personalized treatment strategies.