Cortical Thickness and Clinical Findings in Prescholar Children With Autism Spectrum Disorder

The term autism spectrum disorder (ASD) includes a wide variability of clinical presentation, and this clinical heterogeneity seems to reflect a still unclear multifactorial etiopathogenesis, encompassing different genetic risk factors and susceptibility to environmental factors. Several studies and...

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Bibliographic Details
Published in:Frontiers in neuroscience 2022-02, Vol.15, p.776860-776860
Main Authors: Lucibello, Simona, Bertè, Giovanna, Verdolotti, Tommaso, Lucignani, Martina, Napolitano, Antonio, D'Abronzo, Rosa, Cicala, Maria G, Pede, Elisa, Chieffo, Daniela, Mariotti, Paolo, Colosimo, Cesare, Mercuri, Eugenio, Battini, Roberta
Format: Article
Language:English
Subjects:
Autism
autism spectrum disorder (ASD)
Autistic children
Automation
Behavior
Brain
Communication
cortical thickness
Datasets
Environmental factors
local gyrification index
Magnetic resonance imaging
MRI
Neural networks
Neuroimaging
neuropsychological
Neuroscience
pre-scholar child
Risk factors
Substantia alba
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Summary:The term autism spectrum disorder (ASD) includes a wide variability of clinical presentation, and this clinical heterogeneity seems to reflect a still unclear multifactorial etiopathogenesis, encompassing different genetic risk factors and susceptibility to environmental factors. Several studies and many theories recognize as mechanisms of autism a disruption of brain development and maturation time course, suggesting the existence of common neurobiological substrates, such as defective synaptic structure and aberrant brain connectivity. Magnetic resonance imaging (MRI) plays an important role in both assessment of region-specific structural changes and quantification of specific alterations in gray or white matter, which could lead to the identification of an MRI biomarker. In this study, we performed measurement of cortical thickness in a selected well-known group of preschool ASD subjects with the aim of finding correlation between cortical metrics and clinical scores to understand the underlying mechanism of symptoms and to support early clinical diagnosis. Our results confirm that recent brain MRI techniques combined with clinical data can provide some useful information in defining the cerebral regions involved in ASD although large sample studies with homogeneous analytical and multisite approaches are needed.
ISSN:1662-4548
1662-453X
1662-453X
DOI:10.3389/fnins.2021.776860