Atypical brain connectivity is considered a key neurobiological feature underlying the heterogeneous clinical manifestations of autism spectrum disorder (ASD). However, findings on brain networks in ASD are inconsistent, likely owing to the effects of developmental factors. In addition, how large-scale brain networks in ASD differ across developmental stages remains unclear. We aimed to elucidate the atypical developmental patterns of white matter (WM) structural networks in children and adolescents with ASD using a graph-theoretical approach.
Diffusion/T1-weighted brain imaging data were acquired from 69 individuals with ASD (age: 6–17 years) and 71 age- and sex-matched typically developing controls. Global and nodal topological properties of WM structural networks were computed, and 28 social-related regions were examined through subnetwork and nodal analyses. Case–control comparisons of global and nodal graph metrics were conducted separately for children and adolescents.
The children with ASD exhibited reduced integration of the whole-brain network, reflected by increased characteristic path length and decreased global efficiency. In contrast, the adolescents with ASD showed enhanced segregation within the social-brain subnetwork, indicated by increased clustering coefficient and local efficiency. Nodal analyses revealed reduced nodal efficiency across several social-related regions (e.g., the left inferior frontal gyrus, insula, amygdala, supramarginal gyrus, bilateral superior temporal poles) in children with ASD.
Topological disorganization in the autistic brain network varies across developmental stages, shifting from reduced global integration in childhood to enhanced segregation of social-brain circuits in adolescence. Such atypical WM structural organization may underlie the persistent social cognitive deficits observed in ASD.
Min Li, Kohei Kurita, Takashi Yamada et al.· Frontiers in Neuroscience· 0 citations
Neuroimaging and molecular studies have examined the etiology of attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). However, their findings remain inconsistent because of within-disorder heterogeneity and cross-disorder phenotypic overlap. We sought to identify monoamine-based subtypes across ADHD and ASD and clarify their brain structural characteristics. In 83 children with ADHD and/or ASD, we applied unsupervised machine learning (NbClust with K-means) to identify neurodevelopmental disorder (NDD) phenotypes using urinary monoamine metabolite (MM) profiles. Behavioral symptoms, cognitive performance, cortical surface area, and gray matter volume (GMV) were evaluated for each NDD phenotype and for 83 typically developing (TD) children as controls. Clustering identified two urinary MM-defined NDD phenotypes: NDD-A (n = 18, including 5 ADHD, 2 ASD, and 11 ADHD + ASD cases), characterized by high levels of 4-hydroxy-3-methoxyphenylglycol, 5-hydroxyindoleacetic acid, and homovanillic acid, and NDD-B (n = 65, including 16 ADHD, 19 ASD, and 30 ADHD + ASD cases), characterized by low levels of these metabolites. Urinary 4-hydroxy-3-methoxyphenylglycol levels correlated positively with social communication difficulties in NDD-A. NDD-B showed significantly lower cognitive control, cognitive flexibility, and inhibitory control than TD. Structurally, compared with TD, NDD-A showed significant surface area enlargement in the isthmus cingulate gyrus, whereas NDD-B exhibited significant GMV reductions primarily in fronto-opercular/orbitofrontal regions, with additional reductions in the superior parietal lobule and supramarginal gyrus. These findings suggest that urinary MM-defined NDD-A and NDD-B phenotypes are associated with distinct cognitive and brain structural characteristics. Such phenotype specificity may provide a novel framework for understanding within-disorder heterogeneity and cross-disorder phenotypic overlaps.
Masatoshi Yamashita, Qiulu Shou, Sayo Hamatani et al.· Journal of neural transmissi...· 0 citations