Aug 2026· Frontiers in Neuroscience· Vol 20· 0 citations· 86 references
Medicine
TL;DR
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.
Abstract
Background 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. Methods 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. Results 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. Conclusion 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.
An exploratory association between right precuneus GMV and ADOS social-domain scores suggests a possible link between localized structural variation and social symptom severity, although this finding requires replication in longitudinal and clinically richer datasets given their sensitivity to the harmonization strategy.
Gang Xiao, Xiaoshi Li, Yue Qin et al.· Frontiers in Neuroscience· 0 citations
RCCA revealed three distinct FC patterns in recurrent ASD-related networks, each contributing to predict individual differences in cognitive, social and sensory features, which may shed light on atypical brain network topology associated with specific phenotypic manifestations of ASD.
B. Rodríguez-Herreros, A. Mheich, J. A. Osório et al.· Autism Research· 0 citations
Objective To investigate whether structure-informed functional connectivity patterns derived from the Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL) framework can distinguish children and adolescents with high-functioning autism spectrum disorder (HF-ASD) from typically developing (TD) controls, and to explore the clinical relevance of the identified connectivity features. Methods Multimodal magnetic resonance imaging data, including diffusion tensor imaging (DTI) and resting-state functional MRI (rs-fMRI), were acquired from 37 participants with HF-ASD and 33 TD controls. A total of 358 DICCCOL landmarks were localized in each participant’s individual brain space based on DTI-derived white matter connectivity patterns. rs-fMRI data were aligned to the corresponding DTI space, and whole-brain functional connectivity was calculated among DICCCOL landmarks. Classification was performed using a linear support vector machine within a fully nested leave-one-out cross-validation framework. All supervised procedures, including FDR-corrected group comparisons, correlation-based feature selection, feature standardization, and hyperparameter optimization, were conducted exclusively within the training data of each cross-validation iteration. Stable discriminative functional connections were further characterized according to their functional network affiliations, and exploratory associations with clinical measures were examined. Results The DICCCOL-based functional connectivity model achieved an out-of-fold classification accuracy of 84.29%, with a sensitivity of 83.78%, a specificity of 84.85%, and an area under the receiver operating characteristic curve of 0.832. The stable discriminative functional connections included both increased and decreased connectivity in the HF-ASD group and involved both intra-network and inter-network interactions. These connections were primarily distributed across cognitive-cognitive, cognitive-affective, and affective-affective systems. In addition, several stable functional connections showed significant negative associations with clinical measures, including ADI-R total scores, ADI-R Social Interaction scores, and GEM-PR scores, suggesting potential links between altered connectivity patterns and individual differences in autism-related symptom burden, social functioning, and empathic ability. Conclusions Structure-informed functional connectivity features based on individualized DICCCOL landmarks demonstrated good discriminative potential for identifying HF-ASD in the present sample. The identified connectivity patterns may reflect altered functional integration across cognitive and affective systems and may be related to clinical heterogeneity in ASD. These findings should be considered preliminary, and the identified patterns should be regarded as candidate neuroimaging signatures rather than established diagnostic biomarkers. Validation in larger, longitudinal, independent, and multi-center cohorts is warranted.
Yonglu Wang, Jing-Jing Ma, Zhengwang Xia et al.· Frontiers in Psychiatry· 0 citations
Children and adolescents with ASD exhibited lower empathy capabilities than control subjects, which may be attributed to dysfunctions in the salience and social brain networks.
Yong-Lu Wang, Zhang-Liang Ma, Zhiyi Wang et al.· Frontiers in Psychiatry· 0 citations
FMRI data from 162 ASD and 175 TD adolescents are analyzed to demonstrate statistical associations among altered spatial-functional properties, clinical severity, and transcriptomic profiles related to synaptic signaling, mitochondrial processes, and glial-related functions in ASD, providing a complementary spatial perspective on large-scale functional organization.
Jun Pan, Heng Zhang, Yiran Zhai et al.· Frontiers in Neuroscience· 0 citations
Autism spectrum disorder (ASD) is classically conceptualized as a dysconnectivity syndrome. However, most studies have examined structural and functional connectivity in isolation, leaving the coupling mechanisms between brain structure and function, particularly the contribution of white matter, poorly understood. To systematically characterize connectome pathology in ASD, we analyzed multimodal imaging data from 580 participants (240 with ASD, 340 typical controls) in the Autism Brain Imaging Data Exchange II dataset. We constructed multilayer brain networks integrating gray and white matter layers and quantified topological alterations using multiplex clustering and participation coefficients. Imaging-transcriptomic analysis was performed using the Allen Human Brain Atlas to link network changes to molecular pathways. The results revealed widespread whole-brain topological reorganization in white matter multiplex networks, involving the corpus callosum and major fiber tracts, with gene expression enriched in immune regulation and cellular metabolism pathways. The gray matter multiplex networks exhibited localized hyper-clustering centered on the cortico-striatum-thalamic-cortical circuit and the default mode network associated with genes implicated in cell adhesion and synaptic transmission. Notably, no significant group differences were observed in the multiplex participation coefficient, which indexes cross-layer integration, suggesting that the overall cross-layer connectivity distribution remains relatively stable. These findings delineate the co-occurring patterns of gray matter hyper-clustering and widespread white matter topological alterations in ASD and establish a multilevel framework bridging macroscopic connectome disruptions to the underlying molecular mechanisms, offering an integrated perspective on ASD heterogeneity.
Yingzhuo Wan, Hairong Xiao, Hanrui Chen et al.· Progress in Neuro-psychophar...· 0 citations
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