Group-wise sparse coding for the discovery of functional connectivity-based biomarkers in children and adolescents with autism spectrum disorder

Diagnostic classification of children and adolescents with high-functioning autism spectrum disorder based on brain functional network characteristics of the dense individualized and common connectivity-based cortical landmark model

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. 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. 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. 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.

Cortical Network Overconnectivity Relates to Sensory, Cognitive, and Social Dimensions in Young Children With Autism Spectrum Disorder.

The heterogeneity in both the neurobiological mechanisms and the phenotypic presentations of autism spectrum disorder (ASD) poses a major challenge to clinical and translational research. Alterations in functional connectivity (FC) have been associated with ASD, yet it remains unclear whether and how divergent brain network properties may account for individual differences across ASD-related symptomatology and behaviors. We applied source-level reconstruction to rest-like non-task-related high-density EEG data in a cohort of 104 young children (38 with ASD) to identify global and local alterations of cortical network connectivity. We subsequently used regularized canonical correlation analysis (rCCA) to characterize specific FC patterns linked to variation in cognitive, social and sensory dimensions derived from standard clinical instruments. We found increased low-frequency FC in frontotemporal cross-hemispheric networks and lateral-occipital regions of young ASD children versus healthy peers. RCCA revealed three distinct FC patterns in recurrent ASD-related networks, each contributing to predict individual differences in cognitive, social and sensory features. These linked FC-behavior dimensions may shed light on atypical brain network topology associated with specific phenotypic manifestations of ASD, which might implicate unique underlying neurobiological mechanisms.

Atypical development of white matter structural networks in children and adolescents with autism spectrum disorder: a graph theory study

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.

Structural brain alterations associated with brain age may link to social dysfunction in male adults with autism spectrum disorder

Background While atypical brain development in autism spectrum disorder (ASD) has been extensively characterized during childhood and adolescence, it remains unclear how these neurodevelopmental deviations persist into adulthood and affect brain aging. Existing studies relying on single morphometric measures have yielded inconsistent findings, underscoring the need for integrative, multiscale neuroimaging approaches. Materials and methods Using data from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset, we investigated brain structural alterations in 90 adult males with ASD and 132 age-matched typically developing (TD) controls. All participants were right-handed and aged 18–55 years. Voxel-based morphometry (VBM) was employed to assess gray matter volume (GMV), and surface-based morphometry (SBM) was used to quantify cortical fractal dimension (FD). Global brain aging was evaluated using MRI-derived brain age estimation, from which the brain age gap (BAG) was calculated. Site-related effects were harmonized using the ComBat method. Group comparisons were performed for GMV, FD, and BAG using multiple linear regression, with age, full-scale IQ, and total intracranial volume included as covariates. Associations between neuroimaging metrics and Autism Diagnostic Observation Schedule (ADOS) scores were further examined. Results Cross-sectional comparisons demonstrated that adults with ASD exhibited higher estimated BAG values relative to TD controls (F = 6.838, p = 0.01, partial η2 = 0.031). ComBat-harmonized morphometric analyses revealed exploratory localized GMV and FD differences, including increased GMV and FD in the right precuneus and increased FD in the lingual gyrus and lateral orbitofrontal cortex. GMV in the right precuneus showed an exploratory positive correlation with ADOS social-domain scores (r = 0.214, q = 0.044). Conclusion Adults with ASD exhibited higher estimated BAG relative to TD controls in this cross-sectional sample. 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.

Normative EEG Effective Connectivity as a Neural Marker of Autism Spectrum Disorder

Alterations in Early Alpha-band Connectivity emerge in Infancy among children later diagnosed with Autism

Autism is a heterogeneous neurodevelopmental condition, often accompanied by challenges in language and cognitive development. Although atypical functional connectivity (FC) has been reported in autism, the timing of when it first emerges and its relevance for later behavior remain poorly understood. In this study, we examined developmental trajectories of alpha-band FC and network organization across the first three years of life. We computed global alpha-band measures, including peak alpha connectivity frequency (PACF), mean FC, clustering coefficient, and modularity, to characterize nonlinear developmental trajectories from longitudinal EEGs collected from 238 children (3-to-36-month-olds) with (Autism; n=58) and without (LL-noAutism; n=180) autism. Network-based statistics (NBS-Predict) identified subnetworks contributing to group differences at each age. Exploratory graph analyses (EGA) examined associations among FC, network measures, and language outcomes. We observed that PACF increased linearly with age in both groups. Global alpha-band connectivity measures showed a similar developmental pattern, with mean global FC, clustering coefficient, and modularity all increasing rapidly during the first year in both groups. Thereafter, these measures declined in the Autism group but continued to gradually increase in the LL-noAutism group. Compared to LL-noAutism, NBS-Predict identified both hyper- and hypo-connectivity subnetworks in Autism at 3 months, followed by a hypo-connectivity subnetwork at 24 and 36 months. EGA indicated that early hyperconnectivity predicted later hypoconnectivity and was associated with subsequent network organization and language outcomes. These findings indicate that altered alpha-band connectivity trajectories are detectable in infancy in children later diagnosed with autism and may contribute to later differences in developmental outcomes.