BACKGROUND
Autism spectrum disorder (ASD) is characterized by profound clinical and biological heterogeneity. The neurodynamic profiles associated with divergent developmental trajectories of executive function (EF) during the critical transition from late childhood to early adolescence remain poorly understood. This study aimed to determine whether heterogeneity in EF development is associated with distinct neurodynamic profiles.
METHODS
In a longitudinal study, 68 children with ASD (aged 6-9 years) and 50 age-matched typically developing (TD) controls underwent baseline resting-state fMRI. The ASD group was followed for approximately 4 years. EF was assessed using the Behavior Rating Inventory of Executive Function (BRIEF), alongside follow-up depression, anxiety, and sleep outcomes. Longitudinal EF trajectory clusters were identified to characterize developmental heterogeneity. In parallel, baseline neurofunctional subtypes were derived from ALFF using a normative-deviation framework, with fALFF used for sensitivity analysis. CAP analysis was then applied to examine brain-state dynamics across these complementary stratification approaches.
RESULTS
EF declined longitudinally in the ASD group, particularly in behavioral regulation domains, and these changes were associated with depressive symptoms and sleep problems at follow-up. Three longitudinal EF trajectory clusters were identified, but baseline CAP dynamics showed minimal differences across these groups. In contrast, ALFF-derived neurofunctional subtypes exhibited distinct CAP dynamic profiles, with Subtype 1 showing greater engagement of visual-related states and Subtype 2 exhibiting enhanced transitions among DMN/FPN-related control states. fALFF-based analyses yielded similar subtype assignments, supporting the robustness of the neurofunctional stratification. Critically, similar EF deterioration was associated with distinct neurodynamic profiles, as reflected by subtype-specific state-transition patterns that showed opposite associations with EF changes.
LIMITATIONS
First, the TD group was not followed longitudinally, limiting precise quantification of deviation from normative developmental pathways. Second, the sample size and attrition during follow-up may affect the stability of subtype assignment. Finally, as inferences were based on resting-state fMRI, task-based or ecologically valid measures were not available to validate functional interpretations.
CONCLUSION
These findings provide evidence for neurodynamic heterogeneity in ASD, suggesting that similar clinical outcomes may be associated with divergent brain-state profiles. This work supports the move toward neuro-subtype-informed precision stratification and targeted intervention strategies.
Zenghe Yue, Jinyi Zhu, Yuxuan Wang et al.· Molecular Autism· 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, Jingjing Ma, Zhengwang Xia et al.· Frontiers in Psychiatry· 0 citations