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

Abstract

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.