Cross-attention-guided subject-adaptive graph learning for multimodal autism classification: integrating structural and functional MRI data.

Authors

Yan Tang, Chao Yang, Yihang Xu, Hao Zhang, Hua Xie

Published in

Brain informatics. Jul 08, 2026. Epub Jul 08, 2026.

Abstract

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition marked by structural atypicality and abnormal functional connectivity. It remains challenging to accurately delineate an ASD-associated neural marker due to individual heterogeneity and multi-site data variability. To address these issues, we propose a cross-attention-guided subject-adaptive graph network (CAS-GNN) model that integrates structural MRI and resting-state functional connectivity data, effectively fusing complementary multimodal information. By modeling individualized brain network topologies and incorporating a site-invariant learning strategy, our approach enhances discriminability and cross-site generalization. On the ABIDE-I dataset, CAS-GNN significantly outperformed machine learning baselines and achieved an accuracy of 79.25% ± 4.71% on independent test data and an average accuracy of 78.75% ± 1.56% on five-fold cross-validation. Exploratory analyses identified key ASD-related brain regions and connections, revealing a notable right-hemisphere dominance consistent with atypical asymmetry in ASD. Our framework offers valuable neurobiological insights and provides a promising tool for interpretable and robust ASD diagnosis, accelerating biomarker discovery and development.

PMID:
42420591
Bibliographic data and abstract were imported from PubMed on 09 Jul 2026.

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