Multilevel Correlation-Aware and Modal-Aware Graph Convolutional Network for Diagnosing Neurodevelopmental Disorders

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: 93.11% accuracy for ASD diagnosis, 76.41% accuracy for ADHD diagnosis

• Primary Outcome: Diagnostic accuracy for neurodevelopmental disorders

• Nnt Or Sensitivity: Sensitivity and specificity not reported; only overall accuracy provided

• Confidence Interval: Not reported

Clinical Application

Requires specialized fMRI facilities and computational infrastructure; limited to centers with advanced neuroimaging capabilities Could complement existing RACGP guidelines for ASD/ADHD assessment if validated in Australian populations; would require TGA approval as diagnostic software; cost-effectiveness analysis needed for PBS consideration Pediatric and adult patients with suspected ASD or ADHD requiring diagnostic evaluation

Abstract

OBJECTIVE: Graph-based methods using resting-state functional magnetic resonance imaging demonstrate strong capabilities in modeling brain networks. However, existing graph-based methods often overlook inter-graph relationships, limiting their ability to capture the intrinsic features shared across individuals. Additionally, their simplistic integration strategies may fail to take full advantage of multimodal information. To address these challenges, this paper proposes a Multilevel Correlation-aware and Modal-aware Graph Convolutional Network (MCM-GCN) for the reliable diagnosis of neurodevelopmental disorders. METHODS: At the individual level, we design a correlation-driven feature generation module that incorporates a pooling layer with external graph attention to perceive inter-graph correlations, generating discriminative brain embeddings and identifying disease-related regions. At the population level, to deeply integrate multimodal and multi-atlas information, a multimodal-decoupled feature enhancement module learns unique and shared embeddings from brain graphs and phenotypic data and then fuses them adaptively with graph channel attention for reliable disease classification. RESULTS: Extensive experiments on two public datasets for Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) demonstrate that MCM-GCN outperforms other competing methods, with an accuracy of 93.11% for ASD and 76.41% for ADHD. CONCLUSION: The MCM-GCN framework integrates individual-level and population-level analyses, offering a comprehensive perspective for neurodevelopmental disorder diagnosis, significantly improving diagnostic accuracy while identifying key indicators. SIGNIFICANCE: These findings highlight the potential of the MCM-GCN for imaging-assisted diagnosis of neurodevelopmental diseases, advancing interpretable deep learning in medical imaging analysis.

References

1. 1.Zuo, S., Li, Y., Qi, Y., & Liu, A. (2026). Multilevel Correlation-Aware and Modal-Aware Graph Convolutional Network for Diagnosing Neurodevelopmental Disorders. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/TBME.2025.3617348