Multi-Hop Signed Graph Convolutional Network Under Long-term Dependencies Supervision for Neurodevelopmental Disorder Diagnosis

Brain network analysis leveraging resting-state functional magnetic resonance imaging offers promising prospects for the diagnosis of neurodevelopmental disorders (NDDs). Graph neural networks have demonstrated their powerful ability to extract informative latent features to facilitate diagnostic analysis. Nevertheless, existing signed multi-hop graph convolutional networks (GCNs) are plagued by insufficient prior supervision and the over-smoothing problem, which degrades diagnostic performance. In this study, we propose a long-term guided multi-hop signed graph convolutional network (LT-MSGCN) to identify autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). We construct a dual-branch architecture consisting of an auto-encoding (AE) branch and a multi-hop GCN branch. Specifically, the transformer-augmented AE branch captures long-term dependencies among brain regions, and the resulting relationship matrices serve as regularizers for multi-hop adjacency matrices generated via approximate personalized propagation of neural predictions (APPNP) within the multi-hop GCN branch. After feature extraction by stacked signed graph convolution layers, hop-aware and region-adaptive attention fusion modules further boost discriminative graph representation learning. Under 10-fold cross-validation on the ABIDE-I and ADHD-200 datasets, LT-MSGCN achieves classification accuracies of 71.53% and 70.20%, respectively. The identified discriminative brain regions show good agreement with established neuroscientific findings. Experimental results demonstrate that LT-MSGCN yields competitive classification performance and favorable biological interpretability for the auxiliary diagnosis of NDDs.