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Ting Xu

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25 papers

2 author rows

ECAI Conference 2025 Conference Paper

Cross-Modality Disentanglement and Fusion via Hyperedge-Centric Graph Learning for Brain Network Connectivity Analysis

Manman Yuan
Jiapei Li
Junlin Li
Jiacheng Wang
Ting Xu
Can Yin

Analyzing brain network connectivity (BNC) using multimodal neuroimaging to identify neurodegenerative diseases has attracted increasing attention. However, current methods largely rely on node-centric graphs and assume structural or semantic alignment across modalities, limiting the capture of high-order interactions and modality-specific patterns critical for accurate disease identification. In this paper, we propose a novel Hyperedge-Centric Graph Learning Network (HCGLNet) to address these limitations. Specifically, we present a hyperedge-centric graph construction strategy (HGC) that represents each modality as a hyperedge-centric graph, explicitly modelling high-order connectivity unique to each modality. Moreover, we design a disentangled latent learning module (DLM) that factorize shared and specific representations, preserving modality-specific features from dilution while enabling the extraction of shared cross-modal representations. Finally, we develop a representation-aware routing (RAR) algorithm to adaptively fuse modality-specific and shared features based on learned weights, enhancing discriminability for downstream tasks. Experiments on three real-world datasets show that our HCGLNet abstraction reduces graph size by over 80%, lowers computation, and achieves state-of-the-art performance in neurodegenerative disease classification.

Details

YNIMG Journal 2025 Journal Article

Early wounds, delayed consequences: Brain-behavior modeling reveals neural pathways linking childhood trauma to procrastination

Luo Xu
Yao Yin
Xueke Wang
Ting Xu
Xi Zhang
Tingyong Feng

Childhood trauma has enduring effects on emotional and cognitive functioning, yet its impact on procrastination, particularly from a neurodevelopmental perspective, remains poorly understood. To achieve this, we employed resting-state functional MRI in conjunction with standardized behavioral assessments of childhood trauma, trait anxiety, self-control, and procrastination across two datasets (discovery dataset: n = 760; validation dataset: n = 429). By leveraging the advanced predictive analytics-including connectome-based predictive modeling (CPM) and least absolute shrinkage and selection operator (LASSO) regression-we aimed to elucidate the neural basis linking childhood trauma to procrastination. Our behavioral results revealed that childhood trauma was a significant predictor of elevated procrastination tendencies, with this association mediated by increased trait anxiety and reduced self-control. At the neural level, the predictive modeling using CPM and LASSO regression demonstrated that functional connectivity within and between the frontoparietal network (FPN), salience network (SAN), visual network (VN), and cerebellum significantly predicted childhood trauma. These patterns likely reflect trauma-related disruptions in higher-order cognitive control (e.g., self-control) and increased affective reactivity (e.g., trait anxiety). More importantly, the mediation analyses further confirmed that trait anxiety and self-control jointly mediate the relationship between trauma-related neural network connectivity and procrastination. These findings presented novel evidence that childhood trauma is associated with procrastination via functional alterations in large-scale neural networks implicated in self-control and emotion regulation, providing critical insights into the long-term behavioral consequences of early-life adversity, and informing the development of targeted interventions to reduce procrastination in trauma-exposed individuals.