Topology-Aware Graph Augmentation for Predicting Clinical Trajectories in Neurocognitive Disorders

• URL: http://arxiv.org/abs/2411.00888v1
• Date: Thu, 31 Oct 2024 19:37:20 GMT
• Title: Topology-Aware Graph Augmentation for Predicting Clinical Trajectories in Neurocognitive Disorders
• Authors: Qianqian Wang, Wei Wang, Yuqi Fang, Hong-Jun Li, Andrea Bozoki, Mingxia Liu,
• Abstract summary: We propose a topology-aware graph augmentation (TGA) framework, comprising a pretext model to train a generalizable encoder and a task-specific model to perform downstream tasks. Experiments on 1, 688 fMRI scans suggest that TGA outperforms several state-of-the-art methods.
• Score: 27.280927277680515
• License:
• Abstract: Brain networks/graphs derived from resting-state functional MRI (fMRI) help study underlying pathophysiology of neurocognitive disorders by measuring neuronal activities in the brain. Some studies utilize learning-based methods for brain network analysis, but typically suffer from low model generalizability caused by scarce labeled fMRI data. As a notable self-supervised strategy, graph contrastive learning helps leverage auxiliary unlabeled data. But existing methods generally arbitrarily perturb graph nodes/edges to generate augmented graphs, without considering essential topology information of brain networks. To this end, we propose a topology-aware graph augmentation (TGA) framework, comprising a pretext model to train a generalizable encoder on large-scale unlabeled fMRI cohorts and a task-specific model to perform downstream tasks on a small target dataset. In the pretext model, we design two novel topology-aware graph augmentation strategies: (1) hub-preserving node dropping that prioritizes preserving brain hub regions according to node importance, and (2) weight-dependent edge removing that focuses on keeping important functional connectivities based on edge weights. Experiments on 1, 688 fMRI scans suggest that TGA outperforms several state-of-the-art methods.

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