Topology-Aware Graph Augmentation for Predicting Clinical Trajectories in Neurocognitive Disorders
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910683352793088 |
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| author | Wang, Qianqian Wang, Wei Fang, Yuqi Li, Hong-Jun Bozoki, Andrea Liu, Mingxia |
| author_facet | Wang, Qianqian Wang, Wei Fang, Yuqi Li, Hong-Jun Bozoki, Andrea Liu, Mingxia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00888 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Topology-Aware Graph Augmentation for Predicting Clinical Trajectories in Neurocognitive Disorders Wang, Qianqian Wang, Wei Fang, Yuqi Li, Hong-Jun Bozoki, Andrea Liu, Mingxia Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Neurons and Cognition 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. |
| title | Topology-Aware Graph Augmentation for Predicting Clinical Trajectories in Neurocognitive Disorders |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Neurons and Cognition |
| url | https://arxiv.org/abs/2411.00888 |