Adaptive Critical Subgraph Mining for Cognitive Impairment Conversion Prediction with T1-MRI-based Brain Network

• URL: http://arxiv.org/abs/2403.13338v2
• Date: Thu, 27 Jun 2024 03:04:26 GMT
• Title: Adaptive Critical Subgraph Mining for Cognitive Impairment Conversion Prediction with T1-MRI-based Brain Network
• Authors: Yilin Leng, Wenju Cui, Bai Chen, Xi Jiang, Shuangqing Chen, Jian Zheng,
• Abstract summary: Prediction the conversion to early-stage dementia is critical for mitigating its progression. Traditional T1-weighted magnetic resonance imaging (T1-MRI) research focus on identifying brain atrophy regions. Brain-SubGNN is a novel graph representation network to mine and enhance critical subgraphs based on T1-MRI.
• Score: 4.835051121929712
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prediction the conversion to early-stage dementia is critical for mitigating its progression but remains challenging due to subtle cognitive impairments and structural brain changes. Traditional T1-weighted magnetic resonance imaging (T1-MRI) research focus on identifying brain atrophy regions but often fails to address the intricate connectivity between them. This limitation underscores the necessity of focuing on inter-regional connectivity for a comprehensive understand of the brain's complex network. Moreover, there is a pressing demand for methods that adaptively preserve and extract critical information, particularly specialized subgraph mining techniques for brain networks. These are essential for developing high-quality feature representations that reveal critical spatial impacts of structural brain changes and its topology. In this paper, we propose Brain-SubGNN, a novel graph representation network to mine and enhance critical subgraphs based on T1-MRI. This network provides a subgraph-level interpretation, enhancing interpretability and insights for graph analysis. The process begins by extracting node features and a correlation matrix between nodes to construct a task-oriented brain network. Brain-SubGNN then adaptively identifies and enhances critical subgraphs, capturing both loop and neighbor subgraphs. This method reflects the loop topology and local changes, indicative of long-range connections, and maintains local and global brain attributes. Extensive experiments validate the effectiveness and advantages of Brain-SubGNN, demonstrating its potential as a powerful tool for understanding and diagnosing early-stage dementia. Source code is available at https://github.com/Leng-10/Brain-SubGNN.

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