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.

Related papers

• Topology-Aware Graph Augmentation for Predicting Clinical Trajectories in Neurocognitive Disorders [27.280927277680515]
  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.
  arXiv  Detail & Related papers  (2024-10-31T19:37:20Z)
• Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification [15.24676785238373]
  We propose Contrasformer, a novel contrastive brain network Transformer. It generates a prior-knowledge-enhanced contrast graph to address the distribution shifts across sub-populations. Contrasformer outperforms the state-of-the-art methods for brain networks by achieving up to 10.8% improvement in accuracy.
  arXiv  Detail & Related papers  (2024-09-17T07:26:02Z)
• Contrastive Graph Pooling for Explainable Classification of Brain Networks [9.917580917431293]
  Functional magnetic resonance imaging (fMRI) is a commonly used technique to measure neural activation. Recent analysis of fMRI data models the brain as a graph and extracts features by graph neural networks (GNNs) We propose ContrastPool to better utilize GNN for brain networks, meeting fMRI-specific requirements.
  arXiv  Detail & Related papers  (2023-07-07T11:49:55Z)
• Language Knowledge-Assisted Representation Learning for Skeleton-Based Action Recognition [71.35205097460124]
  How humans understand and recognize the actions of others is a complex neuroscientific problem. LA-GCN proposes a graph convolution network using large-scale language models (LLM) knowledge assistance.
  arXiv  Detail & Related papers  (2023-05-21T08:29:16Z)
• Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks [28.460737693330245]
  We propose TBDS, an end-to-end framework based on underlineTask-aware underlineBrain connectivity underlineDAG for fMRI analysis. The key component of TBDS is the brain network generator which adopts a DAG learning approach to transform the raw time-series into task-aware brain connectivities. Comprehensive experiments on two fMRI datasets demonstrate the efficacy of TBDS.
  arXiv  Detail & Related papers  (2022-11-01T03:59:54Z)
• Functional2Structural: Cross-Modality Brain Networks Representation Learning [55.24969686433101]
  Graph mining on brain networks may facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. We propose a novel graph learning framework, known as Deep Signed Brain Networks (DSBN), with a signed graph encoder. We validate our framework on clinical phenotype and neurodegenerative disease prediction tasks using two independent, publicly available datasets.
  arXiv  Detail & Related papers  (2022-05-06T03:45:36Z)
• Deep Reinforcement Learning Guided Graph Neural Networks for Brain Network Analysis [61.53545734991802]
  We propose a novel brain network representation framework, namely BN-GNN, which searches for the optimal GNN architecture for each brain network. Our proposed BN-GNN improves the performance of traditional GNNs on different brain network analysis tasks.
  arXiv  Detail & Related papers  (2022-03-18T07:05:27Z)
• Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis [17.48272758284748]
  Graph Neural Networks (GNNs) have become a de facto model for analyzing graph-structured data. We develop a novel multiview GNN for multimodal brain networks. In particular, we regard each modality as a view for brain networks and employ contrastive learning for multimodal fusion.
  arXiv  Detail & Related papers  (2021-07-07T13:49:57Z)
• Topological obstructions in neural networks learning [67.8848058842671]
  We study global properties of the loss gradient function flow. We use topological data analysis of the loss function and its Morse complex to relate local behavior along gradient trajectories with global properties of the loss surface.
  arXiv  Detail & Related papers  (2020-12-31T18:53:25Z)
• Context-Aware Refinement Network Incorporating Structural Connectivity Prior for Brain Midline Delineation [50.868845400939314]
  We propose a context-aware refinement network (CAR-Net) to refine and integrate the feature pyramid representation generated by the UNet. For keeping the structural connectivity of the brain midline, we introduce a novel connectivity regular loss. The proposed method requires fewer parameters and outperforms three state-of-the-art methods in terms of four evaluation metrics.
  arXiv  Detail & Related papers  (2020-07-10T14:01:20Z)
• A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression with MEG Brain Networks [59.15734147867412]
  Characterizing the subtle changes of functional brain networks associated with Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G) We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network alterations related to MCI.
  arXiv  Detail & Related papers  (2020-05-08T02:29:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.