Learning Multi-resolution Graph Edge Embedding for Discovering Brain Network Dysfunction in Neurological Disorders

• URL: http://arxiv.org/abs/1912.01181v2
• Date: Thu, 26 Sep 2024 01:26:44 GMT
• Title: Learning Multi-resolution Graph Edge Embedding for Discovering Brain Network Dysfunction in Neurological Disorders
• Authors: Xin Ma, Guorong Wu, Seong Jae Hwang, Won Hwa Kim,
• Abstract summary: We propose Multi-resolution Edge Network (MENET) to detect disease-specific connectomic benchmarks. MENET accurately predicts diagnostic labels and identifies brain connectivities highly associated with neurological disorders.
• Score: 10.12649945620901
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tremendous recent literature show that associations between different brain regions, i.e., brain connectivity, provide early symptoms of neurological disorders. Despite significant efforts made for graph neural network (GNN) techniques, their focus on graph nodes makes the state-of-the-art GNN methods not suitable for classifying brain connectivity as graphs where the objective is to characterize disease-relevant network dysfunction patterns on graph links. To address this issue, we propose Multi-resolution Edge Network (MENET) to detect disease-specific connectomic benchmarks with high discrimination power across diagnostic categories. The core of MENET is a novel graph edge-wise transform that we propose, which allows us to capture multi-resolution ``connectomic'' features. Using a rich set of the connectomic features, we devise a graph learning framework to jointly select discriminative edges and assign diagnostic labels for graphs. Experiments on two real datasets show that MENET accurately predicts diagnostic labels and identify brain connectivities highly associated with neurological disorders such as Alzheimer's Disease and Attention-Deficit/Hyperactivity Disorder.

Related papers

• 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)
• Graph Neural Networks for Brain Graph Learning: A Survey [53.74244221027981]
  Graph neural networks (GNNs) have demonstrated a significant advantage in mining graph-structured data. GNNs to learn brain graph representations for brain disorder analysis has recently gained increasing attention. In this paper, we aim to bridge this gap by reviewing brain graph learning works that utilize GNNs.
  arXiv  Detail & Related papers  (2024-06-01T02:47:39Z)
• Classification of developmental and brain disorders via graph convolutional aggregation [6.6356049194991815]
  We introduce an aggregator normalization graph convolutional network by leveraging aggregation in graph sampling. The proposed model learns discriminative graph node representations by incorporating both imaging and non-imaging features into the graph nodes and edges. We benchmark our model against several recent baseline methods on two large datasets, Autism Brain Imaging Data Exchange (ABIDE) and Alzheimer's Disease Neuroimaging Initiative (ADNI)
  arXiv  Detail & Related papers  (2023-11-13T14:36:29Z)
• DBGDGM: Dynamic Brain Graph Deep Generative Model [63.23390833353625]
  Graphs are a natural representation of brain activity derived from functional magnetic imaging (fMRI) data. It is well known that clusters of anatomical brain regions, known as functional connectivity networks (FCNs), encode temporal relationships which can serve as useful biomarkers for understanding brain function and dysfunction. Previous works, however, ignore the temporal dynamics of the brain and focus on static graphs. We propose a dynamic brain graph deep generative model (DBGDGM) which simultaneously clusters brain regions into temporally evolving communities and learns dynamic unsupervised node embeddings.
  arXiv  Detail & Related papers  (2023-01-26T20:45:30Z)
• Multi-modal Dynamic Graph Network: Coupling Structural and Functional Connectome for Disease Diagnosis and Classification [8.67028273829113]
  We propose a Multi-modal Dynamic Graph Convolution Network (MDGCN) for structural and functional brain network learning. Our method benefits from modeling inter-modal representations and relating attentive multi-model associations into dynamic graphs.
  arXiv  Detail & Related papers  (2022-10-25T02:41:32Z)
• Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model [64.29487107585665]
  Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. Here, we propose an interpretable hierarchical signed graph representation learning model to extract graph-level representations from brain functional networks. In order to further improve the model performance, we also propose a new strategy to augment functional brain network data for contrastive learning.
  arXiv  Detail & Related papers  (2022-07-14T20:03:52Z)
• 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)
• 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)
• Brain Multigraph Prediction using Topology-Aware Adversarial Graph Neural Network [1.6114012813668934]
  We introduce topoGAN architecture, which jointly predicts multiple brain graphs from a single brain graph. Our three key innovations are: (i) designing a novel graph adversarial auto-encoder for predicting multiple brain graphs from a single one, (ii) clustering the encoded source graphs in order to handle the mode collapse issue of GAN and (iii) introducing a topological loss to force the prediction of topologically sound target brain graphs.
  arXiv  Detail & Related papers  (2021-05-06T10:20:45Z)
• 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)
• Dynamic Graph Correlation Learning for Disease Diagnosis with Incomplete Labels [66.57101219176275]
  Disease diagnosis on chest X-ray images is a challenging multi-label classification task. We propose a Disease Diagnosis Graph Convolutional Network (DD-GCN) that presents a novel view of investigating the inter-dependency among different diseases. Our method is the first to build a graph over the feature maps with a dynamic adjacency matrix for correlation learning.
  arXiv  Detail & Related papers  (2020-02-26T17:10:48Z)

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.