• Corpus ID: 235755356

Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis

@article{Zhu2021JointEO,
  title={Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis},
  author={Yanqiao Zhu and Hejie Cui and Lifang He and Lichao Sun and Carl Yang},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.03220}
}
—Multimodal brain networks characterize complex connectivities among different brain regions from both structural and functional aspects and provide a new means for mental disease analysis. Recently, Graph Neural Networks (GNNs) have become a de facto model for analyzing graph-structured data. However, how to employ GNNs to extract effective representations from brain networks in multiple modalities remains rarely explored. Moreover, as brain networks provide no initial node features, how to… 

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References

SHOWING 1-10 OF 30 REFERENCES
Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis
TLDR
This work proposes Multi-view Multi-graph Embedding M2E by stacking multi-graphs into multiple partially-symmetric tensors and using tensor techniques to simultaneously leverage the dependencies and correlations among multi-view and multi- graph brain networks.
Identifying HIV-induced subgraph patterns in brain networks with side information
TLDR
Empirical studies on graph classification tasks for neurological disorders using brain networks demonstrate that subgraph patterns selected by the multi-side-view-guided subgraph selection approach can effectively boost graph classification performances and are relevant to disease diagnosis.
Multi-view Graph Embedding with Hub Detection for Brain Network Analysis
TLDR
An auto-weighted framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain network analysis that learns a unified graph embedding across all the views while reducing the potential influence of the hubs on blurring the boundaries between node clusters in the graph, thus leading to a clear and discriminative node clustering structure for the graph.
Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM
TLDR
Combined multimodal imaging data did not significantly improve classification accuracy compared to the best single measures alone and the support vector machine (SVM) algorithm ran the SVM algorithm and validated the results using leave‐one‐out cross‐validation.
Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease
TLDR
A deep learning method based on Graph Convolutional Networks (GCN) for fusing multiple modalities of brain images in relationship prediction which is useful for distinguishing PD cases from controls is proposed.
How Powerful are Graph Neural Networks?
TLDR
This work characterize the discriminative power of popular GNN variants, such as Graph Convolutional Networks and GraphSAGE, and show that they cannot learn to distinguish certain simple graph structures, and develops a simple architecture that is provably the most expressive among the class of GNNs.
Graph Attention Networks
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior
Hierarchical Graph Representation Learning with Differentiable Pooling
TLDR
DiffPool is proposed, a differentiable graph pooling module that can generate hierarchical representations of graphs and can be combined with various graph neural network architectures in an end-to-end fashion.
GPT-GNN: Generative Pre-Training of Graph Neural Networks
TLDR
The GPT-GNN framework to initialize GNNs by generative pre-training introduces a self-supervised attributed graph generation task to pre-train a GNN so that it can capture the structural and semantic properties of the graph.
Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark
TLDR
This work provides a generic paradigm for the systematic categorization and analysis over the merits of various existing HNE algorithms, and creates four benchmark datasets with various properties regarding scale, structure, attribute/label availability, and \etc.~from different sources towards handy and fair evaluations of H NE algorithms.
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