Deep reinforcement learning guided graph neural networks for brain network analysis

Zhao, Xusheng; Wu, Jia; Peng, Hao; Beheshti, Amin; Monaghan, Jessica J. M.; McAlpine, David; Hernández-Pérez, Heivet; Dras, Mark; Dai, Qiong; Li, Yangyang; Yu, Philip S.; He, Lifang

doi:10.1016/j.neunet.2022.06.035

Cited by 39 publications

(6 citation statements)

References 38 publications

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“…In the field of graph processing, GNNs can embed complex network structures into meaningful low-dimensional representation features [ 44 ]. Two-dimensional convolution is the process of taking the pixel values of the nodes within a certain range adjacent to each node and performing a weighted average.…”

Section: Methodsmentioning

confidence: 99%

Automated diagnosis of schizophrenia based on spatial–temporal residual graph convolutional network

Xu,

Zhu,

et al. 2024

BioMed Eng OnLine

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Background Schizophrenia (SZ), a psychiatric disorder for which there is no precise diagnosis, has had a serious impact on the quality of human life and social activities for many years. Therefore, an advanced approach for accurate treatment is required. New method In this study, we provide a classification approach for SZ patients based on a spatial–temporal residual graph convolutional neural network (STRGCN). The model primarily collects spatial frequency features and temporal frequency features by spatial graph convolution and single-channel temporal convolution, respectively, and blends them both for the classification learning, in contrast to traditional approaches that only evaluate temporal frequency information in EEG and disregard spatial frequency features across brain regions. Results We conducted extensive experiments on the publicly available dataset Zenodo and our own collected dataset. The classification accuracy of the two datasets on our proposed method reached 96.32% and 85.44%, respectively. In the experiment, the dataset using delta has the best classification performance in the sub-bands. Comparison with existing methods Other methods mainly rely on deep learning models dominated by convolutional neural networks and long and short time memory networks, lacking exploration of the functional connections between channels. In contrast, the present method can treat the EEG signal as a graph and integrate and analyze the temporal frequency and spatial frequency features in the EEG signal. Conclusion We provide an approach to not only performs better than other classic machine learning and deep learning algorithms on the dataset we used in diagnosing schizophrenia, but also understand the effects of schizophrenia on brain network features.

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Section: Methodsmentioning

confidence: 99%

Automated diagnosis of schizophrenia based on spatial–temporal residual graph convolutional network

Xu,

Zhu,

et al. 2024

BioMed Eng OnLine

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“…Graph neural networks (GNNs) are a specialized type of deep learning model designed specifically for analyzing graph-structured data, like biological networks [81]. Unlike traditional deep learning models that struggle with the non-linear relationships and complex structures inherent in networks, GNNs excel in this domain due to their unique capabilities.…”

Section: Nearest Neighborsmentioning

confidence: 99%

Exploring the Molecular Terrain: A Survey of Analytical Methods for Biological Network Analysis

Nguyen,

Dao,

Pham

et al. 2024

Symmetry

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Biological systems, characterized by their complex interplay of symmetry and asymmetry, operate through intricate networks of interacting molecules, weaving the elaborate tapestry of life. The exploration of these networks, aptly termed the “molecular terrain”, is pivotal for unlocking the mysteries of biological processes and spearheading the development of innovative therapeutic strategies. This review embarks on a comprehensive survey of the analytical methods employed in biological network analysis, focusing on elucidating the roles of symmetry and asymmetry within these networks. By highlighting their strengths, limitations, and potential applications, we delve into methods for network reconstruction, topological analysis with an emphasis on symmetry detection, and the examination of network dynamics, which together reveal the nuanced balance between stable, symmetrical configurations and the dynamic, asymmetrical shifts that underpin biological functionality. This review equips researchers with a multifaceted toolbox designed to navigate and decipher biological networks’ intricate, balanced landscape, thereby advancing our understanding and manipulation of complex biological systems. Through this detailed exploration, we aim to foster significant advancements in biological network analysis, paving the way for novel therapeutic interventions and a deeper comprehension of the molecular underpinnings of life.

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“…Zhao et al. [52] proposed an algorithm to predict the best feature propagation number in a brain network. They built the initial network with the sampled brain network and its corresponding adjacency matrix.…”

Section: Applications Of Gnn‐based Algorithms In Disease Diagnosismentioning

confidence: 99%

“…They used the proposed multiscale GCN layers to classify graph information in MCI and validated the algorithm on the ADNI public dataset. Zhao et al [52] proposed an algorithm to predict the best feature propagation number in a brain network. They built the initial network with the sampled brain network and its corresponding adjacency matrix.…”

Section: F I G U R Ementioning

confidence: 99%

Graph neural networks for image‐guided disease diagnosis: A review

et al. 2023

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Medical imaging is playing an increasingly crucial role in disease diagnosis. Numerous deep learning-based methods have been developed for imageguided automatic disease diagnosis. Most of the methods have harnessed conventional convolutional neural networks, which are directly applied in the regular image domain. However, some irregular spatial patterns revealed in medical images are also critical to disease diagnosis, since they can describe latent relations in different image regions of a subject (e.g., different focal lesions in an image) or between different groups (e.g., Alzheimer's disease and healthy control). Therefore, how to exploit and analyze irregular spatial patterns and their relations has become a research challenge in the field of imageguided disease diagnosis. To address this challenge, graph neural networks (GNNs) are proposed to perform the convolution operation on graphs. Graphs can naturally represent irregular spatial structures. Because of their ability to aggregate node features, edge features, and graph structure information to capture and learn hidden spatial patterns in irregular structures, GNN-based algorithms have achieved promising results in the detection of various diseases. In this paper, we introduce commonly used GNN-based algorithms and systematically review their applications to disease diagnosis. We summarize the workflow of GNN-based applications in disease diagnosis, ranging from localizing the regions of interest and edge construction to modeling. Furthermore, we discuss the limitations and outline potential research directions for GNNs in disease diagnosis.

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Deep reinforcement learning guided graph neural networks for brain network analysis

Cited by 39 publications

References 38 publications

Automated diagnosis of schizophrenia based on spatial–temporal residual graph convolutional network

Automated diagnosis of schizophrenia based on spatial–temporal residual graph convolutional network

Exploring the Molecular Terrain: A Survey of Analytical Methods for Biological Network Analysis

Graph neural networks for image‐guided disease diagnosis: A review

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