MAMF-GCN: Multi-scale adaptive multi-channel fusion deep graph convolutional network for predicting mental disorder

Pan, Jiacheng; Lin, Haocai; Dong, Yihong; Wang, Y W; Ji, Yonghoon

doi:10.1016/j.compbiomed.2022.105823

Cited by 28 publications

(17 citation statements)

References 27 publications

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“…For instance, DTI provides structural connection information by encoding white matter fiber connections between ROIs, and fMRI records the functional activity routes in each region to examine functional couplings [41,42]. Brain regions are commonly connected based on their correlations (e.g., Pearson's correlation) [8,19,43,44].…”

Section: Graph Constructionmentioning

confidence: 99%

“…ASD is a type of neurodevelopmental disorder often characterized by difficulties in communicating socially, restricted interests, and repetitive behavior [50]. Pan et al [19], Ktena et al [37], and Arya et al [51] proposed algorithms based on GNNs to classify ASD. Pan et al [19] and Ktena et al [37] validated their algorithms on the ABIDE dataset.…”

Section: Autism Spectrum Disordermentioning

confidence: 99%

“…Pan et al [19], Ktena et al [37], and Arya et al [51] proposed algorithms based on GNNs to classify ASD. Pan et al [19] and Ktena et al [37] validated their algorithms on the ABIDE dataset. Specifically, based on the ability of GNNs to process irregular structure data, Pan et al [19] proposed an attention mechanism-based multi-scale adaptive multi-channel fusion deep graph convolutional network (MAMF-GCN) to fusion features of multiple modalities.…”

Section: Autism Spectrum Disordermentioning

confidence: 99%

“…Pan et al [19] and Ktena et al [37] validated their algorithms on the ABIDE dataset. Specifically, based on the ability of GNNs to process irregular structure data, Pan et al [19] proposed an attention mechanism-based multi-scale adaptive multi-channel fusion deep graph convolutional network (MAMF-GCN) to fusion features of multiple modalities. MAMF-GCN constructs graphs for image and non-image data respectively.…”

Section: Autism Spectrum Disordermentioning

confidence: 99%

“…GNN-based algorithms [16][17][18] have been proposed to process and learn from irregular structure data. Because of their ability to represent irregular graph structures and aggregate information in the neighborhood, GNNs have become popular methods that are applied in disease diagnoses, such as brain diseases [19,20], chest diseases [21,22], and eye diseases [6]. In existing reviews, an overview of GNN algorithms [23][24][25] and GNNs in network neuroscience [26] is provided.…”

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confidence: 99%

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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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Section: Graph Constructionmentioning

confidence: 99%

Section: Autism Spectrum Disordermentioning

confidence: 99%

Section: Autism Spectrum Disordermentioning

confidence: 99%

Section: Autism Spectrum Disordermentioning

confidence: 99%

mentioning

confidence: 99%

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

et al. 2023

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Abnormal multimodal neuroimaging patterns associated with social deficits in male autism spectrum disorder

Wei,

Xu,

et al. 2024

Human Brain Mapping

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Atypical social impairments (i.e., impaired social cognition and social communication) are vital manifestations of autism spectrum disorder (ASD) patients, and the incidence rate of ASD is significantly higher in males than in females. Characterizing the atypical brain patterns underlying social deficits of ASD is significant for understanding the pathogenesis. However, there are no robust imaging biomarkers that are specific to ASD, which may be due to neurobiological complexity and limitations of single‐modality research. To describe the multimodal brain patterns related to social deficits in ASD, we highlighted the potential functional role of white matter (WM) and incorporated WM functional activity and gray matter structure into multimodal fusion. Gray matter volume (GMV) and fractional amplitude of low‐frequency fluctuations of WM (WM‐fALFF) were combined by fusion analysis model adopting the social behavior. Our results revealed multimodal spatial patterns associated with Social Responsiveness Scale multiple scores in ASD. Specifically, GMV exhibited a consistent brain pattern, in which salience network and limbic system were commonly identified associated with all multiple social impairments. More divergent brain patterns in WM‐fALFF were explored, suggesting that WM functional activity is more sensitive to ASD's complex social impairments. Moreover, brain regions related to social impairment may be potentially interconnected across modalities. Cross‐site validation established the repeatability of our results. Our research findings contribute to understanding the neural mechanisms underlying social disorders in ASD and affirm the feasibility of identifying biomarkers from functional activity in WM.

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Adaptive Graph Convolutional Fusion Network for Skeleton-Based Abnormal Gait Recognition

Wang,

2024

Lecture Notes in Computer Science

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MAMF-GCN: Multi-scale adaptive multi-channel fusion deep graph convolutional network for predicting mental disorder

Cited by 28 publications

References 27 publications

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

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

Abnormal multimodal neuroimaging patterns associated with social deficits in male autism spectrum disorder

Adaptive Graph Convolutional Fusion Network for Skeleton-Based Abnormal Gait Recognition

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