FSNet: Dual Interpretable Graph Convolutional Network for Alzheimer’s Disease Analysis

Li, Hengxin; Shi, Xiaoshuang; Zhu, Xiaofeng; Wang, Shuihua; Zhang, Zheng

doi:10.1109/tetci.2022.3183679

Cited by 14 publications

(2 citation statements)

References 27 publications

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“…Expanding on the foundational GCN graph convolution, various studies have explored innovative approaches in both structuring graph data and designing model architectures, tailored to a diverse range of applications. For graph data organization, some studies employ population-level graphs as input (Parisot et al ., 2018 ; Huang and Chung, 2022 ; Li et al ., 2022 ; Zhu et al ., 2022 ), where each node represents an individual subject, and the edges denote correlations between subjects. For instance, Parisot et al .…”

Section: Deep Graph Neural Network-based Approaches For Ad Predictionmentioning

confidence: 99%

“…Moreover, Li et al . ( 2022 ) enhanced interpretability by incorporating feature and sample interpretation modules into the population graph learning process, facilitating the interpretation of feature and sample significance. By contrast, other studies use individual-level graphs (Zhang et al ., 2021 , 2020a , 2019 ), with each node representing a ROI, and the edges signifying the connectivity or correlation between these ROI.…”

Section: Deep Graph Neural Network-based Approaches For Ad Predictionmentioning

confidence: 99%

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Exploring Alzheimer's disease: a comprehensive brain connectome-based survey

Zhang,

Qu,

et al. 2024

Psychoradiology

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Dementia is an escalating global health challenge, with Alzheimer's disease (AD) at its forefront. Substantial evidence highlights the accumulation of AD-related pathological proteins in specific brain regions and their subsequent dissemination throughout the broader area along the brain network, leading to disruptions in both individual brain regions and their interconnections. Although a comprehensive understanding of the neurodegeneration-brain network link is lacking, it's undeniable that brain networks play a pivotal role in the development and progression of AD. To thoroughly elucidate the intricate network of elements and connections constituting the human brain, the concept of the brain connectome was introduced. Research based on the connectome holds immense potential for revealing the mechanisms underlying disease development, and it has become a prominent topic that has attracted the attention of numerous researchers. In this review, we aim to systematically summarize studies on brain networks within the context of AD, critically analyze the strengths and weaknesses of existing methodologies, and offer novel perspectives and insights, intending to serve as inspiration for future research.

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