Graph convolutional network with sample and feature weights for Alzheimer’s disease diagnosis

Zeng, Lu; Li, Hengxin; Xiao, Tingsong; Shen, Fumin; Zhi, Zhao

doi:10.1016/j.ipm.2022.102952

Cited by 18 publications

(1 citation statement)

References 31 publications

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“…More and more researchers have utilized graph convolutional networks to detect network abnormalities in the brain under various mental health conditions and have achieved remarkable results for both the classification and prediction of neurological diseases. For example, in the field of Alzheimer's disease (AD) research, a functional connectivity-based GCN framework for early prediction of AD [ 18 ], a new framework based on multiscale augmented GCN for detecting mild cognitive impairment (MCI) achieved a classification accuracy of 90.39% on the ADNI database [ 19 ], sample-weight and feature-weight based on GCN have also made breakthroughs in classification performance and interpretability [ 20 ], and there is a structural MRI-based multirelational GCN for AD diagnosis by learning multirelational perceptual representations of brain regions [ 21 ]. In the field of ADHD research, there is population-based learning of contrastive functional connectivity graphs for ADHD classification verified to be superior on various metrics [ 22 ], and there is a dynamic GCN framework revealing new insights into connectome dysfunction in ADHD [ 23 ].…”

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confidence: 99%

Identifying ADHD-Related Abnormal Functional Connectivity with a Graph Convolutional Neural Network

Hu,

Ran,

Qiao

et al. 2024

Neural Plasticity

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Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder that is characterized by inattention, hyperactivity, and impulsivity. The neural mechanisms underlying ADHD remain inadequately understood, and current approaches do not well link neural networks and attention networks within brain networks. Our objective is to investigate the neural mechanisms related to attention and explore neuroimaging biological tags that can be generalized within the attention networks. In this paper, we utilized resting-state functional magnetic resonance imaging data to examine the differential functional connectivity network between ADHD and typically developing individuals. We employed a graph convolutional neural network model to identify individuals with ADHD. After classification, we visualized brain regions with significant contributions to the classification results. Our results suggest that the frontal, temporal, parietal, and cerebellar regions are likely the primary areas of dysfunction in individuals with ADHD. We also explored the relationship between regions of interest and attention networks, as well as the connection between crucial nodes and the distribution of positively and negatively correlated connections. This analysis allowed us to pinpoint the most discriminative brain regions, including the right orbitofrontal gyrus, the left rectus gyrus and bilateral insula, the right inferior temporal gyrus and bilateral transverse temporal gyrus in the temporal region, and the lingual gyrus of the occipital lobe, multiple regions of the basal ganglia and the upper cerebellum. These regions are primarily involved in the attention executive control network and the attention orientation network. Dysfunction in the functional connectivity of these regions may contribute to the underlying causes of ADHD.

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