Spatial Temporal Graph Convolution with Graph Structure Self-learning for Early MCI Detection

• URL: http://arxiv.org/abs/2211.06161v1
• Date: Fri, 11 Nov 2022 12:29:00 GMT
• Title: Spatial Temporal Graph Convolution with Graph Structure Self-learning for Early MCI Detection
• Authors: Yunpeng Zhao, Fugen Zhou, Bin Guo, Bo Liu
• Abstract summary: We propose a spatial temporal graph convolutional network with a novel graph structure self-learning mechanism for EMCI detection. Results on the Alzheimer's Disease Neuroimaging Initiative database show that our method outperforms state-of-the-art approaches.
• Score: 9.11430195887347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph neural networks (GNNs) have been successfully applied to early mild cognitive impairment (EMCI) detection, with the usage of elaborately designed features constructed from blood oxygen level-dependent (BOLD) time series. However, few works explored the feasibility of using BOLD signals directly as features. Meanwhile, existing GNN-based methods primarily rely on hand-crafted explicit brain topology as the adjacency matrix, which is not optimal and ignores the implicit topological organization of the brain. In this paper, we propose a spatial temporal graph convolutional network with a novel graph structure self-learning mechanism for EMCI detection. The proposed spatial temporal graph convolution block directly exploits BOLD time series as input features, which provides an interesting view for rsfMRI-based preclinical AD diagnosis. Moreover, our model can adaptively learn the optimal topological structure and refine edge weights with the graph structure self-learning mechanism. Results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database show that our method outperforms state-of-the-art approaches. Biomarkers consistent with previous studies can be extracted from the model, proving the reliable interpretability of our method.

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