多通道稀疏图变换网络用于早期阿尔茨海默病识别

doi:10.3969/j.issn.0258-8021.2023.04.007

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Abstract Currently, there is no effective treatment for Alzheimer's disease (AD). Clinical intervention via early diagnosis can delay the AD progression and improve its prognosis. However, the existing methods only consider the neuroimaging features learned from group relationships, not the individual characteristics of the subjects. In this work, we designed a novel multi-modal multi-channel sparse graph transformer network (MSGTN). Our proposed network model included two parts, they are multi-modal data optimization and multi-modal feature learning. Firstly, we acquired the image information (e.g., diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI)) and non-image information (e.g., age and sex) of each subject. Secondly, we utilized locally weighted clustering coefficients (LWCC) to fuse functional and structural information. After that, the fused multi-modal image features were combined with the gender and age information of the subjects to construct a sparse graph. Finally, we input the sparse graph into the MSGTN network for early AD identification. We obtained a total of 170 subjects from the public database ADNI (Alzheimer's disease neuroimaging initiative), including 38 LMCI, 44 EMCI, 44 SMC, and 44 normal controls (NC). Our method achieved classification accuracy of 87.02%, 87.40%, 91.49%, 88.93%, 86.74% and 92.12%, respectively. The experimental results have proved that our proposed model not only can analyze NC versus three different early AD disease states, but also achieved superior classification performance in three different early AD disease states.

Key words： Alzheimer′s disease (AD) multi-modal fusion early identification multi-channel learning graph sparse transformer network.

Received: 11 July 2021

PACS:

R318

Corresponding Authors: ^*E-mail:leiby@szu.edu.cn

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	Articles by authors
	Qiu Yali
	Zhu Yun
	Yu Shuangzhi
	Song Xuegang
	Wang Tianfu
	Lei Baiying

Cite this article:

Qiu Yali,Zhu Yun,Yu Shuangzhi, et al. Multi-Channel Sparse Graph Transformer Network for Early Identification of Alzheimer′s Disease[J]. Chinese Journal of Biomedical Engineering, 2023, 42(4): 442-452.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2023.04.007 OR http://cjbme.csbme.org/EN/Y2023/V42/I4/442

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