Alzheimer's Disease Prediction via Brain Structural-Functional Deep Fusing Network

• URL: http://arxiv.org/abs/2309.16206v2
• Date: Thu, 5 Oct 2023 14:04:00 GMT
• Title: Alzheimer's Disease Prediction via Brain Structural-Functional Deep Fusing Network
• Authors: Qiankun Zuo, Junren Pan, and Shuqiang Wang
• Abstract summary: Cross-modal transformer generative adversarial network (CT-GAN) is proposed to fuse functional and structural information. By analyzing the generated connectivity features, the proposed model can identify AD-related brain connections. Evaluations on the public ADNI dataset show that the proposed CT-GAN can dramatically improve prediction performance and detect AD-related brain regions effectively.
• Score: 5.945843237682432
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fusing structural-functional images of the brain has shown great potential to analyze the deterioration of Alzheimer's disease (AD). However, it is a big challenge to effectively fuse the correlated and complementary information from multimodal neuroimages. In this paper, a novel model termed cross-modal transformer generative adversarial network (CT-GAN) is proposed to effectively fuse the functional and structural information contained in functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). The CT-GAN can learn topological features and generate multimodal connectivity from multimodal imaging data in an efficient end-to-end manner. Moreover, the swapping bi-attention mechanism is designed to gradually align common features and effectively enhance the complementary features between modalities. By analyzing the generated connectivity features, the proposed model can identify AD-related brain connections. Evaluations on the public ADNI dataset show that the proposed CT-GAN can dramatically improve prediction performance and detect AD-related brain regions effectively. The proposed model also provides new insights for detecting AD-related abnormal neural circuits.

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