1. Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference
- Author
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Dai, Haixing, Hu, Mengxuan, Li, Qing, Zhang, Lu, Zhao, Lin, Zhu, Dajiang, Diez, Ibai, Sepulcre, Jorge, Zhang, Fan, Gao, Xingyu, Liu, Manhua, Li, Quanzheng, Li, Sheng, Liu, Tianming, and Li, Xiang
- Subjects
Methodology (stat.ME) ,FOS: Computer and information sciences ,Computer Science - Machine Learning ,Statistics - Methodology ,Machine Learning (cs.LG) - Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition. The accumulation of amyloid-beta in the brain, measured through 18F-florbetapir (AV45) positron emission tomography (PET) imaging, has been widely used for early diagnosis of AD. However, the relationship between amyloid-beta accumulation and AD pathophysiology remains unclear, and causal inference approaches are needed to uncover how amyloid-beta levels can impact AD development. In this paper, we propose a graph varying coefficient neural network (GVCNet) for estimating the individual treatment effect with continuous treatment levels using a graph convolutional neural network. We highlight the potential of causal inference approaches, including GVCNet, for measuring the regional causal connections between amyloid-beta accumulation and AD pathophysiology, which may serve as a robust tool for early diagnosis and tailored care.
- Published
- 2023