Identification of Causal Relationship between Amyloid-beta Accumulation
  and Alzheimer's Disease Progression via Counterfactual Inference
Haixing Dai Mengxuan Hu Qing Li Lu Zhang Lin Zhao Dajiang Zhu Ibai Diez Jorge Sepulcre Fan Zhang Xingyu Gao Manhua Liu Quanzheng Li Sheng Li Tianming Liu Xiang Li
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.