  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.
