Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference

TL;DR

This paper introduces GVCNet, a graph neural network model that estimates the causal effect of amyloid-beta levels on Alzheimer's disease progression, aiming to improve early diagnosis through causal inference.

Contribution

The paper presents a novel graph varying coefficient neural network for estimating causal effects of continuous treatments, specifically applied to amyloid-beta and Alzheimer's disease.

Findings

GVCNet effectively estimates individual causal effects of amyloid-beta levels.

The approach uncovers regional causal connections relevant to AD progression.

Potential for improved early diagnosis and personalized treatment strategies.

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.