Functional magnetic resonance imaging (fMRI) is a commonly used technique to
measure neural activation. Its application has been particularly important in
identifying underlying neurodegenerative conditions such as Parkinson's,
Alzheimer's, and Autism. Recent analysis of fMRI data models the brain as a
graph and extracts features by graph neural networks (GNNs). However, the
unique characteristics of fMRI data require a special design of GNN. Tailoring
GNN to generate effective and domain-explainable features remains challenging.
In this paper, we propose a contrastive dual-attention block and a
differentiable graph pooling method called ContrastPool to better utilize GNN
for brain networks, meeting fMRI-specific requirements. We apply our method to
5 resting-state fMRI brain network datasets of 3 diseases and demonstrate its
superiority over state-of-the-art baselines. Our case study confirms that the
patterns extracted by our method match the domain knowledge in neuroscience
literature, and disclose direct and interesting insights. Our contributions
underscore the potential of ContrastPool for advancing the understanding of
brain networks and neurodegenerative conditions.

本文提出了一种对脑网络进行图神经网络分析的方法，名为 ContrastPool，以满足功能性磁共振成像数据的特殊需求，并在神经退行性疾病的研究中展示了其优势。