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. The source code is available at https://github.com/AngusMonroe/ContrastPool .

中文翻译：

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用于大脑网络可解释分类的对比图池化


功能磁共振成像 （fMRI） 是测量神经激活的常用技术。它的应用在识别潜在的神经退行性疾病（如帕金森氏症、阿尔茨海默氏症和自闭症）方面尤为重要。最近对 fMRI 数据的分析将大脑建模为图形，并通过图形神经网络 （GNN） 提取特征。然而，fMRI 数据的独特特性需要 GNN 的特殊设计。定制 GNN 以生成有效且可域解释的特征仍然具有挑战性。在本文中，我们提出了一种对比双注意力块和一种称为 ContrastPool 的可微分图池化方法，以更好地将 GNN 用于大脑网络，满足 fMRI 的特定要求。我们将我们的方法应用于 3 种疾病的 5 个静息态 fMRI 脑网络数据集，并证明了它相对于最先进的基线的优越性。我们的案例研究证实，通过我们的方法提取的模式与神经科学文献中的领域知识相匹配，并揭示了直接和有趣的见解。我们的贡献强调了 ContrastPool 在促进对大脑网络和神经退行性疾病的理解方面的潜力。源代码可在 https://github.com/AngusMonroe/ContrastPool 上获得。