通过关注脑区间连接关系，提升神经退行性疾病诊断准确率
Edge-boosted graph learning for functional brain connectivity analysis
- 以边为单位建模功能连接，突破传统节点中心方法
- 在ADNI和PPMI数据集上分类性能超越现有GNN方法
- 适合从事脑网络分析与疾病早期诊断的研究者
从功能脑连接预测疾病状态对阿尔茨海默病和帕金森病等严重神经退行性疾病的早期诊断至关重要。现有研究多采用图神经网络（GNN）基于脑区平均fMRI信号的节点间相似性构建的节点级脑连接矩阵进行临床诊断推断。然而，最新神经科学研究表明，此类节点级连接无法准确捕捉大脑内的“功能连接”。本文提出一种新方法，强调边功能连接（eFC），将分析重点转向边之间的关系，并引入共嵌入技术有效融合边功能连接信息。在ADNI和PPMI数据集上的实验结果表明，该方法在分类功能脑网络方面显著优于当前最先进的GNN方法。
原文摘要 · Abstract (English)
Predicting disease states from functional brain connectivity is critical for the early diagnosis of severe neurodegenerative diseases such as Alzheimer's Disease and Parkinson's Disease. Existing studies commonly employ Graph Neural Networks (GNNs) to infer clinical diagnoses from node-based brain connectivity matrices generated through node-to-node similarities of regionally averaged fMRI signals. However, recent neuroscience studies found that such node-based connectivity does not accurately capture ``functional connections" within the brain. This paper proposes a novel approach to brain network analysis that emphasizes edge functional connectivity (eFC), shifting the focus to inter-edge relationships. Additionally, we introduce a co-embedding technique to integrate edge functional connections effectively. Experimental results on the ADNI and PPMI datasets demonstrate that our method significantly outperforms state-of-the-art GNN methods in classifying functional brain networks.
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