The difference between the chronological and biological brain age of a subject can be an important biomarker for neurodegenerative diseases, thus brain age estimation can be crucial in clinical settings. One way to incorporate multimodal information into this estimation is through population graphs, which combine various types of imaging data and capture the associations among individuals within a population. In medical imaging, population graphs have demonstrated promising results, mostly for classification tasks. In most cases, the graph structure is pre-defined and remains static during training. However, extracting population graphs is a non-trivial task and can significantly impact the performance of Graph Neural Networks (GNNs), which are sensitive to the graph structure. In this work, we highlight the importance of a meaningful graph construction and experiment with different population-graph construction methods and their effect on GNN performance on brain age estimation. We use the homophily metric and graph visualizations to gain valuable quantitative and qualitative insights on the extracted graph structures. For the experimental evaluation, we leverage the UK Biobank dataset, which offers many imaging and non-imaging phenotypes. Our results indicate that architectures highly sensitive to the graph structure, such as Graph Convolutional Network (GCN) and Graph Attention Network (GAT), struggle with low homophily graphs, while other architectures, such as GraphSage and Chebyshev, are more robust across different homophily ratios. We conclude that static graph construction approaches are potentially insufficient for the task of brain age estimation and make recommendations for alternative research directions.

翻译：受试者的实际年龄与生物学脑龄之间的差异可能是神经退行性疾病的重要生物标志物，因此脑龄估计在临床环境中至关重要。将多模态信息整合到这一估计中的一种方式是通过人口图，该图结合了多种类型的影像数据，并捕捉人群中个体之间的关联。在医学影像领域，人口图已显示出有前景的结果，主要用于分类任务。在大多数情况下，图结构是预先定义并在训练期间保持静态的。然而，提取人口图并非易事，且会显著影响图神经网络（GNNs）的性能，因为GNNs对图结构敏感。在本研究中，我们强调了有意义图构建的重要性，并实验了不同的人口图构建方法及其对GNN在脑龄估计性能上的影响。我们利用同质性指标和图可视化技术，对提取的图结构获得了有价值的定量与定性见解。在实验评估中，我们利用了英国生物银行（UK Biobank）数据集，该数据集提供了许多影像和非影像表型。我们的结果表明，对图结构高度敏感的架构，例如图卷积网络（GCN）和图注意力网络（GAT），在低同质性图上表现不佳，而其他架构，如GraphSage和Chebyshev，在不同同质性比率下更具鲁棒性。我们得出结论，静态图构建方法可能不足以应对脑龄估计任务，并提出了替代研究方向的建议。