Brain age estimation is clinically important as it can provide valuable information in the context of neurodegenerative diseases such as Alzheimer's. Population graphs, which include multimodal imaging information of the subjects along with the relationships among the population, have been used in literature along with Graph Convolutional Networks (GCNs) and have proved beneficial for a variety of medical imaging tasks. A population graph is usually static and constructed manually using non-imaging information. However, graph construction is not a trivial task and might significantly affect the performance of the GCN, which is inherently very sensitive to the graph structure. In this work, we propose a framework that learns a population graph structure optimized for the downstream task. An attention mechanism assigns weights to a set of imaging and non-imaging features (phenotypes), which are then used for edge extraction. The resulting graph is used to train the GCN. The entire pipeline can be trained end-to-end. Additionally, by visualizing the attention weights that were the most important for the graph construction, we increase the interpretability of the graph. We use the UK Biobank, which provides a large variety of neuroimaging and non-imaging phenotypes, to evaluate our method on brain age regression and classification. The proposed method outperforms competing static graph approaches and other state-of-the-art adaptive methods. We further show that the assigned attention scores indicate that there are both imaging and non-imaging phenotypes that are informative for brain age estimation and are in agreement with the relevant literature.

翻译：脑龄估计在临床上具有重要意义，因为它能为阿尔茨海默病等神经退行性疾病提供有价值的信息。群体图（Population graphs）包含受试者的多模态影像信息及其群体间关系，已在相关文献中与图卷积网络（GCNs）结合使用，并被证明对多种医学影像任务有益。群体图通常是静态的，并利用非影像信息手动构建。然而，图构建并非易事，且可能显著影响GCN的性能——GCN本质上对图结构非常敏感。在本研究中，我们提出了一种框架，能够学习针对下游任务优化的群体图结构。注意力机制为一组影像和非影像特征（表型）分配权重，这些权重随后用于边提取。生成的图用于训练GCN，整个流程可端到端训练。此外，通过可视化对图构建最重要的注意力权重，我们增强了图的可解释性。我们利用UK Biobank提供的丰富神经影像和非影像表型数据，评估了本方法在脑龄回归和分类任务上的表现。所提方法优于竞争性静态图方法及其他当前最优自适应方法。我们进一步表明，分配的注意力分数揭示了影像和非影像表型均对脑龄估计具有信息价值，且与相关文献结论一致。