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.

推断脑龄估计中的群体图结构对图神经网络的影响。

人口图谱构建方法与脑龄回归的图神经网络的比较研究

A Comparative Study of Population-Graph Construction Methods and Graph  Neural Networks for Brain Age Regression

When re-structuring patient cohorts into so-called population graphs,
initially independent data points can be incorporated into one interconnected
graph structure. This population graph can then be used for medical downstream
tasks using graph neural networks (GNNs). The construction of a suitable graph
structure is a challenging step in the learning pipeline that can have severe
impact on model performance. To this end, different graph assessment metrics
have been introduced to evaluate graph structures. However, these metrics are
limited to classification tasks and discrete adjacency matrices, only covering
a small subset of real-world applications. In this work, we introduce extended
graph assessment metrics (GAMs) for regression tasks and continuous adjacency
matrices. We focus on two GAMs in specific: \textit{homophily} and
\textit{cross-class neighbourhood similarity} (CCNS). We extend the notion of
GAMs to more than one hop, define homophily for regression tasks, as well as
continuous adjacency matrices, and propose a light-weight CCNS distance for
discrete and continuous adjacency matrices. We show the correlation of these
metrics with model performance on different medical population graphs and under
different learning settings.

通过引入扩展图评估指标（GAMs），我们研究了患者队列的重组、人口图形的构建以及使用图神经网络进行医学相关的任务，展示了这些评估指标与模型性能之间的相关性。

图神经网络的扩展图评估指标

Extended Graph Assessment Metrics for Graph Neural Networks

We initiate an empirical investigation into differentially private graph
neural networks on population graphs from the medical domain by examining
privacy-utility trade-offs at different privacy levels on both real-world and
synthetic datasets and performing auditing through membership inference
attacks. Our findings highlight the potential and the challenges of this
specific DP application area. Moreover, we find evidence that the underlying
graph structure constitutes a potential factor for larger performance gaps by
showing a correlation between the degree of graph homophily and the accuracy of
the trained model.

通过在真实和合成数据集上不同隐私水平下进行隐私 - 效用权衡和通过成员推断攻击进行审计，从医疗领域的人口图中启动对不同 ially 静态图神经网络的经验调查。我们的发现强调了此特定 DP 应用领域的潜力和挑战。此外，我们通过显示图同质度的程度与训练模型准确度之间的相关性，发现底层图结构构成更大性能差距的潜在因素。