使用深度对抗网络归一化器预测脑图随时间的演变
Foreseeing Brain Graph Evolution Over Time Using Deep Adversarial Network Normalizer
摘要：
将大脑进化预见为一个复杂的高度互连的系统，广泛建模为图形，对于映射健康和疾病中不同解剖学感兴趣区域（ROI）之间的动态相互作用至关重要。有趣的是，脑图进化模型在文献中几乎没有。在这里，我们设计了一个对抗性大脑网络规范化器，用于将每个大脑网络表示为固定中心人口驱动的连接模板的变换。相对于固定参考的这种图形归一化为可靠地识别最相似的训练样本（即，脑图）。然后，测试进化轨迹将由所选择的训练图及其对应的进化轨迹跨越。我们的预测框架基于几何深度学习，它自然地对图进行操作，并很好地保留了它们的拓扑属性。具体来说，我们提出了第一个基于图的生成对抗网络（gGAN），它不仅学习如何相对于固定的连接大脑模板（CBT）（即，选择性地捕获大脑群体中最常见特征的大脑模板），而且还学习大脑图的高阶表示（也称为嵌入）。我们使用这些嵌入来计算训练和测试受试者之间的相似性，这使我们能够在基线时间点选择最接近的训练受试者，以预测测试大脑图随时间的演变。对几种比较方法的一系列基准测试表明，我们提出的方法使用单个基线时间点实现了最低的脑部疾病演变预测误差。我们的gGAN代码可在http://github.com/basiralab/gGAN上获得。
英文摘要：
Foreseeing the brain evolution as a complex highly inter-connected system, widely modeled as a graph, is crucial for mapping dynamic interactions between different anatomical regions of interest (ROIs) in health and disease. Interestingly, brain graph evolution models remain almost absent in the literature. Here we design an adversarial brain network normalizer for representing each brain network as a transformation of a fixed centered population-driven connectional template. Such graph normalization with respect to a fixed reference paves the way for reliably identifying the most similar training samples (i.e., brain graphs) to the testing sample at baseline timepoint. The testing evolution trajectory will be then spanned by the selected training graphs and their corresponding evolution trajectories. We base our prediction framework on geometric deep learning which naturally operates on graphs and nicely preserves their topological properties. Specifically, we propose the first graph-based Generative Adversarial Network (gGAN) that not only learns how to normalize brain graphs with respect to a fixed connectional brain template (CBT) (i.e., a brain template that selectively captures the most common features across a brain population) but also learns a high-order representation of the brain graphs also called embeddings. We use these embeddings to compute the similarity between training and testing subjects which allows us to pick the closest training subjects at baseline timepoint to predict the evolution of the testing brain graph over time. A series of benchmarks against several comparison methods showed that our proposed method achieved the lowest brain disease evolution prediction error using a single baseline timepoint. Our gGAN code is available at http://github.com/basiralab/gGAN.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
