Deep Graph Normalizer：一种用于估计连接脑模板的几何深度学习方法
Deep Graph Normalizer: A Geometric Deep Learning Approach for Estimating Connectional Brain Templates
摘要：
连接脑模板（CBT）是一个基于图形的大脑网络群体的归一化表示，也被视为一个平均连接体。CBTs是在典型和非典型人群中创建大脑连接的代表性地图的强大工具。特别是，估计多视图脑网络（MVBN）群体的中心化和代表性CBT更具挑战性，因为这些网络位于复杂的流形上，并且没有简单的方法来融合不同的异构网络视图。这个问题仍然没有探索，除了一些最近的作品植根于假设连接体之间的关系大多是线性的。然而，这样的假设无法捕捉复杂的模式和个体之间的非线性变化。此外，现有的方法简单地由顺序MVBN处理块组成，没有任何反馈机制，导致错误累积。为了解决这些问题，我们提出了深度图规范化器（DGN），这是第一个几何深度学习（GDL）架构，用于通过将MVBN集成到单个连接大脑模板中来规范化MVBN群体。我们的端到端DGN学习如何融合多视图大脑网络，同时通过利用图卷积神经网络捕获跨主体的非线性模式并保留大脑图拓扑属性。我们还引入了一个随机加权损失函数，它也作为一个正则化，以尽量减少人口的MVBN和估计的CBT之间的距离，从而执行其中心。我们证明，在小规模和大规模连接组数据集上估计CBT时，DGN在代表性和可区分性方面都显著优于现有的最先进方法（即，识别对每个大脑网络群体进行指纹识别的独特连接性）。
英文摘要：
A connectional brain template (CBT) is a normalized graph-based representation of a population of brain networks also regarded as an average connectome. CBTs are powerful tools for creating representative maps of brain connectivity in typical and atypical populations. Particularly, estimating a well-centered and representative CBT for populations of multi-view brain networks (MVBN) is more challenging since these networks sit on complex manifolds and there is no easy way to fuse different heterogeneous network views. This problem remains unexplored with the exception of a few recent works rooted in the assumption that the relationship between connectomes are mostly linear. However, such an assumption fails to capture complex patterns and non-linear variation across individuals. Besides, existing methods are simply composed of sequential MVBN processing blocks without any feedback mechanism, leading to error accumulation. To address these issues, we propose Deep Graph Normalizer (DGN), the first geometric deep learning (GDL) architecture for normalizing a population of MVBNs by integrating them into a single connectional brain template. Our end-to-end DGN learns how to fuse multi-view brain networks while capturing non-linear patterns across subjects and preserving brain graph topological properties by capitalizing on graph convolutional neural networks. We also introduce a randomized weighted loss function which also acts as a regularizer to minimize the distance between the population of MVBNs and the estimated CBT, thereby enforcing its centeredness. We demonstrate that DGN significantly outperforms existing state-of-the-art methods on estimating CBTs on both small-scale and large-scale connectomic datasets in terms of both representativeness and discriminability (i.e., identifying distinctive connectivities fingerprinting each brain network population).
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