While existing predictive frameworks are able to handle Euclidean structured data (i.e, brain images), they might fail to generalize to geometric non-Euclidean data such as brain networks. Besides, these are rooted the sample selection step in using Euclidean or learned similarity measure between vectorized training and testing brain networks. Such sample connectomic representation might include irrelevant and redundant features that could mislead the training sample selection step. Undoubtedly, this fails to exploit and preserve the topology of the brain connectome. To overcome this major drawback, we propose Residual Embedding Similarity-Based Network selection (RESNets) for predicting brain network evolution trajectory from a single timepoint. RESNets first learns a compact geometric embedding of each training and testing sample using adversarial connectome embedding network. This nicely reduces the high-dimensionality of brain networks while preserving their topological properties via graph convolutional networks. Next, to compute the similarity between subjects, we introduce the concept of a connectional brain template (CBT), a fixed network reference, where we further represent each training and testing network as a deviation from the reference CBT in the embedding space. As such, we select the most similar training subjects to the testing subject at baseline by comparing their learned residual embeddings with respect to the pre-defined CBT. Once the best training samples are selected at baseline, we simply average their corresponding brain networks at follow-up timepoints to predict the evolution trajectory of the testing network. Our experiments on both healthy and disordered brain networks demonstrate the success of our proposed method in comparison to RESNets ablated versions and traditional approaches.

中文翻译：

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基于残差嵌入相似性的网络选择，用于从单个观察预测脑网络进化轨迹

虽然现有的预测框架能够处理欧几里得结构化数据（即大脑图像），但它们可能无法推广到几何非欧几里得数据，如大脑网络。此外，这些源于使用欧几里得或学习的向量化训练和测试大脑网络之间的相似性度量的样本选择步骤。这种样本连接组表示可能包括可能误导训练样本选择步骤的不相关和冗余特征。毫无疑问，这无法利用和保留大脑连接组的拓扑结构。为了克服这个主要缺点，我们提出了基于残差嵌入相似性的网络选择（RESNets）来从单个时间点预测大脑网络进化轨迹。RESNets 首先使用对抗性连接组嵌入网络学习每个训练和测试样本的紧凑几何嵌入。这很好地降低了大脑网络的高维性，同时通过图卷积网络保留了它们的拓扑特性。接下来，为了计算受试者之间的相似性，我们引入了连接大脑模板 (CBT) 的概念，一个固定的网络参考，我们进一步将每个训练和测试网络表示为嵌入空间中与参考 CBT 的偏差。因此，我们通过将学习到的残差嵌入与预定义的 CBT 进行比较来选择与基线测试对象最相似的训练对象。一旦在基线选择了最佳训练样本，我们只是在后续时间点平均他们相应的大脑网络，以预测测试网络的进化轨迹。我们对健康和无序大脑网络的实验证明，与 RESNets 消融版本和传统方法相比，我们提出的方法是成功的。