Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used in connectomics for studying the functional relationships between regions of the human brain. rs-fMRI connectomics, however, has inherent analytical challenges, such as how to properly model negative correlations between BOLD time series. In addition, functional relationships between brain regions do not necessarily correspond to their anatomical distance, making the functional topology of the brain less well understood. Recent machine learning techniques, such as word2vec, have used embedding methods to map high-dimensional data into vector spaces, where words with more similar meanings are mapped closer to one another. Inspired by this approach, we have developed the graph embedding pipeline rest2vec for studying the vector space of functional connectomes. We demonstrate how rest2vec uses the phase angle spatial embedding (PhASE) method with dimensionality reduction to embed the connectome into lower dimensions, where the functional definition of a brain region is represented continuously in an intrinsic "functional space." Furthermore, we show how the "functional distance" between brain regions in this space can be applied to discover biologically-relevant connectivity gradients. Interestingly, rest2vec can be conceptualized in the context of the recently proposed maximum mean discrepancy (MMD) metric, followed by a double-centering approach seen in kernel PCA. In sum, rest2vec creates a low-dimensional representation of the rs-fMRI connectome where brain regions are mapped according to their functional relationships, giving a more informed understanding of the functional organization of the brain.

中文翻译：

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rest2vec：使用图嵌入向量化静息状态功能连接组

静息态功能磁共振成像 (rs-fMRI) 广泛用于连接组学，用于研究人脑区域之间的功能关系。然而，rs-fMRI 连接组学具有内在的分析挑战，例如如何正确模拟 BOLD 时间序列之间的负相关。此外，大脑区域之间的功能关系不一定与它们的解剖距离相对应，这使得大脑的功能拓扑结构不太清楚。最近的机器学习技术，例如 word2vec，已经使用嵌入方法将高维数据映射到向量空间中，其中具有更相似含义的单词被映射得更近。受这种方法的启发，我们开发了图嵌入管道 rest2vec 用于研究功能连接组的向量空间。我们演示了 rest2vec 如何使用具有降维的相位角空间嵌入 (PhASE) 方法将连接组嵌入到较低维度中，其中大脑区域的功能定义在固有的“功能空间”中连续表示。此外，我们展示了如何应用该空间中大脑区域之间的“功能距离”来发现与生物学相关的连接梯度。有趣的是，rest2vec 可以在最近提出的最大平均差异 (MMD) 度量的背景下进行概念化，然后是在内核 PCA 中看到的双中心方法。总之，rest2vec 创建了 rs-fMRI 连接组的低维表示，其中大脑区域根据其功能关系进行映射，