Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, P.O.B. 653, 8410501, Beer-Sheva, Israel.
The Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, P.O.B. 653, 8410501, Beer-Sheva, Israel.
Nat Commun. 2018 Jun 5;9(1):2178. doi: 10.1038/s41467-018-04614-w.
Connectomics generates comprehensive maps of brain networks, represented as nodes and their pairwise connections. The functional roles of nodes are defined by their direct and indirect connectivity with the rest of the network. However, the network context is not directly accessible at the level of individual nodes. Similar problems in language processing have been addressed with algorithms such as word2vec that create embeddings of words and their relations in a meaningful low-dimensional vector space. Here we apply this approach to create embedded vector representations of brain networks or connectome embeddings (CE). CE can characterize correspondence relations among brain regions, and can be used to infer links that are lacking from the original structural diffusion imaging, e.g., inter-hemispheric homotopic connections. Moreover, we construct predictive deep models of functional and structural connectivity, and simulate network-wide lesion effects using the face processing system as our application domain. We suggest that CE offers a novel approach to revealing relations between connectome structure and function.
连接组学生成大脑网络的综合图谱，这些网络表示为节点及其两两连接。节点的功能角色由它们与网络其余部分的直接和间接连接来定义。然而，在单个节点的层面上，网络背景并不能直接获取。在语言处理中，也存在类似的问题，可以使用 word2vec 等算法来解决，这些算法可以在有意义的低维向量空间中创建单词及其关系的嵌入。在这里，我们应用这种方法来创建大脑网络或连接组嵌入 (CE) 的嵌入式向量表示。CE 可以描述大脑区域之间的对应关系，并且可以用于推断原始结构扩散成像中缺失的链接，例如，半球间同型连接。此外，我们构建了功能和结构连接的预测性深度模型，并使用面部处理系统作为应用领域，模拟了全网络损伤的影响。我们认为，CE 提供了一种揭示连接组结构与功能之间关系的新方法。
