Mapping higher-order relations between brain structure and function with embedded vector representations of connectomes.
Mapping higher-order relations between brain structure and function with embedded vector representations of connectomes.
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DOI：
10.1038/s41467-018-04614-w
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发表时间：
2018-06-05
影响因子：
16.6
通讯作者：
Sporns O
中科院分区：
文献类型：
--
作者：
Rosenthal G;Váša F;Griffa A;Hagmann P;Amico E;Goñi J;Avidan G;Sporns O
连接组学生成大脑网络的全面地图，表示为节点及其成对连接。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，网络上下文不能在单个节点的级别上直接访问。语言处理中的类似问题已经通过诸如word2vec之类的算法来解决，该算法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体嵌入（CE）的嵌入式矢量表示。CE可以表征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少的链接，例如，半球间的同伦连接此外，我们构建了功能和结构连接的预测性深度模型，并使用人脸处理系统作为我们的应用领域来模拟网络范围的病变效应。我们认为，CE提供了一种新的方法来揭示连接体结构和功能之间的关系。大脑区域的功能由它所嵌入的网络决定。在这里，作者实现了连接体的word2vec算法，为每个节点生成连接结构的向量嵌入，从而可以推断大脑区域之间的功能关系。
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. The function of a brain region is determined by the network it is embedded in. Here the authors implement the word2vec algorithm for connectomes generating a vector embedding of the connectivity structure for each node allowing inference about functional relationships between brain regions.
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DOI：
10.1162/netn_a_00049
发表时间：
2018
期刊：
Network neuroscience (Cambridge, Mass.)
影响因子：
--
作者：
Amico E;Goñi J
通讯作者：
Goñi J
DOI：
10.1145/2939672.2939754
发表时间：
2016-08
期刊：
KDD : proceedings. International Conference on Knowledge Discovery & Data Mining
影响因子：
--
作者：
Grover A;Leskovec J
通讯作者：
Leskovec J
影响因子：
48
作者：
Craddock, R. Cameron;Jbabdi, Saad;Yan, Chao-Gan;Vogelstein, Joshua T.;Castellanos, F. Xavier;Di Martino, Adriana;Kelly, Clare;Heberlein, Keith;Colcombe, Stan;Milham, Michael P.
通讯作者：
Milham, Michael P.
DOI：
10.1098/rstb.2013.0530
发表时间：
2014-10-05
期刊：
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
影响因子：
--
作者：
Avena-Koenigsberger A;Goñi J;Betzel RF;van den Heuvel MP;Griffa A;Hagmann P;Thiran JP;Sporns O
通讯作者：
Sporns O
影响因子：
2.5
作者：
Cordes, D;Haughton, V;Maravilla, K
通讯作者：
Maravilla, K