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.

Original languageEnglish
Pages (from-to)2178
JournalNature Communications
Issue number1
Publication statusPublished - 5 Jun 2018


  • Brain/diagnostic imaging
  • Computer Simulation
  • Connectome/methods
  • Diffusion Tensor Imaging/methods
  • Humans
  • Image Processing, Computer-Assisted
  • Models, Neurological
  • Nerve Net/diagnostic imaging
  • Rotation


Dive into the research topics of 'Mapping higher-order relations between brain structure and function with embedded vector representations of connectomes'. Together they form a unique fingerprint.

Cite this