Mapping higher-order relations between brain structure and function with embedded vector representations of connectomes

Gideon Rosenthal, František Váša, Alessandra Griffa, Patric Hagmann, Enrico Amico, Joaquín Goñi, Galia Avidan and Olaf Sporns ()
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Gideon Rosenthal: Ben-Gurion University of the Negev
František Váša: University of Cambridge
Alessandra Griffa: Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL)
Patric Hagmann: Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL)
Enrico Amico: Purdue University
Joaquín Goñi: Purdue University
Galia Avidan: Ben-Gurion University of the Negev
Olaf Sporns: Indiana University

Nature Communications, 2018, vol. 9, issue 1, 1-12

Abstract: Abstract 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.

Date: 2018
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DOI: 10.1038/s41467-018-04614-w

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