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Nature communications v.9 no.1, 2018년, pp.2178 - 2178   SCI SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

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

Rosenthal, Gideon    (Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, P.O.B. 653, 8410501, Beer-Sheva, Israel   ); Váša, František    (The Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, P.O.B. 653, 8410501, Beer-Sheva, Israel   ); Griffa, Alessandra    (Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK   ); Hagmann, Patric    (Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), 1011, Lausanne, Switzerland   ); Amico, Enrico    (Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland   ); Goñi, Joaquín    (Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), 1011, Lausanne, Switzerland   ); Avidan, Galia    (School of Industrial Engineering, Purdue University, West-Lafayette, 47907, IN, USA   ); Sporns, Olaf    (Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, 47907, IN, USA   );
  • 초록  

    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.


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