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

Détails

ID Serval
serval:BIB_78334C110212
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Mapping higher-order relations between brain structure and function with embedded vector representations of connectomes.
Périodique
Nature communications
Auteur(s)
Rosenthal G., Váša F., Griffa A., Hagmann P., Amico E., Goñi J., Avidan G., Sporns O.
ISSN
2041-1723 (Electronic)
ISSN-L
2041-1723
Statut éditorial
Publié
Date de publication
05/06/2018
Peer-reviewed
Oui
Volume
9
Numéro
1
Pages
2178
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
Publication Status: epublish
Résumé
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.
Mots-clé
Brain/diagnostic imaging, Brain/physiology, Computer Simulation, Connectome/methods, Diffusion Tensor Imaging/methods, Humans, Image Processing, Computer-Assisted, Models, Neurological, Nerve Net/diagnostic imaging, Nerve Net/physiology, Rotation
Pubmed
Web of science
Open Access
Oui
Création de la notice
15/06/2018 17:21
Dernière modification de la notice
20/08/2019 15:34
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