Functional brain networks: great expectations, hard times and the big leap forward

Papo, David; Zanin, Massimiliano; Pineda Pardo, José Ángel; Boccaletti, Stefano y Martín Buldú, Javier (2014). Functional brain networks: great expectations, hard times and the big leap forward. "Philosophical Transactions B: Biological Sciences", v. 369 ; pp. 1-14. ISSN 0962-8436. https://doi.org/10.1098/rstb.2013.0525.

Descripción

Título: Functional brain networks: great expectations, hard times and the big leap forward
Autor/es:
  • Papo, David
  • Zanin, Massimiliano
  • Pineda Pardo, José Ángel
  • Boccaletti, Stefano
  • Martín Buldú, Javier
Tipo de Documento: Artículo
Título de Revista/Publicación: Philosophical Transactions B: Biological Sciences
Fecha: 1 Septiembre 2014
Volumen: 369
Materias:
Palabras Clave Informales: complex networks theory, functional neuroimaging, small-world, robustness, efficiency, synchronizability
Escuela: Centro de Tecnología Biomédica (CTB) (UPM)
Departamento: Otro
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Many physical and biological systems can be studied using complex network theory, a new statistical physics understanding of graph theory. The recent application of complex network theory to the study of functional brain networks has generated great enthusiasm as it allows addressing hitherto non-standard issues in the field, such as efficiency of brain functioning or vulnerability to damage. However, in spite of its high degree of generality, the theory was originally designed to describe systems profoundly different from the brain. We discuss some important caveats in the wholesale application of existing tools and concepts to a field they were not originally designed to describe. At the same time, we argue that complex network theory has not yet been taken full advantage of, as many of its important aspects are yet to make their appearance in the neuroscience literature. Finally, we propose that, rather than simply borrowing from an existing theory, functional neural networks can inspire a fundamental reformulation of complex network theory, to account for its exquisitely complex functioning mode.

Más información

ID de Registro: 38816
Identificador DC: http://oa.upm.es/38816/
Identificador OAI: oai:oa.upm.es:38816
Identificador DOI: 10.1098/rstb.2013.0525
URL Oficial: http://rstb.royalsocietypublishing.org/content/369/1653/20130525
Depositado por: Memoria Investigacion
Depositado el: 29 Abr 2017 07:21
Ultima Modificación: 29 Abr 2017 07:21
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