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

Papo, David and Zanin, Massimiliano and Pineda Pardo, José Ángel and Boccaletti, Stefano and 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.

Description

Title: Functional brain networks: great expectations, hard times and the big leap forward
Author/s:
  • Papo, David
  • Zanin, Massimiliano
  • Pineda Pardo, José Ángel
  • Boccaletti, Stefano
  • Martín Buldú, Javier
Item Type: Article
Título de Revista/Publicación: Philosophical Transactions B: Biological Sciences
Date: 1 September 2014
ISSN: 0962-8436
Volume: 369
Subjects:
Freetext Keywords: complex networks theory, functional neuroimaging, small-world, robustness, efficiency, synchronizability
Faculty: Centro de Tecnología Biomédica (CTB) (UPM)
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 38816
DC Identifier: http://oa.upm.es/38816/
OAI Identifier: oai:oa.upm.es:38816
DOI: 10.1098/rstb.2013.0525
Official URL: http://rstb.royalsocietypublishing.org/content/369/1653/20130525
Deposited by: Memoria Investigacion
Deposited on: 29 Apr 2017 07:21
Last Modified: 29 Apr 2017 07:21
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