Novel Brain Complexity Measures Based on Information Theory

Bonmati Coll, E., Bardera, A., Feixas, M. and Boada, I. 2018. Novel Brain Complexity Measures Based on Information Theory. Entropy. 20 (7) 491. https://doi.org/10.3390/e20070491

TitleNovel Brain Complexity Measures Based on Information Theory
TypeJournal article
AuthorsBonmati Coll, E., Bardera, A., Feixas, M. and Boada, I.
Abstract

Brain networks are widely used models to understand the topology and organization of the brain. These networks can be represented by a graph, where nodes correspond to brain regions and edges to structural or functional connections. Several measures have been proposed to describe the topological features of these networks, but unfortunately, it is still unclear which measures give the best representation of the brain. In this paper, we propose a new set of measures based on information theory. Our approach interprets the brain network as a stochastic process where impulses are modeled as a random walk on the graph nodes. This new interpretation provides a solid theoretical framework from which several global and local measures are derived. Global measures provide quantitative values for the whole brain network characterization and include entropy, mutual information, and erasure mutual information. The latter is a new measure based on mutual information and erasure entropy. On the other hand, local measures are based on different decompositions of the global measures and provide different properties of the nodes. Local measures include entropic surprise, mutual surprise, mutual predictability, and erasure surprise. The proposed approach is evaluated using synthetic model networks and structural and functional human networks at different scales. Results demonstrate that the global measures can characterize new properties of the topology of a brain network and, in addition, for a given number of nodes, an optimal number of edges is found for small-world networks. Local measures show different properties of the nodes such as the uncertainty associated to the node, or the uniqueness of the path that the node belongs. Finally, the consistency of the results across healthy subjects demonstrates the robustness of the proposed measures.

Article number491
JournalEntropy
Journal citation20 (7)
ISSN1099-4300
Year2018
PublisherMDPI
Publisher's version
License
CC BY 4.0
File Access Level
Open (open metadata and files)
Digital Object Identifier (DOI)https://doi.org/10.3390/e20070491
Web address (URL)http://www.mdpi.com/1099-4300/20/7/491
Publication dates
Published25 Jun 2018

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