Paper Info
Title
The Global Dynamical Complexity of the Human Brain Network.
Abstract
How much information do large brain networks integrate as a whole over the sum of their parts? Can the dynamical complexity of such networks be globally quantified in an information-theoretic way and be meaningfully coupled to brain function? Recently, measures of dynamical complexity such as integrated information have been proposed. However, problems related to the normalization and Bell number of partitions associated to these measures make these approaches computationally infeasible for large-scale brain networks. Our goal in this work is to address this problem. Our formulation of network integrated information is based on the Kullback-Leibler divergence between the multivariate distribution on the set of network states versus the corresponding factorized distribution over its parts. We find that implementing the maximum information partition optimizes computations. These methods are well-suited for large networks with linear stochastic dynamics. We compute the integrated information for both, the system's attractor states, as well as non-stationary dynamical states of the network. We then apply this formalism to brain networks to compute the integrated information for the human brain's connectome. Compared to a randomly re-wired network, we find that the specific topology of the brain generates greater information complexity.
Year
DOI
Venue
2016
10.1007/s41109-016-0018-8
Applied Network Science
Keywords
DocType
Volume
Brain networks,Neural dynamics,Complexity measures
Journal
abs/1612.07106
Issue
Citations 
PageRank 
1
0
0.34
References 
Authors
13
2
Name
Order
Citations
PageRank
Xerxes D. Arsiwalla18417.84
Paul F. M. J. Verschure2677116.64