Computing Information Integration in Brain Networks

How much information do large brain networks integrate as a whole over the sum of their parts? Can the complexity of such networks be 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. As an application to brain networks, we compute the integrated information for the human brain’s connectomic data. Compared to a randomly re-wired network, we find that the specific topology of the brain generates greater information complexity.