Information integration in large brain networks

An outstanding problem in neuroscience is to understand how information is integrated across the many modules of the brain. While classic information-theoretic measures have transformed our understanding of feedforward information processing in the brain’s sensory periphery, comparable measures for information flow in the massively recurrent networks of the rest of the brain have been lacking. To address this, recent work in information theory has produced a sound measure of network-wide “integrated information”, which can be estimated from time-series data. But, a computational hurdle has stymied attempts to measure large-scale information integration in real brains. Specifically, the measurement of integrated information involves a combinatorial search for the informational “weakest link” of a network, a process whose computation time explodes super-exponentially with network size. Here, we show that spectral clustering, applied on the correlation matrix of time-series data, provides an approximate but robust solution to the search for the informational weakest link of large networks. This reduces the computation time for integrated information in large systems from longer than the lifespan of the universe to just minutes. We evaluate this solution in brain-like systems of coupled oscillators as well as in high-density electrocortigraphy data from two macaque monkeys, and show that the informational “weakest link” of the monkey cortex splits posterior sensory areas from anterior association areas. Finally, we use our solution to provide evidence in support of the long-standing hypothesis that information integration is maximized by networks with a high global efficiency, and that modular network structures promote the segregation of information.Author summary: Information theory has been key to our understanding of the feedforward pathways of the brain’s sensory periphery. But, traditional information-theoretic measures only quantify communication between pairs of transmitters and receivers, and have been of limited utility in decoding signals in the recurrent networks that dominate the rest of the brain. To address this shortcoming, a theoretically sound measure of information integration has recently been derived, which can quantify communication across an entire brain network. This measure could be pivotal in understanding recurrent brain networks. But, a computational hurdle has made it impossible to quantify this measure in real brains. We present an approximate but robust solution to this hurdle, and use our solution to test long-held assumptions about how brain networks might integrate information.