A hierarchy index for networks in the brain reveals a complex entangled organizational structure

TL;DR

This paper introduces a new hierarchical index to analyze brain networks, revealing a common intertwining of hierarchy and modularity that may be fundamental to information processing in neural systems.

Contribution

The authors propose a novel framework to quantify and uncover hierarchical structures in large networks, applied here to brain connectomes across species.

Findings

Brain networks exhibit a common motif intertwining hierarchy and modularity.

Hierarchical organization may facilitate sequential processing and parallel modular functions.

The hierarchy index reveals complex, entangled organizational structures in neural networks.

Abstract

Networks involved in information processing often have their nodes arranged hierarchically, with the majority of connections occurring in adjacent levels. However, despite being an intuitively appealing concept, the hierarchical organization of large networks, such as those in the brain, are difficult to identify, especially in absence of additional information beyond that provided by the connectome. In this paper, we propose a framework to uncover the hierarchical structure of a given network, that identifies the nodes occupying each level as well as the sequential order of the levels. It involves optimizing a metric that we use to quantify the extent of hierarchy present in a network. Applying this measure to various brain networks, ranging from the nervous system of the nematode Caenorhabditis elegans to the human connectome, we unexpectedly find that they exhibit a common network architectural motif intertwining hierarchy and modularity. This suggests that brain networks may have evolved to simultaneously exploit the functional advantages of these two types of organizations, viz., relatively independent modules performing distributed processing in parallel and a hierarchical structure that allows sequential pooling of these multiple processing streams. An intriguing possibility is that this property we report may be common to information processing networks in general.