Communicability in complex brain networks

TL;DR

This paper introduces a new weighted network communicability measure for brain networks, enabling the distinction of local and global differences in connectivity, which can reveal biologically relevant features without data discretization.

Contribution

The study presents a novel communicability measure that directly utilizes real-valued connectivity data in brain networks, improving analysis of neural connectivity patterns.

Findings

Successfully distinguished diseased from control brain networks

Revealed biologically relevant features not visible in raw data

Avoided data discretization by using real-valued weights

Abstract

Recent advances in experimental neuroscience allow, for the first time, non-invasive studies of the white matter tracts in the human central nervous system, thus making available cutting-edge brain anatomical data describing these global connectivity patterns. This new, non-invasive, technique uses magnetic resonance imaging to construct a snap-shot of the cortical network within the living human brain. Here, we report on the initial success of a new weighted network communicability measure in distinguishing local and global differences between diseased patients and controls. This approach builds on recent advances in network science, where an underlying connectivity structure is used as a means to measure the ease with which information can flow between nodes. One advantage of our method is that it deals directly with the real-valued connectivity data, thereby avoiding the need to discretise the corresponding adjacency matrix, that is, to round weights up to 1 or down to 0, depending upon some threshold value. Experimental results indicate that the new approach is able to highlight biologically relevant features that are not immediately apparent from the raw connectivity data.