  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.
