  Elucidating the intricate relationship between brain structure and function,
both in healthy and pathological conditions, is a key challenge for modern
neuroscience. Recent technical and methodological progress in neuroimaging has
helped advance our understanding of this important issue, with diffusion
weighted images providing information about structural connectivity (SC) and
functional magnetic resonance imaging shedding light on resting state
functional connectivity (rsFC). However, comparing these two distinct datasets,
each of which can be encoded into a different complex network, is by no means
trivial as pairwise link-to-link comparisons represent a relatively restricted
perspective and provide only limited information. Thus, we have adopted a more
integrative systems approach, exploiting theoretical graph analyses to study
both SC and rsFC datasets gathered independently from healthy human subjects.
The aim is to find the main architectural traits shared by the structural and
functional networks by paying special attention to their common hierarchical
modular organization. This approach allows us to identify a common skeleton
from which a new, optimal, brain partition can be extracted, with modules
sharing both structure and function. We describe these emerging common
structure-function modules (SFMs) in detail. In addition, we compare SFMs with
the classical Resting State Networks derived from independent component
analysis of rs-fMRI functional activity, as well as with anatomical
parcellations in the Automated Anatomical Labeling atlas and with the Broadmann
partition, highlighting their similitude and differences. The unveiling of SFMs
brings to light the strong correspondence between brain structure and
resting-state dynamics.
