  Understanding the relationship between the dynamics of neural processes and
the anatomical substrate of the brain is a central question in neuroscience. On
the one hand, modern neuroimaging technologies, such as diffusion tensor
imaging, can be used to construct structural graphs representing the
architecture of white matter streamlines linking cortical and subcortical
structures. On the other hand, temporal patterns of neural activity can be used
to construct functional graphs representing temporal correlations between brain
regions. Although some studies provide evidence that whole-brain functional
connectivity is shaped by the underlying anatomy, the observed relationship
between function and structure is weak, and the rules by which anatomy
constrains brain dynamics remain elusive. In this article, we introduce a
methodology to predict with high accuracy the functional connectivity of a
subject at rest from his or her structural graph. Using our methodology, we are
able to systematically unveil the role of structural paths in the formation of
functional correlations. Furthermore, in our empirical evaluations, we observe
that the eigen-modes of the predicted functional connectivity are aligned with
activity patterns associated with different cognitive systems. Our work offers
the potential to infer properties of brain dynamics in clinical or
developmental populations with low tolerance for functional neuroimaging.
