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Understanding the relationship between the dynamics of neural processes andthe anatomical substrate of the brain is a central question in neuroscience. Onthe one hand, modern neuroimaging technologies, such as diffusion tensorimaging, can be used to construct structural graphs representing thearchitecture of white matter streamlines linking cortical and subcorticalstructures. On the other hand, temporal patterns of neural activity can be usedto construct functional graphs representing temporal correlations between brainregions. Although some studies provide evidence that whole-brain functionalconnectivity is shaped by the underlying anatomy, the observed relationshipbetween function and structure is weak, and the rules by which anatomyconstrains brain dynamics remain elusive. In this article, we introduce amethodology to predict with high accuracy the functional connectivity of asubject at rest from his or her structural graph. Using our methodology, we areable to systematically unveil the role of structural paths in the formation offunctional correlations. Furthermore, in our empirical evaluations, we observethat the eigen-modes of the predicted functional connectivity are aligned withactivity patterns associated with different cognitive systems. Our work offersthe potential to infer properties of brain dynamics in clinical ordevelopmental populations with low tolerance for functional neuroimaging.

Accurately Predicting Functional Connectivity from Diffusion Imaging