  Large bundles of myelinated axons, called white matter, anatomically connect
disparate brain regions together and compose the structural core of the human
connectome. We recently proposed a method of measuring the local integrity
along the length of each white matter fascicle, termed the local connectome. If
communication efficiency is fundamentally constrained by the integrity along
the entire length of a white matter bundle, then variability in the functional
dynamics of brain networks should be associated with variability in the local
connectome. We test this prediction using two statistical approaches that are
capable of handling the high dimensionality of data. First, by performing
statistical inference on distance-based correlations, we show that similarity
in the local connectome between individuals is significantly correlated with
similarity in their patterns of functional connectivity. Second, by employing
variable selection using sparse canonical correlation analysis and
cross-validation, we show that segments of the local connectome are predictive
of certain patterns of functional brain dynamics. These results are consistent
with the hypothesis that structural variability along axon bundles constrains
communication between disparate brain regions.
