  In structural brain networks the connections of interest consist of
white-matter fibre bundles between spatially segregated brain regions. The
presence, location and orientation of these white matter tracts can be derived
using diffusion MRI in combination with probabilistic tractography.
Unfortunately, as of yet no approaches have been suggested that provide an
undisputed way of inferring brain networks from tractography. In this paper, we
provide a computational framework which we refer to as Bayesian connectomics.
Rather than applying an arbitrary threshold to obtain a single network, we
consider the posterior distribution of networks that are supported by the data,
combined with an exponential random graph (ERGM) prior that captures a priori
knowledge concerning the graph-theoretical properties of whole-brain networks.
We show that, on simulated probabilistic tractography data, our approach is
able to reconstruct whole-brain networks. In addition, our approach directly
supports multi-model data fusion and group-level network inference.
