  Recent progress in diffusion MRI and tractography algorithms as well as the
launch of the Human Connectome Project (HCP) have provided brain research with
an abundance of structural connectivity data. In this work, we describe and
evaluate a method that can infer the structural brain network that
interconnects a given set of Regions of Interest (ROIs) from tractography data.
The proposed method, referred to as Minimum Asymmetry Network Inference
Algorithm (MANIA), differs from prior work because it does not determine the
connectivity between two ROIs based on an arbitrary connectivity threshold.
Instead, we exploit a basic limitation of the tractography process: the
observed streamlines from a source to a target do not provide any information
about the polarity of the underlying white matter, and so if there are some
fibers connecting two voxels (or two ROIs) X and Y tractography should be able
in principle to follow this connection in both directions, from X to Y and from
Y to X. We leverage this limitation to formulate the network inference process
as an optimization problem that minimizes the (appropriately normalized)
asymmetry of the observed network. We evaluate the proposed method on a noise
model that randomly corrupts the observed connectivity of synthetic networks.
As a case-study, we apply MANIA on diffusion MRI data from 28 healthy subjects
to infer the structural network between 18 corticolimbic ROIs that are
associated with various neuropsychiatric conditions including depression,
anxiety and addiction.
