  Our understanding of the structure of the brain and its relationships with
human traits is largely determined by how we represent the structural
connectome. Standard practice divides the brain into regions of interest (ROIs)
and represents the connectome as an adjacency matrix having cells measuring
connectivity between pairs of ROIs. Statistical analyses are then heavily
driven by the (largely arbitrary) choice of ROIs. In this article, we propose a
novel tractography-based representation of brain connectomes, which clusters
fiber endpoints to define a data adaptive parcellation targeted to explain
variation among individuals and predict human traits. This representation leads
to Principal Parcellation Analysis (PPA), representing individual brain
connectomes by compositional vectors building on a basis system of fiber
bundles that captures the connectivity at the population level. PPA reduces
subjectivity and facilitates statistical analyses. We illustrate the proposed
approach through applications to data from the Human Connectome Project (HCP)
and show that PPA connectomes improve power in predicting human traits over
state-of-the-art methods based on classical connectomes, while dramatically
improving parsimony and maintaining interpretability. Our PPA package is
publicly available on GitHub, and can be implemented routinely for diffusion
image data.

Applications;Methodology