Title: A Data-Driven Approach to Extract Connectivity Structures from Diffusion Tensor Imaging Data,
Abstract: Diffusion Tensor Imaging (DTI) is an effective tool for the analysis of
structural brain connectivity in normal development and in a broad range of
brain disorders. However efforts to derive inherent characteristics of
structural brain networks have been hampered by the very high dimensionality of
the data, relatively small sample sizes, and the lack of widely acceptable
connectivity-based regions of interests (ROIs). Typical approaches have focused
either on regions defined by standard anatomical atlases that do not
incorporate anatomical connectivity, or have been based on voxel-wise analysis,
which results in loss of statistical power relative to structure-wise
connectivity analysis. In this work, we propose a novel, computationally
efficient iterative clustering method to generate connectivity-based
whole-brain parcellations that converge to a stable parcellation in a few
iterations. Our algorithm is based on a sparse representation of the whole
brain connectivity matrix, which reduces the number of edges from around a half
billion to a few million while incorporating the necessary spatial constraints.
We show that the resulting regions in a sense capture the inherent connectivity
information present in the data, and are stable with respect to initialization
and the randomization scheme within the algorithm. These parcellations provide
consistent structural regions across the subjects of population samples that
are homogeneous with respect to anatomic connectivity. Our method also derives
connectivity structures that can be used to distinguish between population
samples with known different structural connectivity. In particular, new
results in structural differences for different population samples such as
Females vs Males, Normal Controls vs Schizophrenia, and different age groups in
Normal Controls are also shown.