A Data-Driven Approach to Extract Connectivity Structures from Diffusion Tensor Imaging Data

TL;DR

This paper introduces a novel iterative clustering method for deriving stable, connectivity-based brain parcellations from DTI data, improving analysis of structural brain networks across different populations.

Contribution

The authors propose a computationally efficient sparse representation algorithm for whole-brain parcellation based on connectivity, capturing inherent data features and enabling population comparisons.

Findings

Generated stable, connectivity-based brain regions across subjects.

Identified structural differences between populations such as gender, health status, and age.

Reduced data complexity from billions to millions of edges while preserving connectivity information.

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.