PPA: Principal Parcellation Analysis for Brain Connectomes and Multiple Traits

TL;DR

This paper introduces Principal Parcellation Analysis (PPA), a novel data-driven method for representing brain connectomes that enhances trait prediction, reduces subjectivity, and improves interpretability over traditional ROI-based approaches.

Contribution

The paper proposes a new tractography-based brain connectome representation called PPA, which clusters fiber endpoints for adaptive parcellation, improving trait prediction and analysis.

Findings

PPA outperforms classical connectomes in predicting human traits.

PPA enhances statistical power and interpretability.

The method is publicly available on GitHub.

Abstract

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.