Abstract
This work addresses the problem of constructing a unified, topologically optimal connectivity-based brain atlas. The proposed approach aggregates an ensemble partition from individual parcellations without label agreement, providing a balance between sufficiently flexible individual parcellations and intuitive representation of the average topological structure of the connectome. The methods exploit a previously proposed dense connectivity representation, first performing graph-based hierarchical parcellation of individual brains, and subsequently aggregating the individual parcellations into a consensus parcellation. The search for consensus—based on the hard ensemble (HE) algorithm—approximately minimizes the sum of cluster membership distances, effectively estimating a pseudo-Karcher mean of individual parcellations. Computational stability, graph structure preservation, and biological relevance of the simplified representation resulting from the proposed parcellation are assessed on the Human Connectome Project data set. These aspects are assessed using (1) edge weight distribution divergence with respect to the dense connectome representation, (2) interhemispheric symmetry, (3) network characteristics' stability and agreement with respect to individually and anatomically parcellated networks, and (4) performance of the simplified connectome in a biological sex classification task. Ensemble parcellation was found to be highly stable with respect to subject sampling, outperforming anatomical atlases and other connectome-based parcellations in classification as well as preserving global connectome properties. The HE-based parcellation also showed a degree of symmetry comparable with anatomical atlases and a high degree of spatial contiguity without using explicit priors.
Highlights
The ability to quantify how the human brain is interconnected in vivo has opened the door to a number of possible analyses
Connectome markers ranging from the simple graph descriptors such as edge weights and nodal degrees to sophisticated graph theoretical measures have all been invoked in the study of the brain
Central to many of these is some notion of graph clustering, with some enabling local or node-based group-level analysis based on a group partition, and others focusing on normalizing graph metrics without a unified parcellation
Summary
The ability to quantify how the human brain is interconnected in vivo has opened the door to a number of possible analyses. Connectome markers ranging from the simple graph descriptors such as edge weights and nodal degrees to sophisticated graph theoretical measures have all been invoked in the study of the brain. At the time of this writing, dozens of studies examining the effects of genetics and disease on structural and functional brain connectivity have been published (Horovitz and Horwitz, 2012; Jahanshad et al, 2013; Jiang et al, 2019; Lynall et al, 2010; Shah et al, 2017; Sun et al, 2014; Xu et al, 2016). In most of these, brain parcellation plays a crucial role.
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