An exponential random graph modeling approach to creating group-based representative whole-brain connectivity networks

TL;DR

This paper introduces an exponential random graph modeling approach to construct group-based brain connectivity networks, outperforming traditional mean and median correlation methods in capturing topological properties of the group.

Contribution

The paper proposes a novel ERGM-based method for creating representative brain networks that better reflect group topologies compared to conventional approaches.

Findings

ERGM approach outperforms mean and median correlation networks

ERGM captures topological properties more accurately

Proposed method offers flexible and precise group network construction

Abstract

Group-based brain connectivity networks have great appeal for researchers interested in gaining further insight into complex brain function and how it changes across different mental states and disease conditions. Accurately constructing these networks presents a daunting challenge given the difficulties associated with accounting for inter-subject topological variability. Viable approaches to this task must engender networks that capture the constitutive topological properties of the group of subjects' networks that it is aiming to represent. The conventional approach has been to use a mean or median correlation network (Achard et al., 2006; Song et al., 2009) to embody a group of networks. However, the degree to which their topological properties conform with those of the groups that they are purported to represent has yet to be explored. Here we investigate the performance of these mean and median correlation networks. We also propose an alternative approach based on an exponential random graph modeling framework and compare its performance to that of the aforementioned conventional approach. Simpson et al. (2010) illustrated the utility of exponential random graph models (ERGMs) for creating brain networks that capture the topological characteristics of a single subject's brain network. However, their advantageousness in the context of producing a brain network that "represents" a group of brain networks has yet to be examined. Here we show that our proposed ERGM approach outperforms the conventional mean and median correlation based approaches and provides an accurate and flexible method for constructing group-based representative brain networks.