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.