  Principles of network topology have been widely studied in the human
connectome. Of particular interest is the modularity of the human brain, where
the connectome is divided into subnetworks and subsequently changes with
development, aging or disease are investigated. We present a weighted network
measure, the Network Dependency Index (NDI), to identify an individual region's
importance to the global functioning of the network. Importantly, we utilize
NDI to differentiate four subnetworks (Tiers) in the human connectome following
Gaussian Mixture Model fitting. We analyze the topological aspects of each
subnetwork with respect to age and compare it to rich-club based subnetworks
(rich-club, feeder and seeder). Our results first demonstrate the efficacy of
NDI to identify more consistent, central nodes of the connectome across
age-groups, when compared to the rich-club framework. Stratifying the
connectome by NDI led to consistent subnetworks across the life-span revealing
distinct patterns associated with age where, e.g., the key relay nuclei and
cortical regions are contained in a subnetwork with highest NDI. The divisions
of the human connectome derived from our data-driven NDI framework have the
potential to reveal topological alterations described by network measures
through the life-span.
