Principles of network theoretical topology have been widely studied in the human connectome. Of particular interest is the modular nature of the human brain, where the connectome is divided into subnetworks and subsequently studied to investigate changes with development, aging or disease . Here, we present a new, weighted network measure called the Network Dependency Index (NDI), which can be used to identify an individual region's importance to the global functioning of the network. Importantly, we utilize NDI to differentiate four different subnetworks (Tiers) in the human connectome following Gaussian Mixture Model fitting. After identification of these subnetworks, we analyze their individual topological aspects with respect to age and compare it to the rich-club topology, which we use to define three 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 accordingly 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 due to disease and/or development , which are not detectable by utilizing the connectome as a whole.
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 due to disease and/or development which are not detectable by utilizing the connectome as a whole.
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