Network Structural Dependency in the Human Connectome Across the Life-Span

TL;DR

This study introduces the Network Dependency Index (NDI), a new weighted network measure, to identify key brain regions and subnetworks in the human connectome, revealing age-related topological changes across the lifespan.

Contribution

The paper presents NDI as a novel measure for analyzing brain network importance, and demonstrates its effectiveness in identifying consistent subnetworks across different ages, outperforming traditional rich-club analysis.

Findings

NDI effectively identifies central nodes across age groups.

Stratification by NDI reveals age-related topological patterns.

Distinct subnetworks correlate with developmental and aging processes.

Abstract

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.