Network structural dependency in the human connectome across the life-span

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 from which changes with development, aging or disease can be 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 with the rich club framework. Stratifying the connectome by NDI led to consistent subnetworks across the life-span, revealing distinct patterns associated with age where, for example, 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.

Brain connectivity measures improve modeling of functional outcome after acute ischemic stroke

“…Connectomics involves conceptualizing the brain as a graph and allows the exploration of topological properties of brain connectivity with network theoretical measures 7 . This has led to fundamental insights into the brain's organization [8][9][10][11][12] , resilience to injury [13][14][15] , and alterations due to disease [16][17][18][19][20] . Associations between structural features, such as white matter microstructural integrity, and functional post-stroke outcome have recently been established 21 .…”

Heat Kernels with Functional Connectomes Reveal Atypical Energy Transport in Peripheral Subnetworks in Autism

“…Another is our use of node strength to identify hubs when a measure which includes information on shortest path lengths such as betweenness centrality may be more relevant, even though there is great overlap between hubs found using both metrics on ABIDE data [17]. Future work will take these limitations into consideration and incorporate other methods to determine nodal importance [27,25].…”

Network Dependency Index Stratified Subnetwork Analysis of Functional Connectomes: An Application to Autism

“…Subnetworks may be determined on group-averaged connectomes. Such connectomes have been used in multiple studies [21,18,19]. First, the binarized connectivity matrices of all subjects within a group are summarized by only retaining edges that are present in at least 90% of the subjects (group adjacency matrix) with the goal to preserve connections which can be reliably identified.…”

“…These studies stratify groups of nodes in a brain network by a network theoretical measure, often relating it to the underlying network topology. Subsequent analyses often compare "traditional" network measures between groups within the cohort or between subnetworks [18,7,19,6]. However, most subnetwork stratification, afer the brain network has been estimated, relies on a user-defined parameter, which can have significant impact on the subnetwork definition (e.g.…”

“…A recent study investigated the use of the network dependency index (NDI) to identify subnetworks in a data driven fashion and subsequently no user-parameter needs to be defined. In their study, Schirmer et al [19] utilized structural connectomes with 170 brain regions in the NKI-Lifespan cohort to investigate the stability of NDI across age groups, and compared their method with subnetworks defined using the rich-club. NDI assigns a measure of "importance" to each node in the connectome by quantifying the global effect of removing the node on network efficiency.…”