Measuring embeddedness: Hierarchical scale‐dependent information exchange efficiency of the human brain connectome

Abstract

This paper presents a novel approach for understanding information exchange efficiency and its decay across hierarchies of modularity, from local to global, of the structural human brain connectome. Magnetic resonance imaging techniques have allowed us to study the human brain connectivity as a graph, which can then be analyzed using a graph-theoretical approach. Collectively termed brain connectomics, these sophisticated mathematical techniques have revealed that the brain connectome, like many networks, is highly modular and brain regions can thus be organized into communities or modules. Here, using tractography-informed structural connectomes from 46 normal healthy human subjects, we constructed the hierarchical modularity of the structural connectome using bifurcating dendrograms. Moving from fine to coarse (i.e., local to global) up the connectome's hierarchy, we computed the rate of decay of a new metric that hierarchically preferentially weighs the information exchange between two nodes in the same module. By computing “embeddedness”-the ratio between nodal efficiency and this decay rate, one could thus probe the relative scale-invariant information exchange efficiency of the human brain. Results suggest that regions that exhibit high embeddedness are those that comprise the limbic system, the default mode network, and the subcortical nuclei. This supports the presence of near-decomposability overall yet relative embeddedness in select areas of the brain. The areas we identified as highly embedded are varied in function but are arguably linked in the evolutionary role they play in memory, emotion and behavior.

Elucidating Neural Network Functional Connectivity Abnormalities in Bipolar Disorder: Toward a Harmonized Methodological Approach

“…Inferring patterns of altered functional connectivity on the basis of structural connections would provide a framework for deriving more specific hypotheses and constraints over network models, and is supported by existing data (18, 26), and may be valuable for isolating distinct effects of abnormal structure as opposed to phenomena related to state/context, plasticity, compensation or neural inefficiency. In addition, a recent structural study provided important insights into the connectivity of subcortical regions such as the amygdala and striatum (72). Using structural connectivity estimates, the authors developed a metric (embeddedness) describing the interaction between given regions and other regions across different levels of brain hierarchy.…”

Multimodal evaluation of the amygdala's functional connectivity

“…Addressing these outstanding questions about amygdala connectivity is also pertinent in the context of theories of human brain organization; that is, in understanding how the amygdala interacts with other large-scale neural networks, and whether connectivity changes as a function of different brain states. Sophisticated models of brain connectivity that examine properties of information exchange across hierarchies of modularity in the brain have reported that the amygdala shows a high degree of embeddedness (Ye et al 2015). Specifically, the amygdala shows high nodal efficiency and a slower decay rate of information exchange compared to other regions (Ye et al 2015).…”

“…Sophisticated models of brain connectivity that examine properties of information exchange across hierarchies of modularity in the brain have reported that the amygdala shows a high degree of embeddedness (Ye et al 2015). Specifically, the amygdala shows high nodal efficiency and a slower decay rate of information exchange compared to other regions (Ye et al 2015). Taking this view, the amygdala may show a varying degree of connectivity with several large-scale networks, as opposed to ‘belonging’ to a particular network (e.g., the default mode network; DMN, salience network; SN) that is influenced by changes in brain state.…”

Cognitive and connectome properties detectable through individual differences in graphomotor organization

“…Indeed, instead of focusing on few connections linking select regions-of-interest, connectomics allows for a graph-theoretical assessment of system properties in order to quantitatively understand how brain regions or ‘nodes’ communicate and interact. Additionally, advanced graph-theoretical ‘modularity analysis’ investigates how a group of nodes preferentially interact among themselves to form a community or module, which can then be compared between groups of brain networks to assess for ‘modular’ differences (GadElkarim, et al, 2012; Ye, et al, 2015). Understanding not only the cognitive differences associated with graphomotor organization during the CDT but also their connectome neurocircuit underpinnings may enhance our knowledge of the brain-behavior relationships that underlie unprompted (graphomotor) organization during bedside evaluations of overall cognitive performance.…”