Evidence for hubs in human functional brain networks

By Jonathan D. Power, Bradley L. Schlaggar, Christina N. Lessov-Schlaggar, Steven E. Petersen. Neuron, 2013

754 citations recorded by the source corpus.

Summary Hubs integrate and distribute information in powerful ways due to the number and positioning of their contacts in a network. Several resting state functional connectivity MRI reports have implicated regions of the default mode system as brain hubs; we demonstrate that previous degree-based approaches to hub identification may have identified portions of large brain systems rather than critical nodes of brain networks. We utilize two methods to identify hub-like brain regions: 1) finding network nodes that participate in multiple sub-networks of the brain, and 2) finding spatial locations where several systems are represented within a small volume. These methods converge on a distributed set of regions that differ from previous reports on hubs. This work identifies regions that support multiple systems, leading to spatially constrained predictions about brain function that may be tested in terms of lesions, evoked responses, and dynamic patterns of activity.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/5c28e87846af479f1c3fd0046756e7a9cceb443c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation (2018, background): “…proportional to task load like control systems (McKiernan et al., 2003; Singh and Fawcett, 2008) and with peripheral position in network graphs like processing systems (Gratton et al., 2016; Power et al., 2013), suggest that the DMN does not neatly fall into either control or processing categories.”
Graph analysis of functional brain networks: practical issues in translational neuroscience (2014, background): “The well-known signature of hierarchical network structure, for instance, has been shown to be a consequence of degree-correlation biases in the clustering coefficient definition [113]; the identification of high-degree nodes (or hubs) in brain graphs obtained with correlation-based FC measures coul”
Precision functional mapping of individual human brains (2017, positive): “Caution is required in interpreting the global efficiencymetric in functional connectivity data, as the ‘‘paths’’ we observe represent correlated activity rather than physical links (Power et al., 2013).”
Communication dynamics in complex brain networks (2017, background): “On the one hand, nodes with many connections are well positioned to facilitate integration of information both within and between specialized modules 29,40,49,86,99,100 .”
The modular and integrative functional architecture of the human brain (2015, methodology): “Connector nodes are thought to have access to information among different modules and thus potentially integrate information across or coordinate connectivity between the modules, whereas local nodes support the specialized function of individual modules (40, 41).”
Graph theory methods: applications in brain networks (2018, neutral): “networks.”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “Connector hubs tend to interconnect nodes between diﬀerent modules, while the provincial hubs are responsible for linking nodes in the same module (He et al., 2009; Power et al., 2013; Figure 5A ).”
Contributions and challenges for network models in cognitive neuroscience (2014, neutral): “In another example, the use of node degree as an index of centrality in functional networks may be subject to measurement bias, as it tends to covary with the size of network communities 89 .”