Network attributes for segregation and integration in the human brain
Highlights
► Dynamic information flow involves a balance between segregation and integration. ► Network communities promote functional segregation. ► Network hubs ensure efficient communication and integration. ► The balance between segregation and integration changes across time.
Introduction
Recent years have seen a sharp increase in empirical and theoretical studies of networks as models of complex systems. In neuroscience, the rising interest in brain networks is driven by the increasing availability of network data on the structure and function of neural systems. Such networks or graphs, described as collections of nodes (neurons, regions) and edges (connections, pathways) can be analyzed with a wide array of quantitative tools and methods (Figure 1) [1, 2, 3, 4, 5, 6, 7]. Importantly, network science not only provides intuitive and analytically powerful approaches for data analysis and modeling, it also offers a comprehensive theoretical framework for understanding the biological basis of brain function [8]. This framework bridges and unifies the domains of neuroanatomy (‘structural connectivity’ [9]) and brain dynamics (‘functional and effective connectivity’ [10]) by linking neuronal operations (measured empirically or generated computationally) to an underlying anatomical substrate.
This review article surveys a selection of recent studies on large-scale brain networks, mostly obtained from noninvasive imaging of the human brain. What these studies have in common is that they use network approaches to gain insight into the basis of integrative brain function. Structural connections are fundamental in this regard because they allow neural elements to coordinate their activity into coherent dynamic states that support cognition and behavior. To achieve such coherent dynamics, structural networks shape the flow of information between local regions of the brain to accomplish two distinct goals (Figure 1): firstly they promote functional segregation by forming local network communities that are intrinsically densely connected and strongly coupled; and secondly they promote functional integration by enabling global communication between communities through network hubs. The balance between segregation and integration is essential for the operation of distributed networks underlying cognitive function [11, 12]. The remainder of this review will survey recent studies that have identified network architectures and mechanisms that promote segregation, integration, and their dynamic interplay.
Section snippets
Segregation: network communities
Functional segregation refers to neuronal processing carried out among functionally related regions arranged within modules. In networks such modules correspond to ‘communities’ defined by high density of connectivity among members of the same community and low density of connections between members of different communities. This arrangement of connections tends to generate statistical dependence of neural signals within modules and statistical independence between modules, and hence promotes
Integration: network hubs
Integrative processes in networks can be viewed from at least two different perspectives, one based on the efficiency of global communication and another on the ability of the network to integrate distributed information. A widely used measure of global communication efficiency in networks [41] essentially captures the average length of the shortest communication paths between any two nodes. However, this measure is often found to be maximized in networks with random topology, that is networks
Segregation and integration across time and task: network dynamics
To date, most studies of structural or functional brain networks have built on static descriptions of network matrices, which represent a simple summary of brain structure and dynamics sampled over longer time spans. However, structural connections and (on a much faster time scale) functional connections are in constant flux and change across time, both in the course of spontaneous and task-evoked neural activity. The emerging picture is that of a truly ‘restless brain’ [68], and a number of
Conclusions
Numerous studies, only a fraction of which have been included in this brief overview, have documented network attributes such as communities and hubs that accommodate and promote segregation and integration of neural information. What future developments are to be expected? The emerging picture may be one of an increasingly dynamic and flexible multiscale network model, where regions, circuits and communities are demarcated by boundaries of varying degrees of sharpness and temporal stability,
References and recommended reading
Papers of particular interest, published within the period of review, have been highlighted as:
• of special interest
•• of outstanding interest
Acknowledgement
The author acknowledges support by the JS McDonnell Foundation.
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