Cognitive network neuroscience

Abstract

Network science provides theoretical, computational, and empirical tools that can be used to understand the structure and function of the human brain in novel ways using simple concepts and mathematical representations. Network neuroscience is a rapidly growing field that is providing considerable insight into human structural connectivity, functional connectivity while at rest, changes in functional networks over time (dynamics), and how these properties differ in clinical populations. In addition, a number of studies have begun to quantify network characteristics in a variety of cognitive processes and provide a context for understanding cognition from a network perspective. In this review, we outline the contributions of network science to cognitive neuroscience. We describe the methodology of network science as applied to the particular case of neuroimaging data and review its uses in investigating a range of cognitive functions including sensory processing, language, emotion, attention, cognitive control, learning, and memory. In conclusion, we discuss current frontiers and the specific challenges that must be overcome to integrate these complementary disciplines of network science and cognitive neuroscience. Increased communication between cognitive neuroscientists and network scientists could lead to significant discoveries under an emerging scientific intersection known as cognitive network neuroscience.

Figures

Figure 1

From nodes to networks. (A) Brain regions are organized into cytoarchitectonically distinct areas. (B) Each cytoarchitectural configuration has structural properties with different implications for computational functions. (C) Cytoarchitectural regions can be represented as nodes in a network. The nodes have functional associations, represented as edges, that extend beyond spatial boundaries evident in cytoarchitectural organization. Subsystems can be described as network modules. Modules have varying intraconnectivity and intermodule connectivity in the human brain. (D) An example topology of the modular organization of functional brain networks demonstrating the communication between computational resources of different types. Panel D adapted with permission of Yeo et al. (2011).

Figure 2

Network diagnostics. (A) The clustering coefficient is a diagnostic of local network structure. The left panel contains a network with zero connected triangles and therefore no clustering, whereas the right panel contains a network in which additional edges (green) have been added to close the connected triples (i.e., 3 nodes connected by 2 edges) to form triangles (i.e., 3 nodes connected by 3 edges), thereby leading to higher clustering. (B) The average shortest path length is a diagnostic of global network structure. The left panel contains a network with a relatively long average path length. For example, to move from the purple node (top left) to the red node (bottom right) requires one to traverse at least 4 edges. The right panel contains a network in which addition edges have been added to form triangles (green) or to link distant nodes (peach), thereby leading to a shorter average path length in comparison. (C) Mesoscale network structure can take many forms. The left panel contains a network with a core of densely connected nodes (green circles; green edges) and a periphery of sparsely connected nodes (brown circles; gray edges). The right panel contains a network with four densely connected modules (green circles; green edges) and a connector hub (brown circle; gray edges) that links these modules to one another.

Figure 3

Cognitive network neuroscience (C = Cognitive state). A schematic representation of functional brain networks during cognition. Cognitive modules are indicated by collections of identically colored nodes organized into network modules. The organization of brain networks varies across cognitive states and time. Some features of functional network organization may remain relatively stable as a system “core,” and others may vary substantially. Modules may merge and separate. Connections within and between modules may change in strength, configuration, and number. Network organization may change over time as a function of learning processes.

Figure 4

Brain network dynamics during learning. (A) The flexibility of brain network dynamics—defined as the frequency of a brain region when it changes its allegiances to network modules over time—predicts individual differences in learning: More flexible individuals learn better than less flexible individuals (Bassett, Wymbs, et al., 2011). Moreover, brain regions differ in their flexibility. Regions with greater flexibility form a temporal network core, whereas regions with less flexibility form a temporal network periphery. (B) The distribution of the temporal core and periphery nodes in the brain during learning. “Bulk” nodes are those that do not significantly differ from a temporal network null model. (C) The relationship between region flexibility (f ), core–periphery separation (s), and learning (parameterized by κ). Brain regions are represented using data points located at the polar coordinates (fs,fκ). Color indicates flexibility: Blue nodes have lower flexibility, and brown nodes have higher flexibility. Poor learners (straighter spirals) tend to have a small separation between core and periphery (short spirals), whereas good learners (curvier spirals) tend to have large separation between core and periphery (longer spirals). The separation between core and periphery is a good predictor of individual differences in learning success. Adapted with permission from Bassett et al. (2013).

Figure 5

Network-based prediction (M = graph theory metric, S = subject, V = vector). Two complementary approaches to network-based prediction. (A) Trial level prediction. Node time series are sampled from brain regions, and their functional connectivity is estimated. (B) Functional connectivity (e.g., correlation) matrices are created for different trials (e.g., accurate vs. inaccurate trials). The matrices can be reorganized into vectors ( V) representing connectivity values from different trials. (C) Pattern classifiers can be used to associate observed connectivity patterns across subjects to specific performance values. Finally, the connectivity classification can be used to predict whether vectors from a new subject are associated with performance of different types. (D) Graph level prediction of cognitive states. An elaborated approach can be taken at the level of network metrics during cognitive processes. A graph of nodes and edges can be constructed from brain data. Then, graph theoretical metrics for each node (e.g., clustering coefficients, betweenness centrality, node degree) can be calculated. (E) A pattern classifier can be trained on the graph theory metric patterns observed during different cognitive tasks. (F) Summaries of the absolute and relative importance of nodes, measures, and nodes within measures can be used within the pattern classification scheme. (G) Finally, graph theoretical pattern features can be used to predict cognitive states. Note that, in principle, pairwise functional connectivity such as that used to predict trial types (A–C) can be used for cognitive state prediction (D–G) and vice versa. Panels A–C adapted from Heinzle et al. (2012) and Panels D–G adapted from Ekman et al. (2012), with permission.