Dynamic Reconfiguration of Functional Topology in Human Brain Networks: From Resting to Task States

Abstract

Task demands evoke an intrinsic functional network and flexibly engage multiple distributed networks. However, it is unclear how functional topologies dynamically reconfigure during task performance. Here, we selected the resting- and task-state (emotion and working-memory) functional connectivity data of 81 health subjects from the high-quality HCP data. We used the network-based statistic (NBS) toolbox and the Brain Connectivity Toolbox (BCT) to compute the topological features of functional networks for the resting and task states. Graph-theoretic analysis indicated that under high threshold, a small number of long-distance connections dominated functional networks of emotion and working memory that exhibit distinct long connectivity patterns. Correspondently, task-relevant functional nodes shifted their roles from within-module to between-module: the number of connector hubs (mainly in emotional networks) and kinless hubs (mainly in working-memory networks) increased while provincial hubs disappeared. Moreover, the global properties of assortativity, global efficiency, and transitivity decreased, suggesting that task demands break the intrinsic balance between local and global couplings among brain regions and cause functional networks which tend to be more separated than the resting state. These results characterize dynamic reconfiguration of large-scale distributed networks from resting state to task state and provide evidence for the understanding of the organization principle behind the functional architecture of task-state networks.

Figure 1

The number of connected edges varies with the threshold of the t-value from the network-based statistic method. (a) The inflection point occurs at the threshold of t = 4.6 for EMOTION versus resting state. (b) The inflection point occurs at the threshold of t = 6.4 for working memory versus resting state.

Figure 2

Two-dimension and three-dimension depictions of connectivity analysis result in the EMOTION versus FIX condition. The nodal color denotes affiliative community; the nodal size represents the magnitude of nodal betweenness centrality; the edge depicts binarized edge.

Figure 3

Two-dimension and three-dimension depictions of connectivity analysis result in the WM versus FIX condition. WM: working memory.

Figure 4

The hub distribution within 305 nodes for 81 subjects. (a, c, and e) Separately describe the hub distribution of FIX, EMOTION, and WM networks at the threshold of 10%. (b, d, and f) Separately describe the subject ratio of each hub in FIX, EMOTION, and WM networks at the threshold of 10%. (g) Describes the mean ratio of the hubs across all 305 nodes and all subjects at the 5-10% threshold.

Figure 5

The hub distribution within 28 nodes of FIX (a), EMOTION (c), and WM (e) networks for 81 subjects and the subject ratio of each hub in FIX (b), EMOTION (d), and WM (f) at the threshold of 10%. The nodal order is consistent with Table 1. WM: working memory.

Figure 6

The ANOVA results of global property analysis for FIX, EMOTION, and WM: (a) assortativity; (b) global efficiency; (c) transitivity; (d) global efficiency graph enlarged for WM; (e) transitivity graph enlarged for EMOTION and WM at the threshold of 10%. WM: working memory.