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.
Highlights
Understanding how the brain shapes mind, such as cognition and emotion, relies on the knowledge of largescale brain networks [1]
The intrinsic functional network during resting state primarily shapes a standard architecture of task-based functional brain organization and is secondarily evoked by task-relevant networks [6]
Some studies have found that functional networks tend to be of higher global network integration at task state: for example, the performance of cognitive tasks is associated with increased global efficiency and less segregation of processing relative to resting state [36, 37]
Summary
Understanding how the brain shapes mind, such as cognition and emotion, relies on the knowledge of largescale brain networks [1]. Some studies have found that functional networks tend to be of higher global network integration at task state: for example, the performance of cognitive tasks (including WM) is associated with increased global efficiency and less segregation of processing relative to resting state [36, 37]. Other studies have proposed that the global topological properties are largely invariant in order to continually maintaining the balance of efficient local and global processing [38, 39] Another studies demonstrated that functional networks tend to be highly separated (e.g., negative assortativity coefficients) and exhibit a more random configuration at higher levels of task difficulty (e.g., emotional task) [8, 30, 40]. We predicted that under the disturbance of active tasks, the balance between integration and segregation at resting state would be disrupted and functional networks would tend to be more separated and randomized [8, 30, 40]
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