Persistency and flexibility of complex brain networks underlie dual‐task interference

Abstract

Previous studies on multitasking suggest that performance decline during concurrent task processing arises from interfering brain modules. Here, we used graph-theoretical network analysis to define functional brain modules and relate the modular organization of complex brain networks to behavioral dual-task costs. Based on resting-state and task fMRI we explored two organizational aspects potentially associated with behavioral interference when human subjects performed a visuospatial and speech task simultaneously: the topological overlap between persistent single-task modules, and the flexibility of single-task modules in adaptation to the dual-task condition. Participants showed a significant decline in visuospatial accuracy in the dual-task compared with single visuospatial task. Global analysis of topological similarity between modules revealed that the overlap between single-task modules significantly correlated with the decline in visuospatial accuracy. Subjects with larger overlap between single-task modules showed higher behavioral interference. Furthermore, lower flexible reconfiguration of single-task modules in adaptation to the dual-task condition significantly correlated with larger decline in visuospatial accuracy. Subjects with lower modular flexibility showed higher behavioral interference. At the regional level, higher overlap between single-task modules and less modular flexibility in the somatomotor cortex positively correlated with the decline in visuospatial accuracy. Additionally, higher modular flexibility in cingulate and frontal control areas and lower flexibility in right-lateralized nodes comprising the middle occipital and superior temporal gyri supported dual-tasking. Our results suggest that persistency and flexibility of brain modules are important determinants of dual-task costs. We conclude that efficient dual-tasking benefits from a specific balance between flexibility and rigidity of functional brain modules.

The modular and integrative functional architecture of the human brain

“…Crucially, connector nodes are also not involved in all betweenmodule connectivity. Nonconnector node regions play a role as well (28,87,93,94), and, as noted above, increased betweenmodule connectivity that is not routed through connector nodes has been found during many different tasks, and differentiates task connectivity from resting-state connectivity (36). Although the current and previous findings suggest that connector node regions coordinate some of these between-module connections, it is likely that other mechanisms for between-module communication exist that are executed by nonconnector nodes.…”

“…However, the membership changes of connector node regions (i.e., brain regions identified as connectors in our analysis) was only associated with an increase in performance. Moreover, the high-performing subjects (i.e., subjects with connector nodes that changed module membership) had less connectivity between single-task modules (i.e., more modular) (83). Another fMRI study found that increased module membership changes of 11 regions, 7 of which are connector node regions in our analysis, predicted increased learning rates (87).…”

“…Finally, empirical functional connectivity studies have shown that modules are weakly functionally connected to each other, likely because their computations are predominantly distinct, suggesting modular function (6,7,16,36,79). However, connections do occur between modules that transfer information between the modules and influence local activity in the modules, potentially increasing the modules' computational loads (6,7,16,27,28,36,(80)(81)(82)(83). Thus, to validate the autonomous nature of modules, it is necessary to demonstrate that, as more modules are engaged simultaneously and more information is generated across the network and transferred between the modules, the modules' computational loads do not increase (50,51,53) (Fig.…”

Transition of the functional brain network related to increasing cognitive demands

“…The non‐smoothed functional volumes were further investigated to avoid spuriously high correlations between time‐series from the neighboring ROIs [Alavash et al, ; Cole et al, ; Fornito et al, ]. We used CONN Functional Connectivity Toolbox v. 15.f (http://www.nitrc.org/projects/conn/, [Whitfield‐Gabrieli and Nieto‐Castanon, ]) to perform the denoising of the functional time series and to create pairwise correlation matrices.…”

State-dependent variability of dynamic functional connectivity between frontoparietal and default networks relates to cognitive flexibility

“…Functional connectivity operates dynamically on both spatial and temporal scales, which is thought to promote adaptation to changing neural demands and allow for network reconfiguration across behavioral states (Cole et al, 2013; Allen et al, 2014; Alavash et al, 2015; Davison et al, 2015). Task-state fMRI studies investigating learning, memory, and working memory have shown that more dynamic connectivity during task execution, particularly of the FPN and DMN, relates to better cognitive performance (Bassett et al, 2011; Fornito et al, 2012; Spreng and Schacter, 2012; Cole et al, 2013; Monti et al, 2014; Beaty et al, 2015; Braun et al, 2015; Vatansever et al, 2015b).…”