Dynamic coupling of complex brain networks and dual-task behavior
Introduction
Multi-tasking is often concurrent with impairment in performance, and introduces a behavioral challenge. The underlying cognitive architecture of the brain's capacity limit in multi-tasking has been investigated by several behavioral and neuroimaging studies ( Marois and Ivanoff, 2005, Lien et al., 2006, Just et al., 2008, Magen and Cohen, 2010, Remy et al., 2010, Huestegge et al., 2014, Nijboer et al., 2014, Watanabe and Funahashi, 2014). To this end, previous functional magnetic resonance imaging (fMRI) studies have mostly relied on local changes in blood–oxygen-level-dependent (BOLD) activations obtained under different experimental conditions. Recent BOLD-activation studies suggest that the brain's capacity limit in dual-tasking results from interference between neural processes ( Remy et al., 2010, Cohen et al., 2014, Nijboer et al., 2014). Here, we investigate whether alternations in the topological organization of brain functional connectivity networks over time relate to trial by trial variability in dual-task performance.
Recent functional connectivity and graph-theoretical studies suggest that performing two tasks simultaneously or in close succession relies on the flexible reconfiguration of the brain networks ( Ekman et al., 2012, Alavash et al., 2015). In Alavash, Hilgetag, et al. (2015) we showed that (1) topological overlap between single-task network modules was associated with higher dual-task interference and (2) topological reconfiguration of each single-task network modules in adaptation to the dual-task condition was associated with lower dual-task interference. For this, in our previous analyses we assumed that the topology of brain networks changes from one task condition to the other, but is static under each task condition. Thus, brain graphs were constructed based on time-averaged connectivity matrices (i.e. static networks) and between-subject differences in modular reorganization were correlated with between-subject differences in behavioral dual-task costs. As such, the focus of the previous investigations has been on the static topology of brain networks built upon the time-averaged connectivity patterns which disregard the fluctuations in functional brain network organization over time ( Fox et al., 2007, Allen et al., 2012, Zalesky et al., 2014).
In recent years there has been a growing interest in studying the dynamics of the functional connectivity patterns observed in large-scale brain networks ( Hutchison et al., 2013, Lindquist et al., 2014). Several studies have documented that the organization of brain networks are not stable, but dynamically change over time and following changes in task conditions ( Wang et al., 2012, Bassett et al., 2013, Mantzaris et al., 2013, Bola and Sabel, 2015). Many authors have suggested that such patterns are potentially relevant to the fluctuations in cognition and behavior ( Spoormaker et al., 2010, Allen et al., 2012, Jones et al., 2012, Bassett et al., 2013, Tagliazucchi et al., 2013, Schaefer et al., 2014, Zalesky et al., 2014, Sadaghiani et al., 2015). Thus, studying the dual-task interference might profit from the analysis of topological reconfiguration of brain network modules within short time bins (instead of nine-minute runs as it was done in Alavash, Hilgetag, et al., 2015 ), or preferably at a higher temporal resolution. If changes in the modular structure of brain networks across task conditions are potentially relevant to behavior, then fluctuations in behavior within a dual-task condition might also be associated with changes in brain network modularity within short time windows. Thus, while previous studies provide insights into complex brain network correlates of behavioral dual-task costs, it is unknown whether trial by trial variability in dual-task behavior relates to the fluctuations in functional brain network organization during the course of task performance. The aim of the present study is to investigate the coupling of such dynamic network patterns with the dynamics of dual-task behavior at close temporal proximity.
In the present study, we revisited the data of our previous fMRI experiment where subjects performed a visuospatial and a speech task in parallel ( Alavash, Hilgetag, et al., 2015 ; Fig. 1 A). In contrast to the previous static network analysis, here we asked how fluctuations in behavior during the course of the dual-task condition relate to fluctuations in the topology of dynamic brain connectivity networks. To answer this question, we combined a time-resolved correlation analysis ( Zalesky et al., 2014 ) with a sliding window approach applied to the trial by trial behavioral responses to capture the possible temporal coupling of complex brain network metrics with the dual-task behavior ( Fig. 2 ). To best of our knowledge, this is the first study investigating the relation between dynamic brain connectivity networks and dynamics of behavioral performance. A recent study conducted in our lab suggests that performance in different cognitive tasks profits from specific patterns of functional brain network integration at different topological scales, i.e. local, intermediate or global network integration ( Alavash, Doebler, et al., 2015 ). Accordingly, here we expected that distinct yet dynamic patterns of network integration generated at different topological scales correlate with the performance in each dual-task component (i.e. visuospatial or speech task processing). Thus, we hypothesized that moment to moment fluctuations in the dual-task behavior stem from the failure of the brain networks to maintain two different topological configurations at the same time, each supporting the performance in one of the dual-task components.
Section snippets
Subjects
Twenty-four healthy, right-handed subjects (12 females, 12 males, all native German speakers) whose age was in the range of 19–32 years (mean = 24.22 years, standard error of mean [SEM] = 0.74 years) participated in the experiment. Two male subjects showed large head movements during scanning (more than 4 mm of translation or degrees of rotation) and were excluded from further analyses. Ethical approval was obtained from the ethics committee of the University of Oldenburg. All procedures were carried
Behavioral data
The performance of the subjects was measured using the average accuracy (%) for responses to the visuospatial or speech targets within each quarter (35 trials) separately. The average accuracies obtained from responses to target stimuli were compared between different task conditions (i.e. visuospatial vs. speech) and quarters (one to four) by means of an analysis of variance (ANOVA) for repeated measures.
The statistical dependency between responses to each component task in the course of
Dynamics of behavioral performance
The average accuracies (%) for responses to the visuospatial or speech targets across quarters are presented in Fig. 1 B. The results from ANOVA revealed a significant effect of task: the accuracy in the speech task was significantly lower than that of visuospatial task (F(1,21) = 35.27, p < 0.001). To investigate the within-subject dependency between visuospatial and speech performance, we computed the Yule's Q coefficient per subject (see ‘ Analysis of behavioral accuracy ’ section). The mean
Dynamics of functional connectivity networks and its specific coupling with behavior
The overall aim of Alavash, Hilgetag, et al. (2015) and the current study is to find complex network correlates of behavioral impairment often observed under dual-task conditions. In Alavash, Hilgetag, et al. (2015) we were able to associate dual-task behavioral costs with changes in the topological configuration of brain network modules. The current study complements our previous work by featuring the following unique aspects. First, in the current study we analyzed the dynamic changes of
Conclusion
To conclude, the results of this study suggest that the successful performance of two tasks in parallel relies on a specific topological configuration of functional brain connectivity networks. Consistent with our hypothesis, we were able to link the fluctuations in dual-task behavior with the dynamic topological structure of the brain functional connectivity networks.
Acknowledgments
M. A. and C. G. were supported by the Hanse-Wissenschaftskolleg. Parts of the analyses were performed at the High Performance Computer Cluster HERO, located at the University of Oldenburg (Germany) and funded by the DFG through its Major Research Instrumentation Program (INST 184/108-1 FUGG) and the Ministry of Science and Culture (MWK) of the Lower Saxony State.
Conflict of interest
The authors declare no competing financial interests.
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2019, NeuroImageCitation Excerpt :The first goal of the current study was to provide insight into the nature of such network reconfigurations. Although network interactions subserving internal-external dual-tasking have not been studied extensively, many studies have examined the neural correlates of dual-tasking involving two perceptually-based tasks (Alavash et al., 2015; Alavash et al., 2016; Jiang, 2004; Nijboer et al., 2014; Szameitat et al., 2016). Dual-tasking often comes at a behavioral cost compared to performing tasks in isolation, and a general principle that has emerged from this literature is that such behavioral costs emerge to the extent that the neural correlates of the two single tasks overlap.
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2018, NeuroImageCitation Excerpt :Similarly, Shine and colleagues (Shine et al., 2016a) demonstrated that task-related integration in network topology related to fast, effective cognitive performance on the N-back task (Fig. 3). This dissolution of modularity during N-back performance has been corroborated by other groups (Alavash et al., 2016; Chen et al., 2016; Cohen and D'Esposito, 2016; Davison et al., 2015; Shine et al., 2016a; Vatansever et al., 2015), demonstrating that brain-wide integration during cognitively complex tasks may act as a predictive signature of individual differences in executive function (Braun et al., 2015; Gallen et al., 2016; Schultz and Cole, 2016; Shine et al., 2016a). Replicating these fMRI results, a number of studies using electrophysiology during N-back tasks have shown similar increases in network-level integration as a function of cognitive performance (Bola and Borchardt, 2016; de Pasquale et al., 2012; Kitzbichler et al., 2011; Zippo et al., 2016).