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Dynamic coupling of complex brain networks and dual-task behavior
Dynamic coupling of complex brain networks and dual-task behavior Mohsen Alavash, Christiane M. Thiel, Carsten Gießing PII: DOI: Reference:
S1053-8119(16)00034-3 doi: 10.1016/j.neuroimage.2016.01.028 YNIMG 12887
To appear in:
NeuroImage
Received date: Accepted date:
31 July 2015 12 January 2016
Please cite this article as: Alavash, Mohsen, Thiel, Christiane M., Gießing, Carsten, Dynamic coupling of complex brain networks and dual-task behavior, NeuroImage (2016), doi: 10.1016/j.neuroimage.2016.01.028
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ACCEPTED MANUSCRIPT Dynamic coupling of complex brain networks and dual-task behavior
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Revision Nov 2015
Dynamic brain networks and dual-task behavior
Keywords
dual-task accuracy, behavioral fluctuations, dynamic network topology, temporal
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Abbreviated title
Author information
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brain graphs, fMRI 1
Mohsen Alavash , Christiane M. Thiel 1
1,2,3
, Carsten Gießing
1,2
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Biological Psychology Lab, Department of Psychology, European Medical
School, Carl von Ossietzky Universität Oldenburg, 26111 Oldenburg, Germany 2
Research Center Neurosensory Science, Carl von Ossietzky Universität
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Oldenburg, 26111 Oldenburg, Germany 3
Cluster of Excellence Hearing4all, Carl von Ossietzky Universität Oldenburg,
26111 Oldenburg, Germany
[email protected]
the order of
[email protected]
authorship
[email protected]
Corresponding
Mohsen Alavash
author
Department of Psychology, Carl von Ossietzky Universität Oldenburg
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E-mail addresses in
Ammerländer Heer St. 114-118 26111 Oldenburg Germany
Phone: +49 441 798 4385 Fax: +49 441 798 5522
Number of
Pages: 57
Figures: 4
Supp. Figures: 7
Number of words
Abstract: 288
Introduction: 831
Discussion: 3849
Acknowledgements
M. A. and C. G. are 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. The authors declare no competing financial interests.
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Abstract
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Multi-tasking is a familiar situation where behavioral performance is often challenged. To date, fMRI studies investigating the neural underpinning of dual-task interference have mostly relied
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on local brain activation maps or static brain connectivity networks. Here, based on task fMRI we
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explored how fluctuations in behavior during concurrent performance of a visuospatial and a speech task relate to alternations in the topology of dynamic brain connectivity networks. We
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combined a time-resolved functional connectivity and complex network analysis with a sliding window approach applied to the trial by trial behavioral responses to investigate the coupling
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between dynamic brain networks with dual-task behavior at close temporal proximity. Participants showed fluctuations in their dual-task behavior over time, with the accuracy in the
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component tasks being statistically independent from one another. On the global level of brain
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networks we found that dynamic changes of network topology were differentially coupled with
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the behavior in each component task during the course of dual-tasking. While momentary decrease in the global efficiency of dynamic brain networks correlated with subsequent increase in visuospatial accuracy, better speech performance was preceded by higher global network efficiency and was followed by an increase in between-module connectivity over time. Additionally, dynamic alternations in the modular organization of brain networks at the posterior cingulate cortex were differentially predictive for the visuospatial as compared to the speech accuracy over time. Our results provide the first evidence that, during the course of dual-tasking, each component task is supported by a distinct topological configuration of brain connectivity networks. This finding suggests that the failure of functional brain connectivity networks to adapt to an optimal topology supporting the performance in both component tasks at the same time contributes to the moment to moment fluctuations in dual-task behavior.
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Introduction Multi-tasking is often concurrent with impairment in performance, and introduces a behavioral
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challenge. The underlying cognitive architecture of the brain’s capacity limit in multi-tasking has
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been investigated by several behavioral and neuroimaging studies (Marois and Ivanoff, 2005;
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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
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magnetic resonance imaging (fMRI) studies have mostly relied on local changes in bloodoxygen-level-dependent (BOLD) activations obtained under different experimental conditions.
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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
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al., 2014). Here, we investigate whether alternations in the topological organization of brain
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performance.
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functional connectivity networks over time relate to trial by trial variability in dual-task
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., 2015a). In Alavash et al. (2015a) we showed that (1) topological overlap between single-task network modules is associated with higher dual-task interference and (2) topological reconfiguration of each single-task network modules in adaptation to the dual-task condition is 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 betweensubject differences in modular reorganization were correlated with between-subject differences 3
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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
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disregard the fluctuations in functional brain network organization over time (Fox et al., 2007;
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Allen et al., 2012; Zalesky et al., 2014).
In recent years there has been a growing interest in studying the dynamics of the functional
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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
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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
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suggested that such patterns are potentially relevant to the fluctuations in cognition and behavior
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(Spoormaker et al., 2010; Allen et al., 2012; Jones et al., 2012; Bassett et al., 2013; Tagliazucchi
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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 et al. (2015a)), or preferably at a higher temporal resolution. If changes in the modular structure of brain networks within each task condition 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.
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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 et al., 2015a; Figure 1A). In
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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
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connectivity networks. To answer this question, we combined a time-resolved correlation
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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
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metrics with the dual-task behavior (Figure 2). To best of our knowledge, this is the first study
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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 profit from specific patterns of functional brain network integration at
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different topological scales, i.e. local, inter-mediate or global network integration (Alavash et al.,
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2015b). Accordingly, here we expected that distinct yet dynamic patterns of network integration
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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.
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Materials and Methods
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Subjects Twenty-four healthy, right-handed subjects (12 females, 12 males, all native German speakers)
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whose age was in the range of 19-32 years (mean=24.22 years, standard error of mean
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[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
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excluded from further analyses. Ethical approval was obtained from the ethics committee of the
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University of Oldenburg. All procedures were carried out with written informed consent of all subjects and in accordance with the principles of the Declaration of Helsinki. Subjects received a
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Experimental paradigm
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monetary compensation for their participation.
The experimental paradigm and data used in the current study is identical to that of Alavash et al. (2015a). Each subject was instructed to lie still inside the fMRI scanner and complete a dualtask paradigm. The dual-task paradigm was comprised of two component tasks: a visuospatial task and a speech task (Figure 1A). For the visuospatial task (adapted from Hilgetag et al., 2001) subjects were asked to report the location of a small Gabor patch (standard deviation of pixel intensities = 0.026) briefly presented either in the left or right visual periphery (~20 degrees of visual angle) on a grey background using the left middle or index finger respectively. In parallel to the visuospatial task, a speech task was presented where the subjects had the task to discriminate two target consonant-vowels (CVs) as either /da/ or /ga/ (recorded from a male German native speaker) using their right middle or index finger. The correspondence between target CVs and fingers was randomized across subjects. The speech sounds were played together with a band-limited Gaussian white-noise (cut-off frequency=4 kHz). In addition, a third 6
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speech sound /ba/ was played in synchrony with the target CVs, but at a lower intensity to make the task sufficiently challenging (the difference between the intensity of target CVs ( ) and /ba/ =12 dB).
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( ) was 20
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There was no delay between the onsets of the visuospatial and speech stimuli. Each dual-task trial started by presenting a fixation cross for 1000 ms (Figure 1A). Following the fixation cross
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the Gabor patch was briefly presented either in the left or right visual periphery. This was
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concurrent with the presentation of a compound speech sound together with the background noise. After 40 ms the fixation cross turned into a question mark, asking the subject to report the response to each component task as quickly and accurately as possible. In order to reduce the
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subjective expectancy due to successive dual presentation of visuospatial and speech targets, there were also trials on which no visuospatial stimulus, or no speech stimulus, or neither stimuli
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was presented (catch trials). There were 20 trials for each combination of {visuospatial, speech} condition and 60 catch trials in total. The trials were equally distributed within four quarters of the experiment (each comprising 35 trials) with the order of conditions randomized across trials. The duration of the dual-task paradigm was approximately 9 minutes (327 functional volumes). Prior to the functional data acquisition participants completed the visuospatial and speech task as single tasks and were trained for the dual-task paradigm (see Alavash et al., (2015a)).
Analysis of behavioral accuracy Performance of the subjects in response to each component task was analyzed at two time scales. First, and at the larger scale, the average accuracy (%) for responses to the stimuli was measured separately for the visuospatial or speech targets within each quarter. Second, and in order to estimate the dynamics of the dual-task behavior at a higher temporal resolution, a 7
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moving average procedure was applied to the behavioral responses (Figure 2D). This analysis included responses to both joint {visuospatial, speech} trials as well as catch trials (see denote a time series of correct (one) and incorrect (zero)
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‘Experimental paradigm’). Let
responses unevenly spaced over time. The moving average (MA) procedure of length
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defined as:
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(1)
is an observed behavioral response within the sliding window of length
forward in time,
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where
can be
that moves
is one (correct response) or zero (incorrect response), and
is the
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observation time which corresponds to the onset of a dual-task trial. Notably, we analyzed the (i.e. multiples of the
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dynamics of behavior using temporal windows of different lengths
scanning time of repetition (TR)), and each time a temporal window of the same length was used
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for dynamic functional connectivity analysis (see below). The averaged values obtained from this procedure were then linearly interpolated, and the result was down-sampled to one TR. This way, a time series estimates of latent detection ability denoted by
was obtained at multiples of
TR based on the observed behavioral accuracy. The dynamic analysis was applied to both visuospatial and speech responses per subject.
To investigate whether there is a dependency between responses to each component task in the course of dual-tasking, we tested the correlation between visuospatial and speech accuracies by means of Yule’s
coefficient (Yule, 1912; Warrens, 2008). The test statistic provides a measure
of association between two dichotomous variables (e.g. success/failure) based on 2x2 frequency tables (in our case [correct visuospatial, incorrect visuospatial]x [correct speech, incorrect speech]). We first obtained the Yule’s
coefficients per subject as an indication of within-subject 8
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dependency between visuospatial and speech performance. Next, the results were averaged across subjects, and the significance of the mean Yule’s
was tested using a nonparametric
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permutation test (see ‘Statistical analysis’).
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Functional brain data acquisition and preparation
T2*-weighted gradient echo planar imaging (EPI) volumes with BOLD contrast were recorded by
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means of a 3-Tesla MRI scanner (Siemens MAGNETOM Verio (Siemens AG, Erlangen,
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Germany): TR = 1.5 sec, echo time (TE) = 30 milliseconds, flip angle (FA) = 80 degrees, voxel size = 3×3×3.5 millimeters, between-slice gap = 0.87 millimeters, number of slices = 27, image
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size = 64×64 pixels). Structural T1-weighted images (TR = 1.9 sec, TE = 2.52 milliseconds, FA =
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9 degrees, voxel size = 0.9×0.9×1 millimeters, number of slices = 176, image size = 256×256)
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were acquired after completion of functional scans.
After removing the first nine images to account for possible T1 saturation effects, the functional images were corrected for head motion by spatially realigning them to the first volume using SPM8 (Wellcome Trust Centre for Neuroimaging, University College London; Frackowiak et al. 2004). The functional images were spatially normalized to standard stereotaxic MNI space (Montreal Neurological Institute; http://www.mni.mcgill.ca/). No spatial smoothing was applied on the images in order not to induce artificial correlations between time series which subsequently constitute the input data for functional connectivity analysis (Figure 2A and B).
Brain nodes were defined using a parcellated AAL template (Tzourio-Mazoyer et al., 2002) covering 506 cortical regions (Fornito et al., 2010; Zalesky et al., 2010). This template was used 9
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to estimate the mean fMRI time series for each cortical region per subject. In addition to this anatomical template, a functional template (Craddock et al., 2012) comprising 518 cortical nodes
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was used to test whether the effects were present when networks were constructed based on a functional parcellation (results are presented in Figure S2). In order to minimize the effects of
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spurious temporal correlations induced by physiological and movement artifacts, a GLM was
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used to regress out the six rigid-body movement parameters, and the white-mater as well as cerebrospinal fluid (CSF) mean time series (Jo et al., 2013). It has been previously shown that
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behavioral correlates of brain network topology are best observed in the low-frequency
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functional networks (Achard et al., 2006; Bassett et al., 2010; Giessing et al., 2013; Alavash et al., 2015b). Therefore, regional time series were band-pass filtered using an infinite impulse
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response (IIR) filter within the range of 0.01-0.1 Hz (Zalesky et al., 2014).
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Dynamic functional connectivity analysis and network construction To obtain a dynamic measure of association between each pair of cortical regions, weighted Pearson product-moment correlations between regional time series were computed (Pozzi et al., 2012) which in turn gave 506 by 506 correlation matrices for each scan time (multiples of TR) per subject. A similar approach to the one undertaken in this study has been recently used to uncover time-resolved resting-state brain networks (Zalesky et al., 2014). Let mean regional time series for the
brain region obtained from the functional data preparation
step (Figure 2A) where is the onset of a functional scan (i.e. multiples of TR) and number of time points. For a sliding window of length weighting vector
denote a
such that
is the total
that moves forward in time, and a
, the weighted Pearson product-moment
correlation between node and at time point
is given by:
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) at time point
), standard deviations (
or
) and covariances
are:
(3.1)
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(
or
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where the weighted sample means (
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(2)
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(3.2)
The weighting vector
(3.3)
was defined using an exponentially tapered kernel of length
(Pozzi et al., 2012):
, (4.1) where:
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was set to a third of the window length
as suggested by Pozzi et al. (2012)
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The exponent
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(4.2)
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and used in Zalesky et al. (2014). In our analysis, and in order to (1) investigate the window length for which the coupling of the brain networks and dual-task behavior is observed best, and
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(2) to adequately sample the frequency content of the regional time series, the window length was fixed in the range 5xTRs (7.5 sec) to 20xTRs (30 sec) in step of one TR (1.5 sec), together
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with three other larger windows of length 25, 40, and 60xTRs (37.5, 60 and 90 sec respectively). The lengths of the larger windows were similar to those used in recent dynamic connectivity
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studies (Allen et al., 2012; Handwerker et al., 2012; Schaefer et al., 2014; Zalesky et al., 2014;
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Lin et al., 2015; Shen et al., 2015). For every choice, the sliding exponential window was shifted
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forward in time by one TR along the entire time series (Figure 2B). This way, time series estimates of different network diagnostics denoted by
(introduced in the next section) were
obtained at multiples of TR based on the thresholded temporal graphs.
Different approaches have been used to construct brain graphs from functional connectivity matrices and statistically analyze network diagnostics (van Wijk et al., 2010; Fornito et al., 2013). One approach is to investigate brain graphs across different network densities by including edges in the graph according to the rank of their absolute correlation values (Alexander-Bloch et al., 2010; Ginestet et al., 2011). In our analysis, and in order to make the brain graphs comparable in terms of size between subjects and across time points, the number of connections was fixed at 25% of network density (i.e.
where
is the number of
nodes) based on which signed weighted undirected graphs were constructed. To investigate the 12
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robustness of the findings to different network densities, we repeated the main analysis within
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the range of 5%-25% of network density (results presented in Figure S6).
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Network diagnostics
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We used network modularity and efficiency to capture dynamic patterns of network integration at inter-mediate and global scales of network topology respectively. For each topological property,
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we computed a global network metric which characterizes the topological property at the whole-
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brain level (global level) and a regional metric characterizing the same property but for a certain
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brain region (nodal level).
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Network modularity. Unlike most other network measures, the optimal modular structure for a
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given network is typically estimated with optimization algorithms, rather than computed exactly (Steinhaeuser and Chawla, 2010). The quality of the partitions resulting from a given method is often measured by the so-called modularity ( ) index. Here we incorporated a widely-used modularity measure originally proposed by Newman (2006), and used its implementation in the Brain Connectivity Toolbox (Rubinov and Sporns, 2010) which generalizes the modularity maximization algorithm known as Louvain (Blondel et al., 2008) to signed weighted undirected graphs. Let us denote the presence of a positively weighted connection between nodes by
and
, and its corresponding connection weight by
(equivalently for a negatively weighted connection: strength of node
,
, and
is defined as the sum of connection weights of
negative graph representations, i.e.
and
, ,
). The
for each of the positive or
, and the total weight as the sum of all
connection weights (counted twice for each connection), i.e.
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. In order to take the
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sign of connection weights into account, Rubinov and Sporns (2011) proposed the following
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quality index for decomposition of brain graphs into modules:
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where:
(5)
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(6)
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and:
(7)
Similar to the classical measure of network modularity (Newman, 2006),
is the
probability of a connection between and under the assumption of randomly distributed edges, when
and
are in the same module and
otherwise, and
are structural
resolution parameters (Fortunato and Barthelemy, 2007; Lohse et al., 2014) which for simplicity are set to unity in our case (as in Bassett et al. (2010)). For further details and a discussion on the contribution of
and
to the modularity index
the reader is referred to Rubinov and
Sporns (2011). Modularity estimates decomposability of a network into smaller sub-networks which are relatively densely intra-connected, and relatively sparsely interconnected. 14
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The mean functional connectivity was estimated as the mean of the upper-diagonal elements within the temporal connectivity matrices. Based on the modular decomposition three measures
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were computed to characterize the modular organization of networks in more detail. Between/within-module connectivity was estimated by summing the connection weights falling
).
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(i.e.
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between/within modules, and dividing the result by the total number of possible connections
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When considering modular networks, it is reasonable to map the connections between nodes according to the role they fulfill. For this, Guimera and Amaral (2005) have proposed the so-
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called participation coefficient ( ) which determines how node with respect to other modules. For a given node
is positioned within a network
within a binary graph (i.e. a graph with the
where
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connection weights of either one or zero) the participation coefficient of node is defined as:
is the number of links of node
node , and
(8) to nodes in module
, and
is the total degree of
is the total number of network modules. The participation coefficient of a node is
therefore close to one if its links are uniformly distributed among all the modules, and zero if all of its links are within its own module. In the present study, we used the implementation in the Brain Connectivity Toolbox (Rubinov and Sporns, 2010) which generalizes the participation coefficient to weighted undirected graphs, and gives two different
based on once positive and
once negative connections. In each case, the number of links counted in Eq. (8) is replaced by the absolute connection weight in the respective positive or negative graph representations.
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Network efficiency. For a given network
comprised of
nodes, global efficiency
captures the integration of the entire network and is estimated by the inverse of the
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harmonic mean of the minimum weighted path lengths between each pair of nodes
(9)
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A weighted path length is equal to the sum of individual link lengths which are inversely related to connection weights, as large weights typically represent strong associations and
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close proximity (Rubinov and Sporns, 2010). Networks which have a highly integrated organization, characterized by short minimum path lengths between any pair of regional
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nodes, will have high global efficiency. The nodal efficiency at node (
) is likewise
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the rest of the network:
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inversely related to the weighted path length of connections between a particular node and
(10)
Even though nodal efficiency measures regional properties of a network, it still describes the integration of a particular node with the entire network.
Lagged cross-correlation analysis Dynamic coupling between a time series estimates of a single-subject visuospatial or speech accuracy
and a network diagnostic
was quantified by means of lagged cross-correlation
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analysis (Shumway and Stoffer, 2011; Figure 2C). The cross-correlation function between two and
at lag
is given by:
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time series
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is the cross-covariance function:
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where
(11)
(12)
and
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and the denominator is a scaling factor consisting of the variance of each time series. In Eq. (12) indicate the arithmetic means of the corresponding time series over time. The cross-
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correlation function introduced in Eq. (11) ranges between -1 and 1, and measures the linear predictability of a series at time using the values of another series, assuming both series have finite variances. In equations (11) and (12) the cross-covariance function may change as moves along
because the values depend on , i.e. the time lag between the series. In the
present study, the time lag was fixed within the range -20×TR
20×TR (or equivalently
30 sec for TR=1.5 sec) over which it is plausible to expect a degree of coupling between fMRI-derived network metrics and the dual-task behavior (Eichele et al., 2008; Sadaghiani et al., 2015). In Eq. (11) negative lags imply that a given brain network diagnostic
leads the behavior
in the visuospatial or speech component of the dual-task (note, that correlations at different time lags describe the relation between behavior and a BOLD signal-derived network diagnostic, and do not systematically incorporate the time lag between the neural response and the BOLD response produced due to the low-pass filter characteristic of the hemodynamic response function (Friston et al., 2006; chapter 14)). The cross-correlation 17
was computed by
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means of
function in MATLAB (version 2012a; MathWorks, Natick, MA). The value
obtained for a single-subject was then Fisher’s Z transformed, and the results were finally
a network diagnostic
and each visuospatial
and speech
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averaged over subjects at each lag. To measure the specificity of the cross-correlations between performance, we also computed
(13)
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the mean difference of Fisher’s Z-transformed cross-correlations, i.e.:
Statistical analysis
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Behavioral data. Performance of the subjects was measured using the average accuracy (%) for responses to the visuospatial or speech targets within each quarter (35 trials) separately.
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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 a repeated measures analysis of variance (ANOVA).
The statistical dependency between responses to each component task in the course of dualtasking was tested by means of Yule’s
coefficient (Yule, 1912; Warrens, 2008). The Yule’s
coefficients were first estimated per subject as an indication of within-subject dependency between visuospatial and speech performance. Next, the results were averaged across subjects, and the statistical significance of the mean Yule’s
was tested against zero using a two-sided
one-sample permutation test with 10,000 repetitions (Pesarin and Salmaso, 2010).
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In addition, to investigate the possible correlation between visuospatial and speech performance at a larger time scale, the average accuracies in each experimental quarter (35 trials) were
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computed per subject and dual-task component. Next, for each subject, the average visuospatial and speech accuracies across the experimental quarters were correlated by means of
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Spearman’s rho (Spearman, 1904). Finally, the statistical significance of the mean Fisher’s Z-
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transformed correlations averaged over subjects was tested against zero using a two-sided one-
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sample permutation test with 10,000 repetitions (Pesarin and Salmaso, 2010).
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Cross-correlation between global network diagnostics and dual-task behavior. Statistical significance of the lagged cross-correlation was determined by means of a randomization denote a time series of correct (one) and incorrect (zero) responses obtained
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procedure. Let
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from a single-subject. The time at which a correct/incorrect response is modeled by a one/zero
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corresponds to the onset of a dual-task trial. To generate a null distribution of mean Fisher’s Ztransformed cross-correlations at each lag, first the behavioral responses
were randomly
shuffled in time (Figure 2D). For the randomly-shuffled behavioral responses, we performed the same analysis steps as it was done for the empirical behavioral responses (see ‘Analysis of behavioral accuracy’). That is, the moving average procedure defined in Eq. (1) was applied to the randomized . Consistent with the analysis of the empirical behavioral responses, the result of the moving average was linearly interpolated and down-sampled to one TR, which gave us a time series of the null behavioral accuracy Eq. (11) was applied to
. Next, the cross-correlation function defined in
and the time series of a network diagnostic . Finally, the null cross-
correlation obtained for a single-subject was Fisher’s Z-transformed, and the results were averaged over subjects at each lag. The randomization procedure was applied to both visuospatial and speech responses with 10,000 repetitions. To statistically test the significance of the empirical cross-correlations, the observed mean Fisher’s Z-transformed cross-correlation 19
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was compared with the null distribution generated from the randomization procedure (Figure 3). The observed mean cross-correlation was considered significant if it was higher than the 97.5th
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percentile or lower than the 2.5th percentile of the null distribution (upper or lower bounds of the shaded areas in Figure 3 respectively). The corresponding non-parametric p-value was
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computed as the number of times the absolute value of the null observations were found as
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extreme as the absolute value of the empirical mean, divided by the number of randomizations
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(10,000).
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Nodal analysis. At regional level, it was tested whether the time series estimates of visuospatial and speech accuracies differentially correlated with the investigated network diagnostics by
D
means of two-sided paired permutation tests with 10,000 repetitions (van der Voet, 1994). The
and Z
obtained from every subject per brain node. The choice of the regional
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Z
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permutation test was applied to the Fisher’s Z-transformed cross-correlations in Eq. (13), i.e.
network diagnostic
and the length of the sliding window were guided by the results obtained at
the global level of brain networks (see ‘Network diagnostics’ for the definition of the regional diagnostics). Since the aim of the nodal analysis was to investigate the regional network mechanisms which might contribute to the brain-behavior coupling observed at the global level, for each regional diagnostic only the window length
was analyzed for which there was a
significant cross-correlation at global level. In addition, the regional analysis was done at the specific time lag where the corresponding global effect was highest in magnitude (i.e. peak cross-correlation).
Significance thresholds. For all statistical tests (i.e. the inference on the behavioral as well as the global and nodal network effects) we used p 0.05 (two-sided) as the threshold of significance. 20
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In the context of the analysis of nodal effects we used the FDR-adjusted p-values according to Benjamini and Hochberg (1995) to control for alpha inflation entailed by the multiple
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comparisons.
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Note on the computational time
The investigations at the regional level of the brain networks required around 96 hours for a
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given length of the temporal sliding window. This computational time was handled by the use of
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the High Performance Computer Cluster HERO, located at the University of Oldenburg
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(Germany) together with MATLAB Distributed Computing Server (MDCS, version 5.2).
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Results
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Dynamics of behavioral performance The average accuracies (%) for responses to the visuospatial or speech targets across quarters
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are presented in Figure 1B. Results from ANOVA revealed a significant effect of task: accuracy
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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 coefficient per subject (see ‘Analysis of behavioral
applied to the Yule’s Yule’s
coefficient was -0.17 0.14. The permutation test
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accuracy’). The mean and SEM of the Yule’s
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performance, we computed the Yule’s
coefficients revealed no significant result under the null hypothesis of
=0 (p=0.23). This suggests that, in our sample, there was no significant dependency
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between visuospatial and speech accuracy in the course of dual-tasking on trial-by-trial basis. We also found no significant correlation between the performance in speech and visuospatial
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task at a larger time scale. When correct responses to each dual-task component were averaged within each experimental quarter (35 trials) and correlated across quarters per subject, the mean correlation averaged over subjects was not significantly different from zero (mean Spearman’s rho=0.21, p=0.32).
Dynamic coupling of network topology and behavior: Global effects The lagged cross-correlation analysis revealed different patterns of coupling between the global network parameters and each visuospatial or speech performance in the course of dual-tasking. The peak cross-correlation between the time series estimates of visuospatial accuracy global efficiency
and
was the strongest for the sliding window length of 11xTRs (16.5 sec;
Figure 3A, last column, black curve). In this case, the peak cross-correlation between visuospatial accuracy and global efficiency was significantly negative at lag -7xTRs or 22
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equivalently 10.5 sec (
(-7)=-0.06, p=0.05). The results obtained for every length of the sliding
window (i.e. within the range 5xTRs to 20xTRs in step of one TR together with the windows of
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length 25, 40, and 60xTRs) are presented in Figure S1A. There was no significant crosscorrelation between other network diagnostics and visuospatial performance (Figure 3A, first
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three columns; Figure S1A, first three columns). When the functional parcellation template was
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used to construct the brain graphs, the cross-correlations between the network diagnostics and
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visuospatial performance were not significant (Figure S2A).
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The lagged cross-correlation analysis revealed a different pattern of coupling between the brain network diagnostics and the time series estimates of speech accuracy. The peak cross-
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correlation between the time series estimates of speech accuracy
and modularity
was the
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strongest for the sliding window length of 11xTRs (16.5 sec; Figure 3B, second column, black
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curve). In this case, the peak cross-correlation between speech accuracy and modularity was significantly negative at lag 9xTRs or equivalently 13.5 sec (
(9)=-0.08, p=0.007). In addition,
the peak cross-correlation between the time series estimates of speech accuracy between-module connectivity was significantly positive at lag 9xTRs (
and
(9)=0.09, p=0.01;
Figure 3B, third column, black curve). Finally, there was a significantly positive cross-correlation between time series estimates of speech accuracy (
and global efficiency
at lag -7xTRs
(-7)=0.06, p=0.03; Figure 3B, last column, black curve). The results obtained for every length
of the sliding window are presented in Figure S1B. Similar results were found when the functional parcellation template was used to construct the brain graphs (Figure S2B).
To measure the specificity of the cross-correlations between a network diagnostic
and each
visuospatial and speech performance, we also computed the mean difference of Fisher’s Z23
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transformed cross-correlations (Eq. (13)). The peak mean difference of the cross-correlations between modularity
and the time series estimates of each visuospatial
and speech
Z
-
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accuracy was significantly positive at lag 9xTRs or equivalently 13.5 sec (mean(Z
)=0.12, p=0.009; Figure 3C, second column, black curve). In addition, the peak mean
-Z
accuracy was significantly negative at lag 10xTRs
)=-0.12, p=0.01; Figure 3B, third column, black curve). These results were
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(mean(Z
and speech
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estimates of each visuospatial
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difference in the cross-correlations of between-module connectivity and the time series
the strongest for the sliding window length of 11xTRs (16.5 sec). Finally, the peak mean
estimates of each visuospatial
and the time series
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difference of the cross-correlations between global efficiency and speech -Z
)=-0.09, p=0.02; Figure 3B, last column, black
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or equivalently 10.5 sec (mean(Z
accuracy was significantly negative at lag -7xTRs
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curve). This result was the strongest for the sliding window length of 8xTRs (12 sec). The results obtained for every length of the sliding window are presented in Figure S1C. Similar results were
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found when the functional parcellation template was used to construct the brain graphs (Figure S2C) or when brain graphs were thresholded over a range of network densities (Figure S6).
In the absence of prior evidence regarding the appropriate length of a sliding window to identify the coupling between brain network dynamics and behavior, we included a range of window lengths in our analysis at the global level. To investigate whether results are largely affected by differences in window lengths, we computed the mean and SEM of the cross correlations averaged over the sliding window lengths for each network diagnostic (Figure 3, green curves). Our results revealed that the mean cross-correlations between speech performance on one side and mean functional connectivity, modularity, and between-module connectivity on the other side showed similar trends across window lengths (Figure 3B, green curves) as compared to the
24
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strongest cross-correlations (Figure 3B, black curves), whereas for global efficiency the mean
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and strongest cross-correlations deviated from each other (Figure3A/B, last column).
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As mentioned above, the results obtained for every individual length of the sliding window are
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summarized in Figure S1. Besides the relative consistency of cross-correlations between speech performance and mean functional connectivity, modularity, and between-module connectivity
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across window lengths, the strongest significant effects were mostly found for the smaller
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window lengths in the range 7 to 11xTRs (10.5 to 16.5 sec).
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Maps of regional network properties coupled with behavior: Nodal effects
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Guided by the effects obtained at the global level of the brain networks, the difference in cross-
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correlations in Eq. (13) was tested for the regional diagnostics which capture between-module integration and network efficiency at nodal level. That means, in order to find brain regions whose network properties were dynamically coupled with the visuospatial or speech performance, we used the participation coefficient (Eq. (8)) and nodal efficiency (Eq. (10)) in the regional analysis based on Eq. (13). The regional analysis was done at the specific time lags where the peak differences in cross-correlations were observed at the global level. Accordingly, for a given brain node the difference in cross-correlations of participation coefficient time series estimates of each visuospatial
and speech
accuracy was tested at lag 10xTRs
(15 sec), whereas the difference in cross-correlations of nodal efficiency series estimates of each visuospatial
and speech
and the
and the time
accuracy was tested at lag -7xTRs (10.5
sec). We found a significant difference in correlations between the positive participation coefficient
of a brain node within the posterior cingulate cortex (PCC) and each visuospatial
and speech performance (Figure 4A, red node; mean(Z 25
-Z
)=-0.12, FDR-corrected
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p=0.01). For this brain region, the significance of the Fisher’s Z-transformed correlations between
and each visuospatial and speech performance was also tested separately by
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means of one-sample permutation tests. The correlation between the
of the PCC node and
the time series estimates of the visuospatial performance was significantly smaller than zero )=-0.05, p=0.03; Figure 4C). In contrast, the correlation between the
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(mean(Z
of the PCC
)=0.06, p=0.02; Figure 4C). However, there was no significant effect for the
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difference in correlations with nodal efficiency.
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(mean(Z
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node and the time series estimates of the speech performance was significantly larger than zero
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Discussion
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Dynamics of functional connectivity networks and its specific coupling with behavior The overall aim of Alavash et al. (2015a) and the current study is to find complex network
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correlates of behavioral impairment often observed under dual-task conditions. In Alavash et al.
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(2015a) 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
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featuring the following unique aspects. First, in the current study we analyzed the dynamic
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changes of functional brain network topology over time. Dynamic as compared to static networks rely to a larger extent on the brain functional organization as opposed to its structural architecture (Honey et al., 2007; Shen et al., 2015). Therefore, the brain-behavior relations found
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across a sample of subjects are less likely to be confounded by inter-individual differences in structural connectivity (Hermundstad et al., 2013). Second, in the present study we extracted
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global efficiency of dynamic brain networks in addition to modularity. The main motivation for including global efficiency in our analysis comes from our previous study suggesting that performance in different cognitive tasks profit from specific patterns of functional brain network integration at different topological scales (Alavash et al., 2015b). Finally, while the focus of Alavash et al. (2015a) was on the inter-individual differences in dual-tasking, here we aimed at exploring complex brain network correlates of intra-individual variability in dual-task performance over time. Thus, the current study investigates dynamic brain networks on two different scales of network topology (i.e. intermediate and global) and their relation to dual-task behavior. Accordingly, here we related the fluctuations in behavior in the course of dual performance of a visuospatial and a speech task to the dynamic changes of functional brain networks. Our aim was to investigate whether trial by trial fluctuations in dual-tasking result from momentary failure of functional brain connectivity networks to adapt to an optimal topology supporting the performance in both component tasks during the same time. 27
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We showed that dynamic changes of brain network topology were differentially coupled with the
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performance in each single task component during the course of dual-tasking. Each task
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component showed a distinct pattern of cross-correlation with measures of network integration at different topological scales. While better visuospatial performance was preceded by a decrease
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in the global efficiency of brain networks ~10.5 sec earlier, better speech performance was
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preceded by an increase in the global efficiency ~10.5 sec earlier. These findings suggest that, when a visuospatial and a speech task are performed in parallel, while visuospatial performance
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is supported by an earlier decrease in global integration of brain networks over time, speech
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performance is facilitated by an earlier increase in global integration of dynamic brain networks.
Additionally, more accurate speech performance was correlated with a subsequent increase in
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between-module connectivity ~13.5 sec later. In our findings, the first evidence for brainbehavior coupling in the case of speech performance derived from the analysis of mean functional connectivity. This analysis revealed a significant positive correlation between mean functional connectivity and the subsequent speech performance (Figure 3B, first plot). More detailed analyses of brain network topology showed that the latter changes in functional connectivity were accompanied by a significant negative correlation between brain network modularity and the subsequent speech performance (Figure 3B, second plot). This effect seems to result from a reduction in inter-modular connectivity (Figure 3B, third plot). Taken together, these results indicate that better speech performance was followed by an increase in mean functional connectivity, which was dominated by stronger inter-modular connections and in turn reduced network modularity. From theoretical perspective, the anti-correlation between modularity and between-module connectivity is indeed expected: by definition less modular networks tend to show sparser intra-modular and denser inter-modular connections (see 28
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‘Network diagnostics’). In the context of brain functional connectivity networks, Zalesky et al. (2014) also found that 69% of all network connections were inter-modular, and that dynamic
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connections were more prevalent between brain network modules. Thus, in our data and following better speech performance, formation of higher between-module connectivity seems to
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increase mean functional connectivity and diminish modularity of brain networks.
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Although our results showed cross-correlations between measures of network topology and performance in both dual-task components for different time lags, it is difficult to infer the exact
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underlying temporal relationships of the neural and behavioral data because of the delayed hemodynamic response function (HRF) elicited by neural activation (Friston et al., 2006, chapter
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14; Liao et al., 2002) as it is discussed in the following section. Dynamic coupling of behavior and functional connectivity networks at the neural level
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Making a proper inference about brain-behavior coupling based on lead/lag relationships essentially depends on having accurate temporal information about both the underlying neural activity and behavioral patterns. In this regard, one issue is the fact that an fMRI time series is an indirect measure of the neural activity (Buxton et al., 1998; Mandeville et al., 1999; Friston et al., 2000). An fMRI time series can be considered to be a convolution of the hidden neural signal and the canonical HRF, which in turn introduces a temporal delay between the neural response and the BOLD response due to the low-pass filter characteristic of the HRF (Friston et al., 2006, chapter 14; Liao et al., 2002). Thus, deconvolving the HRF from fMRI time series prior to connectivity analysis has been proposed (Gitelman et al., 2003; Ryali et al., 2011). However, the spatial variability of the HRF (Handwerker et al., 2004) confounds the estimation of functional connectivity networks at the neural level (Deshpande et al., 2010; Roebroeck et al., 2011). In fact, since both latent neural activity and the voxel-specific HRF are unknown, a blind 29
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deconvolution technique has to be used that estimates both, the HRF and the underlying neural responses (Havlicek et al., 2011; Wu et al., 2013).
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In the present work, and in order to refine the interpretation of the lead/lag network correlates of
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behavior, we used a recent blind deconvolution approach (Wu et al., 2013) to deconvolve the BOLD time series and estimate the underlying neural signals. The results obtained from this
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procedure reveled very similar patterns for the differences in cross-correlations between a
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network diagnostic and each visuospatial and speech performance (Figure S3). However, the time lag of the peak cross-correlations between a brain network diagnostic and behavioral
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accuracies was influenced by the deconvolution. First, the time lag between changes in visuospatial and speech accuracy and the subsequent alternations in brain network modularity
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was decreased but remained positive, i.e. a backward shift from 13.5 sec to 10.5 sec (Figure S3;
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second colunm). Second, the time lag between alternations in global network efficiency and the subsequent changes in visuospatial and speech accuracy was increased and remained
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negative, i.e. a backward shift from -10.5 sec to -13.5 sec (Figure S3; last colunm). The backward shifts in time can be explained by the temporal precedence of neural signals over BOLD signals.
These results refine the patterns of brain-behavior coupling obtained from BOLD time sereies (Figure 3), and support the finding that global efficincy and modularity of brain neworks were differentialy coupled with behavior depending on the cognitive task (visuospatial or speech) and time (leading or lagging). That is, alternations in global network efficiency was differentially coupled with the subsequent visuospatial and speech task accuracies. In the case of network modularity, however, it seems that changes in brain network modularity lags the behavior: fluctuations in both tasks were differentially coupled with later changes in modular organisation of brain networks. 30
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Taken together, our results provide the evidence for distinct patterns of brain network dynamics
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which are specifically coupled with the behavior in a visuospatial and a speech task when
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performed at the same time, suggesting that trial by trail variability in dual-tasking can be
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associated with the fluctuations in brain network dynamics on different organizational levels.
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Dynamic coupling of visuospatial accuracy and functional connectivity networks
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We note in the present study that less globally integrated brain networks correlated with better visuospatial performance. This significant effect was not as strong as the coupling between brain
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network topology and accuracy in the speech task. The weaker brain-behavior coupling in the
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case of the visuospatial task can be due to a limited variability in the time series estimates of visuospatial accuracy over time. To investigate this, we grouped the subjects into low versus
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high performers, once based on their average visuospatial accuracy and once based on the variance of their visuospatial accuracy over time. Subsequently, we repeated the crosscorrelation analysis separately for each group. The results showed that, the peak crosscorrelation between visuospatial accuracy and global efficiency at lag -7xTRs showed no significant difference between low and high performance groups (non-parametric p=0.90 for grouping according to the mean visuospatial accuracy, non-parametric p=0.98 for grouping based on the variance of visuospatial accuracy; see Figure S4A). Note, that, on the numerical level, subjects with high mean accuracy showed even more pronounced correlations than for low performers. In the case of the coupling with brain network modularity, we also found no significant difference between the performance groups (non-parametric p=0.38 for grouping according to the mean visuospatial accuracy, non-parametric p=0.53 for grouping based on the variance of visuospatial accuracy). Thus, the ability to detect correlations between visuospatial 31
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accuracy and the brain network diagnostics seems not to be affected by the performance level of
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each subject.
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Additionally, we correlated the measures obtained from individuals’ visual performance (i.e.
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mean and variance of their accuracy) with the cross-correlation between behavioral time series and global efficiency at lag -7xTRs. None of the correlations were significant (Spearman’s rank
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correlations and non-parametric p-values: =0.002, p=0.9; =-0.05, p=0.8, respectively; see also
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Figure S4B).
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By inspecting the individual cross-correlations obtained from each subject, we observed that the
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cross-correlation for one of the subjects largely deviated from the rest, pushing the group-
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averaged cross-correlation to zero (Figure S4B, arrow; see also Figure S5A). After excluding the corresponding subject from the analysis, we found a stronger significant cross-correlation between visuospatial accuracy and global efficiency at lag -10.5 sec based on both anatomical and functional parcellation templates (Figure S5B). This exclusion did not affect the groupaveraged cross-correlation between visuospatial accuracy and network modularity, and reproduced a non-significant brain-behavior coupling in this case. Thus, performance in the visuospatial task component seems to be independent from brain network integration on the inter-mediate level (i.e. network modularity), but negatively correlated with brain network integration on the global level (i.e. global network efficiency).
The significant negative correlation between global efficiency and the subsequent visuospatial accuracy might be related to the dual combination of the visuospatial and the speech task. In a dual situation where more accurate speech performance is supported by an increase in the 32
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global network efficiency, changes in global network integration in the opposite direction might reduce interference between distributed brain areas involved in both tasks.
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Dynamic coupling of speech accuracy and functional connectivity networks
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In our data, speech performance was facilitated by higher global integration of brain networks
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over time. This facilitation seems to break down brain network modularity by increasing the between-module connectivity: following more accurate speech performance, mean connectivity
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strength between brain network modules was increased. The emergence of stronger betweenmodule connectivity might be due to a functional network integration subserving accurate
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processing of the speech task. Such functional integration might be related to a higher connectivity within a widely distributed network of cortical areas which has been shown to
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improve speech perception under adverse listening conditions (Obleser et al., 2007). Perhaps, in
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the current dual-task setting, the opposite directions in the coupling of each task component with
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global network integration is an indication of a limiting organizational factor underlying the behavioral impairment in the dual-task condition. Moment to moment fluctuations in the dual-task behavior seems to arise 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 dualtask components.
PCC as a hub of transient functional interactions: implications for behavior At the regional level of the brain networks we found a significant difference in the correlations between positive participation coefficient (
; Eq. (8)) and each visuospatial and speech
performance. More specifically, while speech performance positively correlated with
at a
brain node within the PCC, there was a significant negative correlation between visuospatial performance and
at the same node (Figure 4C). The participation coefficient is a topological 33
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network property which quantifies how ‘well-distributed’ the connections of node different network modules. When node
are among
is uniformly connected to other network modules (i.e.
However, when node
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tightly integrated with other modules) the participation coefficient gets a value close to one. is connected to only its own module, the participation coefficient is zero.
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Therefore, temporal alternation in nodal participation coefficient is a network signature of
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transient functional interactions occurring within and between functional modules. Thus, our data show that subsequent to higher speech and lower visuospatial accuracy the PCC gets more
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integrated with connections to the nodes of other modules. The effect we found at the regional
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level was observed only for the positive participation coefficient (i.e. when negative connections were removed, and Eq. (8) was calculated only based on the strength of the positive
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connections) and was specific to a brain node within the posterior cingulate cortex.
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Previous resting-state studies point to the posterior cingulate cortex as a hub region of the default mode network (Fox et al., 2005; Fransson and Marrelec, 2008; van den Heuvel and Hulshoff Pol, 2010) and recent studies provide important hints on the dynamic functional connectivity of the PCC during rest (Uddin et al., 2009). Using a time-frequency analysis of resting-state functional connectivity, Chang and Glover (2010) observed that coherence and phase between the PCC and the anti-correlated task-positive network as well as other nodes of the default mode network was variable in time and frequency. The dynamic functional connectivity of PCC to other brain regions during rest has been also investigated by Handwerker et al. (2012), where the authors found that many brain voxels shift between being correlated and anticorrelated to the PCC with distinct frequency profiles. Regarding task data, several functional connectivity studies could show that PCC is part of the task-negative network, but strongly interacts with nodes of the task-positive network (Kelly et al., 2008; Piccoli et al., 2015) suggesting that the PCC modulates activity in the task-positive network and, thereby, influence 34
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cognitive performance. For example, the fMRI experiment of Lin et al. (2015) shows that a large number of functional network connections originating from PCC correlate with performance in a
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visual attention task. In summary, many studies have documented that the PCC is part of the task negative network, but similar to our results exhibit dynamic changes in functional
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connectivity with other brain regions over time and during resting-state as well as task
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performance. In contrast to the studies reviewed above, our nodal results point to the important role that PCC may play within the task-induced modular organization of brain networks (see
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Guimera and Amaral (2005) for the definition and discussion of node roles in the context of
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complex modular networks).
Link-wise dynamic functional connectivity and its coupling with behavior
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In recent years studies have begun to relate link-based metrics extracted from brain graphs to
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behavior or diseases (Zalesky et al., 2010; Ahn et al., 2010; Jin et al., 2015). In the present
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study, it is plausible to also expect coupling between a network edge metric and behavior in each dual-task component. To investigate whether changes in connectivity between brain regions correlated with behavior, we used a similar approach to that of Zalesky et al. (2014). In a first step, we identified links whose connection weights dynamically changed over time. To select these dynamic links a statistic was computed that accesses the dynamic fluctuations in timeresolved correlation coefficients for each pair of brain regions (see Figure S7 for further details). Based on this statistic, seven connections were identified whose functional connectivity showed dynamic fluctuations in all subjects. Using the cross-correlation analysis described above (Eq. (11)) the coupling between the connectivity strength of these common dynamic links and dualtask behavior was tested for the specific window length were highest in magnitude.
35
and time lag where the global effects
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The results revealed significant group-averaged correlations between functional connectivity of three connections and time series of each visuospatial and speech accuracy (one sample
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permutation test; p<0.05, FDR corrected; see Figure S7). Specifically, a network link connecting a node within the right somatomotor cortex to a node within the left middle frontal gyrus showed
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a significantly negative correlation with speech accuracy over time. In our experiment, subjects
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responded to the visuospatial targets using their left hand, and to the speech targets using their right hand, whose corresponding somatomotoric representations are intrinsically correlated (Fox
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et al., 2007). Decrease in functional connectivity between the frontal and somatomotor regions
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might be an indication of a top-down control to attenuate the interference between left and right motor cortices, thereby facilitating the performance in the speech task component (see also
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Alavash et al. 2015b, Figure 4C).
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Methodological considerations
A unique aspect of the present study is the dynamic link between the functional brain network topology and behavior. In this regard, temporal windows of different lengths were included in our analysis, and were kept consistent across the dynamic analysis of brain network topology and dual-task behavior. Sliding-window correlation analysis of functional brain data is an emerging method (Lindquist et al., 2014), and the choice of the window size is a matter of debate (Leonardi and Van De Ville, 2015). Recently Zalesky and Breakspear (2015) stressed the importance of recognizing spurious connectivity dynamics due to inappropriate window lengths, and encouraged the use of appropriate statistical testing. In our study, we approached this by (1) testing the association between dynamic connectivity and network patterns with dual-task behavior by comparing the measured correlations over time with a null distribution of the correlations based on randomly shuffled behavioral responses and (2) including a range of window sizes in the analysis. In our data, the strongest degree of coupling between brain 36
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network topology and dual-task behavior was observed for the window length of 11xTRs (16.5 sec). Although one might get more reliable estimates of network topology by increasing the
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window size, longer windows might miss the relevant fluctuations in the dual-task behavior. We therefore argue that the length of the temporal sliding window has to be optimized for both
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functional and behavioral data, and the optimal length depends on the specific experimental task
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and behavior being investigated. We also note that in our data the time lags between behavioral time series and those of the brain network metrics vary with the choice of window size (Figure 3,
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green curves). This variance could be related to the uncertainty in the estimates of latent
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detection ability obtained at multiples of TR based on the observed responses (Eq. (1)). Another possible explanation for this variance could be the differential spectral sensitivity of the time windows with varying lengths. In our analysis functional brain data were band-pass filtered in the
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frequency range 0.01-0.1 Hz, and we found the significant brain-behavior couplings within
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window lengths 8-11xTRs (equivalently 0.06-0.08 Hz). To increase the sensitivity to detect
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significant brain behavior coupling future studies might benefit from an additional high-pass filtering based on the length of the sliding window in order to reduce spurious fluctuations (Leonardi & Van De Ville, 2015). Frequency-specific brain-behavior coupling is conceptually similar to the behavioral relevance of the brain neurophysiological rhythms found in perceptual tasks (Landau et al., 2015). Hence, due to the importance of information within time and frequency domain, future work could also benefit from time-frequency analyses such as wavelet coherence in order to circumvent the limitations of the sliding-window correlation method (Chang and Glover, 2010; Smith et al., 2011).
Nonetheless, important for making inferences on lead-lag mechanisms is whether the brainbehavior coupling occurs within a positive or negative range of time lags. In the present study, the results obtained from the deconvolution approach facilitated making such inferences, since 37
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they rely on the dynamic brain networks derived from estimates of the neural signals which underlie the behavior. These results showed that global efficincy and modularity of brain
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neworks were differentialy coupled with behavior depending on the cognitive task (visuospatial
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or speech) and time (leading or lagging) in our dual-task situation (Figure S3).
Towards the chronnectometery of cognition and behavior
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In recent years the importance of an integrated framework that links brain functional connectivity
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with brain dynamics has been acknowledged by the neuroscience community. It has been argued that what is needed is not only what is connected, but also how functional regions of the
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brain are topologically connected, dynamically coordinated (hence the name ‘chronnectome’),
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and support distinct aspects of cognitive operations and behavioral outcome (van den Heuvel and Sporns, 2013; Calhoun et al., 2014; Kopell et al., 2014; Sporns, 2014). This has led into a
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surge in studies investigating the dynamics of functional connectivity networks not only during rest (Allen et al., 2012; Jones et al., 2012; Kucyi and Davis, 2014; Mitra et al., 2014; Schaefer et al., 2014; Zalesky et al., 2014) but also task conditions (Cribben et al., 2012; Cribben et al., 2013; Monti et al., 2014; Sadaghiani et al., 2015). This approach offers the potential to dissociate the brain functional organization from its structural architecture, as previous studies suggest that structure-function correspondence increases as the length of the temporal window used to observe the brain function increases (Honey et al., 2007; Shen et al., 2015). Most of the previous studies investigated the correlation between functional brain network topology and performance without distinguishing the effects of functional versus structural network organization. Dynamic patterns in the course of behavioral experiments, on the other hand, have long been observed and studied (Schöner and Kelso, 1988; Wijnants, 2014). Examples include learning 38
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(Smith et al., 2004; Smith et al., 2007; Kostrubiec et al., 2012), coordinated actions (Banerjee et al., 2012; Kelso, 2012) and detection of near-threshold stimuli (Palva et al., 2005; Eichele et al.,
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2008; Monto et al., 2008; Palva et al., 2013; Weisz et al., 2014; Sadaghiani et al., 2015). Performance in a multi-tasking situation, however, has been often measured at a large temporal
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scale where the intra-individual fluctuations in behavior over time (Palva and Palva, 2012) were
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not taken into account. Nevertheless, the underlying functional connectivity network patterns of moment to moment fluctuations in perception, attention or goal-directed behavior are still not
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well understood. Future studies will be important to find a theoretical framework to chart the
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dynamics of brain network topology under task conditions and the corresponding behavioral patterns in order to better understand the underlying functional network organization as well as
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temporal structure of cognition and behavior.
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Conclusion To conclude, the results of this study suggest that successful performance of two tasks in
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parallel relies on a specific topological configuration of functional brain connectivity networks.
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Consistent with our hypothesis, we were able to link the fluctuations in dual-task behavior with
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the dynamic topological structure of the brain functional connectivity networks.
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Figure 1: Illustration of the dual-task paradigm and the behavioral data. (A) Dual-task paradigm. The dual-task paradigm was comprised of two component tasks: a
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visuospatial task and a speech task. For the visuospatial task subjects reported the location of a
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small Gabor patch briefly presented either in the left or right visual periphery on a grey background using the left middle or index finger respectively. In parallel, a speech task was
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presented where the subjects had the task to discriminate two target consonant-vowel (CV)
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compounds as either /da/ or /ga/ using their right middle or index finger. The correspondence between the target CVs and fingers was randomized across subjects. The speech sounds were
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played together with a band-limited Gaussian white-noise and a third speech sound /ba/ (at a lower intensity) to make the task sufficiently challenging. The dual-task trials were equally
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distributed within four quarters (each comprising 35 trials) with the order of conditions
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randomized across trials. The duration of the paradigm was approximately 9 minutes.
(B) Behavioral data. Average accuracies (%) for responses to the visuospatial or speech targets across quarters. Bars illustrate mean accuracy, and error bars represent standard error of means. While average speech accuracies were significantly lower than that of visuospatial task, there was no significant correlation between the performance in visuospatial and speech task.
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Figure 2: The methodological steps undertaken in the study. To investigate the dynamic coupling between functional brain network topology and the behavior
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in each dual-task component over time, different methodological steps were employed in order
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to (A) acquire and prepare the functional brain data for dynamic connectivity and network analyses, (B) construct and analyze temporal brain networks, and (C) compute the correlation
over time for the time lags in the range
20×TRs. To analyze the
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each dual-task component
(see ‘Materials and methods’) and the performance in
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dynamics of brain functional connectivity networks and behavioral accuracy in each visuospatial (multiples of TR) were shifted in time at
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or speech task, temporal windows of different lengths
steps of one TR. For a given length of the temporal window, the choice of window size was kept
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analysis pipeline was applied to each single-subject data, and the result of the Fisher’s Ztransformed cross-correlations were averaged across subjects and compared with the results
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Figure 3: Lagged cross-correlations between global metrics of brain network topology and behavioral accuracy in each component of the dual-task.
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The lagged cross-correlation analysis revealed different patterns of coupling between the
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topological metrics of dynamic functional brain connectivity networks at global level and behavioral accuracy in (A) visuospatial or (B) speech performance during the course of dual-
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global efficiency of brain networks ~10.5 seconds later (A/C, last column), increase in betweenmodule connectivity predicted better speech performance ~13.5 seconds following the changes
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Performance of the subjects in response to each component task was analyzed at the temporal resolution of one TR by means of a moving average procedure. Dynamic correlation between
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each pair of cortical regions was computed using an exponentially tapered sliding window, and correlation matrices were generated at each scan time (multiples of TR) per subject. Subsequently, signed weighted undirected graphs were constructed and the dynamics of mean functional connectivity, network modularity, between-module connectivity and global network efficiency were extracted. Finally, the temporal coupling between each network property and the behavioral accuracy in (A) visuospatial or (B) speech performance was investigated using Fisher’s Z-transformed lagged cross-correlations averaged over subjects. To measure the specificity of the cross-correlations between a network diagnostic and each visuospatial and speech performance, we also computed the mean difference of Fisher’s Z-transformed crosscorrelations (C).
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Sliding window length ( ). The length of the temporal sliding window applied to both behavioral and functional brain data was fixed in the range 5xTRs to 20xTRs in step of one TR, together
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with three other larger windows of length 25, 40, and 60xTRs. The cross-correlations obtained from the window length for which the significant coupling of the brain networks and a component
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task behavior was the strongest are shown in black, together with the mean and SEM of the
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cross-correlations averaged over the sliding window lengths (green curves).
Statistical significance. The statistical significance of the lagged cross-correlations was
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determined based on a null distribution of mean Fisher’s Z-transformed cross-correlations at each lag which was generated by randomly shuffling the behavioral responses (10,000
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randomizations). The observed mean cross-correlation was considered significant (red vertical
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lines) if it was higher than the 97.5th percentile or lower than the 2.5th percentile of the null
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distribution (upper and lower bounds of the shaded areas respectively). The corresponding nonparametric p-value was computed as the number of times the absolute value of the null observations were found as extreme as the absolute value of the empirical mean, divided by the number of randomizations (10,000).
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Figure 4: Module participation of the posterior cingulate cortex differentially predicts the behavioral accuracy in each dual-task component.
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To find brain regions where the topology of dynamic brain networks was differentially coupled
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with the visuospatial and speech performance, the difference in the correlations between time series estimates of behavioral accuracy in each component of the dual-task and the time and (B) nodal efficiency was statistically
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courses of (A) positive participation coefficient
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tested. We found a significant difference in cross-correlations between the participation coefficient of a brain node within the posterior cingulate cortex (red node) and each visuospatial
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and speech performance. Specifically, the individual correlations with each component task were
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significant at the same node and differed in sign (C).
The nodal analysis was done for the sliding window length and the specific time lag for which the
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peak differences in the cross-correlations were observed at the global level (Figure 3C). That means here we report the nodal results for (A/C) window length of 11 TRs at lag 10 TRs and (B) window length of 11 TRs at lag -7 TRs. For each regional network diagnostic (A/B) the subject-specific Fisher’s Z-transformed correlations with behavioral accuracy in each visuospatial and speech task were submitted to a paired permutation test with 10,000 repetitions per brain node. Asterisks in (C) indicate significant correlations tested under the null hypothesis of zero correlation (p 0.05, two-sided) using a one-sample permutation test with 10,000 repetitions.
Statistical significance. The nodal p-values were FDR-adjusted according to Benjamini and Hochberg (1995) at the significance level of p 0.05 two-sided (red node). The nodal results
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uncorrected for multiple comparisons at the significance level of p 0.001 are color-coded in
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orange.
coordinates = [3,-65,13])
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PCC. Posterior cingulate cortex (MNI
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Brain slices. L and R indicate the left and right hemispheres respectively. The brain volumetric slices represent the group-averaged T1 image and are ordered from the bottom to the top of the coordinate (mm).
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brain (left to right) according to their MNI
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Highlights
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Dynamics of brain network topology predict fluctuations in dual-task performance Network integration differentially correlates with behavior in each component task Global integration of dynamic brain networks predicts decrease in visual accuracy Global integration of dynamic brain networks predicts increase in speech accuracy Dynamic integration of PCC with brain network modules underlies dual-task behavior
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● ● ● ● ●
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S1053-8119(16)00034-3 doi: 10.1016/j.neuroimage.2016.01.028 YNIMG 12887
To appear in:
NeuroImage
Received date: Accepted date:
31 July 2015 12 January 2016
Please cite this article as: Alavash, Mohsen, Thiel, Christiane M., Gießing, Carsten, Dynamic coupling of complex brain networks and dual-task behavior, NeuroImage (2016), doi: 10.1016/j.neuroimage.2016.01.028
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Dynamic coupling of complex brain networks and dual-task behavior
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Revision Nov 2015
Dynamic brain networks and dual-task behavior
Keywords
dual-task accuracy, behavioral fluctuations, dynamic network topology, temporal
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Abbreviated title
Author information
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brain graphs, fMRI 1
Mohsen Alavash , Christiane M. Thiel 1
1,2,3
, Carsten Gießing
1,2
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Biological Psychology Lab, Department of Psychology, European Medical
School, Carl von Ossietzky Universität Oldenburg, 26111 Oldenburg, Germany 2
Research Center Neurosensory Science, Carl von Ossietzky Universität
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Oldenburg, 26111 Oldenburg, Germany 3
Cluster of Excellence Hearing4all, Carl von Ossietzky Universität Oldenburg,
26111 Oldenburg, Germany
[email protected]
the order of
[email protected]
authorship
[email protected]
Corresponding
Mohsen Alavash
author
Department of Psychology, Carl von Ossietzky Universität Oldenburg
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E-mail addresses in
Ammerländer Heer St. 114-118 26111 Oldenburg Germany
Phone: +49 441 798 4385 Fax: +49 441 798 5522
Number of
Pages: 57
Figures: 4
Supp. Figures: 7
Number of words
Abstract: 288
Introduction: 831
Discussion: 3849
Acknowledgements
M. A. and C. G. are 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. The authors declare no competing financial interests.
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Abstract
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Multi-tasking is a familiar situation where behavioral performance is often challenged. To date, fMRI studies investigating the neural underpinning of dual-task interference have mostly relied
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on local brain activation maps or static brain connectivity networks. Here, based on task fMRI we
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explored how fluctuations in behavior during concurrent performance of a visuospatial and a speech task relate to alternations in the topology of dynamic brain connectivity networks. We
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combined a time-resolved functional connectivity and complex network analysis with a sliding window approach applied to the trial by trial behavioral responses to investigate the coupling
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between dynamic brain networks with dual-task behavior at close temporal proximity. Participants showed fluctuations in their dual-task behavior over time, with the accuracy in the
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component tasks being statistically independent from one another. On the global level of brain
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networks we found that dynamic changes of network topology were differentially coupled with
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the behavior in each component task during the course of dual-tasking. While momentary decrease in the global efficiency of dynamic brain networks correlated with subsequent increase in visuospatial accuracy, better speech performance was preceded by higher global network efficiency and was followed by an increase in between-module connectivity over time. Additionally, dynamic alternations in the modular organization of brain networks at the posterior cingulate cortex were differentially predictive for the visuospatial as compared to the speech accuracy over time. Our results provide the first evidence that, during the course of dual-tasking, each component task is supported by a distinct topological configuration of brain connectivity networks. This finding suggests that the failure of functional brain connectivity networks to adapt to an optimal topology supporting the performance in both component tasks at the same time contributes to the moment to moment fluctuations in dual-task behavior.
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Introduction Multi-tasking is often concurrent with impairment in performance, and introduces a behavioral
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challenge. The underlying cognitive architecture of the brain’s capacity limit in multi-tasking has
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been investigated by several behavioral and neuroimaging studies (Marois and Ivanoff, 2005;
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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
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magnetic resonance imaging (fMRI) studies have mostly relied on local changes in bloodoxygen-level-dependent (BOLD) activations obtained under different experimental conditions.
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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
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al., 2014). Here, we investigate whether alternations in the topological organization of brain
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performance.
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functional connectivity networks over time relate to trial by trial variability in dual-task
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., 2015a). In Alavash et al. (2015a) we showed that (1) topological overlap between single-task network modules is associated with higher dual-task interference and (2) topological reconfiguration of each single-task network modules in adaptation to the dual-task condition is 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 betweensubject differences in modular reorganization were correlated with between-subject differences 3
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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
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disregard the fluctuations in functional brain network organization over time (Fox et al., 2007;
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Allen et al., 2012; Zalesky et al., 2014).
In recent years there has been a growing interest in studying the dynamics of the functional
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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
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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
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suggested that such patterns are potentially relevant to the fluctuations in cognition and behavior
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(Spoormaker et al., 2010; Allen et al., 2012; Jones et al., 2012; Bassett et al., 2013; Tagliazucchi
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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 et al. (2015a)), or preferably at a higher temporal resolution. If changes in the modular structure of brain networks within each task condition 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.
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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 et al., 2015a; Figure 1A). In
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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
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connectivity networks. To answer this question, we combined a time-resolved correlation
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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
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metrics with the dual-task behavior (Figure 2). To best of our knowledge, this is the first study
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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 profit from specific patterns of functional brain network integration at
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different topological scales, i.e. local, inter-mediate or global network integration (Alavash et al.,
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2015b). Accordingly, here we expected that distinct yet dynamic patterns of network integration
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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.
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Materials and Methods
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Subjects Twenty-four healthy, right-handed subjects (12 females, 12 males, all native German speakers)
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whose age was in the range of 19-32 years (mean=24.22 years, standard error of mean
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[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
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excluded from further analyses. Ethical approval was obtained from the ethics committee of the
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University of Oldenburg. All procedures were carried out with written informed consent of all subjects and in accordance with the principles of the Declaration of Helsinki. Subjects received a
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Experimental paradigm
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monetary compensation for their participation.
The experimental paradigm and data used in the current study is identical to that of Alavash et al. (2015a). Each subject was instructed to lie still inside the fMRI scanner and complete a dualtask paradigm. The dual-task paradigm was comprised of two component tasks: a visuospatial task and a speech task (Figure 1A). For the visuospatial task (adapted from Hilgetag et al., 2001) subjects were asked to report the location of a small Gabor patch (standard deviation of pixel intensities = 0.026) briefly presented either in the left or right visual periphery (~20 degrees of visual angle) on a grey background using the left middle or index finger respectively. In parallel to the visuospatial task, a speech task was presented where the subjects had the task to discriminate two target consonant-vowels (CVs) as either /da/ or /ga/ (recorded from a male German native speaker) using their right middle or index finger. The correspondence between target CVs and fingers was randomized across subjects. The speech sounds were played together with a band-limited Gaussian white-noise (cut-off frequency=4 kHz). In addition, a third 6
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speech sound /ba/ was played in synchrony with the target CVs, but at a lower intensity to make the task sufficiently challenging (the difference between the intensity of target CVs ( ) and /ba/ =12 dB).
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( ) was 20
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There was no delay between the onsets of the visuospatial and speech stimuli. Each dual-task trial started by presenting a fixation cross for 1000 ms (Figure 1A). Following the fixation cross
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the Gabor patch was briefly presented either in the left or right visual periphery. This was
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concurrent with the presentation of a compound speech sound together with the background noise. After 40 ms the fixation cross turned into a question mark, asking the subject to report the response to each component task as quickly and accurately as possible. In order to reduce the
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subjective expectancy due to successive dual presentation of visuospatial and speech targets, there were also trials on which no visuospatial stimulus, or no speech stimulus, or neither stimuli
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was presented (catch trials). There were 20 trials for each combination of {visuospatial, speech} condition and 60 catch trials in total. The trials were equally distributed within four quarters of the experiment (each comprising 35 trials) with the order of conditions randomized across trials. The duration of the dual-task paradigm was approximately 9 minutes (327 functional volumes). Prior to the functional data acquisition participants completed the visuospatial and speech task as single tasks and were trained for the dual-task paradigm (see Alavash et al., (2015a)).
Analysis of behavioral accuracy Performance of the subjects in response to each component task was analyzed at two time scales. First, and at the larger scale, the average accuracy (%) for responses to the stimuli was measured separately for the visuospatial or speech targets within each quarter. Second, and in order to estimate the dynamics of the dual-task behavior at a higher temporal resolution, a 7
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moving average procedure was applied to the behavioral responses (Figure 2D). This analysis included responses to both joint {visuospatial, speech} trials as well as catch trials (see denote a time series of correct (one) and incorrect (zero)
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‘Experimental paradigm’). Let
responses unevenly spaced over time. The moving average (MA) procedure of length
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defined as:
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(1)
is an observed behavioral response within the sliding window of length
forward in time,
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where
can be
that moves
is one (correct response) or zero (incorrect response), and
is the
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observation time which corresponds to the onset of a dual-task trial. Notably, we analyzed the (i.e. multiples of the
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dynamics of behavior using temporal windows of different lengths
scanning time of repetition (TR)), and each time a temporal window of the same length was used
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for dynamic functional connectivity analysis (see below). The averaged values obtained from this procedure were then linearly interpolated, and the result was down-sampled to one TR. This way, a time series estimates of latent detection ability denoted by
was obtained at multiples of
TR based on the observed behavioral accuracy. The dynamic analysis was applied to both visuospatial and speech responses per subject.
To investigate whether there is a dependency between responses to each component task in the course of dual-tasking, we tested the correlation between visuospatial and speech accuracies by means of Yule’s
coefficient (Yule, 1912; Warrens, 2008). The test statistic provides a measure
of association between two dichotomous variables (e.g. success/failure) based on 2x2 frequency tables (in our case [correct visuospatial, incorrect visuospatial]x [correct speech, incorrect speech]). We first obtained the Yule’s
coefficients per subject as an indication of within-subject 8
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dependency between visuospatial and speech performance. Next, the results were averaged across subjects, and the significance of the mean Yule’s
was tested using a nonparametric
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permutation test (see ‘Statistical analysis’).
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Functional brain data acquisition and preparation
T2*-weighted gradient echo planar imaging (EPI) volumes with BOLD contrast were recorded by
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means of a 3-Tesla MRI scanner (Siemens MAGNETOM Verio (Siemens AG, Erlangen,
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Germany): TR = 1.5 sec, echo time (TE) = 30 milliseconds, flip angle (FA) = 80 degrees, voxel size = 3×3×3.5 millimeters, between-slice gap = 0.87 millimeters, number of slices = 27, image
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size = 64×64 pixels). Structural T1-weighted images (TR = 1.9 sec, TE = 2.52 milliseconds, FA =
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9 degrees, voxel size = 0.9×0.9×1 millimeters, number of slices = 176, image size = 256×256)
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were acquired after completion of functional scans.
After removing the first nine images to account for possible T1 saturation effects, the functional images were corrected for head motion by spatially realigning them to the first volume using SPM8 (Wellcome Trust Centre for Neuroimaging, University College London; Frackowiak et al. 2004). The functional images were spatially normalized to standard stereotaxic MNI space (Montreal Neurological Institute; http://www.mni.mcgill.ca/). No spatial smoothing was applied on the images in order not to induce artificial correlations between time series which subsequently constitute the input data for functional connectivity analysis (Figure 2A and B).
Brain nodes were defined using a parcellated AAL template (Tzourio-Mazoyer et al., 2002) covering 506 cortical regions (Fornito et al., 2010; Zalesky et al., 2010). This template was used 9
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to estimate the mean fMRI time series for each cortical region per subject. In addition to this anatomical template, a functional template (Craddock et al., 2012) comprising 518 cortical nodes
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was used to test whether the effects were present when networks were constructed based on a functional parcellation (results are presented in Figure S2). In order to minimize the effects of
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spurious temporal correlations induced by physiological and movement artifacts, a GLM was
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used to regress out the six rigid-body movement parameters, and the white-mater as well as cerebrospinal fluid (CSF) mean time series (Jo et al., 2013). It has been previously shown that
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behavioral correlates of brain network topology are best observed in the low-frequency
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functional networks (Achard et al., 2006; Bassett et al., 2010; Giessing et al., 2013; Alavash et al., 2015b). Therefore, regional time series were band-pass filtered using an infinite impulse
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response (IIR) filter within the range of 0.01-0.1 Hz (Zalesky et al., 2014).
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Dynamic functional connectivity analysis and network construction To obtain a dynamic measure of association between each pair of cortical regions, weighted Pearson product-moment correlations between regional time series were computed (Pozzi et al., 2012) which in turn gave 506 by 506 correlation matrices for each scan time (multiples of TR) per subject. A similar approach to the one undertaken in this study has been recently used to uncover time-resolved resting-state brain networks (Zalesky et al., 2014). Let mean regional time series for the
brain region obtained from the functional data preparation
step (Figure 2A) where is the onset of a functional scan (i.e. multiples of TR) and number of time points. For a sliding window of length weighting vector
denote a
such that
is the total
that moves forward in time, and a
, the weighted Pearson product-moment
correlation between node and at time point
is given by:
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) at time point
), standard deviations (
or
) and covariances
are:
(3.1)
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(
or
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where the weighted sample means (
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(2)
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(3.2)
The weighting vector
(3.3)
was defined using an exponentially tapered kernel of length
(Pozzi et al., 2012):
, (4.1) where:
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was set to a third of the window length
as suggested by Pozzi et al. (2012)
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The exponent
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(4.2)
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and used in Zalesky et al. (2014). In our analysis, and in order to (1) investigate the window length for which the coupling of the brain networks and dual-task behavior is observed best, and
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(2) to adequately sample the frequency content of the regional time series, the window length was fixed in the range 5xTRs (7.5 sec) to 20xTRs (30 sec) in step of one TR (1.5 sec), together
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with three other larger windows of length 25, 40, and 60xTRs (37.5, 60 and 90 sec respectively). The lengths of the larger windows were similar to those used in recent dynamic connectivity
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studies (Allen et al., 2012; Handwerker et al., 2012; Schaefer et al., 2014; Zalesky et al., 2014;
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Lin et al., 2015; Shen et al., 2015). For every choice, the sliding exponential window was shifted
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forward in time by one TR along the entire time series (Figure 2B). This way, time series estimates of different network diagnostics denoted by
(introduced in the next section) were
obtained at multiples of TR based on the thresholded temporal graphs.
Different approaches have been used to construct brain graphs from functional connectivity matrices and statistically analyze network diagnostics (van Wijk et al., 2010; Fornito et al., 2013). One approach is to investigate brain graphs across different network densities by including edges in the graph according to the rank of their absolute correlation values (Alexander-Bloch et al., 2010; Ginestet et al., 2011). In our analysis, and in order to make the brain graphs comparable in terms of size between subjects and across time points, the number of connections was fixed at 25% of network density (i.e.
where
is the number of
nodes) based on which signed weighted undirected graphs were constructed. To investigate the 12
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robustness of the findings to different network densities, we repeated the main analysis within
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the range of 5%-25% of network density (results presented in Figure S6).
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Network diagnostics
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We used network modularity and efficiency to capture dynamic patterns of network integration at inter-mediate and global scales of network topology respectively. For each topological property,
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we computed a global network metric which characterizes the topological property at the whole-
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brain level (global level) and a regional metric characterizing the same property but for a certain
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brain region (nodal level).
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Network modularity. Unlike most other network measures, the optimal modular structure for a
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given network is typically estimated with optimization algorithms, rather than computed exactly (Steinhaeuser and Chawla, 2010). The quality of the partitions resulting from a given method is often measured by the so-called modularity ( ) index. Here we incorporated a widely-used modularity measure originally proposed by Newman (2006), and used its implementation in the Brain Connectivity Toolbox (Rubinov and Sporns, 2010) which generalizes the modularity maximization algorithm known as Louvain (Blondel et al., 2008) to signed weighted undirected graphs. Let us denote the presence of a positively weighted connection between nodes by
and
, and its corresponding connection weight by
(equivalently for a negatively weighted connection: strength of node
,
, and
is defined as the sum of connection weights of
negative graph representations, i.e.
and
, ,
). The
for each of the positive or
, and the total weight as the sum of all
connection weights (counted twice for each connection), i.e.
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. In order to take the
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sign of connection weights into account, Rubinov and Sporns (2011) proposed the following
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quality index for decomposition of brain graphs into modules:
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where:
(5)
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(6)
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and:
(7)
Similar to the classical measure of network modularity (Newman, 2006),
is the
probability of a connection between and under the assumption of randomly distributed edges, when
and
are in the same module and
otherwise, and
are structural
resolution parameters (Fortunato and Barthelemy, 2007; Lohse et al., 2014) which for simplicity are set to unity in our case (as in Bassett et al. (2010)). For further details and a discussion on the contribution of
and
to the modularity index
the reader is referred to Rubinov and
Sporns (2011). Modularity estimates decomposability of a network into smaller sub-networks which are relatively densely intra-connected, and relatively sparsely interconnected. 14
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The mean functional connectivity was estimated as the mean of the upper-diagonal elements within the temporal connectivity matrices. Based on the modular decomposition three measures
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were computed to characterize the modular organization of networks in more detail. Between/within-module connectivity was estimated by summing the connection weights falling
).
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(i.e.
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between/within modules, and dividing the result by the total number of possible connections
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When considering modular networks, it is reasonable to map the connections between nodes according to the role they fulfill. For this, Guimera and Amaral (2005) have proposed the so-
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called participation coefficient ( ) which determines how node with respect to other modules. For a given node
is positioned within a network
within a binary graph (i.e. a graph with the
where
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connection weights of either one or zero) the participation coefficient of node is defined as:
is the number of links of node
node , and
(8) to nodes in module
, and
is the total degree of
is the total number of network modules. The participation coefficient of a node is
therefore close to one if its links are uniformly distributed among all the modules, and zero if all of its links are within its own module. In the present study, we used the implementation in the Brain Connectivity Toolbox (Rubinov and Sporns, 2010) which generalizes the participation coefficient to weighted undirected graphs, and gives two different
based on once positive and
once negative connections. In each case, the number of links counted in Eq. (8) is replaced by the absolute connection weight in the respective positive or negative graph representations.
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Network efficiency. For a given network
comprised of
nodes, global efficiency
captures the integration of the entire network and is estimated by the inverse of the
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harmonic mean of the minimum weighted path lengths between each pair of nodes
(9)
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A weighted path length is equal to the sum of individual link lengths which are inversely related to connection weights, as large weights typically represent strong associations and
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close proximity (Rubinov and Sporns, 2010). Networks which have a highly integrated organization, characterized by short minimum path lengths between any pair of regional
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nodes, will have high global efficiency. The nodal efficiency at node (
) is likewise
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the rest of the network:
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inversely related to the weighted path length of connections between a particular node and
(10)
Even though nodal efficiency measures regional properties of a network, it still describes the integration of a particular node with the entire network.
Lagged cross-correlation analysis Dynamic coupling between a time series estimates of a single-subject visuospatial or speech accuracy
and a network diagnostic
was quantified by means of lagged cross-correlation
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analysis (Shumway and Stoffer, 2011; Figure 2C). The cross-correlation function between two and
at lag
is given by:
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time series
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is the cross-covariance function:
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where
(11)
(12)
and
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and the denominator is a scaling factor consisting of the variance of each time series. In Eq. (12) indicate the arithmetic means of the corresponding time series over time. The cross-
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correlation function introduced in Eq. (11) ranges between -1 and 1, and measures the linear predictability of a series at time using the values of another series, assuming both series have finite variances. In equations (11) and (12) the cross-covariance function may change as moves along
because the values depend on , i.e. the time lag between the series. In the
present study, the time lag was fixed within the range -20×TR
20×TR (or equivalently
30 sec for TR=1.5 sec) over which it is plausible to expect a degree of coupling between fMRI-derived network metrics and the dual-task behavior (Eichele et al., 2008; Sadaghiani et al., 2015). In Eq. (11) negative lags imply that a given brain network diagnostic
leads the behavior
in the visuospatial or speech component of the dual-task (note, that correlations at different time lags describe the relation between behavior and a BOLD signal-derived network diagnostic, and do not systematically incorporate the time lag between the neural response and the BOLD response produced due to the low-pass filter characteristic of the hemodynamic response function (Friston et al., 2006; chapter 14)). The cross-correlation 17
was computed by
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means of
function in MATLAB (version 2012a; MathWorks, Natick, MA). The value
obtained for a single-subject was then Fisher’s Z transformed, and the results were finally
a network diagnostic
and each visuospatial
and speech
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averaged over subjects at each lag. To measure the specificity of the cross-correlations between performance, we also computed
(13)
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the mean difference of Fisher’s Z-transformed cross-correlations, i.e.:
Statistical analysis
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Behavioral data. Performance of the subjects was measured using the average accuracy (%) for responses to the visuospatial or speech targets within each quarter (35 trials) separately.
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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 a repeated measures analysis of variance (ANOVA).
The statistical dependency between responses to each component task in the course of dualtasking was tested by means of Yule’s
coefficient (Yule, 1912; Warrens, 2008). The Yule’s
coefficients were first estimated per subject as an indication of within-subject dependency between visuospatial and speech performance. Next, the results were averaged across subjects, and the statistical significance of the mean Yule’s
was tested against zero using a two-sided
one-sample permutation test with 10,000 repetitions (Pesarin and Salmaso, 2010).
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In addition, to investigate the possible correlation between visuospatial and speech performance at a larger time scale, the average accuracies in each experimental quarter (35 trials) were
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computed per subject and dual-task component. Next, for each subject, the average visuospatial and speech accuracies across the experimental quarters were correlated by means of
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Spearman’s rho (Spearman, 1904). Finally, the statistical significance of the mean Fisher’s Z-
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transformed correlations averaged over subjects was tested against zero using a two-sided one-
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sample permutation test with 10,000 repetitions (Pesarin and Salmaso, 2010).
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Cross-correlation between global network diagnostics and dual-task behavior. Statistical significance of the lagged cross-correlation was determined by means of a randomization denote a time series of correct (one) and incorrect (zero) responses obtained
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procedure. Let
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from a single-subject. The time at which a correct/incorrect response is modeled by a one/zero
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corresponds to the onset of a dual-task trial. To generate a null distribution of mean Fisher’s Ztransformed cross-correlations at each lag, first the behavioral responses
were randomly
shuffled in time (Figure 2D). For the randomly-shuffled behavioral responses, we performed the same analysis steps as it was done for the empirical behavioral responses (see ‘Analysis of behavioral accuracy’). That is, the moving average procedure defined in Eq. (1) was applied to the randomized . Consistent with the analysis of the empirical behavioral responses, the result of the moving average was linearly interpolated and down-sampled to one TR, which gave us a time series of the null behavioral accuracy Eq. (11) was applied to
. Next, the cross-correlation function defined in
and the time series of a network diagnostic . Finally, the null cross-
correlation obtained for a single-subject was Fisher’s Z-transformed, and the results were averaged over subjects at each lag. The randomization procedure was applied to both visuospatial and speech responses with 10,000 repetitions. To statistically test the significance of the empirical cross-correlations, the observed mean Fisher’s Z-transformed cross-correlation 19
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was compared with the null distribution generated from the randomization procedure (Figure 3). The observed mean cross-correlation was considered significant if it was higher than the 97.5th
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percentile or lower than the 2.5th percentile of the null distribution (upper or lower bounds of the shaded areas in Figure 3 respectively). The corresponding non-parametric p-value was
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computed as the number of times the absolute value of the null observations were found as
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extreme as the absolute value of the empirical mean, divided by the number of randomizations
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(10,000).
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Nodal analysis. At regional level, it was tested whether the time series estimates of visuospatial and speech accuracies differentially correlated with the investigated network diagnostics by
D
means of two-sided paired permutation tests with 10,000 repetitions (van der Voet, 1994). The
and Z
obtained from every subject per brain node. The choice of the regional
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Z
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permutation test was applied to the Fisher’s Z-transformed cross-correlations in Eq. (13), i.e.
network diagnostic
and the length of the sliding window were guided by the results obtained at
the global level of brain networks (see ‘Network diagnostics’ for the definition of the regional diagnostics). Since the aim of the nodal analysis was to investigate the regional network mechanisms which might contribute to the brain-behavior coupling observed at the global level, for each regional diagnostic only the window length
was analyzed for which there was a
significant cross-correlation at global level. In addition, the regional analysis was done at the specific time lag where the corresponding global effect was highest in magnitude (i.e. peak cross-correlation).
Significance thresholds. For all statistical tests (i.e. the inference on the behavioral as well as the global and nodal network effects) we used p 0.05 (two-sided) as the threshold of significance. 20
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In the context of the analysis of nodal effects we used the FDR-adjusted p-values according to Benjamini and Hochberg (1995) to control for alpha inflation entailed by the multiple
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comparisons.
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Note on the computational time
The investigations at the regional level of the brain networks required around 96 hours for a
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given length of the temporal sliding window. This computational time was handled by the use of
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the High Performance Computer Cluster HERO, located at the University of Oldenburg
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(Germany) together with MATLAB Distributed Computing Server (MDCS, version 5.2).
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Results
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Dynamics of behavioral performance The average accuracies (%) for responses to the visuospatial or speech targets across quarters
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are presented in Figure 1B. Results from ANOVA revealed a significant effect of task: accuracy
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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 coefficient per subject (see ‘Analysis of behavioral
applied to the Yule’s Yule’s
coefficient was -0.17 0.14. The permutation test
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accuracy’). The mean and SEM of the Yule’s
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performance, we computed the Yule’s
coefficients revealed no significant result under the null hypothesis of
=0 (p=0.23). This suggests that, in our sample, there was no significant dependency
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between visuospatial and speech accuracy in the course of dual-tasking on trial-by-trial basis. We also found no significant correlation between the performance in speech and visuospatial
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task at a larger time scale. When correct responses to each dual-task component were averaged within each experimental quarter (35 trials) and correlated across quarters per subject, the mean correlation averaged over subjects was not significantly different from zero (mean Spearman’s rho=0.21, p=0.32).
Dynamic coupling of network topology and behavior: Global effects The lagged cross-correlation analysis revealed different patterns of coupling between the global network parameters and each visuospatial or speech performance in the course of dual-tasking. The peak cross-correlation between the time series estimates of visuospatial accuracy global efficiency
and
was the strongest for the sliding window length of 11xTRs (16.5 sec;
Figure 3A, last column, black curve). In this case, the peak cross-correlation between visuospatial accuracy and global efficiency was significantly negative at lag -7xTRs or 22
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equivalently 10.5 sec (
(-7)=-0.06, p=0.05). The results obtained for every length of the sliding
window (i.e. within the range 5xTRs to 20xTRs in step of one TR together with the windows of
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length 25, 40, and 60xTRs) are presented in Figure S1A. There was no significant crosscorrelation between other network diagnostics and visuospatial performance (Figure 3A, first
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three columns; Figure S1A, first three columns). When the functional parcellation template was
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used to construct the brain graphs, the cross-correlations between the network diagnostics and
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visuospatial performance were not significant (Figure S2A).
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The lagged cross-correlation analysis revealed a different pattern of coupling between the brain network diagnostics and the time series estimates of speech accuracy. The peak cross-
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correlation between the time series estimates of speech accuracy
and modularity
was the
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strongest for the sliding window length of 11xTRs (16.5 sec; Figure 3B, second column, black
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curve). In this case, the peak cross-correlation between speech accuracy and modularity was significantly negative at lag 9xTRs or equivalently 13.5 sec (
(9)=-0.08, p=0.007). In addition,
the peak cross-correlation between the time series estimates of speech accuracy between-module connectivity was significantly positive at lag 9xTRs (
and
(9)=0.09, p=0.01;
Figure 3B, third column, black curve). Finally, there was a significantly positive cross-correlation between time series estimates of speech accuracy (
and global efficiency
at lag -7xTRs
(-7)=0.06, p=0.03; Figure 3B, last column, black curve). The results obtained for every length
of the sliding window are presented in Figure S1B. Similar results were found when the functional parcellation template was used to construct the brain graphs (Figure S2B).
To measure the specificity of the cross-correlations between a network diagnostic
and each
visuospatial and speech performance, we also computed the mean difference of Fisher’s Z23
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transformed cross-correlations (Eq. (13)). The peak mean difference of the cross-correlations between modularity
and the time series estimates of each visuospatial
and speech
Z
-
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accuracy was significantly positive at lag 9xTRs or equivalently 13.5 sec (mean(Z
)=0.12, p=0.009; Figure 3C, second column, black curve). In addition, the peak mean
-Z
accuracy was significantly negative at lag 10xTRs
)=-0.12, p=0.01; Figure 3B, third column, black curve). These results were
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(mean(Z
and speech
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estimates of each visuospatial
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difference in the cross-correlations of between-module connectivity and the time series
the strongest for the sliding window length of 11xTRs (16.5 sec). Finally, the peak mean
estimates of each visuospatial
and the time series
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difference of the cross-correlations between global efficiency and speech -Z
)=-0.09, p=0.02; Figure 3B, last column, black
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or equivalently 10.5 sec (mean(Z
accuracy was significantly negative at lag -7xTRs
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curve). This result was the strongest for the sliding window length of 8xTRs (12 sec). The results obtained for every length of the sliding window are presented in Figure S1C. Similar results were
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found when the functional parcellation template was used to construct the brain graphs (Figure S2C) or when brain graphs were thresholded over a range of network densities (Figure S6).
In the absence of prior evidence regarding the appropriate length of a sliding window to identify the coupling between brain network dynamics and behavior, we included a range of window lengths in our analysis at the global level. To investigate whether results are largely affected by differences in window lengths, we computed the mean and SEM of the cross correlations averaged over the sliding window lengths for each network diagnostic (Figure 3, green curves). Our results revealed that the mean cross-correlations between speech performance on one side and mean functional connectivity, modularity, and between-module connectivity on the other side showed similar trends across window lengths (Figure 3B, green curves) as compared to the
24
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strongest cross-correlations (Figure 3B, black curves), whereas for global efficiency the mean
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and strongest cross-correlations deviated from each other (Figure3A/B, last column).
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As mentioned above, the results obtained for every individual length of the sliding window are
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summarized in Figure S1. Besides the relative consistency of cross-correlations between speech performance and mean functional connectivity, modularity, and between-module connectivity
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across window lengths, the strongest significant effects were mostly found for the smaller
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window lengths in the range 7 to 11xTRs (10.5 to 16.5 sec).
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Maps of regional network properties coupled with behavior: Nodal effects
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Guided by the effects obtained at the global level of the brain networks, the difference in cross-
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correlations in Eq. (13) was tested for the regional diagnostics which capture between-module integration and network efficiency at nodal level. That means, in order to find brain regions whose network properties were dynamically coupled with the visuospatial or speech performance, we used the participation coefficient (Eq. (8)) and nodal efficiency (Eq. (10)) in the regional analysis based on Eq. (13). The regional analysis was done at the specific time lags where the peak differences in cross-correlations were observed at the global level. Accordingly, for a given brain node the difference in cross-correlations of participation coefficient time series estimates of each visuospatial
and speech
accuracy was tested at lag 10xTRs
(15 sec), whereas the difference in cross-correlations of nodal efficiency series estimates of each visuospatial
and speech
and the
and the time
accuracy was tested at lag -7xTRs (10.5
sec). We found a significant difference in correlations between the positive participation coefficient
of a brain node within the posterior cingulate cortex (PCC) and each visuospatial
and speech performance (Figure 4A, red node; mean(Z 25
-Z
)=-0.12, FDR-corrected
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p=0.01). For this brain region, the significance of the Fisher’s Z-transformed correlations between
and each visuospatial and speech performance was also tested separately by
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means of one-sample permutation tests. The correlation between the
of the PCC node and
the time series estimates of the visuospatial performance was significantly smaller than zero )=-0.05, p=0.03; Figure 4C). In contrast, the correlation between the
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(mean(Z
of the PCC
)=0.06, p=0.02; Figure 4C). However, there was no significant effect for the
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difference in correlations with nodal efficiency.
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(mean(Z
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node and the time series estimates of the speech performance was significantly larger than zero
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Discussion
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Dynamics of functional connectivity networks and its specific coupling with behavior The overall aim of Alavash et al. (2015a) and the current study is to find complex network
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correlates of behavioral impairment often observed under dual-task conditions. In Alavash et al.
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(2015a) 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
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featuring the following unique aspects. First, in the current study we analyzed the dynamic
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changes of functional brain network topology over time. Dynamic as compared to static networks rely to a larger extent on the brain functional organization as opposed to its structural architecture (Honey et al., 2007; Shen et al., 2015). Therefore, the brain-behavior relations found
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across a sample of subjects are less likely to be confounded by inter-individual differences in structural connectivity (Hermundstad et al., 2013). Second, in the present study we extracted
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global efficiency of dynamic brain networks in addition to modularity. The main motivation for including global efficiency in our analysis comes from our previous study suggesting that performance in different cognitive tasks profit from specific patterns of functional brain network integration at different topological scales (Alavash et al., 2015b). Finally, while the focus of Alavash et al. (2015a) was on the inter-individual differences in dual-tasking, here we aimed at exploring complex brain network correlates of intra-individual variability in dual-task performance over time. Thus, the current study investigates dynamic brain networks on two different scales of network topology (i.e. intermediate and global) and their relation to dual-task behavior. Accordingly, here we related the fluctuations in behavior in the course of dual performance of a visuospatial and a speech task to the dynamic changes of functional brain networks. Our aim was to investigate whether trial by trial fluctuations in dual-tasking result from momentary failure of functional brain connectivity networks to adapt to an optimal topology supporting the performance in both component tasks during the same time. 27
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We showed that dynamic changes of brain network topology were differentially coupled with the
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performance in each single task component during the course of dual-tasking. Each task
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component showed a distinct pattern of cross-correlation with measures of network integration at different topological scales. While better visuospatial performance was preceded by a decrease
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in the global efficiency of brain networks ~10.5 sec earlier, better speech performance was
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preceded by an increase in the global efficiency ~10.5 sec earlier. These findings suggest that, when a visuospatial and a speech task are performed in parallel, while visuospatial performance
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is supported by an earlier decrease in global integration of brain networks over time, speech
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performance is facilitated by an earlier increase in global integration of dynamic brain networks.
Additionally, more accurate speech performance was correlated with a subsequent increase in
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between-module connectivity ~13.5 sec later. In our findings, the first evidence for brainbehavior coupling in the case of speech performance derived from the analysis of mean functional connectivity. This analysis revealed a significant positive correlation between mean functional connectivity and the subsequent speech performance (Figure 3B, first plot). More detailed analyses of brain network topology showed that the latter changes in functional connectivity were accompanied by a significant negative correlation between brain network modularity and the subsequent speech performance (Figure 3B, second plot). This effect seems to result from a reduction in inter-modular connectivity (Figure 3B, third plot). Taken together, these results indicate that better speech performance was followed by an increase in mean functional connectivity, which was dominated by stronger inter-modular connections and in turn reduced network modularity. From theoretical perspective, the anti-correlation between modularity and between-module connectivity is indeed expected: by definition less modular networks tend to show sparser intra-modular and denser inter-modular connections (see 28
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‘Network diagnostics’). In the context of brain functional connectivity networks, Zalesky et al. (2014) also found that 69% of all network connections were inter-modular, and that dynamic
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connections were more prevalent between brain network modules. Thus, in our data and following better speech performance, formation of higher between-module connectivity seems to
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increase mean functional connectivity and diminish modularity of brain networks.
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Although our results showed cross-correlations between measures of network topology and performance in both dual-task components for different time lags, it is difficult to infer the exact
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underlying temporal relationships of the neural and behavioral data because of the delayed hemodynamic response function (HRF) elicited by neural activation (Friston et al., 2006, chapter
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14; Liao et al., 2002) as it is discussed in the following section. Dynamic coupling of behavior and functional connectivity networks at the neural level
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Making a proper inference about brain-behavior coupling based on lead/lag relationships essentially depends on having accurate temporal information about both the underlying neural activity and behavioral patterns. In this regard, one issue is the fact that an fMRI time series is an indirect measure of the neural activity (Buxton et al., 1998; Mandeville et al., 1999; Friston et al., 2000). An fMRI time series can be considered to be a convolution of the hidden neural signal and the canonical HRF, which in turn introduces a temporal delay between the neural response and the BOLD response due to the low-pass filter characteristic of the HRF (Friston et al., 2006, chapter 14; Liao et al., 2002). Thus, deconvolving the HRF from fMRI time series prior to connectivity analysis has been proposed (Gitelman et al., 2003; Ryali et al., 2011). However, the spatial variability of the HRF (Handwerker et al., 2004) confounds the estimation of functional connectivity networks at the neural level (Deshpande et al., 2010; Roebroeck et al., 2011). In fact, since both latent neural activity and the voxel-specific HRF are unknown, a blind 29
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deconvolution technique has to be used that estimates both, the HRF and the underlying neural responses (Havlicek et al., 2011; Wu et al., 2013).
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In the present work, and in order to refine the interpretation of the lead/lag network correlates of
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behavior, we used a recent blind deconvolution approach (Wu et al., 2013) to deconvolve the BOLD time series and estimate the underlying neural signals. The results obtained from this
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procedure reveled very similar patterns for the differences in cross-correlations between a
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network diagnostic and each visuospatial and speech performance (Figure S3). However, the time lag of the peak cross-correlations between a brain network diagnostic and behavioral
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accuracies was influenced by the deconvolution. First, the time lag between changes in visuospatial and speech accuracy and the subsequent alternations in brain network modularity
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was decreased but remained positive, i.e. a backward shift from 13.5 sec to 10.5 sec (Figure S3;
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second colunm). Second, the time lag between alternations in global network efficiency and the subsequent changes in visuospatial and speech accuracy was increased and remained
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negative, i.e. a backward shift from -10.5 sec to -13.5 sec (Figure S3; last colunm). The backward shifts in time can be explained by the temporal precedence of neural signals over BOLD signals.
These results refine the patterns of brain-behavior coupling obtained from BOLD time sereies (Figure 3), and support the finding that global efficincy and modularity of brain neworks were differentialy coupled with behavior depending on the cognitive task (visuospatial or speech) and time (leading or lagging). That is, alternations in global network efficiency was differentially coupled with the subsequent visuospatial and speech task accuracies. In the case of network modularity, however, it seems that changes in brain network modularity lags the behavior: fluctuations in both tasks were differentially coupled with later changes in modular organisation of brain networks. 30
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Taken together, our results provide the evidence for distinct patterns of brain network dynamics
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which are specifically coupled with the behavior in a visuospatial and a speech task when
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performed at the same time, suggesting that trial by trail variability in dual-tasking can be
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associated with the fluctuations in brain network dynamics on different organizational levels.
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Dynamic coupling of visuospatial accuracy and functional connectivity networks
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We note in the present study that less globally integrated brain networks correlated with better visuospatial performance. This significant effect was not as strong as the coupling between brain
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network topology and accuracy in the speech task. The weaker brain-behavior coupling in the
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case of the visuospatial task can be due to a limited variability in the time series estimates of visuospatial accuracy over time. To investigate this, we grouped the subjects into low versus
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high performers, once based on their average visuospatial accuracy and once based on the variance of their visuospatial accuracy over time. Subsequently, we repeated the crosscorrelation analysis separately for each group. The results showed that, the peak crosscorrelation between visuospatial accuracy and global efficiency at lag -7xTRs showed no significant difference between low and high performance groups (non-parametric p=0.90 for grouping according to the mean visuospatial accuracy, non-parametric p=0.98 for grouping based on the variance of visuospatial accuracy; see Figure S4A). Note, that, on the numerical level, subjects with high mean accuracy showed even more pronounced correlations than for low performers. In the case of the coupling with brain network modularity, we also found no significant difference between the performance groups (non-parametric p=0.38 for grouping according to the mean visuospatial accuracy, non-parametric p=0.53 for grouping based on the variance of visuospatial accuracy). Thus, the ability to detect correlations between visuospatial 31
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accuracy and the brain network diagnostics seems not to be affected by the performance level of
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each subject.
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Additionally, we correlated the measures obtained from individuals’ visual performance (i.e.
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mean and variance of their accuracy) with the cross-correlation between behavioral time series and global efficiency at lag -7xTRs. None of the correlations were significant (Spearman’s rank
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correlations and non-parametric p-values: =0.002, p=0.9; =-0.05, p=0.8, respectively; see also
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Figure S4B).
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By inspecting the individual cross-correlations obtained from each subject, we observed that the
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cross-correlation for one of the subjects largely deviated from the rest, pushing the group-
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averaged cross-correlation to zero (Figure S4B, arrow; see also Figure S5A). After excluding the corresponding subject from the analysis, we found a stronger significant cross-correlation between visuospatial accuracy and global efficiency at lag -10.5 sec based on both anatomical and functional parcellation templates (Figure S5B). This exclusion did not affect the groupaveraged cross-correlation between visuospatial accuracy and network modularity, and reproduced a non-significant brain-behavior coupling in this case. Thus, performance in the visuospatial task component seems to be independent from brain network integration on the inter-mediate level (i.e. network modularity), but negatively correlated with brain network integration on the global level (i.e. global network efficiency).
The significant negative correlation between global efficiency and the subsequent visuospatial accuracy might be related to the dual combination of the visuospatial and the speech task. In a dual situation where more accurate speech performance is supported by an increase in the 32
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global network efficiency, changes in global network integration in the opposite direction might reduce interference between distributed brain areas involved in both tasks.
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Dynamic coupling of speech accuracy and functional connectivity networks
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In our data, speech performance was facilitated by higher global integration of brain networks
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over time. This facilitation seems to break down brain network modularity by increasing the between-module connectivity: following more accurate speech performance, mean connectivity
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strength between brain network modules was increased. The emergence of stronger betweenmodule connectivity might be due to a functional network integration subserving accurate
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processing of the speech task. Such functional integration might be related to a higher connectivity within a widely distributed network of cortical areas which has been shown to
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improve speech perception under adverse listening conditions (Obleser et al., 2007). Perhaps, in
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the current dual-task setting, the opposite directions in the coupling of each task component with
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global network integration is an indication of a limiting organizational factor underlying the behavioral impairment in the dual-task condition. Moment to moment fluctuations in the dual-task behavior seems to arise 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 dualtask components.
PCC as a hub of transient functional interactions: implications for behavior At the regional level of the brain networks we found a significant difference in the correlations between positive participation coefficient (
; Eq. (8)) and each visuospatial and speech
performance. More specifically, while speech performance positively correlated with
at a
brain node within the PCC, there was a significant negative correlation between visuospatial performance and
at the same node (Figure 4C). The participation coefficient is a topological 33
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network property which quantifies how ‘well-distributed’ the connections of node different network modules. When node
are among
is uniformly connected to other network modules (i.e.
However, when node
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tightly integrated with other modules) the participation coefficient gets a value close to one. is connected to only its own module, the participation coefficient is zero.
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Therefore, temporal alternation in nodal participation coefficient is a network signature of
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transient functional interactions occurring within and between functional modules. Thus, our data show that subsequent to higher speech and lower visuospatial accuracy the PCC gets more
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integrated with connections to the nodes of other modules. The effect we found at the regional
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level was observed only for the positive participation coefficient (i.e. when negative connections were removed, and Eq. (8) was calculated only based on the strength of the positive
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connections) and was specific to a brain node within the posterior cingulate cortex.
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Previous resting-state studies point to the posterior cingulate cortex as a hub region of the default mode network (Fox et al., 2005; Fransson and Marrelec, 2008; van den Heuvel and Hulshoff Pol, 2010) and recent studies provide important hints on the dynamic functional connectivity of the PCC during rest (Uddin et al., 2009). Using a time-frequency analysis of resting-state functional connectivity, Chang and Glover (2010) observed that coherence and phase between the PCC and the anti-correlated task-positive network as well as other nodes of the default mode network was variable in time and frequency. The dynamic functional connectivity of PCC to other brain regions during rest has been also investigated by Handwerker et al. (2012), where the authors found that many brain voxels shift between being correlated and anticorrelated to the PCC with distinct frequency profiles. Regarding task data, several functional connectivity studies could show that PCC is part of the task-negative network, but strongly interacts with nodes of the task-positive network (Kelly et al., 2008; Piccoli et al., 2015) suggesting that the PCC modulates activity in the task-positive network and, thereby, influence 34
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cognitive performance. For example, the fMRI experiment of Lin et al. (2015) shows that a large number of functional network connections originating from PCC correlate with performance in a
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visual attention task. In summary, many studies have documented that the PCC is part of the task negative network, but similar to our results exhibit dynamic changes in functional
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connectivity with other brain regions over time and during resting-state as well as task
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performance. In contrast to the studies reviewed above, our nodal results point to the important role that PCC may play within the task-induced modular organization of brain networks (see
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Guimera and Amaral (2005) for the definition and discussion of node roles in the context of
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complex modular networks).
Link-wise dynamic functional connectivity and its coupling with behavior
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In recent years studies have begun to relate link-based metrics extracted from brain graphs to
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behavior or diseases (Zalesky et al., 2010; Ahn et al., 2010; Jin et al., 2015). In the present
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study, it is plausible to also expect coupling between a network edge metric and behavior in each dual-task component. To investigate whether changes in connectivity between brain regions correlated with behavior, we used a similar approach to that of Zalesky et al. (2014). In a first step, we identified links whose connection weights dynamically changed over time. To select these dynamic links a statistic was computed that accesses the dynamic fluctuations in timeresolved correlation coefficients for each pair of brain regions (see Figure S7 for further details). Based on this statistic, seven connections were identified whose functional connectivity showed dynamic fluctuations in all subjects. Using the cross-correlation analysis described above (Eq. (11)) the coupling between the connectivity strength of these common dynamic links and dualtask behavior was tested for the specific window length were highest in magnitude.
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and time lag where the global effects
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The results revealed significant group-averaged correlations between functional connectivity of three connections and time series of each visuospatial and speech accuracy (one sample
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permutation test; p<0.05, FDR corrected; see Figure S7). Specifically, a network link connecting a node within the right somatomotor cortex to a node within the left middle frontal gyrus showed
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a significantly negative correlation with speech accuracy over time. In our experiment, subjects
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responded to the visuospatial targets using their left hand, and to the speech targets using their right hand, whose corresponding somatomotoric representations are intrinsically correlated (Fox
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et al., 2007). Decrease in functional connectivity between the frontal and somatomotor regions
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might be an indication of a top-down control to attenuate the interference between left and right motor cortices, thereby facilitating the performance in the speech task component (see also
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Alavash et al. 2015b, Figure 4C).
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Methodological considerations
A unique aspect of the present study is the dynamic link between the functional brain network topology and behavior. In this regard, temporal windows of different lengths were included in our analysis, and were kept consistent across the dynamic analysis of brain network topology and dual-task behavior. Sliding-window correlation analysis of functional brain data is an emerging method (Lindquist et al., 2014), and the choice of the window size is a matter of debate (Leonardi and Van De Ville, 2015). Recently Zalesky and Breakspear (2015) stressed the importance of recognizing spurious connectivity dynamics due to inappropriate window lengths, and encouraged the use of appropriate statistical testing. In our study, we approached this by (1) testing the association between dynamic connectivity and network patterns with dual-task behavior by comparing the measured correlations over time with a null distribution of the correlations based on randomly shuffled behavioral responses and (2) including a range of window sizes in the analysis. In our data, the strongest degree of coupling between brain 36
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network topology and dual-task behavior was observed for the window length of 11xTRs (16.5 sec). Although one might get more reliable estimates of network topology by increasing the
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window size, longer windows might miss the relevant fluctuations in the dual-task behavior. We therefore argue that the length of the temporal sliding window has to be optimized for both
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functional and behavioral data, and the optimal length depends on the specific experimental task
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and behavior being investigated. We also note that in our data the time lags between behavioral time series and those of the brain network metrics vary with the choice of window size (Figure 3,
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green curves). This variance could be related to the uncertainty in the estimates of latent
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detection ability obtained at multiples of TR based on the observed responses (Eq. (1)). Another possible explanation for this variance could be the differential spectral sensitivity of the time windows with varying lengths. In our analysis functional brain data were band-pass filtered in the
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frequency range 0.01-0.1 Hz, and we found the significant brain-behavior couplings within
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window lengths 8-11xTRs (equivalently 0.06-0.08 Hz). To increase the sensitivity to detect
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significant brain behavior coupling future studies might benefit from an additional high-pass filtering based on the length of the sliding window in order to reduce spurious fluctuations (Leonardi & Van De Ville, 2015). Frequency-specific brain-behavior coupling is conceptually similar to the behavioral relevance of the brain neurophysiological rhythms found in perceptual tasks (Landau et al., 2015). Hence, due to the importance of information within time and frequency domain, future work could also benefit from time-frequency analyses such as wavelet coherence in order to circumvent the limitations of the sliding-window correlation method (Chang and Glover, 2010; Smith et al., 2011).
Nonetheless, important for making inferences on lead-lag mechanisms is whether the brainbehavior coupling occurs within a positive or negative range of time lags. In the present study, the results obtained from the deconvolution approach facilitated making such inferences, since 37
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they rely on the dynamic brain networks derived from estimates of the neural signals which underlie the behavior. These results showed that global efficincy and modularity of brain
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neworks were differentialy coupled with behavior depending on the cognitive task (visuospatial
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or speech) and time (leading or lagging) in our dual-task situation (Figure S3).
Towards the chronnectometery of cognition and behavior
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In recent years the importance of an integrated framework that links brain functional connectivity
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with brain dynamics has been acknowledged by the neuroscience community. It has been argued that what is needed is not only what is connected, but also how functional regions of the
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brain are topologically connected, dynamically coordinated (hence the name ‘chronnectome’),
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and support distinct aspects of cognitive operations and behavioral outcome (van den Heuvel and Sporns, 2013; Calhoun et al., 2014; Kopell et al., 2014; Sporns, 2014). This has led into a
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surge in studies investigating the dynamics of functional connectivity networks not only during rest (Allen et al., 2012; Jones et al., 2012; Kucyi and Davis, 2014; Mitra et al., 2014; Schaefer et al., 2014; Zalesky et al., 2014) but also task conditions (Cribben et al., 2012; Cribben et al., 2013; Monti et al., 2014; Sadaghiani et al., 2015). This approach offers the potential to dissociate the brain functional organization from its structural architecture, as previous studies suggest that structure-function correspondence increases as the length of the temporal window used to observe the brain function increases (Honey et al., 2007; Shen et al., 2015). Most of the previous studies investigated the correlation between functional brain network topology and performance without distinguishing the effects of functional versus structural network organization. Dynamic patterns in the course of behavioral experiments, on the other hand, have long been observed and studied (Schöner and Kelso, 1988; Wijnants, 2014). Examples include learning 38
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(Smith et al., 2004; Smith et al., 2007; Kostrubiec et al., 2012), coordinated actions (Banerjee et al., 2012; Kelso, 2012) and detection of near-threshold stimuli (Palva et al., 2005; Eichele et al.,
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2008; Monto et al., 2008; Palva et al., 2013; Weisz et al., 2014; Sadaghiani et al., 2015). Performance in a multi-tasking situation, however, has been often measured at a large temporal
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scale where the intra-individual fluctuations in behavior over time (Palva and Palva, 2012) were
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not taken into account. Nevertheless, the underlying functional connectivity network patterns of moment to moment fluctuations in perception, attention or goal-directed behavior are still not
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well understood. Future studies will be important to find a theoretical framework to chart the
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dynamics of brain network topology under task conditions and the corresponding behavioral patterns in order to better understand the underlying functional network organization as well as
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temporal structure of cognition and behavior.
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Conclusion To conclude, the results of this study suggest that successful performance of two tasks in
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parallel relies on a specific topological configuration of functional brain connectivity networks.
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Consistent with our hypothesis, we were able to link the fluctuations in dual-task behavior with
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the dynamic topological structure of the brain functional connectivity networks.
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Figure 1: Illustration of the dual-task paradigm and the behavioral data. (A) Dual-task paradigm. The dual-task paradigm was comprised of two component tasks: a
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visuospatial task and a speech task. For the visuospatial task subjects reported the location of a
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small Gabor patch briefly presented either in the left or right visual periphery on a grey background using the left middle or index finger respectively. In parallel, a speech task was
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presented where the subjects had the task to discriminate two target consonant-vowel (CV)
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compounds as either /da/ or /ga/ using their right middle or index finger. The correspondence between the target CVs and fingers was randomized across subjects. The speech sounds were
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played together with a band-limited Gaussian white-noise and a third speech sound /ba/ (at a lower intensity) to make the task sufficiently challenging. The dual-task trials were equally
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randomized across trials. The duration of the paradigm was approximately 9 minutes.
(B) Behavioral data. Average accuracies (%) for responses to the visuospatial or speech targets across quarters. Bars illustrate mean accuracy, and error bars represent standard error of means. While average speech accuracies were significantly lower than that of visuospatial task, there was no significant correlation between the performance in visuospatial and speech task.
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Figure 2: The methodological steps undertaken in the study. To investigate the dynamic coupling between functional brain network topology and the behavior
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in each dual-task component over time, different methodological steps were employed in order
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to (A) acquire and prepare the functional brain data for dynamic connectivity and network analyses, (B) construct and analyze temporal brain networks, and (C) compute the correlation
over time for the time lags in the range
20×TRs. To analyze the
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each dual-task component
(see ‘Materials and methods’) and the performance in
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between different network diagnostics
dynamics of brain functional connectivity networks and behavioral accuracy in each visuospatial (multiples of TR) were shifted in time at
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or speech task, temporal windows of different lengths
steps of one TR. For a given length of the temporal window, the choice of window size was kept
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consistent between the analysis of functional brain data (B) and dual-task behavior (D). The
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analysis pipeline was applied to each single-subject data, and the result of the Fisher’s Ztransformed cross-correlations were averaged across subjects and compared with the results
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obtained from randomly shuffled behavioral responses (D).
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Figure 3: Lagged cross-correlations between global metrics of brain network topology and behavioral accuracy in each component of the dual-task.
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The lagged cross-correlation analysis revealed different patterns of coupling between the
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topological metrics of dynamic functional brain connectivity networks at global level and behavioral accuracy in (A) visuospatial or (B) speech performance during the course of dual-
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tasking. While decrease in visuospatial accuracy correlated with subsequent increase in the
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global efficiency of brain networks ~10.5 seconds later (A/C, last column), increase in betweenmodule connectivity predicted better speech performance ~13.5 seconds following the changes
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in network modularity (B/C, third column).
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Performance of the subjects in response to each component task was analyzed at the temporal resolution of one TR by means of a moving average procedure. Dynamic correlation between
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each pair of cortical regions was computed using an exponentially tapered sliding window, and correlation matrices were generated at each scan time (multiples of TR) per subject. Subsequently, signed weighted undirected graphs were constructed and the dynamics of mean functional connectivity, network modularity, between-module connectivity and global network efficiency were extracted. Finally, the temporal coupling between each network property and the behavioral accuracy in (A) visuospatial or (B) speech performance was investigated using Fisher’s Z-transformed lagged cross-correlations averaged over subjects. To measure the specificity of the cross-correlations between a network diagnostic and each visuospatial and speech performance, we also computed the mean difference of Fisher’s Z-transformed crosscorrelations (C).
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Sliding window length ( ). The length of the temporal sliding window applied to both behavioral and functional brain data was fixed in the range 5xTRs to 20xTRs in step of one TR, together
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with three other larger windows of length 25, 40, and 60xTRs. The cross-correlations obtained from the window length for which the significant coupling of the brain networks and a component
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task behavior was the strongest are shown in black, together with the mean and SEM of the
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cross-correlations averaged over the sliding window lengths (green curves).
Statistical significance. The statistical significance of the lagged cross-correlations was
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determined based on a null distribution of mean Fisher’s Z-transformed cross-correlations at each lag which was generated by randomly shuffling the behavioral responses (10,000
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randomizations). The observed mean cross-correlation was considered significant (red vertical
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lines) if it was higher than the 97.5th percentile or lower than the 2.5th percentile of the null
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distribution (upper and lower bounds of the shaded areas respectively). The corresponding nonparametric p-value was computed as the number of times the absolute value of the null observations were found as extreme as the absolute value of the empirical mean, divided by the number of randomizations (10,000).
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Figure 4: Module participation of the posterior cingulate cortex differentially predicts the behavioral accuracy in each dual-task component.
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To find brain regions where the topology of dynamic brain networks was differentially coupled
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with the visuospatial and speech performance, the difference in the correlations between time series estimates of behavioral accuracy in each component of the dual-task and the time and (B) nodal efficiency was statistically
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courses of (A) positive participation coefficient
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tested. We found a significant difference in cross-correlations between the participation coefficient of a brain node within the posterior cingulate cortex (red node) and each visuospatial
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and speech performance. Specifically, the individual correlations with each component task were
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significant at the same node and differed in sign (C).
The nodal analysis was done for the sliding window length and the specific time lag for which the
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peak differences in the cross-correlations were observed at the global level (Figure 3C). That means here we report the nodal results for (A/C) window length of 11 TRs at lag 10 TRs and (B) window length of 11 TRs at lag -7 TRs. For each regional network diagnostic (A/B) the subject-specific Fisher’s Z-transformed correlations with behavioral accuracy in each visuospatial and speech task were submitted to a paired permutation test with 10,000 repetitions per brain node. Asterisks in (C) indicate significant correlations tested under the null hypothesis of zero correlation (p 0.05, two-sided) using a one-sample permutation test with 10,000 repetitions.
Statistical significance. The nodal p-values were FDR-adjusted according to Benjamini and Hochberg (1995) at the significance level of p 0.05 two-sided (red node). The nodal results
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uncorrected for multiple comparisons at the significance level of p 0.001 are color-coded in
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orange.
coordinates = [3,-65,13])
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PCC. Posterior cingulate cortex (MNI
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Brain slices. L and R indicate the left and right hemispheres respectively. The brain volumetric slices represent the group-averaged T1 image and are ordered from the bottom to the top of the coordinate (mm).
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brain (left to right) according to their MNI
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Highlights
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Dynamics of brain network topology predict fluctuations in dual-task performance Network integration differentially correlates with behavior in each component task Global integration of dynamic brain networks predicts decrease in visual accuracy Global integration of dynamic brain networks predicts increase in speech accuracy Dynamic integration of PCC with brain network modules underlies dual-task behavior
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