Attentional load modulates large-scale functional brain connectivity beyond the core attention networks

NeuroImage 109 (2015) 260–272

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Attentional load modulates large-scale functional brain connectivity beyond the core attention networks Dag Alnæs a, Tobias Kaufmann b, Geneviève Richard b, Eugene P. Duff c, Markus H. Sneve a, Tor Endestad a, Jan Egil Nordvik d, Ole A. Andreassen b, Stephen M. Smith c, Lars T. Westlye a,b,⁎ a

Department of Psychology, University of Oslo, Norway Norwegian Centre for Mental Disorders Research (NORMENT), KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Norway FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, UK d Sunnaas Rehabilitation Hospital HT, Nesodden, Norway b c

a r t i c l e

i n f o

Article history: Accepted 6 January 2015 Available online 13 January 2015 Keywords: fMRI Decoding Network modeling Brain network connectivity Attentional effort Visual attention

a b s t r a c t In line with the notion of a continuously active and dynamic brain, functional networks identified during rest correspond with those revealed by task-fMRI. Characterizing the dynamic cross-talk between these network nodes is key to understanding the successful implementation of effortful cognitive processing in healthy individuals and its breakdown in a variety of conditions involving aberrant brain biology and cognitive dysfunction. We employed advanced network modeling on fMRI data collected during a task involving sustained attentive tracking of objects at two load levels and during rest. Using multivariate techniques, we demonstrate that attentional load levels can be significantly discriminated, and from a resting-state condition, the accuracy approaches 100%, by means of estimates of between-node functional connectivity. Several network edges were modulated during task engagement: The dorsal attention network increased connectivity with a visual node, while decreasing connectivity with motor and sensory nodes. Also, we observed a decoupling between left and right hemisphere dorsal visual streams. These results support the notion of dynamic network reconfigurations based on attentional effort. No simple correspondence between node signal amplitude change and node connectivity modulations was found, thus network modeling provides novel information beyond what is revealed by conventional taskfMRI analysis. The current decoding of attentional states confirms that edge connectivity contains highly predictive information about the mental state of the individual, and the approach shows promise for the utilization in clinical contexts. © 2015 Elsevier Inc. All rights reserved.

Introduction Structural and functional connectivity reflect fundamental organizational principles of the human brain. Even the simplest behaviour depends on the coordination of distributed brain circuits. Thus, the functions of a brain structure and its role in perceptual, cognitive, emotional or motor processes are best understood in terms of its connections. Recent advances in functional brain imaging have ignited a tremendous interest in the functional connectivity (FC) of brain networks, its relation to cognitive performance and its breakdown in disorders of brain biology. An intuitive and compelling view emerging from this work is that modulation of FC, defined as the temporal correlation between brain regions, plays a critical role in complex cognitive processes (van den Heuvel and Hulshoff Pol, 2010).

⁎ Corresponding author at: Norwegian Centre for Mental Disorders Research (NORMENT), KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, PO Box 4956 Nydalen, 0424 Oslo, Norway. E-mail address: [email protected] (L.T. Westlye).

http://dx.doi.org/10.1016/j.neuroimage.2015.01.026 1053-8119/© 2015 Elsevier Inc. All rights reserved.

A comprehensive meta-analysis comparing the spatial distribution of activation patterns derived from resting-state and task-based imaging studies documented that the large-scale intrinsic functional organization of the human brain during periods of no specific stimulation reflects the task-related networks which have been identified during experimental modulation (Smith et al., 2009). Whereas the resting brain’s functional organization mimics the engaged brain (Raichle, 2010), task-modulation of the large-scale between-network FC has been suggested, supporting the intriguing and intuitive notion that even simple cognitive processes reflect the dynamic orchestration of a range of brain networks at various levels. However, the spatiotemporal associations between resting-state and task-based networks have only recently been investigated (Breckel et al., 2013; Kitzbichler et al., 2011), and the dynamic network repertoire underlying cognitive function in general and attentional effort in particular is yet to be fully characterized and understood. The dynamic modulation of betweennode synchronization may provide highly valuable information as a supplement to conventional contrast-based analysis in the characterization of neuronal mechanisms of complex cognitive processing, as well as in the search for neuronal hallmarks and intermediate phenotypes

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for disorders characterized by cognitive dysfunction (Duff et al., 2013; Shirer et al., 2012) Visual attention relies on the integrated coordination of an ensemble of inter-connected networks in the brain (Corbetta et al., 2008; Madden and Parks, 2013). Using the multiple object tracking task (MOT), requiring sustained multifocal attention (Cavanagh and Alvarez, 2005), several brain regions have been shown to preferentially activate by attentive tracking (Howe et al., 2009), among them the frontal eye fields (FEF), superior parietal lobule (SPL), the anterior and posterior intraparietal sulcus (aIPS and pIPS respectively), all considered to reflect core nodes in an extended brain network associated with goal-driven or topdown attention (Corbetta et al., 2008; Fox et al., 2005). This dorsal frontoparietal attention network has been shown to dynamically and differentially couple with visual cortical areas processing task-relevant information (Chadick and Gazzaley, 2011). Whereas previous studies have identified potential core hubs of the brain networks underlying object tracking, fewer studies have explored the temporal dynamics between the network nodes and its modulation by cognitive effort. A recent study investigated changes in FC as measured by functional connectivity density (FCD) during continuous attentive tracking of visual objects compared to resting state (Tomasi et al., 2013). The authors reported decreased FCD in visual, auditory, language and motor cortices during tracking compared to the resting state. Importantly, when comparing areas with the largest connectivity changes with areas showing significant BOLD activations and deactivations using a blocked version of MOT, they observed minimal overlap, suggesting a decoupling between cortical areas involved in the tracking task and task-irrelevant networks during periods of attentive tracking. In the current study, we employ independent component analysis (ICA) and advanced network modelling (Smith et al., 2011, 2013a,b) to estimate and compare the network synchronization between resting-state data and data collected during two load conditions of a continuous tracking version (Fig. 1, Inline Supplementary Video S1) of the MOT task (Pylyshyn and Storm, 1988; Tomasi et al., 2013). We utilize the estimated node-by-node connectivity matrices within each condition in multivariate machine learning in order to assess the degree to which the different conditions can be automatically classified. These

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assessments provide tools to quantify how individual connections contribute to the modulation of attention (e.g. Duff et al., 2013; Heinzle et al., 2012; Shirer et al., 2012). We also investigate edge-wise connectivity changes between network nodes related to attentional effort. In addition, we estimate the signal amplitude of each network node during a blocked version of the same task, enabling us to compare the network modeling approach with a standard analysis of the MOT task, where we contrast attentive tracking of one or two objects (L1/L2) with passively viewing (PV) objects. We hypothesize that including all estimated network edges in multivariate classification algorithms yields robust discrimination between resting-state and data recorded during sustained attentional effort. At the edge-level, we hypothesize a decrease in the synchronization as measured by the temporal correlations between network nodes encompassing the dorsal attention network and task-irrelevant sensory and motor networks when comparing states of attentive tracking of objects to the resting-state. Since the full range of network node modulation during task performance is unknown, we include the full estimated connectivity matrices in the analysis, allowing for a comprehensive investigation of the functional network modulation in response to increased cognitive load, and compare multivariate and univariate approaches for identifying important edges. In order to minimize the impact of noise and global correlations on the estimated network structures, we utilise a sensitive data-driven approach for fMRI data denoising (Griffanti et al., 2014; Salimi-Khorshidi et al., 2014) and infer functional connectivity on full and regularized partial temporal correlations which have been shown to be suitable for network analyses (Duff et al., 2013; Smith et al., 2011). Methods Sample Forty-three subjects took part in the study. None had present or previous history of psychiatric or neurological disorders. 4 participants were excluded due to excessive motion in the scanner (see fMRI preprocessing), and 2 due to poor performance (accuracy below 50%). Thus, 37

Fig. 1. The Multiple Object Tracking (MOT) task. The participants are instructed to keep fixation and, when assigned with targets (objects turning red), to track these objects covertly. When one of the objects turn green (probe), the participants give a response indicating whether the probed object was one of the previously assigned target objects. In the continuous tracking version, the objects never stopped moving, instead the targets and probes were marked in colors while the objects were in motion. In the blocked version, the objects started moving after targets had been assigned, and stopped moving before the probe was presented.

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participants (20 females) with a mean age of 29.24 years (SD = 9.98, range 21-60) were included. 7 were left-handed. The study was approved by the Regional Committee for Medical and Health Research Ethics (South-East Norway), and conducted in accordance with the Helsinki declaration. All participants signed an informed consent. Task and display Fig. 1 shows the MOT task design, while Video S1 illustrates the continuous version of the multiple object tracking (MOT) task. In addition to one run without any specific cognitive demands (resting-state), all participants performed two different versions of MOT in the MRI scanner during the same scan session, including two identical blocked runs, and two runs comprising continuous tracking (L1 and L2 in separate runs). For both versions, the participant was looking through a mirror mounted on the head coil at a calibrated MR-compatible LCD screen (NNL LCD Monitor®, NordicNeuroLab, Bergen, Norway) placed behind the scanner bore, with a screen resolution of 1920x10080@60Hz. All stimuli were generated using MATLAB and the Psychophysics Toolbox extensions (Brainard, 1997; Pelli, 1997). Participants produced their responses using a MR-compatible subject response collection system (ResponseGrip®, NordicNeuroLab, Bergen, Norway). A trigger pulse from the scanner synchronized the onset of the experiment to the beginning of the acquisition of an fMRI volume. The screen covered 32.43° of visual angle, and the tracking area covered 17.32° of visual angle at a viewing distance of 1.2 meters. The objects were circular disks with a diameter of 0.7° of visual angle moving at a speed of 4° / second. Objects changed direction when closer than 1° to another object or the edge of the tracking area, and also made random changes (one random turn pr. second on average) to make object movements unpredictable. Colors used for the objects were isoluminant (as measured using Spyder 4, Datacolor, Lawrenceville, NJ). A fixation circle of 0.5° of visual angle was located in the center of the tracking area. The participant’s task consisted of covertly tracking target objects while fixating on the central fixation point. Detailed instructions were given before entering the scanner room as well as before each sequence. The blocked version contained 24 trials divided into six blocks. Each block contained three different conditions (Passive Viewing, one target object (L1), and two target objects (L2), followed by rest. The order of the three conditions was random and counter-balanced so that each condition was followed by a rest period in two of the six parts, i.e. the Passive Viewing was presented twice followed by rest, and so was L1 and L2. The blocked version started with 1.5 seconds of instructions followed by 0.5 second fixation. Then, 10 identical objects were presented on a grey background. All objects were blue for 0.5 second; and then either zero (passive viewing condition), one (L1) or two (L2) of the objects turned red (designating them as targets) for 2.5 seconds before turning color back to blue (during tracking all objects were identical). After another 1.5 seconds, the objects started moving randomly and independently of each other for 12 seconds. At the end of each trial, the objects stopped moving before one of the objects turned green (probe) for 2.5 seconds. The participant was instructed to respond as quickly and accurately as possible, “yes” or “no” to whether the green probe was one of the objects originally designated as a target. In passive viewing trials, there were no targets, nor probe, but the participants were still instructed to keep fixation during the length of the trial. Accuracy and response time (RT) were recorded for each button press. The continuous MOT version was very similar to the blocked version except that the objects were continually moving throughout the entire run and did not include rest periods. Each participant performed two sessions with the continuous MOT task, one with tracking load of 1 object, and the other with 2 objects. In addition, we collected a resting-state run (fixation only). Each continuous tracking block lasted 7.5 minutes and contained 14 trials. The continuous tracking versions

started with 1.5 second instruction, followed by 0.5 second fixation. After that, 10 identical blue objects were presented on a grey background screen, and after another 0.5 second the objects started moving. The first cued object(s), or target(s), turned red after the first 0.5 second, and remained red for a duration of 2.5 seconds. The tracking period lasted on average 32 seconds (range, 27-37) after which the participants were instructed to respond to a probe (green object), before a new target assignment took place. The duration of the target and the probe presentation was 2.5 seconds. Again, the participants were instructed to fixate on the centrally presented fixation point during the length of the run. To ensure that participants were actively tracking during the whole scan period, they were instructed to push a button to be reminded which objects were the targets if they lost track of them at any point during the run, and the target object(s) would then turn red again for another 2.5 seconds. Accuracy, RT, and the number of help presses were recorded.

MRI acquisition MRI was performed at the Intervention Centre, Oslo University Hospital on a 3 T Achieva MRI scanner (Philips, Eindhoven) equipped with an 8-channel Philips SENSE head coil. All participants were scanned in a single session (total duration of 75 minutes). We collected data from five functional runs (two blocked, two continuous, one resting state), and a high-resolution T1-weighted anatomical scan. The order of the functional runs was fixed; first two rounds of the blocked MOT was performed, next the continuous tracking of one and then two objects, before concluding with a resting state run. fMRI data were collected using a BOLD-sensitive T2*-weighted echoplanar imaging sequence with a time of repetition (TR) of 2207 ms, echo time (TE) of 30 ms, voxel size of 3 × 3 × 3 mm, a field of view (FOV) of 192 x 192 mm, and 45 axial slices, flip angle: 80°. The slices were oriented to cover the whole cortex, cerebellum and brain stem. To avoid T1 saturation effects, 5 dummy scans were discarded from the start of each run. The number of dynamic volumes collected was 245 for the blocked runs and 212 for the continuous runs. The T1-weighted sequence consisted of 180 sagittal slices acquired using a turbo field echo pulse sequence with the following parameters (TR: 6.6 ms, TE: 3.06 ms, flip angle: 8°, voxel size: 1 × 1 × 1.2 mm, FOV: 256 x 256 mm).

fMRI preprocessing Processing and analysis was performed at the NORMENT Multimodal Imaging Lab, Oslo University Hospital and University of Oslo.

Blocked runs Data preprocessing was performed using FMRI Expert Analysis Tool (FEAT) Version 6.00, from FMRIB’s Software Library (FSL, Jenkinson et al., 2012; Smith, 2004) and custom MATLAB tools. Intra-session head motion was corrected using MCFLIRT (Jenkinson et al., 2002), before linear trends and low-frequency drifts were removed (high-pass filter of 0.01 Hz). Image sequences were examined for excessive head motion causing image artefacts. The mean relative motion parameter for all runs was extracted, and participants in which any run exceeded 2.5 standard deviation from the average of all runs (across participants) were discarded. Brain extraction tool (BET, Smith, 2002) was used to remove non-brain tissue. Spatial smoothing was performed using a Gaussian kernel filter with a full width at half maximum (FWHM) of 6 mm (SUSAN, Smith and Brady, 1997). FMRIB's Nonlinear Image Registration tool (FNIRT) was used to register the participant's fMRI volumes to Montreal Neurological Institute (MNI) 152 standard space using the T1-weighted scan as an intermediate.

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Automated ICA-based denoising Single-session ICA was performed for each run using Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC, Beckmann, DeLuca, Devlin, and Smith, 2005), and the resulting components from 16 randomly selected runs were manually classified into signal and noise components and used to create a studyspecific training set for FIX (Griffanti et al., 2014; Salimi-Khorshidi et al., 2014) in order to automatically classify noise components from the single session MELODIC analysis for all the functional runs (blocked, continuous and resting runs), resulting in datasets with those components removed from the time series. Data cleaning also included corrections based on the estimated motion parameters for each run using a linear regression procedure. Continuous runs Initial preprocessing steps performed on the continuous datasets were identical to the blocked data preprocessing. Data from the five functional runs (2 blocked, 2 continuous, and a resting-state) from a sub-sample of 20 subjects (mean age = 29.75, SD = 9.9) were included in a temporal concatenation group ICA using MELODIC. A sub-sample was used due to memory limitations. The sub-sample did not significantly differ in age from the sample as a whole (t(20) = -0.18, p = 0.86). Network modeling ICA – Network nodes Inaccurate definitions of regions of interest (ROIs) that do not follow functional boundaries may have deleterious effects on the estimation of network properties (Duff et al., 2013; Smith et al., 2011). Therefore, in the current study, we used ICA as a data-driven approach to define our network nodes, and dual regression (Beckmann et al., 2009; Filippini et al., 2009) to estimate subject specific independent component spatial maps and associated time series. Whereas the dimensionality of a group-ICA decomposition is somewhat arbitrary, the number of estimated components is critical for the interpretation of the resulting maps: If estimating a low number of components, each component will in itself represent a large-scale neural network (Kiviniemi et al., 2003). Conversely, when estimating a higher number of components, each component will to a larger extent represent independent functional sub-networks (Kiviniemi et al., 2009; Smith et al., 2011). We fixed the number of components to 80 in order to facilitate network modeling. Both the continuous and blocked runs were fed into MELODIC in order to inform the decomposition. Dual regression (Beckmann et al., 2009; Filippini et al., 2009) was performed on the full set of 80 components in order to estimate individual component spatial maps and corresponding time courses. Only time series from the continuous runs from the whole group (N = 37) were included in the network modeling, resulting in 80 4D-volumes comprising 111 individual component spatial maps and corresponding time series (3 per subject for each of the 37 subjects). Of the 80 group-level components, 34 were removed for various reasons: Some were identified as vascular artefacts, others were artefacts related to CSF and subject motion. The component time courses were submitted to network modeling (Smith et al., 2011) using custom MATLAB scripts and FSLNets tools distributed with FSL. First, the time series of the 34 discarded components were regressed out of the time series of the 46 remaining components. We then calculated the full and the L1-norm regularized (Duff et al., 2013; Smith et al., 2011) partial (for regularization parameters, λ =1, 5 and 20, using L1precision: http://www.di.ens.fr/~mschmidt/ Software/L1precision.html) correlations (edges) between each of the component pairs (nodes), each yielding a 46 × 46 correlation matrix for each of the 111 datasets, before converting the correlation coefficients to z-scores. The average full correlation matrix was then submitted to hierarchical clustering scheme using the linkage and dendrogram functions in MATLAB based on the Euclidean distances of the temporal

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correlations, yielding a hierarchical cluster tree representing the functional organization of the brain network across subjects and conditions. Partial correlations represent the strength of edges when all other time series have been regressed out, thereby putatively representing direct links between nodes. We used regularization in the estimation of partial correlation matrices to improve robustness to noise (Duff et al., 2013; Smith et al., 2011). Classification analysis Since the matrices are symmetrical, we used the above-diagonal edges from the unthresholded full and partial correlation matrices as features for classification. Data was iteratively separated into a training (N = 36 participants) and a test (N = 1) set using subject-wise leaveone-out cross-validation. A 3-class regularized linear discriminant analysis (rLDA; Friedman, 1989; Schäfer and Strimmer, 2005; as implemented in covshrink-kpm, strimmerlab.org) classifier was trained on each training set to identify the combination of edges showing the largest discrimination between conditions (Rest vs L1; Rest vs L2; and L1 vs L2). The model was then tested on each of the corresponding test sets. In order to assess the statistical significance of the classification accuracies (i.e., deviation from the null of chance classification), we performed permutation testing following the procedure described by Radmacher et al. (2002). The classification procedure was identical to the procedure performed on true-labeled data, except for the permuted labels. Briefly, we compared our results to an empirical null distribution of discrimination accuracies obtained by permutation testing where we randomly permuted class labels of the network matrices within subjects across 10 000 iterations. First, we created 10,000 streams of random numbers using MATLAB (Release 2014a, The MathWorks, Inc., Natick, MA, US) and assured these streams were independent (highest correlation between two streams: r = .61, p = .07). Second, we used the streams to permute the class labels of the network matrices (L1, L2, rest) within subjects (taking into account the repeated measures design). Note that this resulted in 10,000 different sets of class labels, yet left the actual network matrices untouched. Third, we performed subject-wise leave-one-out cross-validated classifications on each of the sets. The analysis was performed on the Linux cluster Abel at University of Oslo, Norway. Edgewise univariate analysis To explore the patterns of synchronization and desyncronization between network nodes in relation to task demands, we calculated weight-difference maps by subtracting the classification weights assigned for each edge for the following classes: L2-Rest, L1-Rest and L2-L1. For each of these contrasts, we extracted the edges with classification weight differences and univariate t-values above the 95th and 99th percentile. The t-values were derived from the univariate pairedsamples t-test of the regularized partial correlation matrices for the corresponding contrast. In order to test if nodes showing the largest connectivity changes correspond to nodes showing the largest signal change in response to increased task demands, we submitted the blocked MOT functional runs to dual regression, extracting time series for each of the 46 components included in the network modeling, and performed a GLM-analysis using the subject specific HRF-convolved design matrices for the blocked runs to estimate each component’s time series fit to the different conditions of the blocked runs (L1, L2 and PV). Results ICA Fig. 2 (Panel A) shows the hierarchical clustering (Smith et al., 2013a,b) of the 46 included components (network nodes, Panel B, based on the full correlations across conditions and participants (Panel C, Full correlation bellow the diagonal). The components group into 4

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Fig. 2. Hierarchical clustering of brain nodes. Panel A: Dendrogram showing the clustering of the nodes, based on the full correlations across conditions and participants. Panel B: The 46 nodes/components from the group ICA. Panel C: Below the diagonal is the full correlation matrix across conditions and participants. Above the diagonal are the regularized partial correlations across conditions. Warm colors denote positive correlations and cold colors negative correlations.

clusters, largely corresponding to visual, frontoparietal and cerebellar/ brainstem, default mode network (DMN)/subcortical, and motor/ somatosensory clusters: (1) visual sensory nodes: cuneus, lingual gyrus (IC5); cuneus (IC12); occipital, MT, superior parietal lobule (IC27); occipital, MT, superior parietal lobule, (IC37); occipital pole (IC23); lateral occipital cortex, fusiform cortex (IC13). (2) Taskpositive nodes: Right and left lateral occipital cortex, middle frontal gyrus, frontal pole (IC1 & IC7, respectively); inferior frontal gyrus (IC21); inferior parietal lobule, middle frontal gyrus (IC11); MT, superior parietal lobule, intraparietal sulcus, frontal eye fields (IC2); lateral occipital cortex, superior parietal lobule (IC17); the right middle, inferior frontal gyrus (IC22); middle frontal gyrus, temporoparietal junction (IC8); medial prefrontal frontal cortex (IC16); frontal pole (IC35, IC28 and IC42); supplementary motor area (IC14); inferior parietal lobule, middle temporal gyrus, right inferior frontal gyrus (IC19); angular gyrus, superior temporal gyrus, middle and inferior frontal gyrus, frontal operculum (IC6); the left superior frontal gyrus, right supramarginal gyrus, right precentral gyrus (IC20); the cerebellum (IC24); the temporal pole (IC26) and the brain stem pons (IC39). (3) Default mode network (DMN): mPFC, precuneus, LOC (IC3); PCC (IC31); PCC, precuneus (IC9); ACC, paracingulate (IC4); insula (IC39); parahippocampal gyrus (IC34); parahippocampal cortex, amygdala (IC43), and also several sub-cortical components: Caudate nucleus (IC29); right thalamus and bilateral thalamus (IC44 and IC45, respectively); putamen (IC32); brainstem, midbrain tectum (IC36); cerebellum (IC33 & IC41), brainstem pons, thalamus (IC40 & IC46). (4) Motor/somatosensory networks: Post-central gyrus (IC10); Heschl’s gyri (IC15), the left and right precentral gyrus (IC18 and IC38, respectively), and the posterior paracentral lobule (IC25). Since we included runs from all conditions in the group ICA we wanted to verify that the spatial distribution of components were similar across the runs that were submitted to network analysis. Any

reported differences in FC between conditions rest on the assumption that the component maps are comparable between runs, i.e. that our experimental conditions affect connectivity between nodes rather than the spatial distribution of the nodes themselves. We therefore performed one-sample t-tests on the individual level dual-regression spatial maps within each condition (L1, L2, Rest), and computed pairwise and average spatial correlations between the unthresholded t-maps

Fig. 3. Confusion matrix. The figure depicts classification results obtained for the three classes (L1, L2 and Rest, on the diagonal) based on data from regularized partial correlations (λ = 1). Permutation testing revealed that accuracies were significantly better than chance (N33%, L1: p b .0001; L2: p b .0003; rest: p b .0001).

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for each component. The distributions of the correlations are presented in Inline Supplementary Fig. S1. The average correlation for all nodes across the three conditions was r = .77, std = .06, range .64-.86. Since these correlations are based on the full unthresholded maps, voxels outside the canonical spatial networks influence the correlations. However, as can be seen in Inline Supplementary Fig. S2 showing a selection of the components, there is a large degree of spatial similarity across conditions also for those nodes showing the lowest correlations, suggesting that the spatial distribution of the components are not highly sensitive to condition. Inline Supplementary Figs. S1 and S2 can be found online at http:// dx.doi.org/10.1016/j.neuroimage.2015.01.026. Classification analysis In a subject-wise leave-one-out cross-validation procedure, we trained and tested an rLDA classifier using the above described network matrices as features in a three-class discrimination task. Best results were achieved with regularized partial correlation matrices, with λ = 1 (λ-values of 1, 5, 20 were tested). The reported accuracies are the per class hit rate. Fig. 3 depicts the resulting confusion matrix. Importantly, the algorithm distinguished the Rest condition from both Load conditions (Tracking of 1 object, L1; Tracking of 2 objects, L2) without any prediction error (mean classification accuracy across participants:

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M = 100 %). Furthermore, both load conditions could be classified at high average accuracies (L1 and L2, both: M = 70.27%, SD = 7.6%). Permutation testing revealed that the obtained classification accuracies were significantly better than chance (N 33%, L1: p b .0001; L2: p b .0003; rest: p b .0001). Inline Supplementary Fig. S3 shows distributions of accuracies obtained by classifying on each of the 10,000 sets of labelpermuted network matrices. The distribution centralizes at chance level of 33.3% with the 95% confidence interval including accuracies of at least 51.4% and the 99% confidence interval including those of 56.8% or higher. For other network matrices tested (full correlation matrix and various regularized partial correlation matrices), we obtained similar classification accuracies (Inline Supplementary Fig. S4). Inline Supplementary Figs. S3 and S4 can be found online at http:// dx.doi.org/10.1016/j.neuroimage.2015.01.026. In order to investigate whether the classification accuracy depends on a few highly predictive edges, we ran an iterative exclusion analysis where classification accuracies were calculated when removing either the strongest (Fig. 4, Panel A) or weakest (Panel B) edges in a stepwise fashion (1035 edges in total) using the following procedure: For each condition (L1, L2, Rest), we created a separate stream of edges, sorted by the classification weight assigned to the edge (regularized partial, λ = 1) for the three conditions across subjects. Classification was then performed iteratively with a stepwise exclusion of the edges. For each of the three conditions, this iterative exclusion was performed for

Fig. 4. Edgewise exclusion. In order to investigate whether classification of effort was driven by a few highly predictive edges, we ran an iterative exclusion analysis. The plots reflect the classification accuracies obtained when iteratively excluding the strongest (A) and weakest (B) edges, respectively. Shaded area represents SEM for the accuracies obtained at each step.

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each of the three streams (edges sorted by weights for each of the three conditions) and the average accuracy and standard error were calculated at each step. To classify Rest at close to 100% accuracy, it was sufficient to include only a few of the strongest edges. However, these edges were not crucial, as classification accuracy remains at a high level even when discarding 800 (77%) of the strongest edges. For classification of both L1 and L2, removing the weakest edges does not impact the classification accuracies substantially; however, there is a gradual decline in accuracy and reliability when the number of edges exceeds 200 (9%). Furthermore, there is a drop in accuracy as the number of edges falls below 50 (4.8%). Also, as can be seen in the stepwise removal of the strongest edges, the classification of L1 and L2 drops at a faster rate compared to Rest. We also compared classification accuracy when only including the edges involving the dorsal attention network (IC2), and also on all edges except IC2. For classification of Rest, it was sufficient to include edges involving IC2 to reach 100% accuracy; however, we also obtain 100% accuracy when including all edges except IC2. For classification of L1 and L2, accuracy is not affected by excluding IC2, and when classifying only on edges involving this node, classification accuracies drop to 50% for both, i.e. discrimination between L1 and L2 is no longer possible. Exclusion of motor and visual nodes Since the resting state was the only run that did not include a response, we wanted to investigate whether the high classification accuracy for rest was driven solely by a difference in motor activity. We recomputed the connectivity matrices for the full, partial and regularized partial (λ = 1) correlations after regressing out the time series of

the two nodes encompassing the motor cortices (IC18 and IC38), before rerunning the classification algorithm. For the full and partial correlation matrices, the resulting classification accuracies were almost identical. L1: 59.46% / 62.16%, L2: 75% / 70.27%, Rest: 97.3/100%, respectively. For the regularized partial correlation (λ = 1), there was a slight decrease for L1 (59.46%), while accuracies for L2 and Rest remained unchanged (L2: 70.27, Rest: 100%). In order to investigate the impact of differences in visual features in the task display (i.e. during target assignment in L2 vs L1), we performed the same steps as above, this time regressing out time series for all visual components. Again, we obtained similar classification accuracies. L1: 64.86% / 62.16%, 62.16%, L2: 59.46% / 81.08%, 83.78%, Rest: 100%/100%/100% (full, partial, and regularized, respectively). Edgewise analysis of attentional effort Fig. 5 (Panel D) depicts the edges that show the highest classification weight difference above the diagonal, and edges showing the largest connectivity change (t-values representing univariate FC differences) below the diagonal, for the contrast L2 N Rest (cut off at the 95th percentile, two-tailed, FDR corrected, independently for each measure, see Figs. 6 and 7 for L1 N Rest and L2 N L1, respectively). The classification weights and the t-values from the edgewise univariate analysis represent two different approaches for describing the importance of individual edges: The classification weights represents the importance of an edge in predicting the load condition in the presence of all other edges within a multivariate framework, i.e. its importance is dependent of the other edges. Still, the correlation between edge weights and paired

Fig 5. Edge modulation and node amplitude responses for L2 N Rest. Panel A: Dendrogram showing the clustering of the nodes. Panel B: The 46 components/nodes from the ICA. Panel C: Node signal amplitude response for the blocked MOT (FDR corrected, q = .05). Panel D: Colored elements in the matrix reflect edges given the highest classification weights (above the 95th percentile) and the strongest univariate effects (regularized partial correlations, below the diagonal) for the same contrast. Warm colors denote edges showing increased synchronization during L2 compared to Rest, and cold colors the opposite relationship. Crosses designate edges assigned the most extreme weights and t-values (99th percentile, t-values are significant, FDR, q = .05).

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Fig. 6. Edge modulation and node amplitude responses for L1 N Rest. Panel A: Dendrogram showing the clustering of the nodes. Panel B: The 46 components/nodes from the ICA. Panel C: Node signal amplitude response for the blocked MOT (FDR corrected, q = .05). Panel D: Colored elements in the matrix reflect edges given the highest classification weights (above the 95th percentile) and the strongest univariate effects (regularized partial correlations, below the diagonal) for the same contrast. Warm colors denote edges showing increased synchronization during L1 compared to Rest, and cold colors the opposite relationship. Crosses designate edges assigned the most extreme weights and t-values (99th percentile, t-values are significant (FDR, q = .05).

t-test scores were very high (r = .97, for both L2 and L1 vs Rest, as well as for L2 vs L1) confirming that edges which are highly modulated by task demands also contain the most predictive information for classifying attentional effort. The node-pairs and their associated connectivity changes for the edges assigned the top one percent weight difference are summarized in Inline Supplementary Table S1 (all univariate connectivity changes reported are significant, FDR, q = .05, two-tailed). Briefly, for both L1 and L2, the dorsal attention network (IC2: MT/IPS/SPL/FEF) decreased connectivity with early visual (IC5, IC12) ventral visual (IC13) and motor/somatosensory (IC18) networks, with the exception of the occipital pole (IC23) in which connectivity increased, when contrasted with Rest. For L1 N Rest, the dorsal attention network (IC2) also decreased connectivity with a task positive network encompassing inferior parietal lobule/middle frontal gyrus (IC11), while IC11 increased its connectivity with IC13 in both load conditions compared to rest. Further, IC37 and IC27, encompassing areas of the left and right hemisphere dorsal visual stream respectively, decreased connectivity with each other, while IC37 also decreased connectivity with the parahippocampus and amygdala (IC43) in the active tracking runs. The SMA/precentral gyrus (IC14) showed decreased connectivity with the putamen (IC32) and increased connectivity with the SPL (IC17), when contrasting L2 N Rest, while decreasing its connectivity with auditory cortex (IC15) in both L1 and L2 vs Rest. Inline Supplementary Table S1 can be found online at http://dx.doi. org/10.1016/j.neuroimage.2015.01.026. The correlation between edge weights and univariate t-values for the contrast L2 N L1 was as high (r = .97) as for L2 and L1 vs Rest; however, when exploring the edges with classification weights and

paired t-values representing connectivity changes above the 99th percentile (Fig. 7, Panel D), the overlap is substantially lower than for L2 and L1 vs. Rest, i.e. the two approaches diverge to a much larger extend than for L2 and L1 vs. Rest in terms of identifying important single edges. Inline Supplementary Fig. S5 shows the percentage overlap between the most extreme weights and t-values for L2 N Rest, L1 N Rest and L2 N L1 when stepwise including edges sorted by their absolute edge weights, and shows again that while the two approaches converge for the most extreme values when contrasting L2 and L1 with Rest, this is not the case for the more subtle difference between L2 and L1. Also, none of these edges overlap with those identified when contrasting L2 and L1 versus Rest, and none of the edge t-values survive correction for multiple comparisons (FDR, q = .05). Among the edges identified in the L2 and L1 vs. Rest, three show a nominal effect of Load (p b .05): The tracking related decrease in connectivity between the left and right hemisphere dorsal visual stream nodes (IC37 and IC27, respectively), and between MT/IPS/SPL/FEF (dorsal attention; IC2) and IPL/MFP (IC11), is smaller for L2 than for L1. The tracking related decrease between SMA/precentral gyrus (IC14) and putamen (IC32) is larger for L2 compared to L1. Inline Supplementary Fig. S5 can be found online at http://dx.doi. org/10.1016/j.neuroimage.2015.01.026. Load effects on in-scanner subject motion To rule out that the classification and edge-wise effects were driven by a difference in subject motion between the task runs and the resting state run, we extracted the relative motion parameters estimated during the preprocessing step, and submitted them to paired-samples

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Fig. 7. Edge modulation and node amplitude responses for L2 N L1 Panel A: Dendrogram showing the clustering of the nodes. Panel B: The 46 components/nodes from the ICA. Panel C: Node signal amplitude response for the blocked MOT (FDR corrected, q = .05). Panel D: Colored elements in the matrix reflect edges given the highest classification weights (above the 95th percentile) and the strongest univariate effects (regularized partial correlations, below the diagonal) for the same contrast. Warm colors denote edges showing increased synchronization during L2 compared to L1, and cold colors the opposite relationship. Crosses designate edges assigned the most extreme weights and t-values (99th percentile, t-values are significant (FDR, q = .05).

t-tests. No significant difference was observed between neither L1 (t(36) = 1.47, p = .15)] or L2 [t(36) = -.54, p = .59] compared to Rest. Also, no significant difference was observed between L1 and L2 [t(36) = 1.16, p = .25]. These results, along with the comprehensive denoising of the functional timeseries data including FIX and regressing out motion parameters, make it very unlikely that the classification is driven by subject motion. Blocked MOT In order to investigate associations between task-based activation and connectivity changes for the edges associated with the one percent

highest classification weights, we estimated the model fit of each nodes’ time-series to the three conditions of the blocked version of the MOT task: L1, L2 and PV (Figs. 5, 6 and 7, respectively, panels C). MT/IPS/SPL/FEF (dorsal attention; IC2), SMA/precentral gyrus (IC14), SPL (IC17), left hemisphere motor/somatosensory cortex (IC18), putamen (IC32), and left hemisphere dorsal visual stream (IC37) showed increased amplitude during L2 and L1 blocks compared to PV. The opposite pattern was seen for middle frontal gyrus/ temporoparietal junction (IC8) and inferior frontal gyrus (IC21), which showed decreased amplitude during L2 and L1 trials. All these nodes except left hemisphere motor/somatosensory cortex (IC18) and left hemisphere dorsal visual stream (IC37) also showed increased or

Fig. 8. Blocked MOT behavioral performance. Bar plots depicting mean accuracy and RT (in ms.) during the blocked version of MOT. L1 and L2 denote the tracking loads of one and two objects, respectively. Error bars represent the SEM. Stars indicate a significant difference. The help button was included in the continuous tracking to ensure that the participants were actively tracking throughout the run.

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decreased amplitude with increasing effort (L2 N L1). Nodes that showed significant amplitude increases in L2 N PV, included IPL/MFP (IC11), ventral visual (IC13), and right hemisphere dorsal visual stream (IC27). All of these showed a significant increase during L2 compared to L1. A significant decrease during L2 compared to PV was seen for the auditory cortex (IC15), which also showed a significant decrease in L2 compared to L1. Early visual (IC5, IC12), occipital pole (IC23), and parahippocampus/amygdala (IC43) showed no significant signal change in any of the tracking conditions compared to PV; however, IC23 showed a significant decrease with increasing effort. Behavioral data For both the blocked and the continuous runs the average accuracy and RT for each participant was calculated for the two load conditions and submitted to paired samples t-tests. For the continuous runs we also calculated the average number of help presses. Mean RT and accuracy for the blocked runs are presented in Fig. 8. There was a significant difference in response accuracy between L1 (mean accuracy = 96.18 %, SE = 1.28) and L2 (mean accuracy = 92.57%, SE = 1.67 %) [t(36) = 2.4, p b .05]. Further, RTs were significantly higher in the L2 condition (mean = 1115 ms, SE = 39.16 ms) compared to L1 (mean = 1026 ms, SE = 36.08 ms) [t(36) = 5.25, p b .001]. For the continuous runs (Fig. 9) paired samples t-tests showed no significant difference in accuracy between L1 (mean = 92.29%, SE = 1.28%) and L2 (mean = 89.19 %, SE = 2.08%) [t(36) = 1.41, p = 0.167]. The number of help button presses were significantly higher during L2 (mean = 3.46, SE = 0.03) compared to L1 (mean = 1.43, SE = 0.03) [t(36) = 3.73, p b .001]. There was no significant [t(36) = 0.78, p = 0.44] difference in RT between L1 (mean = 1096.35 ms, SE = 29.37 ms) and L2 (mean = 1110.67 ms, SE = 26.67 ms). Discussion By employing advanced network modeling on fMRI data collected during attentive tracking of objects and during a resting-state condition, we have demonstrated that states characterized by sustained top-down attention and cognitive effort can be discriminated with an accuracy of 100% from an unconstrained resting-state condition by means of datadriven estimates of between-node functional connectivity. The same approach yielded reduced but well above chance discrimination between two different load levels of the tracking task. Edgewise analysis revealed that the most discriminative edges identified by the multivariate approach when comparing attentional effort with resting state connectivity reflected changes in functional links between core areas of the top-down attention network and visual, motor and somatosensory networks, but also demonstrated that the functional connectivity fingerprint of differences in attentional load is characterized by a complex pattern of synchronization and desynchronization between a wide

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array of functional networks. This complex pattern is in line with the current notion that even the simplest behavior is dependent on the successful cross-talk between a range of more or less independent functional modules, which are present and continuously active also in the absence of external task demands (Raichle, 2010; Smith et al., 2009). These novel results demonstrate that the current functional connectivity approach is sensitive to shifts in the spatiotemporal configuration of brain networks, and that alterations in cognitive effort are robustly reflected in changes in the large-scale functional connectivity patterns of the brain. The continuous version of the MOT task enabled us to compare brain connectivity patterns during sustained effortful attentional processing to those observed during an unconstrained resting state condition. We hypothesized that the changes in synchronization between nodes provided predictive information about the cognitive states induced by the different conditions extending beyond the conventional contrast based analysis. By taking into account the full range of edges from our network modeling approach, we were able to classify the resting state from the active tracking conditions with 100% accuracy, while classification of the two continuous tracking conditions, differing only in task demands, were well above chance level. Permutation testing revealed that all classification accuracies were highly significant. Also, while edges involving nodes encompassing core areas of the dorsal attention network in itself were enough to discriminate resting state connectivity from states of attentional effort, they were not critical, as the same performance is seen also when classifying using all other edges except the ones including this node. When discriminating between different levels of tracking load, we were not able to identify any particular single edge as important in either the multivariate or univariate approach, instead the successful classification seemed to involve a range of edges implicating various brain nodes. Another interesting finding is that although we obtained similar classification accuracies for L1 and L2 when including the full set of features, the classification of L2 was more robust in response to the removal of edges than L1, suggesting that increasing attentional effort affects a wide range of the included edges. These results further support our hypothesis that the full extent of the between-network synchronizations and desynchronizations contain predictive information about cognitive states. Multivariate approaches have the potential to dramatically increase the sensitivity of neuroimaging data (Haynes and Rees, 2006), and recent studies have shown that participants’ brain states can be decoded from the spatially distributed pattern of activity in a selection of voxels, such as object perception (Haxby et al., 2001; Haynes and Rees, 2005), contents of visual working memory (Harrison and Tong, 2009), but also higher-order cognitive control (Esterman et al., 2009) and attentional shifts (Chiu et al., 2011). Some recent studies have extended the machine learning approach to classification on continuous data, showing that cognitive states can be classified based on patterns of functional connectivity (Duff et al., 2013; Richiardi et al., 2011; Shirer et al., 2012),

Fig. 9. Continuous MOT behavioral performance. Bar plots depicting mean accuracy RT (in milliseconds), and the number of help button presses during the continuous MOT version. L1 and L2 is tracking load of one and two objects, respectively. Error bars represent the SEM. Stars indicate a significant difference. The help button was included in the continuous tracking to ensure that the participants were actively tracking throughout the run.

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and also to successfully discriminate healthy controls from patients with major depression (Craddock et al., 2009; Zeng et al., 2012), schizophrenia (Shen et al., 2010) and attention-deficit/hyperactivity disorder (Zhu et al., 2008). With the exception of Duff et al. (2013) and Zhu et al. (2008), all the above studies used atlas-based parcellation of brain regions. Duff et al. (2013) reported that classification accuracies tended to be worse for non-ICA feature sets (task-based parcellations) compared to feature sets based on ICA, especially when decoding based on partial correlation matrices. The above studies also classified on qualitatively different mental states or conditions. Here, we extend these findings by showing that a parametric increase in attentional load is associated with alterations in between-node connectivity and that the edges contain highly predictive information of the level of attentional load. Along with previous work on classification of mental states using fMRI, the current successful decoding of attentional load and also effortful versus resting-state conditions shows promise for the utilization of similar methods in other contexts, including clinical settings where fMRI connectivity may aid assessments and evaluations of diagnostics, prognostics and in the evaluation of targeted intervention programs. We identified highly overlapping spatial distributions of the nodes within each of the conditions, including the resting-state run, supporting that the overall anatomical weighting of the components are comparable across different levels of cognitive effort (Smith et al., 2009; Toro et al., 2008). The current results thus confirm that the large-scale brain networks are active and identifiable across a range of psychological contexts. We combined multivariate and univariate approaches in order to identify important single edges. When comparing the tracking condition with resting state, we observed, in line with our hypothesis, that the dorsal attention network (IC2: MT/IPS/SPL/FEF), which has previously been reported as highly engaged during tracking (Howe et al., 2009), decreased connectivity with several nodes encompassing early visual cortex and ventral visual stream areas (Goodale and Milner, 1992), as well as with motor and somatosensory areas. In contrast, an increase was seen with the occipital pole. This differential coupling and decoupling between the dorsal attention and visual nodes may reflect the fact that the objects were tracked predominantly in the central parts of the visual field, since the occipital pole is known to retinotopically represent the central parts of the visual field (Wandell et al., 2007). Interestingly, we also observed a strong decoupling between nodes encompassing the left and the right hemisphere dorsal visual stream. There is evidence that the two hemispheres have independent resources for attentive tracking of objects (Alvarez and Cavanagh, 2005), and a decoupling between these nodes during attentive tracking may facilitate such independence of early level tracking resources. Further, we observed a decoupling between dorsal visual stream areas and the parahippocampal cortex. These complex patterns of connectivity changes support previous observations that sensory nodes are differentially and dynamically coupled with task-positive nodes and with the DMN on the basis of task goals (Chadick and Gazzaley, 2011). Few studies have compared intrinsic connectivity in the resting state directly to those obtained when actively engaging in cognitively demanding tasks. One exception is a recent study investigating changes in functional connectivity as measured by FCD when performing attentive tracking of visual objects compared to resting state (Tomasi et al., 2013). The authors reported decreased FCD in visual, auditory, language and motor cortices during tracking compared to a resting state. In line with our current results, the authors observed minimal overlap when comparing areas with the largest connectivity changes with areas showing significant amplitude activations during blocked tracking. Further, connectivity decreases in regions that responded weakly or negatively in the task-fMRI analysis, showed a positive correlation with tracking performance, indicating a functional disconnection of seemingly task-irrelevant networks, which may interfere with the tracking task. Our results support this notion of a functional disconnection of taskirrelevant motor and sensory nodes.

We observed characteristic signal amplitude modulations by task demands in nodes encompassing task-positive and dorsal attention networks as well as in nodes encompassing the dorsal visual stream. Earlier studies have implicated the dorsal attention network, including MT, SPL, IPS and FEF, in mental tracking of objects (Howe et al., 2009) and it has been shown that parietal cortical areas increase BOLD activity in a parametric fashion with increasing number of targets (Jovicich et al., 2001). Compared to previous MOT studies, which included tracking load of up to five objects, ours included only tracking loads of one and two objects. Still, we observed increased signal amplitude in core areas involved in mental tracking with increasing effort, suggesting that the task indeed recruited relevant neuronal networks even at a relatively low load. The observed decrease in response accuracy with higher tracking load also suggests a higher degree of attentional effort when tracking two versus one object in the blocked MOT. We did however not observe a decrease in accuracy for the continuous tracking task. In this version of the task the help button was intended to facilitate active tracking throughout the duration of the run, and may have mitigated any differences in accuracy and response times. Thus, in addition to the experimental manipulation of load, a difference in attentional effort between the two tracking loads in the continuous MOT can be inferred from the observed difference in the blocked version and the difference in the number of help button presses. The connectivity analysis and the node signal amplitude GLM analysis converge on, and confirm that the dorsal attention network, including the MT, SPL, IPS and FEF, are core areas of a tracking network, as the node comprising these areas (IC2) showed a complex pattern of increases and decreases in connectivity with several other nodes. The current approach revealed novel associations between brain network dynamics and levels of cognitive effort. Indeed, whereas some of the edges assigned the highest weights in the multivariate classification analysis involved nodes showing large task-based amplitude modulations, there were no simple relationship between the amplitude modulations and the classification weights across nodes. For example, the dorsal attention node (IC2), which primarily displayed decreased connectivity with several visual areas with altering levels of effort, showed a strong increase in amplitude in both load conditions compared to PV. Moreover, while the dorsal attention node showed decreased connectivity with ventral visual stream areas when contrasting Tracking to Rest, both nodes show increased amplitude in response to Tracking. The dorsal attention node also displayed decreased connectivity with cuneus/lingual gyrus (IC5) and increased connectivity with the occipital pole (IC23); however, these two latter nodes showed no significant amplitude modulation. The same was true for the parahippocampus and amygdala node (IC43) and the left dorsal visual stream (IC37), which both showed decreased connectivity with tracking load, but only the latter showed a significant signal amplitude increase during tracking. While the right and left hemisphere dorsal visual stream (IC27 and IC37, respectively) both showed increased signal amplitude in response to tracking load, they showed a strong decoupling in response to tracking. This supports that the amplitude of brain responses does not necessarily reflect their functional significance (Sidtis, 2012), and investigating large-scale connectivity during task engagement provides a highly useful supplement to conventional contrast based analyses. The present results do not come without limitations. Our characterization of brain nodes and between-network connectivity are limited by the temporal and spatial limitations inherent in fMRI data. Data collected at a higher temporal sampling rate may capture reorganization of resting state connectivity networks as it changes dynamically over time and possibly also in response to task demands (Smith et al., 2012). It would be informative to compare the current approach with results obtained using methods which are sensitive to other properties of the temporal dynamics, including the frequency domain (Sun et al., 2005). Also, the true dimensionality of the brain is unknown, and future studies should investigate alternative approaches for parcellation and

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model order selection. Future studies should also compare a wider range of cognitive states in order to assess the specificity and sensitivity of the method. Also, our results cannot assess to which degree the identified nodes and edges are necessary and sufficient for attentive tracking, and further studies including patients with lesions in areas corresponding to these nodes (e.g. after stroke) are needed. In addition, studies perturbing specific brain nodes using non-invasive stimulation (e.g. transcranial magnetic stimulation, TMS) would be informative. The maximum tracking load of two objects combined with the help button in the current study was intended to reduce between-subject variability to facilitate reliable estimation of between-network connectivity. In order to investigate relationships between connectivity indices and task performance, future studies should include either higher tracking loads or populations with reduced cognitive capacity, e.g. aging or clinical populations. Including eye-tracking measurements would also be ideal, in order to confirm that participants are in fact tracking covertly. In the current study, we did not collect gaze data, so even though we emphasized the importance of keeping fixation to our participants, we cannot rule out that some participants may have tracked by using overt eye movements. However, while the generalizability to the current continuous tracking runs is unclear, preliminary data from an ongoing study combining fMRI and eye-tracking suggests that subjects are indeed able to maintain fixation during blocked MOT runs (Alnæs et al., unpublished results). Cognitive tasks have been shown to induce lasting effects on resting state connectivity (Breckel et al., 2013; Grigg and Grady, 2010). Thus, in order to reduce between-subject variability, we fixed the order of the runs across subject. However, we can therefore not rule out that fatigue or scanner heating could have impacted the classification results. Lastly, the interpretation of parameters from machine learning approaches poses some challenges compared to the interpretation of those from univariate approaches. A high classification weight in a multivariate framework cannot be interpreted in isolation and does not entail a direct statistical dependence with the observed brain dynamics. We observed a high correlation between edge weights and t-values, and the two approaches converged in terms of which edges showed the strongest effects. Importantly, all edge-wise results and interpretations reported in the current study are based on edges that show significant univariate effects. However, methods on how to transform multivariate parameters into forward model parameters have been proposed (Haufe et al., 2014), which may improve interpretability of multivariate classification weights in absence of information provided by univariate analysis in future work. In conclusion, our present results confirm that there is highly predictive information regarding ongoing attentional processes in betweennode connectivity measures, which do not correspond in any direct fashion to the patterns of BOLD signal amplitude changes. The patterns of synchronization and desynchronization between nodes support the notion of network reconfiguration based on task goals during topdown attention. We propose that the current approach may prove highly useful also for decoding and characterizing altered functional connectivity related to abnormal cognitive functioning in clinical populations. Supplementary data related to this article can be found online at http://dx.doi.org/10.1016/j.neuroimage.2015.01.026.

Acknowledgments and funding This study was supported by the Department of Psychology, University of Oslo, Norway. G.R. was funded by a MedIm Bridging Funds grant. L.T.W. was supported by the Research Council of Norway (#204966/F20).

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