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THE NETWORK PROPERTY OF THE THALAMUS IN THE DEFAULT MODE NETWORK IS CORRELATED WITH TRAIT MINDFULNESS X. WANG, a,b  M. XU, a,b  Y. SONG, a,b X. LI, a,b Z. ZHEN, a,b Z. YANG a,b AND J. LIU a,b,c*

INTRODUCTION Experience is a steady stream that is ever flowing when we are not in a coma or deep sleep. Yet we are not always aware of what we are experiencing. Sometimes, we snack without being aware of what we eat; we hear the loudness and the beat, but do not listen to the timbre and the variation; we walk along a street, unaware of our surroundings until we step on something. At some point in our daily activities, we all ‘‘zone out.’’ Mindfulness is typically defined as nonjudgmental or receptive awareness of and attention to experiences in the present moment (Brown and Ryan, 2003; Kabat-Zinn, 2003; Bishop et al., 2004; Brown et al., 2007). A roughly opposed construct to mindfulness is mind-wandering, which is defined as a shift of attention away from task at hand toward internal information (Smallwood and Schooler, 2006), in contrast to the fundamental component of sustained attentiveness in mindfulness. Mindfulness has become increasingly popular in both the general public and scientific community because it produces a wide range of beneficial effects on mental well-being, cognitive functioning, and physical health (Brown and Ryan, 2003; Baer et al., 2006; Cardaciotto et al., 2008; Chiesa and Serretti, 2009; Hofmann et al., 2010; Holzel et al., 2011b). Recent neuroimaging studies indicate that the default mode network (DMN) plays a pivotal role in mindfulness. The DMN is a collection of brain regions which are typically deactivated in goal-directed tasks and activated during rest periods (Shulman et al., 1997; Gusnard and Raichle, 2001; Mazoyer et al., 2001; Raichle et al., 2001), including the precuneus/posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC), lateral parietal cortex (LPC), lateral temporal cortex (LTC), parahippocampal gyrus (PHG), and thalamus (e.g., Beckmann and Smith, 2004; Fox et al., 2005; Fransson, 2005; Mantini and Vanduffel, 2013). A decade of research indicates that the DMN is involved in a variety of tasks that are generally related to mindfulness. For example, the DMN is activated during mind-wandering (Mason et al., 2007; Christoff et al., 2009; Dickenson et al., 2013), self-referential processing (Northoff et al., 2006; Herwig et al., 2010), stimulus-independent thoughts (Buckner et al., 2008; Buckner, 2012), and retrieving memories and envisioning the future (Schacter et al., 2007; Andrews-Hanna et al., 2010; Spreng and Grady, 2010). More specifically, empirical exploration of mindfulness has mainly focused on cultivation of mindfulness by meditation practice (Baer, 2003; Kabat-Zinn, 2003).

a

State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China b Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China c

School of Psychology, Beijing Normal University, Beijing, China

Abstract—Mindfulness is typically defined as nonjudgmental awareness of experiences in the present moment, which is beneficial for mental and physical well-being. Previous studies have identified multiple regions in the default mode network (DMN) that are involved in mindfulness, but little is known about how these regions work collaboratively as a network. Here, we used resting-state functional magnetic resonance imaging to investigate the role of the DMN in trait mindfulness by correlating spontaneous functional connectivity among DMN nodes with self-reported trait mindfulness in a large population of young human adults. Among all pairs of the DMN nodes, we found that individuals with weaker functional connectivity between the thalamus and posterior cingulate cortex (PCC) were more mindful of the present. Post-hoc analyses of these two nodes further revealed that graph-based nodal properties of the thalamus, not the PCC, were negatively correlated with trait mindfulness, suggesting that a low involvement of the thalamus in the DMN is relevant for high trait mindfulness. Our findings not only suggest the thalamus as a switch between mind-wandering and mindfulness, but also invite future studies on mechanisms of how mindfulness produces beneficial effects by modulating the thalamus. Ó 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

Key words: mindfulness, default mode network, posterior cingulate cortex, thalamus, graph-based network analysis, resting-state functional connectivity.

*Corresponding author. Current address: Room 405, Yingdong Building, 19 Xinjiekouwai Street, Haidian District, Beijing 100875, China. Tel/fax: +86-10-58806154. E-mail address: [email protected] (J. Liu).   Equal contribution. Abbreviations: ARAS, ascending reticular activating system; BA, Brodmann’s area; DMN, default mode network; EPI, echo-planarimaging; FDR, false discovery rate; FSL, FMRIB Software Library; GM, gray matter; LTC, lateral temporal cortex; LPC, lateral parietal cortex; MAAS, Mindful Attention Awareness Scale; MNI, Montreal Neurological Institute; MPFC, medial prefrontal cortex; MRI, magnetic resonance imaging; PCC, posterior cingulate cortex; PHG, parahippocampal gyrus; rs-fMRI, resting-state functional MRI; SD, standard deviation; SFG, superior frontal gyrus; TRL, threshold range length. http://dx.doi.org/10.1016/j.neuroscience.2014.08.006 0306-4522/Ó 2014 IBRO. Published by Elsevier Ltd. All rights reserved. 291

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Functional magnetic resonance imaging (MRI) studies have demonstrated differences in the DMN activations between experienced meditators and novices. Nevertheless, it is unclear whether meditation training is related to increased or decreased activation of the DMN. For example, the MPFC and PCC are deactivated in both long-term meditators when they view emotional pictures in a mindful state (vs. a non-mindful state) (Taylor et al., 2011) and short-term meditators (after 8-week meditation training) when they attend to the present experience (vs. attend to enduring traits) (Farb et al., 2007). On the other hand, the MPFC shows increased activation in monks during meditation (vs. rest), but not in novices (Manna et al., 2010). The discrepancy of these results is likely due to differences in experimental and baseline tasks employed during scanning, duration of training, and participant characteristics. The findings of structural MRI studies are more consistent, with several cross-sectional studies reporting greater gray matter (GM) volume or density of the DMN regions in meditators than novices, including the PCC, LTC, LPC, PHG, and thalamus (Holzel et al., 2008; Luders et al., 2009; Leung et al., 2013). Further, one longitudinal study showed that the GM density in the PCC is enlarged after 8 weeks of mindfulness training (Holzel et al., 2011a). Finally, meditation training is associated with differences in functional connectivity between DMN nodes comparing meditators with novices (Brewer et al., 2011; Jang et al., 2011; Taylor et al., 2013). In short, mindfulness meditation is related to both function and structure of the DMN regions. A growing number of studies have conceptualized mindfulness as a dispositional characteristic, i.e. trait mindfulness, which varies naturally across individuals even without mindfulness practice (Brown and Ryan, 2003). Although there is some debate over whether trait mindfulness and trained mindfulness represent the same construct and share the same neural basis (e.g., Davidson, 2010), previous neuroimaging studies on trait mindfulness also indicate the involvement of the DMN. For example, trait mindfulness is negatively correlated with resting-state activity in the PCC (Way et al., 2010) and self-referential processing activity in the MPFC (Herwig et al., 2010). And a recent structural MRI study found that trait mindfulness negatively correlated with GM volume in the PCC (Lu et al., 2014). However, it is unclear how the collaboration between the DMN nodes reflects trait mindfulness. Here, we aimed to address two questions. First, what is the relationship between trait mindfulness and functional connectivity of the DMN nodes? Second, what is the relevant node for trait mindfulness in the DMN? To address these questions, we first acquired resting-state functional MRI (rs-fMRI) data and assessed trait mindfulness in a large population of young adults (N = 245). Because awareness of the present experience is a fundamental element in all definitions of mindfulness, we adopted the Mindful Attention Awareness Scale (MAAS) (Brown and Ryan, 2003), a widely used self-reported measure of trait mindfulness in the literature, to assess mindfulness as a dispositional general tendency to ‘‘be attentive to and aware of

present-moment experiences’’ (Brown and Ryan, 2003). We then explored the role of the DMN in self-reported trait mindfulness by correlating the functional connectivity among DMN nodes with MAAS scores across individuals. Finally, we used graph-based network analyses to identify the relevant node for trait mindfulness in the DMN.

EXPERIMENTAL PROCEDURES Participants Two hundred and forty-five (129 females and 116 males; 18–25 years of age, mean age = 21.7 years, standard deviation [SD] = 1.05) college students from Beijing Normal University, Beijing, China, participated in the study. Participants with self-reported history of medication and neurological or psychiatric disorders were excluded. Both behavior and fMRI protocols were approved by the Institutional Review Board of Beijing Normal University. Informed written consent was obtained from all participants before the experiment. Note that the participants in the current study were a subset of the sample in a previous voxel-based morphometry (VBM) study on trait mindfulness (Lu et al., 2014). Measuring mindfulness Trait mindfulness was assessed by the MAAS, which is a well-established self-report questionnaire with excellent internal consistency and test–retest reliability (Brown and Ryan, 2003). The MAAS contains 15 items with a single factor, and exemplar items are ‘‘I find it difficult to stay focused on what’s happening in the present,’’ and, ‘‘I find myself preoccupied with the future or the past.’’ Participants rate each MAAS item on a 6-point Likert-type scale (1 = almost always, 6 = almost never), and the MAAS score, which is calculated by averaging the participant’s scores for the 15 items, is used as the index for a participant’s trait mindfulness. The MAAS score ranges from 1 (mind-wandering) to 6 (mindful). Outliers were defined as being three SDs below or above the population mean of the MAAS score. One male participant (0.4% of the participant population) was excluded from further analyses because his MAAS score was three SDs below the mean. All participants completed the MAAS in a separate session after the MRI scan. Because the acquisition of MRI data was time-consuming for the large sample of participants, the MAAS was measured at least a month after MRI data acquisition. Given that the temporal stability of trait mindfulness as measured by the MAAS scale has been shown over a month (Brown and Ryan, 2003), the time interval may have little impact on the results in the present study. rs-fMRI data acquisition and preprocessing The rs-fMRI scan was performed on a 3T scanner (Siemens Magnetom Trio, A Tim System) with a 12-channel phased-array head coil at Beijing Normal University Imaging Center for Brain Research, Beijing, China. Participants were instructed to relax without engaging in any specific task and to remain still with their

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eyes closed during the scan. The resting-state scan lasted 8 min and consisted of 240 contiguous echoplanar-imaging (EPI) volumes (TR = 2000 ms; TE = 30 ms; flip angle = 90°; number of slices = 33; matrix = 64  64; FOV = 200  200 mm2; acquisition voxel size = 3.125  3.125  3.6 mm3). High-resolution T1-weighted images also were acquired with magnetization prepared gradient echo sequence (MPRAGE: TR/ TE/TI = 2530/3.39/1100 ms; flip angle = 7°; matrix = 256  256) for spatial registration. One hundred and twenty-eight contiguous sagittal slices were obtained with 1  1 mm2 in-plane resolution and 1.33-mm slice thickness. For each participant, image preprocessing was performed with FMRIB Software Library (FSL, www.fmrib.ox.ac.uk/fsl/). Preprocessing included head motion correction (by aligning each volume to the middle volume of the image with MCFLIRT), spatial smoothing (with a Gaussian kernel of 6-mm full-width half-maximum), intensity normalization, and removal of linear trend. Next, a temporal band-pass filter (0.01– 0.1 Hz) was applied with FSLMATHS to reduce lowfrequency drifts and high-frequency noise. Registration of each participant’s high-resolution anatomical image to a common stereotaxic space (the Montreal Neurological Institute (MNI) 152-brain template with a resolution of 2  2  2 mm3, MNI152) was accomplished using a two-step process (Andersson et al., 2007). Firstly, a 12-degrees-of-freedom linear affine was carried out with FLIRT (Jenkinson and Smith, 2001; Jenkinson et al., 2002). Secondly, the registration was further refined with FNIRT nonlinear registration (Andersson et al., 2007). Registration of each participant’s functional images to the high-resolution anatomical images was carried out with FLIRT to produce a 6-degrees-of-freedom affine transformation matrix. To eliminate physiological noise, such as fluctuations caused by motion or cardiac and respiratory cycles, nuisance signals were regressed out using the methods described in previous studies (Fox et al., 2005; Biswal et al., 2010). Nuisance regressors included averaged cerebrospinal fluid signal, averaged white matter signal, global signal averaged across the whole brain, six head realignment parameters obtained by rigid-body head motion correction, and the derivatives of each of these signals. The 4-D residual time series obtained after removing the nuisance covariates were registered to MNI152 standard space by applying the previously calculated transformation matrix. Participants whose head motion was greater than 3.0° or 3.0 mm throughout the rs-fMRI scan were excluded from further analyses. One male participant (0.4% of participant population) met this criterion with an in-scanner head motion of maximal rotation greater than 3.0°. Identifying the DMN The DMN is commonly defined as a set of brain regions that are positively correlated with the PCC at restingstate (e.g., Fox et al., 2005; Fransson, 2005). Therefore, we followed previous studies (e.g., Fox et al., 2005;

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Fransson, 2005) that have used the PCC as a seed to identify the DMN. The anatomical mask of the PCC from the Harvard–Oxford atlas with a probability greater than 60% (MNI coordinates: 0, 40, 30; size: 887 voxels) was taken as the seed. We then calculated Pearson’s correlation coefficients between the mean time course of the seed and that of each voxel across the whole brain of each participant, to generate a participant-level correlation map of all voxels that were positively or negatively correlated with the seed. Finally, the participant-level correlation map was transformed to a Z-score map using Fisher’s r-to-z transformation and then registered to MNI152 space. Group-level analyses were carried out using FMRIB’s Local Analysis of Mixed Effects (FLAME) (Beckmann et al., 2003) implemented in FSL, which is widely used in group-level seed-based resting-state functional connectivity analyses (Uddin et al., 2009; Mennes et al., 2010). The resulting z statistic map showing significant positive functional connectivity with the PCC (p < 0.001, false discovery rate (FDR) corrected) was defined as the DMN. DMN nodes were defined as spheres with a radius of 5 mm that centered in the maximal activation in each significant cluster of the z statistic map; edges (i.e., functional connectivity between two nodes) were defined as pair-wise Pearson’s correlations between the nodes’ mean time-courses. Based on the group-level DMN, an N (number of nodes)  N correlation matrix of the DMN was defined for each participant. The correlation coefficients in the matrix of each participant were transformed to Z-score using Fisher’s r-to-z transformation. Correlation between DMN and mindfulness To explore which DMN edge predicted a participant’s mindfulness, we calculated correlation coefficients between the functional connectivity strength and MAAS scores across participants for each edge. Thus, there were N  (N  1)/2 possible correlation coefficients for the N  N matrix of the DMN. The statistical significance level was set to p < 0.05 with Bonferroni correction for multiple comparisons. To identify the relevant node of the DMN involved in mindfulness, nodes with edges that showed significant correlations with the MAAS score were chosen for graph-based network analyses (Bullmore and Sporns, 2009; Rubinov and Sporns, 2010). Three nodal network metrics in graph-based network, nodal degree, nodal efficiency, and clustering coefficient were used to characterize the network property of DMN nodes in mindfulness by correlating them with participants’ mindfulness. To calculate these network metrics, we first converted each participant’s raw correlation matrix into a binary graph (adjacency matrix) with a predetermined correlation threshold at T = 0.3 based on previous studies (e.g., Cordes et al., 2002; Wang and Li, 2013; Wu et al., 2013). That is, edges with absolute connectivity strength r(i, j) larger than T were set to 1; otherwise, they were set to 0. The nodal network metrics are illustrated in a graph G consisting of N nodes and K edges. The nodal degree of a given node i is defined as the number of edges linked to

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P the node: knod ðiÞ ¼ j–i2G aij , where aij is the ith row and jth column element in the adjacency matrix (Rubinov and Sporns, 2010). The nodal efficiency of a node i is meaP 1 1 sured as Enod ðiÞ ¼ N1 j–i2G dði;jÞ, where d(i, j) is the shortest path length between node i and node j (Achard and Bullmore, 2007). The nodal clustering coefficient of a node i is calculated as Cnod ðiÞ ¼ knod ðiÞðk2Nnodi ðiÞ1Þ , where Ni denotes the number of existing connections among the neighbors of node i, and knod(i) represents the degree of node i (Watts and Strogatz, 1998). These three nodal network metrics are related, but they reflect different network properties of a node. The nodal degree reflects the importance of a node in the network, the nodal efficiency quantifies the importance of a node for communication within a network, and the nodal clustering coefficient illustrates local interconnectivity and aggregation of a network. Network metric calculations were performed using in-house MATLAB code and the Brain Connectivity Toolbox (http://www.brain-connectivity-toolbox.net/). To examine whether the results depended on a particular threshold selected to binarize the raw correlation matrix, a wide range of thresholds were tested, with threshold T ranging from 0.25 to 0.60 in steps of 0.01, totaling 36 threshold levels for each network metric. Because there were 3n metrics (n candidate DMN nodes multiplied by three nodal metrics) and each had 36 threshold levels in the DMN-mindfulness correlation analyses, we performed multiple comparisons correction based on the hypothesis that the true correlation between network metrics and mindfulness covered a wide range of thresholds (Wang et al., 2012). That is, the combination of both individual significance level (p) at each threshold and threshold range length (TRL, the number of continuous thresholds with significant correlations) was used to define the true significant correlation (overall false-positive probability P < 0.05) from random noise. Specifically, we set the significance level as p < 0.05 for each individual threshold, which corresponded to the absolute value of Pearson’s correlation coefficients larger than 0.126 with 243 participants. Then, we used AlphaSim of the AFNI (Cox, 1996) to estimate the false-positive probability distribution of the threshold range via Monte-Carlo simulations (Ward, 2000) for the 3n  36 matrix. In addition, recent studies have shown that functional connectivity, especially among DMN nodes, is significantly affected by head motion (Power et al., 2012; Satterthwaite et al., 2012; Van Dijk et al., 2012). To rule out this confounding factor, we replicated the correlation among three network metrics and the MAAS score by controlling for head motion. Specifically, we calculated the mean head motion that represented the mean absolute displacement of each brain volume compared to the previous volume in three translation motion parameters (Van Dijk et al., 2012). Confounding effect may also come from age. For example, the functional connectivity property of the brain changes with age (6–30 years of age) (Dosenbach et al., 2010). To examine whether the correlation results were influenced by the age of the participants, we recalculated the correlation between three network metrics and the MAAS score, with age as a confounding covariate.

RESULTS Behavioral results The MAAS score was used as a self-reported measure of a participant’s trait mindfulness, with higher scores indicating greater mindfulness. The kurtosis (0.036) and skewness (0.132) of the MAAS scores ranged between 1 and +1, which indicated the normality of the data (Marcoulides and Hershberger, 1997). The internal consistency of the MAAS was also satisfying (Cronbach’s a = 0.84). In addition, there was a significant amount of individual differences in self-reported trait mindfulness among participants, with scores ranging from 2.20 to 5.53 (mean = 3.80; SD = 0.59). There was no significant difference in trait mindfulness between men and women (t(241) = 0.91, p = 0.36). Next, we explored the network property of the DMN that underlies individual differences in trait mindfulness. Connectivity in the DMN To identify the DMN, we explored brain regions that showed significant positive correlations with the PCC (p < 0.001, FDR corrected) when participants relaxed without engaging in any specific task (i.e., resting-state) (Fox et al., 2005; Fransson, 2005). As shown in Fig. 1, the DMN consisted of the PCC, MPFC, left and right superior frontal gyrus (SFG), left and right LPC, left and right LTC, left and right PHG, and thalamus (Talairach Daemon Labels in FSL), which is in agreement with the DMN identified in previous studies (e.g., Raichle et al., 2001; Fox et al., 2005; Fransson, 2005; Buckner et al., 2008; Raichle, 2011; Mantini and Vanduffel, 2013). Table 1 shows the MNI coordinates and z-values of the peak voxel of the 11 identified DMN nodes (peaks with z-value larger than 14). Relationship between DMN and trait mindfulness After identifying the DMN, we examined which DMN edges predicted self-reported trait mindfulness. Correlation analyses were performed on all 55 edges constructed from the 11 DMN nodes (see Methods and Materials) and MAAS scores across participants. We found that the edge connecting the thalamus and PCC was negatively correlated with the MAAS score (r = 0.23, p < 0.001, Bonferroni corrected p < 0.05), indicating that individuals with weaker functional connectivity between the thalamus and PCC were more mindful of the present (Fig. 2). To examine the reliability of this finding, we randomly assigned the participants to one of two groups (group 1: 120 participants; group 2: 123 participants). A correlation between the thalamus– PCC functional connectivity and MAAS score was observed in both groups (group1: r = 0.26, p = 0.004; group 2: r = 0.20, p = 0.03). Furthermore, the thalamus node was located mainly in the right hemisphere, which may possibly because the anatomical seed PCC taken from the Harvard–Oxford atlas to construct the DMN is not exactly symmetrical between both hemispheres, with more voxels in the right (400 voxels) than in the left hemisphere (335 voxels).

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Fig. 1. The DMN identified by the positive functional connectivity with the seed at the PCC (p < 0.001, FDR corrected). There were 11 DMN nodes in total shown as red dots in the brain. Color bar shows the z value of functional connectivity. PCC, posterior cingulate cortex; MPFC, medial prefrontal cortex; SFG, superior frontal gyrus; LPC, lateral parietal cortex; LTC, lateral temporal cortex; PHG, parahippocampal gyrus. The visualization was provided by BrainNet Viewer (http://www.nitrc.org/projects/bnv/). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Table 1. Descriptive information of DMN nodes DMN nodes

PCC MPFC R.LPC L.LPC R.SFG L.SFG R.LTC L.LTC L.PHG R.PHG Thalamus

MNI Coordinates x

y

z

6 2 48 42 24 22 60 60 26 28 4

46 44 60 70 34 30 2 14 36 34 10

22 6 30 34 44 40 24 20 14 12 0

Peak z value

Brodmann’s areas

27.0 20.4 20.9 20.3 18.3 16.9 17.7 16.8 17.4 17.1 15.2

BA 23, 29, 30, 31 BA 9, 10, 11, 24, 32 BA 39 BA 39 BA 6, 8 BA 6, 8 BA 20, 21 BA 20, 21 BA 35, 36 BA 35, 36 Not available

PCC, posterior cingulate cortex; MPFC, medial prefrontal cortex; SFG, superior frontal gyrus; LPC, lateral parietal cortex; LTC, lateral temporal cortex; PHG, parahippocampal gyrus. L, left hemisphere; R, right hemisphere; BA, Brodmann’s area.

Note that the lateralization of the thalamus node relative to the PCC node unlikely accounted for the observed association, because the functional connectivity between the thalamus node and a PCC node in the left hemisphere based on the second maximal voxel (MNI: 4, 44, 24) of the PCC in the group DMN was also negatively correlated with the MAAS score (r = 0.25, p < 0.0001). Finally, the correlations between the other edges and MAAS score were not significant (in all cases, p > 0.05, uncorrected). To determine whether the thalamus or PCC node was the relevant node of the DMN involved in mindfulness, we examined the graph-based relationship between each of these nodes (the thalamus or PCC) and the rest of the DMN nodes in response to trait mindfulness. Three

graph-based metrics including nodal degree, nodal efficiency, and nodal clustering coefficient of the two nodes were correlated with the MAAS score across participants. For the thalamus, we found that all three nodal metrics were able to predict self-reported trait mindfulness. First, the nodal degree of the thalamus was negatively correlated with the MAAS score (r = 0.19, p = 0.002, Bonferroni corrected p < 0.05), indicating that individuals whose thalamus had fewer connections with other DMN nodes were more mindful. Second, the nodal efficiency that indexes the importance of the thalamus for communication within the DMN was also negatively correlated with the MAAS score (r = 0.17, p = 0.009, Bonferroni corrected p = 0.05). Finally, the nodal clustering coefficient that

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Fig. 2. The DMN and mindfulness. (A) Correlations between DMN edges and mindfulness indexed by MAAS score. The thickness of lines indicates the magnitude of Pearson’s correlation coefficients between DMN edges and MAAS score. The thickest line is the edge connecting the thalamus and PCC. Edges with low correlation coefficients (the absolute value is less than 0.05) are not shown. (B) Scatter plot between the MAAS score and thalamus-PCC edge across participants.

describes the local interconnectivity and aggregation of the sub-network constructed by neighboring nodes of the thalamus was also negatively correlated with the MAAS score (r = 0.20, p = 0.002, Bonferroni corrected p < 0.05). In contrast, the nodal metrics of the PCC were not significantly correlated with the MAAS score (nodal degree: r = 0.06, p = 0.35; nodal efficiency: r = 0.09, p = 0.16; nodal clustering coefficient: r = 0.05, p = 0.47). In short, the more the thalamus was isolated from the other DMN nodes, the more attentive and aware the individual was to the present. However, there are several confounding factors that need to be ruled out. First, in the graph-based analysis we used a predetermined threshold (i.e., T = 0.3) to binarize the DMN edges, so it is possible that our result is not robust and might have depended on the threshold selected. To rule out this alternative, we tested a wide range of thresholds ranging from 0.25 to 0.60. The Monte-Carlo simulation showed that if correlations between the nodal metric of the thalamus and mindfulness were significant (p < 0.05) at more than three consecutive thresholds (i.e., TRL > 3), then the correlations were unlikely accounted for by noise. Here, we found that in a wide range of thresholds, the nodal degree (thresholds from 0.25 to 0.40, TRL = 16), nodal efficiency (thresholds from 0.29 to 0.44, TRL = 16), and nodal clustering coefficient (thresholds from 0.25 to 0.34, TRL = 10) of the thalamus were significantly correlated with the MAAS score (Fig. 3). Therefore, the relationship between the thalamus and mindfulness was stable and largely insensitive to the threshold selection. Second, we replicated the previous analysis after controlling for head motion estimated from the mean absolute displacement of adjacent volumes (Van Dijk et al., 2012), which yielded similar results. That is, correlations between the nodal metrics of the thalamus and mindfulness remained significant (nodal degree: r = 0.19, p = 0.004; nodal efficiency: r = 0.16,

p = 0.01; nodal clustering coefficient: r = 0.19, p = 0.003). Therefore, head motion had little impact on the relationship between the thalamus and mindfulness. Third, we recalculated the correlation between three network metrics and the MAAS score with age as a confounding covariate, and the correlation results were still significant after controlling for age (nodal degree: r = 0.19, p = 0.003; nodal efficiency: r = 0.17, p = 0.01; nodal clustering coefficient: r = 0.20, p = 0.002). Finally, the correlation between the thalamus and mindfulness remained significant even after controlling for gender (nodal degree: r = 0.19, p = 0.003; nodal efficiency: r = 0.17, p = 0.01; nodal clustering coefficient: r = 0.19, p = 0.003), which is consistent with the behavioral observation of no gender difference in mindfulness.

DISCUSSION In this study, we used graph-based network analyses to identify the relevant node of the DMN involved in our awareness of present experiences. Three network metrics including nodal degree, nodal efficiency, and nodal clustering coefficient converged to demonstrate that a low involvement of the thalamus within the DMN was relevant for high trait mindfulness. That is, if the spontaneous neural activity in an individual’s thalamus was weakly correlated with those of other DMN nodes, the individual was more mindful. Previous neuroimaging studies on mindfulness have identified multiple regions of the DMN, including the PCC, MPFC, LPC, LTC, PHG, and thalamus, that are involved in mindfulness (Holzel et al., 2008; Luders et al., 2009; Brewer et al., 2011; Dickenson et al., 2013; Leung et al., 2013; Shaurya Prakash et al., 2013). Our study extends these findings by providing the first empirical evidence of how these DMN regions work collaboratively at the network level where the thalamus is the relevant node for trait mindfulness.

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Fig. 3. Correlation coefficients between the MAAS score and nodal network metrics of the thalamus as a function of thresholds used to binarize DMN edges (top, nodal degree; middle, nodal efficiency; bottom, nodal clustering coefficient). Dotted horizontal lines indicate that correlations reached significance level (r = 0.126, p = 0.05), and shaded areas represent significant threshold range length (TRL).

Our results fit strikingly with the functionality of the thalamus. A large body of evidence suggests that the thalamus is the generator of the alpha rhythm (8–12 Hz) (Ohmoto et al., 1978; Schreckenberger et al., 2004; Hughes and Crunelli, 2005), which is evident during mind-wandering or spontaneous self-referential thoughts (Knyazev et al., 2011; Macdonald et al., 2011; Ros et al., 2013). Further, the alpha rhythm can modulate the connectivity of the DMN (Mantini et al., 2007; Jann et al., 2009; Hlinka et al., 2010; Ros et al., 2013), especially during mind-wandering (Ros et al., 2013). Taken together with our finding of the negative correlation between the network properties of the thalamus and mindfulness, it is possible that this thalamic alpha rhythm, which may propagate to other DMN nodes through functional connectivity, is the neural source for mindwandering. Moreover, the excessive connectivity between the thalamus and other DMN nodes may further maintain the state of mind-wandering. The thalamus is a crucial component not only in the DMN, but also in the ascending reticular activating system (ARAS) (Maldonato, 2014), which is the relay system comprising the reticular formation, thalamus, and cerebral cortex, responsible for governing wakefulness and vigilance (Kinomura et al., 1996; Maldonato, 2014). Importantly, the ARAS and the DMN are two competing systems within a ‘‘global workspace’’ model of consciousness (Dehaene and Naccache, 2001; Baars et al., 2003; Silva et al., 2010), with the ARAS modulating conscious awareness to external stimuli (Maldonato, 2014) and the DMN involved in self-referential processing (Northoff et al., 2006), respectively. As the thalamus plays a crucial role in both the DMN and the ARAS, it may serve as a switch

between different brain states, especially between mind-wandering and mindfulness, through its functional interaction with the two systems. For example, during the transition from wakefulness to sleep, the thalamus generates spindle waves (Steriade et al., 1993; Bal et al., 1995; McCormick and Bal, 1997), and stimulating the thalamus helps to recover consciousness in patients with traumatic brain injury (Schiff et al., 2007; Giacino et al., 2012). Evidence that is more direct comes from the studies showing that the thalamus is activated during the shift from mind-wandering to attention (Kinomura et al., 1996; Hasenkamp et al., 2012). Therefore, the excessive connectivity between the thalamus and other DMN regions may decrease the ability of the ARAS to regulate bottom-up arousal and make it more susceptible to distracting influences of arousal, and thus associated with more mind-wandering and less mindfulness in individuals. Further studies are awaited to investigate how the mindfulness is related to the functional interaction between the DMN and the ARAS through the thalamus. On the other hand, the thalamus may also be associated with mindfulness through emotion processing. Recent studies have shown that mindful individuals are not only less depressed and anxious, but also more optimistic and satisfied (Brown and Ryan, 2003; Baer et al., 2006; Feldman et al., 2007; Cardaciotto et al., 2008; Howell et al., 2010; Deng et al., 2012). In contrast, people are less happy when their minds are wandering than when they are not (Killingsworth and Gilbert, 2010). Considering that the thalamus is involved in processing of negative emotions, especially depression (Marchand et al., 2011, 2012; Diener et al., 2012; Sexton et al., 2013), the thalamus and its connectivity with the DMN may provide the neural

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basis for the mechanism that mindfulness exerts benefits on emotion and well-being (Holzel et al., 2011b).

CONCLUSION Our study showed that individual differences in trait mindfulness were associated with functional connectivity properties of the thalamus within the DMN. This finding contributes to the understanding of network-level neural mechanisms related to trait mindfulness. However, there are several limitations in the current study which need to be addressed by future research. First, trait mindfulness was measured by self-report questionnaire in our study, which may partly reflect subjective perception and evaluation of individual’s actual mindfulness. Nevertheless, several studies have shown that selfreported mindfulness measured by the MAAS is negatively correlated with objective measures of mindwandering (e.g., errors in the Sustained Attention to Response Task) (Deng et al., 2012; Mrazek et al., 2012), supporting the validity of using the MAAS as a measure of mindfulness. Future research is needed to further investigate the association between the DMN and objective measures of mindfulness. Second, the narrow age range of our participants (SD = 1.05) limits the generalizability of the results to other age population. Future studies with a wider age range are needed to explore the association between mindfulness and the functionality of the DMN as a function of age. Third, the cross-sectional nature of our findings does not permit inferences about the causal relationships between functional connectivity of the DMN and individual differences in trait mindfulness. Future longitudinal studies should provide a better understanding of the causal links between brain function and trait mindfulness. Fourth, the thalamus contains multiple nuclei, each of which has a distinct function and connectivity pattern with multiple cortical regions (e.g., Jones, 2007). Future work is needed to illustrate the exact nuclei of the thalamus that contributes to mindfulness, as well as the specific cognitive functions contributing to mindfulness. Finally, future studies are needed to examine the relationship between trait mindfulness and more constructs such as personality, impulsivity, perceived stress, and positive and negative affect, to illuminate the neural basis underlying which mindfulness produces its beneficial effects through its relationship with these constructs.

AUTHOR CONTRIBUTIONS Jia Liu conceived the experiment; Xu Wang, Miao Xu, Yiying Song, Xueting Li, Zonglei Zhen, and Zetian Yang performed the experiment; Xu Wang analyzed the data; Xu Wang, Miao Xu, Yiying Song, and Jia Liu wrote the paper.

COMPETING FINANCIAL INTERESTS The authors declare no competing financial interests. Acknowledgments—This study was funded by the National Social Science Foundation of China (11&ZD187) and National Natural Science Foundation of China (31221003, 30800295).

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(Accepted 7 August 2014) (Available online 15 August 2014)