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Journal of Cognitive Enhancement

, Volume 1, Issue 2, pp 228–234 | Cite as

Differences in Default Mode Network Connectivity in Meditators and Non-meditators During an Attention Task

  • Elisa H. Kozasa
  • João R. Sato
  • Tamara A. Russell
  • Maria A. M. Barreiros
  • Shirley S. Lacerda
  • João Radvany
  • Luiz E. A. M. Mello
  • Edson AmaroJr
Original Article

Abstract

Activity in the default mode network (DMN) is reduced during non-self-referential goal-directed tasks in healthy individuals. In this study, we investigated differences in DMN functional connectivity between regular meditators and non-meditators during an attention paradigm. Non-meditators and regular meditators, matched by age, years of education, and gender were instructed to name the color of single words visually presented in a Stroop Word-Color Task (SWCT) adapted for functional magnetic resonance imaging (fMRI). The task was performed when the participants were not formally meditating. Logistic analysis based on imaging data indicated that the connectivity between the PCC (precuneus/posterior cingulate cortex) and the right and left parietal lobules helps differentiating regular meditators from non-meditators. Granger causality results showed that the activity in the PCC contains information to predict the activity in the right lateral parietal cortex and that the accuracy in this prediction is higher in regular meditators when compared to non-meditators. This suggests a stronger link between these two regions in regular meditators. In contrast to regular meditators, the PCC is more influenced by the left parietal region (related to the process of reading—which is the interference in the SWCT), and this region is more influenced by the PCC in non-meditators. These functional connectivity differences in the DMN between groups possibly reflect a higher degree of interference and probably more distraction during the SWCT in non-meditators compared with meditators.

Keywords

Meditation Attention Default mode Stroop task fMRI 

Introduction

The default mode network (DMN) is a set of areas in the human brain collectively characterized by functions which have a self-referential nature. Some of the core regions related to the DMN are the medial prefrontal cortex (MPFC), the precuneus/posterior cingulate cortex (PCC), the inferior parietal lobule (IPL), the lateral temporal cortex (LTC), and the hippocampal formation (HF) (Raichle et al. 2001; Buckner et al. 2008). Activity in DMN is reduced during non-self-referential goal-directed tasks in healthy individuals (Gruberger et al. 2011). This reduction may indicate the existence of an organized, baseline default mode of brain function, which is suspended during specific goal-directed behaviors (Raichle et al. 2001). However, the exact nature of DMN is unclear. Activity in DMN may reflect the occurrence of mind-wandering, i.e., cognitive processes unrelated to the current task and decoupled from current sensory information (stimulus-independent thought). It may also reflect enhanced watchfulness in relation to external environment (e.g., waiting for upcoming task-relevant stimuli during resting state or stimulus-oriented thought). Moreover, activity in DMN may reflect a combination of both (Gilbert et al. 2007).

Although DMN is active in internal modes of cognition, its activation decreases during the performance of cognitively demanding tasks. Indeed, previous reports have shown reduced blood-oxygen-level dependent (BOLD) response of the DMN regions during cognitive demanding tasks such as the Stroop Word-Color Task (SWCT) (Fox et al. 2005; Sridharan et al. 2008; Harrison et al. 2005). The SWCT involves neural circuits subserving attention, working memory, response selection, and inhibition, among others (Harrison et al. 2005; Peterson et al. 1999).

Meditation is another cognitively demanding task. It is a form of mental training which results in attention regulation, body awareness, emotion regulation, and change in the perspective of the self (Hölzel et al. 2011; Slagter et al. 2007).

Meditation training improves attention-related behavioral responses, most likely enhancing functioning of the subcomponents of attention, including attention allocation and processing of new information (Jha et al. 2007; Slagter et al. 2009). Comparing mindfulness expert meditators with naïve-meditators, some authors found that expert meditators performed significantly better on measures of Stroop interference than naïve ones in all measures of attention and self-reported mindfulness (Moore and Malinowski 2009).

In an activation likelihood estimation (ALE) meta-analysis of meditation studies, the subtraction of meditation condition from a control task showed DMN regions decreased activity, probably due to reduced mind-wandering (Tomasino et al. 2013). A functional magnetic resonance imaging (fMRI) study, during resting state, showed that the amplitude of spontaneous fluctuations in long-term mindfulness meditation (MM) practitioners was significantly enhanced in the visual cortex and reduced in DMN compared to naïve controls. During a visual recognition memory task, the MM group showed increased visual cortex responsivity and weaker negative responses in DMN areas. Mindfulness meditators had significantly faster behavioral performance than controls. Results showed, therefore, opposite changes in the visual and default mode systems in long-term meditators during rest and task (Berkovich-Ohana et al. 2016a, 2016b). Another study with the same sample showed that DMN functional connectivity is reduced during meditation, compared to resting state; a significant negative correlation was found between DMN functional connectivity and meditation expertise (Berkovich-Ohana et al. 2016a, 2016b).

In a comparison between experienced and novice meditator groups using an event-related fMRI paradigm with distracting sounds versus silence in meditation versus resting state, expert meditators had less involvement of the DMN regions (related to task-irrelevant thoughts), such as the PCC and the MPFC, and were less distracted by the sounds than the novices (Brefczynski-lewis et al. 2007). Another fMRI study using a simplified meditative condition interspersed with a lexical decision task compared to regular Zen practitioners and matched control subjects. The study aimed at investigating neural correlates of conceptual processing during meditation. Behavioral performance was not different between groups. However, Zen practitioners displayed a reduced duration of neural response linked to conceptual processing in DMN regions, suggesting meditative training might foster the ability to voluntarily regulate the flow of spontaneous mentation (Pagnoni et al. 2008).

In order to understand the relationship between focused attention meditation and activity in DMN regions, one study proposed the comparison between internal and external attention, as well as among different phases within one meditation practice: mindful attention, mind-wandering, and refocusing. Meditation naïve participants were trained in external (focused on a sound) and internal (focused on breathing) attention meditation for 4 days. They then performed the focused attention meditations during fMRI scanning. At pseudorandom intervals, participants were asked whether they had stayed focused exclusively on the task or had been distracted. During mindful attention, brain regions typically associated with the DMN showed significantly less neural activation compared to mind-wandering phases. Reduced activity of DMN was found during both external and internal attention, with stronger deactivation in the posterior cingulate cortex during internal attention compared to external attention (Scheibner et al. 2017).

The objective of this study was to compare functional connectivity within DMN regions in meditators and non-meditators during the SWCT. Considering that meditation is a cognitively demanding practice, which trains attention, we hypothesized that regular meditators would present decreased connectivity within DMN regions during the SWCT compared with non-meditators as a result of less interference in the attention process.

Materials and Methods

Participants

We selected 39 right-handed participants: 19 non-meditators (without previous practice of meditation or whose practice was not considered extensive and with a frequency lower than once a week) and 20 regular meditators (with at least 3 years of practice, three times a week) matched by age, years of education, and gender. There were eight and nine males in each group, respectively. Data from these participants were also presented in a previous study (Kozasa et al. 2012). The mean time of practice for regular meditators was 8.53 years (SD = 4.07). Modalities of meditation practices reported by regular meditators were Zen (N = 9) (traditional Japanese meditation focused on the sensations and contents of the mind), mindfulness of breathing (N = 6) (focused on breathing), and kriya meditation (N = 5) (focused on gentle yoga postures and breathing exercises).

Participants’ mean ages were 46.39 years for regular meditators and 43.80 years for non-meditators (SD = 9.30 and 9.35, respectively). Most participants were university graduates and post graduates. All participants provided written informed consent, and the protocol was approved by the Ethics Committee of Instituto Israelita de Ensino e Pesquisa Albert Einstein, São Paulo, Brazil.

Stroop Word-Color Task

Subjects were instructed to name the color (red, blue, or green) of single words presented in three conditions: congruent, neutral, and incongruent (Carter et al. 1995). Firstly, participants were familiarized with the task before being subjected to the fMRI sessions. After viewing the word for 1 s, the participant had to select one of the three colored buttons representing red, blue, or green colors using three fingers of their right hand. In the SWCT congruent condition, word and color are the same, and thus, the word “red” is written in red color. In the neutral condition, a single word which is not related to any color (e.g., house) is written in red, blue, or green color. In the incongruent condition, word and color are not the same. For example, the word “red” is written in green and, therefore, the correct answer is “green”. In this block-designed fMRI paradigm, each condition was presented in epochs of 10 trials for six sets of the sequence congruent-neutral-incongruent conditions, in a total of 180 trials.

Image Acquisition

Participants were evaluated during an fMRI adapted SWCT task using a block design. Image acquisition (3.0 T MR system—Siemens Tim Trio, 12ch head coil), visual stimuli presentation via goggles (NNL systems), and participant response were synchronized (NNL systems, www.nordicneurolab.com). fMRI acquisition was based on T2*-weighted echo planar (EPI) images for the whole brain. Acquisition parameters were EPI GRE T2—BOLD PACE: TR = 2000 ms, TE = 50 ms, 32 slices, 3.3 mm of slice thickness, 0.5 mm of interslice gap, FOV = 200 mm, and matrix 64 × 64, 3 mm3 voxels, with 180 volumes (first four EPI volumes were discarded to prevent T1 saturation effects). Each word stimulus was presented for 1 s interspersed by the appearance of a fixation cross for 1 s. Total task time is 6 min.

Image Processing

Images were processed using FSL toolbox (freely available and documented at www.fmrib.ox.ac.uk/fsl/). The fMRI data was processed by temporal filtering (high pass filter cutoff = 100 s), motion correction (MCFLIRT), BET brain extraction, spatial smoothing (full width at half maximum—FWHM = 8 mm), and spatial normalization (affine transformation, 12° of freedom) to standard space using the MNI152 template (http://nist.mni.mcgill.ca/?p=858).

Functional connectivity analysis was carried out in three steps: (i) independent component analysis to identify regions of interest (clusters) representing DMN; (ii) Granger analysis to estimate link strength, and (iii) logistic regression to compare regular meditators and non-meditators. It is worth noticing that even though Granger causality analyses were carried out using signals extracted from regions defined by independent component analysis (ICA) maps, this was not a double dipping analysis. Note that ICA was conducted considering all subjects of the sample (regular meditators and non-meditators) as a single group. Since Granger causality analyses were performed regardless of group comparison, there is no bias towards group differences.

In order to identify neural systems present during SWCT, we applied the ICA to all imaging data collected during the fMRI run. It is important to mention that ICA analyzes the components from the whole run, not discriminating between experimental conditions. Tensor ICA was performed using FSL in a joint group analysis, since the experimental design and paradigm were the same for all subjects (Beckmann and Smith 2005). Fifteen ICA maps were extracted in this analysis using FSL MELODIC ICA pipeline (variance-normalized timecourses, IC maps threshold p > 0.5) and visually inspected in descending order of variance contribution. The first component in which the thresholded map unequivocally highlighted DMN regions (precuneus/PCC, medial frontal and bilateral parietal cortices) was chosen as DMN representative (see Table 1 and Fig. 1). These four regions of interest (ROIs) were then used for connectivity analysis. It is important to stress that the main aim of performing an ICA on this data was to show that we could identify the presence of a common component spread on DMN. A secondary purpose was to use the identified DMN nodes in the ICA map to delimit functional ROIs for this network (instead of using pre-specified coordinates). In addition, it is important to mention that both ICA and connectivity analysis were carried out using BOLD signal of the whole session of each subject and, thus, results are not discriminated by conditions of SWCT. Our main concern was not to compare connectivity conditions but rather to compare regular meditators and non-meditators groups. The task was used solely to modulate different levels of attentional demand, which lead to changes in BOLD signal. In other words, we were not interested in quantifying connectivity strength by exploring random fluctuations of signal at each condition, but in estimating the strength of links derived from variations of BOLD signal induced by the task.
Table 1

Peak coordinates of the DMN ROIs identified in ICA maps

MNI coordinates

   

X

Y

Z

z value

Extent (voxels)

Region

50

−58

36

3.946

167

Right parietal cortex

−10

−42

36

4.796

515

Precuneus/posterior cingulate cortex

−14

54

32

6.124

2168

Medial prefrontal cortex

−54

−58

32

5.399

415

Left parietal cortex

Fig. 1

DMN regions identified in tensor independent components analysis (ICA) (N = 39). LTC lateral temporal cortex, MPFC medial prefrontal cortex, PCC precuneus/posterior cingulate, IPL inferior parietal lobule. Color bar represents the ICA Z-Score

The connectivity analysis was then carried out using the bivariate Granger causality analysis between the average BOLD signals of the chosen ROIs (Roebroeck et al. 2005) in order to quantify the link strength. Granger causality allows quantification and identification of the direction of influence by exploring the temporal relationships between signals (Roebroeck et al. 2005). A region “A” is said to Granger-cause a region “B”, if the past values of signal from “A” help on predicting the present and future values of “B”. In other words, Granger causality uses time precedence as a feature to identify temporal precedence. In this paper, we analyzed the connections to and from PCC (our seed ROI), since it is found to play a pivotal role in DMN (Fransson and Marrelec 2008). For each pair of region (PCC and three ROIs), an autoregressive bivariate vector model was fit, which is the most common approach applied to measure Granger causality.

The comparison of the connectivity structure between regular meditators and non-meditators was carried out using a single logistic regression (response variable: regular meditator vs. non-meditator). Connections (z values from Granger causality analysis) from and to PCC were included as predictor variables of this logistic regression. Thus, the significance of regression coefficients can be used to evaluate whether the connectivity strength between the nodes of DMN and PCC contains information to predict whether a subject is a regular meditator or not. Statistical significance level was set to uncorrected p value < 0.05.

Results

To identify functional networks present during SWCT, we applied ICA to all imaging data collected during the fMRI run. Results from ICA maps showed all DMN regions in component 8 (from a descending rank of variance contribution, the ICA maps of the first seven components were unequivocally not related to DMN) obtained from all subjects included in the analysis. The ICA coefficients group map is shown in Fig. 1.

Table 1 represents peak coordinates of ROIs identified in ICA maps, highlighting the right parietal cortex, precuneus/posterior cingulate cortex, medial prefrontal cortex, and left parietal cortex.

Table 2 shows results of the logistic regression analyses based on Granger causality z values as predictor variables. We acknowledge that the findings do not survive for multiple comparisons correction. However, from the six possible connections evaluated, three presented a p value below 5%. In regular meditators, the PCC presented more influence over the right inferior parietal lobule than in non-meditators. In contrast, in non-meditators, the PCC had more influence and was also more influenced by the left inferior parietal lobule than in meditators. Thus, we found differences in connectivity patterns between PCC and parietal regions between the two groups.
Table 2

Logistic regression parameter estimates using group (regular meditator vs non-meditators) as response variable and Granger causality z values of PCC connections as predictors

Connection

Slope

S.E.

p value

PCC to right IPL

0.925

0.452

0.041

PCC to MPFC

0.351

0.320

0.274

PCC to left IPL

−0.728

0.341

0.033

PCC from right IPL

0.073

0.299

0.807

PCC from MPFC

0.088

0.243

0.718

PCC from left IPL

−0.790

0.386

.041

A positive coefficient sign indicates that the strength of influence (in Granger causality sense) is higher in meditators than in non-meditators. A negative coefficient indicates the opposite. Statistical significance at 0.05

S.E. standard error, PCC precuneus/posterior cingulate cortex, IPL inferior parietal lobule, MPFC medial prefrontal cortex

Analysis of head movement differences between meditators and non-meditators during the fMRI acquisition did not show statistical difference (Student’s t test, regarding mean relative displacement p = 0.435 and for mean absolute displacement p = 0.733).

Behavioral data of SWCT from this same acquisition did not show differences in the Stroop effect (reaction time contrast [incongruent minus congruent]) between groups (regular meditators: 823.64 ± 97.66; non-meditators: 809.62 ± 152.25; p = 0.736). No difference was found between regular meditators and non-meditators (regular meditators: 9.50 ± 0.51; p = 0.833; non-meditators: 9.43 ± 0.73; p = 0.954) (Kozasa et al. 2012).

Discussion

We aimed at investigating whether functional connectivity between DMN brain regions was different comparing meditators and non-meditators during the attention paradigm SWCT.

Although we still do not have a single, widely accepted explanation for DMN, it has been associated with mind-wandering or “disruptions” during attentional tasks (Peterson et al. 2009). Activity in the DMN may reflect the occurrence of mind-wandering (stimulus-independent thoughts). It may also reflect enhanced watchfulness in relation to external environment. Moreover, activity in DMN may reflect a combination of both (Gilbert et al. 2007). In addition, a lack of attention during an attention task is related to increased DMN activity (Weissman et al. 2006). Meditators who train attentional skills are probably less distracted by mind-wandering or external environment than non-meditators and may keep attention on the tasks at hand. For example, in an fMRI paradigm with distracting sounds during meditation and rest blocks, expert meditators had less involvement of DMN regions (related to task-irrelevant thoughts), such as the PCC, and were less distracted by distracting sounds than novices (Brefczynski-lewis et al. 2007). It is possible that expert meditators could maintain sustained attention on the given task and, thus, be less captured by distracting sounds.

Comparing experienced meditators and matched naïve controls, as they performed concentration, loving-kindness and choiceless awareness meditation practices, medial prefrontal, and posterior cingulate cortices were relatively deactivated in experienced meditators across all meditation practices. Functional connectivity analysis indicated stronger coupling between posterior cingulate, dorsal anterior cingulate, and dorsolateral prefrontal cortices at baseline and during meditation in experienced meditators (Brewer et al. 2011). These findings are consistent with decreased mind-wandering. Our study differs from these reports, since we observed that regular meditators differ from non-meditators in DMN brain regions during an attention task in which participants were not meditating or instructed to meditate.

A study comparing meditation to an active cognitive task (judgments about adjectives in response to a cue) indicated that meditation is associated with reduced activations in the DMN regions (posterior cingulate/precuneus and anterior cingulate cortex) relative to the task in meditators compared to controls. These findings suggest that meditation practice leads to relatively reduced DMN processing (Garrison et al. 2015). In a study investigating whether a Mindfulness Based Stress Reduction (MBSR) course modulates neural representation networks of interoception, untrained participants, and MBSR trained participants had their performance compared during an interoceptive attention task (breath attention) and an exteroceptive attention task (cognitive suppression and working memory maintenance). Following MBSR, dorsomedial prefrontal cortex (DMPFC) showed interoceptive attention-specific negative connectivity to primary interoceptive cortex (posterior insula) (Farb et al. 2013). Deactivation of DMPFC is related to DMN disengagement, which happens in tasks requiring cognitive control (Seeley et al. 2007). Another study showed that DMN functional connectivity is reduced during meditation, compared to resting state. Moreover, a significant negative correlation was found between DMN functional connectivity and meditation expertise (Berkovich-Ohana et al. 2016a, 2016b).

Some authors state that mindfulness practice enhances functional connectivity within attentional networks as well as increases connectivity across distributed brain regions subserving attention, self-referential, and emotional processes. They observed increased functional connectivity, such as increased connectivity between dorsal attention network (DAN) and DMN, during meditation as opposed to rest. Mindfulness meditation appears to involve a shift from a functionally restricted default mode during rest into a more functionally integrated large-scale network subserving attention, salience, and self-reflection (Froeliger et al. 2012). Meditation naïve participants, trained in external and internal attention meditation, performed focused attention meditations during fMRI scanning. During mindful attention, brain regions typically associated with DMN showed significantly less neural activation, compared to mind-wandering (Scheibner et al. 2017).

We hypothesized that regular meditators would present decreased connectivity within DMN regions during SWCT, compared with non-meditators, as a result of less interference in the attention process. To test this hypothesis, we chose PCC as a ROI, because it is one of the key structures of DMN (Fransson and Marrelec 2008). PCC is an active region of the brain, which may continuously gather information about the world around and within us. When task performance demands focused attention, PCC activity is interrupted (Raichle et al. 2001). Table 1 indicates that the strength of Granger causality between PCC and other DMN regions may help discriminating the two groups. In regular meditators, PCC has greater influence over the right parietal region than in non-meditators. However, connectivity strength between PCC and left parietal region is greater in non-meditators than in regular meditators. Considering the important role of the left inferior parietal lobule in reading (van der Mark et al. 2011), it is plausible to suggest that non-meditators have more interference of word reading (the distractor in SWCT), compared to regular meditators, although non-meditators and meditators have the same performance in SWCT, probably due to the high educational level of our sample, which resulted in a ceiling effect. However, the right parietal region is more activated by subjects with non-verbal style. In contrast, subjects with a verbal style activate more the left parietal region (Gevins and Smith 2000). This fact reinforces the hypothesis of non-meditators having more interference from reading words. Contrasting connectivity maps between participants with high versus low meditation experience, more experienced meditators exhibited increased connectivity within attentional networks and between attentional regions and medial frontal regions. These neural networks are probably involved in the development of cognitive skills, such as maintaining attention and disengaging from distraction. Considering this altered connectivity of brain regions in experienced meditators was observed in a non-meditative state (the resting state), this may represent a transfer of cognitive abilities developed during formal meditation practice into daily life (Hasenkamp and Barsalou 2012). Results of our study point to the same direction: connectivity in regular meditators reflects less distraction or less interference during SWCT, outside the formal meditation practice. Simon and Engström (2015) have also suggested that DMN can be a biomarker to evaluate meditation effects on the treatment of mental disorders, such as anxiety or attention-deficit hyperactivity, because DMN abnormalities have been associated with the severity of their symptoms, and, on the other hand, meditation practice has been associated with DMN modulation.

One of the aims of this study was focused on DMN connectivity, and we investigated this aspect in one of its important ROIs, PCC. New studies with regular meditators and non-meditators, comparing other ROIs in attentional sub-systems and DMN during different attention probes may bring additional information to our results. Failure to suppress mental processes associated with medial DMN regions is present in attention-deficit and hyperactive disorder (ADHD) (Peterson et al. 2009). Depressed patients seem to exhibit a failure to normally downregulate activity within DMN (Sheline et al. 2009). We suggest that clinical implications of meditation training should be evaluated in these groups of patients, and we also speculate that meditation could be a strategy to control the automatic cascade of thoughts which interrupts one’s engagement in meaningful activities. Considering our results and the literature regarding meditation effects on attention, we conjecture that regular meditators are less likely to have disruptions from the object of attention even when not formally meditating.

Notes

Acknowledgments

This work was supported by Instituto Israelita de Ensino e Pesquisa Albert Einstein—IIEPAE, Fundação de Amparo à Pesquisa do Estado de São Paulo—FAPESP, Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq (476288/2009-6), and Associação Fundo de Incentivo à Pesquisa—AFIP. Authors would like to thank Coen sensei, Zendo Brasil staff for discussing the inclusion/exclusion criteria, Marta O. S. Freitas for helping with the recruitment of volunteers for this study, and Liana G. Sanches for the technical support.

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Copyright information

© Springer International Publishing 2017

Authors and Affiliations

  • Elisa H. Kozasa
    • 1
    • 2
  • João R. Sato
    • 3
    • 4
  • Tamara A. Russell
    • 5
  • Maria A. M. Barreiros
    • 1
  • Shirley S. Lacerda
    • 1
  • João Radvany
    • 6
  • Luiz E. A. M. Mello
    • 7
  • Edson AmaroJr
    • 1
    • 3
  1. 1.Instituto do CérebroHospital Israelita Albert EinsteinSão PauloBrazil
  2. 2.Department of PsychobiologyUniversidade Federal De São PauloSão PauloBrazil
  3. 3.Universidade Federal do ABCSanto AndréBrazil
  4. 4.Instituto de RadiologiaUniversidade de São PauloSão PauloBrazil
  5. 5.Institute of Psychiatry, King’s College LondonLondonUK
  6. 6.Department of ImagingHospital Israelita Albert EinsteinSão PauloBrazil
  7. 7.Department of PhysiologyUniversidade Federal de São PauloSão PauloBrazil

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