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Resting-state functional connectivity of the default mode network associated with happiness

Resting-state functional connectivity of the default mode network associated with happiness Happiness refers to people’s cognitive and affective evaluation of their life. Why are some people happier than others? One reason might be that unhappy people are prone to ruminate more than happy people. The default mode network (DMN) is normally active during rest and is implicated in rumination. We hypothesized that unhappiness may be associated with increased default-mode functional connectivity during rest, including the medial prefrontal cortex (MPFC), posterior cingu- late cortex (PCC) and inferior parietal lobule (IPL). The hyperconnectivity of these areas may be associated with higher levels of rumination. One hundred forty-eight healthy participants underwent a resting-state fMRI scan. A group-independent component analysis identified the DMNs. Results indicated increased functional connectivity in the DMN was associated with lower levels of happiness. Specifically, relative to happy people, unhappy people exhibited greater functional connect- ivity in the anterior medial cortex (bilateral MPFC), posterior medial cortex regions (bilateral PCC) and posterior parietal cortex (left IPL). Moreover, the increased functional connectivity of the MPFC, PCC and IPL, correlated positively with the inclination to ruminate. These results highlight the important role of the DMN in the neural correlates of happiness, and suggest that rumination may play an important role in people’s perceived happiness. Key words: happiness; default mode network; rumination; functional connectivity; resting-state fMRI Introduction investigate the neurobiological sources of these individual dif- Happiness is a fundamental human pursuit (Diener, 2000; Parks ferences (Kringelbach and Berridge, 2009); doing so may provide et al., 2012). However, not everyone is equipped with the same new insights into our understanding of happiness. ability to be happy (Lyubomirsky, 2001; Diener and Seligman, A starting point for this study is the observation that happi- 2002). In our daily lives, it is common for some people to be ness is often negatively associated with excessive, negative, happy while others often experience fits of gloom. So, why are self-focused processing; i.e. rumination. Emerging evidence some people happier than others? There has been considerable shows that unhappy people are inclined to dwell on their nega- interest in the sources of individual differences in happiness. A tive life events, focus on their self-emotions and feel self- great deal of research has shown that both internal factors conscious, which results in a variety of adverse consequences (e.g. self-reference, emotion regulation, and social comparison) (Lyubomirsky et al., 2003, 2011; Nolen-Hoeksema et al., 2008; and external factors (e.g. age, gender, education, marriage and Killingsworth and Gilbert, 2010; Stawarczyk et al., 2012; income) influence happiness (Diener et al., 1999; Lyubomirsky, Andrews-Hanna et al., 2013; Mason et al., 2013). For 2001; Diener, 2013). However, little work has been done to instance, Killingsworth and Gilbert (2010) collected real-time Received: 16 October 2014; Revised: 11 October 2015; Accepted: 20 October 2015 V The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com 516 Y. Luo et al. | 517 self-reported instances of mind wandering and happiness from Although the relationship between happiness and the DMN 2250 participants and found that people were less happy when has yet to be examined, previous research has examined the their minds wandered than when not. Unhappy individuals, relationship between emotion-related disorders (e.g. depres- unlike their happy peers, showed increased self-focused sion) and the DMN (Greicius, 2008; Broyd et al., 2009; Whitfield- thoughts and were more sensitive to unfavorable achievement Gabrieli and Ford, 2012). For example, hyperactivation and feedback, which adversely affected their performance on im- hyperconnectivity of the DMN has been found in patients with portant academic tasks (Lyubomirsky et al., 2011). In addition, depression who were characterized by high levels of rumination excessive self-focused cognition (rumination) is implicated in (Whitfield-Gabrieli and Ford, 2012). Moreover, during both pas- depressive and anxiety symptoms (Nolen-Hoeksema et al., sive viewing and actively appraising negative images, individ- 2008). Finally, recent work utilizing a longitudinal design uals with depression exhibited an overactive DMN (Sheline reported that low self-esteem predicted subsequent rumination, et al., 2009). Additional studies have reported that participants which in turn predicted subsequent depression, and that with depression showed increased resting-state functional con- rumination partially mediated the prospective effect of low self- nectivity of the MPFC, PCC, and thalamus within the DMN com- esteem on depression (Kuster et al., 2012). pared with the healthy control participants (Greicius et al., 2007; In recent years, neuroimaging studies on happiness have Berman et al., 2011; Sheline et al., 2010; Zhou et al., 2010). advanced our understanding of its neural underpinnings (Urry Moreover, the increased level of DMN dominance (greater DMN et al., 2004; van Reekum et al., 2007; Heller et al., 2013; activity relative to the task-positive network) was also found in Cunningham and Kirkland, 2014; Lewis et al., 2014; Luo et al., people with depression (Hamilton et al., 2011). Notably, the 2014, 2015; Kong et al., 2015a,b,c,d). For example, one resting- degree of resting-state functional connectivity correlated with state electroencephalography (EEG) study revealed that individ- the person’s tendency to ruminate (Greicius et al., 2007; Sheline uals with greater activation in the left superior prefrontal cortex et al., 2010; Berman et al., 2011; Hamilton et al., 2011). However, were happier (compared with the right superior prefrontal cor- an overactive DMN in patients with depression is not always tex (Urry et al., 2004). Structural magnetic resonance imaging observed. For example, decreased functional connectivity in pa- (MRI) studies have reported gray matter volume associations tients with major depressive disorder has been reported (Veer with aspects of happiness; eudaimonic happiness, character- et al., 2010). In addition, an independent study reported evi- ized by meaning and self-realization, was shown to be posi- dence for the dissociation between anterior and posterior func- tively associated with gray matter volume in the right insular tional connectivity of the resting-state DMN in people with cortex (Lewis et al., 2014). Additionally, global life satisfaction depression, with an increase in functional connectivity in the has been linked to the parahippocampal gyrus (Kong et al., anterior medial cortical regions and a decreased functional 2015a). More recently, two task-based functional MRI studies connectivity in the posterior medial cortical regions (Zhu et al., found that happier individuals showed greater amygdala 2012). Therefore, the relationship between functional connect- responses to positive stimuli (Cunningham and Kirkland, 2014), ivity in the resting-state DMN and depression remains as well as sustained activity in the striatum and dorsolateral unclear. prefrontal cortex in response to positive stimuli (Heller et al., Although there is considerable evidence for abnormal pat- 2013). However, none of these studies has looked at how a spe- terns of DMN activity in people with depression, it is important cific psychological process, such as rumination, relates to the to investigate the relationship between DMN activities and neural basis of happiness. happiness in non-clinical samples. We treat happiness and sub- As a complement to the EEG and task-based studies men- jective well-being as interchangeable terms in this study. tioned earlier, the disposition of happiness warrants investiga- Happiness refers to people’s cognitive and affective evaluation tion from the perspective of resting-state functional of their life. Thus, happiness is a broad construct that encom- connectivity (Biswal et al., 1995; Fox and Raichle, 2007). This passes frequent positive affects, occasional negative affects and method may provide insight into individual differences in hap- high level of life satisfaction (Diener et al., 1999, 2002). In add- piness as they relate to spontaneous self-referential processing. ition, we conceptualized happiness as a trait rather than a tran- Functional connectivity refers to the temporal correlations of sient emotion state (Lyubomirsky et al., 2005). In this study, spontaneous brain activity in spatially remote areas (Friston happiness was measured by the Chinese Happiness Inventory et al., 1993). These connectivity patterns, namely resting-state (CHI) (Lu and Shih, 1997; based on the Oxford Happiness networks (RSNs), are thought to characterize the individual dif- Inventory) with 28 items (Argyle et al., 1989) alongside an add- ferences in intrinsic brain functions (Fox and Raichle, 2007). One itional 20 items to accurately cover the sources of happiness for such RSN, the default mode network (DMN) includes the medial Chinese people. These Chinese sources of happiness include prefrontal cortex (MPFC), posterior cingulate cortex (PCC), precu- ‘harmony of interpersonal relationships’, ‘being praised and re- neus (PCU) and inferior parietal lobule (IPL). The DMN is active spected by others’, ‘satisfaction of material needs’, ‘achieve- during introspectively oriented mental activity at rest (e.g. self- ment at work’, ‘downward social comparisons’ and ‘peace of reflection, theory of mind or mind wandering), and is sup- mind’ (Lu and Shih, 1997). pressed in the presence of an external task (Raichle et al., 2001; There are several concepts related to happiness, such as de- Buckner et al., 2008). The DMN has been theorized to play a piv- pression and self-esteem. Although those concepts may partly otal role in unconstrained, self-referential cognition (Raichle overlap and be related, they are distinct constructs et al., 2001; Weissman et al., 2006; Mason et al., 2007; Buckner (Lyubomirsky et al., 2006; Ryff et al., 2006). Given that the ab- et al., 2008; Andrews-Hanna et al., 2014). In our previous study, sence of depression and high self-esteem does not guarantee we reported that local functional homogeneity of intrinsic brain happiness, they are therefore not sufficient conditions for hap- activity was altered within DMN areas (e.g. the MPFC and PCC) piness (Lyubomirsky et al., 2006). Furthermore, more and more among happy and unhappy individuals (Luo et al., 2014). In this researchers argue that happiness and unhappiness are not study, we sought to extend our previous findings by directly opposite ends of a bipolar continuum, but rather are distinct investigating how the functional connectivity of DMN is associ- domains of mental functioning (Ryff et al., 2006). Despite the ated with happiness. considerable amount of neural evidence and research 518 | Social Cognitive and Affective Neuroscience, 2016, Vol. 11, No. 3 Fig. 1. The spatial pattern in the aDMN and pDMN Note: aDMN, anterior default mode network, pDMN, posterior default mode network. L, left; R, Right. excluded due to excessive head motion (see below for details) Table1. Demographics and another two participants were excluded because they failed to complete self-report measures. The results reported here are Mean SD Range from the remaining 148 participants (61 men, 81 women; mean Age (years) 20.90 1.65 18–26 age¼ 20.90 years, SD¼ 1.65 years). They were healthy under- Head Motion (mm) 0.11 0.038 0.023–0.142 graduate and postgraduate students with no history of neuro- CHI (n¼ 148) 115.96 18.04 73–167 logical conditions or psychiatric episodes. In accordance with RRS-Brooding (n¼ 70) 10.27 2.45 5–18 the Declaration of Helsinki, written informed consent was RRS-Reflection (n¼ 70) 11.01 2.76 6–20 obtained from all participants. The study protocol was approved by the local ethics committee. Note: CHI, Chinese Happiness Inventory; RRS, Ruminative Responses Scale. All participants’ happiness levels were assessed with the CHI, full version (Lu et al., 1997) (alpha ¼ 0.94). The CHI is a on depression (Greicius et al., 2007; Sheline et al., 2010; standardized measure of 48 items; higher total scores indicate a Hamilton et al., 2011) and self-esteem (Chavez and Heatherton, higher level of overall happiness. The CHI is composed of a uni- 2015), it is still necessary to investigate the associations versal component of happiness and a specific component of the between DMN and happiness. Chinese conceptualization of happiness (Lu and Shih, 1997). In this study, we investigated whether happiness is associ- Rumination was measured with the Ruminative Responses ated with the DMN and whether this relationship is associated Scale (RRS) (Treynor et al., 2003). We did not measure the rumin- with rumination. Our hypotheses were derived from the conclu- ation at first (78 participants), and the RRS was included for the sion that depression is associated with hyperconnectivity of the final 70 participants. The RRS can be divided into three compo- DMN (Whitfield-Gabrieli and Ford, 2012), and that within the nents: depression, brooding and reflection. According to DMN, the MPFC, PCC and IPL are theorized to be involved in Treynor et al. (2003), rumination is composed of two compo- self-referential processes (Northoff et al., 2006; Andrews-Hanna nents: brooding and reflection. Therefore, we focused on the et al., 2010). Therefore, it is possible that unhappiness is associ- Brooding and Reflection subscales of RRS. The Brooding sub- ated with increased functional connectivity between the MPFC, scale assesses the tendency to passively compare current mood PCC and IPL in a non-clinical population. Furthermore, the with ideal standards, and the Reflection subscale assesses the inclination to ruminate may be related to the hypothesized tendency to consider why one is depressed (Treynor et al., 2003). increased functional connectivity between the aforementioned The Chinese version of the RRS has 21 items (rated on 1¼ Never areas when at rest. to 4¼ Always) (Yang et al., 2009) (total RRS alpha¼ 0.89; Brooding alpha¼ 0.65 and Reflection alpha¼ 0.78). Methods Participants and behavioral measures Data acquisition Participants included 168 healthy young adults, who volun- The experiment was performed on a 3.0-T scanner (Magnetom teered as part of an ongoing project investigating the associ- Trio, Siemens, Erlangen, Germany). Functional images were ation between brain imaging and mental health. Data from 51 acquired using a single-shot, gradient-recalled echo planar of these participants have been previously published in a study imaging sequence (TR ¼ 2000 ms, TE¼ 30 ms, flip angle¼ 90 ,32 that investigated the association between regional homogeneity axial slices, FOV¼ 192 192 cm, acquisition matrix¼ 64 64, and happiness (Luo et al., 2014). Eighteen participants were slice thickness ¼ 3 mm, without gap, voxel size¼ 3 3 4mm). Y. Luo et al. | 519 Fig. 2. Regions showing significant correlations between the DMNs and happiness. The left panel shows regions with significant correlations between the aDMN and happiness; the right panel shows regions with significant correlations between the pDMN and happiness. Numbers in the upper left corner of each image refer to the x-, y- or z-plane coordinates of the MNI space (R, right; L, left). Abbreviations: MPFC, medial prefrontal cortex; PCC, poster cingulate cortex; IPL, inferior parietal lobule. images were sinc interpolated in time to correct for slice time Table 2. Brain regions demonstrating significant correlations be- differences. Then, the images were realigned with a six- tween the functional connectivity of the DMN and happiness. parameter (rigid body) linear transformation to correct for head movements. Next, the functional images were co-registered Anatomical region Side Bas MNI Voxel Peak with the corresponding T1-volume and warped into the x y z Size T-value Montreal Neurological Institute (MNI) space at a resolution of 3 3 3 mm, using Diffeomorphic Anatomical Registration aDMN Through Exponentiated Lie algebra in SPM8 (Ashburner, 2007). MPFC B 9/10/32 0 51 39 82 3.41 Finally, the images were spatially smoothed using a Gaussian pDMN filter of 4-mm full width at half maximum (FWHM) to reduce PCC B 31 6 42 42 42 3.17 noise. IPL L 7/40 33 60 54 29 3.13 Note: Side refers to the hemisphere (B, bilateral; R, right; and L, left). Brodmann Head motion areas (BAs), coordinates of peak t-value in MNI space, volume in voxels, and To rule out the confounding effect of head motion, we com- peak t-values are specified for each region showing significant correlations. puted frame-wise displacement (FD) for our data (Power et al., 2012, 2014). We used a ‘scrubbing’ method, as recent studies For each participant, a total of 8 min resting data were acquired. have emphasized the confounding effect of transient head mo- Participants were instructed to simply rest with their eyes tion on the time course of resting-state data (Power et al., 2012, closed, not to think of anything in particular, and not to fall 2015; Van Dijk et al., 2012; Yan et al., 2013a,b). For each partici- asleep. To minimize head motion, the participants’ heads were pant, the volumes with a FD above 0.2 mm, and the 1-back and restricted with foam cushions. For spatial normalization and lo- 2-forward neighbors, were scrubbed (Power et al., 2012, 2013). calization, high-resolution T1-weighted anatomical images Participants with excessive motion (<5 min of useable data) were also acquired along the sagittal orientation using a 3D were excluded as an outlier (Power et al., 2014). A total of 18 par- magnetization prepared rapid gradient-echo sequence (176 ticipants were identified as outliers and eliminated from the slices, TR¼ 1900 ms, TE¼ 2.53 ms, flip angle¼ 9 , reso- final analyses. The FD of the remaining data was low (M¼ 0.11, lution¼ 256 256 and voxel size¼ 1 1 1 mm) on each SD¼ 0.04), and there were no correlations between FD and be- participant. havioral measures (rs< 0.08, Ps> 0.36). Lastly, mean FD of each participant was included in the multiple regression models as nuisance covariates to remove the residual effect of motion Data pre-processing effect at the group level. Data were pre-processed using DPARSF (Data Processing Assistant and Resting-state FMRI, version 2.3, http://www.rest Independent component analysis fmri.net/forum/DPARSF) (Chao-Gan and Yu-Feng, 2010). The first 10 volumes were removed to account for the T1 equilibrium Group spatial independent component analysis (ICA) was car- effect, leaving 230 volumes for the final analysis. The functional ried out by the Group ICA for fMRI Toolbox (GIFT v2.0a, 520 | Social Cognitive and Affective Neuroscience, 2016, Vol. 11, No. 3 Fig. 3. Correlations between scores on the RRS subscales and mean Z values of the MPFC, PCC and IPL. MPFC, medial prefrontal cortex; PCC, poster cingulate cortex; IPL, inferior parietal lobule. icatb.sourceforge.net) (Calhoun et al., 2001) for 148 participants. the independent components (Beckmann et al., 2005; Liao et al., The number of components was set to 20 (Smith et al., 2009). To 2010). The ICASSSO results showed that the component sta- perform group ICA, dimensionality of the data was first reduced bility ranged above 0.95 for all included independent using principle component analysis (PCA), then the reduced components. data were concatenated over the time domain using the info- Components corresponding to the DMN were then identi- max algorithm. This algorithm was repeated 20 times by run- fied. Components were identified by template matching and ning the ICASSO toolbox, using both ‘randinit’ and ‘bootstrap’ visual inspection. First, using the DMN template according to methods, to ensure the reliability of the derived components Smith et al. (2009), we performed a spatial template matching (Himberg et al., 2004; Correa et al., 2007; Das et al., 2014). procedure (von dem Hagen et al., 2013). Two components were Afterward, individual image maps were reconstructed from the identified as having a highest spatial overlap with the template aggregated data based on matrices stored during PCA. The (correlation values: 0.32, 0.19). Then, two components matching resulting image maps and time courses were then back- the DMN were further conformed by visual inspection. reconstructed and calibrated using Z values to normalize According to the group ICA results, the DMN were split into the the signal. It has been reported that Z values can be indirectly anterior DMN (aDMN) and the posterior DMN (pDMN), which used to measure the functional connectivity within a network, was consistent with previous research (Liemburg et al., 2012; since Z values represented the contribution of the voxels to Ding and Lee, 2013; von dem Hagen et al., 2013). Y. Luo et al. | 521 Statistical analysis of independent components Discussion Statistical analyses were performed using SPM8 (http://www.fil. Our results provide neural evidence that rumination may ion.ucl.ac.uk/spm/software/spm8/). For each DMN component, underlie the association between DMN activities and individual random-effect analyses using one-sample t-tests were per- differences in happiness within a healthy population. formed respectively. We set a stringent threshold criterion to Consistent with our hypothesis, a group ICA approach found examine regions typically found in the DMN (T> 12, k> 20). The that the increased functional connectivity of the DMN was asso- resulting statistical maps were used as masks in following ana- ciated with lower levels of happiness. Compared with their lysis to restrict the analysis within DMN areas. happy peers, individuals reporting greater levels of unhappi- Multiple regression analyses with happiness, gender (covari- ness showed increased functional connectivity in core DMN ate of no interest), age (covariate of no interest), FD (covariate of areas, including the MPFC, PCC and IPL. Furthermore, the no interest) and the Z values were performed. The results of strength of functional connectivity in the MPFC, PCC and IPL multiple regressions were corrected using a Monte Carlo simu- was positively correlated with scores on the two subscales of lation (Ledberg et al., 1998), resulting in a corrected threshold of rumination: brooding and reflection. The increased functional P< 0.05 (AlphaSim program within AFNI (http://afni.nimh.nih. connectivity within the DMN among unhappy people may sug- gov/pub/dist/doc/program_help/AlphaSim.html). The following gest the unhappy people are associated with excessive negative parameters were used: single voxel P¼ 0.05, 10 000 simulations, self-reflection. FWHM¼ 4 mm, cluster connection radius r¼ 5 mm). Minimum The current results provide further evidence that unhappi- cluster sizes of aDMN and pDMN were 30 voxels (810 mm ) and ness is associated with hyperconnectivity of DMN areas in a 27 voxels (729 mm ), respectively. non-clinical sample, and that the strength of the hyperconnec- tivity between the MPFC, PCC and IPL is associated with the trait of rumination. Unhappy people may spend more time ruminat- Connectivity-behavior analysis ing on negative self-feelings, thoughts and emotions To investigate the relationship between Z values in the network (Lyubomirsky et al., 2003, 2011; Nolen-Hoeksema et al., 2008; maps and the level of rumination, a post-hoc Pearson correlation Killingsworth and Gilbert, 2010; Stawarczyk et al., 2012; analysis was performed. The voxels in the DMN showing signifi- Andrews-Hanna et al., 2013; Mason et al., 2013). The DMN is cant correlations between Z values and happiness were active when people are engaged in unconstrained self- extracted as a mask consisting of several regions of interests referential processing and deactivates during goal-oriented ac- (ROIs). These averaged masks (ROIs) (at group level) were tivity. As the midline hubs of the DMN (MPFC and PCC) are applied to all participants. The mean Z values for each individual highly implicated in personally significant evaluations, e.g. dur- within these ROIs were correlated with the scores on the ing self-relevant affective decision-making (Andrews-Hanna Brooding and Reflection subscales of the RRS. Considering these et al., 2010), it is not surprising that unhappy people showed analyses were exploratory in nature, a statistical significance increased functional connectivity in the DMN. Prior studies also level of P< 0.05 uncorrected and P< 0.01 uncorrected, were used. found the intrinsic activities of DMN areas were associated with happiness (Luo et al., 2014, 2015). For example, our prior resting- state study found the local synchronization of intrinsic brain Results activities in the MPFC and PCC were altered between happy and Demographic results unhappy people (Luo et al., 2014). However, although previous studies speculated as to the existence of an association between Spatial pattern of DMN. The results of the Group ICA analyses the altered intrinsic activities of DMN areas and rumination are shown in Figure 1 and visualized with BrainNet Viewer (Xia (Luo et al., 2014, 2015), no prior research had directly tested this et al., 2013). Visual inspection indicated that the aDMN was speculation. The present results provide direct novel evidence mainly composed of the MPFC and anterior cingulate cortices, that the increased functional connectivity of DMN areas among the PCC/PCU, and the bilateral IPL. The pDMN included the PCC, unhappy people is associated with the inclination to ruminate. retrosplenial cortex and PCU (Table 1). More specifically, we found that unhappy people exhibited increased functional connectivity of the bilateral dorsal MPFC, DMN and happiness. Multiple regression analysis (P< 0.05, within the anterior DMN, relative to happy people. Moreover, Alphasim corrected) indicated that Z values of some areas the Z values of the dorsal MPFC were positively correlated with within the DMN were negatively correlated with happiness (see rumination. The MPFC, consisting of the midline core areas of Figure 2, Table 2). More specifically, within the aDMN, the Z val- the DMN, are highly implicated in self-referential and emotional ues of the bilateral MPFC were negatively correlated with happi- processing (Andrews-Hanna et al., 2010). Importantly, the dorsal ness. Similarly, within the pDMN, the z values of the bilateral MPFC has been termed the ‘dorsal nexus’ because it connects PCC, and left IPL were negatively correlated with happiness. the cognitive control network, DMN and affective network (Sheline et al., 2010). In clinical samples, patients with depres- Connectivity-behavior results. There were significant positive cor- sion demonstrated the hyperconnectivity of dorsal MPFC more relations between the RRS subscale scores and the Z values of so than healthy people (Sheline et al., 2010; Zhu et al., 2012). the MPFC within the aDMN, and between the Z values of the Moreover, experienced meditators, people who are character- PCC and IPL within the pDMN (Figure 3). Specifically, the mean ized by attention to the present and less mind wandering, dem- Z values of the MPFC were positively correlated with the scores onstrated relatively deactivated MPFC compared with matched on the two RRS subscales [Brooding (r¼ 0.30, P¼ 0.013) and controls (Brewer et al., 2011). Taken together, our study provides Reflection (r¼ 0.25, P¼ 0.039)]. In addition, the mean Z values of novel evidence that demonstrates that the MPFC is involved in the PCC were positively correlated with scores for Brooding unhappy individual’s rumination, in a non-clinical sample, (r¼ 0.26, P¼ 0.029), and the mean Z values of the IPL was mar- which may indicate that individuals who report greater levels of ginally correlated with scores for Reflection (r¼ 0.23, P¼ 0.054). unhappiness spend excessive amounts of time think about how 522 | Social Cognitive and Affective Neuroscience, 2016, Vol. 11, No. 3 they feel about themselves and find it is difficult to disengage ‘the Fundamental Research Funds for the Central from these thoughts. Universities’ [GK201503009 to Y. L.]’; ‘the Youth foundation Furthermore, the posterior DMN areas, including the PCC and for Humanities and Social Science Research of Ministry of IPL, showed increased functional connectivity among unhappy Education’ [15YJC190016 to S.Q.]; ‘the Fundamental Research people. Additionally, the Z values of the PCC were positively cor- Funds for the Central Universities’ [GK201503006 to S.Q.]. related with scores for brooding, and those of the IPL were posi- Conflict of interest. None declared. tively correlated with scores for reflection, two subscales of rumination. The results were consistent with previous studies, References which found increased functional connectivity in the PCC of pa- tients with depression (Greicius et al., 2007; Berman et al., 2011; Andrews-Hanna, J.R., Kaiser, R.H., Turner, A.E.J., et al. (2013). A but see Zhu et al., 2012) and recovered anorexia nervosa (Cowdrey penny for your thoughts: dimensions of self-generated et al., 2014), relative to healthy controls. Those posterior parietal thought content and relationships with individual differences regions play an important role in episodic memory, especially in emotional wellbeing. 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Resting-state functional connectivity of the default mode network associated with happiness

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Abstract

Happiness refers to people’s cognitive and affective evaluation of their life. Why are some people happier than others? One reason might be that unhappy people are prone to ruminate more than happy people. The default mode network (DMN) is normally active during rest and is implicated in rumination. We hypothesized that unhappiness may be associated with increased default-mode functional connectivity during rest, including the medial prefrontal cortex (MPFC), posterior cingu- late cortex (PCC) and inferior parietal lobule (IPL). The hyperconnectivity of these areas may be associated with higher levels of rumination. One hundred forty-eight healthy participants underwent a resting-state fMRI scan. A group-independent component analysis identified the DMNs. Results indicated increased functional connectivity in the DMN was associated with lower levels of happiness. Specifically, relative to happy people, unhappy people exhibited greater functional connect- ivity in the anterior medial cortex (bilateral MPFC), posterior medial cortex regions (bilateral PCC) and posterior parietal cortex (left IPL). Moreover, the increased functional connectivity of the MPFC, PCC and IPL, correlated positively with the inclination to ruminate. These results highlight the important role of the DMN in the neural correlates of happiness, and suggest that rumination may play an important role in people’s perceived happiness. Key words: happiness; default mode network; rumination; functional connectivity; resting-state fMRI Introduction investigate the neurobiological sources of these individual dif- Happiness is a fundamental human pursuit (Diener, 2000; Parks ferences (Kringelbach and Berridge, 2009); doing so may provide et al., 2012). However, not everyone is equipped with the same new insights into our understanding of happiness. ability to be happy (Lyubomirsky, 2001; Diener and Seligman, A starting point for this study is the observation that happi- 2002). In our daily lives, it is common for some people to be ness is often negatively associated with excessive, negative, happy while others often experience fits of gloom. So, why are self-focused processing; i.e. rumination. Emerging evidence some people happier than others? There has been considerable shows that unhappy people are inclined to dwell on their nega- interest in the sources of individual differences in happiness. A tive life events, focus on their self-emotions and feel self- great deal of research has shown that both internal factors conscious, which results in a variety of adverse consequences (e.g. self-reference, emotion regulation, and social comparison) (Lyubomirsky et al., 2003, 2011; Nolen-Hoeksema et al., 2008; and external factors (e.g. age, gender, education, marriage and Killingsworth and Gilbert, 2010; Stawarczyk et al., 2012; income) influence happiness (Diener et al., 1999; Lyubomirsky, Andrews-Hanna et al., 2013; Mason et al., 2013). For 2001; Diener, 2013). However, little work has been done to instance, Killingsworth and Gilbert (2010) collected real-time Received: 16 October 2014; Revised: 11 October 2015; Accepted: 20 October 2015 V The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com 516 Y. Luo et al. | 517 self-reported instances of mind wandering and happiness from Although the relationship between happiness and the DMN 2250 participants and found that people were less happy when has yet to be examined, previous research has examined the their minds wandered than when not. Unhappy individuals, relationship between emotion-related disorders (e.g. depres- unlike their happy peers, showed increased self-focused sion) and the DMN (Greicius, 2008; Broyd et al., 2009; Whitfield- thoughts and were more sensitive to unfavorable achievement Gabrieli and Ford, 2012). For example, hyperactivation and feedback, which adversely affected their performance on im- hyperconnectivity of the DMN has been found in patients with portant academic tasks (Lyubomirsky et al., 2011). In addition, depression who were characterized by high levels of rumination excessive self-focused cognition (rumination) is implicated in (Whitfield-Gabrieli and Ford, 2012). Moreover, during both pas- depressive and anxiety symptoms (Nolen-Hoeksema et al., sive viewing and actively appraising negative images, individ- 2008). Finally, recent work utilizing a longitudinal design uals with depression exhibited an overactive DMN (Sheline reported that low self-esteem predicted subsequent rumination, et al., 2009). Additional studies have reported that participants which in turn predicted subsequent depression, and that with depression showed increased resting-state functional con- rumination partially mediated the prospective effect of low self- nectivity of the MPFC, PCC, and thalamus within the DMN com- esteem on depression (Kuster et al., 2012). pared with the healthy control participants (Greicius et al., 2007; In recent years, neuroimaging studies on happiness have Berman et al., 2011; Sheline et al., 2010; Zhou et al., 2010). advanced our understanding of its neural underpinnings (Urry Moreover, the increased level of DMN dominance (greater DMN et al., 2004; van Reekum et al., 2007; Heller et al., 2013; activity relative to the task-positive network) was also found in Cunningham and Kirkland, 2014; Lewis et al., 2014; Luo et al., people with depression (Hamilton et al., 2011). Notably, the 2014, 2015; Kong et al., 2015a,b,c,d). For example, one resting- degree of resting-state functional connectivity correlated with state electroencephalography (EEG) study revealed that individ- the person’s tendency to ruminate (Greicius et al., 2007; Sheline uals with greater activation in the left superior prefrontal cortex et al., 2010; Berman et al., 2011; Hamilton et al., 2011). However, were happier (compared with the right superior prefrontal cor- an overactive DMN in patients with depression is not always tex (Urry et al., 2004). Structural magnetic resonance imaging observed. For example, decreased functional connectivity in pa- (MRI) studies have reported gray matter volume associations tients with major depressive disorder has been reported (Veer with aspects of happiness; eudaimonic happiness, character- et al., 2010). In addition, an independent study reported evi- ized by meaning and self-realization, was shown to be posi- dence for the dissociation between anterior and posterior func- tively associated with gray matter volume in the right insular tional connectivity of the resting-state DMN in people with cortex (Lewis et al., 2014). Additionally, global life satisfaction depression, with an increase in functional connectivity in the has been linked to the parahippocampal gyrus (Kong et al., anterior medial cortical regions and a decreased functional 2015a). More recently, two task-based functional MRI studies connectivity in the posterior medial cortical regions (Zhu et al., found that happier individuals showed greater amygdala 2012). Therefore, the relationship between functional connect- responses to positive stimuli (Cunningham and Kirkland, 2014), ivity in the resting-state DMN and depression remains as well as sustained activity in the striatum and dorsolateral unclear. prefrontal cortex in response to positive stimuli (Heller et al., Although there is considerable evidence for abnormal pat- 2013). However, none of these studies has looked at how a spe- terns of DMN activity in people with depression, it is important cific psychological process, such as rumination, relates to the to investigate the relationship between DMN activities and neural basis of happiness. happiness in non-clinical samples. We treat happiness and sub- As a complement to the EEG and task-based studies men- jective well-being as interchangeable terms in this study. tioned earlier, the disposition of happiness warrants investiga- Happiness refers to people’s cognitive and affective evaluation tion from the perspective of resting-state functional of their life. Thus, happiness is a broad construct that encom- connectivity (Biswal et al., 1995; Fox and Raichle, 2007). This passes frequent positive affects, occasional negative affects and method may provide insight into individual differences in hap- high level of life satisfaction (Diener et al., 1999, 2002). In add- piness as they relate to spontaneous self-referential processing. ition, we conceptualized happiness as a trait rather than a tran- Functional connectivity refers to the temporal correlations of sient emotion state (Lyubomirsky et al., 2005). In this study, spontaneous brain activity in spatially remote areas (Friston happiness was measured by the Chinese Happiness Inventory et al., 1993). These connectivity patterns, namely resting-state (CHI) (Lu and Shih, 1997; based on the Oxford Happiness networks (RSNs), are thought to characterize the individual dif- Inventory) with 28 items (Argyle et al., 1989) alongside an add- ferences in intrinsic brain functions (Fox and Raichle, 2007). One itional 20 items to accurately cover the sources of happiness for such RSN, the default mode network (DMN) includes the medial Chinese people. These Chinese sources of happiness include prefrontal cortex (MPFC), posterior cingulate cortex (PCC), precu- ‘harmony of interpersonal relationships’, ‘being praised and re- neus (PCU) and inferior parietal lobule (IPL). The DMN is active spected by others’, ‘satisfaction of material needs’, ‘achieve- during introspectively oriented mental activity at rest (e.g. self- ment at work’, ‘downward social comparisons’ and ‘peace of reflection, theory of mind or mind wandering), and is sup- mind’ (Lu and Shih, 1997). pressed in the presence of an external task (Raichle et al., 2001; There are several concepts related to happiness, such as de- Buckner et al., 2008). The DMN has been theorized to play a piv- pression and self-esteem. Although those concepts may partly otal role in unconstrained, self-referential cognition (Raichle overlap and be related, they are distinct constructs et al., 2001; Weissman et al., 2006; Mason et al., 2007; Buckner (Lyubomirsky et al., 2006; Ryff et al., 2006). Given that the ab- et al., 2008; Andrews-Hanna et al., 2014). In our previous study, sence of depression and high self-esteem does not guarantee we reported that local functional homogeneity of intrinsic brain happiness, they are therefore not sufficient conditions for hap- activity was altered within DMN areas (e.g. the MPFC and PCC) piness (Lyubomirsky et al., 2006). Furthermore, more and more among happy and unhappy individuals (Luo et al., 2014). In this researchers argue that happiness and unhappiness are not study, we sought to extend our previous findings by directly opposite ends of a bipolar continuum, but rather are distinct investigating how the functional connectivity of DMN is associ- domains of mental functioning (Ryff et al., 2006). Despite the ated with happiness. considerable amount of neural evidence and research 518 | Social Cognitive and Affective Neuroscience, 2016, Vol. 11, No. 3 Fig. 1. The spatial pattern in the aDMN and pDMN Note: aDMN, anterior default mode network, pDMN, posterior default mode network. L, left; R, Right. excluded due to excessive head motion (see below for details) Table1. Demographics and another two participants were excluded because they failed to complete self-report measures. The results reported here are Mean SD Range from the remaining 148 participants (61 men, 81 women; mean Age (years) 20.90 1.65 18–26 age¼ 20.90 years, SD¼ 1.65 years). They were healthy under- Head Motion (mm) 0.11 0.038 0.023–0.142 graduate and postgraduate students with no history of neuro- CHI (n¼ 148) 115.96 18.04 73–167 logical conditions or psychiatric episodes. In accordance with RRS-Brooding (n¼ 70) 10.27 2.45 5–18 the Declaration of Helsinki, written informed consent was RRS-Reflection (n¼ 70) 11.01 2.76 6–20 obtained from all participants. The study protocol was approved by the local ethics committee. Note: CHI, Chinese Happiness Inventory; RRS, Ruminative Responses Scale. All participants’ happiness levels were assessed with the CHI, full version (Lu et al., 1997) (alpha ¼ 0.94). The CHI is a on depression (Greicius et al., 2007; Sheline et al., 2010; standardized measure of 48 items; higher total scores indicate a Hamilton et al., 2011) and self-esteem (Chavez and Heatherton, higher level of overall happiness. The CHI is composed of a uni- 2015), it is still necessary to investigate the associations versal component of happiness and a specific component of the between DMN and happiness. Chinese conceptualization of happiness (Lu and Shih, 1997). In this study, we investigated whether happiness is associ- Rumination was measured with the Ruminative Responses ated with the DMN and whether this relationship is associated Scale (RRS) (Treynor et al., 2003). We did not measure the rumin- with rumination. Our hypotheses were derived from the conclu- ation at first (78 participants), and the RRS was included for the sion that depression is associated with hyperconnectivity of the final 70 participants. The RRS can be divided into three compo- DMN (Whitfield-Gabrieli and Ford, 2012), and that within the nents: depression, brooding and reflection. According to DMN, the MPFC, PCC and IPL are theorized to be involved in Treynor et al. (2003), rumination is composed of two compo- self-referential processes (Northoff et al., 2006; Andrews-Hanna nents: brooding and reflection. Therefore, we focused on the et al., 2010). Therefore, it is possible that unhappiness is associ- Brooding and Reflection subscales of RRS. The Brooding sub- ated with increased functional connectivity between the MPFC, scale assesses the tendency to passively compare current mood PCC and IPL in a non-clinical population. Furthermore, the with ideal standards, and the Reflection subscale assesses the inclination to ruminate may be related to the hypothesized tendency to consider why one is depressed (Treynor et al., 2003). increased functional connectivity between the aforementioned The Chinese version of the RRS has 21 items (rated on 1¼ Never areas when at rest. to 4¼ Always) (Yang et al., 2009) (total RRS alpha¼ 0.89; Brooding alpha¼ 0.65 and Reflection alpha¼ 0.78). Methods Participants and behavioral measures Data acquisition Participants included 168 healthy young adults, who volun- The experiment was performed on a 3.0-T scanner (Magnetom teered as part of an ongoing project investigating the associ- Trio, Siemens, Erlangen, Germany). Functional images were ation between brain imaging and mental health. Data from 51 acquired using a single-shot, gradient-recalled echo planar of these participants have been previously published in a study imaging sequence (TR ¼ 2000 ms, TE¼ 30 ms, flip angle¼ 90 ,32 that investigated the association between regional homogeneity axial slices, FOV¼ 192 192 cm, acquisition matrix¼ 64 64, and happiness (Luo et al., 2014). Eighteen participants were slice thickness ¼ 3 mm, without gap, voxel size¼ 3 3 4mm). Y. Luo et al. | 519 Fig. 2. Regions showing significant correlations between the DMNs and happiness. The left panel shows regions with significant correlations between the aDMN and happiness; the right panel shows regions with significant correlations between the pDMN and happiness. Numbers in the upper left corner of each image refer to the x-, y- or z-plane coordinates of the MNI space (R, right; L, left). Abbreviations: MPFC, medial prefrontal cortex; PCC, poster cingulate cortex; IPL, inferior parietal lobule. images were sinc interpolated in time to correct for slice time Table 2. Brain regions demonstrating significant correlations be- differences. Then, the images were realigned with a six- tween the functional connectivity of the DMN and happiness. parameter (rigid body) linear transformation to correct for head movements. Next, the functional images were co-registered Anatomical region Side Bas MNI Voxel Peak with the corresponding T1-volume and warped into the x y z Size T-value Montreal Neurological Institute (MNI) space at a resolution of 3 3 3 mm, using Diffeomorphic Anatomical Registration aDMN Through Exponentiated Lie algebra in SPM8 (Ashburner, 2007). MPFC B 9/10/32 0 51 39 82 3.41 Finally, the images were spatially smoothed using a Gaussian pDMN filter of 4-mm full width at half maximum (FWHM) to reduce PCC B 31 6 42 42 42 3.17 noise. IPL L 7/40 33 60 54 29 3.13 Note: Side refers to the hemisphere (B, bilateral; R, right; and L, left). Brodmann Head motion areas (BAs), coordinates of peak t-value in MNI space, volume in voxels, and To rule out the confounding effect of head motion, we com- peak t-values are specified for each region showing significant correlations. puted frame-wise displacement (FD) for our data (Power et al., 2012, 2014). We used a ‘scrubbing’ method, as recent studies For each participant, a total of 8 min resting data were acquired. have emphasized the confounding effect of transient head mo- Participants were instructed to simply rest with their eyes tion on the time course of resting-state data (Power et al., 2012, closed, not to think of anything in particular, and not to fall 2015; Van Dijk et al., 2012; Yan et al., 2013a,b). For each partici- asleep. To minimize head motion, the participants’ heads were pant, the volumes with a FD above 0.2 mm, and the 1-back and restricted with foam cushions. For spatial normalization and lo- 2-forward neighbors, were scrubbed (Power et al., 2012, 2013). calization, high-resolution T1-weighted anatomical images Participants with excessive motion (<5 min of useable data) were also acquired along the sagittal orientation using a 3D were excluded as an outlier (Power et al., 2014). A total of 18 par- magnetization prepared rapid gradient-echo sequence (176 ticipants were identified as outliers and eliminated from the slices, TR¼ 1900 ms, TE¼ 2.53 ms, flip angle¼ 9 , reso- final analyses. The FD of the remaining data was low (M¼ 0.11, lution¼ 256 256 and voxel size¼ 1 1 1 mm) on each SD¼ 0.04), and there were no correlations between FD and be- participant. havioral measures (rs< 0.08, Ps> 0.36). Lastly, mean FD of each participant was included in the multiple regression models as nuisance covariates to remove the residual effect of motion Data pre-processing effect at the group level. Data were pre-processed using DPARSF (Data Processing Assistant and Resting-state FMRI, version 2.3, http://www.rest Independent component analysis fmri.net/forum/DPARSF) (Chao-Gan and Yu-Feng, 2010). The first 10 volumes were removed to account for the T1 equilibrium Group spatial independent component analysis (ICA) was car- effect, leaving 230 volumes for the final analysis. The functional ried out by the Group ICA for fMRI Toolbox (GIFT v2.0a, 520 | Social Cognitive and Affective Neuroscience, 2016, Vol. 11, No. 3 Fig. 3. Correlations between scores on the RRS subscales and mean Z values of the MPFC, PCC and IPL. MPFC, medial prefrontal cortex; PCC, poster cingulate cortex; IPL, inferior parietal lobule. icatb.sourceforge.net) (Calhoun et al., 2001) for 148 participants. the independent components (Beckmann et al., 2005; Liao et al., The number of components was set to 20 (Smith et al., 2009). To 2010). The ICASSSO results showed that the component sta- perform group ICA, dimensionality of the data was first reduced bility ranged above 0.95 for all included independent using principle component analysis (PCA), then the reduced components. data were concatenated over the time domain using the info- Components corresponding to the DMN were then identi- max algorithm. This algorithm was repeated 20 times by run- fied. Components were identified by template matching and ning the ICASSO toolbox, using both ‘randinit’ and ‘bootstrap’ visual inspection. First, using the DMN template according to methods, to ensure the reliability of the derived components Smith et al. (2009), we performed a spatial template matching (Himberg et al., 2004; Correa et al., 2007; Das et al., 2014). procedure (von dem Hagen et al., 2013). Two components were Afterward, individual image maps were reconstructed from the identified as having a highest spatial overlap with the template aggregated data based on matrices stored during PCA. The (correlation values: 0.32, 0.19). Then, two components matching resulting image maps and time courses were then back- the DMN were further conformed by visual inspection. reconstructed and calibrated using Z values to normalize According to the group ICA results, the DMN were split into the the signal. It has been reported that Z values can be indirectly anterior DMN (aDMN) and the posterior DMN (pDMN), which used to measure the functional connectivity within a network, was consistent with previous research (Liemburg et al., 2012; since Z values represented the contribution of the voxels to Ding and Lee, 2013; von dem Hagen et al., 2013). Y. Luo et al. | 521 Statistical analysis of independent components Discussion Statistical analyses were performed using SPM8 (http://www.fil. Our results provide neural evidence that rumination may ion.ucl.ac.uk/spm/software/spm8/). For each DMN component, underlie the association between DMN activities and individual random-effect analyses using one-sample t-tests were per- differences in happiness within a healthy population. formed respectively. We set a stringent threshold criterion to Consistent with our hypothesis, a group ICA approach found examine regions typically found in the DMN (T> 12, k> 20). The that the increased functional connectivity of the DMN was asso- resulting statistical maps were used as masks in following ana- ciated with lower levels of happiness. Compared with their lysis to restrict the analysis within DMN areas. happy peers, individuals reporting greater levels of unhappi- Multiple regression analyses with happiness, gender (covari- ness showed increased functional connectivity in core DMN ate of no interest), age (covariate of no interest), FD (covariate of areas, including the MPFC, PCC and IPL. Furthermore, the no interest) and the Z values were performed. The results of strength of functional connectivity in the MPFC, PCC and IPL multiple regressions were corrected using a Monte Carlo simu- was positively correlated with scores on the two subscales of lation (Ledberg et al., 1998), resulting in a corrected threshold of rumination: brooding and reflection. The increased functional P< 0.05 (AlphaSim program within AFNI (http://afni.nimh.nih. connectivity within the DMN among unhappy people may sug- gov/pub/dist/doc/program_help/AlphaSim.html). The following gest the unhappy people are associated with excessive negative parameters were used: single voxel P¼ 0.05, 10 000 simulations, self-reflection. FWHM¼ 4 mm, cluster connection radius r¼ 5 mm). Minimum The current results provide further evidence that unhappi- cluster sizes of aDMN and pDMN were 30 voxels (810 mm ) and ness is associated with hyperconnectivity of DMN areas in a 27 voxels (729 mm ), respectively. non-clinical sample, and that the strength of the hyperconnec- tivity between the MPFC, PCC and IPL is associated with the trait of rumination. Unhappy people may spend more time ruminat- Connectivity-behavior analysis ing on negative self-feelings, thoughts and emotions To investigate the relationship between Z values in the network (Lyubomirsky et al., 2003, 2011; Nolen-Hoeksema et al., 2008; maps and the level of rumination, a post-hoc Pearson correlation Killingsworth and Gilbert, 2010; Stawarczyk et al., 2012; analysis was performed. The voxels in the DMN showing signifi- Andrews-Hanna et al., 2013; Mason et al., 2013). The DMN is cant correlations between Z values and happiness were active when people are engaged in unconstrained self- extracted as a mask consisting of several regions of interests referential processing and deactivates during goal-oriented ac- (ROIs). These averaged masks (ROIs) (at group level) were tivity. As the midline hubs of the DMN (MPFC and PCC) are applied to all participants. The mean Z values for each individual highly implicated in personally significant evaluations, e.g. dur- within these ROIs were correlated with the scores on the ing self-relevant affective decision-making (Andrews-Hanna Brooding and Reflection subscales of the RRS. Considering these et al., 2010), it is not surprising that unhappy people showed analyses were exploratory in nature, a statistical significance increased functional connectivity in the DMN. Prior studies also level of P< 0.05 uncorrected and P< 0.01 uncorrected, were used. found the intrinsic activities of DMN areas were associated with happiness (Luo et al., 2014, 2015). For example, our prior resting- state study found the local synchronization of intrinsic brain Results activities in the MPFC and PCC were altered between happy and Demographic results unhappy people (Luo et al., 2014). However, although previous studies speculated as to the existence of an association between Spatial pattern of DMN. The results of the Group ICA analyses the altered intrinsic activities of DMN areas and rumination are shown in Figure 1 and visualized with BrainNet Viewer (Xia (Luo et al., 2014, 2015), no prior research had directly tested this et al., 2013). Visual inspection indicated that the aDMN was speculation. The present results provide direct novel evidence mainly composed of the MPFC and anterior cingulate cortices, that the increased functional connectivity of DMN areas among the PCC/PCU, and the bilateral IPL. The pDMN included the PCC, unhappy people is associated with the inclination to ruminate. retrosplenial cortex and PCU (Table 1). More specifically, we found that unhappy people exhibited increased functional connectivity of the bilateral dorsal MPFC, DMN and happiness. Multiple regression analysis (P< 0.05, within the anterior DMN, relative to happy people. Moreover, Alphasim corrected) indicated that Z values of some areas the Z values of the dorsal MPFC were positively correlated with within the DMN were negatively correlated with happiness (see rumination. The MPFC, consisting of the midline core areas of Figure 2, Table 2). More specifically, within the aDMN, the Z val- the DMN, are highly implicated in self-referential and emotional ues of the bilateral MPFC were negatively correlated with happi- processing (Andrews-Hanna et al., 2010). Importantly, the dorsal ness. Similarly, within the pDMN, the z values of the bilateral MPFC has been termed the ‘dorsal nexus’ because it connects PCC, and left IPL were negatively correlated with happiness. the cognitive control network, DMN and affective network (Sheline et al., 2010). In clinical samples, patients with depres- Connectivity-behavior results. There were significant positive cor- sion demonstrated the hyperconnectivity of dorsal MPFC more relations between the RRS subscale scores and the Z values of so than healthy people (Sheline et al., 2010; Zhu et al., 2012). the MPFC within the aDMN, and between the Z values of the Moreover, experienced meditators, people who are character- PCC and IPL within the pDMN (Figure 3). Specifically, the mean ized by attention to the present and less mind wandering, dem- Z values of the MPFC were positively correlated with the scores onstrated relatively deactivated MPFC compared with matched on the two RRS subscales [Brooding (r¼ 0.30, P¼ 0.013) and controls (Brewer et al., 2011). Taken together, our study provides Reflection (r¼ 0.25, P¼ 0.039)]. In addition, the mean Z values of novel evidence that demonstrates that the MPFC is involved in the PCC were positively correlated with scores for Brooding unhappy individual’s rumination, in a non-clinical sample, (r¼ 0.26, P¼ 0.029), and the mean Z values of the IPL was mar- which may indicate that individuals who report greater levels of ginally correlated with scores for Reflection (r¼ 0.23, P¼ 0.054). unhappiness spend excessive amounts of time think about how 522 | Social Cognitive and Affective Neuroscience, 2016, Vol. 11, No. 3 they feel about themselves and find it is difficult to disengage ‘the Fundamental Research Funds for the Central from these thoughts. Universities’ [GK201503009 to Y. L.]’; ‘the Youth foundation Furthermore, the posterior DMN areas, including the PCC and for Humanities and Social Science Research of Ministry of IPL, showed increased functional connectivity among unhappy Education’ [15YJC190016 to S.Q.]; ‘the Fundamental Research people. Additionally, the Z values of the PCC were positively cor- Funds for the Central Universities’ [GK201503006 to S.Q.]. related with scores for brooding, and those of the IPL were posi- Conflict of interest. None declared. tively correlated with scores for reflection, two subscales of rumination. The results were consistent with previous studies, References which found increased functional connectivity in the PCC of pa- tients with depression (Greicius et al., 2007; Berman et al., 2011; Andrews-Hanna, J.R., Kaiser, R.H., Turner, A.E.J., et al. (2013). A but see Zhu et al., 2012) and recovered anorexia nervosa (Cowdrey penny for your thoughts: dimensions of self-generated et al., 2014), relative to healthy controls. Those posterior parietal thought content and relationships with individual differences regions play an important role in episodic memory, especially in emotional wellbeing. 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Journal

Social Cognitive and Affective Neuroscience – Oxford University Press

Published: Mar 1, 2016

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