| Response style theory (RST) suggest that there are two response styles in facing with depressed mood, rumination and distraction. Rumination refers to cognitions that focus the individual’s attention on their depressed mood and the causes and consequences of depressed mood. Distraction refers to cognitions and behaviors that take the individual’s mind off their depressed mood. RST predicts that individuals who ruminate in response to their depressed mood will suffer an intensification and prolongation of that mood, whereas individuals who engage in distraction responses will attenuate and alleviate their depressed mood. The negative response style, rumination, may amplify and impede the recovery from physiological response.Rumination is associated with the state (including the activity and functional connectivity) of the default mode network (DMN) (Berman et al.,2010; Hamilton et al.,2011). Berman et al. (2010) found that patients with major depressive disorder (MDD) showed more neural functional connectivity between the subgenual-cingulate cortex and the posterior-cingulate cortex than healthy individuals during rest periods. Significantly, this resting state connectivity is positively correlated with behavioral measures of rumination. Hamilton et al. (2011) revealed that in the MDD participants, increasing levels of DMN dominance (the number of temporal frames for which DMN BOLD was greater) were associated with higher levels of maladaptive, depressive rumination and lower levels of adaptive, reflective rumination. Different levels of rumination may result from the integrity of DMN, or the information transformation efficacy of DMN. In addition, DMN is commonly anti-correlated (negatively correlated, as the DMN rise or fall in activation over time, the other network fall or rise in systematic reverse direction) with regions belonging to the central executive network (CEN), which includes a set of regions that are generally activated during performance of goal-directed or attention- demanding tasks (Fox et al.,2005). Chen et al. (2013) further revealed that the CEN node have a causal regulatory relationship of the DMN. They combined transcranial magnetic stimulation (TMS) with functional MRI to test this hypothesis by causally excite or inhibit nodes within the CEN or salience network (SN) and determine consequent effects on the DMN. They found that excitatory stimulations delivered to the CEN node induced negative DMN connectivity with the CEN and SN. Conversely, inhibitory TMS to the CEN node resulted in a disinhibition of DMN activity. In addition, Sridharan et al. (2008) used Granger causality analysis (GCA) to explore the relationship between DMN, CEN and SN, they found that the SN, particularly the right fronto-insular cortex (rFIC), plays an important and causal role in switching between the CEN and the DMN. Goulden et al. (2014) replicated this result with the method of dynamic causal modelling (DCM).In the first study,235 subjects were eligible as part of our ongoing project exploring the relationship between brain imaging and mental health. All the participants completed Adolescent Self-Rating Life Events Checklist (ASLEC) and Rumination (or Ruminative) Responses Scale (RRS), which were part of a battery of psychological tests the subject need to take. In addition, the MRI data was also acquired from these subjects. We used whole brain analysis to examine the association between regional grey matter volume (rGMV) and average impact score of ASLEC (individual differences in sensitivity to NLEs). To control for possible confounding variables, participants’ age, gender, general intelligence, anxiety, depression, and global volume of gray matter were entered as confounding covariates into the regression model. We found that the rGMV of the left VLPFC was positively correlated with NLEs sensitivity. Subsequently, partial correlation statistics revealed a positive association between the rumination scores and NLEs sensitivity (P< 0.001). After controlling for gender, general intelligence, age, anxiety, depression, and global volume of GM, we also found a significantly positive correlation between rumination scores and the rGMV of the left VLPFC (P< 0.001). Finally, we found that rumination served as a mediator between the rGMV of the VLPFC and individual NLEs sensitivity. These findings suggest that people with greater VLPFC might be more inclined to ruminate and the ruminative response style might make them more sensitive to NLEs.In the second study, according to the relationship between DMN, CEN and SN, we want to test two assumption in healthy college student:1. whether the functional connectivity in the DMN is positively related with the rumination, greater DMN connectivity makes higher rumination; 2. whether the functional connectivity between the DMN and CEN (negative correlation) is associated with rumination.287 subjects were eligible as part of our ongoing project exploring the relationship between brain imaging and mental health. All the participants completed Rumination (or Ruminative) Responses Scale (RRS), MRI data was also acquired from these subjects. We found that the anti-correlation strength between DMN and CEN was negatively related with rumination, greater anti-correlation strength between DMN and CEN makes lower rumination, which may represent that better inhibition of DMN activity from CEN makes lower rumination. Because SN plays an important role in switching between the CEN and the DMN. We further tested that if the activity (or excitability) of SN can drive the anti-correlation between DMN and CEN. We combined the GCA and dynamic functional connectivity to verify our idea. Specifically, a new time series was acquired by calculating the dynamic standard deviation of SN, which represent the activity (or excitability) of SN, and we called this time series "X". Another new time series was obtained by calculating the dynamic functional connectivity between CEN and DMN, and we called this time series "Y". Subsequently, GCA was used to analysis the prediction relationship between X and Y. In addition, we also explored that if the strength of drive is associated with rumination. Consistent with our hypothesis, we found that activity of SN can drive the anti-correlation between DMN and CEN, and low ruminators have greater strength of drive than high ruminators. This result demonstrate that the resting-state connectivity variations can be driven by intrinsic activities of specific network or region, which can provide insights on the dynamic changes in large-scale brain connectivity and network configurations. In addition, high ruminators may have problems in this process. |
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