Neural Substrates Of Rumination Subtypes In Healthy Participant

Background:Rumination has evolved as a critical construct in understanding the development and persistence of mental disorders,especially depression.Rumination is a mode of responding to distress that involves repetitively and passively focusing on symptoms of distress and on the possible causes and consequences of these symptoms.In view of the negative consequences of rumination,the study of its cognitive neural mechanism has become one of the important contents of behavioral and brain science research.Rumination is a trait that includes two subcomponents,namely brooding(B)and reflection(R),respectively construed as maladaptive and adaptive response styles to negative experiences.Overall,brooding rumination is consistently associated with negative outcomes.However,reflection is a purposeful turning inwards to engage in cognitive problem-solving to alleviate one’s depressive symptoms.Thus,brooding and reflection were suggested to have different clinical implications.Therefore,the current study will systematically explore the neural mechanisms of rumination subtypes in healthy people from the perspective of brain structure and function,and compare the commonality and uniqueness of network mechanisms related to the two subcomponents.Method: Using the resting state functional Magnetic Resonance Imaging(rs-f MRI)and structural magnetic resonance imaging(s-MRI),we explored multimodal relationships between two subcomponents of rumination,gray matter volume and resting-state cerebral blood flow in healthy participant(N=84).Experiment 1,using the value of gray matter volume as the dependent variable and individual B and R scores as independent variables,explored the anatomical basis of the two subcomponents through multiple regression model analysis.Experiment 2,in order to understand the brain functional characteristics of the two subcomponents,the value of cerebral blood flow was taken as the dependent variable,and the B scores and R scores of individuals were taken as independent variables.The relationship between individual the rumination subtype and cerebral blood flow was explored through multiple regression model analysis.Then,according to the multiple regression results of gray matter volume and cerebral blood flow,Meng’s Z test analysis was used to explore the brain regions that were common and statistically significant differences between B and R.Experiment 3,the dorsolateral prefrontal cortex(DLPFC)and anterior cingulate cortex(ACC)were used as regions of interest(ROI)to further investigate the structural and functional differences with B and R using Pearson partial correlation analysis.Experiment 4,Network-based statistics(NBS)to explore the relationship between the two subcomponents and default mode network(DMN),salience network(SN)and fronto-Parietal Network(FPN).Then,on the basis of the NBS results,Meng’s Z test was used to compare the differences between the resting state functional connectivity(FC)and the correlation coefficients of B and R scores.Results: Experiment 1,brain regions negatively correlated with B and gray matter volume included the inferior frontal gyrus of the opercular part,bilateral posterior cingulate,bilateral temporal lobes,median cingulate and paracingulate gyri,medial superior frontal gyrus and dorsolateral superior frontal gyrus,but no significant positive correlation was found.Additionally,we found the gray matter volume in the posterior cingulate gyrus,middle frontal gyrus,medial superior frontal gyrus,precuneus,dorsalateral superior frontal gyrus,hippocampus,cuneus,and fusiform gyrus were significantly positively correlated with R scores.Meng’s Z test results showed that there were statistically significant differences between the two subcomponents and the gray matter volume in the areas of lingual gyrus,fusiform gyrus,calcarine,precuneus,superior occipital gyrus and cuneus.Experiment 2,B scores were negatively correlated with cerebral blood flow in bilateral Median cingulate and paracingulate gyri,bilateral posterior cingulate,bilateral lingual gyrus and bilateral precuneus.Additionally,R scores were negatively correlated with cerebral blood flow in median cingulate and paracingulate gyri,right postcentral gyrus,bilateral middle frontal gyrus,bilateral medial superior frontal gyrus,right dorsalateral superior frontal gyrus,bilateral superior parietal gyrus and bilateral precuneus.Meng’s Z test results showed that there were statistically significant differences between the two subcomponents factors and cerebral blood flow in the medial superior frontal gyrus,middle frontal gyrus,dorsalateral superior frontal gyrus,orbital inferior frontal gyrus and precental gyrus.Experiment 3,using the ROI analysis method and found the cerebral blood flow and gray matter volume values of DLPFC and ACC were negatively correlated respectively.Experiment4,using the NBS analysis method and found that the maximum connectivity component significantly correlated with R involved the intra-network connections of DMN,FPN and SN and the connections between the three networks.Among them,right precental gyrus(FPN),left middle temporal gyrus of temporal pole(DMN),left angular gyrus(DMN),left middle temporal gyrus(DMN),right inferior temporal gyrus,left precuneus(DMN),left supplementary motor area(SN),Inferior frontal gyrus of triangular part(FPN)and anterior cingulate cortex(DMN)are the hub nodes of this connected component.R is mainly negatively correlated with FC between nodes,while a few connections are positively correlated,but no FC is found to be significantly correlated with B.Using the Meng’s Z test method and found that the different connections between FC and R and B were mainly distributed in the internal and inter-network connections between DMN and FPN,as well as the connections between SN and DMN and FPN.Among them,right parahippocampal gyrus(DMN),middle temporal gyrus of temporal pole(DMN),Inferior frontal gyrus of triangular part(FPN),middle temporal gyrus(DMN),medial superior frontal gyrus(DMN),left superior frontal gyrus of orbital part(DMN),right middle frontal gyrus(FPN)and left anterior cingulate(SN)are the central hub nodes of the network with different connections related to the rumination subtype.Conclusion: Our findings indicate that the two subtypes of rumination might share some common components yet also have distinct neural basis.It highlight the importance of the neural correlates of cognitive control and visual for understanding adaptive reflection and also support the value of large-scale functional connectivity networks for understanding the subtypes of rumination.Our findings also provide new and important insights into how the potentially shared and distinct cognitive,affective and neural processes of brooding and reflection can be extended to clinical populations to further elucidate the neurobehavioral relationships between the two subtypes of rumination and neural network mode.In conclusion,the current study using multimodal brain imaging techniques to systematically reveal “the neural mechanism of the subtype of rumination” from the perspective of brain structure and function.