Shared whole-brain network profiles during rumination in depressive patients and healthy adults
Poster No:
500
Submission Type:
Abstract Submission
Authors:
Xiao Chen1, Feng-Nan Jia2, Yan-Song Liu3, Chao-gan Yan4
Institutions:
1Institute of Psychology, Chinese Academy of Sciences, Beijing, China, 2Soochow University, Suzhou, Jiangsu, 3Suzhou Guangji Hospital, Suzhou, Jiangsu, 4Tsinghua University, Beijing, China
First Author:
Co-Author(s):
Introduction:
Rumination can be conceptualized as a specific style of spontaneous thought, characterized by its automatic and repetitive nature, as well as negative valence and focus on oneself (Christoff, Irving, Fox, Spreng, & Andrews-Hanna, 2016). Rumination can be observed in healthy individuals who are vulnerable to mental disorders and across several common psychiatric conditions, especially depression (Shaw, Hilt, & Starr, 2019). Recent neuroimaging studies have highlighted that rumination might be associated with the recruitment of a certain brain network and the functional connectivity (FC) alterations among the different brain regions of this network (Chen et al., 2020; Zhou et al., 2020). Specifically, we have previously shown that the FCs among the subsystems of the default mode networks (DMN) were altered during an active rumination state in a group of healthy adults (Andrews-Hanna, 2012; Chen et al., 2020). However, it remains unknown the FCs among the DMN subsystems and the other large-scale brain networks (e.g., frontoparietal network, FPN; dorsal attention network, DAN) during active rumination and whether these FC alterations differ in patients with major depression disorder (MDD).
Methods:
The participants were recruited from Guangji Hospital in Suzhou, Jiangsu, China. After quality control, a total of 45 patients with MDD and 46 healthy controls (HC) were included in the final analysis. For the details of the participant recruitment and quality control, refer to Jia et al. (2023). All participants finished a Rumination State Task (Chen et al., 2020), in which participants were induced to either focus on themselves (e.g., "Think: Why can't I handle things better in these events I just remembered?") or think about an unrelated, objective scenery (distraction; e.g., "Think about: A train stopped at a station"). We next extracted the whole-brain networks using the atlas of Schaefer and colleagues (2018) and calculated Fisher's r-to-z-transformed Pearson's correlation as FCs. We grouped all parcellations into 7 large-scale brain networks and averaged FCs among all regions of the same brain networks to get the network-wised FCs. A general linear model (GLM) was applied to analyze a two-way mixed-effects ANOVA with a two-level fixed within-subject factor (rumination vs. distraction) and a two-level random between-subject factor (MDD patients vs. HCs). We included the head motion as the additional covariates. Multiple comparison correction was performed using the network-based statistic (NBS) (Zalesky, Fornito, & Bullmore, 2010). We further conducted independent sample t-tests and paired t-tests to characterize the Condition effect and the Group effect.
Results:
We did not find any Interaction effect between the Group and the Condition (p > 0.05). Condition effects were revealed between the Core subsystem and the medial temporal lobe (MTL) subsystem of DMN in both MDD patients (t = 3.66, p < 0.001, Cohen's f2 = 0.31) and HCs (t = 4.10, p < 0.001, Cohen's f2 = 0.38)(Figure 1), indicating an enhancement of FC during rumination as compared to the distraction state. A more detailed node-level analysis showed that both HCs and MDD patients showed significantly enhanced FCs among the DMN subsystems, frontoparietal network (FPN), and dorsal attention network (DAN)(Figure 2). Interestingly, the MDD patients seemed to have more reduced FCs among these networks during rumination.
·Figure 1. The functional connectivities (FC) between the Core and the medial temporal lobe (MTL) subsystems were enhanced in both patients with depression and the healthy controls (HC).
·Figure 2. Wide-spread alterations in functional connectivities (FC) were observed among nodes.
Conclusions:
We found shared brain network structures during an active rumination state in both MDD patients and HCs. Our results highlighted the prominent role of the FCs between the Core and MTL subsystems of DMN in the neural basis of rumination. This shared brain network of rumination indicates that healthy adults could be readily leveraged to explore the brain network features underlying rumination, and the Core-MTL FCs might be potential targets for brain stimulation methodology to suppress rumination.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Emotion, Motivation and Social Neuroscience:
Self Processes 2
Emotion and Motivation Other
Keywords:
Affective Disorders
DISORDERS
MRI
Psychiatric
Psychiatric Disorders
1|2Indicates the priority used for review
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Provide references using APA citation style.
Chen, X., Chen, N. X., Shen, Y. Q., Li, H. X., Li, L., Lu, B., Zhu, Z. C., Fan, Z., & Yan, C. G. (2020). The subsystem mechanism of default mode network underlying rumination: A reproducible neuroimaging study. Neuroimage, 221, 117185. https://doi.org/10.1016/j.neuroimage.2020.117185
Christoff, K., Irving, Z. C., Fox, K. C., Spreng, R. N., & Andrews-Hanna, J. R. (2016). Mind-wandering as spontaneous thought: A dynamic framework. Nat Rev Neurosci, 17(11), 718-731. https://doi.org/10.1038/nrn.2016.113
Jia, F. N., Chen, X., Du, X. D., Tang, Z., Ma, X. Y., Ning, T. T., Zou, S. Y., Zuo, S. F., Li, H. X., Cui, S. X., Deng, Z. Y., Fu, J. L., Fu, X. Q., Huang, Y. X., Li, X. Y., Lian, T., Liao, Y. F., Liu, L. L., Lu, B.,…Liu, Y. S. (2023). Aberrant degree centrality profiles during rumination in major depressive disorder. Hum Brain Mapp, 44(17), 6245-6257. https://doi.org/10.1002/hbm.26510
Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X. N., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. T. (2018). Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb Cortex, 28(9), 3095-3114. https://doi.org/10.1093/cercor/bhx179
Shaw, Z. A., Hilt, L. M., & Starr, L. R. (2019). The developmental origins of ruminative response style: An integrative review. Clin Psychol Rev, 74, 101780. https://doi.org/10.1016/j.cpr.2019.101780
Zalesky, A., Fornito, A., & Bullmore, E. T. (2010). Network-based statistic: identifying differences in brain networks. Neuroimage, 53(4), 1197-1207. https://doi.org/10.1016/j.neuroimage.2010.06.041
Zhou, H. X., Chen, X., Shen, Y. Q., Li, L., Chen, N. X., Zhu, Z. C., Castellanos, F. X., & Yan, C. G. (2020). Rumination and the default mode network: Meta-analysis of brain imaging studies and implications for depression. Neuroimage, 206, 116287. https://doi.org/10.1016/j.neuroimage.2019.116287