Functional Connectome Gradient Features in Rumination States and Major Depression Disorder
Poster No:
630
Submission Type:
Abstract Submission
Authors:
Zheng-Jia-Yi Hu1, Chao-Gan Yan1
Institutions:
1Institute of Psychology, Chinese Academy of Sciences, Beijing, Beijing
First Author:
Co-Author:
Introduction:
Major depression disorder (MDD) is a common mental illness with high rates of relapse, disability and suicide. The default mode network (DMN) plays an important role in exploring the brain mechanism in depression through fMRI. In recent years, researchers have found that rumination is often regarded as a psychological expression of DMN abnormalities in patients with depression (Kaiser et al., 2015). Lately, a popular approach to define the brain as a set of continuous scores along multiple axes is called functional connectome gradient (Vos de Wael et al., 2020). Previous study (Margulies et al., 2016) identified the first two gradients as the primary-transmodal gradient and the visual-sensorimotor gradient. Here is a universally proven result that the patients with MDD showed a narrower range of gradient scores, and regionally, the MDD group showed lower gradient scores mostly in the DMN than healthy controls (Xia et al., 2020). Therefore, this study aims to investigate cortical gradient features in healthy people and depression patients.
Methods:
The discovery sample includes task-state fMRI data from 41 young healthy adults, which were collected in a previously published study. (Chen et al., 2020). The discovery set, which included 41 healthy control participants (HCs) and 45 MDD patients, was recruited from Guangji Hospital in Suzhou, China. The procedure was approved by the Ethics Committee of Institute of Psychology. The details of experimental design and MRI data analysis were introduced in the previous paper (Chen et al., 2020).
Following the data preprocessing, the time series of the region of interest (ROIs) defined by Schaefer2018_400Parcels were extracted to yield a functional connectivity matrix for each participant. The BrainSpace toolbox based on Matlab (Vos de Wael et al., 2020) was utilized to calculate gradient values. Paired t-tests were then used to explore the significant variations between different states after false discovery rate (FDR) corrections. And the gradient metrics like explanation ratio, range, variance, and dispersion were also computed to explore global differences.
For the validation states, after calculating the gradient matrix, a two-way mixed-effects ANOVA was conducted, incorporating a two-level fixed within-subject factor (rumination vs. distraction) and a two-level random between-subject factor (MDD patients vs. HCs).
Following the data preprocessing, the time series of the region of interest (ROIs) defined by Schaefer2018_400Parcels were extracted to yield a functional connectivity matrix for each participant. The BrainSpace toolbox based on Matlab (Vos de Wael et al., 2020) was utilized to calculate gradient values. Paired t-tests were then used to explore the significant variations between different states after false discovery rate (FDR) corrections. And the gradient metrics like explanation ratio, range, variance, and dispersion were also computed to explore global differences.
For the validation states, after calculating the gradient matrix, a two-way mixed-effects ANOVA was conducted, incorporating a two-level fixed within-subject factor (rumination vs. distraction) and a two-level random between-subject factor (MDD patients vs. HCs).
Results:
Consistent with previous findings, all the results showed the primary-transmodal gradient and the visual-sensorimotor gradient (Fig1.A). In brief, focused on the results of the first gradient, the results indicated that the rumination state had significantly lower range and variation than distraction state in individual sites (Fig2). As for the validation dataset, a significant condition effect of gradient variance was observed.
From a regional perspective, compared with the distraction state, the rumination state presented lower gradient score in visual cortex and the posterior cingulate cortex within the DMN, with higher gradient score in the prefrontal cortical areas within the DMN and the frontoparietal network (FPN) (Fig1.B).
Paired t-tests in validation data proved the variation of DMN and FPN. We used these two brain regions as masks for conducting the mixed-effect analysis on the validation dataset. There was not any significant interaction effect after the FDR correlation. However, before the correlation, the results indicate that the group and the different rumination states have interaction effect in DMN and frontoparietal network (Fig1.C).
From a regional perspective, compared with the distraction state, the rumination state presented lower gradient score in visual cortex and the posterior cingulate cortex within the DMN, with higher gradient score in the prefrontal cortical areas within the DMN and the frontoparietal network (FPN) (Fig1.B).
Paired t-tests in validation data proved the variation of DMN and FPN. We used these two brain regions as masks for conducting the mixed-effect analysis on the validation dataset. There was not any significant interaction effect after the FDR correlation. However, before the correlation, the results indicate that the group and the different rumination states have interaction effect in DMN and frontoparietal network (Fig1.C).
·Figure 1. Gradient values and gradient differences between groups. (A) Primary-transmodal gradient and visual-sensorimotor gradient of rumination state at the IPCAS site. (B) Gradient differences in r
·Figure 2. Global gradient difference. (A) Gradient range difference in PKUSIEMENS site. (B) Gradient variance difference in PKUSIEMENS site.
Conclusions:
In the present study, both healthy subjects and MDD patients maintained the first and second gradients of the primary-transmodel in both rumination and distraction states. Rumination had a narrower gradient range and a lower gradient value in the DMN than distraction. However, unlike previous studies, we also found a rising gradient value in specific regions of the DMN in MDD patients and rumination states, which is worth exploring.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Keywords:
FUNCTIONAL MRI
Other - Major Depression Disorder; Rumination; Functional Connectome Gradient
1|2Indicates the priority used for review
Provide references using author date format
Kaiser, R. H., Andrews-Hanna, J. R., Wager, T. D., & Pizzagalli, D. A. (2015). Large-scale network dysfunction in major depressive disorder. JAMA Psychiatry, 72(6), 603. https://doi.org/10.1001/jamapsychiatry.2015.0071
Margulies, D. S., Ghosh, S. S., Goulas, A., Falkiewicz, M., Huntenburg, J. M., Langs, G., Bezgin, G., Eickhoff, S. B., Castellanos, F. X., Petrides, M., Jefferies, E., & Smallwood, J. (2016). Situating the default-mode network along a principal gradient of macroscale cortical organization. Proceedings of the National Academy of Sciences, 113(44), 12574–12579. https://doi.org/10.1073/pnas.1608282113
Vos de Wael, R., Benkarim, O., Paquola, C., Lariviere, S., Royer, J., Tavakol, S., Xu, T., Hong, S.-J., Langs, G., Valk, S., Misic, B., Milham, M., Margulies, D., Smallwood, J., & Bernhardt, B. C. (2020). BrainSpace: A toolbox for the analysis of macroscale gradients in neuroimaging and Connectomics datasets. Communications Biology, 3(1). https://doi.org/10.1038/s42003-020-0794-7
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