Characterizing gradients of functional connectome underpinning rumination and their alteration in depression

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Emerging studies have highlighted that brain areas are organized along several macroscale gradients, which could be a powerful framework to better understand how the functional connectome was organized to underlie rumination. In this study, two datasets (Rum-Beijing and Rum-MDD) were leveraged in the present study. Rum-Beijing consisted of 41 healthy controls (HC) who underwent 3 repetitive scans, while Rum-MDD consisted of 45 patients with major depressive disorder (MDD) and 46 HCs. We used a modified rumination state task (RST) to induce participants into a continuous, active rumination state and characterized the gradient profiles. RST also included a distraction state as the control condition. The explanation ratio of gradient and the regional differences of each gradient were also compared. Leveraging the Rum-MDD dataset, we further examined the interaction effect between the group (MDD vs. HC) and condition (rumination vs. distraction) regarding the gradient’s global and local metrics. Two gradients were identified: the primary-transmodal gradient and the visual-sensorimotor gradient. We found that the rumination state exhibited reduced gradient values in the default mode network (DMN) but elevated gradient values in the frontoparietal network (FPN) as compared to the distraction state. In global perspective, during the rumination state, individuals with MDD reflected significantly higher values in explanation ratio, gradient range, and gradient variance along visual-sensorimotor gradient compared to HCs. Finally, we observed significantly reduced correlations between functional connectivity and the primary-transmodal gradient during rumination compared to distraction in HCs. In conclusion, the present study showed that rumination may correspond to a specific underlying functional gradient profile, and such profile was altered in patients with MDD. These results shed new light on the neural mechanisms underlying rumination, highlighting a global functional coupling characteristics across the whole brain during an active rumination state. Biological sciences/Neuroscience Biological sciences/Psychology gradient default mode network frontoparietal network major depressive disorder rumination Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Rumination is repetitive thinking on the symptoms, situations, causes, meanings, and possible causes as well as consequences of distress 1 . Rumination is associated with higher susceptibility to depressive episodes and predicts the onset of depression 2,3 . Furthermore, it not only impairs problem-solving behavior but also predicts delayed treatment response and higher relapse rates in major depressive disorder (MDD) 3,4 . Existing evidence has shown that rumination is a treatable psychological process and could be alleviated by several interventions such as mindfulness meditation, rumination-focused cognitive behavioral therapy, repetitive transcranial magnetic stimulation (rTMS), real-time neurofeedback, and transcranial Direct Current stimulation (tDCS) 5–9 . Given the recently highlighted association between the clinical efficiency of neuromodulation techniques and the degree to which they have targeted the underlying functional network underpinnings of a given symptom 10,11 , a better understanding of the rumination’s underlying functional network mechanism may lead to the development of next-generation rumination-targeted treatment of MDD 12–14 . Existing research has highlighted the pivotal roles of the local brain activities and within-network interactions of large-scale brain networks, especially the default mode network (DMN) and frontoparietal network (FPN), in the neural underpinnings of rumination 15–19 . Task-based functional magnetic resonance imaging (fMRI) studies have found increased activity in the typical DMN regions, including the posterior cingulate cortex (PCC), medial temporal regions and medial prefrontal cortex (MPFC), as well as the FPN regions, including the inferior parietal lobe 20–24 . Furthermore, brain activities in DMN and FPN regions and functional coupling among nodes of these two networks, typically characterized using resting-state fMRI (R-fMRI), were also found to be associated with participants’ self-reported rumination tendency 25–27 . Traditional functional connectivity (FC) methods may be insufficient to fully characterize the dynamic changes and complex functional organization of the brain. The cortical gradient is an analytical framework that captures the continuous, spatially organized pattern of variation in a specific characteristic across the cerebral cortex 28–30 . Since rumination is a recurrent and automatic form of emotional regulation and a particularly intense and concentrated form of inner speech 31 , it may bear specific alterations regarding the global properties of the functional brain network. The cortical gradients place discrete large-scale networks and associated regional units on a continuous spectrum, extending from unimodal systems that support perception and action to association areas involved in more abstract cognitive functions. Such an approach reveals that the organization of the brain is not merely a collection of isolated regions and networks but rather a dynamic and continuous system. This holistic and continuous perspective provides a more comprehensive understanding of the brain's structure and function and can effectively capture reliable features of the interpretable functional biology of the human brain 32–34 . By analyzing connectivity data in both humans and macaques, Margulies and colleagues 28 identified a primary-transmodal gradient (Gradient 1) that ranges from a major sensorimotor functional area on one end to a cross-modal area known as the DMN on the other and a visual-sensorimotor gradient (Gradient 2) differentiates from the sensorimotor and auditory cortex to the visual cortex. It is worth noting that some studies have explored gradient abnormality in MDD. Patients with MDD showed a compressed primary-transmodal gradient. The MDD group showed lower gradient scores, mostly in the DMN, than healthy controls. The regional changes also include parallel alterations in subcortical regions such as the caudate, amygdala, and thalamus. In addition, gene transcription profiles accounted for 53.9% of the gradient pattern changes. The patients' baseline gradient maps significantly predicted symptom improvement after treatment 35,36 . Similarly, the study finds reduced cortical gradient dispersion in the primary intrinsic brain networks of treatment-resistant depression patients. Decreased dispersion within nodes of the default mode, control, and salience networks correlates with baseline levels of trait anxiety, depression, and mindfulness. Meanwhile, baseline dispersion in the salience network predicts trait anxiety scores at 24 weeks post-intervention 37 . These insightful studies have shed light on rumination’s neural underpinnings, albeit caveats remain. The block-designed task-fMRI studies can reveal brain activities during active rumination, but it is relatively difficult to investigate the functional coupling among remote brain regions. R-fMRI is suitable for investigating the functional network features and cortical gradients, but its association with self-reported rumination traits only provides indirect evidence of rumination’s network mechanism. To address these issues, we developed a rumination state task that induced participants into an active, continuous rumination state and investigated the network underpinnings of rumination. Our previous studies using this paradigm have revealed reproducible FC and dynamic stability mechanisms underlying rumination 38,39 , as well as abnormalities regarding degree centrality in MDD patients 40 . The induced, continuous, active rumination state is suitable for characterizing cortical gradients as this metric is calculated based on an FC matrix, which reveals the synchronization among activities of remote brain regions during a period of time (~ 8 minutes). The recent "replication crisis" across various scientific disciplines has sparked widespread concerns about the reliability of published findings, especially those neuroimaging results 41,42 . Therefore, it is crucial to first ensure the replicability of any novel findings 43 . Here, we first aimed to examine the gradient profile of the functional connectome during an active rumination state in a group of healthy adults. Of note, we ensured the replicability of our findings by implementing a repeated-measured design using different scanners at different locations. Then we further explored the abnormality of this rumination gradient profile in MDD patients using an independent validation dataset. We hypothesized that both healthy and MDD individuals would present a narrower primary-to-transmodal cortical gradient during the rumination state regarding the global gradient metrics and lower gradient scores in the DMN. We further hypothesized that, compared to healthy individuals, the MDD group exhibits abnormal macroscale organizations alongside both gradients. Methods 2.1 Participants This study used two independent datasets 39,40 . We have openly shared our neuroimaging data from Rum-Beijing through the R-fMRI Maps project ( http://rfmri.org/RuminationfMRIData ). The Rum-Beijing dataset consists of 41 healthy adults recruited from the local community around the Institute of Psychology, Chinese Academy of Sciences, Beijing, China. The local Institutional Review Board approved the study protocol for this dataset, and all participants signed an informed consent form. For more details about this dataset, please read our previous study The Subsystem Mechanism of Default Mode Network Underlying Rumination: a Reproducible Neuroimaging Study 39 . For the Rum-MDD dataset, we recruited participants Guangji Hospital in Suzhou, Jiangsu, China. The local Institutional Review Board approved the study protocol for this dataset as well, and all participants signed an informed consent form. All individuals diagnosed with MDD underwent a 17-item assessment using the Hamilton Depression Rating Scale (HAMD). Healthy controls (HCs) were recruited from the local community through advertisements. MDD patients were required to meet DSM-5 criteria through a structured clinical interview conducted by a trained evaluator and to have a HAMD score of ≥ 17. For more detailed exclusion criteria for both MDD and HC, please refer to Jia et al. 36 . The analytical sample comprised 45 MDD patients (33 females, mean age = 26.09) and 46 healthy controls (35 females, mean age = 29.22, Table 1 ). Table 1 Demographical information of participants N Sex (female/male) Age RRS RRS-B RRS-R HAMD Rum-Beijing Healthy Control 40 22/41 22.71 (4.01) 46 (11.32) 11.24 (3.28) 10.85 (2.33) Rum-MDD MDD Patients 45 33/45 26.09 (8.32) 61.08 (9.80) 14.58 (2.95) 12.16 (2.86) 23.29 (5.41) Healthy Control 46 35/46 29.22 (10.25) 36.10 (8.54) 9.10 (2.50) 8.35 (2.63) RRS: Ruminative Response Scale; RRS-R: the reflection subscale of RRS; RRS-B: the brooding subscale of RRS; HAMD: Hamilton depression rating scale; HC, healthy control; MDD, major depressive disorder. Table 2 Differences in gradient explanation ratio, range, variance, and gradient dispersion in three sites between rumination and distraction on the Rum-Beijing dataset and the Rum-MDD dataset IPCAS PKUGE PKUSIEMENS MDD HC t p Cohen’s f 2 t p Cohen’s f 2 t p Cohen’s f 2 t p Cohen’s f 2 t p Cohen’s f 2 Visual-sensorimotor gradient Explanation ratio -0.674 0.504 0.016 6.301 < 0.001*** 1.265 3.789 < 0.001*** 0.493 1.287 0.204 0.036 -0.756 0.453 0.017 Range 3.158 0.003** 0.137 3.940 < 0.001*** 0.563 4.577 < 0.001*** 0.376 3.284 0.002** 0.265 1.038 0.304 0.020 Variance 1.896 0.062 0.046 2.251 0.030* 0.185 4.007 < 0.001*** 0.609 4.264 < 0.001*** 0.501 1.698 0.095 0.061 *p < 0.05; **p < 0.01; ***p < 0.001 2.2 Experimental design All participants of these two datasets finished RST. Before the MRI scan, the researcher interviewed the participants to brief the purpose of the study and the definition of the psychological state during the scan. The rumination state was defined as "passive and repetitive thinking about negative events and their possible consequences," and the distraction state was defined as "imagining images unrelated to oneself.” The effect of the distraction state was to prevent the subject from continuously immersing in the rumination state and to provide the comparison condition 39 . The fMRI scan consists of four states. First, during the resting state, the subject was asked to look at the fixation on the screen without engaging in specific thoughts. Then, during the sad memory state, the subject was prompted to recall negative autobiographical events triggered by keywords collected before the scanning. During the rumination state, participants were asked to reflect on themselves. Finally, during the distraction state, participants were prompted to imagine some objective scenarios. Except for the resting state, all mental states (sad memory, rumination, and distraction) included four sequentially displayed stimuli (keywords or prompts). An 8-minute continuous mental state was created by transitioning from one stimulus to the next every two minutes, without any inter-stimulus intervals (ISI). While the order of rumination and distraction states was balanced among subjects, the resting state and recall of negative autobiographical events always came first and second. 2.3 MRI data acquisition and preprocessing All participants from the Rum-Beijing dataset repetitively underwent MRI scans on 3 different scanners at two sites: two 3 Tesla GE MR750 scanners at the Institute of Psychology, Chinese Academy of Sciences (IPCAS), and Peking University Magnetic Resonance Imaging Research Center (PKUGE), as well as a 3 Tesla SIEMENS PRISMA scanner (PKUSIEMENS) at Peking University Magnetic Resonance Imaging Research Center. MRI images from the RUM-MDD dataset were acquired in a 3 Tesla SIEMENS SKYRA scanner at Guangji Hospital. Before functional image acquisition, all participants underwent a 3D T1 scan, and functional images for both resting state and mental states (sad memory, rumination, and distraction) were obtained. Data preprocessing was implemented with the toolbox for Data Processing & Analysis for Brain Imaging on Surface (DPABISurf) 44 . For details, please refer to the Supplementary Information. 2.4 Functional connectome and gradient mapping Following data preprocessing, cortical data were mapped onto the cortical parcellation using an atlas proposed by Schaefer and colleagues 45 , consisting of 400 parcellations. The 54-parcellation Tian's subcortical atlas 46 was used to extract functional signals from the subcortical regions. Subsequently, the time series of regions of interest (ROIs) were extracted to calculate Pearson correlation coefficients which underwent the Fisher’s r -to- z transformation, yielding a 454×454 FC matrix. We used the BrainSpace toolbox 30 run in a Matlab R2020a (The MathWorks Inc., Natick, MA, US) environment to calculate gradient matrices for resting state, sad memory, rumination, and distraction states within each group. The input matrix was sparsified to achieve 10% sparsity, and then a cosine similarity matrix was calculated, according to Vos de Wael et al. 30 . Subsequently, a normalized angle matrix was adjusted to eliminate negative values within the similarity matrix, following the methods outlined by Vos de Wael et al. 30 and Paquola et al. 47 . Diffusion map embedding was used to capture the gradient component that explains the variance in the functional connectome pattern. The Procrustes rotation was applied to realign all the connectome gradients. Furthermore, the explanation ratio of each gradient was also computed to provide insight into the relative contribution of that gradient, calculated by dividing the eigenvalue of a specific gradient by the total sum of eigenvalues. 2.5 Statistical analysis We first computed the average first and second gradients across all the subjects in the rumination/distractions condition. To test the replicability, we calculated these average gradient distribution maps in all the repetitive scans in the Rum-Beijing dataset and both groups in the Rum-MDD dataset. We further examined the network-level gradient features by aggregating the gradients across all subjects and grouping the 400 parcellations according to Yeo’s 7-network atlas, followed by calculating the mean gradient values within each network. A number of global metrics including explanation ratio, range, and variance of both gradients were examined. We also examined the gradient values at the node level. We applied paired t -tests by fitting a general linear model (GLM) to test the condition effect (rumination vs. distraction) in both Rum-Beijing and Rum-MDD datasets. The head motion was included as a covariate. In the Rum-MDD dataset, we further examined the interaction effect between the condition (rumination vs. distraction) and the group (MDD vs. HC) effect by fitting a linear mixed-effects model (LMMs). y ~ Group*Condition + Head motion + (1 | Subject) (1) An independent sample t -test was performed to examine the group effect in the rumination and distraction conditions, with head motion controlled as the additional covariate. All analyses were conducted in R (version 4.4.2). The False Discovery Rate (FDR) correction was utilized for multiple comparisons. Results 3.1 Connectome gradient mapping during rumination The first two gradients accounting for the greatest variance were selected for the present study. Generally speaking, we found that the overall gradient architecture revealed by previous studies 28,35 remains during both rumination and distraction states in MDD patients and HCs. As illustrated in Fig. 1 , the first gradient was organized along an axis from the primary visual/sensorimotor area to the association area (named “primary-transmodal gradient” henceforth). Meanwhile, the second gradient is anchored at one end by the visual areas and by the sensorimotor area at the other end (named “visual-sensorimotor gradient” henceforth, Figs. 1 & 2 ). These gradient architectures were highly reproducible across all different conditions, scanners and datasets. 3.2 Network-wised gradient alterations during rumination We first displayed the ranges of the network-wised gradients during the rumination state and the distraction state, using both the Rum-Beijing dataset and the Rum-MDD dataset (Fig. 3 A). As expected, all the networks exhibited distributed gradients across the primary-transmodal gradient, with the DMN at one end and the SMN on the other end. On the other hand, the scores of the visual-sensorimotor gradient clearly discriminated the VN and SMN, with these two networks on the opposite sides along this gradient. Next, we examined network-level alterations in gradient values between the rumination and distraction conditions. We extracted and averaged the gradient values within each functional network, and then conducted t -tests to examine the variations of the gradient across conditions. Compared to the distraction condition, the rumination condition revealed specific alterations in gradient organization both in HC and individuals with MDD. Specifically, for the primary-transmodal gradient, we found reduced mean DMN gradient values during rumination as compared to distraction. This effect could be reproduced in both datasets (IPCAS: t (39) = -1.562, q FDR = 0.126, Cohen’s f 2 = 0.063; PKUGE: t (39) = -2.712, q FDR = 0.010, Cohen’s f 2 = 0.189; PKUSIEMENS: t (39) = -1.358, q FDR = 0.182, Cohen’s f 2 = 0.047; rum-MDD HC: t (44) = -2.554, q FDR = 0.014, Cohen’s f 2 = 0.148; rum-MDD MDD: t (43) = -2.322, q FDR = 0.025, Cohen’s f 2 = 0.125). We also found enhanced mean FPN gradient valuess (IPCAS: t (39) = 1.453, q FDR = 0.154, Cohen’s f 2 = 0.054; PKUGE: t (39) = 1.665, q FDR = 0.104, Cohen’s f 2 = 0.071; PKUSIEMENS: t (39) = -0.465, q FDR = 0.645, Cohen’s f 2 = 0.006; rum-MDD HC: t (44) = 2.777, q FDR = 0.008, Cohen’s f 2 = 0.175; rum-MDD MDD: t (43) = 2.655, q FDR = 0.011, Cohen’s f 2 = 0.164)(Fig. 3 B). We further performed paired t -tests at the parcellation level. Again, we observed the most prevalent and reproducible regional effects of the primary-transmodal gradient in the FPN and DMN regions. Those results were consistent across all three sites in the Rum-Beijing dataset. In detail, compared with the distraction state, the rumination state exhibited significantly lower gradient scores in the posterior cingulate cortex (PCC) region, which is one of the core areas of DMN (Fig. 3 B). In addition, the rumination state presented higher gradient scores in the left dorsal lateral prefrontal cortex (DLPFC), a typical area of the FPN. Furthermore, for the visual-sensorimotor gradient, participants repetitively showed higher gradient scores in the rumination state than in the distraction state in the dorsal medial prefrontal cortex (DMPFC), a typical region of DMN (Fig. 4 ). Unfortunately, these findings were not replicated in the Rum-MDD dataset. Nevertheless, we observed decreased primary-transmodal gradient values in DMN regions and increased values in FPN regions, other than PCC and DLPFC. On the other hand, the increased visual-sensorimotor gradient in the DMPFC regions could be replicated in the HCs of the Rum-MDD dataset, while not in the MDD group. No significant interaction effect or group effect was found in the Rum-MDD dataset. 3.3 Gradient alterations regarding the global metrics during rumination We observed a significantly higher explanation ratio ( t (88) = 2.124, p = 0.037, Cohen’s f² = 0.051), range ( t (88) = 2.278, p = 0.025, Cohen’s f² = 0.059), and variance ( t (88) = 2.664, p = 0.009, Cohen’s f² = 0.081) in the visual-sensorimotor gradient during rumination in patients with MDD compared to healthy controls (see Fig. 5 ). Discussion This is the first study that intends to delineate the alterations of functional gradients during an active ruminative state in both healthy adults and patients with MDD. In two independent datasets, we repetitively found that brain’s two prominent macro-scale organization dimensions, the primary-to-transmodal gradient as well as the visual-sensorimotor gradient, remained in the rumination state. We further found that large-scaled brain networks were distributed along the gradient following a specific order that was similar to resting state. Importantly, we found a reduced DMN score and an enhanced FPN score in the primary-to-transmodal gradient during rumination in both HCs and patients with MDD. Furthermore, we identified that in the rumination state, individuals with MDD exhibited significantly greater explanation ratio, gradient range, and gradient variance than healthy controls in the visual-sensorimotor gradient. Recent advances in the human brain mapping has revealed that the human brain’s activity exhibit macroscale spatial organization which could be characterized as gradients 29 . Such cortical hierarchy captured by the gradient might serve as an organizational axis of controlled psychological processes including depressive rumination 48 . An outstanding question remained, though, that whether or to what extend gradients would be altered in an active rumination state. Here, leveraging two independent datasets, we primarily found a replicable significantly decreased gradient values in the DMN during the rumination state along the primary-transmodal gradient compared to the distraction state. In previous studies, it has been observed that patients with MDD exhibit lower gradient values in the DMN compared to healthy controls 35 . From the perspective of cortical gradient architecture, a decrease in the gradient value of DMN indicates reduced functional differentiation within the macroscale topology of the brain, suggesting a shift toward lower-order, more sensorimotor-like functional characteristics 28,36 . This finding adds to a growing literature highlighting the prominent role of DMN in the neural mechanism underpinning rumination 24,38,39,49–53 . Such reductions in gradient scores may indicate a more dynamic involvement of these regions in self-referential and past-oriented cognitive processes, which are core features of ruminative thinking 21,25 . Moreover, the FPN showed a repetitive enhancement along the visual-sensorimotor gradient during the rumination state compared to the distraction state. Such enhancement in the FPN may suggest reduced interaction of this brain network with primary regions, thereby displaying a more unique functional connectivity pattern and implying decreased involvement of executive control functions during the rumination state. Importantly, the FPN plays a critical role in inhibitory control, and one potential mechanism underlying rumination is a deficiency in this capacity. Therefore, the reduced engagement of the FPN in executive control during the rumination state may indicate weakened inhibitory control, which could in turn contribute to persistent self-focus and negative ruminative thought patterns 16,54,55 . Likewise, the higher gradient values observed in the FPN during the rumination state may suggest a reduced capacity to regulate and manage spontaneous thought processes, potentially leading individuals to become trapped in repetitive negative thinking 56 . Moreover, the distinct changes observed in the DMN and FPN during the rumination state further support previous findings that an imbalance between these two networks appears to be central to MDD and may underlie the cognitive impairments associated with the disorder, such as impaired executive control and persistent rumination 57 . This also explains the results from other studies, which showed a reduction in FCs between the SFG region and both the DAN and FPN during rumination in MDD patients 40 , as well as the observed correlation between repetitive negative thinking and decreased resting-state functional connectivity (rsFC) between the DMN and FPN circuitry 58 . Regionally, in the Rum-Beijing dataset, the rumination state mainly exhibited a lower gradient score in the PCC within the DMN. This change observed in participants during rumination state suggested a decreased dissimilarity in the embedded FC pattern between primary and transmodal systems 35,59 . Previous studies have proposed that the DMN has three subsystems, they are a midline Core system, a dorsal medial prefrontal cortex (DMPFC) subsystem and a MTL subsystem 39 . The results of the current study focus on the midline core system and the DMPFC system. According to previous investigations, the midline Core system is theorized to integrate the functions of other subsystems and is implicated in introspective processes regarding one's own mental states. And the DMPFC subsystem is associated with processes such as mentalizing and metacognition 60 . The reduction in the gradient within the PCC region indicates increased similarity in its functional connectivity patterns with other brain regions, highlighting the pivotal role of the PCC in rumination. For instance, higher connectivity between the Amygdala and PCC was correlated with increased rumination, while alterations in connectivity between the left PCC and right inferior temporal gyri were linked to changes in depressive symptoms and rumination 61,62 . This is consistent with the central role of the midline core system in integrating other subsystems' functions, as well as the crucial role of self-referential processing in rumination 17,63 . When compared to the distraction state, there was a considerable drop in FC between the core and DMPFC subsystem during rumination 39 . It can be inferred that the functional differences between these two subsystems are amplified in the ruminating state. Despite the replicable alteration in the primary-to-transmodal gradient during rumination, we only found significant case-control differences regarding visual-sensorimotor gradient’s global metrics. Specifically, in the rumination state, individuals with MDD exhibited significantly greater explanation ratio, gradient range, and gradient variance than healthy controls in visual-sensorimotor gradient. According to previous findings, a higher explanation ratio suggests greater functional dominance or integrative involvement of the visual-sensorimotor gradient, whereas increased gradient range and variance reflect a more segregated and less stable functional organization 59,64 . Some studies have found that ruminative thinking is closely associated with depressive cognition presented in the form of visual mental imagery. Moreover, the type of visual information influences the extent of ruminative thinking, and the association between visual depressive cognition and the severity of depressive symptoms appears to be stronger than that of verbal depressive cognition 31,65–67 . Furthermore, the SMN is often found to shift in its gradient embedding in MDD 68 . In line with our findings, prior studies have reported reduced FC within the VIS and between the VIS and SMN in MDD. Inter-network FC involving the VIS correlates positively with depressive severity, while intra-VIS FC shows negative associations with both symptoms and cognitive deficits. These alterations support the notion that the VIS and the SMN—networks involved in sensory integration and motor control—are commonly disrupted in affective disorders 69–71 . These findings indicate enhanced differentiation and topographic reorganization along visual-sensorimotor gradient in the functional connectome of individuals with MDD during rumination, and further reflect greater functional segregation between the visual and sensorimotor cortices 28,72 . In this state, the VIS appears to be predominantly engaged in generating internal imagery rather than responding to external stimuli, while the SMN, typically involved in action preparation and execution, becomes disengaged or behaviorally decoupled. As a result, these two systems become functionally decoupled and spatially distant within connectome gradient space—indicative of a shift from an externally oriented, integrated perceptual-motor-visual mode of processing toward a highly internalized, perceptually decoupled state characteristic of rumination in MDD 73–75 . Interestingly, no significant gradient value difference was found between MDD and HC in the distraction state in the Rum-MDD dataset. Distraction states have the impact of diverting attention, attempting to manage attention, and lowering unpleasant feelings 76 . It has been reported that in a rumination state, negative emotions sustain it by narrowing attention span and leading to difficulty separation from information when it is no longer relevant. However, this situation can be most effectively affected by the distraction state in a short period of time 55 . Studies have indicated that rumination involves greater inner speech than non-rumination, and it might manifest as verbal or mental images. Research has demonstrated that rumination was linked to comparable emotional states, high-frequency heart rate variability, and skin conductance response, independent of whether subjects were prompted to rumination state by verbal thinking or mental image. However, compared with the distraction state in the form of verbal thinking, the distraction state in the form of mental imagery resulted in greater affective improvement and greater high-frequency heart rate variability, while there was no significant difference in skin conductance response 76,77 . These evidences suggest that distraction may be utilized as a vital measure to improve the adverse reactions caused by rumination state, and may be applied to the clinical treatment of depression. One strength of our study is the test-retest reliability and out-of-sample validation. However, this study also comes with certain limitations that could be addressed in future research. The use of the Schaefer’s 400 template for whole brain gradient calculation may be considered too coarse. To enhance precision, it is suggested that future studies employ a more detailed template, potentially down to the level of cortical vertices, following the trend observed in current gradient research. Furthermore, the absence of a correlation between gradient scores and behavioral data in this study highlights an avenue for improvement. Subsequent investigations may benefit from employing a broader range of meta-correlations to explore the functional significance of gradient scores. Besides, considering that the MDD patients included in this study were undergoing medication treatment, the potential effects of medication on the study's results should be taken into account. In addition, this study did not investigate the changes in rumination status before and after depression treatment, and more follow-up researches are needed to explore this point. Lastly, given the pivotal role of the DMN in the gradient distribution of brain FC and its connection to rumination, future studies could delve into exploring the gradient within the DMN. This could provide deeper insights into the mechanism of DMN changes in the rumination state of patients with depression. In the present study, both healthy subjects and MDD patients maintained the primary-transmodal gradient and visual-sensorimotor gradients in both the rumination and distraction states. At the network level, convergent alterations in network gradients emerged during rumination, characterized by decreased DMN scores and elevated FPN scores on the primary-transmodal gradient in both groups. Notably, this pattern was consistently observed in the MDD group. Furthermore, MDD-specific dysregulation manifested as significantly heightened gradient range, variance, and explanation ratio in visual-sensorimotor gradient during rumination. Collectively, our study identified the characteristic features of functional connectivity gradients during the rumination state, including enhanced processing of self-referential and negative thinking, alongside diminished regulation and coping mechanisms. Critically, we observed prominent differences in these patterns between MDD individuals and HCs. These findings suggest that the distinct gradient patterns in MDD patients reflect dysfunctional large-scale network reorganization, which may underlie the neural mechanisms of depressive rumination. Declarations Acknowledgment This work was funded by the National Natural Science Foundation of China (No. 32300933, No. 82122035, No. 81671774, and No. 81630031), the Beijing Nova Program of Science and Technology (No. 20230484465), the Beijing Natural Science Foundation (No. J230040), the Sci-Tech Innovation 2030 - Major Project of Brain Science and Brain-inspired Intelligence Technology (No. 2021ZD0200600), the Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences (No. E2CX4425YZ, No. E3CX1315 and No. Y9CX422005). Conflict of Interest The authors declare no conflict of interest. References Watkins, E. R. & Roberts, H. Reflecting on rumination: Consequences, causes, mechanisms and treatment of rumination. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7277358","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":497754287,"identity":"4e14ae58-0106-4eea-8d81-de09413d2031","order_by":0,"name":"Xiao Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYDACCRBhAGYYPgBSPHykaDEGUTxsxGmBMMzAbIJa+Gc3H3vMU2AnZy7dvK3ya46dDBsD88NHN/BZcudYuuEMg2RjyznHym7LbksGOozN2DgHjxYDiRwziQ8GzIkbbuSY3ZbcxgzUwsMmjV9L/jeJBIN6sJZiyW31xGjJYQPachishfHjtsOEtUjcSDOTnGFw3NhyRlqxNOO24zxszAT8wj8j+Zk0z59qOXOJ5I0ff26rtudnb374GJ8WhAuBmJkHxGImRjlMC+MPYlWPglEwCkbBiAIA4uFBD9l1hKYAAAAASUVORK5CYII=","orcid":"","institution":"Institute of Psychology, Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Chen","suffix":""},{"id":497754288,"identity":"180b61e2-5e2e-4fbf-be55-5d91b43fecb2","order_by":1,"name":"Zheng-Jia-Yi Hu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zheng-Jia-Yi","middleName":"","lastName":"Hu","suffix":""},{"id":497754289,"identity":"7717f7c5-e007-4137-8632-506b1dfa71f1","order_by":2,"name":"Feng Jia","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Jia","suffix":""},{"id":497754290,"identity":"46a07a1d-1020-4558-888b-870676f1a224","order_by":3,"name":"Yan-Song Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yan-Song","middleName":"","lastName":"Liu","suffix":""},{"id":497754291,"identity":"9a621c6c-dc49-4082-9069-856eb3819e2c","order_by":4,"name":"Chaogan Yan","email":"","orcid":"https://orcid.org/0000-0003-3413-5977","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Chaogan","middleName":"","lastName":"Yan","suffix":""}],"badges":[],"createdAt":"2025-08-02 10:00:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7277358/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7277358/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89230724,"identity":"f35c0ee2-d9f8-4ac7-a864-6e58511684f7","added_by":"auto","created_at":"2025-08-17 14:15:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2761523,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the primary-transmodal gradient and the visual-sensorimotor gradient of the rumination and distraction states in 3 sites in the Rum-Beijing and the Rum-MDD datasets.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7277358/v1/86a76a3b688a914bbd7211d1.png"},{"id":89232057,"identity":"2ab09a4d-fc5f-4c5f-bb97-463b913279e9","added_by":"auto","created_at":"2025-08-17 14:23:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3002452,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots of the gradient score for the first two connectome gradients in rumination states in 3 sites in the Rum-Beijing and the Rum-MDD datasets. Each dot was colored according to 7 networks of cortical parcellation and a subcortical network. Abbreviations: SMN, somatomotor network; VN, visual network; DAN, dorsal attention network; VAN, ventral attention network; LN, limbic network; FPN, fronto-parietal network; DMN, default mode network; SC, subcortical network.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7277358/v1/e3b8c229635abc2c7b8994f6.png"},{"id":89232058,"identity":"6915467b-5bb9-4f3f-b827-bb93837172a0","added_by":"auto","created_at":"2025-08-17 14:23:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3368383,"visible":true,"origin":"","legend":"\u003cp\u003eA) The range of gradient values across the 7 networks in all 3 sites in the Rum-Beijing dataset and in the Rum-MDD dataset in the primary-to-transmodal gradient as well as the visual-sensorimotor gradient. B) Bar plot showing network-wise comparison of gradient value differences between rumination and distraction states. Note the replicability of the reduced DMN’s primary-transmodal gradient values in the rumination condition as compared to the distraction condition. Abbreviations: SMN, somatomotor network; VN, visual network; DAN, dorsal attention network; VAN, ventral attention network; LN, limbic network; FPN, fronto-parietal network; DMN, default mode network. *: \u003cem\u003eq\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7277358/v1/a216bf0c3b9205e06df77f04.png"},{"id":89230720,"identity":"7215166c-ecf9-4b44-a2b4-6cac98f6e4b9","added_by":"auto","created_at":"2025-08-17 14:15:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1177443,"visible":true,"origin":"","legend":"\u003cp\u003eRegions with significant differences between the rumination state and the distraction state in primary-transmodal gradients and visual-sensorimotor gradients in all 3 sites in the Rum-Beijing dataset and in the Rum-MDD dataset. Abbreviations: rum., rumination; dis., distraction.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7277358/v1/f1078a572f415d0c71c3ed18.png"},{"id":89230735,"identity":"6e1c4df7-044a-4b67-aa45-39897751674e","added_by":"auto","created_at":"2025-08-17 14:15:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":914464,"visible":true,"origin":"","legend":"\u003cp\u003eA) The global metrics of the visual-sensorimotor gradient in rumination state of the Rum-MDD dataset. B) Distribution of the visual-sensorimotor gradient of the rumination in the Rum-MDD datasets. Abbreviations: HC, healthy controls; MDD, major depressive disorder. *: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; **: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7277358/v1/21a2d409c6fabcd110fe0c76.png"},{"id":89233634,"identity":"b6cae0cb-1112-473c-bcdd-bd5cd02af619","added_by":"auto","created_at":"2025-08-17 14:39:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10879185,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7277358/v1/67aa1fe4-479f-4b26-97ca-f97f8fa24385.pdf"},{"id":89232056,"identity":"691780ca-c811-4a8f-9d2e-e360b1dc747e","added_by":"auto","created_at":"2025-08-17 14:23:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5772409,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7277358/v1/e37d2c8379b35902dda13437.docx"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Characterizing gradients of functional connectome underpinning rumination and their alteration in depression","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRumination is repetitive thinking on the symptoms, situations, causes, meanings, and possible causes as well as consequences of distress \u003csup\u003e1\u003c/sup\u003e. Rumination is associated with higher susceptibility to depressive episodes and predicts the onset of depression \u003csup\u003e2,3\u003c/sup\u003e. Furthermore, it not only impairs problem-solving behavior but also predicts delayed treatment response and higher relapse rates in major depressive disorder (MDD) \u003csup\u003e3,4\u003c/sup\u003e. Existing evidence has shown that rumination is a treatable psychological process and could be alleviated by several interventions such as mindfulness meditation, rumination-focused cognitive behavioral therapy, repetitive transcranial magnetic stimulation (rTMS), real-time neurofeedback, and transcranial Direct Current stimulation (tDCS) \u003csup\u003e5\u0026ndash;9\u003c/sup\u003e. Given the recently highlighted association between the clinical efficiency of neuromodulation techniques and the degree to which they have targeted the underlying functional network underpinnings of a given symptom \u003csup\u003e10,11\u003c/sup\u003e, a better understanding of the rumination\u0026rsquo;s underlying functional network mechanism may lead to the development of next-generation rumination-targeted treatment of MDD \u003csup\u003e12\u0026ndash;14\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eExisting research has highlighted the pivotal roles of the local brain activities and within-network interactions of large-scale brain networks, especially the default mode network (DMN) and frontoparietal network (FPN), in the neural underpinnings of rumination \u003csup\u003e15\u0026ndash;19\u003c/sup\u003e. Task-based functional magnetic resonance imaging (fMRI) studies have found increased activity in the typical DMN regions, including the posterior cingulate cortex (PCC), medial temporal regions and medial prefrontal cortex (MPFC), as well as the FPN regions, including the inferior parietal lobe \u003csup\u003e20\u0026ndash;24\u003c/sup\u003e. Furthermore, brain activities in DMN and FPN regions and functional coupling among nodes of these two networks, typically characterized using resting-state fMRI (R-fMRI), were also found to be associated with participants\u0026rsquo; self-reported rumination tendency \u003csup\u003e25\u0026ndash;27\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTraditional functional connectivity (FC) methods may be insufficient to fully characterize the dynamic changes and complex functional organization of the brain. The cortical gradient is an analytical framework that captures the continuous, spatially organized pattern of variation in a specific characteristic across the cerebral cortex \u003csup\u003e28\u0026ndash;30\u003c/sup\u003e. Since rumination is a recurrent and automatic form of emotional regulation and a particularly intense and concentrated form of inner speech \u003csup\u003e31\u003c/sup\u003e, it may bear specific alterations regarding the global properties of the functional brain network. The cortical gradients place discrete large-scale networks and associated regional units on a continuous spectrum, extending from unimodal systems that support perception and action to association areas involved in more abstract cognitive functions. Such an approach reveals that the organization of the brain is not merely a collection of isolated regions and networks but rather a dynamic and continuous system. This holistic and continuous perspective provides a more comprehensive understanding of the brain's structure and function and can effectively capture reliable features of the interpretable functional biology of the human brain \u003csup\u003e32\u0026ndash;34\u003c/sup\u003e. By analyzing connectivity data in both humans and macaques, Margulies and colleagues \u003csup\u003e28\u003c/sup\u003e identified a primary-transmodal gradient (Gradient 1) that ranges from a major sensorimotor functional area on one end to a cross-modal area known as the DMN on the other and a visual-sensorimotor gradient (Gradient 2) differentiates from the sensorimotor and auditory cortex to the visual cortex.\u003c/p\u003e\u003cp\u003eIt is worth noting that some studies have explored gradient abnormality in MDD. Patients with MDD showed a compressed primary-transmodal gradient. The MDD group showed lower gradient scores, mostly in the DMN, than healthy controls. The regional changes also include parallel alterations in subcortical regions such as the caudate, amygdala, and thalamus. In addition, gene transcription profiles accounted for 53.9% of the gradient pattern changes. The patients' baseline gradient maps significantly predicted symptom improvement after treatment \u003csup\u003e35,36\u003c/sup\u003e. Similarly, the study finds reduced cortical gradient dispersion in the primary intrinsic brain networks of treatment-resistant depression patients. Decreased dispersion within nodes of the default mode, control, and salience networks correlates with baseline levels of trait anxiety, depression, and mindfulness. Meanwhile, baseline dispersion in the salience network predicts trait anxiety scores at 24 weeks post-intervention \u003csup\u003e37\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThese insightful studies have shed light on rumination\u0026rsquo;s neural underpinnings, albeit caveats remain. The block-designed task-fMRI studies can reveal brain activities during active rumination, but it is relatively difficult to investigate the functional coupling among remote brain regions. R-fMRI is suitable for investigating the functional network features and cortical gradients, but its association with self-reported rumination traits only provides indirect evidence of rumination\u0026rsquo;s network mechanism. To address these issues, we developed a rumination state task that induced participants into an active, continuous rumination state and investigated the network underpinnings of rumination. Our previous studies using this paradigm have revealed reproducible FC and dynamic stability mechanisms underlying rumination \u003csup\u003e38,39\u003c/sup\u003e, as well as abnormalities regarding degree centrality in MDD patients \u003csup\u003e40\u003c/sup\u003e. The induced, continuous, active rumination state is suitable for characterizing cortical gradients as this metric is calculated based on an FC matrix, which reveals the synchronization among activities of remote brain regions during a period of time (~\u0026thinsp;8 minutes). The recent \"replication crisis\" across various scientific disciplines has sparked widespread concerns about the reliability of published findings, especially those neuroimaging results \u003csup\u003e41,42\u003c/sup\u003e. Therefore, it is crucial to first ensure the replicability of any novel findings \u003csup\u003e43\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHere, we first aimed to examine the gradient profile of the functional connectome during an active rumination state in a group of healthy adults. Of note, we ensured the replicability of our findings by implementing a repeated-measured design using different scanners at different locations. Then we further explored the abnormality of this rumination gradient profile in MDD patients using an independent validation dataset. We hypothesized that both healthy and MDD individuals would present a narrower primary-to-transmodal cortical gradient during the rumination state regarding the global gradient metrics and lower gradient scores in the DMN. We further hypothesized that, compared to healthy individuals, the MDD group exhibits abnormal macroscale organizations alongside both gradients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Participants\u003c/h2\u003e\u003cp\u003eThis study used two independent datasets \u003csup\u003e39,40\u003c/sup\u003e. We have openly shared our neuroimaging data from Rum-Beijing through the R-fMRI Maps project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rfmri.org/RuminationfMRIData\u003c/span\u003e\u003cspan address=\"http://rfmri.org/RuminationfMRIData\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe Rum-Beijing dataset consists of 41 healthy adults recruited from the local community around the Institute of Psychology, Chinese Academy of Sciences, Beijing, China. The local Institutional Review Board approved the study protocol for this dataset, and all participants signed an informed consent form. For more details about this dataset, please read our previous study \u003cb\u003eThe Subsystem Mechanism of Default Mode Network Underlying Rumination: a Reproducible Neuroimaging Study\u003c/b\u003e\u003csup\u003e\u003cb\u003e39\u003c/b\u003e\u003c/sup\u003e. For the Rum-MDD dataset, we recruited participants Guangji Hospital in Suzhou, Jiangsu, China. The local Institutional Review Board approved the study protocol for this dataset as well, and all participants signed an informed consent form. All individuals diagnosed with MDD underwent a 17-item assessment using the Hamilton Depression Rating Scale (HAMD). Healthy controls (HCs) were recruited from the local community through advertisements. MDD patients were required to meet DSM-5 criteria through a structured clinical interview conducted by a trained evaluator and to have a HAMD score of \u0026ge;\u0026thinsp;17. For more detailed exclusion criteria for both MDD and HC, please refer to Jia et al.\u003csup\u003e36\u003c/sup\u003e. The analytical sample comprised 45 MDD patients (33 females, mean age\u0026thinsp;=\u0026thinsp;26.09) and 46 healthy controls (35 females, mean age\u0026thinsp;=\u0026thinsp;29.22, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographical information of participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSex (female/male)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRRS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRRS-B\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRRS-R\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHAMD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eRum-Beijing\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealthy Control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22/41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.71 (4.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46 (11.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.24 (3.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.85 (2.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eRum-MDD\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDD Patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33/45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.09 (8.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61.08 (9.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.58 (2.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.16 (2.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e23.29 (5.41)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealthy Control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35/46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.22 (10.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.10 (8.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.10 (2.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.35 (2.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eRRS: Ruminative Response Scale; RRS-R: the reflection subscale of RRS; RRS-B: the brooding subscale of RRS; HAMD: Hamilton depression rating scale; HC, healthy control; MDD, major depressive disorder.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifferences in gradient explanation ratio, range, variance, and gradient dispersion in three sites between rumination and distraction on the Rum-Beijing dataset and the Rum-MDD dataset\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"21\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" 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nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eIPCAS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003ePKUGE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003ePKUSIEMENS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c16\"\u003e\u003cp\u003eMDD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c20\"\u003e\u003cp\u003eHC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCohen\u0026rsquo;s f\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eCohen\u0026rsquo;s f\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eCohen\u0026rsquo;s f\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003eCohen\u0026rsquo;s f\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003eCohen\u0026rsquo;s f\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eVisual-sensorimotor gradient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExplanation ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3.789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e-0.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e0.453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4.577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e3.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.002**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e1.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e0.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.030*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e4.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e1.698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"21\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Experimental design\u003c/h2\u003e\u003cp\u003eAll participants of these two datasets finished RST. Before the MRI scan, the researcher interviewed the participants to brief the purpose of the study and the definition of the psychological state during the scan. The rumination state was defined as \"passive and repetitive thinking about negative events and their possible consequences,\" and the distraction state was defined as \"imagining images unrelated to oneself.\u0026rdquo; The effect of the distraction state was to prevent the subject from continuously immersing in the rumination state and to provide the comparison condition \u003csup\u003e39\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe fMRI scan consists of four states. First, during the resting state, the subject was asked to look at the fixation on the screen without engaging in specific thoughts. Then, during the sad memory state, the subject was prompted to recall negative autobiographical events triggered by keywords collected before the scanning. During the rumination state, participants were asked to reflect on themselves. Finally, during the distraction state, participants were prompted to imagine some objective scenarios. Except for the resting state, all mental states (sad memory, rumination, and distraction) included four sequentially displayed stimuli (keywords or prompts). An 8-minute continuous mental state was created by transitioning from one stimulus to the next every two minutes, without any inter-stimulus intervals (ISI). While the order of rumination and distraction states was balanced among subjects, the resting state and recall of negative autobiographical events always came first and second.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 MRI data acquisition and preprocessing\u003c/h2\u003e\u003cp\u003eAll participants from the Rum-Beijing dataset repetitively underwent MRI scans on 3 different scanners at two sites: two 3 Tesla GE MR750 scanners at the Institute of Psychology, Chinese Academy of Sciences (IPCAS), and Peking University Magnetic Resonance Imaging Research Center (PKUGE), as well as a 3 Tesla SIEMENS PRISMA scanner (PKUSIEMENS) at Peking University Magnetic Resonance Imaging Research Center. MRI images from the RUM-MDD dataset were acquired in a 3 Tesla SIEMENS SKYRA scanner at Guangji Hospital. Before functional image acquisition, all participants underwent a 3D T1 scan, and functional images for both resting state and mental states (sad memory, rumination, and distraction) were obtained. Data preprocessing was implemented with the toolbox for Data Processing \u0026amp; Analysis for Brain Imaging on Surface (DPABISurf) \u003csup\u003e44\u003c/sup\u003e. For details, please refer to the Supplementary Information.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Functional connectome and gradient mapping\u003c/h2\u003e\u003cp\u003eFollowing data preprocessing, cortical data were mapped onto the cortical parcellation using an atlas proposed by Schaefer and colleagues \u003csup\u003e45\u003c/sup\u003e, consisting of 400 parcellations. The 54-parcellation Tian's subcortical atlas \u003csup\u003e46\u003c/sup\u003e was used to extract functional signals from the subcortical regions. Subsequently, the time series of regions of interest (ROIs) were extracted to calculate Pearson correlation coefficients which underwent the Fisher\u0026rsquo;s \u003cem\u003er\u003c/em\u003e-to-\u003cem\u003ez\u003c/em\u003e transformation, yielding a 454\u0026times;454 FC matrix.\u003c/p\u003e\u003cp\u003eWe used the BrainSpace toolbox \u003csup\u003e30\u003c/sup\u003e run in a Matlab R2020a (The MathWorks Inc., Natick, MA, US) environment to calculate gradient matrices for resting state, sad memory, rumination, and distraction states within each group. The input matrix was sparsified to achieve 10% sparsity, and then a cosine similarity matrix was calculated, according to Vos de Wael et al. \u003csup\u003e30\u003c/sup\u003e. Subsequently, a normalized angle matrix was adjusted to eliminate negative values within the similarity matrix, following the methods outlined by Vos de Wael et al. \u003csup\u003e30\u003c/sup\u003e and Paquola et al. \u003csup\u003e47\u003c/sup\u003e. Diffusion map embedding was used to capture the gradient component that explains the variance in the functional connectome pattern. The Procrustes rotation was applied to realign all the connectome gradients. Furthermore, the explanation ratio of each gradient was also computed to provide insight into the relative contribution of that gradient, calculated by dividing the eigenvalue of a specific gradient by the total sum of eigenvalues.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\u003cp\u003eWe first computed the average first and second gradients across all the subjects in the rumination/distractions condition. To test the replicability, we calculated these average gradient distribution maps in all the repetitive scans in the Rum-Beijing dataset and both groups in the Rum-MDD dataset. We further examined the network-level gradient features by aggregating the gradients across all subjects and grouping the 400 parcellations according to Yeo\u0026rsquo;s 7-network atlas, followed by calculating the mean gradient values within each network. A number of global metrics including explanation ratio, range, and variance of both gradients were examined. We also examined the gradient values at the node level.\u003c/p\u003e\u003cp\u003eWe applied paired \u003cem\u003et\u003c/em\u003e-tests by fitting a general linear model (GLM) to test the condition effect (rumination vs. distraction) in both Rum-Beijing and Rum-MDD datasets. The head motion was included as a covariate. In the Rum-MDD dataset, we further examined the interaction effect between the condition (rumination vs. distraction) and the group (MDD vs. HC) effect by fitting a linear mixed-effects model (LMMs).\u003c/p\u003e\u003cp\u003ey\u0026thinsp;~\u0026thinsp;Group*Condition\u0026thinsp;+\u0026thinsp;Head motion + (1 | Subject) (1)\u003c/p\u003e\u003cp\u003eAn independent sample \u003cem\u003et\u003c/em\u003e-test was performed to examine the group effect in the rumination and distraction conditions, with head motion controlled as the additional covariate. All analyses were conducted in R (version 4.4.2). The False Discovery Rate (FDR) correction was utilized for multiple comparisons.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Connectome gradient mapping during rumination\u003c/h2\u003e\u003cp\u003eThe first two gradients accounting for the greatest variance were selected for the present study. Generally speaking, we found that the overall gradient architecture revealed by previous studies \u003csup\u003e28,35\u003c/sup\u003e remains during both rumination and distraction states in MDD patients and HCs. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the first gradient was organized along an axis from the primary visual/sensorimotor area to the association area (named \u0026ldquo;primary-transmodal gradient\u0026rdquo; henceforth). Meanwhile, the second gradient is anchored at one end by the visual areas and by the sensorimotor area at the other end (named \u0026ldquo;visual-sensorimotor gradient\u0026rdquo; henceforth, Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026amp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These gradient architectures were highly reproducible across all different conditions, scanners and datasets.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Network-wised gradient alterations during rumination\u003c/h2\u003e\u003cp\u003eWe first displayed the ranges of the network-wised gradients during the rumination state and the distraction state, using both the Rum-Beijing dataset and the Rum-MDD dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). As expected, all the networks exhibited distributed gradients across the primary-transmodal gradient, with the DMN at one end and the SMN on the other end. On the other hand, the scores of the visual-sensorimotor gradient clearly discriminated the VN and SMN, with these two networks on the opposite sides along this gradient.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNext, we examined network-level alterations in gradient values between the rumination and distraction conditions. We extracted and averaged the gradient values within each functional network, and then conducted \u003cem\u003et\u003c/em\u003e-tests to examine the variations of the gradient across conditions. Compared to the distraction condition, the rumination condition revealed specific alterations in gradient organization both in HC and individuals with MDD. Specifically, for the primary-transmodal gradient, we found reduced mean DMN gradient values during rumination as compared to distraction. This effect could be reproduced in both datasets (IPCAS: \u003cem\u003et\u003c/em\u003e(39) = -1.562, \u003cem\u003eq\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 0.126, Cohen\u0026rsquo;s \u003cem\u003ef\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.063; PKUGE: \u003cem\u003et\u003c/em\u003e(39) = -2.712, \u003cem\u003eq\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 0.010, Cohen\u0026rsquo;s \u003cem\u003ef\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.189; PKUSIEMENS: \u003cem\u003et\u003c/em\u003e(39) = -1.358, \u003cem\u003eq\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e = 0.182, Cohen\u0026rsquo;s \u003cem\u003ef\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.047; rum-MDD HC: \u003cem\u003et\u003c/em\u003e(44) = -2.554, \u003cem\u003eq\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 0.014, Cohen\u0026rsquo;s \u003cem\u003ef\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.148; rum-MDD MDD: \u003cem\u003et\u003c/em\u003e(43) = -2.322, \u003cem\u003eq\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 0.025, Cohen\u0026rsquo;s \u003cem\u003ef\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.125). We also found enhanced mean FPN gradient valuess (IPCAS: \u003cem\u003et\u003c/em\u003e(39)\u0026thinsp;=\u0026thinsp;1.453, \u003cem\u003eq\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 0.154, Cohen\u0026rsquo;s \u003cem\u003ef\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.054; PKUGE: \u003cem\u003et\u003c/em\u003e(39)\u0026thinsp;=\u0026thinsp;1.665, \u003cem\u003eq\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 0.104, Cohen\u0026rsquo;s \u003cem\u003ef\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.071; PKUSIEMENS: \u003cem\u003et\u003c/em\u003e(39) = -0.465, \u003cem\u003eq\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e = 0.645, Cohen\u0026rsquo;s \u003cem\u003ef\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.006; rum-MDD HC: \u003cem\u003et\u003c/em\u003e(44)\u0026thinsp;=\u0026thinsp;2.777, \u003cem\u003eq\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 0.008, Cohen\u0026rsquo;s \u003cem\u003ef\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.175; rum-MDD MDD: \u003cem\u003et\u003c/em\u003e(43)\u0026thinsp;=\u0026thinsp;2.655, \u003cem\u003eq\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 0.011, Cohen\u0026rsquo;s \u003cem\u003ef\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.164)(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eWe further performed paired \u003cem\u003et\u003c/em\u003e-tests at the parcellation level. Again, we observed the most prevalent and reproducible regional effects of the primary-transmodal gradient in the FPN and DMN regions. Those results were consistent across all three sites in the Rum-Beijing dataset. In detail, compared with the distraction state, the rumination state exhibited significantly lower gradient scores in the posterior cingulate cortex (PCC) region, which is one of the core areas of DMN (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In addition, the rumination state presented higher gradient scores in the left dorsal lateral prefrontal cortex (DLPFC), a typical area of the FPN. Furthermore, for the visual-sensorimotor gradient, participants repetitively showed higher gradient scores in the rumination state than in the distraction state in the dorsal medial prefrontal cortex (DMPFC), a typical region of DMN (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Unfortunately, these findings were not replicated in the Rum-MDD dataset. Nevertheless, we observed decreased primary-transmodal gradient values in DMN regions and increased values in FPN regions, other than PCC and DLPFC. On the other hand, the increased visual-sensorimotor gradient in the DMPFC regions could be replicated in the HCs of the Rum-MDD dataset, while not in the MDD group.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNo significant interaction effect or group effect was found in the Rum-MDD dataset.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Gradient alterations regarding the global metrics during rumination\u003c/h2\u003e\u003cp\u003eWe observed a significantly higher explanation ratio (\u003cem\u003et\u003c/em\u003e(88)\u0026thinsp;=\u0026thinsp;2.124, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037, Cohen\u0026rsquo;s f\u0026sup2; = 0.051), range (\u003cem\u003et\u003c/em\u003e(88)\u0026thinsp;=\u0026thinsp;2.278, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025, Cohen\u0026rsquo;s f\u0026sup2; = 0.059), and variance (\u003cem\u003et\u003c/em\u003e(88)\u0026thinsp;=\u0026thinsp;2.664, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009, Cohen\u0026rsquo;s f\u0026sup2; = 0.081) in the visual-sensorimotor gradient during rumination in patients with MDD compared to healthy controls (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first study that intends to delineate the alterations of functional gradients during an active ruminative state in both healthy adults and patients with MDD. In two independent datasets, we repetitively found that brain\u0026rsquo;s two prominent macro-scale organization dimensions, the primary-to-transmodal gradient as well as the visual-sensorimotor gradient, remained in the rumination state. We further found that large-scaled brain networks were distributed along the gradient following a specific order that was similar to resting state. Importantly, we found a reduced DMN score and an enhanced FPN score in the primary-to-transmodal gradient during rumination in both HCs and patients with MDD. Furthermore, we identified that in the rumination state, individuals with MDD exhibited significantly greater explanation ratio, gradient range, and gradient variance than healthy controls in the visual-sensorimotor gradient.\u003c/p\u003e\u003cp\u003eRecent advances in the human brain mapping has revealed that the human brain\u0026rsquo;s activity exhibit macroscale spatial organization which could be characterized as gradients \u003csup\u003e29\u003c/sup\u003e. Such cortical hierarchy captured by the gradient might serve as an organizational axis of controlled psychological processes including depressive rumination \u003csup\u003e48\u003c/sup\u003e. An outstanding question remained, though, that whether or to what extend gradients would be altered in an active rumination state. Here, leveraging two independent datasets, we primarily found a replicable significantly decreased gradient values in the DMN during the rumination state along the primary-transmodal gradient compared to the distraction state. In previous studies, it has been observed that patients with MDD exhibit lower gradient values in the DMN compared to healthy controls \u003csup\u003e35\u003c/sup\u003e. From the perspective of cortical gradient architecture, a decrease in the gradient value of DMN indicates reduced functional differentiation within the macroscale topology of the brain, suggesting a shift toward lower-order, more sensorimotor-like functional characteristics \u003csup\u003e28,36\u003c/sup\u003e. This finding adds to a growing literature highlighting the prominent role of DMN in the neural mechanism underpinning rumination \u003csup\u003e24,38,39,49\u0026ndash;53\u003c/sup\u003e. Such reductions in gradient scores may indicate a more dynamic involvement of these regions in self-referential and past-oriented cognitive processes, which are core features of ruminative thinking \u003csup\u003e21,25\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMoreover, the FPN showed a repetitive enhancement along the visual-sensorimotor gradient during the rumination state compared to the distraction state. Such enhancement in the FPN may suggest reduced interaction of this brain network with primary regions, thereby displaying a more unique functional connectivity pattern and implying decreased involvement of executive control functions during the rumination state. Importantly, the FPN plays a critical role in inhibitory control, and one potential mechanism underlying rumination is a deficiency in this capacity. Therefore, the reduced engagement of the FPN in executive control during the rumination state may indicate weakened inhibitory control, which could in turn contribute to persistent self-focus and negative ruminative thought patterns \u003csup\u003e16,54,55\u003c/sup\u003e. Likewise, the higher gradient values observed in the FPN during the rumination state may suggest a reduced capacity to regulate and manage spontaneous thought processes, potentially leading individuals to become trapped in repetitive negative thinking \u003csup\u003e56\u003c/sup\u003e. Moreover, the distinct changes observed in the DMN and FPN during the rumination state further support previous findings that an imbalance between these two networks appears to be central to MDD and may underlie the cognitive impairments associated with the disorder, such as impaired executive control and persistent rumination \u003csup\u003e57\u003c/sup\u003e. This also explains the results from other studies, which showed a reduction in FCs between the SFG region and both the DAN and FPN during rumination in MDD patients \u003csup\u003e40\u003c/sup\u003e, as well as the observed correlation between repetitive negative thinking and decreased resting-state functional connectivity (rsFC) between the DMN and FPN circuitry \u003csup\u003e58\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRegionally, in the Rum-Beijing dataset, the rumination state mainly exhibited a lower gradient score in the PCC within the DMN. This change observed in participants during rumination state suggested a decreased dissimilarity in the embedded FC pattern between primary and transmodal systems \u003csup\u003e35,59\u003c/sup\u003e. Previous studies have proposed that the DMN has three subsystems, they are a midline Core system, a dorsal medial prefrontal cortex (DMPFC) subsystem and a MTL subsystem \u003csup\u003e39\u003c/sup\u003e. The results of the current study focus on the midline core system and the DMPFC system. According to previous investigations, the midline Core system is theorized to integrate the functions of other subsystems and is implicated in introspective processes regarding one's own mental states. And the DMPFC subsystem is associated with processes such as mentalizing and metacognition \u003csup\u003e60\u003c/sup\u003e. The reduction in the gradient within the PCC region indicates increased similarity in its functional connectivity patterns with other brain regions, highlighting the pivotal role of the PCC in rumination. For instance, higher connectivity between the Amygdala and PCC was correlated with increased rumination, while alterations in connectivity between the left PCC and right inferior temporal gyri were linked to changes in depressive symptoms and rumination \u003csup\u003e61,62\u003c/sup\u003e. This is consistent with the central role of the midline core system in integrating other subsystems' functions, as well as the crucial role of self-referential processing in rumination \u003csup\u003e17,63\u003c/sup\u003e. When compared to the distraction state, there was a considerable drop in FC between the core and DMPFC subsystem during rumination \u003csup\u003e39\u003c/sup\u003e. It can be inferred that the functional differences between these two subsystems are amplified in the ruminating state.\u003c/p\u003e\u003cp\u003eDespite the replicable alteration in the primary-to-transmodal gradient during rumination, we only found significant case-control differences regarding visual-sensorimotor gradient\u0026rsquo;s global metrics. Specifically, in the rumination state, individuals with MDD exhibited significantly greater explanation ratio, gradient range, and gradient variance than healthy controls in visual-sensorimotor gradient. According to previous findings, a higher explanation ratio suggests greater functional dominance or integrative involvement of the visual-sensorimotor gradient, whereas increased gradient range and variance reflect a more segregated and less stable functional organization \u003csup\u003e59,64\u003c/sup\u003e. Some studies have found that ruminative thinking is closely associated with depressive cognition presented in the form of visual mental imagery. Moreover, the type of visual information influences the extent of ruminative thinking, and the association between visual depressive cognition and the severity of depressive symptoms appears to be stronger than that of verbal depressive cognition \u003csup\u003e31,65\u0026ndash;67\u003c/sup\u003e. Furthermore, the SMN is often found to shift in its gradient embedding in MDD \u003csup\u003e68\u003c/sup\u003e. In line with our findings, prior studies have reported reduced FC within the VIS and between the VIS and SMN in MDD. Inter-network FC involving the VIS correlates positively with depressive severity, while intra-VIS FC shows negative associations with both symptoms and cognitive deficits. These alterations support the notion that the VIS and the SMN\u0026mdash;networks involved in sensory integration and motor control\u0026mdash;are commonly disrupted in affective disorders \u003csup\u003e69\u0026ndash;71\u003c/sup\u003e. These findings indicate enhanced differentiation and topographic reorganization along visual-sensorimotor gradient in the functional connectome of individuals with MDD during rumination, and further reflect greater functional segregation between the visual and sensorimotor cortices \u003csup\u003e28,72\u003c/sup\u003e. In this state, the VIS appears to be predominantly engaged in generating internal imagery rather than responding to external stimuli, while the SMN, typically involved in action preparation and execution, becomes disengaged or behaviorally decoupled. As a result, these two systems become functionally decoupled and spatially distant within connectome gradient space\u0026mdash;indicative of a shift from an externally oriented, integrated perceptual-motor-visual mode of processing toward a highly internalized, perceptually decoupled state characteristic of rumination in MDD \u003csup\u003e73\u0026ndash;75\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eInterestingly, no significant gradient value difference was found between MDD and HC in the distraction state in the Rum-MDD dataset. Distraction states have the impact of diverting attention, attempting to manage attention, and lowering unpleasant feelings \u003csup\u003e76\u003c/sup\u003e. It has been reported that in a rumination state, negative emotions sustain it by narrowing attention span and leading to difficulty separation from information when it is no longer relevant. However, this situation can be most effectively affected by the distraction state in a short period of time \u003csup\u003e55\u003c/sup\u003e. Studies have indicated that rumination involves greater inner speech than non-rumination, and it might manifest as verbal or mental images. Research has demonstrated that rumination was linked to comparable emotional states, high-frequency heart rate variability, and skin conductance response, independent of whether subjects were prompted to rumination state by verbal thinking or mental image. However, compared with the distraction state in the form of verbal thinking, the distraction state in the form of mental imagery resulted in greater affective improvement and greater high-frequency heart rate variability, while there was no significant difference in skin conductance response \u003csup\u003e76,77\u003c/sup\u003e. These evidences suggest that distraction may be utilized as a vital measure to improve the adverse reactions caused by rumination state, and may be applied to the clinical treatment of depression.\u003c/p\u003e\u003cp\u003eOne strength of our study is the test-retest reliability and out-of-sample validation. However, this study also comes with certain limitations that could be addressed in future research. The use of the Schaefer\u0026rsquo;s 400 template for whole brain gradient calculation may be considered too coarse. To enhance precision, it is suggested that future studies employ a more detailed template, potentially down to the level of cortical vertices, following the trend observed in current gradient research. Furthermore, the absence of a correlation between gradient scores and behavioral data in this study highlights an avenue for improvement. Subsequent investigations may benefit from employing a broader range of meta-correlations to explore the functional significance of gradient scores. Besides, considering that the MDD patients included in this study were undergoing medication treatment, the potential effects of medication on the study's results should be taken into account. In addition, this study did not investigate the changes in rumination status before and after depression treatment, and more follow-up researches are needed to explore this point. Lastly, given the pivotal role of the DMN in the gradient distribution of brain FC and its connection to rumination, future studies could delve into exploring the gradient within the DMN. This could provide deeper insights into the mechanism of DMN changes in the rumination state of patients with depression.\u003c/p\u003e\u003cp\u003eIn the present study, both healthy subjects and MDD patients maintained the primary-transmodal gradient and visual-sensorimotor gradients in both the rumination and distraction states. At the network level, convergent alterations in network gradients emerged during rumination, characterized by decreased DMN scores and elevated FPN scores on the primary-transmodal gradient in both groups. Notably, this pattern was consistently observed in the MDD group. Furthermore, MDD-specific dysregulation manifested as significantly heightened gradient range, variance, and explanation ratio in visual-sensorimotor gradient during rumination. Collectively, our study identified the characteristic features of functional connectivity gradients during the rumination state, including enhanced processing of self-referential and negative thinking, alongside diminished regulation and coping mechanisms. Critically, we observed prominent differences in these patterns between MDD individuals and HCs. These findings suggest that the distinct gradient patterns in MDD patients reflect dysfunctional large-scale network reorganization, which may underlie the neural mechanisms of depressive rumination.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the National Natural Science Foundation of China (No. 32300933, No. 82122035, No. 81671774, and No. 81630031), the Beijing Nova Program of Science and Technology (No. 20230484465), the Beijing Natural Science Foundation (No. J230040), the Sci-Tech Innovation 2030 - Major Project of Brain Science and Brain-inspired Intelligence Technology (No. 2021ZD0200600), the Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences (No. E2CX4425YZ, No. E3CX1315 and No. Y9CX422005).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u0026nbsp;\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWatkins, E. 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E.\u003cem\u003e et al.\u003c/em\u003e Understanding the experience of rumination and worry: A descriptive qualitative survey study. \u003cem\u003eBr J Clin Psychol\u003c/em\u003e https://doi.org/10.1111/bjc.12367 (2022).\u003c/li\u003e\n\u003cli\u003eLawrence, H. R., Siegle, G. J. \u0026amp; Schwartz-Mette, R. A. Reimagining rumination? The unique role of mental imagery in adolescents\u0026apos; affective and physiological response to rumination and distraction. \u003cem\u003eJ Affect Disord\u003c/em\u003e https://doi.org/10.1016/j.jad.2023.02.066 (2023).\u003c/li\u003e\n\u003cli\u003eMoffatt, J., Mitrenga, K. J., Alderson-Day, B., Moseley, P. \u0026amp; Fernyhough, C. Inner experience differs in rumination and distraction without a change in electromyographical correlates of inner speech. \u003cem\u003ePLoS One\u003c/em\u003e https://doi.org/10.1371/journal.pone.0238920 (2020).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"gradient, default mode network, frontoparietal network, major depressive disorder, rumination","lastPublishedDoi":"10.21203/rs.3.rs-7277358/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7277358/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs a prominent psychopathological process of major depression disorder (MDD), rumination\u0026rsquo;s brain underpinnings remain unclear. Emerging studies have highlighted that brain areas are organized along several macroscale gradients, which could be a powerful framework to better understand how the functional connectome was organized to underlie rumination. In this study, two datasets (Rum-Beijing and Rum-MDD) were leveraged in the present study. Rum-Beijing consisted of 41 healthy controls (HC) who underwent 3 repetitive scans, while Rum-MDD consisted of 45 patients with major depressive disorder (MDD) and 46 HCs. We used a modified rumination state task (RST) to induce participants into a continuous, active rumination state and characterized the gradient profiles. RST also included a distraction state as the control condition. The explanation ratio of gradient and the regional differences of each gradient were also compared. Leveraging the Rum-MDD dataset, we further examined the interaction effect between the group (MDD vs. HC) and condition (rumination vs. distraction) regarding the gradient\u0026rsquo;s global and local metrics. Two gradients were identified: the primary-transmodal gradient and the visual-sensorimotor gradient. We found that the rumination state exhibited reduced gradient values in the default mode network (DMN) but elevated gradient values in the frontoparietal network (FPN) as compared to the distraction state. In global perspective, during the rumination state, individuals with MDD reflected significantly higher values in explanation ratio, gradient range, and gradient variance along visual-sensorimotor gradient compared to HCs. Finally, we observed significantly reduced correlations between functional connectivity and the primary-transmodal gradient during rumination compared to distraction in HCs. In conclusion, the present study showed that rumination may correspond to a specific underlying functional gradient profile, and such profile was altered in patients with MDD. These results shed new light on the neural mechanisms underlying rumination, highlighting a global functional coupling characteristics across the whole brain during an active rumination state.\u003c/p\u003e","manuscriptTitle":"Characterizing gradients of functional connectome underpinning rumination and their alteration in depression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-17 14:15:29","doi":"10.21203/rs.3.rs-7277358/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-12-23T10:13:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-12-05T01:42:50+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-11-20T17:12:11+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-11-14T22:25:56+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-10-27T17:06:23+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-08-08T14:19:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-04T09:40:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-04T09:33:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Translational Psychiatry","date":"2025-08-02T09:55:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f5b79178-c0c0-4b53-b7a4-bd58bcdeee01","owner":[],"postedDate":"August 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":52879050,"name":"Biological sciences/Neuroscience"},{"id":52879051,"name":"Biological sciences/Psychology"}],"tags":[],"updatedAt":"2026-05-12T15:16:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-17 14:15:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7277358","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7277358","identity":"rs-7277358","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}