Abstract
BACKGROUND
Rumination is a critical psychological factor contributing to the relapse of major depressive episodes (MDEs) and a core residual symptom in remitted MDEs. Investigating its neural correlations is essential for developing strategies to prevent MDE relapse. Despite its clinical importance, the brain network mechanisms underlying rumination in remitted MDE patients have yet to be fully elucidated.
AIM
To investigate the brain network mechanism underlying rumination in patients with remitted MDEs using functional magnetic resonance imaging (fMRI).
METHODS
We conducted an fMRI-based rumination-distraction task to induce rumination and distraction states in 51 patients with remitted MDEs. Functional connectivity (FC) was analyzed using the network-based statistic (NBS) approach, and eight topological metrics were calculated to compare the network topological properties between the two states. Correlation analyses were further performed to identify the relationships between individual rumination levels and the significantly altered brain network metrics.
RESULTS
The NBS analysis revealed that the altered FCs between the rumination and distraction states were located primarily in the frontoparietal, default mode, and cerebellar networks. No significant correlation was detected between these altered FCs and individual rumination levels. Among the eight topological metrics, the clustering coefficient, shortest path length, and local efficiency were significantly lower during rumination and positively correlated with individual rumination levels. In contrast, global efficiency was greater in the rumination state than in the distraction state and was negatively correlated with individual rumination levels.
CONCLUSION
Our work revealed the altered FC and topological properties during rumination in remitted MDE patients, offering valuable insights into the neural mechanisms of rumination from a brain network perspective.
Keywords: Neural mechanism, Network topology, Functional connectivity, Rumination, Major depressive episode
Core Tip: Rumination is a key risk factor for relapse in major depressive episode (MDE) patients, yet its neural mechanisms in remitted MDEs remain unclear. Using a rumination induction neuroimaging task, we analyzed brain network alterations during rumination. The alterations in functional connectivity between the rumination and distraction states occurred mainly in the frontoparietal, default mode, and cerebellar networks. Topology analysis revealed that the whole-brain network was more functionally integrated and less segregated during rumination. These altered network topological characteristics were associated with individual rumination levels, providing insights into the neural basis of rumination in remitted MDE patients.
INTRODUCTION
Rumination is defined as repetitive thinking about the symptoms, causes, circumstances, meanings, implications, and consequences of depressed mood and distress[1]. According to response styles theory, rumination plays an important role in both the onset and maintenance of major depressive episodes (MDEs)[2]. Remission following multiple MDEs is characterized by a return to normal functioning with minimal symptoms lasting over two months[3]. However, research indicates that nearly 80% of remitters continue to experience one or more residual symptoms[4,5], and rumination is frequently identified as a core residual symptom[6,7]. Furthermore, longitudinal studies have demonstrated that rumination is a critical factor contributing to the relapse of MDE[8,9]. Despite its recognized importance, the neural mechanisms underlying rumination in remitted patients with MDE remain insufficiently characterized, warranting further investigation.
Previous neuroimaging studies have revealed that the functional abnormalities associated with rumination are distributed across multiple brain networks rather than being localized to a single brain region[10-12]. Within the framework of network neuroscience, the brain is modeled as a complex system comprising nodes (representing discrete brain regions) and edges [functional connectivity (FC)][13]. A meta-analysis of 14 studies revealed the default mode network (DMN) as the principal neural substrate underlying rumination[14], whereas the salience network (SAN)[15,16] and frontoparietal network (FPN)[17] were also frequently reported in rumination-related processes. In addition to these findings, accumulating evidence indicates that rumination is associated with abnormal connectivity between these subnetworks. For example, altered FC among the FPN, DMN, and SAN has been demonstrated to be correlated with individual differences in rumination levels[18].
In addition to FC analyses, network topology analysis has been widely applied to investigate the functional alterations of brain networks in patients with depression[19]. Grounded in graph theory, topological metrics provide a macroscopic perspective on brain function by reflecting functional segregation (the capacity for specialized processing within distinct regions) and functional integration (the capacity to combine specialized information across distributed regions). Previous studies have identified aberrant topological architectures in individuals experiencing depressive rumination. Specifically, Zhang et al[20] demonstrated altered functional integration and segregation in brain networks that were significantly correlated with depressive rumination in patients compared with healthy controls in the resting state. Jacob et al[21] found that individual differences in rumination levels were significantly associated with the degree centrality of the right precuneus, a key node of the DMN, highlighting its role in self-referential processing. Additionally, Jia et al[22] reported significant interaction effects during an active rumination task in the left superior frontal gyrus, a key node of the dorsal attention network (DAN), suggesting a potential dynamic interaction between attention and self-referential processes during rumination. Collectively, these findings underscore the complexity of distinct brain network mechanisms underlying depressive rumination and reveal inconsistencies across different studies. The whole-brain functional network organization of rumination in remitted MDE remains to be fully elucidated.
In this work, we conducted a rumination-distraction functional magnetic resonance imaging (fMRI) task paradigm and applied two approaches, the network-based statistic (NBS) and graph-theory methods, to investigate the whole-brain network characteristics associated with rumination in remitted MDE patients. NBS is a powerful statistical approach for identifying significantly altered FC in the whole brain without the need for mass univariate testing in conventional comparisons[23]. Additionally, eight topological metrics were calculated to measure the functional integration and segregation of the brain networks. This study aimed to address three key questions: Compared with the distraction state: (1) Which FCs show changes during rumination; (2) How are the network topological properties altered during rumination; and (3) Are these changes in network characteristics associated with individual differences in rumination levels?
MATERIALS AND METHODS
Participants
A total of 55 remitted MDE patients [diagnosed with major depressive disorder (MDD) or bipolar II] were recruited at the Second Xiangya Hospital in China from 2021 to 2024. The inclusion criteria were as follows: (1) Aged 18 to 60 years; (2) At least six years of education, ensuring the ability to independently complete all scales and assessments; (3) Right-handed; (4) A diagnosis of MDD or bipolar II disorder confirmed by two attending psychiatrists using the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, with the most recent MDE in remission; and (5) For MDD patients, a total Hamilton Depression Rating Scale-17 (HAMD-17) score ≤ 7 for at least two months; for bipolar II disorder patients, both a Young Mania Rating Scale score ≤ 7 and a HAMD-17 score ≤ 7 for at least two months. Four patients were excluded because of excessive head motion (defined as a mean framewise displacement > 0.2 mm) in either the rumination or distraction state. Ultimately, 51 patients (43 with MDD and 8 with bipolar II) were enrolled in the final study.
This study was approved by the Ethics Committee of the Second Xiangya Hospital and registered on ClinicalTrials.gov (NCT05585047). Written informed consent was obtained from all participants.
Measures
The individual rumination level was measured using the adapted Chinese version of the Ruminative Response Scale (RRS), which was originally developed by Nolen-Hoeksema and Morrow[24] and translated into Chinese by Xiu and Yang[25]. The scale consists of 22 items across three dimensions: Depression, brooding, and reflection. The items on the RRS are rated on a four-point Likert scale (1 = “never” to 4 = “always”). In Chinese samples, the RRS has demonstrated strong internal consistency, with an overall Cronbach’s α of 0.90, and acceptable reliability, with α coefficients for the three dimensions ranging from 0.68 to 0.85[25].
Rumination-distraction task
During the magnetic resonance imaging (MRI) scan, patients completed a task lasting approximately 40 minutes, comprising six sequential states, each lasting six minutes: Resting state I, negative autobiographical state, rumination state, mindfulness state, distraction state, and resting state II (Figure 1). Before beginning the task, patients were asked to recall the four most distressing events in their lives and to provide three descriptive words for each event. During resting states I and II, they were instructed to fixate on a cross displayed at the center of the screen. In the negative autobiographical state, patients viewed the three words corresponding to their worst life event and were directed to recall its details. In the rumination state, they reflected on all four life events, guided by stimulus texts on the screen. During the mindfulness state, patients were encouraged to adopt a nonjudgmental awareness of their thoughts and emotions to foster a mindful state. For the distraction state, prompts such as “Imagine a cloud in the sky” were presented to redirect patients' attention to neutral, objective imagery. This distraction state is widely used in comparison to the rumination state[14]. After each state, participants completed a questionnaire assessing their thought content and phenomenology using a Likert 4-point scale, where 1 represented “not at all” and 4 represented “all the time”. Details about the task paradigm are shown in Supplementary Figure 1 and Supplementary Table 1.
Figure 1.
The rumination-distraction task paradigm. The picture from top to bottom shows six states in the task: Resting state I, negative autobiographical state, rumination state, mindfulness state, distraction state, resting state II. The left four columns show the stimulus prompts in the six states. The right columns show the questions after each state. NCEE: National college entrance examination.
MRI acquisition
MRI data were collected using a 3.0 Tesla United Imaging uMR 790 scanner equipped with a 32-channel head coil. T1-weighted structural images were obtained using a magnetization-prepared rapid acquisition gradient-echo sequence. The imaging parameters were as follows: Repetition time (TR) = 6.97 millisecond, echo time (TE) = 3.1 millisecond, flip angle = 10°, field of view (FOV) = 230 mm × 256 mm, slice thickness = 1 mm, slice gap = 0 mm, voxel size = 1.33 mm × 1 mm × 1 mm, and scan time = 3 minutes and 12 seconds. fMRI data were acquired using T2-weighted echo planar imaging sequences with the following parameters: TR = 700 millisecond, TE = 30 millisecond, flip angle = 52°, FOV = 210 mm × 210 mm, slice thickness = 2.5 mm, voxel size = 2.5 mm × 2.5 mm × 2.5 mm, with a simultaneous multi-slice acceleration factor = 7.
Data preprocessing
The T1-weighted structural images were nonlinearly registered to the standard Montreal Neurological Institute space to obtain the transformation matrix. Following slice-time correction and motion correction, the fMRI signals were coregistered to the corresponding structural image. Nuisance covariates, including linear, quadratic, and cubic drift, 24 motion parameters (six translational parameters, six rotational parameters, and their first-order derivatives), the mean cerebrospinal fluid signal and the mean white matter signal, were regressed out. The functional image was then temporally smoothed using a low-pass Gaussian filter with a cutoff frequency of 0.12 Hz. The inverse transformation matrix from the T1 normalization step was subsequently applied to warp the Shen 268 atlas[26] back into individual subject spaces. The atlas defines 268 nodes, which are organized into 11 functional networks[27]. These include motor and somatosensory, cingulo-opercular, auditory, visual, subcortical, ventral attention, and cerebellar network (CBN), along with the DMN, FPN, SAN, and DAN, which were mentioned earlier.
Functional network construction
After preprocessing, the mean time series from each region of interest (ROI) was extracted for each patient. Pearson correlation coefficients were calculated between all pairs of ROI time series, resulting in a 268 × 268 correlation matrix for each patient. These matrices contained 35778 unique edges. The negative correlations were set to zero following previous studies[28,29]. Finally, the correlation coefficients were normalized using Fisher’s Z-transformation.
NBS
The NBS approach was employed to identify the FC alterations between the rumination and distraction states[23]. First, paired t tests using a repeated measure general linear model, with head motion included as a covariate, were applied to each edge to compare the two states. A two-tailed significance threshold of P < 0.001 was used to identify suprathreshold edges. Suprathreshold edges that were connected formed components, with the number of links within a component defined as its true size. Next, a total of 1000 random permutations were independently generated. For each permutation, the group of each edge was randomly exchanged, and the largest component size from each permutation was recorded to construct an empirical null distribution. The statistical significance of each observed component was evaluated by estimating K, defined as the number of permutations in which the maximal component size exceeded the true size of the observed component. The one-tailed familywise error rate-corrected P value for each component was defined as K/1000, with results deemed significant at a P value < 0.05.
Topological metrics
To further investigate the topological properties of the brain network during rumination, eight topological metrics were calculated. These metrics included the clustering coefficient, normalized clustering coefficient, local efficiency, and modularity, which reflect the functional segregation of the brain network. Additionally, the shortest path length, normalized shortest path length, and global efficiency were computed to assess functional integration. The small-world coefficient, defined as the ratio of the normalized clustering coefficient to the normalized shortest path length, was also included as a measure of network organization[29].
Prior to calculating the graph metrics, a sparsity threshold was applied to the connections in the brain network. The thresholds ranged from 0.1 (representing the top 10% of the strongest connections in the individual brain network) to 0.4, with steps of 0.01. For each patient, the area under the curve across this range of sparsity was calculated to derive a summarized scalar for each topological metric in both the rumination and distraction states.
Statistical analysis
The Kolmogorov-Smirnov test was used to assess the normality of the demographic and clinical data. The self-reported scores obtained during the fMRI scan, including those for the self-focus item (i.e., “During the past 6 minutes, I was thinking something about myself”) and the sadness item (i.e., “During the past 6 minutes, what I was thinking was negative”), were compared between the different states using the paired t test to examine the effectiveness of the induction of rumination and distraction. Differences in the graph metrics between two states were evaluated using permutation tests, where the state labels were randomly permuted 10000 times. The significance threshold was set at two-tailed P < 0.05, with Bonferroni correction applied to account for multiple comparisons. For both the FC and topology analyses, metrics that showed significant alterations between the rumination and distraction states were identified. Pearson correlation coefficients were then calculated to examine the relationships between the differences in these metrics and individual rumination levels, with false discovery rates correction for multiple comparisons.
RESULTS
Demographics and clinical data
The demographic and clinical characteristics of the 51 patients with remitted MDEs included in this study are summarized in Table 1. Among the patients, 74.5% were female, with a mean age of 23 years. The mean HAMD-17 score was 3.10. A total of 34 patients (66.7%) had a history of antidepressant use, and 29 patients (56.9%) had experienced their first depressive episode.
Table 1.
Characteristics of patients with remitted depressive episodes
|
Characteristics
|
Value
|
| Demographic, P50 (P25, P75) | |
| Age, year | 23 (20, 25) |
| Sex (female%) | 38/51 (74.5) |
| Education, year | 15 (13, 16) |
| Clinical characteristics, P50 (P25, P75) | |
| HAMD-17 (SD) | 3.10 (2.29) |
| Onset age, year | 21.0 (18, 24) |
| First onset (%) | 29/51 (56.9) |
| Number of episodes | 1 (1, 2) |
| Current illness duration, month | 8 (1, 15) |
| Total illness duration, month | 16 (8, 36) |
| Antidepressant (%) | 34/51 (66.7) |
P50: 50th percentile; P25: 25th percentile; P75: 75th percentile; HAMD-17: Hamilton Depression Scale-17.
The effective induction of rumination
During the fMRI scan, patients exhibited significantly higher levels of sadness and self-focus in the rumination state than in the resting state (sadness ratings: T = 4.025, P = 0.000; self-focus ratings: T = 3.957, P = 0.000), confirming the successful induction of rumination. Furthermore, both of these two ratings decreased following distraction relative to the rumination state (sadness ratings: T = -7.382, P = 0.000; self-focus ratings: T = -8.105, P = 0.000), demonstrating the effectiveness of distraction in alleviating the sadness and self-focus induced by rumination.
Comparison of FC between the rumination and distraction states
The NBS-identified 82 FCs that were significantly stronger in the rumination state than in the distraction state (Figure 2A and B, Supplementary Table 2). Among these FCs, that between the FPN and DMN was the greatest (number of edges = 25), followed by that between the FPN and SAN (number of edges = 10). In contrast, 164 FCs were significantly weaker during rumination than during distraction (Figure 2C and D, Supplementary Table 3). The FC within the CBN presented the greatest number of reduced edges (30), followed by 23 edges between the CBN and the FPN. In addition, no significant correlations were found between the above altered FCs and individual rumination levels.
Figure 2.
Results of significantly different edges between rumination and distraction states. A: The chord map of the higher functional connectivity (FC) during rumination. Different color represents different subnetwork. A chord connecting two subnetworks indicates that there is a significant FC between the two subnetworks, where the thickness represents the number of significant FC; B: The matrix map shows the number of significantly higher FC between subnetworks during rumination; C: The chord map of the lower FC during rumination; D: The matrix map shows the number of significantly lower FC between subnetworks during rumination, compared to distraction state. SMot: Motor and somatosensory; CON: Cingulo-opercular; AUD: Auditory; DMN: Default mode; VIS: Visual; FPN: Frontoparietal; SAN: Salience; Subc: Subcortical; VAN: Ventral attention; DAN: Dorsal attention; CBN: Cerebellar network. All the figures above were drawn by Bioimage Suite (https://bioimagesuiteweb.github.io/webapp/connviewer.html?species=human#).
Comparison of topological properties between the rumination and distraction states
Compared with the distraction state, the rumination state presented significantly lower values for the clustering coefficient (P = 0.002), shortest path length (P = 0.002), local efficiency (P = 0.002), and normalized shortest path length (P = 0.003) but exhibited significantly greater global efficiency (P = 0.002; Table 2 and Figure 3). No significant differences were observed in small-worldness, modularity or the normalized clustering coefficient between the two states (Table 2 and Figure 3). For the above five graph metrics that showed significant differences, the change in global efficiency between the two states was negatively correlated with individual RRS scores, whereas the changes in the other four metrics were positively correlated with individual RRS scores (Table 3).
Table 2.
The topological metrics in rumination and distraction states
|
|
Rumination state
|
Distraction state
|
P value
|
| Clustering coefficient | 0.1627 (0.1586, 0.1710) | 0.1653 (0.1605, 0.1775) | 0.002a |
| Shortest path length | 0.5825 (0.5777, 0.5891) | 0.5838 (0.5787, 0.6010) | 0.002a |
| Global efficiency | 0.1872 (0.1861, 0.1879) | 0.1870 (0.1844, 0.1878) | 0.002a |
| Local efficiency | 0.2346 (0.2327, 0.2390) | 0.2359 (0.2340, 0.2415) | 0.002a |
| Small-worldness | 0.5849 (0.5643, 0.6059) | 0.5835 (0.5593, 0.6066) | 0.391 |
| Modularity | 0.1017 (0.0976, 0.1079) | 0.1051 (0.1017, 0.1128) | 0.015 |
| After normalization | |||
| Clustering coefficient | 0.6294 (0.5988, 0.6500) | 0.6294 (0.6088, 0.6582) | 0.914 |
| Shortest path length | 0.3290 (0.3264, 0.3329) | 0.3295 (0.3276, 0.3379) | 0.003a |
P < 0.05 after Bonferroni correction.
Figure 3.
Comparisons of the topological metrics between rumination and distraction states. aP < 0.05 after Bonferroni correction; NS: No significant difference between two states after Bonferroni correction.
Table 3.
The correlations between the topological metrics and the three dimensions of the Ruminative Response Scale
|
RRS brooding
|
RRS reflection
|
RRS depression
|
|||||
|
r
|
P value
|
r
|
P value
|
r
|
P value
|
||
| Clustering coefficient | 0.3374 | 0.017a | 0.3874 | 0.005a | 0.3087 | 0.029a | |
| Shortest path length | 0.3456 | 0.014a | 0.3833 | 0.006a | 0.3000 | 0.034a | |
| Global efficiency | -0.3611 | 0.010a | -0.3953 | 0.004a | -0.3130 | 0.027a | |
| Local efficiency | 0.3320 | 0.018a | 0.3901 | 0.005a | 0.3221 | 0.023a | |
| Normalized shortest path length | 0.3499 | 0.013a | 0.3919 | 0.004a | 0.3104 | 0.028a | |
P < 0.05 after false discovery rates correction.
r = Pearson's r. RRS: The Ruminative Response Scale.
DISCUSSION
In this study, we identified significant changes in the FCs and topological properties of brain networks during rumination in patients with remitted MDEs. The FPN, DMN, and CBN were identified as the core subnetworks with altered FC during rumination. The clustering coefficient, local efficiency, and shortest path length were significantly reduced during rumination and positively correlated with individual RRS levels. In contrast, global efficiency was greater in the rumination state than in the distraction state and was negatively associated with the RRS score.
The FPN is a task-positive network critical for higher-order control, such as cognitive control and executive function[30-32]. A meta-analysis of resting-state FC studies conducted by Kaiser et al[33] revealed increased connectivity between the FPN and DMN in patients with depression compared with controls. Consistent with this finding, the increased FC between the FPN and DMN observed in our study during rumination may reflect a bias toward excessive self-referential processing at the expense of attending to external tasks or stimuli. Additionally, we observed increased FC between the FPN and SAN during rumination. Greater FPN-SAN connection is associated with enhanced emotional reactivity in individuals with depression[34]. Considering that the past event memories recalled during rumination were negative and induced strong depressive emotions, we hypothesize that the increased FPN-SAN FC may represent an adaptive response or regulatory effort to manage negative moods[35,36].
The CBN is widely connected to the cerebral cortex and is involved in various aspects of cognition and self-reflection[37,38]. It also plays a role in automatically regulating emotions without conscious awareness[39-42]. Increased bilateral cerebellum recruitment has been associated with fewer depression symptoms and is linked to greater complexity in cognitive and emotional tasks[43,44]. The observed reduction in FC within the CBN during rumination may indicate impairments in automatic cognitive processing that typically operate without conscious engagement. Furthermore, we also found that the FC between the CBN and FPN was lower during rumination than in the distraction state. Previous studies have reported consistent findings regarding reduced connection between the CBN and FPN in MDE patients[45]. The reduced FC between the CBN and FPN observed in our study may suggest abnormalities in the integration of cognitive and affective representations during rumination in remitted MDE patients[42,44].
Compared with those in the distraction state, the clustering coefficient, local efficiency, and shortest path length of the whole-brain network were lower, whereas the global efficiency was greater in the rumination state. The clustering coefficient and local efficiency are indicators of functional segregation, whereas the shortest path length and global efficiency reflect the functional integration of brain networks[29]. These results from the topology analysis suggest that during rumination, the whole-brain network in remitted MDE patients becomes more functionally integrated and less segregated. Rumination is thought to occupy significant cognitive resources and contribute to various types of cognitive dysfunction[46], including impairments in executive function[47], learning[48], and attention[49]. We speculate that the observed high level of integration reflects increased cognitive resource demands during rumination, whereas low segregation may indicate a reduced capacity for task-specific processing. Notably, all changes in the above topological metrics between the two states were significantly correlated with individual RRS levels. Those metrics that were lower during rumination (i.e., the clustering coefficient, local efficiency, and shortest path length) were positively correlated with the RRS, whereas the metric that increased during rumination (i.e., global efficiency) was negatively correlated with individual rumination levels. In other words, the more prominent the network property of high integration and low segregation during rumination is, the lower the individual RRS scores. This trend may reflect that the observed topological organization in MDEs was a neural compensatory or adaptive mechanism rather than a cause of rumination in remitted MDE patients[20].
Several limitations of this study should be acknowledged. First, the sample size was relatively small, and healthy controls were not included. Future studies with larger samples are needed to validate the observed within-state findings and explore between-group differences. Second, we included both unipolar and bipolar depression patients in the study. Although rumination is a transdiagnostic characteristic among mood disorders, heterogeneity may have been introduced by combining these two types of depression. More balanced samples and stratified analyses are necessary to explore the disorder-specific brain network mechanisms underlying rumination. Third, the potential effects of antidepressants were not excluded. Although this study focused on within-state comparisons, the influence of medication might differ across states even within the same individuals. Future research could address this issue by dividing participants into subgroups on the basis of antidepressant usage in larger samples.
CONCLUSION
In summary, this study revealed significant alterations in FC between the rumination and distraction states, primarily involving the frontoparietal, default mode, and cerebellar networks. Topology analysis revealed functional organization properties characterized by high integration and low segregation during rumination. Furthermore, correlation analysis revealed significant associations between topology properties and patients’ rumination levels. From a whole-brain network perspective, these findings provide valuable insights into the neural mechanisms underlying rumination in remitted MDE patients.
ACKNOWLEDGEMENTS
We would like to thank all the patients who participated in our study.
Footnotes
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of the Second Xiangya Hospital (No. 2021-022) and registered on ClinicalTrials.gov (NCT05585047).
Informed consent statement: All involved persons gave their informed written consent prior to study inclusion.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement—checklist of items—and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind.
Specialty type: Psychiatry
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade A, Grade B, Grade B
Novelty: Grade A, Grade B
Creativity or Innovation: Grade A, Grade B
Scientific Significance: Grade A, Grade B
P-Reviewer: Liu L; Mazza M S-Editor: Li L L-Editor: A P-Editor: Zhang L
Contributor Information
Kang-Ning Li, Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan Province, China.
Shi-Xiong Tang, Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, Hunan Province, China.
You-Fu Tao, Xiangya Medical School, The Second Xiangya Hospital, Central South University, Changsha 410011, Hunan Province, China.
Hai-Ruo He, Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan Province, China.
Mo-Han Ma, Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan Province, China.
Qian-Qian Zhang, Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan Province, China.
Mei Huang, Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan Province, China.
Wen-Tao Chen, Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan Province, China.
Hui Liang, Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan Province, China.
Ao-Qian Deng, Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan Province, China.
Si-Rui Gao, Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan Province, China.
Fan-Yu Meng, Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan Province, China.
Yi-Lin Peng, Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan Province, China.
Yu-Meng Ju, Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan Province, China.
Wen-Wen Ou, Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan Province, China.
Su Shu, Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan Province, China. shusujy@163.com.
Yan Zhang, Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan Province, China.
Data sharing statement
Technical appendix, statistical code, and dataset are available from the corresponding author upon reasonable request at shusujy@163.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Technical appendix, statistical code, and dataset are available from the corresponding author upon reasonable request at shusujy@163.com.