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Topologically state-independent and dependent functional connectivity patterns in current and remitted depression
Journal of Affective Disorders 250 (2019) 178–185
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Research paper
Topologically state-independent and dependent functional connectivity patterns in current and remitted depression
T
Dong Daifenga,b,c, Li Chutinga,b,c, Ming Qingsend, Zhong Xuea,b,c, Zhang Xiaocuia,b,c, ⁎ Sun Xiaoqianga,b,c, Jiang Yalia,b,c, Gao Yidiana,b,c, Wang Xianga,b,c, Yao Shuqiaoa,b,c, a
Medical Psychological Center, The Second Xiangya Hospital of Central South University, Changsha, Hunan, PR China Medical Psychological Institute of Central South University, Changsha, Hunan, PR China c China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, PR China d Department of Psychiatry, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, PR China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Current depression Remitted depression Graph theory
Objective: Identification of state-independent and -dependent neural biomarkers may provide insight into the pathophysiology and effective treatment of major depressive disorder (MDD), therefore we aimed to investigate the state-independent and -dependent topological alterations of MDD. Method: Brain resting-state functional magnetic resonance imaging (fMRI) data were acquired from 59 patients with unmedicated first episode current MDD (cMDD), 48 patients with remitted MDD (rMDD) and 60 demographically matched healthy controls (HCs). Using graph theory, we systematically studied the topological organization of their whole-brain functional networks at the global and nodal level. Results: At a global level, both patient groups showed decreased normalized clustering coefficient in relative to HCs. On a nodal level, both patient groups showed decreased nodal centrality, predominantly in cortex-moodregulation brain regions including the dorsolateral prefrontal cortex, posterior parietal cortex and posterior cingulate cortex. By comparison to cMDD patients, rMDD group had a higher nodal centrality in right parahippocampal gyrus. Limitations: The present study, an exploratory analysis, may require further confirmation with task-based and experimental studies. Conclusions: Deficits in the topological organization of the whole brain and cortex-mood-regulation brain regions in both rMDD and cMDD represent state-independent biomarkers.
1. Introduction
depression or current depression) (Arnone et al., 2013; Ming et al., 2017; Van et al., 2013) that distinguish remitted MDD (rMDD) from current MDD may inform the development of treatments that may prevent relapse and support persistent remission. Hence, identification of state-independent and -dependent neural biomarkers in first-episode, medication-naive current MDD (cMDD) patients and rMDD patients may provide insights into MDD pathophysiology and therapeutic targets. Accumulating structural and functional neuroimaging data suggest that rMDD patients exhibit dysfunction in prefrontal, limbic, and paralimbic brain regions (Burkhouse et al., 2017; Dong et al., 2019; Ming et al., 2017; Stange et al., 2017; Van et al., 2013) and abnormal frontal-limbic interactions (Figueroa et al., 2017; Jacobs et al., 2016, 2014). Similar alterations have been observed in cMDD revealed by a
Major depressive disorder (MDD) is a high-morbidity mental disorder (Eaton et al., 2008; Kruijshaar et al., 2005) with a 15-year recurrence rate of 35% in the general population and as high as 85% in specialized mental health care settings (Hardeveld et al., 2010). This high recurrence is a major clinical issue in MDD management (Fava and Kendler, 2000). Residual characteristics after first episode, including subclinical symptoms and underlying state-independent neurobiology (traits existing across different illness courses)(Arnone et al., 2013; Ming et al., 2017; Van et al., 2013), have been proposed as possible explanations of this recurrence (Hardeveld et al., 2010; Post and Weiss, 1998; Robinson and Sahakian, 2008; Solomon et al., 2000). Furthermore, state-dependent neural changes (state-specific in the remitted
⁎ Corresponding author at: Medical Psychological Institute, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road of Changsha, Hunan, 410011, PR China. E-mail address: [email protected] (S. Yao).
https://doi.org/10.1016/j.jad.2019.03.030 Received 12 December 2018; Received in revised form 23 February 2019; Accepted 4 March 2019 Available online 05 March 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.
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meta-analysis with 31 resting studies of first episode, medication-naïve MDD (Zhong et al., 2016), consistent with the limbic-cortical dysregulation model of depression which suggests that the disruption of a functional interactive network of cortical-limbic pathway is critical to regulation of mood (Mayberg, 1997). However, direct neuroimaging comparisons between rMDD and cMDD are sparse and inconclusive. Our previous study exploring neural responses to psychosocial stress which induced by the Montreal Imaging Task showed hyperactivity in the dorsolateral prefrontal cortex (dlPFC) and striatum in patients with rMDD, relative to observations in patients with cMDD (Ming et al., 2017), and a recent study (Dong et al., 2019) investigating alterations within and between the salience network(SN), default mode network (DMN) as well as the central executive network (CEN) with an independent component analysis(ICA) did not find state-dependent patterns. Additionally, a resting-state functional magnetic resonance imaging (fMRI) study exploring low-frequency fluctuation showed hypoactivity in the medial frontal gyrus in rMDD (Jing et al., 2013). Longitudinal studies exploring antidepressant effects on neurophysiology in MDD also documented some state-dependent differences in the prefrontal cortex (An et al., 2017; Cheng et al., 2017; Fu et al., 2015; Heller et al., 2013), limbic system (An et al., 2017), and frontal-limbic connectivity (Anand et al., 2007; Anand et al., 2005). Notably, there have been mixed results regarding state-dependent and-independent characteristics of MDD, especially in prefrontal and limbic regions. These inconsistencies may be related to behavioral protocol, imaging modality, or cohort (e.g., antidepressant use) differences. Therefore, a systematic comparison of cMDD, rMDD, and healthy controls (HCs) in the resting state is needed to clarify this topic clearly and supplement previous findings. Graph theory provides a powerful mathematical framework for describing the topological organization of resting-state brain networks in the form of nodes (functional or structural defined regions of interest) and edges (functional connectivity) (Zuo et al., 2012) and depicts topological patterns of the brain networks from three levels: global properties, modularity and regional nodal properties (Gong and He, 2015). Previous structural and functional studies using graph theory have reported that cMDD patients had atypical global properties (Gong et al., 2017; Lu et al., 2017; Meng et al., 2014; Zhang et al., 2011) and atypical nodal centralities in frontal (Gong et al., 2017; Lu et al., 2017; Meng et al., 2014; Qin et al., 2015; Tao et al., 2013; Wang et al., 2014; Zhang et al., 2011), parietal (Ajilore et al., 2014; Chen et al., 2017), limbic (Gong et al., 2017; Lu et al., 2017; Qin et al., 2015; Tao et al., 2013; Zhang et al., 2011), and paralimbic (Singh et al., 2013; Zhang et al., 2011) brains regions as well as in the basal ganglia (Gong et al., 2017; Meng et al., 2014; Tao et al., 2013; Zhang et al., 2011). However, the functional and structural studies using graph theory in rMDD also found alterations in global properties (Bai et al., 2012; Wang et al., 2016) and atypical nodal centralities in frontal (Bai et al., 2012; Qin et al., 2015; Wang et al., 2016), insula (Qin et al., 2015), occipital (Wang et al., 2016), and basal ganglia (Qin et al., 2015; Wang et al., 2016). Together, the current findings were still inconsistent, and whether such large-scale topological organization changes in whole-brain functional networks in MDD are state-dependent or -independent is still unclear. In the current study, we acquired resting-state fMRI data from firstepisode unmedicated cMDD patients, rMDD patients, and HCs. Then, we examined the topological organization of whole-brain intrinsic functional networks and analyzed between-group differences in topological parameters from both global and regional levels. Based on the limbic-cortical dysregulation model of depression, we hypothesized that both state-dependent and -independent topological patterns would be observed in limbic-cortical system in the current and remitted depression.
2. Methods 2.1. Participants Patients with MDD were recruited from the outpatient department of the Second Xiangya Hospital affiliated with Central South University in Changsha, Hunan, PR China. Age-, gender-, and education-matched HCs were recruited through advertisements and posters from two colleges and the Changsha community. The study was conducted in accordance with the declaration of Helsinki and was approved by the Ethics committee of the Second Xiangya Hospital of Central South University. All participants were aware of the study's purpose and signed an informed consent form. Psychiatric evaluations were conducted independently by two psychiatrists using the Structured Clinical Interview for DSM-IV-TR Axis Ⅰ Disorder-Patient edition (Depressive disorders, Schizophrenia Spectrum and other Psychotic disorders, Bipolar and Related disorders, Anxiety disorders, Substance-Related and Addictive Disorders et al.) (First et al., 2002). Only patients meeting the DSM-IV-TR criteria for the first MDD episode were included in the cMDD group to exclude potential confounding effects from antidepressant medication (Schaefer et al., 2006), multiple depression episodes (Takami et al., 2007), and comorbidities. Participants enrolled in the rMDD group had experienced at least one episode of depression within the past 10 years, did not meet the DSMIV-TR criteria for active MDD within the 30-day period preceding scanning, and had a 17-item HAM-D score ≤ 7. For all three groups (cMDD, rMDD and HCs), the exclusion criteria were: (1) any prior DSMIV-TR Axis Ⅰ disorder (except MDD in the cMDD and rMDD groups); (2) a history of taking antidepressants or undergoing psychotherapy; (3) a history of alcohol/substance abuse; and (4) any neurological disorder diagnosis, structural brain abnormalities, or contradictions to MRI. Ultimately, 60 medication-naive, first episode depressed patients (34 females), 48 remitted patients (25 females), and 63 healthy subjects (34 females) were enrolled in the study in the cMDD, rMDD, and HC groups, respectively.
2.2. Clinical evaluation The Beck Depression Inventory-Ⅱ (BDI) (Steer, 1996), a validated self-report scale of depressive symptoms, was used to evaluate the depressive symptoms of all participants. Additionally, the 17-item Hamilton Depression Rating Scale (HAMD) (Hamilton, 1960) was used to evaluate the depressive symptoms of MDD patients.
2.3. Image acquisition Scanning was conducted in a 3.0-T Siemens Magnetom Skyra scanner (Siemens Healthineers, Erlangen, Germany) with a 32-channel head coil. All patients were instructed to lie in a supine position with their eyes closed, to remain still, and to think of nothing in particular, but to avoid falling asleep. Their heads were fixed snugly with foam pads and straps to minimize head movement. After scanning, each participant was asked whether he/she fall asleep during the scan. Once falling asleep, the data would not be used. Blood-oxygen-level-dependent data were acquired with an echoplanar imaging (EPI) sequence (repetition time/echo time = 2000/ 30 ms, thickness/gap = 4/1 mm, field of view = 256 × 256 mm, flip angel = 80°, matrix = 64 × 64, and 32 slices). The total time of resting acquisition was 7 min 12 s and 216 vol were collected. Besides, the T1 weighted structural images were acquired with a magnetization-prepared rapid gradient echo for checking the organic brain lesion and preprocessing the fMRI data (repetition time/echo time = 1900/ 2.01 ms, thickness/gap = 1/0 mm, field of view = 256 × 256 mm, flip angel = 9°, matrix = 256 × 256, and 176 slices). 179
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parameters, including both local efficiency and global efficiency. Specifically, the clustering coefficient of a network is the average clustering coefficients of all nodes and quantifies the extent of local interconnectivity or cliquishness of a graph. The characteristic path length of a network is the shortest path length (number of edges) required to transfer from one node to another averaged over all pairs of nodes, which reflects overall routing efficiency of a graph. The local efficiency of a network is the mean of all the local efficiency of nodes in the graph and reflects the fault tolerance of a graph by measuring the capacity of its subgraphs for information exchange when the index nodes are eliminated. Meanwhile, the global efficiency of a network quantifies the capacity for parallel information transfer of a graph. Finally, the normalized clustering coefficient and normalized characteristic path length are defined as the ration of the clustering coefficient and characteristic path length of the network to matched random networks respectively and are used to examine the small-world properties quantitatively. The small-worldness (small-worldness = normalized clustering coefficient / normalized characteristic path length) reflects a typical balance between functional segregation and functional integration. For regional nodal network measures, we employed two nodal centrality metrics: degree centrality and nodal efficiency (Rubinov and Sporns, 2010). Degree centrality is the number of connections lined to a given node; nodal efficiency is the inverse of the harmonic mean of the shortest path length between a given node and all other nodes. Detailed descriptions of these topological measures are presented in Table S2. Furthermore, we calculated the area under the curve (AUC) for each network metric analyzed. The AUC datum provides a summarized scalar value for topological characterization of a network independent of the individual threshold of each network. The integrated AUC metric has been shown to be sensitive for detecting topological alterations in association with brain disorders (He et al., 2009; Wang et al., 2009; Yu et al., 2017; Zhang et al., 2011).
2.4. Image preprocessing The images were processed in Data Processing Assistant for RestingState FMRI (version 2.3, http://restfmri.net) software (Yan and Zang, 2010) in nine steps. (1) The first 10 EPI volumes of resting data were removed. (2) The slice timing was corrected. (3) Data from participants who display excessive head motion (translation > 1.5 mm or rotation > 1.5°; cMDD group, N = 1; and HC group, N = 3) were excluded from further analyses. Ultimately, 59 cMDD, 48 remitted patients and 60 HCs were included in the further statistical analyses. (4) individual structural images were co-registered to the mean functional image, and the transformed structural images were then segmented into gray matter, white matter and cerebrospinal fluid. Wherein the white matter and cerebrospinal fluid were used as masks in nuisance regression. (5) Nuisance variable regression was performed by regressing out mean signals for white matter, cerebrospinal fluid, and Friston-24 head motion parameters. (6) The images were spatially normalized to a standard Montreal Neurological Institute (MNI) template and resampled at a voxel size of 3 × 3 × 3 mm. (7) Spatial smoothing was completed with a Gaussian Kernel (6 × 6 × 6 mm full-width at half maximum). (8) Linear and quadratic trends were removed. (9) Finally, band-pass filtering (0.01–0.08 Hz) was performed. The residuals were used in our subsequent brain network analyses. 2.5. Network construction Using the graph theoretical network analysis toolbox (GRETNA, https://www.nitrc.org/projects/gretna) (Wang et al., 2015), we constructed a brain functional network for each subject according to the functional atlas designed by Fan et al. (2016). Functional parcellation is designed to improve the test-retest network reliabilities relative to structural parcellation (Gong and He, 2015). We employed a human brainnetcome atlas, a fine-grained, cross-validated atlas with 210 cortical and 36 subcortical regions that contains information on both anatomical and functional connections. We parcellated the brain into 246 regions of interest (ROIs) (123 in each hemisphere). The names of the ROIs and their corresponding abbreviations are listed in Supplementary Table S1. The time series of each ROI was estimated by averaging the residual time series of all voxels within that region. Second, to measure the functional connectivity among regions, we computed Pearson correlation coefficients between the regional time series of all possible brain region pairs, yielding a correlation matrix (246 × 246) for each subject, and the Person correlation rather than the partial-based correlations has higher test-retest network reliabilities (Gong and He, 2015). Each correlation matrix (Pearson correlation coefficients, absolute values) was thresholded into an undirected binarized matrix with a fixed sparsity or density value (i.e., the number of edges in the network divided by the maximum possible number of edges). Setting a sparsity-specific threshold ensured that the networks for all three groups had the same number of edges. We computed brain network properties over a wide range of network sparsity/density (8% ≤ s ≤ 50%) at the intervals of 0.01; smallworld parameters (reflecting the optimal balance between functional segregation and functional integration) were estimated and the number of spurious edges was minimized, as described previously (Achard et al., 2006; He et al., 2007; Yu et al., 2017). Through this thresholding, a set of 246 × 246 binarized matrices was obtained for each subject.
2.7. Statistical analysis 2.7.1. Behavioral analyses One-way analyses of variance (ANOVAs) and chi-squared tests were used to detect group differences (three levels: cMDD, rMDD, and HCs) in our demographic and psychometric results followed by post hoc twosample t-tests. A two-tailed p < .05 was considered significant. The behavioral analyses were conducted in the SPSS.22.0. 2.7.2. Network metrics analysis In current study, there were not significant differences in head motion (volume-level framewise displacement (FD) defined by Power et al.) (Power et al., 2012) among the three groups (F2,164 = 0.723, p = .487). Given recent studies demonstrated that head motion may have both noisy and neuronal effect on functional connectivity measures (Dijk et al., 2012; Zeng et al., 2014b), we analyzed all the network metrics by adding FD value as a covariate to further exclude possible effects of head motion. To detect group differences in global network measures and regional nodal characteristics, one-way analyses of covariance (ANCOVAs) were performed on the AUCs for each univariate network metric with age, gender, education level and FD value as covariates. For both global measures and nodal characteristics, the significant level was set as false discovery rate (FDR) correction, p < .05. Further pair-wise post hoc t-tests with age, gender, education level and FD value as covariates were then conducted for brain regions detected as significant in an ANCOVA. To control for the effects of residual depressive symptoms, BDI score was employed as a covariate, with age, gender, education level and FD value, when comparing the rMDD and HC groups. All network metric analyses were conducted with metric comparison tools in GRETNA. For all post hoc t-tests, the significance level was set at FDR correction, p < .05.
2.6. Network analysis We calculated both global and regional network measures for brain networks at each sparsity threshold. The global measures included small-world parameters, namely clustering coefficient, characteristic path length, normalized clustering coefficient, normalized characteristic path length, and small-worldness, as well as network efficiency 180
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Table 1 Demographics and clinical characteristics of study participants. Characteristic
HCs (n = 60)
Cmdd (n = 59)
rMDD (n = 48)
χ2/F/t
p
Mean age (s.d.) Mean FD (s.d) Sex, males:females Mean years of education (s.d.) Mean illness duration, years (s.d.) Mean remission duration, years (s.d.) Mean BDI score (s.d.) Mean HAMD-D17, score (s.d.)
21.02 (1.13) 26.45 (10.34) 26:34 14.28 (0.61) _ _ 2.83 (2.44) –
22.05 (4.09) 30.37 (23.89) 25:34 13.88 (2.15) 0.89 (1.32) _ 31.48 (9.03) 24.3 (4.57)
21.00 (3.74) 27.59 (15.96) 23:25 13.96 (2.54) 1.01 (0.78) 0.50 (0.30) 5.42 (5.08) 2.93 (2.28)
1.995 0.723 0.366 0.738 0.295 _ 378.058 - 27.645
.139 .487 .833 .480 .769 _ <.001 <.001
HCs, healthy controls; cMDD, current major depressive disorder; rMDD, remitted major depressive disorder; FD, Framewise Displacement.
Fig. 2B and Table S4) . In comparison to cMDD group, the rMDD patients had a significant higher degree of centrality in right PhG (Fig. 2C and Table S4; post hoc qFDR = 0.006).
2.7.3. Correlation analyses Upon detection of significant group differences in any network metrics, partial correlation analyses, with age, gender, education level and FD value as covariates, were conducted in each group (HC, rMDD and cMDD) separately to probe the relationship between AUC of altered metrics and psychometric scores (BDI and HAMD score). The correlation analyses were conducted in SPSS.22.0. We calculated the FDR (p < .05) to control multiple comparisons.
3.4. Correlations between network measures and psychometric scores Both low nodal efficiency values and degree centrality values for right superior parietal lobule in the cMDD group correlated negatively with HAMD scores (r = −0.409, qFDR = 0.040; r = −0.415, qFDR = 0.040; Fig. 2D). No other significant correlations were detected. Besides, partial correlation analyses between the degree centrality of right PhG (state-dependent metric) and illness duration as well as previous episodes were conducted in order to exclude potential influence of these factors on our interpretation our results and no significant correlations were detected.
3. Results 3.1. Demographic and clinical characteristics Fifty-nine cMDD, 48 remitted patients and 60 HCs were included in the ultimate analyses in the present study. The three groups were statistically similar in terms of age, gender, and education. The cMDD group scored higher than the rMDD group on the 17-item HAM-D scale (t = −27.645, df = 98, p < .001). One-way ANOVA indicated that there were main effects of group on BDI (F2, 164 = 378.058, p < .001). As reported in Table 1, the BDI scores of each group differed from those of the other two groups (cMDD > rMDD > HCs, all post hoc tests p < .05).
4. Discussion Using graph theory to explore topological alterations of functional brain networks in cMDD and rMDD patients, we found some state-dependent and -independent topological functional patterns in MDD. The global-level finding was that both rMDD and cMDD patients had decreased normalized clustering coefficient. On a nodal level, we found that both patient groups had decreased nodal centrality, predominantly in cortex-mood-regulation brain regions (dlPFC, PCC, posterior parietal cortex) in relation to HCs. Additionally, by comparison to cMDD patients, rMDD patients had significant higher degree centrality of the right PhG. Regarding to global topological properties, both the rMDD and cMDD groups showed decreased normalized clustering coefficient in relative to HCs, which consistent with previous studies (Gong et al., 2017; Li et al., 2017). The normalized clustering coefficient reflects function segregation of network which refers to the ability of local specialization. Therefore, this finding may reveal that both cMDD and rMDD patients have dysfunction in functional segregation. Besides, there was a slight reduction in the small-worldness since the p value was much closed to the 0.05 after the FDR correction, which is consistent with previous neuroimaging studies examining functional (Gong et al., 2017) and structural networks (Lu et al., 2017). Since the small worldness characterizes an optimal balance between functional segregation and functional integration which is crucial for high synchronizability and fast information transmission in the brain, our results may indicate that both rMDD and cMDD patients have less effective balance between local specialization and global integration in comparison to healthy subjects. Our findings reflected both state-dependent and -independent alterations of nodal centrality related to the limbic-cortical dysregulation model of depression. As suggested by limbic-cortical model (Mayberg, 1997, 2003; Mayberg, 2009), dorsal compartment decreases (metabolism and blood flow) and ventral compartment increases (metabolism and blood flow) characterize MDD morbidity; concurrent inhibition of ventral compartment regions and normalization of
3.2. Global topological organization of functional brain networks Statistical analyses on the AUC (sparsity ranges from 0.08 to 0.50) of global parameters revealed significant group differences in normalized clustering coefficient (qFDR = 0.047). Post hoc comparisons revealed that, relative to HCs, both the cMDD and rMDD groups had significantly lower normalized clustering coefficient (all qFDR < 0.05). Besides, it also revealed a tendency toward significant group differences in characteristic path length and small-worldness (qFDR = 0.061, qFDR = 0.062). Detailed information of global network properties among the cMDD patients, rMDD patients and HCs were depicted in Fig. 1A–D and Table S3. 3.3. Nodal topological organization of functional brain networks We identified brain regions showing significant between-group differences in at least one nodal metric. ANCOVAs (p < .05, FDR corrected) revealed significant differences in nodal efficiency primarily in cortical and limbic brain regions. Post hoc analysis further revealed that, compared to HCs, both the cMDD and rMDD groups showed decreased nodal efficiency in the right dlPFC, left precentral gyrus (PrG), bilateral posterior parietal cortex and right posterior cingulate cortex (PCC) (all p < .05, FDR corrected) (Fig. 2A and Table S4). ANCOVAs (p < .05, FDR corrected) revealed significant differences in degree centrality, primarily in cortical and paralimbic brain regions. Post hoc analyses revealed that, compared to HCs, both cMDD and rMDD patients had a decreased degree of centrality in the right dlPFC, right posterior parietal cortex and an increased degree of centrality in the right parahippocampal gyrus (PhG) (all p < .05, FDR corrected; 181
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Fig. 1. Global network properties among the cMDD patients, rMDD patients, and HCs. (A) Normalized characteristic path length (A), small worldness (B), and normalized clustering coefficient (C) across the sparsity range among the cMDD, rMDD, and HC groups. (D) Global network measure AUCs by group. Cp, Lp, gamma, lambda, sigma, Eglob, and Eloc denote the clustering coefficient, characteristic path length, normalized clustering coefficient, normalized characteristic path length, small worldness, global efficiency, and local efficiency, respectively. Error bars denote standard errors. cMDD, current major depressive disorder; rMDD, remitted major depressive disorder; HCs, healthy controls. *FDR correction, p < .05.
HAMD scores in the cMDD group, indicates that a lower nodal centrality of the posterior parietal cortex associates with more severe depressive symptoms. Overall, all above findings may support the notion that the state-independent patterns may play vital roles in pathophysiology of depression. Besides, in comparison to HCs, both patient groups showed aberrant nodal centrality in primary motor cortex. Decreased nodal centrality of primary motor cortex was consistent with a previous diffusion tensor imaging network study in geriatric remitted depression patients (Bai et al., 2012). However, another structural connectivity study found that late-life depression was associated with increased nodal centrality in similar brain regions (Ajilore et al., 2014). This apparent conflict might be related to distinct types of psychomotor symptoms (agitation or retardation) in MDD. Beyond the trait-like alterations of nodal centrality in MDD, we also found one state-dependent topological pattern in right PhG: both MDD groups have significant higher degree centrality in right PhG in relation to HCs; rMDD group has increased degree centrality of PhG in relative to cMDD. Consistently, a previous study (Zhang et al., 2011) also found that cMDD patients had increased nodal centrality in PhG in relation to HCs. Our finding further suggests that the PhG may play a strengthened role of coordinating whole-brain networks in both current and remitted depression. Besides, our finding of atypically increased nodal centrality of the PhG in rMDD, relative to cMDD, is noteworthy given that the PhG has been implicated in MDD pathophysiology (Zeng et al., 2012). The PhG has frequently been regarded as part of default mode network and is involved in self-referential activity (Zhang et al., 2011). Strengthened degree centrality of PhG may favor enhanced self-focused processing
hypofunctioning dorsal compartment sites characterize remission of depression. The dorsal compartment, which includes both neocortical and midline limbic elements (e.g., dlPFC, dorsal cingulate gyrus, PCC, IPL, and striatum), has been postulated to be principally involved in attentional and cognitive features of MDD (Mayberg, 1997). The ventral compartment, which is composed of paralimbic cortical, subcortical, and brainstem regions (e.g., subgenual anterior cingulate cortex, hippocampus, ventral frontal, and hypothalamus), has been hypothesized to mediate the vegetative and somatic aspects of MDD (Mayberg, 1997). Our findings of decreased nodal centrality in the dorsal compartment (right dlPFC, right PCC, and posterior parietal cortex) in both cMDD and rMDD patients indicates that these alterations may represent state-independent biomarkers of MDD. Consistent with this interpretation, prior functional and structural network studies with small theory method also found decreased nodal centrality in the dlPFC (Bai et al., 2012; Jerzy et al., 2012; Qin et al., 2014; Singh et al., 2013; Zhang et al., 2011), PCC (Singh et al., 2013), and IPL (Ajilore et al., 2014; Chen et al., 2017). Decreased nodal centrality of these regions suggest their weakened roles of coordinating whole-brain networks. The dorsal compartment is important for executive function and mood regulation (Mayberg, 1997, 2003; Mayberg, 2009), and both dlPFC and posterior parietal cortex are two key brain regions of central executive network. Thus, weakened nodal centrality in these regions may reflect weakened top-down mood regulation. Notably, a resting study (Liu et al., 2013) which examined the spontaneous activity of cMDD and their siblings also found state-independent heritable markers in dlPFC, which may suggest that our finding have its specific hereditary basis. Our finding of SPL nodal centrality correlating negatively with 182
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Fig. 2. State-independent and state-dependent nodal measures of MDD. (A) Brain regions showing atypical nodal efficiency in cMDD and rMDD groups, indicative of state-independent patterns of nodal efficiency. (B) Brain regions showing atypical degree centrality in both patient groups, indicative of the state-independent patterns of degree centrality. (C) State-dependent patterns including nodal efficiency of the right PhG. (D) Correlations between nodal centrality in SPL and clinical symptoms in cMDD. Error bars denote standard errors. PhG, parahippocampal gyrus; SPL, superior parietal lobule; HAMD, Hamilton Depression Rating Scale; rMDD, remitted major depressive disorder; cMDD, current major depressive disorder; HC, heathy control. *FDR correction, p < .05.
current depression (Jacobs et al., 2016; Meng et al., 2014). Therefore, episode-dependent and -independent effects should be further investigated with longitudinal studies. Third, the current study is an exploratory analysis and further confirmation with task-based and experimental studies is needed. Fourth, we did not collect hormones data and control for menstrual circle during the fMRI scan, which may affect our results. Finally, some of cMDD patients recruited in the current study were likely to be bipolar disorder since these patients were during their first depressive episodes, which may influence the interpretation of our results.
and support the important role of default mode network as suggested by previous studies (Greicius et al., 2007; Li et al., 2013; Zeng et al., 2014a). Given higher self-referential activity such as rumination (Nolan et al., 1998) is a high-risk factor of depressive episode, our finding may further clarify the potential neural mechanism of higher recurrence of rMDD. Note the degree centrality of PhG is not associated with illness duration and the number of previous episodes in current study and the gray matter volume of PhG is state-independent revealed by a structural research (Zeng et al., 2015), which suggest the differences of illness duration, number of previous episodes and the volume of gray matter may do not influence the interpretations of observed state-dependent topological patterns in right PhG. Considering we did not find significant correlations between the degree centrality of right PhG and depressive symptoms, more future researches are needed to support the interpretations of this finding. Of note, in our previous study, we did not find state-dependent network changes within and between the salience network, default mode network and central executive network using the independent component analysis (ICA). Although both the graph theory analysis and ICA are good methods to explore functional connectivity of brain. Our previous study mainly highlighted three core brain networks (SN, CEN, DMN) (Dong et al., 2019), whereas the current study mainly concentrates on the global efficiency and node efficiency of the whole brain. Therefore, it is not surprising that these two studies do not have identical results. Overall, both studies have found state-dependent alterations in the brain regions associated with central executive network such as dlPFC, suggesting the crucial role of this network in depression. Several study limitations should be addressed. First, our 30-day remission criterion was relatively short and thus might represent a temporary remission, rather than a definite recovery, in at least some of the patients. Second, previous studies have suggested that the number of depression episodes might influence functional connectivity in
5. Conclusion To our knowledge, this is the first study to investigate state-independent and -dependent biomarkers of MDD from the perspective of topological organization. State-independent dysfunction in cortexmood-regulation brain regions may demonstrate potential neural mechanism of the high recurrence of depression and inform the development of future treatment strategies for MDD. Competing interest The authors declare that they have no competing interests. Funding This study was supported by the National Natural Science Foundation of China (Grant No. 81471384, 81501178). Authors' contributions S.Q.Y, X.W, and X.C.Z designed the study; D.F.D, C.T.L, Q.S.M, X.Z, 183
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X.Q.S, Y.L.J and Y.D.G collected the data; D.F.D analyzed the data and wrote the first manuscript; D.F.D, C.T.L and Q.S.M revised the manuscript.
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Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad
Research paper
Topologically state-independent and dependent functional connectivity patterns in current and remitted depression
T
Dong Daifenga,b,c, Li Chutinga,b,c, Ming Qingsend, Zhong Xuea,b,c, Zhang Xiaocuia,b,c, ⁎ Sun Xiaoqianga,b,c, Jiang Yalia,b,c, Gao Yidiana,b,c, Wang Xianga,b,c, Yao Shuqiaoa,b,c, a
Medical Psychological Center, The Second Xiangya Hospital of Central South University, Changsha, Hunan, PR China Medical Psychological Institute of Central South University, Changsha, Hunan, PR China c China National Clinical Research Center on Mental Disorders (Xiangya), Changsha, Hunan, PR China d Department of Psychiatry, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, PR China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Current depression Remitted depression Graph theory
Objective: Identification of state-independent and -dependent neural biomarkers may provide insight into the pathophysiology and effective treatment of major depressive disorder (MDD), therefore we aimed to investigate the state-independent and -dependent topological alterations of MDD. Method: Brain resting-state functional magnetic resonance imaging (fMRI) data were acquired from 59 patients with unmedicated first episode current MDD (cMDD), 48 patients with remitted MDD (rMDD) and 60 demographically matched healthy controls (HCs). Using graph theory, we systematically studied the topological organization of their whole-brain functional networks at the global and nodal level. Results: At a global level, both patient groups showed decreased normalized clustering coefficient in relative to HCs. On a nodal level, both patient groups showed decreased nodal centrality, predominantly in cortex-moodregulation brain regions including the dorsolateral prefrontal cortex, posterior parietal cortex and posterior cingulate cortex. By comparison to cMDD patients, rMDD group had a higher nodal centrality in right parahippocampal gyrus. Limitations: The present study, an exploratory analysis, may require further confirmation with task-based and experimental studies. Conclusions: Deficits in the topological organization of the whole brain and cortex-mood-regulation brain regions in both rMDD and cMDD represent state-independent biomarkers.
1. Introduction
depression or current depression) (Arnone et al., 2013; Ming et al., 2017; Van et al., 2013) that distinguish remitted MDD (rMDD) from current MDD may inform the development of treatments that may prevent relapse and support persistent remission. Hence, identification of state-independent and -dependent neural biomarkers in first-episode, medication-naive current MDD (cMDD) patients and rMDD patients may provide insights into MDD pathophysiology and therapeutic targets. Accumulating structural and functional neuroimaging data suggest that rMDD patients exhibit dysfunction in prefrontal, limbic, and paralimbic brain regions (Burkhouse et al., 2017; Dong et al., 2019; Ming et al., 2017; Stange et al., 2017; Van et al., 2013) and abnormal frontal-limbic interactions (Figueroa et al., 2017; Jacobs et al., 2016, 2014). Similar alterations have been observed in cMDD revealed by a
Major depressive disorder (MDD) is a high-morbidity mental disorder (Eaton et al., 2008; Kruijshaar et al., 2005) with a 15-year recurrence rate of 35% in the general population and as high as 85% in specialized mental health care settings (Hardeveld et al., 2010). This high recurrence is a major clinical issue in MDD management (Fava and Kendler, 2000). Residual characteristics after first episode, including subclinical symptoms and underlying state-independent neurobiology (traits existing across different illness courses)(Arnone et al., 2013; Ming et al., 2017; Van et al., 2013), have been proposed as possible explanations of this recurrence (Hardeveld et al., 2010; Post and Weiss, 1998; Robinson and Sahakian, 2008; Solomon et al., 2000). Furthermore, state-dependent neural changes (state-specific in the remitted
⁎ Corresponding author at: Medical Psychological Institute, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road of Changsha, Hunan, 410011, PR China. E-mail address: [email protected] (S. Yao).
https://doi.org/10.1016/j.jad.2019.03.030 Received 12 December 2018; Received in revised form 23 February 2019; Accepted 4 March 2019 Available online 05 March 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.
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meta-analysis with 31 resting studies of first episode, medication-naïve MDD (Zhong et al., 2016), consistent with the limbic-cortical dysregulation model of depression which suggests that the disruption of a functional interactive network of cortical-limbic pathway is critical to regulation of mood (Mayberg, 1997). However, direct neuroimaging comparisons between rMDD and cMDD are sparse and inconclusive. Our previous study exploring neural responses to psychosocial stress which induced by the Montreal Imaging Task showed hyperactivity in the dorsolateral prefrontal cortex (dlPFC) and striatum in patients with rMDD, relative to observations in patients with cMDD (Ming et al., 2017), and a recent study (Dong et al., 2019) investigating alterations within and between the salience network(SN), default mode network (DMN) as well as the central executive network (CEN) with an independent component analysis(ICA) did not find state-dependent patterns. Additionally, a resting-state functional magnetic resonance imaging (fMRI) study exploring low-frequency fluctuation showed hypoactivity in the medial frontal gyrus in rMDD (Jing et al., 2013). Longitudinal studies exploring antidepressant effects on neurophysiology in MDD also documented some state-dependent differences in the prefrontal cortex (An et al., 2017; Cheng et al., 2017; Fu et al., 2015; Heller et al., 2013), limbic system (An et al., 2017), and frontal-limbic connectivity (Anand et al., 2007; Anand et al., 2005). Notably, there have been mixed results regarding state-dependent and-independent characteristics of MDD, especially in prefrontal and limbic regions. These inconsistencies may be related to behavioral protocol, imaging modality, or cohort (e.g., antidepressant use) differences. Therefore, a systematic comparison of cMDD, rMDD, and healthy controls (HCs) in the resting state is needed to clarify this topic clearly and supplement previous findings. Graph theory provides a powerful mathematical framework for describing the topological organization of resting-state brain networks in the form of nodes (functional or structural defined regions of interest) and edges (functional connectivity) (Zuo et al., 2012) and depicts topological patterns of the brain networks from three levels: global properties, modularity and regional nodal properties (Gong and He, 2015). Previous structural and functional studies using graph theory have reported that cMDD patients had atypical global properties (Gong et al., 2017; Lu et al., 2017; Meng et al., 2014; Zhang et al., 2011) and atypical nodal centralities in frontal (Gong et al., 2017; Lu et al., 2017; Meng et al., 2014; Qin et al., 2015; Tao et al., 2013; Wang et al., 2014; Zhang et al., 2011), parietal (Ajilore et al., 2014; Chen et al., 2017), limbic (Gong et al., 2017; Lu et al., 2017; Qin et al., 2015; Tao et al., 2013; Zhang et al., 2011), and paralimbic (Singh et al., 2013; Zhang et al., 2011) brains regions as well as in the basal ganglia (Gong et al., 2017; Meng et al., 2014; Tao et al., 2013; Zhang et al., 2011). However, the functional and structural studies using graph theory in rMDD also found alterations in global properties (Bai et al., 2012; Wang et al., 2016) and atypical nodal centralities in frontal (Bai et al., 2012; Qin et al., 2015; Wang et al., 2016), insula (Qin et al., 2015), occipital (Wang et al., 2016), and basal ganglia (Qin et al., 2015; Wang et al., 2016). Together, the current findings were still inconsistent, and whether such large-scale topological organization changes in whole-brain functional networks in MDD are state-dependent or -independent is still unclear. In the current study, we acquired resting-state fMRI data from firstepisode unmedicated cMDD patients, rMDD patients, and HCs. Then, we examined the topological organization of whole-brain intrinsic functional networks and analyzed between-group differences in topological parameters from both global and regional levels. Based on the limbic-cortical dysregulation model of depression, we hypothesized that both state-dependent and -independent topological patterns would be observed in limbic-cortical system in the current and remitted depression.
2. Methods 2.1. Participants Patients with MDD were recruited from the outpatient department of the Second Xiangya Hospital affiliated with Central South University in Changsha, Hunan, PR China. Age-, gender-, and education-matched HCs were recruited through advertisements and posters from two colleges and the Changsha community. The study was conducted in accordance with the declaration of Helsinki and was approved by the Ethics committee of the Second Xiangya Hospital of Central South University. All participants were aware of the study's purpose and signed an informed consent form. Psychiatric evaluations were conducted independently by two psychiatrists using the Structured Clinical Interview for DSM-IV-TR Axis Ⅰ Disorder-Patient edition (Depressive disorders, Schizophrenia Spectrum and other Psychotic disorders, Bipolar and Related disorders, Anxiety disorders, Substance-Related and Addictive Disorders et al.) (First et al., 2002). Only patients meeting the DSM-IV-TR criteria for the first MDD episode were included in the cMDD group to exclude potential confounding effects from antidepressant medication (Schaefer et al., 2006), multiple depression episodes (Takami et al., 2007), and comorbidities. Participants enrolled in the rMDD group had experienced at least one episode of depression within the past 10 years, did not meet the DSMIV-TR criteria for active MDD within the 30-day period preceding scanning, and had a 17-item HAM-D score ≤ 7. For all three groups (cMDD, rMDD and HCs), the exclusion criteria were: (1) any prior DSMIV-TR Axis Ⅰ disorder (except MDD in the cMDD and rMDD groups); (2) a history of taking antidepressants or undergoing psychotherapy; (3) a history of alcohol/substance abuse; and (4) any neurological disorder diagnosis, structural brain abnormalities, or contradictions to MRI. Ultimately, 60 medication-naive, first episode depressed patients (34 females), 48 remitted patients (25 females), and 63 healthy subjects (34 females) were enrolled in the study in the cMDD, rMDD, and HC groups, respectively.
2.2. Clinical evaluation The Beck Depression Inventory-Ⅱ (BDI) (Steer, 1996), a validated self-report scale of depressive symptoms, was used to evaluate the depressive symptoms of all participants. Additionally, the 17-item Hamilton Depression Rating Scale (HAMD) (Hamilton, 1960) was used to evaluate the depressive symptoms of MDD patients.
2.3. Image acquisition Scanning was conducted in a 3.0-T Siemens Magnetom Skyra scanner (Siemens Healthineers, Erlangen, Germany) with a 32-channel head coil. All patients were instructed to lie in a supine position with their eyes closed, to remain still, and to think of nothing in particular, but to avoid falling asleep. Their heads were fixed snugly with foam pads and straps to minimize head movement. After scanning, each participant was asked whether he/she fall asleep during the scan. Once falling asleep, the data would not be used. Blood-oxygen-level-dependent data were acquired with an echoplanar imaging (EPI) sequence (repetition time/echo time = 2000/ 30 ms, thickness/gap = 4/1 mm, field of view = 256 × 256 mm, flip angel = 80°, matrix = 64 × 64, and 32 slices). The total time of resting acquisition was 7 min 12 s and 216 vol were collected. Besides, the T1 weighted structural images were acquired with a magnetization-prepared rapid gradient echo for checking the organic brain lesion and preprocessing the fMRI data (repetition time/echo time = 1900/ 2.01 ms, thickness/gap = 1/0 mm, field of view = 256 × 256 mm, flip angel = 9°, matrix = 256 × 256, and 176 slices). 179
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parameters, including both local efficiency and global efficiency. Specifically, the clustering coefficient of a network is the average clustering coefficients of all nodes and quantifies the extent of local interconnectivity or cliquishness of a graph. The characteristic path length of a network is the shortest path length (number of edges) required to transfer from one node to another averaged over all pairs of nodes, which reflects overall routing efficiency of a graph. The local efficiency of a network is the mean of all the local efficiency of nodes in the graph and reflects the fault tolerance of a graph by measuring the capacity of its subgraphs for information exchange when the index nodes are eliminated. Meanwhile, the global efficiency of a network quantifies the capacity for parallel information transfer of a graph. Finally, the normalized clustering coefficient and normalized characteristic path length are defined as the ration of the clustering coefficient and characteristic path length of the network to matched random networks respectively and are used to examine the small-world properties quantitatively. The small-worldness (small-worldness = normalized clustering coefficient / normalized characteristic path length) reflects a typical balance between functional segregation and functional integration. For regional nodal network measures, we employed two nodal centrality metrics: degree centrality and nodal efficiency (Rubinov and Sporns, 2010). Degree centrality is the number of connections lined to a given node; nodal efficiency is the inverse of the harmonic mean of the shortest path length between a given node and all other nodes. Detailed descriptions of these topological measures are presented in Table S2. Furthermore, we calculated the area under the curve (AUC) for each network metric analyzed. The AUC datum provides a summarized scalar value for topological characterization of a network independent of the individual threshold of each network. The integrated AUC metric has been shown to be sensitive for detecting topological alterations in association with brain disorders (He et al., 2009; Wang et al., 2009; Yu et al., 2017; Zhang et al., 2011).
2.4. Image preprocessing The images were processed in Data Processing Assistant for RestingState FMRI (version 2.3, http://restfmri.net) software (Yan and Zang, 2010) in nine steps. (1) The first 10 EPI volumes of resting data were removed. (2) The slice timing was corrected. (3) Data from participants who display excessive head motion (translation > 1.5 mm or rotation > 1.5°; cMDD group, N = 1; and HC group, N = 3) were excluded from further analyses. Ultimately, 59 cMDD, 48 remitted patients and 60 HCs were included in the further statistical analyses. (4) individual structural images were co-registered to the mean functional image, and the transformed structural images were then segmented into gray matter, white matter and cerebrospinal fluid. Wherein the white matter and cerebrospinal fluid were used as masks in nuisance regression. (5) Nuisance variable regression was performed by regressing out mean signals for white matter, cerebrospinal fluid, and Friston-24 head motion parameters. (6) The images were spatially normalized to a standard Montreal Neurological Institute (MNI) template and resampled at a voxel size of 3 × 3 × 3 mm. (7) Spatial smoothing was completed with a Gaussian Kernel (6 × 6 × 6 mm full-width at half maximum). (8) Linear and quadratic trends were removed. (9) Finally, band-pass filtering (0.01–0.08 Hz) was performed. The residuals were used in our subsequent brain network analyses. 2.5. Network construction Using the graph theoretical network analysis toolbox (GRETNA, https://www.nitrc.org/projects/gretna) (Wang et al., 2015), we constructed a brain functional network for each subject according to the functional atlas designed by Fan et al. (2016). Functional parcellation is designed to improve the test-retest network reliabilities relative to structural parcellation (Gong and He, 2015). We employed a human brainnetcome atlas, a fine-grained, cross-validated atlas with 210 cortical and 36 subcortical regions that contains information on both anatomical and functional connections. We parcellated the brain into 246 regions of interest (ROIs) (123 in each hemisphere). The names of the ROIs and their corresponding abbreviations are listed in Supplementary Table S1. The time series of each ROI was estimated by averaging the residual time series of all voxels within that region. Second, to measure the functional connectivity among regions, we computed Pearson correlation coefficients between the regional time series of all possible brain region pairs, yielding a correlation matrix (246 × 246) for each subject, and the Person correlation rather than the partial-based correlations has higher test-retest network reliabilities (Gong and He, 2015). Each correlation matrix (Pearson correlation coefficients, absolute values) was thresholded into an undirected binarized matrix with a fixed sparsity or density value (i.e., the number of edges in the network divided by the maximum possible number of edges). Setting a sparsity-specific threshold ensured that the networks for all three groups had the same number of edges. We computed brain network properties over a wide range of network sparsity/density (8% ≤ s ≤ 50%) at the intervals of 0.01; smallworld parameters (reflecting the optimal balance between functional segregation and functional integration) were estimated and the number of spurious edges was minimized, as described previously (Achard et al., 2006; He et al., 2007; Yu et al., 2017). Through this thresholding, a set of 246 × 246 binarized matrices was obtained for each subject.
2.7. Statistical analysis 2.7.1. Behavioral analyses One-way analyses of variance (ANOVAs) and chi-squared tests were used to detect group differences (three levels: cMDD, rMDD, and HCs) in our demographic and psychometric results followed by post hoc twosample t-tests. A two-tailed p < .05 was considered significant. The behavioral analyses were conducted in the SPSS.22.0. 2.7.2. Network metrics analysis In current study, there were not significant differences in head motion (volume-level framewise displacement (FD) defined by Power et al.) (Power et al., 2012) among the three groups (F2,164 = 0.723, p = .487). Given recent studies demonstrated that head motion may have both noisy and neuronal effect on functional connectivity measures (Dijk et al., 2012; Zeng et al., 2014b), we analyzed all the network metrics by adding FD value as a covariate to further exclude possible effects of head motion. To detect group differences in global network measures and regional nodal characteristics, one-way analyses of covariance (ANCOVAs) were performed on the AUCs for each univariate network metric with age, gender, education level and FD value as covariates. For both global measures and nodal characteristics, the significant level was set as false discovery rate (FDR) correction, p < .05. Further pair-wise post hoc t-tests with age, gender, education level and FD value as covariates were then conducted for brain regions detected as significant in an ANCOVA. To control for the effects of residual depressive symptoms, BDI score was employed as a covariate, with age, gender, education level and FD value, when comparing the rMDD and HC groups. All network metric analyses were conducted with metric comparison tools in GRETNA. For all post hoc t-tests, the significance level was set at FDR correction, p < .05.
2.6. Network analysis We calculated both global and regional network measures for brain networks at each sparsity threshold. The global measures included small-world parameters, namely clustering coefficient, characteristic path length, normalized clustering coefficient, normalized characteristic path length, and small-worldness, as well as network efficiency 180
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Table 1 Demographics and clinical characteristics of study participants. Characteristic
HCs (n = 60)
Cmdd (n = 59)
rMDD (n = 48)
χ2/F/t
p
Mean age (s.d.) Mean FD (s.d) Sex, males:females Mean years of education (s.d.) Mean illness duration, years (s.d.) Mean remission duration, years (s.d.) Mean BDI score (s.d.) Mean HAMD-D17, score (s.d.)
21.02 (1.13) 26.45 (10.34) 26:34 14.28 (0.61) _ _ 2.83 (2.44) –
22.05 (4.09) 30.37 (23.89) 25:34 13.88 (2.15) 0.89 (1.32) _ 31.48 (9.03) 24.3 (4.57)
21.00 (3.74) 27.59 (15.96) 23:25 13.96 (2.54) 1.01 (0.78) 0.50 (0.30) 5.42 (5.08) 2.93 (2.28)
1.995 0.723 0.366 0.738 0.295 _ 378.058 - 27.645
.139 .487 .833 .480 .769 _ <.001 <.001
HCs, healthy controls; cMDD, current major depressive disorder; rMDD, remitted major depressive disorder; FD, Framewise Displacement.
Fig. 2B and Table S4) . In comparison to cMDD group, the rMDD patients had a significant higher degree of centrality in right PhG (Fig. 2C and Table S4; post hoc qFDR = 0.006).
2.7.3. Correlation analyses Upon detection of significant group differences in any network metrics, partial correlation analyses, with age, gender, education level and FD value as covariates, were conducted in each group (HC, rMDD and cMDD) separately to probe the relationship between AUC of altered metrics and psychometric scores (BDI and HAMD score). The correlation analyses were conducted in SPSS.22.0. We calculated the FDR (p < .05) to control multiple comparisons.
3.4. Correlations between network measures and psychometric scores Both low nodal efficiency values and degree centrality values for right superior parietal lobule in the cMDD group correlated negatively with HAMD scores (r = −0.409, qFDR = 0.040; r = −0.415, qFDR = 0.040; Fig. 2D). No other significant correlations were detected. Besides, partial correlation analyses between the degree centrality of right PhG (state-dependent metric) and illness duration as well as previous episodes were conducted in order to exclude potential influence of these factors on our interpretation our results and no significant correlations were detected.
3. Results 3.1. Demographic and clinical characteristics Fifty-nine cMDD, 48 remitted patients and 60 HCs were included in the ultimate analyses in the present study. The three groups were statistically similar in terms of age, gender, and education. The cMDD group scored higher than the rMDD group on the 17-item HAM-D scale (t = −27.645, df = 98, p < .001). One-way ANOVA indicated that there were main effects of group on BDI (F2, 164 = 378.058, p < .001). As reported in Table 1, the BDI scores of each group differed from those of the other two groups (cMDD > rMDD > HCs, all post hoc tests p < .05).
4. Discussion Using graph theory to explore topological alterations of functional brain networks in cMDD and rMDD patients, we found some state-dependent and -independent topological functional patterns in MDD. The global-level finding was that both rMDD and cMDD patients had decreased normalized clustering coefficient. On a nodal level, we found that both patient groups had decreased nodal centrality, predominantly in cortex-mood-regulation brain regions (dlPFC, PCC, posterior parietal cortex) in relation to HCs. Additionally, by comparison to cMDD patients, rMDD patients had significant higher degree centrality of the right PhG. Regarding to global topological properties, both the rMDD and cMDD groups showed decreased normalized clustering coefficient in relative to HCs, which consistent with previous studies (Gong et al., 2017; Li et al., 2017). The normalized clustering coefficient reflects function segregation of network which refers to the ability of local specialization. Therefore, this finding may reveal that both cMDD and rMDD patients have dysfunction in functional segregation. Besides, there was a slight reduction in the small-worldness since the p value was much closed to the 0.05 after the FDR correction, which is consistent with previous neuroimaging studies examining functional (Gong et al., 2017) and structural networks (Lu et al., 2017). Since the small worldness characterizes an optimal balance between functional segregation and functional integration which is crucial for high synchronizability and fast information transmission in the brain, our results may indicate that both rMDD and cMDD patients have less effective balance between local specialization and global integration in comparison to healthy subjects. Our findings reflected both state-dependent and -independent alterations of nodal centrality related to the limbic-cortical dysregulation model of depression. As suggested by limbic-cortical model (Mayberg, 1997, 2003; Mayberg, 2009), dorsal compartment decreases (metabolism and blood flow) and ventral compartment increases (metabolism and blood flow) characterize MDD morbidity; concurrent inhibition of ventral compartment regions and normalization of
3.2. Global topological organization of functional brain networks Statistical analyses on the AUC (sparsity ranges from 0.08 to 0.50) of global parameters revealed significant group differences in normalized clustering coefficient (qFDR = 0.047). Post hoc comparisons revealed that, relative to HCs, both the cMDD and rMDD groups had significantly lower normalized clustering coefficient (all qFDR < 0.05). Besides, it also revealed a tendency toward significant group differences in characteristic path length and small-worldness (qFDR = 0.061, qFDR = 0.062). Detailed information of global network properties among the cMDD patients, rMDD patients and HCs were depicted in Fig. 1A–D and Table S3. 3.3. Nodal topological organization of functional brain networks We identified brain regions showing significant between-group differences in at least one nodal metric. ANCOVAs (p < .05, FDR corrected) revealed significant differences in nodal efficiency primarily in cortical and limbic brain regions. Post hoc analysis further revealed that, compared to HCs, both the cMDD and rMDD groups showed decreased nodal efficiency in the right dlPFC, left precentral gyrus (PrG), bilateral posterior parietal cortex and right posterior cingulate cortex (PCC) (all p < .05, FDR corrected) (Fig. 2A and Table S4). ANCOVAs (p < .05, FDR corrected) revealed significant differences in degree centrality, primarily in cortical and paralimbic brain regions. Post hoc analyses revealed that, compared to HCs, both cMDD and rMDD patients had a decreased degree of centrality in the right dlPFC, right posterior parietal cortex and an increased degree of centrality in the right parahippocampal gyrus (PhG) (all p < .05, FDR corrected; 181
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Fig. 1. Global network properties among the cMDD patients, rMDD patients, and HCs. (A) Normalized characteristic path length (A), small worldness (B), and normalized clustering coefficient (C) across the sparsity range among the cMDD, rMDD, and HC groups. (D) Global network measure AUCs by group. Cp, Lp, gamma, lambda, sigma, Eglob, and Eloc denote the clustering coefficient, characteristic path length, normalized clustering coefficient, normalized characteristic path length, small worldness, global efficiency, and local efficiency, respectively. Error bars denote standard errors. cMDD, current major depressive disorder; rMDD, remitted major depressive disorder; HCs, healthy controls. *FDR correction, p < .05.
HAMD scores in the cMDD group, indicates that a lower nodal centrality of the posterior parietal cortex associates with more severe depressive symptoms. Overall, all above findings may support the notion that the state-independent patterns may play vital roles in pathophysiology of depression. Besides, in comparison to HCs, both patient groups showed aberrant nodal centrality in primary motor cortex. Decreased nodal centrality of primary motor cortex was consistent with a previous diffusion tensor imaging network study in geriatric remitted depression patients (Bai et al., 2012). However, another structural connectivity study found that late-life depression was associated with increased nodal centrality in similar brain regions (Ajilore et al., 2014). This apparent conflict might be related to distinct types of psychomotor symptoms (agitation or retardation) in MDD. Beyond the trait-like alterations of nodal centrality in MDD, we also found one state-dependent topological pattern in right PhG: both MDD groups have significant higher degree centrality in right PhG in relation to HCs; rMDD group has increased degree centrality of PhG in relative to cMDD. Consistently, a previous study (Zhang et al., 2011) also found that cMDD patients had increased nodal centrality in PhG in relation to HCs. Our finding further suggests that the PhG may play a strengthened role of coordinating whole-brain networks in both current and remitted depression. Besides, our finding of atypically increased nodal centrality of the PhG in rMDD, relative to cMDD, is noteworthy given that the PhG has been implicated in MDD pathophysiology (Zeng et al., 2012). The PhG has frequently been regarded as part of default mode network and is involved in self-referential activity (Zhang et al., 2011). Strengthened degree centrality of PhG may favor enhanced self-focused processing
hypofunctioning dorsal compartment sites characterize remission of depression. The dorsal compartment, which includes both neocortical and midline limbic elements (e.g., dlPFC, dorsal cingulate gyrus, PCC, IPL, and striatum), has been postulated to be principally involved in attentional and cognitive features of MDD (Mayberg, 1997). The ventral compartment, which is composed of paralimbic cortical, subcortical, and brainstem regions (e.g., subgenual anterior cingulate cortex, hippocampus, ventral frontal, and hypothalamus), has been hypothesized to mediate the vegetative and somatic aspects of MDD (Mayberg, 1997). Our findings of decreased nodal centrality in the dorsal compartment (right dlPFC, right PCC, and posterior parietal cortex) in both cMDD and rMDD patients indicates that these alterations may represent state-independent biomarkers of MDD. Consistent with this interpretation, prior functional and structural network studies with small theory method also found decreased nodal centrality in the dlPFC (Bai et al., 2012; Jerzy et al., 2012; Qin et al., 2014; Singh et al., 2013; Zhang et al., 2011), PCC (Singh et al., 2013), and IPL (Ajilore et al., 2014; Chen et al., 2017). Decreased nodal centrality of these regions suggest their weakened roles of coordinating whole-brain networks. The dorsal compartment is important for executive function and mood regulation (Mayberg, 1997, 2003; Mayberg, 2009), and both dlPFC and posterior parietal cortex are two key brain regions of central executive network. Thus, weakened nodal centrality in these regions may reflect weakened top-down mood regulation. Notably, a resting study (Liu et al., 2013) which examined the spontaneous activity of cMDD and their siblings also found state-independent heritable markers in dlPFC, which may suggest that our finding have its specific hereditary basis. Our finding of SPL nodal centrality correlating negatively with 182
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Fig. 2. State-independent and state-dependent nodal measures of MDD. (A) Brain regions showing atypical nodal efficiency in cMDD and rMDD groups, indicative of state-independent patterns of nodal efficiency. (B) Brain regions showing atypical degree centrality in both patient groups, indicative of the state-independent patterns of degree centrality. (C) State-dependent patterns including nodal efficiency of the right PhG. (D) Correlations between nodal centrality in SPL and clinical symptoms in cMDD. Error bars denote standard errors. PhG, parahippocampal gyrus; SPL, superior parietal lobule; HAMD, Hamilton Depression Rating Scale; rMDD, remitted major depressive disorder; cMDD, current major depressive disorder; HC, heathy control. *FDR correction, p < .05.
current depression (Jacobs et al., 2016; Meng et al., 2014). Therefore, episode-dependent and -independent effects should be further investigated with longitudinal studies. Third, the current study is an exploratory analysis and further confirmation with task-based and experimental studies is needed. Fourth, we did not collect hormones data and control for menstrual circle during the fMRI scan, which may affect our results. Finally, some of cMDD patients recruited in the current study were likely to be bipolar disorder since these patients were during their first depressive episodes, which may influence the interpretation of our results.
and support the important role of default mode network as suggested by previous studies (Greicius et al., 2007; Li et al., 2013; Zeng et al., 2014a). Given higher self-referential activity such as rumination (Nolan et al., 1998) is a high-risk factor of depressive episode, our finding may further clarify the potential neural mechanism of higher recurrence of rMDD. Note the degree centrality of PhG is not associated with illness duration and the number of previous episodes in current study and the gray matter volume of PhG is state-independent revealed by a structural research (Zeng et al., 2015), which suggest the differences of illness duration, number of previous episodes and the volume of gray matter may do not influence the interpretations of observed state-dependent topological patterns in right PhG. Considering we did not find significant correlations between the degree centrality of right PhG and depressive symptoms, more future researches are needed to support the interpretations of this finding. Of note, in our previous study, we did not find state-dependent network changes within and between the salience network, default mode network and central executive network using the independent component analysis (ICA). Although both the graph theory analysis and ICA are good methods to explore functional connectivity of brain. Our previous study mainly highlighted three core brain networks (SN, CEN, DMN) (Dong et al., 2019), whereas the current study mainly concentrates on the global efficiency and node efficiency of the whole brain. Therefore, it is not surprising that these two studies do not have identical results. Overall, both studies have found state-dependent alterations in the brain regions associated with central executive network such as dlPFC, suggesting the crucial role of this network in depression. Several study limitations should be addressed. First, our 30-day remission criterion was relatively short and thus might represent a temporary remission, rather than a definite recovery, in at least some of the patients. Second, previous studies have suggested that the number of depression episodes might influence functional connectivity in
5. Conclusion To our knowledge, this is the first study to investigate state-independent and -dependent biomarkers of MDD from the perspective of topological organization. State-independent dysfunction in cortexmood-regulation brain regions may demonstrate potential neural mechanism of the high recurrence of depression and inform the development of future treatment strategies for MDD. Competing interest The authors declare that they have no competing interests. Funding This study was supported by the National Natural Science Foundation of China (Grant No. 81471384, 81501178). Authors' contributions S.Q.Y, X.W, and X.C.Z designed the study; D.F.D, C.T.L, Q.S.M, X.Z, 183
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X.Q.S, Y.L.J and Y.D.G collected the data; D.F.D analyzed the data and wrote the first manuscript; D.F.D, C.T.L and Q.S.M revised the manuscript.
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