Reconfiguration of Cortical Networks in MDD Uncovered by Multiscale Community Detection with fMRI

Reconfiguration of Cortical Networks in MDD Uncovered by Multiscale Community Detection with fMRI Abstract Major depressive disorder (MDD) is known to be associated with altered interactions between distributed brain regions. How these regional changes relate to the reorganization of cortical functional systems, and their modulation by antidepressant medication, is relatively unexplored. To identify changes in the community structure of cortical functional networks in MDD, we performed a multiscale community detection algorithm on resting-state functional connectivity networks of unmedicated MDD (uMDD) patients (n = 46), medicated MDD (mMDD) patients (n = 38), and healthy controls (n = 50), which yielded a spectrum of multiscale community partitions. we selected an optimal resolution level by identifying the most stable community partition for each group. uMDD and mMDD groups exhibited a similar reconfiguration of the community structure of the visual association and the default mode systems but showed different reconfiguration profiles in the frontoparietal control (FPC) subsystems. Furthermore, the central system (somatomotor/salience) and 3 frontoparietal subsystems showed strengthened connectivity with other communities in uMDD but, with the exception of 1 frontoparietal subsystem, returned to control levels in mMDD. These findings provide evidence for reconfiguration of specific cortical functional systems associated with MDD, as well as potential effects of medication in restoring disease-related network alterations, especially those of the FPC system. Antidepressant, Functional Connectivity, Graph Theory, MDD, Resting-state fMRI Introduction Many neuroimaging studies have demonstrated that major depressive disorder (MDD) is associated with altered functional and structural connectivity of distributed brain regions (Sheline et al. 2010; Zeng et al. 2012; Dutta et al. 2014; Korgaonkar et al. 2014; Gong and He 2015; Mulders et al. 2015). Altered connections include those mediating interactions within and between multiple brain systems supporting several specific functions, such as emotion, attention, cognitive control, and self-referential processing. Furthermore, the neuropathology of MDD involves disrupted interactions between different brain systems (Drevets 2001; Price and Drevets 2010). Therefore, MDD may be thought of as a “network disease,” where a small local change may lead to global dysfunction expressed across the whole brain (Fornito et al. 2015). The dysfunction of 3 systems are particularly relevant to MDD (Kaiser et al. 2015; Mulders et al. 2015): default mode (DM) system, frontoparietal control (FPC) system, and salience system. The DM system involved in self-referential process (Raichle et al. 2001; Greicius et al. 2003; Buckner et al. 2008) is related to pathological introspection of MDD patients like rumination (Zhu et al. 2017). The dysfunctional top–down control by FPC system (Corbetta and Shulman 2002; Fox et al. 2005) is associated with deficient cognitive control in depression (Stange et al. 2017). The salience system (overlapping with ventral attention system), supporting detecting and orientating to salient stimuli (Seeley et al. 2007), has a role in biased attention in MDD patients (Beevers et al. 2015). Moreover, abnormal interactions among these 3 systems have been observed in MDD patients (Manoliu et al. 2014; Kaiser et al. 2015; Mulders et al. 2015). Neurocognitive models of MDD suggested that imbalanced connectivity of FPC with DM and salience systems results in imbalanced control over introspection and external attention and furthermore affects mood regulation (Disner et al. 2011; Rayner et al. 2016). The triple-network model (Menon 2011), a common framework for multiple psychiatric disorders, also emphasizes that the deficient communication of these 3 core systems plays an important role in the psychopathology of psychiatric disorders. Thus, investigating the intrinsic organization and interactions of different functional systems in the brain network can help to identify preserved, dysfunctional, and compensatory subsystems in MDD and further help to understand the psychopathology of MDD, which may lead to targeted treatments. Graph theory provides a useful tool to characterize the brain network (Sporns et al. 2005; Bullmore and Sporns 2009). The community structure is one of the most functionally relevant graph metrics to study the organization and interaction of functional systems in the brain network (Petersen and Sporns 2015). A community is a set of more densely interconnected nodes (or brain areas) within the set compared with between other sets (Fortunato 2010; Fortunato and Hric 2016; Sporns and Betzel 2016). The community structure (or modular organization) can delineate the functional segregation and integration of the whole-brain network. Researchers have identified community structure in both structural and functional networks in the healthy human brain (Chen et al. 2008; He et al. 2009; Meunier et al. 2009). Identifying the community structure of the brain is also of great interest to clinical researchers because disrupted community structures were found in diverse brain disorders (Cary et al. 2016; Glerean et al. 2016; Lerman-Sinkoff and Barch 2016), including MDD (Bohr et al. 2012; Lord et al. 2012; Tao et al. 2013; Peng et al. 2014). Lord et al. (2012) detected community structure of functional network for individuals with unipolar depression. They found that superior regions in frontal and parietal cortex increased intercommunity connections, whereas inferior regions like occipital, temporal, and inferior frontal cortex increased intracommunity connections in patients. The participation indices (an index of how dense nodes are connected across multiple communities) of 15 nodes mainly located in frontal, parietal, and occipital cortex occupied top 25 informative metrics, which can identify patients from the healthy by machine learning. These findings indicate that the community structures are significantly reorganized in depressive patients by the alterations in widespread brain areas. However, they did not describe the specific alterations of the community structures in depression. Peng et al. (2014) reported different community structures between MDD group and the healthy controls (HC). They observed that an integrated community comprised the DM and FPC systems in the HC, but parts of this community belonging to the FPC system were separated in MDD group. Another community responsible for affective processing also split in MDD group. They suggested that these alterations might account for abnormal cognitive and affective processing in MDD. Tao et al. (2013) applied a reference community structure based on HC to first-episode MDD and resistant MDD patients and found widespread altered connections, mostly belonging to the attention system. These studies indicate that MDD-related alterations of functional connectivity underling altered community structures are distributed in different functional systems like the default, cognitive control, attention, and affective systems. This corresponds to the theoretical model. However, a limited number of studies on community structure in MDD reported inconsistent results, which calls for more investigations with solid methodology for robust identification of the neural alterations in MDD. Previous studies have reported that antidepressant treatment modifies functional connectivity in distributed regions in MDD (Dichter et al. 2015; Gudayol-Ferre et al. 2015). Gudayol-Ferre et al. (2015) reviewed that antidepressant treatment mainly affected connectivity of portions of the DM and cortical–limbic networks. This suggests that the medication effect is widespread and complex, highlighting the need to examine its effect on the large-scale system level. Although these studies indicate that changes in functional network communities accompany MDD, none so far have 1) systematically examined the community structure of functional networks in both medicated and unmedicated MDD (uMDD), 2) addressed methodological problems with modularity maximization such as the resolution limit (Fortunato and Barthelemy 2007) and degeneracy (Good et al. 2010; Lancichinetti and Fortunato 2012), or 3) taken into account community partitions across multiple scales (Betzel et al. 2014b). The primary aim of this study is to address these limitations, by examining changes in the community structure of cortical functional networks in both uMDD and medicated MDD (mMDD), across multiple resolutions. To achieve this goal, we identified an optimal resolution parameter for each group based on the stability of the community structure against small changes in the resolution parameter, quantified as the variation of information (Meila 2003). We hypothesized that both MDD patient groups would exhibit altered community structure compared with that of HC group. Materials and Methods Participants A total of 134 participants aged 18–65 years were included, containing 46 drug-naive MDD patients, 38 mMDD patients, and 50 age-, gender-, and education-matched HC participants (Table 1). No significant differences of age, gender, education, and head motion existed among the 3 groups. All participants were part of an ongoing depression research project, which was conducted by Southwest University, China. All the MDD patients were recruited from the psychiatric department of the First Affiliated Hospital of Chongqing Medical University and diagnosed with the Structured Clinical Interview of the DSM-IV by experienced psychiatric physicians. HC participants were screened without current or past Axis I or II disorder. All participants had 1) no neurological, organic brain diseases and other psychiatric disorders; 2) no family history of psychiatric disorders on their first-degree relatives; and 3) no physical contraindication for undergoing magnetic resonance imaging (MRI). The medicated participants had accepted antidepressant treatment before the MRI scan, in most cases including selective serotonin reuptake inhibitors including citalopram, escitalopram, fluoxetine, paroxetine, and sertraline; serotonin–norepinephrine reuptake inhibitors such as venlafaxine; serotonin antagonists and reuptake inhibitors such as trazodone; tetracyclic antidepressant such as mirtazapine; and tricyclic antidepressant such as amitriptyline and melitracen-flupentixol. All patients underwent assessment for depressive symptom by the 17-item Hamilton Rating Scale for Depression (HRSD) and the Beck Depression Inventory-II (BDI). The mMDD group had lower scores on BDI than the uMDD group, but there was no difference on HRSD and illness duration (Table 1). This study was approved by the Ethics Committee of Southwest University and First Affiliated Hospital of Chongqing Medical School. All participants provided informed written consent. Table 1 Demographic information   Unmedicated  Medicated  Control  Age (years)  39.46 (18–63)  39.77 (19–66)  40.4 (19–63)  Gender (male/female)  12/34  10/28  19/31  Education (years)  12.28 ± 3.55  11.13 ± 3.26  12.04 ± 4.11  meanFD (mm)  0.07 ± 0.04  0.06 ± 0.03  0.07 ± 0.03  BDI  23.82 ± 7.71**  18.86 ± 8.59**  —  HRSD  23.61 ± 4.33  23 ± 4.33  —  Duration (weeks)  106.11 ± 166.45  119.25 ± 132.45  —    Unmedicated  Medicated  Control  Age (years)  39.46 (18–63)  39.77 (19–66)  40.4 (19–63)  Gender (male/female)  12/34  10/28  19/31  Education (years)  12.28 ± 3.55  11.13 ± 3.26  12.04 ± 4.11  meanFD (mm)  0.07 ± 0.04  0.06 ± 0.03  0.07 ± 0.03  BDI  23.82 ± 7.71**  18.86 ± 8.59**  —  HRSD  23.61 ± 4.33  23 ± 4.33  —  Duration (weeks)  106.11 ± 166.45  119.25 ± 132.45  —  **P < 0.01, obtained by 2-sample t-test. Image Acquisition Magnetic resonance images were acquired from a 3 T Siemens TrioTim scanner. The resting-state functional images were obtained with an echo-planar imaging (EPI) sequence (volumes = 242, TR/TE = 2000/30 ms, flip angle = 90°, matrix = 64 × 64, thickness/gap = 3/1 mm, voxel size = 3.4 × 3.4 × 3 mm3). The structural images were from a high-resolution, T1-weighted magnetization–prepared rapid gradient echo sequence (TR/TE/TI = 1900 ms/2.52 ms/900 ms, flip angle = 9°, matrix = 256 × 256, slices = 176, voxel size = 1 × 1 × 1 mm3). Image Preprocessing All the imaging data were preprocessed by using the Connectome Computation System pipeline (Xu et al. 2015), which provide a platform for processing multimodal images by integrating functions of analysis of functional neuroimages (Cox 1996), FreeSurfer (Dale et al. 1999; Fischl et al. 1999a), and FMRIB Software Library (Jenkinson et al. 2012). Structural MRI preprocessing included following steps of removing spatial noise by a nonlocal mean filtering (Xing et al. 2011; Zuo and Xing 2011) and then running recon-all command in FreeSurfer 5.1 to conduct brain extraction, structural segmentation, and surface reconstruction. The preprocessing of functional images was completed by 1) removing the first 5 EPI volumes; 2) replacing temporal spikes; 3) slice timing correction; 4) head motion correction; 5) generating brain mask; 6) normalization of global mean intensity of 10 000; 7) nuisance variable regression (Fox et al. 2005; Lund et al. 2006) by regressing out mean signals from white matter, cerebrospinal fluid, Friston-24 head motion parameters (Friston et al. 1996; Yan et al. 2013) and the global mean signal; 8) band-pass filtering (0.01–0.1 HZ); 9) removing linear and quadratic trends; and 10) spatial smoothing with a 6-mm full-width at half-maximum filter. Functional images were coregistered to the native anatomical images by using boundary-based registration (BBR, Greve and Fischl 2009) and then were projected onto the fsaverage surface template and resampled to fsaverage5 surface (Fischl et al. 1999b). The global mean signal was removed in order to reduce physiological noise, such as movement and respiration (Power et al. 2014). We performed quality control procedure on all imaging data to ensure the quality of preprocessing. First, visual inspection was conducted on screenshots of the following steps, including brain exaction, structural segmentation, surface reconstruction, and BBR-based functional images registration. Then, the mean frame-wise displacement (meanFD) was calculated as the head motion criterion (meanFD < 0.2 mm). All the participants included in the final study passed this criterion. Functional Network Construction The whole-brain surface was parceled into 114 regions derived from a previously established functional parcellation of 17 networks (Yeo et al. 2011). Functional connectivity can be estimated from intrinsic spontaneous activity of brain regions (Biswal et al. 1995; van den Heuvel and Hulshoff Pol 2010), by computing the Pearson correlations of time series from all pairs of regions. For each subject group, the connection matrices obtained from each subject were averaged into a single group matrix used to detect network communities. Community Detection We used a multiscale community detection technique to identify the community structure of a large-scale functional network for each group (Fig. 1). One widely used strategy is to maximize connections within each community while minimizing connections between communities (Newman and Girvan 2004; Rubinov and Sporns 2010; Sporns and Betzel 2016). To achieve this, we maximize the quality function:   Q(γ)=12m∑ij[Aij−γ]δ(gi,gj) Figure 1. View largeDownload slide Multiscale community detection schematic. (A) The whole-brain surface comprised 114 parcels. A weighted adjacency matrix of functional connectivity of 114 brain regions was constructed for each subject. The matrices of subjects in each group were averaged into a single group-level matrix as the functional network. (B) The multiscale community detection was applied on each group matrix across a range of resolution parameter values. For each resolution parameter γ, 1) we repeated community detection 10 000 times and 2) finally obtained a single consensus community structure. (C) We chose the optimal γ based on the resolution stability, by calculating the averaged variation of information (VI) between the partition of each γ and its two contiguous neighbors (γ(i−1) and γ(i + 1)). Then, we targeted the broadest range of continuous γ values for which the VI remained equal to zero. For example, in the bottom plot, γ = 0.18 is chosen as the optimal. Figure 1. View largeDownload slide Multiscale community detection schematic. (A) The whole-brain surface comprised 114 parcels. A weighted adjacency matrix of functional connectivity of 114 brain regions was constructed for each subject. The matrices of subjects in each group were averaged into a single group-level matrix as the functional network. (B) The multiscale community detection was applied on each group matrix across a range of resolution parameter values. For each resolution parameter γ, 1) we repeated community detection 10 000 times and 2) finally obtained a single consensus community structure. (C) We chose the optimal γ based on the resolution stability, by calculating the averaged variation of information (VI) between the partition of each γ and its two contiguous neighbors (γ(i−1) and γ(i + 1)). Then, we targeted the broadest range of continuous γ values for which the VI remained equal to zero. For example, in the bottom plot, γ = 0.18 is chosen as the optimal. A = [Aij] is a weighted and signed functional connectivity matrix where Aij represents the connection weight between node i and j estimated by Pearson correlation coefficient between 2 regional time series. gi is the assignment of the node i to a community. Modularity maximization is subject to a “resolution limit”; that is, it is unable to detect communities smaller than a given scale (Fortunato and Barthelemy 2007; Fortunato and Hric 2016). Here, we included a tunable resolution parameter γ to mitigate this problem (Reichardt and Bornholdt 2006). By adjusting the value of γ from low to high, one can detect few large-size communities to more numerous small-size communities, which also corresponds to the “constant Potts model” (Traag et al. 2011) suitable for networks based on correlation matrices (MacMahon and Garlaschelli 2015). We used a Louvain algorithm implemented in a MATLAB software package (Blondel et al. 2008; Jutla et al. 2011) to perform community detection. We varied γ within a range from 0.1 to 0.4 in increments of 0.001. The limits of this range were chosen such that they retrieve community partitions with significantly lower (γ = 0.1) or higher (γ = 0.4) numbers of communities than are included in a standard resting-state network partition (Yeo et al. 2011). For each of 301 settings of the γ parameter, we performed community detection 10 000 times and then identified a single consensus partition from the agreement matrix (Lancichinetti and Fortunato 2012). After deriving the consensus partition at each γ, we selected a single level of γ for further analysis. Prior work used stability as a criterion, by looking for a plateau in the number of communities (Swanson et al. 2016) or by calculating the similarity of partitions across a range of γ values (Misic et al. 2016). Here, we adopted an approach similar to the latter criterion by defining the stability of γ across a small window at each point across the range. Specifically, we used the variation of information (VI) (Meila 2003) to evaluate the variability between different partitions computed under small variations of γ for multiscale resolution stability. First, we calculated the VI averaged between the partition of each γ and each of its two contiguous neighbors (γ(i-1) and γ(i + 1)). This measure represents the stability of the partition when γ is changed slightly. Then, we targeted the broadest range of continuous γ values for which the averaged VI remained equal to zero (Fig. 1), taken to represent the most stable partition across multiscale resolutions. Network Properties To further examine the connectivity patterns of each community and the relationship between communities, we compared each community’s intracommunity and intercommunity connectivity strengths based on the community structure from HC as a reference. First, we averaged all the intracommunity functional connectivity between each pair of nodes within each community. Second, we averaged all intercommunity functional connectivity between each community and all other communities. Finally, we compared the averaged intracommunity and intercommunity connectivity among 3 groups. Statistics Permutation testing was used to assess differences of network properties between groups with 10 000 permutations and tested at significance level 0.05 for a 2-tailed test. Before permutation test, age, gender, and the meanFD were regressed out from each network metric as covariates. In order to control any putative differences in whole-brain connection strength and focus on community-specific connectivity, individual mean whole-brain connection strength was also regressed out as covariate. The False Discovery Rate (FDR) was used for multiple comparison correction at α = 0.05 (Benjamini and Hochberg 1995; Genovese et al. 2002). Results Optimal Parameter of Resolution in Multiscale Community Detection We identified the optimal resolution parameter γ for each group based on the resolution stability (see Materials and Methods). As a result, we identified the optimal γ as 0.238 for HC, 0.173 for uMDD, and 0.181 for mMDD (Fig. 2). Figure 2. View largeDownload slide Selection of the optimal partition for each group. The plot illustrates the resolution stability VI value (shown as the histogram) and the number of communities (shown as the continuous line) obtained across a range of resolution parameter γ in 3 subject groups. For 3 groups, the optimal γ was marked in the plot, as 0.238 for the HC, 0.173 for uMDD, and 0.181 mMDD, for which the VI is equal to zero with the broadest range. Figure 2. View largeDownload slide Selection of the optimal partition for each group. The plot illustrates the resolution stability VI value (shown as the histogram) and the number of communities (shown as the continuous line) obtained across a range of resolution parameter γ in 3 subject groups. For 3 groups, the optimal γ was marked in the plot, as 0.238 for the HC, 0.173 for uMDD, and 0.181 mMDD, for which the VI is equal to zero with the broadest range. Community Structures The community structure of functional network of each group is shown in Figure 3. Figure 4 illustrates the community assignment of each brain region in 3 subject groups. Overall, the decomposition of the functional network using our multiscale community detection method (and the optimal γ) was similar to previous functional parcellations (Power et al. 2011; Yeo et al. 2011); we compared our community structures with the 7 networks previously reported in Yeo et al. (2011) by calculating their mutual information (Fig. 3). For the 3 groups, we obtained a mutual information of 0.718 (HC), 0.650 (uMDD), and 0.710 (mMDD). Our community detection approach disclosed 12 communities in the HC, 9 communities in uMDD, and 10 communities in mMDD, and the community structures of both patient groups appeared similar to each other while differing from that of the HC group (Fig. 4). In addition, the mMDD group exhibited some new features of community structure that differed from the other two groups. Figure 3. View largeDownload slide The community structures for 3 groups and the mapping with Yeo’s 7 network. The upper panel shows community structures of functional networks of 3 groups. Twelve communities for HC, 9 communities for uMDD, and 10 communities for mMDD were separately identified. The bottom panel illustrates how our partitions compare with the 7 networks of Yeo et al. (2011), represented by 7 different colors. VN: visual network; SMN: somatomotor network; DAN: dorsal attention network; VAN: ventral attention network; LMN: limbic network; FPN: frontoparietal network; DMN: default mode network; TEM: TempPar (independent regions not belong to 7 networks). Figure 3. View largeDownload slide The community structures for 3 groups and the mapping with Yeo’s 7 network. The upper panel shows community structures of functional networks of 3 groups. Twelve communities for HC, 9 communities for uMDD, and 10 communities for mMDD were separately identified. The bottom panel illustrates how our partitions compare with the 7 networks of Yeo et al. (2011), represented by 7 different colors. VN: visual network; SMN: somatomotor network; DAN: dorsal attention network; VAN: ventral attention network; LMN: limbic network; FPN: frontoparietal network; DMN: default mode network; TEM: TempPar (independent regions not belong to 7 networks). Figure 4. View largeDownload slide Community assignment of 114 brain regions. The left alluvial diagram (modified from https://github.com/mbojan/alluvial) illustrates changes of community assignments of brain regions across 3 groups. Each block represents a community and each line corresponds to a brain region. For example, comparing to HC, Community 1 fragmented to 2 parts, one of which joined Community 2 in uMDD and mMDD. The right image shows the brain surface mapping of community partitions. The color-coding is as same as the left diagram, corresponding to distinct communities. Major communities in HC includes (1) visual association (VA), (2) central system, (4) frontoparietal control-A (FPC-A), (9) frontoparietal control-B (FPC-B), (10) frontoparietal control-C (FPC-C), (11) DM system. Figure 4. View largeDownload slide Community assignment of 114 brain regions. The left alluvial diagram (modified from https://github.com/mbojan/alluvial) illustrates changes of community assignments of brain regions across 3 groups. Each block represents a community and each line corresponds to a brain region. For example, comparing to HC, Community 1 fragmented to 2 parts, one of which joined Community 2 in uMDD and mMDD. The right image shows the brain surface mapping of community partitions. The color-coding is as same as the left diagram, corresponding to distinct communities. Major communities in HC includes (1) visual association (VA), (2) central system, (4) frontoparietal control-A (FPC-A), (9) frontoparietal control-B (FPC-B), (10) frontoparietal control-C (FPC-C), (11) DM system. In particular, in the HC group, the communities we detected consist of visual association (VA), central (somatomotor/salience), FPC-A, FPC-B, FPC-C, and DM systems (ignoring small-sized communities with only 1 or 2 nodes). In contrast, the uMDD group showed that the VA and FPC-A systems have partly been fragmented, with parts merging with the central system (e.g., bilateral occipito-temporal sulcus, parieto-occipital gyrus, and superior parietal lobe from VA and bilateral posterior part of superior frontal sulcus, intraparietal sulcus, posterior part of inferior temporal gyrus, left inferior part of precentral sulcus, left anterior part of inferior frontal sulcus, and right posterior part of inferior frontal sulcus from FPC-A). FPC-B was split into 2 parts, one of which joined the DM system including the bilateral middle frontal gyrus and right angular gyrus. For the DM system, some regions including the bilateral superior temporal sulcus and the left inferior frontal gyrus formed an independent community, and the left posterior region of inferior parietal lobule combined with FPC-C. In addition, the bilateral medial orbital gyrus and temporal pole integrated into one community. However, mMDD exhibited some new features of community structure in FPC-A and FPC-B that differed from the other 2 groups (Fig. 4). A part of FPC-A (including bilateral posterior part of inferior temporal gyrus, intraparietal sulcus, left posterior part of superior frontal sulcus, and left inferior frontal sulcus) and FPC-B (including bilateral posterior part of inferior temporal sulcus, lateral frontopolar cortex, middle part of medial superior frontal gyrus, left posterior middle frontal gyrus, and left anterior angular gyrus) combined together with bilateral posterior part of anterior cingulate cortex into an independent community. The right posterior region of inferior parietal lobule split from the DM system to join the FPC-C. In addition, some single nodes merging with other large communities in uMDD, like left orbital sulcus and right middle part of ACC, became independent in mMDD. Intracommunity and Intercommunity Connectivity To further determine the origin of the changes in the community structure in uMDD and mMDD patients, we examined intracommunity and intercommunity connectivity of each community based on the community structure of HC (see Materials and Methods for details). No significant differences of intracommunity connectivity among the 3 groups were found (Fig. 5). However, several communities in the uMDD group, including the central, FPC-A, FPC-B, and FPC-C systems, exhibited increased intercommunity connectivity compared with the HC (central, P = 0.004; FPC-A, P = 0.002; FPC-B, P = 0.006; FPC-C, P = 0.040; FDR corrected) and mMDD except FPC-A system (central, P = 0.001; FPC-B, P = 0.007; FPC-C, P = 0.05; FPC-A, P = 0.086; FDR corrected). Meanwhile, the mMDD group showed reduced intercommunity connectivity in the DM system compared with the other 2 groups (HC, P = 0.003; uMDD, P < 0.001; FDR corrected). Figure 5. View largeDownload slide The intracommunity and intercommunity connectivity strengths. The intracommunity connectivity strength was quantified as the averaged functional connectivity between each pair of nodes within each community. The intercommunity connectivity strength was calculated as the averaged functional connectivity between the nodes within a community and all the other nodes outside the community. There was no significant difference in intracommunity connectivity among 3 groups. But significant differences were found in intercommunity connectivity (shown in rectangles of bottom panel). For C2 (central), C9 (FPC-B), C10 (FPC-C), uMDD showed increased intercommunity connectivity than the other 2 groups. For C4 (FPC-A), uMDD showed increased connectivity than HC. For C11 (DM), mMDD had lower connectivity strength than others. The gray dots represent outliers. Figure 5. View largeDownload slide The intracommunity and intercommunity connectivity strengths. The intracommunity connectivity strength was quantified as the averaged functional connectivity between each pair of nodes within each community. The intercommunity connectivity strength was calculated as the averaged functional connectivity between the nodes within a community and all the other nodes outside the community. There was no significant difference in intracommunity connectivity among 3 groups. But significant differences were found in intercommunity connectivity (shown in rectangles of bottom panel). For C2 (central), C9 (FPC-B), C10 (FPC-C), uMDD showed increased intercommunity connectivity than the other 2 groups. For C4 (FPC-A), uMDD showed increased connectivity than HC. For C11 (DM), mMDD had lower connectivity strength than others. The gray dots represent outliers. Discussion Using a multiscale community detection approach, we identified the most stable community partitions of functional networks for each group. Importantly, we could delineate patterns of reorganization in the community structure of functional brain networks in MDD patients with or without antidepressant treatment. In particular, we observed disturbances in the community structure of VA and DM systems that were similar in uMDD and mMDD groups, while also identifying reconfigurations of the FPC subsystems that differed in the 2 patient groups. Moreover, further analysis of intracommunity and intercommunity connectivity revealed that changes in intercommunity connectivity mainly accounted for the observed changes in community organization in MDD patients. Specifically, in the uMDD group, the intercommunity connectivity in the central system and 3 FPC subsystems was stronger than both the HC and medicated patients (except the FPC-A system). The mMDD group additionally showed attenuated intercommunity connectivity in the DM system compared with the other 2 groups. Multiscale Community Detection Previous studies have demonstrated that structural and functional human brain networks are organized into a modular architecture (Hagmann et al. 2008; He et al. 2009; Meunier et al. 2009; Power et al. 2011). Functional communities are robustly identified with a variety of community detection methods (Sporns and Betzel 2016) and the resulting communities have been shown to correspond to functional brain systems that are coherently engaged in both rest and task conditions (Crossley et al. 2013; Bertolero et al. 2015). Thus, the community organization of large-scale human functional networks appears to reflect underlying neurobiological mechanisms, and variations in community structure may signal significant changes in the brain’s functional capacities. Although examining the community organization of human brain networks has been extremely fruitful for uncovering the organizing principles of the human brain, methodological limitations of community detection methods such as resolution limit and degeneracy of optimal solutions of community structure have so far remained largely unaddressed (Fortunato and Barthelemy 2007; Good et al. 2010; Lancichinetti and Fortunato 2012; Fortunato and Hric 2016). In the present study, we addressed some of these limitations by identifying an optimal resolution parameter γ associated with a highly stable community structure based on resolution stability measure for each group. The community structure of the control group was broadly similar to that identified in previous studies (Power et al. 2011; Yeo et al. 2011); yet, we found characteristic patterns of reconfiguration of functional networks, which differed among unmedicated and medicated patient groups. Changes in Functional Networks in uMDD We compared the community structure of MDD groups with the HC to locate disease-related changes in organization of functional networks. Notable differences were found in the composition of communities, especially in the DM, FPC-B, and VA systems. Each of them fragmented into 2 parts in the uMDD group compared with the healthy group. One segregated part of FPC-B joined the DM system, and one part of VA together with FPC-A joined the central system. This reconfiguration of community structure was associated with increased intercommunity connections, like the central system, 3 FPC subsystems in uMDD. These findings support the “hyperconnectivity hypothesis” of depression, which suggests that increased connectivity might be a biomarker of the mood disorder (Perrin et al. 2012). This is also consistent with a recent meta-analysis, which concluded that MDD involved imbalanced connectivity among functional networks, for example, DM, FPC, and attention networks (Kaiser et al. 2015). The alterations of the DM, FPC, and central systems (overlap with salience system) have been widely reported in MDD (Mulders et al. 2015) and with high power for discriminating MDD from HC (Qin et al. 2015a, 2015b). Disturbed DM functional connectivity is related to excessive negative rumination in MDD (Zhu et al. 2017), which may underlie the negative internal processes occurring in MDD. The FPC system supports tasks/cognition control (Corbetta and Shulman 2002; Fox et al. 2005), which is suggested to be associated with maladaptive cognitive control in MDD, like controlling over attention and emotion (Joormann and Gotlib 2010; Joormann and Quinn 2014). The salience system (ventral attention system) is involved in detecting salient stimuli (Seeley et al. 2007), which is known to be affected in MDD (Peckham et al. 2010; Disner et al. 2011; Beevers et al. 2015). Our findings support the neurocognitive models of MDD, which propose that the symptoms of MDD can be attributed to disturbed connections of the FPC network with other networks involving attention, emotion, and internal mentation (Disner et al. 2011; Kaiser et al. 2015; Rayner et al. 2016). The imbalanced connectivity between FPC and DM results in the domination of self-focused introspection over external orientation. The disturbed connectivity between FPC and salience system causes the disruption of cognitive control of biased attention. Here, the altered community structure and increased connectivity of 3 FPC subsystems imply that FPC plays a vital role in the pathophysiology of MDD. Their separate recombination with the DM and central systems may reflect their different roles in abnormal control on emotional internal thought and external attention (Manoliu et al. 2014; Kaiser et al. 2015). The central system (salience and somatomotor systems) showed stronger intercommunity connectivity in uMDD. This hyperconnectivity may explain the community recombination of the central system with parts of the VA system and the FPC-A system (Fig. 4). The shifted part of VA included the superior parietal, tempo-occipital sulcus, and parieto-occipital gyrus, are respectively in 2 visual processing pathways supporting cognitive processing of visual input, as the ventral and dorsal visual pathway (Goodale and Milner 1992). Studies suggested that altered occipital regions were related to negatively biased processing of visual inputs in uMDD (Graham et al. 2013; Cheng et al. 2016). Here, the recombination of the central system with a part of the VA system may reflect abnormal control of attention in visual processing in MDD, which lead to negative perception (Peckham et al. 2010; Zhou et al. 2010; Desseilles et al. 2011; Disner et al. 2011; Beevers et al. 2015; Tozzi et al. 2017). In addition, the somatomotor system has been found to be associated with depression severity in MDD, suggesting that it was related to abnormal pain processing (Tadayonnejad et al. 2015). The dysfunction of somatomotor regions may be associated with physical symptoms like pain and fatigue in MDD (Bair et al. 2003). Together, hyperconnectivity of the central system may underlie nonemotional symptoms involving biased attention and somatosensation. Effects of Medication on Network Topology of MDD Several studies have reported that various kinds of antidepressants have heterogeneous effects on functional connectivity across the visual, DM, salience, and cognitive control networks (Dichter et al. 2015; Gudayol-Ferre et al. 2015; Wang et al. 2015). However, most of these studies investigated seed-based connectivity, without addressing the whole-brain system level. Using network analysis, we found that antidepressants affected the community structure mainly by modulating patterns of intercommunity connectivity, which more closely resembled those found in HC. In mMDD patients, decreased intercommunity connectivity appeared in almost all affected systems as opposed to increased intercommunity connections in unmedicated patients (except FPC-A), which may indicate normalization/restoration effects of medication. This normalization parallels recent findings showing that antidepressants can renormalize increased cortical thickness of widespread regions in depressive patients (Bansal et al. 2017) and could reduce increased functional connectivity of orbitofrontal cortex with distributed regions (Cheng et al. 2016). However, the community structure in medicated patients was largely the same as that of uMDD, except that FPC-A and FPC-B systems combined together. But the community structures of mMDD and the HC appear more similar to Yeo’s network partition (Yeo et al. 2011). We speculate that antidepressants may contribute more to adaptive connectivity changes between communities rather than the general organization of the community structure. A key finding of the antidepressant effect is that the FPC subsystems exhibited alterations in both community organization and connectivity. Previous studies have found that depression treatment could change functional connectivity of FPC (Aizenstein et al. 2009; Perrin et al. 2012; Liston et al. 2014). Perrin et al. (2012) found a reduced average global functional connectivity of dorsolateral prefrontal cortex after electroconvulsive therapy, which is consistent with our findings. Both the FPC and the DM systems exhibited reduced functional connectivity after antidepressant administration in healthy participants (McCabe et al. 2011; van de Ven et al. 2013). As we have shown that the FPC system is vital in the pathophysiology of MDD, we propose that it is also a potential therapeutic target of antidepressant medication. As the number of studies about the antidepressant effect on the FPC system is limited, further studies are needed. A growing body of studies have reported altered connections by antidepressant treatment within DM system or between DM and other systems, like FPC system or limbic system, implying its prominent involvement in depression (Dichter et al. 2015; Gudayol-Ferre et al. 2015). Of note, in our findings, the intercommunity connectivity of the DM system in mMDD decreased below the level of the control group and uMDD. This may imply a compensatory mechanism of antidepressants on the DM system. In MDD patients, the connectivity of the DM system was associated with the antidepressant response (Pizzagalli 2011; Wang et al. 2015), and whole-brain connectivity of regions in the DM system can be reduced by the antidepressant (Fu et al. 2015; Wang et al. 2015). Its alteration of intracommunication and intercommunication is associated with MDD remission (Qin et al. 2015a, 2015b). However, some studies suggested that the normalization effect of antidepressants only took part in certain subsystems of the DM system (Li et al. 2013; Shen et al. 2015). Additional work is needed to provide more solid conclusions and interpretations regarding the role of antidepressants in modifying functional brain connectivity. Limitations Several limitations in the current study need to be noted. First, functional networks using only cortical regions are explored in this study. However, subcortical regions also play an important role in the pathophysiology of MDD (Heller 2016). Second, while other parcellations may be explored, our choice of a previously published parcellation based on data from 1000 participants (42) was based on its consistency with other canonical maps of brain functional systems (Power et al. 2011) and wide usage in the field (Baker et al. 2014; Betzel et al. 2014a; Yeo et al. 2015). Third, the antidepressant treatment is quite heterogeneous. The type of medication and treatment duration differs across subjects, which does not allow us to directly examine the effects of specific drugs or drug regimens on MDD. Previous studies have also incorporated diversely medicated patients because the subjects are patients that limits the sample size (Heller et al. 2013; Li et al. 2013; Qin et al. 2015a, 2015b; Gong et al. 2018). Studies comparing different medications have found similar effects on brain functional connections (Lisiecka et al. 2011; McCabe and Mishor 2011). In addition, many studies using different medications have found a convergent normalization effect on elevated functional connectivity, which are in line with our results. This suggests that there might be a general normalization effect across antidepressant treatments (Lisiecka et al. 2011; McCabe and Mishor 2011; Li et al. 2013; Wang et al. 2015; Fu et al. 2015; An et al. 2017). Thus, we included all the types of drugs to see if there is a general modulation effect of treatments. In order to characterize drug effects on functional connectivity more rigorously, longitudinal design with more uniform patient groups and drug treatments are needed in the future. Conclusion In the current study, we examined the community structure of cortical functional networks in uMDD, mMDD, and HC using a multiscale community detection method. Although uMDD and mMDD patients showed similar reorganization of community structure in VA and DM systems, the FPC subsystems, in particular, reconfigured differently in the 2 patient groups. uMDD patients were characterized by intercommunity connectivity increased in the central system and 3 FPC subsystems implicating altered communication among multiple large-scale systems, which appears partially restored or overly compensated in mMDD patients. Together, these findings provide a global picture of community structure alterations of cortical functional networks in uMDD and mMDD patients and suggest potential medication effects in restoring disease-related network alterations. Funding This work was supported by grants from the National Basic Research (973) Program (2015CB351702), the Natural Science Foundation of China (NSFC 31271189 to P.X.; 81471740 to X.-N.Z.), Beijing Municipal Science and Tech Commission (Z161100002616023, Z171100000117012), the China - Netherlands CAS-NWO Programme (153111KYSB20160020), and the Major Project of National Social Science Foundation of China (14ZDB161). X.-N.Z. and O.S. are members of an international collaboration team supported by the NSFC Major Joint Fund for International Cooperation and Exchange (81220108014). O.S. is supported by the National Institutes of Health grant (R01AR009036). We would also like to thank the National R&D Infrastructure and Facility Development Program of China, “Fundamental Science Data Sharing Platform” (DKA2017−12−02−21) who funded the computational platform. Notes We acknowledge valuable comments by Andrea Avena-Koenigsberger, Department of Psychological and Brain Sciences, Indiana University, and Richard F. Betzel, Department of Bioengineering, University of Pennsylvania. We also gratefully acknowledge Dr. Enrico Glerean, Department of Neuroscience and Biomedical Engineering, Aalto University, Finland, for sharing code for the alluvial plot. All authors report no biomedical financial interests or potential conflicts of interest. Conflict of Interest: None declared. References Aizenstein HJ, Butters MA, Wu M, Mazurkewicz LM, Stenger VA, Gianaros PJ, Becker JT, Reynolds CF 3rd, Carter CS. 2009. Altered functioning of the executive control circuit in late-life depression: episodic and persistent phenomena. Am J Geriatr Psychiatry . 17: 30– 42. Google Scholar CrossRef Search ADS PubMed  An J, Wang L, Li K, Zeng Y, Su Y, Jin Z, Yu X, Si T. 2017. Differential effects of antidepressant treatment on long-range and short-range functional connectivity strength in patients with major depressive disorder. Sci Rep . 7: 10214. Google Scholar CrossRef Search ADS PubMed  Bair MJ, Robinson RL, Katon W, Kroenke K. 2003. Depression and pain comorbidity: a literature review. Arch Intern Med . 163: 2433– 2445. Google Scholar CrossRef Search ADS PubMed  Baker JT, Holmes AJ, Masters GA, Yeo BT, Krienen F, Buckner RL, Ongur D. 2014. Disruption of cortical association networks in schizophrenia and psychotic bipolar disorder. JAMA Psychiatry . 71: 109– 118. Google Scholar CrossRef Search ADS PubMed  Bansal R, Hellerstein DJ, Peterson BS. 2017. Evidence for neuroplastic compensation in the cerebral cortex of persons with depressive illness. Mol Psychiatry . (October 2016), 1–9. Beevers CG, Clasen PC, Enock PM, Schnyer DM. 2015. Attention bias modification for major depressive disorder: Effects on attention bias, resting state connectivity, and symptom change. J Abnorm Psychol . 124: 463– 475. Google Scholar CrossRef Search ADS PubMed  Benjamini Y, Hochberg Y. 1995. Controlling the false discovery rate – a practical and powerful approach to multiple testing. J Roy Stat Soc B Met . 57: 289– 300. Bertolero MA, Yeo BT, D’Esposito M. 2015. The modular and integrative functional architecture of the human brain. Proc Natl Acad Sci U S A . 112: E6798– E6807. Google Scholar CrossRef Search ADS PubMed  Betzel RF, Byrge L, He Y, Goni J, Zuo XN, Sporns O. 2014a. Changes in structural and functional connectivity among resting-state networks across the human lifespan. Neuroimage . 102( Pt 2): 345– 357. Google Scholar CrossRef Search ADS PubMed  Betzel RF, Griffa A, Avena-Koenigsberger A, GoÑI J, Thiran J-P, Hagmann P, Sporns O. 2014b. Multi-scale community organization of the human structural connectome and its relationship with resting-state functional connectivity. Netw Sci . 1: 353– 373. Google Scholar CrossRef Search ADS   Biswal B, Yetkin FZ, Haughton VM, Hyde JS. 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnet Reson Med . 34: 537– 541. Google Scholar CrossRef Search ADS   Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E. 2008. Fast unfolding of communities in large networks. J Stat Mech-Theory E . P10008. Bohr IJ, Kenny E, Blamire A, O’Brien JT, Thomas AJ, Richardson J, Kaiser M. 2012. Resting-state functional connectivity in late-life depression: higher global connectivity and more long distance connections. Front Psychiatry . 3: 116. Google Scholar PubMed  Buckner RL, Andrews-Hanna JR, Schacter DL. 2008. The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci . 1124: 1– 38. Google Scholar CrossRef Search ADS PubMed  Bullmore E, Sporns O. 2009. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci . 10: 186– 198. Google Scholar CrossRef Search ADS PubMed  Cary RP, Ray S, Grayson DS, Painter J, Carpenter S, Maron L, Sporns O, Stevens AA, Nigg JT, Fair DA. 2016. Network structure among brain systems in adult ADHD is uniquely modified by stimulant administration. Cereb Cortex . 27: 3970– 3979. Chen ZJ, He Y, Rosa-Neto P, Germann J, Evans AC. 2008. Revealing modular architecture of human brain structural networks by using cortical thickness from MRI. Cereb Cortex . 18: 2374– 2381. Google Scholar CrossRef Search ADS PubMed  Cheng W, Rolls ET, Qiu J, Liu W, Tang Y, Huang CC, Wang X, Zhang J, Lin W, Zheng L, et al.  . 2016. Medial reward and lateral non-reward orbitofrontal cortex circuits change in opposite directions in depression. Brain . 139: 3296– 3309. Google Scholar CrossRef Search ADS PubMed  Corbetta M, Shulman GL. 2002. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci . 3: 201– 215. Google Scholar CrossRef Search ADS PubMed  Cox RW. 1996. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res . 29: 162– 173. Google Scholar CrossRef Search ADS PubMed  Crossley NA, Mechelli A, Vertes PE, Winton-Brown TT, Patel AX, Ginestet CE, McGuire P, Bullmore ET. 2013. Cognitive relevance of the community structure of the human brain functional coactivation network. Proc Natl Acad Sci U S A . 110: 11583– 11588. Google Scholar CrossRef Search ADS PubMed  Dale AM, Fischl B, Sereno MI. 1999. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage . 9: 179– 194. Google Scholar CrossRef Search ADS PubMed  Desseilles M, Schwartz S, Dang-Vu TT, Sterpenich V, Ansseau M, Maquet P, Phillips C. 2011. Depression alters “top-down” visual attention: a dynamic causal modeling comparison between depressed and healthy subjects. Neuroimage . 54: 1662– 1668. Google Scholar CrossRef Search ADS PubMed  Dichter GS, Gibbs D, Smoski MJ. 2015. A systematic review of relations between resting-state functional-MRI and treatment response in major depressive disorder. J Affect Disord . 172: 8– 17. Google Scholar CrossRef Search ADS PubMed  Disner SG, Beevers CG, Haigh EA, Beck AT. 2011. Neural mechanisms of the cognitive model of depression. Nat Rev Neurosci . 12: 467– 477. Google Scholar CrossRef Search ADS PubMed  Drevets WC. 2001. Neuroimaging and neuropathological studies of depression: implications for the cognitive-emotional features of mood disorders. Curr Opin Neurobiol . 11: 240– 249. Google Scholar CrossRef Search ADS PubMed  Dutta A, McKie S, Deakin JF. 2014. Resting state networks in major depressive disorder. Psychiatry Res . 224: 139– 151. Google Scholar CrossRef Search ADS PubMed  Fischl B, Sereno MI, Dale AM. 1999. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage . 9: 195– 207. Google Scholar CrossRef Search ADS PubMed  Fischl B, Sereno MI, Tootell RB, Dale AM. 1999. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp . 8: 272– 284. Google Scholar CrossRef Search ADS PubMed  Fornito A, Zalesky A, Breakspear M. 2015. The connectomics of brain disorders. Nat Rev Neurosci . 16: 159– 172. Google Scholar CrossRef Search ADS PubMed  Fortunato S. 2010. Community detection in graphs. Phys Rep . 486: 75– 174. Google Scholar CrossRef Search ADS   Fortunato S, Barthelemy M. 2007. Resolution limit in community detection. Proc Natl Acad Sci U S A . 104: 36– 41. Google Scholar CrossRef Search ADS PubMed  Fortunato S, Hric D. 2016. Community detection in networks: a user guide. Phys Rep . 659: 1– 44. Google Scholar CrossRef Search ADS   Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. 2005. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A . 102: 9673– 9678. Google Scholar CrossRef Search ADS PubMed  Friston KJ, Williams S, Howard R, Frackowiak RSJ, Turner R. 1996. Movement-related effects in fMRI time-series. Magnet Reson Med . 35: 346– 355. Google Scholar CrossRef Search ADS   Fu CH, Costafreda SG, Sankar A, Adams TM, Rasenick MM, Liu P, Donati R, Maglanoc LA, Horton P, Marangell LB. 2015. Multimodal functional and structural neuroimaging investigation of major depressive disorder following treatment with duloxetine. BMC Psychiatry . 15: 82. Google Scholar CrossRef Search ADS PubMed  Genovese CR, Lazar NA, Nichols T. 2002. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage . 15: 870– 878. Google Scholar CrossRef Search ADS PubMed  Glerean E, Pan RK, Salmi J, Kujala R, Lahnakoski JM, Roine U, Nummenmaa L, Leppamaki S, Nieminen-von Wendt T, Tani P, et al.  . 2016. Reorganization of functionally connected brain subnetworks in high-functioning autism. Hum Brain Mapp . 37: 1066– 1079. Google Scholar CrossRef Search ADS PubMed  Gong Q, He Y. 2015. Depression, neuroimaging and connectomics: a selective overview. Biol Psychiatry . 77: 223– 235. Google Scholar CrossRef Search ADS PubMed  Gong L, Hou Z, Wang Z, He C, Yin Y, Yuan Y, Zhang H, Lv L, Zhang H, Xie C, et al.  . 2018. Disrupted topology of hippocampal connectivity is associated with short-term antidepressant response in major depressive disorder. J Affect Disord . 225: 539– 544. Google Scholar CrossRef Search ADS PubMed  Good BH, de Montjoye YA, Clauset A. 2010. Performance of modularity maximization in practical contexts. Phys Rev E Stat Nonlin Soft Matter Phys . 81: 046106. Google Scholar CrossRef Search ADS PubMed  Goodale MA, Milner AD. 1992. Separate visual pathways for perception and action. Trends Neurosci . 15: 20– 25. Google Scholar CrossRef Search ADS PubMed  Graham J, Salimi-Khorshidi G, Hagan C, Walsh N, Goodyer I, Lennox B, Suckling J. 2013. Meta-analytic evidence for neuroimaging models of depression: state or trait? J Affect Disord . 151: 423– 431. Google Scholar CrossRef Search ADS PubMed  Greicius MD, Krasnow B, Reiss AL, Menon V. 2003. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A . 100: 253– 258. Google Scholar CrossRef Search ADS PubMed  Greve DN, Fischl B. 2009. Accurate and robust brain image alignment using boundary-based registration. Neuroimage . 48: 63– 72. Google Scholar CrossRef Search ADS PubMed  Gudayol-Ferre E, Pero-Cebollero M, Gonzalez-Garrido AA, Guardia-Olmos J. 2015. Changes in brain connectivity related to the treatment of depression measured through fMRI: a systematic review. Front Hum Neurosci . 9:582. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, Sporns O. 2008. Mapping the structural core of human cerebral cortex. PLoS Biol . 6: e159. Google Scholar CrossRef Search ADS PubMed  He Y, Wang J, Wang L, Chen ZJ, Yan C, Yang H, Tang H, Zhu C, Gong Q, Zang Y, et al.  . 2009. Uncovering intrinsic modular organization of spontaneous brain activity in humans. PLoS One . 4: e5226. Google Scholar CrossRef Search ADS PubMed  Heller AS. 2016. Cortical-subcortical interactions in depression: from animal models to human psychopathology. Front Syst Neurosci . 10: 20. Google Scholar CrossRef Search ADS PubMed  Heller AS, Johnstone T, Light SN, Peterson MJ, Kolden GG, Kalin NH, Davidson RJ. 2013. Relationships between changes in sustained fronto-striatal connectivity and positive affect in major depression resulting from antidepressant treatment. Am J Psychiat . 170: 197– 206. Google Scholar CrossRef Search ADS PubMed  Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. 2012. FSL. Neuroimage . 62: 782– 790. Google Scholar CrossRef Search ADS PubMed  Joormann J, Gotlib IH. 2010. Emotion regulation in depression: relation to cognitive inhibition. Cogn Emot . 24: 281– 298. Google Scholar CrossRef Search ADS PubMed  Joormann J, Quinn ME. 2014. Cognitive processes and emotion regulation in depression. Depress Anxiety . 31: 308– 315. Google Scholar CrossRef Search ADS PubMed  Jutla IS, Jeub LG, Mucha PJ 2011. A generalized louvain method for community detection implemented in matlab. URL http://netwiki/amath unc edu/GenLouvain. Kaiser RH, Andrews-Hanna JR, Wager TD, Pizzagalli DA. 2015. Large-scale network dysfunction in major depressive disorder: a meta-analysis of resting-state functional connectivity. JAMA Psychiatry . 72: 603– 611. Google Scholar CrossRef Search ADS PubMed  Korgaonkar MS, Fornito A, Williams LM, Grieve SM. 2014. Abnormal structural networks characterize major depressive disorder: a connectome analysis. Biol Psychiatry . 76: 567– 574. Google Scholar CrossRef Search ADS PubMed  Lancichinetti A, Fortunato S. 2012. Consensus clustering in complex networks. Sci Rep . 2: 336. Google Scholar CrossRef Search ADS PubMed  Lerman-Sinkoff DB, Barch DM. 2016. Network community structure alterations in adult schizophrenia: identification and localization of alterations. Neuroimage Clin . 10: 96– 106. Google Scholar CrossRef Search ADS PubMed  Li B, Liu L, Friston KJ, Shen H, Wang L, Zeng LL, Hu D. 2013. A treatment-resistant default mode subnetwork in major depression. Biol Psychiatry . 74: 48– 54. Google Scholar CrossRef Search ADS PubMed  Lisiecka D, Meisenzahl E, Scheuerecker J, Schoepf V, Whitty P, Chaney A, Moeller HJ, Wiesmann M, Frodl T. 2011. Neural correlates of treatment outcome in major depression. Int J Neuropsychopharmacol . 14: 521– 534. Google Scholar CrossRef Search ADS PubMed  Liston C, Chen AC, Zebley BD, Drysdale AT, Gordon R, Leuchter B, Voss HU, Casey BJ, Etkin A, Dubin MJ. 2014. Default mode network mechanisms of transcranial magnetic stimulation in depression. Biol Psychiatry . 76: 517– 526. Google Scholar CrossRef Search ADS PubMed  Lord A, Horn D, Breakspear M, Walter M. 2012. Changes in community structure of resting state functional connectivity in unipolar depression. PLoS One . 7: e41282. Google Scholar CrossRef Search ADS PubMed  Lund TE, Madsen KH, Sidaros K, Luo WL, Nichols TE. 2006. Non-white noise in fMRI: does modelling have an impact? Neuroimage . 29: 54– 66. Google Scholar CrossRef Search ADS PubMed  MacMahon M, Garlaschelli D. 2015. Community detection for correlation matrices. Phys Rev . 5:21006. Manoliu A, Meng C, Brandl F, Doll A, Tahmasian M, Scherr M, Schwerthoffer D, Zimmer C, Forstl H, Bauml J, et al.  . 2014. Insular dysfunction within the salience network is associated with severity of symptoms and aberrant inter-network connectivity in major depressive disorder. Frontiers in Human Neuroscience . 7:930. McCabe C, Mishor Z. 2011. Antidepressant medications reduce subcortical-cortical resting-state functional connectivity in healthy volunteers. Neuroimage . 57: 1317– 1323. Google Scholar CrossRef Search ADS PubMed  McCabe C, Mishor Z, Filippini N, Cowen PJ, Taylor MJ, Harmer CJ. 2011. SSRI administration reduces resting state functional connectivity in dorso-medial prefrontal cortex. Mol Psychiatry . 16: 592– 594. Google Scholar CrossRef Search ADS PubMed  Meila M. 2003. Comparing clusterings by the variation of information. Learning Theory and Kernel Machines . 2777: 173– 187. Google Scholar CrossRef Search ADS   Menon V. 2011. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci . 15: 483– 506. Google Scholar CrossRef Search ADS PubMed  Meunier D, Lambiotte R, Fornito A, Ersche KD, Bullmore ET. 2009. Hierarchical modularity in human brain functional networks. Front Neuroinform . 3: 37. Google Scholar CrossRef Search ADS PubMed  Misic B, Betzel RF, de Reus MA, van den Heuvel MP, Berman MG, McIntosh AR, Sporns O. 2016. Network-level structure-function relationships in human neocortex. Cereb Cortex . 26: 3285– 3296. Google Scholar CrossRef Search ADS PubMed  Mulders PC, van Eijndhoven PF, Schene AH, Beckmann CF, Tendolkar I. 2015. Resting-state functional connectivity in major depressive disorder: a review. Neurosci Biobehav Rev . 56: 330– 344. Google Scholar CrossRef Search ADS PubMed  Newman ME, Girvan M. 2004. Finding and evaluating community structure in networks. Phys Rev E Stat Nonlin Soft Matter Phys . 69: 026113. Google Scholar CrossRef Search ADS PubMed  Peckham AD, McHugh RK, Otto MW. 2010. A meta-analysis of the magnitude of biased attention in depression. Depress Anxiety . 27: 1135– 1142. Google Scholar CrossRef Search ADS PubMed  Peng D, Shi F, Shen T, Peng Z, Zhang C, Liu X, Qiu M, Liu J, Jiang K, Fang Y, et al.  . 2014. Altered brain network modules induce helplessness in major depressive disorder. J Affect Disord . 168: 21– 29. Google Scholar CrossRef Search ADS PubMed  Perrin JS, Merz S, Bennett DM, Currie J, Steele DJ, Reid IC, Schwarzbauer C. 2012. Electroconvulsive therapy reduces frontal cortical connectivity in severe depressive disorder. Proc Natl Acad Sci U S A . 109: 5464– 5468. Google Scholar CrossRef Search ADS PubMed  Petersen SE, Sporns O. 2015. Brain networks and cognitive architectures. Neuron . 88: 207– 219. Google Scholar CrossRef Search ADS PubMed  Pizzagalli DA. 2011. Frontocingulate dysfunction in depression: toward biomarkers of treatment response. Neuropsychopharmacology . 36: 183– 206. Google Scholar CrossRef Search ADS PubMed  Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, Vogel AC, Laumann TO, Miezin FM, Schlaggar BL, et al.  . 2011. Functional network organization of the human brain. Neuron . 72: 665– 678. Google Scholar CrossRef Search ADS PubMed  Power JD, Mitra A, Laumann TO, Snyder AZ, Schlaggar BL, Petersen SE. 2014. Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage . 84: 320– 341. Google Scholar CrossRef Search ADS PubMed  Price JL, Drevets WC. 2010. Neurocircuitry of mood disorders. Neuropsychopharmacology . 35: 192– 216. Google Scholar CrossRef Search ADS PubMed  Qin J, Shen H, Zeng LL, Jiang WX, Liu L, Hu DW. 2015a. Predicting clinical responses in major depression using intrinsic functional connectivity. Neuroreport . 26: 675– 680. Google Scholar CrossRef Search ADS PubMed  Qin J, Wei M, Liu H, Chen J, Yan R, Yao Z, Lu Q. 2015b. Altered anatomical patterns of depression in relation to antidepressant treatment: evidence from a pattern recognition analysis on the topological organization of brain networks. J Affect Disord . 180: 129– 137. Google Scholar CrossRef Search ADS PubMed  Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. 2001. A default mode of brain function. Proc Natl Acad Sci U S A . 98: 676– 682. Google Scholar CrossRef Search ADS PubMed  Rayner G, Jackson G, Wilson S. 2016. Cognition-related brain networks underpin the symptoms of unipolar depression: evidence from a systematic review. Neurosci Biobehav Rev . 61: 53– 65. Google Scholar CrossRef Search ADS PubMed  Reichardt J, Bornholdt S. 2006. Statistical mechanics of community detection. Phys Rev E Stat Nonlin Soft Matter Phys . 74: 016110. Google Scholar CrossRef Search ADS PubMed  Rubinov M, Sporns O. 2010. Complex network measures of brain connectivity: uses and interpretations. Neuroimage . 52: 1059– 1069. Google Scholar CrossRef Search ADS PubMed  Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, Reiss AL, Greicius MD. 2007. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci . 27: 2349– 2356. Google Scholar CrossRef Search ADS PubMed  Sheline YI, Price JL, Yan Z, Mintun MA. 2010. Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proc Natl Acad Sci U S A . 107: 11020– 11025. Google Scholar CrossRef Search ADS PubMed  Shen Y, Yao J, Jiang X, Zhang L, Xu L, Feng R, Cai L, Liu J, Wang J, Chen W. 2015. Sub-hubs of baseline functional brain networks are related to early improvement following two-week pharmacological therapy for major depressive disorder. Hum Brain Mapp . 36: 2915– 2927. Google Scholar CrossRef Search ADS PubMed  Sporns O, Betzel RF. 2016. Modular brain networks. Annu Rev Psychol . 67: 613– 640. Google Scholar CrossRef Search ADS PubMed  Sporns O, Tononi G, Kotter R. 2005. The human connectome: a structural description of the human brain. PLoS Comput Biol . 1: e42. Google Scholar CrossRef Search ADS PubMed  Stange JP, Bessette KL, Jenkins LM, Peters AT, Feldhaus C, Crane NA, Ajilore O, Jacobs RH, Watkins ER, Langenecker SA. 2017. Attenuated intrinsic connectivity within cognitive control network among individuals with remitted depression: temporal stability and association with negative cognitive styles. Hum Brain Mapp . 38: 2939– 2954. Google Scholar CrossRef Search ADS PubMed  Swanson LW, Sporns O, Hahn JD. 2016. Network architecture of the cerebral nuclei (basal ganglia) association and commissural connectome. Proc Natl Acad Sci U S A . 113: E5972– E5981. Google Scholar CrossRef Search ADS PubMed  Tadayonnejad R, Yang S, Kumar A, Ajilore O. 2015. Clinical, cognitive, and functional connectivity correlations of resting-state intrinsic brain activity alterations in unmedicated depression. J Affect Disord . 172: 241– 250. Google Scholar CrossRef Search ADS PubMed  Tao H, Guo S, Ge T, Kendrick KM, Xue Z, Liu Z, Feng J. 2013. Depression uncouples brain hate circuit. Mol Psychiatry . 18: 101– 111. Google Scholar CrossRef Search ADS PubMed  Tozzi L, Doolin K, Farrel C, Joseph S, O’Keane V, Frodl T. 2017. Functional magnetic resonance imaging correlates of emotion recognition and voluntary attentional regulation in depression: a generalized psycho-physiological interaction study. J Affect Disord . 208: 535– 544. Google Scholar CrossRef Search ADS PubMed  Traag VA, Van Dooren P, Nesterov Y. 2011. Narrow scope for resolution-limit-free community detection. Phys Rev E Stat Nonlin Soft Matter Phys . 84: 016114. Google Scholar CrossRef Search ADS PubMed  van de Ven V, Wingen M, Kuypers KP, Ramaekers JG, Formisano E. 2013. Escitalopram decreases cross-regional functional connectivity within the default-mode network. PLoS One . 8: e68355. Google Scholar CrossRef Search ADS PubMed  van den Heuvel MP, Hulshoff Pol HE. 2010. Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol . 20: 519– 534. Google Scholar CrossRef Search ADS PubMed  Wang L, Xia M, Li K, Zeng Y, Su Y, Dai W, Zhang Q, Jin Z, Mitchell PB, Yu X, et al.  . 2015. The effects of antidepressant treatment on resting-state functional brain networks in patients with major depressive disorder. Hum Brain Mapp . 36: 768– 778. Google Scholar CrossRef Search ADS PubMed  Xing XX, Zhou YL, Adelstein JS, Zuo XN. 2011. PDE-based spatial smoothing: a practical demonstration of impacts on MRI brain extraction, tissue segmentation and registration. Magn Reson Imaging . 29: 731– 738. Google Scholar CrossRef Search ADS PubMed  Xu T, Yang Z, Jiang LL, Xing XX, Zuo XN. 2015. A connectome computation system for discovery science of brain. Sci Bull . 60: 86– 95. Google Scholar CrossRef Search ADS   Yan CG, Cheung B, Kelly C, Colcombe S, Craddock RC, Di Martino A, Li Q, Zuo XN, Castellanos FX, Milham MP. 2013. A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage . 76: 183– 201. Google Scholar CrossRef Search ADS PubMed  Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, Roffman JL, Smoller JW, Zollei L, Polimeni JR, et al.  . 2011. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol . 106: 1125– 1165. Google Scholar CrossRef Search ADS PubMed  Yeo BT, Tandi J, Chee MW. 2015. Functional connectivity during rested wakefulness predicts vulnerability to sleep deprivation. Neuroimage . 111: 147– 158. Google Scholar CrossRef Search ADS PubMed  Zeng LL, Shen H, Liu L, Wang L, Li B, Fang P, Zhou Z, Li Y, Hu D. 2012. Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain . 135: 1498– 1507. Google Scholar CrossRef Search ADS PubMed  Zhou Y, Yu C, Zheng H, Liu Y, Song M, Qin W, Li K, Jiang T. 2010. Increased neural resources recruitment in the intrinsic organization in major depression. J Affect Disord . 121: 220– 230. Google Scholar CrossRef Search ADS PubMed  Zhu X, Zhu Q, Shen H, Liao W, Yuan F. 2017. Rumination and default mode network subsystems connectivity in first-episode, drug-naive young patients with major depressive disorder. Sci Rep . 7: 43105. Google Scholar CrossRef Search ADS PubMed  Zuo XN, Xing XX. 2011. Effects of non-local diffusion on structural MRI preprocessing and default network mapping: statistical comparisons with isotropic/anisotropic diffusion. PLoS One . 6: e26703. Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2017. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Cerebral Cortex Oxford University Press

Reconfiguration of Cortical Networks in MDD Uncovered by Multiscale Community Detection with fMRI

Loading next page...
 
/lp/ou_press/reconfiguration-of-cortical-networks-in-mdd-uncovered-by-multiscale-MW6DdiUf8G
Publisher
Oxford University Press
Copyright
© The Author(s) 2017. Published by Oxford University Press.
ISSN
1047-3211
eISSN
1460-2199
D.O.I.
10.1093/cercor/bhx335
Publisher site
See Article on Publisher Site

Abstract

Abstract Major depressive disorder (MDD) is known to be associated with altered interactions between distributed brain regions. How these regional changes relate to the reorganization of cortical functional systems, and their modulation by antidepressant medication, is relatively unexplored. To identify changes in the community structure of cortical functional networks in MDD, we performed a multiscale community detection algorithm on resting-state functional connectivity networks of unmedicated MDD (uMDD) patients (n = 46), medicated MDD (mMDD) patients (n = 38), and healthy controls (n = 50), which yielded a spectrum of multiscale community partitions. we selected an optimal resolution level by identifying the most stable community partition for each group. uMDD and mMDD groups exhibited a similar reconfiguration of the community structure of the visual association and the default mode systems but showed different reconfiguration profiles in the frontoparietal control (FPC) subsystems. Furthermore, the central system (somatomotor/salience) and 3 frontoparietal subsystems showed strengthened connectivity with other communities in uMDD but, with the exception of 1 frontoparietal subsystem, returned to control levels in mMDD. These findings provide evidence for reconfiguration of specific cortical functional systems associated with MDD, as well as potential effects of medication in restoring disease-related network alterations, especially those of the FPC system. Antidepressant, Functional Connectivity, Graph Theory, MDD, Resting-state fMRI Introduction Many neuroimaging studies have demonstrated that major depressive disorder (MDD) is associated with altered functional and structural connectivity of distributed brain regions (Sheline et al. 2010; Zeng et al. 2012; Dutta et al. 2014; Korgaonkar et al. 2014; Gong and He 2015; Mulders et al. 2015). Altered connections include those mediating interactions within and between multiple brain systems supporting several specific functions, such as emotion, attention, cognitive control, and self-referential processing. Furthermore, the neuropathology of MDD involves disrupted interactions between different brain systems (Drevets 2001; Price and Drevets 2010). Therefore, MDD may be thought of as a “network disease,” where a small local change may lead to global dysfunction expressed across the whole brain (Fornito et al. 2015). The dysfunction of 3 systems are particularly relevant to MDD (Kaiser et al. 2015; Mulders et al. 2015): default mode (DM) system, frontoparietal control (FPC) system, and salience system. The DM system involved in self-referential process (Raichle et al. 2001; Greicius et al. 2003; Buckner et al. 2008) is related to pathological introspection of MDD patients like rumination (Zhu et al. 2017). The dysfunctional top–down control by FPC system (Corbetta and Shulman 2002; Fox et al. 2005) is associated with deficient cognitive control in depression (Stange et al. 2017). The salience system (overlapping with ventral attention system), supporting detecting and orientating to salient stimuli (Seeley et al. 2007), has a role in biased attention in MDD patients (Beevers et al. 2015). Moreover, abnormal interactions among these 3 systems have been observed in MDD patients (Manoliu et al. 2014; Kaiser et al. 2015; Mulders et al. 2015). Neurocognitive models of MDD suggested that imbalanced connectivity of FPC with DM and salience systems results in imbalanced control over introspection and external attention and furthermore affects mood regulation (Disner et al. 2011; Rayner et al. 2016). The triple-network model (Menon 2011), a common framework for multiple psychiatric disorders, also emphasizes that the deficient communication of these 3 core systems plays an important role in the psychopathology of psychiatric disorders. Thus, investigating the intrinsic organization and interactions of different functional systems in the brain network can help to identify preserved, dysfunctional, and compensatory subsystems in MDD and further help to understand the psychopathology of MDD, which may lead to targeted treatments. Graph theory provides a useful tool to characterize the brain network (Sporns et al. 2005; Bullmore and Sporns 2009). The community structure is one of the most functionally relevant graph metrics to study the organization and interaction of functional systems in the brain network (Petersen and Sporns 2015). A community is a set of more densely interconnected nodes (or brain areas) within the set compared with between other sets (Fortunato 2010; Fortunato and Hric 2016; Sporns and Betzel 2016). The community structure (or modular organization) can delineate the functional segregation and integration of the whole-brain network. Researchers have identified community structure in both structural and functional networks in the healthy human brain (Chen et al. 2008; He et al. 2009; Meunier et al. 2009). Identifying the community structure of the brain is also of great interest to clinical researchers because disrupted community structures were found in diverse brain disorders (Cary et al. 2016; Glerean et al. 2016; Lerman-Sinkoff and Barch 2016), including MDD (Bohr et al. 2012; Lord et al. 2012; Tao et al. 2013; Peng et al. 2014). Lord et al. (2012) detected community structure of functional network for individuals with unipolar depression. They found that superior regions in frontal and parietal cortex increased intercommunity connections, whereas inferior regions like occipital, temporal, and inferior frontal cortex increased intracommunity connections in patients. The participation indices (an index of how dense nodes are connected across multiple communities) of 15 nodes mainly located in frontal, parietal, and occipital cortex occupied top 25 informative metrics, which can identify patients from the healthy by machine learning. These findings indicate that the community structures are significantly reorganized in depressive patients by the alterations in widespread brain areas. However, they did not describe the specific alterations of the community structures in depression. Peng et al. (2014) reported different community structures between MDD group and the healthy controls (HC). They observed that an integrated community comprised the DM and FPC systems in the HC, but parts of this community belonging to the FPC system were separated in MDD group. Another community responsible for affective processing also split in MDD group. They suggested that these alterations might account for abnormal cognitive and affective processing in MDD. Tao et al. (2013) applied a reference community structure based on HC to first-episode MDD and resistant MDD patients and found widespread altered connections, mostly belonging to the attention system. These studies indicate that MDD-related alterations of functional connectivity underling altered community structures are distributed in different functional systems like the default, cognitive control, attention, and affective systems. This corresponds to the theoretical model. However, a limited number of studies on community structure in MDD reported inconsistent results, which calls for more investigations with solid methodology for robust identification of the neural alterations in MDD. Previous studies have reported that antidepressant treatment modifies functional connectivity in distributed regions in MDD (Dichter et al. 2015; Gudayol-Ferre et al. 2015). Gudayol-Ferre et al. (2015) reviewed that antidepressant treatment mainly affected connectivity of portions of the DM and cortical–limbic networks. This suggests that the medication effect is widespread and complex, highlighting the need to examine its effect on the large-scale system level. Although these studies indicate that changes in functional network communities accompany MDD, none so far have 1) systematically examined the community structure of functional networks in both medicated and unmedicated MDD (uMDD), 2) addressed methodological problems with modularity maximization such as the resolution limit (Fortunato and Barthelemy 2007) and degeneracy (Good et al. 2010; Lancichinetti and Fortunato 2012), or 3) taken into account community partitions across multiple scales (Betzel et al. 2014b). The primary aim of this study is to address these limitations, by examining changes in the community structure of cortical functional networks in both uMDD and medicated MDD (mMDD), across multiple resolutions. To achieve this goal, we identified an optimal resolution parameter for each group based on the stability of the community structure against small changes in the resolution parameter, quantified as the variation of information (Meila 2003). We hypothesized that both MDD patient groups would exhibit altered community structure compared with that of HC group. Materials and Methods Participants A total of 134 participants aged 18–65 years were included, containing 46 drug-naive MDD patients, 38 mMDD patients, and 50 age-, gender-, and education-matched HC participants (Table 1). No significant differences of age, gender, education, and head motion existed among the 3 groups. All participants were part of an ongoing depression research project, which was conducted by Southwest University, China. All the MDD patients were recruited from the psychiatric department of the First Affiliated Hospital of Chongqing Medical University and diagnosed with the Structured Clinical Interview of the DSM-IV by experienced psychiatric physicians. HC participants were screened without current or past Axis I or II disorder. All participants had 1) no neurological, organic brain diseases and other psychiatric disorders; 2) no family history of psychiatric disorders on their first-degree relatives; and 3) no physical contraindication for undergoing magnetic resonance imaging (MRI). The medicated participants had accepted antidepressant treatment before the MRI scan, in most cases including selective serotonin reuptake inhibitors including citalopram, escitalopram, fluoxetine, paroxetine, and sertraline; serotonin–norepinephrine reuptake inhibitors such as venlafaxine; serotonin antagonists and reuptake inhibitors such as trazodone; tetracyclic antidepressant such as mirtazapine; and tricyclic antidepressant such as amitriptyline and melitracen-flupentixol. All patients underwent assessment for depressive symptom by the 17-item Hamilton Rating Scale for Depression (HRSD) and the Beck Depression Inventory-II (BDI). The mMDD group had lower scores on BDI than the uMDD group, but there was no difference on HRSD and illness duration (Table 1). This study was approved by the Ethics Committee of Southwest University and First Affiliated Hospital of Chongqing Medical School. All participants provided informed written consent. Table 1 Demographic information   Unmedicated  Medicated  Control  Age (years)  39.46 (18–63)  39.77 (19–66)  40.4 (19–63)  Gender (male/female)  12/34  10/28  19/31  Education (years)  12.28 ± 3.55  11.13 ± 3.26  12.04 ± 4.11  meanFD (mm)  0.07 ± 0.04  0.06 ± 0.03  0.07 ± 0.03  BDI  23.82 ± 7.71**  18.86 ± 8.59**  —  HRSD  23.61 ± 4.33  23 ± 4.33  —  Duration (weeks)  106.11 ± 166.45  119.25 ± 132.45  —    Unmedicated  Medicated  Control  Age (years)  39.46 (18–63)  39.77 (19–66)  40.4 (19–63)  Gender (male/female)  12/34  10/28  19/31  Education (years)  12.28 ± 3.55  11.13 ± 3.26  12.04 ± 4.11  meanFD (mm)  0.07 ± 0.04  0.06 ± 0.03  0.07 ± 0.03  BDI  23.82 ± 7.71**  18.86 ± 8.59**  —  HRSD  23.61 ± 4.33  23 ± 4.33  —  Duration (weeks)  106.11 ± 166.45  119.25 ± 132.45  —  **P < 0.01, obtained by 2-sample t-test. Image Acquisition Magnetic resonance images were acquired from a 3 T Siemens TrioTim scanner. The resting-state functional images were obtained with an echo-planar imaging (EPI) sequence (volumes = 242, TR/TE = 2000/30 ms, flip angle = 90°, matrix = 64 × 64, thickness/gap = 3/1 mm, voxel size = 3.4 × 3.4 × 3 mm3). The structural images were from a high-resolution, T1-weighted magnetization–prepared rapid gradient echo sequence (TR/TE/TI = 1900 ms/2.52 ms/900 ms, flip angle = 9°, matrix = 256 × 256, slices = 176, voxel size = 1 × 1 × 1 mm3). Image Preprocessing All the imaging data were preprocessed by using the Connectome Computation System pipeline (Xu et al. 2015), which provide a platform for processing multimodal images by integrating functions of analysis of functional neuroimages (Cox 1996), FreeSurfer (Dale et al. 1999; Fischl et al. 1999a), and FMRIB Software Library (Jenkinson et al. 2012). Structural MRI preprocessing included following steps of removing spatial noise by a nonlocal mean filtering (Xing et al. 2011; Zuo and Xing 2011) and then running recon-all command in FreeSurfer 5.1 to conduct brain extraction, structural segmentation, and surface reconstruction. The preprocessing of functional images was completed by 1) removing the first 5 EPI volumes; 2) replacing temporal spikes; 3) slice timing correction; 4) head motion correction; 5) generating brain mask; 6) normalization of global mean intensity of 10 000; 7) nuisance variable regression (Fox et al. 2005; Lund et al. 2006) by regressing out mean signals from white matter, cerebrospinal fluid, Friston-24 head motion parameters (Friston et al. 1996; Yan et al. 2013) and the global mean signal; 8) band-pass filtering (0.01–0.1 HZ); 9) removing linear and quadratic trends; and 10) spatial smoothing with a 6-mm full-width at half-maximum filter. Functional images were coregistered to the native anatomical images by using boundary-based registration (BBR, Greve and Fischl 2009) and then were projected onto the fsaverage surface template and resampled to fsaverage5 surface (Fischl et al. 1999b). The global mean signal was removed in order to reduce physiological noise, such as movement and respiration (Power et al. 2014). We performed quality control procedure on all imaging data to ensure the quality of preprocessing. First, visual inspection was conducted on screenshots of the following steps, including brain exaction, structural segmentation, surface reconstruction, and BBR-based functional images registration. Then, the mean frame-wise displacement (meanFD) was calculated as the head motion criterion (meanFD < 0.2 mm). All the participants included in the final study passed this criterion. Functional Network Construction The whole-brain surface was parceled into 114 regions derived from a previously established functional parcellation of 17 networks (Yeo et al. 2011). Functional connectivity can be estimated from intrinsic spontaneous activity of brain regions (Biswal et al. 1995; van den Heuvel and Hulshoff Pol 2010), by computing the Pearson correlations of time series from all pairs of regions. For each subject group, the connection matrices obtained from each subject were averaged into a single group matrix used to detect network communities. Community Detection We used a multiscale community detection technique to identify the community structure of a large-scale functional network for each group (Fig. 1). One widely used strategy is to maximize connections within each community while minimizing connections between communities (Newman and Girvan 2004; Rubinov and Sporns 2010; Sporns and Betzel 2016). To achieve this, we maximize the quality function:   Q(γ)=12m∑ij[Aij−γ]δ(gi,gj) Figure 1. View largeDownload slide Multiscale community detection schematic. (A) The whole-brain surface comprised 114 parcels. A weighted adjacency matrix of functional connectivity of 114 brain regions was constructed for each subject. The matrices of subjects in each group were averaged into a single group-level matrix as the functional network. (B) The multiscale community detection was applied on each group matrix across a range of resolution parameter values. For each resolution parameter γ, 1) we repeated community detection 10 000 times and 2) finally obtained a single consensus community structure. (C) We chose the optimal γ based on the resolution stability, by calculating the averaged variation of information (VI) between the partition of each γ and its two contiguous neighbors (γ(i−1) and γ(i + 1)). Then, we targeted the broadest range of continuous γ values for which the VI remained equal to zero. For example, in the bottom plot, γ = 0.18 is chosen as the optimal. Figure 1. View largeDownload slide Multiscale community detection schematic. (A) The whole-brain surface comprised 114 parcels. A weighted adjacency matrix of functional connectivity of 114 brain regions was constructed for each subject. The matrices of subjects in each group were averaged into a single group-level matrix as the functional network. (B) The multiscale community detection was applied on each group matrix across a range of resolution parameter values. For each resolution parameter γ, 1) we repeated community detection 10 000 times and 2) finally obtained a single consensus community structure. (C) We chose the optimal γ based on the resolution stability, by calculating the averaged variation of information (VI) between the partition of each γ and its two contiguous neighbors (γ(i−1) and γ(i + 1)). Then, we targeted the broadest range of continuous γ values for which the VI remained equal to zero. For example, in the bottom plot, γ = 0.18 is chosen as the optimal. A = [Aij] is a weighted and signed functional connectivity matrix where Aij represents the connection weight between node i and j estimated by Pearson correlation coefficient between 2 regional time series. gi is the assignment of the node i to a community. Modularity maximization is subject to a “resolution limit”; that is, it is unable to detect communities smaller than a given scale (Fortunato and Barthelemy 2007; Fortunato and Hric 2016). Here, we included a tunable resolution parameter γ to mitigate this problem (Reichardt and Bornholdt 2006). By adjusting the value of γ from low to high, one can detect few large-size communities to more numerous small-size communities, which also corresponds to the “constant Potts model” (Traag et al. 2011) suitable for networks based on correlation matrices (MacMahon and Garlaschelli 2015). We used a Louvain algorithm implemented in a MATLAB software package (Blondel et al. 2008; Jutla et al. 2011) to perform community detection. We varied γ within a range from 0.1 to 0.4 in increments of 0.001. The limits of this range were chosen such that they retrieve community partitions with significantly lower (γ = 0.1) or higher (γ = 0.4) numbers of communities than are included in a standard resting-state network partition (Yeo et al. 2011). For each of 301 settings of the γ parameter, we performed community detection 10 000 times and then identified a single consensus partition from the agreement matrix (Lancichinetti and Fortunato 2012). After deriving the consensus partition at each γ, we selected a single level of γ for further analysis. Prior work used stability as a criterion, by looking for a plateau in the number of communities (Swanson et al. 2016) or by calculating the similarity of partitions across a range of γ values (Misic et al. 2016). Here, we adopted an approach similar to the latter criterion by defining the stability of γ across a small window at each point across the range. Specifically, we used the variation of information (VI) (Meila 2003) to evaluate the variability between different partitions computed under small variations of γ for multiscale resolution stability. First, we calculated the VI averaged between the partition of each γ and each of its two contiguous neighbors (γ(i-1) and γ(i + 1)). This measure represents the stability of the partition when γ is changed slightly. Then, we targeted the broadest range of continuous γ values for which the averaged VI remained equal to zero (Fig. 1), taken to represent the most stable partition across multiscale resolutions. Network Properties To further examine the connectivity patterns of each community and the relationship between communities, we compared each community’s intracommunity and intercommunity connectivity strengths based on the community structure from HC as a reference. First, we averaged all the intracommunity functional connectivity between each pair of nodes within each community. Second, we averaged all intercommunity functional connectivity between each community and all other communities. Finally, we compared the averaged intracommunity and intercommunity connectivity among 3 groups. Statistics Permutation testing was used to assess differences of network properties between groups with 10 000 permutations and tested at significance level 0.05 for a 2-tailed test. Before permutation test, age, gender, and the meanFD were regressed out from each network metric as covariates. In order to control any putative differences in whole-brain connection strength and focus on community-specific connectivity, individual mean whole-brain connection strength was also regressed out as covariate. The False Discovery Rate (FDR) was used for multiple comparison correction at α = 0.05 (Benjamini and Hochberg 1995; Genovese et al. 2002). Results Optimal Parameter of Resolution in Multiscale Community Detection We identified the optimal resolution parameter γ for each group based on the resolution stability (see Materials and Methods). As a result, we identified the optimal γ as 0.238 for HC, 0.173 for uMDD, and 0.181 for mMDD (Fig. 2). Figure 2. View largeDownload slide Selection of the optimal partition for each group. The plot illustrates the resolution stability VI value (shown as the histogram) and the number of communities (shown as the continuous line) obtained across a range of resolution parameter γ in 3 subject groups. For 3 groups, the optimal γ was marked in the plot, as 0.238 for the HC, 0.173 for uMDD, and 0.181 mMDD, for which the VI is equal to zero with the broadest range. Figure 2. View largeDownload slide Selection of the optimal partition for each group. The plot illustrates the resolution stability VI value (shown as the histogram) and the number of communities (shown as the continuous line) obtained across a range of resolution parameter γ in 3 subject groups. For 3 groups, the optimal γ was marked in the plot, as 0.238 for the HC, 0.173 for uMDD, and 0.181 mMDD, for which the VI is equal to zero with the broadest range. Community Structures The community structure of functional network of each group is shown in Figure 3. Figure 4 illustrates the community assignment of each brain region in 3 subject groups. Overall, the decomposition of the functional network using our multiscale community detection method (and the optimal γ) was similar to previous functional parcellations (Power et al. 2011; Yeo et al. 2011); we compared our community structures with the 7 networks previously reported in Yeo et al. (2011) by calculating their mutual information (Fig. 3). For the 3 groups, we obtained a mutual information of 0.718 (HC), 0.650 (uMDD), and 0.710 (mMDD). Our community detection approach disclosed 12 communities in the HC, 9 communities in uMDD, and 10 communities in mMDD, and the community structures of both patient groups appeared similar to each other while differing from that of the HC group (Fig. 4). In addition, the mMDD group exhibited some new features of community structure that differed from the other two groups. Figure 3. View largeDownload slide The community structures for 3 groups and the mapping with Yeo’s 7 network. The upper panel shows community structures of functional networks of 3 groups. Twelve communities for HC, 9 communities for uMDD, and 10 communities for mMDD were separately identified. The bottom panel illustrates how our partitions compare with the 7 networks of Yeo et al. (2011), represented by 7 different colors. VN: visual network; SMN: somatomotor network; DAN: dorsal attention network; VAN: ventral attention network; LMN: limbic network; FPN: frontoparietal network; DMN: default mode network; TEM: TempPar (independent regions not belong to 7 networks). Figure 3. View largeDownload slide The community structures for 3 groups and the mapping with Yeo’s 7 network. The upper panel shows community structures of functional networks of 3 groups. Twelve communities for HC, 9 communities for uMDD, and 10 communities for mMDD were separately identified. The bottom panel illustrates how our partitions compare with the 7 networks of Yeo et al. (2011), represented by 7 different colors. VN: visual network; SMN: somatomotor network; DAN: dorsal attention network; VAN: ventral attention network; LMN: limbic network; FPN: frontoparietal network; DMN: default mode network; TEM: TempPar (independent regions not belong to 7 networks). Figure 4. View largeDownload slide Community assignment of 114 brain regions. The left alluvial diagram (modified from https://github.com/mbojan/alluvial) illustrates changes of community assignments of brain regions across 3 groups. Each block represents a community and each line corresponds to a brain region. For example, comparing to HC, Community 1 fragmented to 2 parts, one of which joined Community 2 in uMDD and mMDD. The right image shows the brain surface mapping of community partitions. The color-coding is as same as the left diagram, corresponding to distinct communities. Major communities in HC includes (1) visual association (VA), (2) central system, (4) frontoparietal control-A (FPC-A), (9) frontoparietal control-B (FPC-B), (10) frontoparietal control-C (FPC-C), (11) DM system. Figure 4. View largeDownload slide Community assignment of 114 brain regions. The left alluvial diagram (modified from https://github.com/mbojan/alluvial) illustrates changes of community assignments of brain regions across 3 groups. Each block represents a community and each line corresponds to a brain region. For example, comparing to HC, Community 1 fragmented to 2 parts, one of which joined Community 2 in uMDD and mMDD. The right image shows the brain surface mapping of community partitions. The color-coding is as same as the left diagram, corresponding to distinct communities. Major communities in HC includes (1) visual association (VA), (2) central system, (4) frontoparietal control-A (FPC-A), (9) frontoparietal control-B (FPC-B), (10) frontoparietal control-C (FPC-C), (11) DM system. In particular, in the HC group, the communities we detected consist of visual association (VA), central (somatomotor/salience), FPC-A, FPC-B, FPC-C, and DM systems (ignoring small-sized communities with only 1 or 2 nodes). In contrast, the uMDD group showed that the VA and FPC-A systems have partly been fragmented, with parts merging with the central system (e.g., bilateral occipito-temporal sulcus, parieto-occipital gyrus, and superior parietal lobe from VA and bilateral posterior part of superior frontal sulcus, intraparietal sulcus, posterior part of inferior temporal gyrus, left inferior part of precentral sulcus, left anterior part of inferior frontal sulcus, and right posterior part of inferior frontal sulcus from FPC-A). FPC-B was split into 2 parts, one of which joined the DM system including the bilateral middle frontal gyrus and right angular gyrus. For the DM system, some regions including the bilateral superior temporal sulcus and the left inferior frontal gyrus formed an independent community, and the left posterior region of inferior parietal lobule combined with FPC-C. In addition, the bilateral medial orbital gyrus and temporal pole integrated into one community. However, mMDD exhibited some new features of community structure in FPC-A and FPC-B that differed from the other 2 groups (Fig. 4). A part of FPC-A (including bilateral posterior part of inferior temporal gyrus, intraparietal sulcus, left posterior part of superior frontal sulcus, and left inferior frontal sulcus) and FPC-B (including bilateral posterior part of inferior temporal sulcus, lateral frontopolar cortex, middle part of medial superior frontal gyrus, left posterior middle frontal gyrus, and left anterior angular gyrus) combined together with bilateral posterior part of anterior cingulate cortex into an independent community. The right posterior region of inferior parietal lobule split from the DM system to join the FPC-C. In addition, some single nodes merging with other large communities in uMDD, like left orbital sulcus and right middle part of ACC, became independent in mMDD. Intracommunity and Intercommunity Connectivity To further determine the origin of the changes in the community structure in uMDD and mMDD patients, we examined intracommunity and intercommunity connectivity of each community based on the community structure of HC (see Materials and Methods for details). No significant differences of intracommunity connectivity among the 3 groups were found (Fig. 5). However, several communities in the uMDD group, including the central, FPC-A, FPC-B, and FPC-C systems, exhibited increased intercommunity connectivity compared with the HC (central, P = 0.004; FPC-A, P = 0.002; FPC-B, P = 0.006; FPC-C, P = 0.040; FDR corrected) and mMDD except FPC-A system (central, P = 0.001; FPC-B, P = 0.007; FPC-C, P = 0.05; FPC-A, P = 0.086; FDR corrected). Meanwhile, the mMDD group showed reduced intercommunity connectivity in the DM system compared with the other 2 groups (HC, P = 0.003; uMDD, P < 0.001; FDR corrected). Figure 5. View largeDownload slide The intracommunity and intercommunity connectivity strengths. The intracommunity connectivity strength was quantified as the averaged functional connectivity between each pair of nodes within each community. The intercommunity connectivity strength was calculated as the averaged functional connectivity between the nodes within a community and all the other nodes outside the community. There was no significant difference in intracommunity connectivity among 3 groups. But significant differences were found in intercommunity connectivity (shown in rectangles of bottom panel). For C2 (central), C9 (FPC-B), C10 (FPC-C), uMDD showed increased intercommunity connectivity than the other 2 groups. For C4 (FPC-A), uMDD showed increased connectivity than HC. For C11 (DM), mMDD had lower connectivity strength than others. The gray dots represent outliers. Figure 5. View largeDownload slide The intracommunity and intercommunity connectivity strengths. The intracommunity connectivity strength was quantified as the averaged functional connectivity between each pair of nodes within each community. The intercommunity connectivity strength was calculated as the averaged functional connectivity between the nodes within a community and all the other nodes outside the community. There was no significant difference in intracommunity connectivity among 3 groups. But significant differences were found in intercommunity connectivity (shown in rectangles of bottom panel). For C2 (central), C9 (FPC-B), C10 (FPC-C), uMDD showed increased intercommunity connectivity than the other 2 groups. For C4 (FPC-A), uMDD showed increased connectivity than HC. For C11 (DM), mMDD had lower connectivity strength than others. The gray dots represent outliers. Discussion Using a multiscale community detection approach, we identified the most stable community partitions of functional networks for each group. Importantly, we could delineate patterns of reorganization in the community structure of functional brain networks in MDD patients with or without antidepressant treatment. In particular, we observed disturbances in the community structure of VA and DM systems that were similar in uMDD and mMDD groups, while also identifying reconfigurations of the FPC subsystems that differed in the 2 patient groups. Moreover, further analysis of intracommunity and intercommunity connectivity revealed that changes in intercommunity connectivity mainly accounted for the observed changes in community organization in MDD patients. Specifically, in the uMDD group, the intercommunity connectivity in the central system and 3 FPC subsystems was stronger than both the HC and medicated patients (except the FPC-A system). The mMDD group additionally showed attenuated intercommunity connectivity in the DM system compared with the other 2 groups. Multiscale Community Detection Previous studies have demonstrated that structural and functional human brain networks are organized into a modular architecture (Hagmann et al. 2008; He et al. 2009; Meunier et al. 2009; Power et al. 2011). Functional communities are robustly identified with a variety of community detection methods (Sporns and Betzel 2016) and the resulting communities have been shown to correspond to functional brain systems that are coherently engaged in both rest and task conditions (Crossley et al. 2013; Bertolero et al. 2015). Thus, the community organization of large-scale human functional networks appears to reflect underlying neurobiological mechanisms, and variations in community structure may signal significant changes in the brain’s functional capacities. Although examining the community organization of human brain networks has been extremely fruitful for uncovering the organizing principles of the human brain, methodological limitations of community detection methods such as resolution limit and degeneracy of optimal solutions of community structure have so far remained largely unaddressed (Fortunato and Barthelemy 2007; Good et al. 2010; Lancichinetti and Fortunato 2012; Fortunato and Hric 2016). In the present study, we addressed some of these limitations by identifying an optimal resolution parameter γ associated with a highly stable community structure based on resolution stability measure for each group. The community structure of the control group was broadly similar to that identified in previous studies (Power et al. 2011; Yeo et al. 2011); yet, we found characteristic patterns of reconfiguration of functional networks, which differed among unmedicated and medicated patient groups. Changes in Functional Networks in uMDD We compared the community structure of MDD groups with the HC to locate disease-related changes in organization of functional networks. Notable differences were found in the composition of communities, especially in the DM, FPC-B, and VA systems. Each of them fragmented into 2 parts in the uMDD group compared with the healthy group. One segregated part of FPC-B joined the DM system, and one part of VA together with FPC-A joined the central system. This reconfiguration of community structure was associated with increased intercommunity connections, like the central system, 3 FPC subsystems in uMDD. These findings support the “hyperconnectivity hypothesis” of depression, which suggests that increased connectivity might be a biomarker of the mood disorder (Perrin et al. 2012). This is also consistent with a recent meta-analysis, which concluded that MDD involved imbalanced connectivity among functional networks, for example, DM, FPC, and attention networks (Kaiser et al. 2015). The alterations of the DM, FPC, and central systems (overlap with salience system) have been widely reported in MDD (Mulders et al. 2015) and with high power for discriminating MDD from HC (Qin et al. 2015a, 2015b). Disturbed DM functional connectivity is related to excessive negative rumination in MDD (Zhu et al. 2017), which may underlie the negative internal processes occurring in MDD. The FPC system supports tasks/cognition control (Corbetta and Shulman 2002; Fox et al. 2005), which is suggested to be associated with maladaptive cognitive control in MDD, like controlling over attention and emotion (Joormann and Gotlib 2010; Joormann and Quinn 2014). The salience system (ventral attention system) is involved in detecting salient stimuli (Seeley et al. 2007), which is known to be affected in MDD (Peckham et al. 2010; Disner et al. 2011; Beevers et al. 2015). Our findings support the neurocognitive models of MDD, which propose that the symptoms of MDD can be attributed to disturbed connections of the FPC network with other networks involving attention, emotion, and internal mentation (Disner et al. 2011; Kaiser et al. 2015; Rayner et al. 2016). The imbalanced connectivity between FPC and DM results in the domination of self-focused introspection over external orientation. The disturbed connectivity between FPC and salience system causes the disruption of cognitive control of biased attention. Here, the altered community structure and increased connectivity of 3 FPC subsystems imply that FPC plays a vital role in the pathophysiology of MDD. Their separate recombination with the DM and central systems may reflect their different roles in abnormal control on emotional internal thought and external attention (Manoliu et al. 2014; Kaiser et al. 2015). The central system (salience and somatomotor systems) showed stronger intercommunity connectivity in uMDD. This hyperconnectivity may explain the community recombination of the central system with parts of the VA system and the FPC-A system (Fig. 4). The shifted part of VA included the superior parietal, tempo-occipital sulcus, and parieto-occipital gyrus, are respectively in 2 visual processing pathways supporting cognitive processing of visual input, as the ventral and dorsal visual pathway (Goodale and Milner 1992). Studies suggested that altered occipital regions were related to negatively biased processing of visual inputs in uMDD (Graham et al. 2013; Cheng et al. 2016). Here, the recombination of the central system with a part of the VA system may reflect abnormal control of attention in visual processing in MDD, which lead to negative perception (Peckham et al. 2010; Zhou et al. 2010; Desseilles et al. 2011; Disner et al. 2011; Beevers et al. 2015; Tozzi et al. 2017). In addition, the somatomotor system has been found to be associated with depression severity in MDD, suggesting that it was related to abnormal pain processing (Tadayonnejad et al. 2015). The dysfunction of somatomotor regions may be associated with physical symptoms like pain and fatigue in MDD (Bair et al. 2003). Together, hyperconnectivity of the central system may underlie nonemotional symptoms involving biased attention and somatosensation. Effects of Medication on Network Topology of MDD Several studies have reported that various kinds of antidepressants have heterogeneous effects on functional connectivity across the visual, DM, salience, and cognitive control networks (Dichter et al. 2015; Gudayol-Ferre et al. 2015; Wang et al. 2015). However, most of these studies investigated seed-based connectivity, without addressing the whole-brain system level. Using network analysis, we found that antidepressants affected the community structure mainly by modulating patterns of intercommunity connectivity, which more closely resembled those found in HC. In mMDD patients, decreased intercommunity connectivity appeared in almost all affected systems as opposed to increased intercommunity connections in unmedicated patients (except FPC-A), which may indicate normalization/restoration effects of medication. This normalization parallels recent findings showing that antidepressants can renormalize increased cortical thickness of widespread regions in depressive patients (Bansal et al. 2017) and could reduce increased functional connectivity of orbitofrontal cortex with distributed regions (Cheng et al. 2016). However, the community structure in medicated patients was largely the same as that of uMDD, except that FPC-A and FPC-B systems combined together. But the community structures of mMDD and the HC appear more similar to Yeo’s network partition (Yeo et al. 2011). We speculate that antidepressants may contribute more to adaptive connectivity changes between communities rather than the general organization of the community structure. A key finding of the antidepressant effect is that the FPC subsystems exhibited alterations in both community organization and connectivity. Previous studies have found that depression treatment could change functional connectivity of FPC (Aizenstein et al. 2009; Perrin et al. 2012; Liston et al. 2014). Perrin et al. (2012) found a reduced average global functional connectivity of dorsolateral prefrontal cortex after electroconvulsive therapy, which is consistent with our findings. Both the FPC and the DM systems exhibited reduced functional connectivity after antidepressant administration in healthy participants (McCabe et al. 2011; van de Ven et al. 2013). As we have shown that the FPC system is vital in the pathophysiology of MDD, we propose that it is also a potential therapeutic target of antidepressant medication. As the number of studies about the antidepressant effect on the FPC system is limited, further studies are needed. A growing body of studies have reported altered connections by antidepressant treatment within DM system or between DM and other systems, like FPC system or limbic system, implying its prominent involvement in depression (Dichter et al. 2015; Gudayol-Ferre et al. 2015). Of note, in our findings, the intercommunity connectivity of the DM system in mMDD decreased below the level of the control group and uMDD. This may imply a compensatory mechanism of antidepressants on the DM system. In MDD patients, the connectivity of the DM system was associated with the antidepressant response (Pizzagalli 2011; Wang et al. 2015), and whole-brain connectivity of regions in the DM system can be reduced by the antidepressant (Fu et al. 2015; Wang et al. 2015). Its alteration of intracommunication and intercommunication is associated with MDD remission (Qin et al. 2015a, 2015b). However, some studies suggested that the normalization effect of antidepressants only took part in certain subsystems of the DM system (Li et al. 2013; Shen et al. 2015). Additional work is needed to provide more solid conclusions and interpretations regarding the role of antidepressants in modifying functional brain connectivity. Limitations Several limitations in the current study need to be noted. First, functional networks using only cortical regions are explored in this study. However, subcortical regions also play an important role in the pathophysiology of MDD (Heller 2016). Second, while other parcellations may be explored, our choice of a previously published parcellation based on data from 1000 participants (42) was based on its consistency with other canonical maps of brain functional systems (Power et al. 2011) and wide usage in the field (Baker et al. 2014; Betzel et al. 2014a; Yeo et al. 2015). Third, the antidepressant treatment is quite heterogeneous. The type of medication and treatment duration differs across subjects, which does not allow us to directly examine the effects of specific drugs or drug regimens on MDD. Previous studies have also incorporated diversely medicated patients because the subjects are patients that limits the sample size (Heller et al. 2013; Li et al. 2013; Qin et al. 2015a, 2015b; Gong et al. 2018). Studies comparing different medications have found similar effects on brain functional connections (Lisiecka et al. 2011; McCabe and Mishor 2011). In addition, many studies using different medications have found a convergent normalization effect on elevated functional connectivity, which are in line with our results. This suggests that there might be a general normalization effect across antidepressant treatments (Lisiecka et al. 2011; McCabe and Mishor 2011; Li et al. 2013; Wang et al. 2015; Fu et al. 2015; An et al. 2017). Thus, we included all the types of drugs to see if there is a general modulation effect of treatments. In order to characterize drug effects on functional connectivity more rigorously, longitudinal design with more uniform patient groups and drug treatments are needed in the future. Conclusion In the current study, we examined the community structure of cortical functional networks in uMDD, mMDD, and HC using a multiscale community detection method. Although uMDD and mMDD patients showed similar reorganization of community structure in VA and DM systems, the FPC subsystems, in particular, reconfigured differently in the 2 patient groups. uMDD patients were characterized by intercommunity connectivity increased in the central system and 3 FPC subsystems implicating altered communication among multiple large-scale systems, which appears partially restored or overly compensated in mMDD patients. Together, these findings provide a global picture of community structure alterations of cortical functional networks in uMDD and mMDD patients and suggest potential medication effects in restoring disease-related network alterations. Funding This work was supported by grants from the National Basic Research (973) Program (2015CB351702), the Natural Science Foundation of China (NSFC 31271189 to P.X.; 81471740 to X.-N.Z.), Beijing Municipal Science and Tech Commission (Z161100002616023, Z171100000117012), the China - Netherlands CAS-NWO Programme (153111KYSB20160020), and the Major Project of National Social Science Foundation of China (14ZDB161). X.-N.Z. and O.S. are members of an international collaboration team supported by the NSFC Major Joint Fund for International Cooperation and Exchange (81220108014). O.S. is supported by the National Institutes of Health grant (R01AR009036). We would also like to thank the National R&D Infrastructure and Facility Development Program of China, “Fundamental Science Data Sharing Platform” (DKA2017−12−02−21) who funded the computational platform. Notes We acknowledge valuable comments by Andrea Avena-Koenigsberger, Department of Psychological and Brain Sciences, Indiana University, and Richard F. Betzel, Department of Bioengineering, University of Pennsylvania. We also gratefully acknowledge Dr. Enrico Glerean, Department of Neuroscience and Biomedical Engineering, Aalto University, Finland, for sharing code for the alluvial plot. All authors report no biomedical financial interests or potential conflicts of interest. Conflict of Interest: None declared. References Aizenstein HJ, Butters MA, Wu M, Mazurkewicz LM, Stenger VA, Gianaros PJ, Becker JT, Reynolds CF 3rd, Carter CS. 2009. Altered functioning of the executive control circuit in late-life depression: episodic and persistent phenomena. Am J Geriatr Psychiatry . 17: 30– 42. Google Scholar CrossRef Search ADS PubMed  An J, Wang L, Li K, Zeng Y, Su Y, Jin Z, Yu X, Si T. 2017. Differential effects of antidepressant treatment on long-range and short-range functional connectivity strength in patients with major depressive disorder. Sci Rep . 7: 10214. Google Scholar CrossRef Search ADS PubMed  Bair MJ, Robinson RL, Katon W, Kroenke K. 2003. Depression and pain comorbidity: a literature review. Arch Intern Med . 163: 2433– 2445. Google Scholar CrossRef Search ADS PubMed  Baker JT, Holmes AJ, Masters GA, Yeo BT, Krienen F, Buckner RL, Ongur D. 2014. Disruption of cortical association networks in schizophrenia and psychotic bipolar disorder. JAMA Psychiatry . 71: 109– 118. Google Scholar CrossRef Search ADS PubMed  Bansal R, Hellerstein DJ, Peterson BS. 2017. Evidence for neuroplastic compensation in the cerebral cortex of persons with depressive illness. Mol Psychiatry . (October 2016), 1–9. Beevers CG, Clasen PC, Enock PM, Schnyer DM. 2015. Attention bias modification for major depressive disorder: Effects on attention bias, resting state connectivity, and symptom change. J Abnorm Psychol . 124: 463– 475. Google Scholar CrossRef Search ADS PubMed  Benjamini Y, Hochberg Y. 1995. Controlling the false discovery rate – a practical and powerful approach to multiple testing. J Roy Stat Soc B Met . 57: 289– 300. Bertolero MA, Yeo BT, D’Esposito M. 2015. The modular and integrative functional architecture of the human brain. Proc Natl Acad Sci U S A . 112: E6798– E6807. Google Scholar CrossRef Search ADS PubMed  Betzel RF, Byrge L, He Y, Goni J, Zuo XN, Sporns O. 2014a. Changes in structural and functional connectivity among resting-state networks across the human lifespan. Neuroimage . 102( Pt 2): 345– 357. Google Scholar CrossRef Search ADS PubMed  Betzel RF, Griffa A, Avena-Koenigsberger A, GoÑI J, Thiran J-P, Hagmann P, Sporns O. 2014b. Multi-scale community organization of the human structural connectome and its relationship with resting-state functional connectivity. Netw Sci . 1: 353– 373. Google Scholar CrossRef Search ADS   Biswal B, Yetkin FZ, Haughton VM, Hyde JS. 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnet Reson Med . 34: 537– 541. Google Scholar CrossRef Search ADS   Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E. 2008. Fast unfolding of communities in large networks. J Stat Mech-Theory E . P10008. Bohr IJ, Kenny E, Blamire A, O’Brien JT, Thomas AJ, Richardson J, Kaiser M. 2012. Resting-state functional connectivity in late-life depression: higher global connectivity and more long distance connections. Front Psychiatry . 3: 116. Google Scholar PubMed  Buckner RL, Andrews-Hanna JR, Schacter DL. 2008. The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci . 1124: 1– 38. Google Scholar CrossRef Search ADS PubMed  Bullmore E, Sporns O. 2009. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci . 10: 186– 198. Google Scholar CrossRef Search ADS PubMed  Cary RP, Ray S, Grayson DS, Painter J, Carpenter S, Maron L, Sporns O, Stevens AA, Nigg JT, Fair DA. 2016. Network structure among brain systems in adult ADHD is uniquely modified by stimulant administration. Cereb Cortex . 27: 3970– 3979. Chen ZJ, He Y, Rosa-Neto P, Germann J, Evans AC. 2008. Revealing modular architecture of human brain structural networks by using cortical thickness from MRI. Cereb Cortex . 18: 2374– 2381. Google Scholar CrossRef Search ADS PubMed  Cheng W, Rolls ET, Qiu J, Liu W, Tang Y, Huang CC, Wang X, Zhang J, Lin W, Zheng L, et al.  . 2016. Medial reward and lateral non-reward orbitofrontal cortex circuits change in opposite directions in depression. Brain . 139: 3296– 3309. Google Scholar CrossRef Search ADS PubMed  Corbetta M, Shulman GL. 2002. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci . 3: 201– 215. Google Scholar CrossRef Search ADS PubMed  Cox RW. 1996. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res . 29: 162– 173. Google Scholar CrossRef Search ADS PubMed  Crossley NA, Mechelli A, Vertes PE, Winton-Brown TT, Patel AX, Ginestet CE, McGuire P, Bullmore ET. 2013. Cognitive relevance of the community structure of the human brain functional coactivation network. Proc Natl Acad Sci U S A . 110: 11583– 11588. Google Scholar CrossRef Search ADS PubMed  Dale AM, Fischl B, Sereno MI. 1999. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage . 9: 179– 194. Google Scholar CrossRef Search ADS PubMed  Desseilles M, Schwartz S, Dang-Vu TT, Sterpenich V, Ansseau M, Maquet P, Phillips C. 2011. Depression alters “top-down” visual attention: a dynamic causal modeling comparison between depressed and healthy subjects. Neuroimage . 54: 1662– 1668. Google Scholar CrossRef Search ADS PubMed  Dichter GS, Gibbs D, Smoski MJ. 2015. A systematic review of relations between resting-state functional-MRI and treatment response in major depressive disorder. J Affect Disord . 172: 8– 17. Google Scholar CrossRef Search ADS PubMed  Disner SG, Beevers CG, Haigh EA, Beck AT. 2011. Neural mechanisms of the cognitive model of depression. Nat Rev Neurosci . 12: 467– 477. Google Scholar CrossRef Search ADS PubMed  Drevets WC. 2001. Neuroimaging and neuropathological studies of depression: implications for the cognitive-emotional features of mood disorders. Curr Opin Neurobiol . 11: 240– 249. Google Scholar CrossRef Search ADS PubMed  Dutta A, McKie S, Deakin JF. 2014. Resting state networks in major depressive disorder. Psychiatry Res . 224: 139– 151. Google Scholar CrossRef Search ADS PubMed  Fischl B, Sereno MI, Dale AM. 1999. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage . 9: 195– 207. Google Scholar CrossRef Search ADS PubMed  Fischl B, Sereno MI, Tootell RB, Dale AM. 1999. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp . 8: 272– 284. Google Scholar CrossRef Search ADS PubMed  Fornito A, Zalesky A, Breakspear M. 2015. The connectomics of brain disorders. Nat Rev Neurosci . 16: 159– 172. Google Scholar CrossRef Search ADS PubMed  Fortunato S. 2010. Community detection in graphs. Phys Rep . 486: 75– 174. Google Scholar CrossRef Search ADS   Fortunato S, Barthelemy M. 2007. Resolution limit in community detection. Proc Natl Acad Sci U S A . 104: 36– 41. Google Scholar CrossRef Search ADS PubMed  Fortunato S, Hric D. 2016. Community detection in networks: a user guide. Phys Rep . 659: 1– 44. Google Scholar CrossRef Search ADS   Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. 2005. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A . 102: 9673– 9678. Google Scholar CrossRef Search ADS PubMed  Friston KJ, Williams S, Howard R, Frackowiak RSJ, Turner R. 1996. Movement-related effects in fMRI time-series. Magnet Reson Med . 35: 346– 355. Google Scholar CrossRef Search ADS   Fu CH, Costafreda SG, Sankar A, Adams TM, Rasenick MM, Liu P, Donati R, Maglanoc LA, Horton P, Marangell LB. 2015. Multimodal functional and structural neuroimaging investigation of major depressive disorder following treatment with duloxetine. BMC Psychiatry . 15: 82. Google Scholar CrossRef Search ADS PubMed  Genovese CR, Lazar NA, Nichols T. 2002. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage . 15: 870– 878. Google Scholar CrossRef Search ADS PubMed  Glerean E, Pan RK, Salmi J, Kujala R, Lahnakoski JM, Roine U, Nummenmaa L, Leppamaki S, Nieminen-von Wendt T, Tani P, et al.  . 2016. Reorganization of functionally connected brain subnetworks in high-functioning autism. Hum Brain Mapp . 37: 1066– 1079. Google Scholar CrossRef Search ADS PubMed  Gong Q, He Y. 2015. Depression, neuroimaging and connectomics: a selective overview. Biol Psychiatry . 77: 223– 235. Google Scholar CrossRef Search ADS PubMed  Gong L, Hou Z, Wang Z, He C, Yin Y, Yuan Y, Zhang H, Lv L, Zhang H, Xie C, et al.  . 2018. Disrupted topology of hippocampal connectivity is associated with short-term antidepressant response in major depressive disorder. J Affect Disord . 225: 539– 544. Google Scholar CrossRef Search ADS PubMed  Good BH, de Montjoye YA, Clauset A. 2010. Performance of modularity maximization in practical contexts. Phys Rev E Stat Nonlin Soft Matter Phys . 81: 046106. Google Scholar CrossRef Search ADS PubMed  Goodale MA, Milner AD. 1992. Separate visual pathways for perception and action. Trends Neurosci . 15: 20– 25. Google Scholar CrossRef Search ADS PubMed  Graham J, Salimi-Khorshidi G, Hagan C, Walsh N, Goodyer I, Lennox B, Suckling J. 2013. Meta-analytic evidence for neuroimaging models of depression: state or trait? J Affect Disord . 151: 423– 431. Google Scholar CrossRef Search ADS PubMed  Greicius MD, Krasnow B, Reiss AL, Menon V. 2003. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A . 100: 253– 258. Google Scholar CrossRef Search ADS PubMed  Greve DN, Fischl B. 2009. Accurate and robust brain image alignment using boundary-based registration. Neuroimage . 48: 63– 72. Google Scholar CrossRef Search ADS PubMed  Gudayol-Ferre E, Pero-Cebollero M, Gonzalez-Garrido AA, Guardia-Olmos J. 2015. Changes in brain connectivity related to the treatment of depression measured through fMRI: a systematic review. Front Hum Neurosci . 9:582. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, Sporns O. 2008. Mapping the structural core of human cerebral cortex. PLoS Biol . 6: e159. Google Scholar CrossRef Search ADS PubMed  He Y, Wang J, Wang L, Chen ZJ, Yan C, Yang H, Tang H, Zhu C, Gong Q, Zang Y, et al.  . 2009. Uncovering intrinsic modular organization of spontaneous brain activity in humans. PLoS One . 4: e5226. Google Scholar CrossRef Search ADS PubMed  Heller AS. 2016. Cortical-subcortical interactions in depression: from animal models to human psychopathology. Front Syst Neurosci . 10: 20. Google Scholar CrossRef Search ADS PubMed  Heller AS, Johnstone T, Light SN, Peterson MJ, Kolden GG, Kalin NH, Davidson RJ. 2013. Relationships between changes in sustained fronto-striatal connectivity and positive affect in major depression resulting from antidepressant treatment. Am J Psychiat . 170: 197– 206. Google Scholar CrossRef Search ADS PubMed  Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. 2012. FSL. Neuroimage . 62: 782– 790. Google Scholar CrossRef Search ADS PubMed  Joormann J, Gotlib IH. 2010. Emotion regulation in depression: relation to cognitive inhibition. Cogn Emot . 24: 281– 298. Google Scholar CrossRef Search ADS PubMed  Joormann J, Quinn ME. 2014. Cognitive processes and emotion regulation in depression. Depress Anxiety . 31: 308– 315. Google Scholar CrossRef Search ADS PubMed  Jutla IS, Jeub LG, Mucha PJ 2011. A generalized louvain method for community detection implemented in matlab. URL http://netwiki/amath unc edu/GenLouvain. Kaiser RH, Andrews-Hanna JR, Wager TD, Pizzagalli DA. 2015. Large-scale network dysfunction in major depressive disorder: a meta-analysis of resting-state functional connectivity. JAMA Psychiatry . 72: 603– 611. Google Scholar CrossRef Search ADS PubMed  Korgaonkar MS, Fornito A, Williams LM, Grieve SM. 2014. Abnormal structural networks characterize major depressive disorder: a connectome analysis. Biol Psychiatry . 76: 567– 574. Google Scholar CrossRef Search ADS PubMed  Lancichinetti A, Fortunato S. 2012. Consensus clustering in complex networks. Sci Rep . 2: 336. Google Scholar CrossRef Search ADS PubMed  Lerman-Sinkoff DB, Barch DM. 2016. Network community structure alterations in adult schizophrenia: identification and localization of alterations. Neuroimage Clin . 10: 96– 106. Google Scholar CrossRef Search ADS PubMed  Li B, Liu L, Friston KJ, Shen H, Wang L, Zeng LL, Hu D. 2013. A treatment-resistant default mode subnetwork in major depression. Biol Psychiatry . 74: 48– 54. Google Scholar CrossRef Search ADS PubMed  Lisiecka D, Meisenzahl E, Scheuerecker J, Schoepf V, Whitty P, Chaney A, Moeller HJ, Wiesmann M, Frodl T. 2011. Neural correlates of treatment outcome in major depression. Int J Neuropsychopharmacol . 14: 521– 534. Google Scholar CrossRef Search ADS PubMed  Liston C, Chen AC, Zebley BD, Drysdale AT, Gordon R, Leuchter B, Voss HU, Casey BJ, Etkin A, Dubin MJ. 2014. Default mode network mechanisms of transcranial magnetic stimulation in depression. Biol Psychiatry . 76: 517– 526. Google Scholar CrossRef Search ADS PubMed  Lord A, Horn D, Breakspear M, Walter M. 2012. Changes in community structure of resting state functional connectivity in unipolar depression. PLoS One . 7: e41282. Google Scholar CrossRef Search ADS PubMed  Lund TE, Madsen KH, Sidaros K, Luo WL, Nichols TE. 2006. Non-white noise in fMRI: does modelling have an impact? Neuroimage . 29: 54– 66. Google Scholar CrossRef Search ADS PubMed  MacMahon M, Garlaschelli D. 2015. Community detection for correlation matrices. Phys Rev . 5:21006. Manoliu A, Meng C, Brandl F, Doll A, Tahmasian M, Scherr M, Schwerthoffer D, Zimmer C, Forstl H, Bauml J, et al.  . 2014. Insular dysfunction within the salience network is associated with severity of symptoms and aberrant inter-network connectivity in major depressive disorder. Frontiers in Human Neuroscience . 7:930. McCabe C, Mishor Z. 2011. Antidepressant medications reduce subcortical-cortical resting-state functional connectivity in healthy volunteers. Neuroimage . 57: 1317– 1323. Google Scholar CrossRef Search ADS PubMed  McCabe C, Mishor Z, Filippini N, Cowen PJ, Taylor MJ, Harmer CJ. 2011. SSRI administration reduces resting state functional connectivity in dorso-medial prefrontal cortex. Mol Psychiatry . 16: 592– 594. Google Scholar CrossRef Search ADS PubMed  Meila M. 2003. Comparing clusterings by the variation of information. Learning Theory and Kernel Machines . 2777: 173– 187. Google Scholar CrossRef Search ADS   Menon V. 2011. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci . 15: 483– 506. Google Scholar CrossRef Search ADS PubMed  Meunier D, Lambiotte R, Fornito A, Ersche KD, Bullmore ET. 2009. Hierarchical modularity in human brain functional networks. Front Neuroinform . 3: 37. Google Scholar CrossRef Search ADS PubMed  Misic B, Betzel RF, de Reus MA, van den Heuvel MP, Berman MG, McIntosh AR, Sporns O. 2016. Network-level structure-function relationships in human neocortex. Cereb Cortex . 26: 3285– 3296. Google Scholar CrossRef Search ADS PubMed  Mulders PC, van Eijndhoven PF, Schene AH, Beckmann CF, Tendolkar I. 2015. Resting-state functional connectivity in major depressive disorder: a review. Neurosci Biobehav Rev . 56: 330– 344. Google Scholar CrossRef Search ADS PubMed  Newman ME, Girvan M. 2004. Finding and evaluating community structure in networks. Phys Rev E Stat Nonlin Soft Matter Phys . 69: 026113. Google Scholar CrossRef Search ADS PubMed  Peckham AD, McHugh RK, Otto MW. 2010. A meta-analysis of the magnitude of biased attention in depression. Depress Anxiety . 27: 1135– 1142. Google Scholar CrossRef Search ADS PubMed  Peng D, Shi F, Shen T, Peng Z, Zhang C, Liu X, Qiu M, Liu J, Jiang K, Fang Y, et al.  . 2014. Altered brain network modules induce helplessness in major depressive disorder. J Affect Disord . 168: 21– 29. Google Scholar CrossRef Search ADS PubMed  Perrin JS, Merz S, Bennett DM, Currie J, Steele DJ, Reid IC, Schwarzbauer C. 2012. Electroconvulsive therapy reduces frontal cortical connectivity in severe depressive disorder. Proc Natl Acad Sci U S A . 109: 5464– 5468. Google Scholar CrossRef Search ADS PubMed  Petersen SE, Sporns O. 2015. Brain networks and cognitive architectures. Neuron . 88: 207– 219. Google Scholar CrossRef Search ADS PubMed  Pizzagalli DA. 2011. Frontocingulate dysfunction in depression: toward biomarkers of treatment response. Neuropsychopharmacology . 36: 183– 206. Google Scholar CrossRef Search ADS PubMed  Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, Vogel AC, Laumann TO, Miezin FM, Schlaggar BL, et al.  . 2011. Functional network organization of the human brain. Neuron . 72: 665– 678. Google Scholar CrossRef Search ADS PubMed  Power JD, Mitra A, Laumann TO, Snyder AZ, Schlaggar BL, Petersen SE. 2014. Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage . 84: 320– 341. Google Scholar CrossRef Search ADS PubMed  Price JL, Drevets WC. 2010. Neurocircuitry of mood disorders. Neuropsychopharmacology . 35: 192– 216. Google Scholar CrossRef Search ADS PubMed  Qin J, Shen H, Zeng LL, Jiang WX, Liu L, Hu DW. 2015a. Predicting clinical responses in major depression using intrinsic functional connectivity. Neuroreport . 26: 675– 680. Google Scholar CrossRef Search ADS PubMed  Qin J, Wei M, Liu H, Chen J, Yan R, Yao Z, Lu Q. 2015b. Altered anatomical patterns of depression in relation to antidepressant treatment: evidence from a pattern recognition analysis on the topological organization of brain networks. J Affect Disord . 180: 129– 137. Google Scholar CrossRef Search ADS PubMed  Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. 2001. A default mode of brain function. Proc Natl Acad Sci U S A . 98: 676– 682. Google Scholar CrossRef Search ADS PubMed  Rayner G, Jackson G, Wilson S. 2016. Cognition-related brain networks underpin the symptoms of unipolar depression: evidence from a systematic review. Neurosci Biobehav Rev . 61: 53– 65. Google Scholar CrossRef Search ADS PubMed  Reichardt J, Bornholdt S. 2006. Statistical mechanics of community detection. Phys Rev E Stat Nonlin Soft Matter Phys . 74: 016110. Google Scholar CrossRef Search ADS PubMed  Rubinov M, Sporns O. 2010. Complex network measures of brain connectivity: uses and interpretations. Neuroimage . 52: 1059– 1069. Google Scholar CrossRef Search ADS PubMed  Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, Reiss AL, Greicius MD. 2007. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci . 27: 2349– 2356. Google Scholar CrossRef Search ADS PubMed  Sheline YI, Price JL, Yan Z, Mintun MA. 2010. Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proc Natl Acad Sci U S A . 107: 11020– 11025. Google Scholar CrossRef Search ADS PubMed  Shen Y, Yao J, Jiang X, Zhang L, Xu L, Feng R, Cai L, Liu J, Wang J, Chen W. 2015. Sub-hubs of baseline functional brain networks are related to early improvement following two-week pharmacological therapy for major depressive disorder. Hum Brain Mapp . 36: 2915– 2927. Google Scholar CrossRef Search ADS PubMed  Sporns O, Betzel RF. 2016. Modular brain networks. Annu Rev Psychol . 67: 613– 640. Google Scholar CrossRef Search ADS PubMed  Sporns O, Tononi G, Kotter R. 2005. The human connectome: a structural description of the human brain. PLoS Comput Biol . 1: e42. Google Scholar CrossRef Search ADS PubMed  Stange JP, Bessette KL, Jenkins LM, Peters AT, Feldhaus C, Crane NA, Ajilore O, Jacobs RH, Watkins ER, Langenecker SA. 2017. Attenuated intrinsic connectivity within cognitive control network among individuals with remitted depression: temporal stability and association with negative cognitive styles. Hum Brain Mapp . 38: 2939– 2954. Google Scholar CrossRef Search ADS PubMed  Swanson LW, Sporns O, Hahn JD. 2016. Network architecture of the cerebral nuclei (basal ganglia) association and commissural connectome. Proc Natl Acad Sci U S A . 113: E5972– E5981. Google Scholar CrossRef Search ADS PubMed  Tadayonnejad R, Yang S, Kumar A, Ajilore O. 2015. Clinical, cognitive, and functional connectivity correlations of resting-state intrinsic brain activity alterations in unmedicated depression. J Affect Disord . 172: 241– 250. Google Scholar CrossRef Search ADS PubMed  Tao H, Guo S, Ge T, Kendrick KM, Xue Z, Liu Z, Feng J. 2013. Depression uncouples brain hate circuit. Mol Psychiatry . 18: 101– 111. Google Scholar CrossRef Search ADS PubMed  Tozzi L, Doolin K, Farrel C, Joseph S, O’Keane V, Frodl T. 2017. Functional magnetic resonance imaging correlates of emotion recognition and voluntary attentional regulation in depression: a generalized psycho-physiological interaction study. J Affect Disord . 208: 535– 544. Google Scholar CrossRef Search ADS PubMed  Traag VA, Van Dooren P, Nesterov Y. 2011. Narrow scope for resolution-limit-free community detection. Phys Rev E Stat Nonlin Soft Matter Phys . 84: 016114. Google Scholar CrossRef Search ADS PubMed  van de Ven V, Wingen M, Kuypers KP, Ramaekers JG, Formisano E. 2013. Escitalopram decreases cross-regional functional connectivity within the default-mode network. PLoS One . 8: e68355. Google Scholar CrossRef Search ADS PubMed  van den Heuvel MP, Hulshoff Pol HE. 2010. Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol . 20: 519– 534. Google Scholar CrossRef Search ADS PubMed  Wang L, Xia M, Li K, Zeng Y, Su Y, Dai W, Zhang Q, Jin Z, Mitchell PB, Yu X, et al.  . 2015. The effects of antidepressant treatment on resting-state functional brain networks in patients with major depressive disorder. Hum Brain Mapp . 36: 768– 778. Google Scholar CrossRef Search ADS PubMed  Xing XX, Zhou YL, Adelstein JS, Zuo XN. 2011. PDE-based spatial smoothing: a practical demonstration of impacts on MRI brain extraction, tissue segmentation and registration. Magn Reson Imaging . 29: 731– 738. Google Scholar CrossRef Search ADS PubMed  Xu T, Yang Z, Jiang LL, Xing XX, Zuo XN. 2015. A connectome computation system for discovery science of brain. Sci Bull . 60: 86– 95. Google Scholar CrossRef Search ADS   Yan CG, Cheung B, Kelly C, Colcombe S, Craddock RC, Di Martino A, Li Q, Zuo XN, Castellanos FX, Milham MP. 2013. A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage . 76: 183– 201. Google Scholar CrossRef Search ADS PubMed  Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, Roffman JL, Smoller JW, Zollei L, Polimeni JR, et al.  . 2011. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol . 106: 1125– 1165. Google Scholar CrossRef Search ADS PubMed  Yeo BT, Tandi J, Chee MW. 2015. Functional connectivity during rested wakefulness predicts vulnerability to sleep deprivation. Neuroimage . 111: 147– 158. Google Scholar CrossRef Search ADS PubMed  Zeng LL, Shen H, Liu L, Wang L, Li B, Fang P, Zhou Z, Li Y, Hu D. 2012. Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain . 135: 1498– 1507. Google Scholar CrossRef Search ADS PubMed  Zhou Y, Yu C, Zheng H, Liu Y, Song M, Qin W, Li K, Jiang T. 2010. Increased neural resources recruitment in the intrinsic organization in major depression. J Affect Disord . 121: 220– 230. Google Scholar CrossRef Search ADS PubMed  Zhu X, Zhu Q, Shen H, Liao W, Yuan F. 2017. Rumination and default mode network subsystems connectivity in first-episode, drug-naive young patients with major depressive disorder. Sci Rep . 7: 43105. Google Scholar CrossRef Search ADS PubMed  Zuo XN, Xing XX. 2011. Effects of non-local diffusion on structural MRI preprocessing and default network mapping: statistical comparisons with isotropic/anisotropic diffusion. PLoS One . 6: e26703. Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2017. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

Journal

Cerebral CortexOxford University Press

Published: Apr 1, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve Freelancer

DeepDyve Pro

Price
FREE
$49/month

$360/year
Save searches from
Google Scholar,
PubMed
Create lists to
organize your research
Export lists, citations
Read DeepDyve articles
Abstract access only
Unlimited access to over
18 million full-text articles
Print
20 pages/month
PDF Discount
20% off