Magnetic Resonance in Medicine 72:1397–1407 (2014)

Abnormal Brain Anatomical Topological Organization of the Cognitive-Emotional and the Frontoparietal Circuitry in Major Depressive Disorder Jiaolong Qin,1 Maobin Wei,1 Haiyan Liu,2 Rui Yan,2 Guoping Luo,1 Zhijian Yao,2,3 and Qing Lu1* Purpose: Despite the increasing understanding of major depressive disorder (MDD) using neuroimaging techniques, the topological organization of anatomical networks underlying MDD remains unclear. Methods: The topological organization of brain anatomical networks was explored using complex network approaches. Diffusion tensor image data of 29 MDD patients and 30 healthy controls were collected. Network metrics such as strength, efficiency, and centrality were computed after the construction of brain anatomical networks. Between-group differences and correlations with clinical measurements were further explored. Results: Compared with the healthy controls, MDD patients exhibited widely damaged information interactions in both cognitive-emotional circuitry and frontoparietal circuitry. Moreover, the centralities in the right prefrontal cortex involving the right middle frontal gyrus and the right gyrus rectus were negatively correlated with the duration of disease. Additionally, the centrality in the right inferior frontal gyrus (triangular part) and the efficiency in the right superior frontal gyrus (orbital part) were both positively related to the depression severity. Conclusion: These findings suggest that altered connectivity involved in affective and cognitive processing procedures might contribute to the pathogenesis of MDD. Magn Reson C 2013 Wiley Periodicals, Inc. Med 72:1397–1407, 2014. V Key words: connectome; diffusion tensor imaging; graph theory; whole brain anatomical network

INTRODUCTION Major depressive disorder (MDD) is a common mental disorder, which is characterized by persistent depressed mood, anxiety and dysphoria, psychomotor changes, alterations of motivation and social behavior, and sleep 1 Research Centre for Learning Science, Southeast University, Nanjing, China. 2 Academic Department of Psychiatry, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China. 3 Medical College of Nanjing University, Nanjing, China. Grant sponsor: The National Natural Science Foundation of China; Grant numbers: 81371552; 61372032; Grant sponsor: Jiangsu Clinical Medicine Technology Foundation; Grant number: BL2012052; Grant sponsor: Jiangsu Natural Science Foundation; Grant number: BK2012740; Grant sponsor: University graduates’ Scientific Research Innovation Project of Jiangsu; Grant number: CXLX13_116. *Correspondence to: Qing Lu, Ph.D., Research Centre for Learning Science, Southeast University, Nanjing, 210096, China. E-mail: [email protected]

Additional Supporting information may be found in the online version of this article. Received 31 July 2013; revised 15 October 2013; accepted 16 October 2013 DOI 10.1002/mrm.25036 Published online 22 November 2013 in Wiley Online Library (wileyonlinelibrary.com). C 2013 Wiley Periodicals, Inc. V

abnormalities (1). Neuroimaging studies had shown widely distributed functional and structural abnormalities in depressed patients (2–4). Based on these studies, MDD has been recognized as a disconnection problem (5), which meant such a disease not only affected the discrete brain regions in anatomy but also the integrated circuits in function in the patients. In this research, a powerful framework of the network for characterizing topological properties of brain networks was used (6). Many studies had used such frameworks to investigate the changes in patients with MDD (7–15). Specifically, several studies on functional brain networks found lesions in the global topological properties, such as small-worldness properties in MDD patients (15,16). Interestingly, in addition to the abnormalities of global topological properties, the abnormalities in the brain hate circuit were also reported (7) and the network degree of amygdala was positively correlated with the duration of depression in adolescents (17). It was also suggested that the network metrics were the useful features for construction of the diagnostic model for MDD (11). In parallel, a second line of evidence from the anatomical brain networks was revealed. Several nodes attached to default model network (DMN) exhibited significant differences between groups, suggesting a higher degree of ruminative self-reflections in depression (13). In addition, Fang et al (14) used a machine learning method and found that the strengths of all the most discriminating connections within the cortical-limbic network changed in patients with MDD. Nevertheless, different from the study on functional connectivity, anatomical connectivity was seldom used to investigate abnormal brain networks in patients with MDD. It remains unclear as to whether the topological organization of anatomical brain networks is abnormal in patients with MDD. In this study, we used diffusion tensor imaging (DTI) to investigate the topological infrastructure of brain anatomical networks in MDD patients. DTI is a noninvasive imaging technique for mapping the diffusion process of water molecules, helping to explore the development of the white matter (18). Many studies applied DTI to investigate MDD-related changes in white matter (19,20) and mainly focused on fractional anisotropy (FA) metric, which can provide more detailed information on fiber tracts. Hence, FA metric has been selected as the weights of a network to involve additional information on the strength of interactions. Recently, research studies had used the FAweighted network approach to examine the topological organization in diseases (21). To explore changes on anatomical networks in MDD patients, we collected DTI data from 29 MDD patients and 30 healthy controls. Their brain

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Table 1 Demographic and Clinical Characteristics of the Subjectsa Variables Sample size Age (years) Gender(male/female) Handedness(R/L) Number of previous episodes Scare of 17-item HDRS Duration of illness(months)

DP

NC

P value

29 22–53 (38.97 6 9.95) 14/15 29/0 1.59 6 0.69 30.36 6 5.61 4.28 6 5.66

30 23–54 (35.57 6 11.73) 12/18 30/0 – – –

– 0.236b 0.374c – – – –

a

Data were presented as the range of minimum-maximum (mean 6 SD). DP ¼ depressive patients; NC ¼ healthy controls; L, left; R, right. b The P value was obtained by two-sample two-tailed t test. c The P value was obtained by two-tailed Pearson chi-square test. The P value more than 0.05 indicated no statistically significant difference between the two groups.

anatomical networks were analyzed using complex network approaches. Between-group differences and correlations with clinical variables were further discussed. METHODS Subjects Twenty-nine patients with unipolar MDD were recruited from in-patient facilities. Eligibility screening procedures included the Structured Clinical Interview for the DSMIV (SCID), the 17-item Hamilton depression rating scale (HDRS), and the Brief Psychiatric Rating Scale (BPRS). Patients had a minimum score of 20 rated with the 17item HDRS on the day of scanning. Furthermore, MDD patients with other psychiatric illness or a history of electroconvulsive therapy were excluded. Thirty age-, gender-, and handedness-matched healthy subjects were recruited from a roll of persons living in the same places or nearby as the depressed patients. Moreover, healthy subjects with a family history of major psychiatric or neurological illness in their first-degree relatives were excluded. All subjects met eligibility criteria to undergo the MRI scan. After a complete description of the study to all subjects, we obtained written informed consents. This study was approved by the Research Ethics Review Board. Table 1 summarized the demographic and clinical characteristics of these subjects. Image Acquisition Imaging data were acquired using a GE Signa 1.5 Tesla (T) MRI scanner. T1-weighted axial images: repetition time/ echo time (TR/TE) ¼ 500/14 ms, thickness/gap ¼ 1.0/0 mm, flip angle ¼ 15 , inversion time ¼ 400 ms, matrix ¼ 256  128, field of view (FOV) ¼ 240  240 mm2, in-plane resolution ¼ 256  192. DTI scans: Diffusion was measured along 25 noncollinear directions (b value ¼ 1000 s/mm2), and an additional image without diffusion weighting (i.e., b ¼ 0 s/ mm2), TR/TE ¼ 10,000 ms/81.2 ms, FOV ¼ 240 mm  240 mm, Matrix ¼ 128  128, NEX ¼2, slice thickness ¼ 4 mm without gap. The DTI acquisition time was 9 min. A total of 780 files were achieved for each subject. Data Preprocessing DTI preprocessing was computed using FMRIB’s Diffusion Toolbox (http://www.fmrib.ox.ac.uk/fsl/fdt/

index.html) and included the following steps: eddy current and motion artifact correction of the DTI data, estimation of the diffusion tensor, and calculation of the FA. Briefly, the eddy current distortions and motion artifacts in the DTI data were corrected for by applying a rigidbody transformation of each diffusion-weighted image to the b0 image. Subsequently, the diffusion tensor matrix was calculated according to the Stejskal and Tanner equation. Three eigenvalues and eigenvectors were obtained by diagonalization of the tensor matrix, and then, FA map was calculated. It should be noted that a small number of baseline images (i.e., b ¼ 0 s/mm2) may affect the accuracy of calculating the ADC values and the tensor D, although it is theoretically reasonable. After that, each b0 image was registered to the T1-weighted image, and then registered to MNI-152 space. The transformation matrix from diffusion space to MNI space was calculated by the transformation matrices created in the above two register processing steps and was stored for later use. Network Construction Nodes and edges are the two basic elements of a network. In the current study, each network’ nodes and edges were defined as follows. All the processing pipelines were shown in Figure 1. Network node definition We defined 396 nodes based on the automated anatomical labeling (AAL) template with 82 brain regions (Table 2) using the parcellation method proposed by Zalesky et al (22). Note that, in our case, the cerebellum and subcortical (i.e., caudate, putamen, pallidum, and thalamus) were removed. The parcellation algorithm randomly parcellated gray matter into several contiguous regions-of-interest (ROIs), which would serve as distinct nodes and minimized the variation in nodal volume (for more details, see Zalesky et al) (22). The partitioned template then remained in the MNI space for the subsequent procedures. White matter tractography Diffusion toolkit (http://www.trackvis.org) toolbox was used for fiber tract reconstruction. Specifically, each streamline was propagated using the FACT algorithm (23), and the propagation was terminated if either a minimum angle threshold ¼ 50 was violated or a voxel was

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FIG. 1. The construction of the anatomical network model. Along with the flow, the steps began with a fiber tracking, then followed a nodal mask, an adjacency matrix for network model, and last, the visualization of the anatomical network model.

encountered with FA below 0.2. In addition, to enable examination of these tracts on a group level, individual tracts were normalized to a standard space using the transformation matrix stored in the DTI preprocessing stage. Network edge definition The connection of two nodes was then determined by a fiber that touched both nodes i and j. Moreover, we selected a threshold value for the fiber tracks. Two

regions were considered structurally connected if at least two fibers with the fiber length greater than 5 mm were found. Such a threshold selection reduced the risk of false-positive connections due to noise or the limitations in the deterministic tractography and simultaneously ensured the size of the largest connected component in the anatomical networks across all the participants. After defining the network edges, the weighted network analyses were performed. For the weighted networks, we defined the mean FA and fiber number (FN) values of

Table 2 Cortical Regions of Interest Defined in the Studya Index

a

Regions

1,2 3,4 5,6 7,8 9, 10 11,12 13,14 15,16 17,18 19,20

Precental gyrus Superior frontal gyrus, dorsolateral Superior frontal gyrus, orbital part Middle frontal gyrus Middle frontal gyrus, orbital part Inferior frontal gyrus, opercular part Inferior frontal gyrus, triangular part Inferior frontal gyrus, orbital part Rolandic operculum Supplementary motor area

21,22 23,24 25,26 27,28 29,30 31,32 33,34 35,36 37,38 39,40 41,42

Olfactory cortex Superior frontal gyrus, medial Superior frontal gyrus, medial orbital Gyrus rectus Insula Anterior cingulate and paracingulate gyri Median cingulate and paracingulate gyri Posterior cingulate gyrus Hippocampus Parahippocampal gyrus Amygdala

Abbreviation

Index

Regions

Abbreviation

PreCG SFGdor ORBsup MFG ORBmid IFGoperc IFGtriang ORBinf ROL SMA

43,44 45,46 47,48 49,50 51,52 53,54 55,56 57,58 59,60 61,62

CAL CUN LING SOG MOG IOG FFG PoCG SPG IPL

OLF SFGmed ORBsupmed REC INS ACG DCG PCG HIP PHG AMYG

63,64 65,66 67,68 69,70 79,80 81,82 83,84 85,86 87,88 89,90

Calcarine fissure and surrounding cortex cuneus Lingual gyrus Superior occipital gyrus Middle occipital gyrus Inferior occipital gyrus Fusiform gyrus Postcentral gyrus Superior parietal gyrus Inferior parietal, but supramarginal and angular gyri Supramarginal gyrus Angular gyrus Precuneus Paracentral lobule Heschl gyrus Superior temporal gyrus Temporal pole: superior temporal gyrus Middle temporal gyrus Temporal pole: middle temporal gyrus Inferior temporal gyrus

The regions were listed based on the template obtained from the AAL atlas.

SMG ANG PCUN PCL HES STG TPOsup MTG TPOmid ITG

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the connected streamlines between two regions as the weights of the network edges. Briefly, each fiber’s FA value was calculated by the average of FA values of all voxels on each fiber track, and each edge’s weight (namely FA value) was calculated by averaging the FA values of all fibers. In our case, we also constructed the FN-weighted anatomical brain networks using the FN threshold values ranged from 2 to 12 with an interval of 2. (For details, see the FN-weighted network construction in Supp. Materials, which are available online.)

Eglob ðGÞ ¼

1X ei n i2N

[4]

The local efficiency of G was computed as: 1X Eglob ðGi Þ n i2 G

Eloc ðGÞ ¼

[5]

For all connectivity matrix G, the weights of G were normalized to the corresponding maximum of G. Then, we calculated overall level network characteristics (including the network strength S, the network clustering coefficient C and the small-world efficiency) and nodal characteristics (including the nodal strength si, the local clustering coefficient ci, the nodal global efficiency ei, the nodal betweenness centrality bi, and the nodal eigenvector centrality eci) for the normalized weighted brain anatomical networks using the brain connectivity toolbox (24). The graph organizational measures were computed as follows.

where Gi referred to the subgraph composed of the nearest neighbors of node i. To explore the small-world efficiency, the Eglob and Eloc of the brain networks were compared with those of random networks. Here, we generated 100 matched random networks, which had the same number of nodes and edges, and degree distribution as the real networks (25). In particular, we retained the weight of each edge during the rewiring procedure such that the weight distribution of the network was preserved. A real network would be considered smallworld if Eloc(G)/Eloc (random) > 1 and Eglob(G)/Eglob (random)  1, where Eloc (random) and Eglob (random) were the mean Eglob and the mean Eloc of 100 matched random networks. Namely, a small-world network has not only the higher local efficiency but also the approximately equivalent global efficiency compared with the random networks.

Network strength

Local clustering coefficient

In a weighted graph G with n nodes, the si for node i was defined as the sum of the weights of direct connections of node i: X si ¼ Wij [1]

In a weighted graph G with n nodes, ci of a node i measured the density of connections between the node i’ s neighbors. Formally:

Network Analysis

ci ¼

j2N

where N was the set of all nodes in the graph, and Wij was the weight between node i and node j in the graph. Obviously, the S of the graph G was computed as the average of si for all nodes inside set N. Formally: S¼

1X si n i2N

[2]



Small-world efficiency

[6]

where ti referred to the actual number of edges in Gi (the subgraph of neighbors of i) and ki meant the number of nodes in Gi. ki ðk2i -1Þ expressed the maximum possible number of edges in Gi. In turn, network clustering coefficient C was calculated as the average of ci among all nodes in the graph, defined as:

where n was the number of nodes.

1X ci n i2N

[7]

where N was the set of all nodes in the graph.

In this study, we used a network efficiency measure to quantify the small-world behavior of the anatomical brain networks in healthy controls and MDD patients, respectively. In a weighted graph G with n nodes, the nodal global efficiency ei of a node i was computed as the average of the inverse of the distances of all nodes that directly connected node i except the node itself. Formally: ei ¼

2ti ki ðki  1Þ

X 1 1 n  1 j2N;j6¼i dij

[3]

where dij was the shortest path length between node i and j in G, and N was the set of all nodes in the graph. This measure quantified the importance of the nodes for communication within the network. In turn, the global efficiency of a network G with n nodes was calculated as:

Nodal centrality and hubs We calculated the two different metrics of nodal centrality including betweenness centrality and eigenvector centrality as follows. Betweenness centrality. In a weighted graph G with n nodes, the bi of a node i represents a central node that played pivotal role in control over the information transfer within the graph, which was calculated as the fraction of shortest paths that passed through node i between other nodes. Formally: bi ¼

X sphj ðiÞ 1 ðn  1Þðn  2Þ h;j2N;h6¼i;j;i6¼j sphj

[8]

where sphj (i) was the number of shortest path between

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node h and node j passing through node i, and N was the set of all nodes in the graph. Hubs. As for identification of hubs in the anatomical brain network, we calculated the normalized betweenness as hi = bi/hbii, where hbii was the mean nodal betweenness of the average FA-weighted anatomical network across all healthy controls. A region was identified as a hub, depending on whether its hi value was 1.5 times greater than the mean betweenness of the average FA-network. Eigenvector centrality. Compared with bi, eci takes the connections of different lengths and dispersion into account, rather than only considering the shortest paths between the nodes. In a weighted graph, the eci of a node i was defined according to the formula below (26), eci ¼

n 1X wij ej l j¼1

[9]

where k denoted the largest eigenvalue of the graph and e denoted the corresponding principal eigenvector of the graph. In addition, because only the largest eigenvalue and corresponding eigenvector should be obtained, a power iteration algorithm was implemented to improve computational efficiency as recommended by (27) .

Statistical Analysis Si correction for nodal metrics A nodal degree has a strong influence on other graph characteristics. In a weighted graph, the natural generalization of the degree ki of a node i is the node strength si. Therefore, an additional analysis, called si correction for ei, ci, and eci was performed. The procedure of si correction for nodal characteristics has been previously described in (28). Taking ei for example, the interaction between si and ei within each group (MDD patients and healthy controls) was regressed separately. The group comparison was then calculated by using nonparametric permutation test, which would be introduced in the next section (between-group differences). After si correction, the group differences in corrected ei could be interpreted as the group differences of interaction between si and ei. Between-group differences From the definition of overall level network characteristic (i.e., S), these overall level network characteristics are an averaged value of nodal level network characteristic (i.e., si) and they are macro-descriptions of network. Many details of between-group difference might be omitted by average. Therefore, we should further probe the between-group difference in nodal level. To determine the statistical significance of group differences in network metrics, a nonparametric permutation method was implemented. In particular, for each network characteristic (S, C, Eglob, and Eloc) or each nodal characteristic (si, ci, ei, and eci), we first calculated the actual observed value O using the following formula:

O ¼ hMDDcharacteristic i  hControlcharacteristic i

[10]

where hMDDcharacteristici and hControlcharacteristici meant the average nodal/network characteristic values of depressions and of healthy controls respectively. To test the null hypothesis that the observed group difference could happen by chance, we then randomly reallocated each kind of the characteristic values into either of the two groups. Subsequently, we recomputed the observed value onew by using formula 10 between the randomized groups. This randomization procedure was repeated 10,000 times, and the one-tailed P value was the ratio between the occurrence times (onew > O/onew < O) and 10,000. A significance threshold of P ¼ 0.05 (uncorrected) was used for testing the overall level network characteristics. To address the problem of multiple comparisons in the nodal characteristics, a false discovery rate (FDR) correction was performed with the threshold of P ¼ 0.05. Testing Model Network topological properties are strongly related to each other. Thus, we explored whether the groupbetween differences in eci, ci, and ei were caused by changing si or whether they reflected anatomical organization differences. In this case, a testing model was implemented on the FA-weighted network of both groups. Briefly, the testing model was established as follows: (i) We randomly selected 29 of 30 graphs of healthy controls for further constructing the simulated depression graph, and then randomly paired up the 29 selected healthy graphs with 29 depression graphs. (ii) A simulated depression graph was constructed in the following way. The selected healthy graphs’ nodal weights that were found to have a significant group difference in si (P  0.05, uncorrected) between healthy controls and depressions were replaced with the corresponding nodal weights of the paired depression graphs. (iii) We calculated the network characteristics including eci, ci, and ei, for the simulated depression graphs and also applied the si correction for the three network characteristics. (iv) The comparison between the simulated depression graphs and healthy controls’ graphs in the network characteristics was conducted using independent two-sample t test analysis with the uncorrected P ¼ 0.05. All the above four steps were repeated 550 times (more than 0.6 times of the maximum repeated times, 870 without duplicate) (28). When comparing the characteristics (eci, ci, and ei) between the simulated depression patients and healthy controls, if the comparison resulted in similar group differences with those between the actual depression patients and healthy controls, this would suggest that part of the MDD-related eci/ci/ei differences were the result of disorganization of connections. In other words, those MDD-related eci/ci/ei differences may be indirectly induced by changing si. Clinical Effects The correlation between clinical scores (including the sum score of the 17-item Hamilton value and the

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FIG. 2. Mean connectivity matrix, the small-world efficiency and hubs distribution. A: Mean connectivity matrix of healthy controls. B: Mean connectivity matrix of depressed patients. The color bar represented the FA weight of connections. Results were indexed in 396  396 symmetric matrices. C: The small-world efficiency of the brain anatomical networks in the healthy controls (HC) and depressed patients (MDD), which had a much higher local efficiency and a similar global efficiency compared with the matched random networks (Random-HC and Random-MDD). D: Hub regions in the human anatomical networks. The 33 regions were identified as hubs, including 13 regions of the heteromodal or unimodal association cortex (STG.R, SPG.R, SFGmed.L, STG.L, PCUN.L, MFG.R, LING.L, SFGdor.R, PCUN.R, LING.R, SFGmed.R, MOG.R, SFGdor.L), 12 regions of the paralimbic cortex (INS.R, ORBinf.L, DCG.R, INS.L, ACG.R, ORBmid.R, TPOsup.L, ORBmid.L, ACG.L, DCG.L, ORBsup.L, TPOsup.R), 6 primary sensory and motor cortices (CAL.L, CUN.L, CAL.R, CUN.R, PreCG.R, PoCG.L), and 2 limbic regions (HIP.L, HIP.R). Nodes represented brain regions, and the size of the nodes (i.e., diameter) represented the magnitude of normalized nodal betweenness centrality (Supp. Table S5). [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

duration of disease) and the nodal characteristics which survived after FDR correction in MDD group was explored by means of a Pearson correlation method. Recently, in brain-behavior correlation analysis, the outlier problem was associated with resolutions. In our research, the outliers were identified according to the strategy proposed by means of the work of Schwarzkopf et al (29). Briefly, the outliers were identified by bootstrapping the Mahalanobis distance Ds of each observation from the bivariate mean and excluding all points whose average Ds was six or greater. The Pearson correlation coefficient analysis was further performed between the selected nodal characteristics and clinical scores in the depression group to analyze the simple effect. A significance level of P < 0.05 was set for all correlation analysis. RESULTS In the present study, we constructed two different kinds of networks for each subject, including FA-weighted and FNweighted networks. Here, we focused mainly on the results obtained from the analyses on the FA-weighted networks. The FA-weighted averaged adjacency matrixes for each group are represented in Figure 2A and B (for the results of the FN-weighted networks, see the Supp. Materials).

To make the presentation of our results clear, the following network characteristic results would be categorized into two levels: (i) overall level network characteristic, including small-world efficiency, S and C; (ii) nodal characteristic, including hubs, si, ci, ei, and eci. Overall Level Network Characteristics of the Brain Anatomical Networks Small-world efficiency Using small-world efficiency analyses, we found that the brain anatomical networks of both healthy controls and MDD patients exhibited a similar global efficiency and a much higher local efficiency compared with the matched random networks (average healthy group: Eloc (G)/Eloc (random) ¼ 9.107, Eglob (G)/Eglob (random) ¼ 0.820; average MDD patient group: Eloc (G)/Eloc (random) ¼ 8.682, Eglob (G)/Eglob (random) ¼0.820 ; see Fig. 2C). The results suggested that there were small-world characteristics of the brain anatomical networks in both groups. Between-group differences in overall level network characteristics The overall level of S (P ¼ 0.5497),C (P ¼ 0.6725), Eloc (P ¼ 0.445), and Eglob (P ¼ 0.718) did not differ between the

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Table 3 Regions Showing Disrupted ei in the MDD Group Compared with the Health Groupa Brain regions Decrease Left superior frontal gyrus (dorsolateral) Left middle frontal gyrus Right superior frontal gyrus (orbital part) Left middle temporal gyrus Right angular gyrus

P values after Si correction 0.00003* 0.00056* 0.00061*,#3 0.00042* 0.00059*,#2

a

Regions were considered disrupted in the MDD group under the circumstance of their ei exhibiting significant between-group differences (P < 0.05, corrected). Decrease denoted that an ei significantly was reduced in the MDD group compared with the healthy group. * indicated that the regions survived critical FDR threshold for multiple comparisons. # denoted that the region was a hub node, while the number following the # was the region’s hub score.

MDD patients and the healthy controls, suggesting an intact overall organization of the brain anatomical network in the MDD patients. Nodal Characteristics of the Brain Anatomical Networks Identification of hubs We used the nodal betweenness centrality to identify hubs in the anatomical network of the human brain, and 33 regions were identified as hubs (see Fig. 2D and Supp. Table S5). These hubs included 13 regions of the heteromodal or unimodal association cortex (STG.R, SPG.R, SFGmed.L, STG.L, PCUN.L, MFG.R, LING.L, SFGdor.R, PCUN.R, LING.R, SFGmed.R, MOG.R, SFGdor.L), 12 regions of the paralimbic cortex (INS.R, ORBinf.L, DCG.R, INS.L, ACG.R, ORBmid.R, TPOsup.L, ORBmid.L, ACG.L, DCG.L, ORBsup.L, TPOsup.R), 6 primary sensory and motor cortices (CAL.L, CUN.L, CAL.R, CUN.R, PreCG.R, PoCG.L), and 2 limbic regions (HIP.L, HIP.R). Between-group differences in nodal strength si Compared with the healthy controls, the MDD patients exhibited an abnormal change of si, predominately in the prefrontal cortex (PFC) and temporal pole. Effects did not survive FDR correction. Between-group differences in nodal global efficiency ei

Between-group differences in nodal eigenvector centrality eci Compared with the healthy controls, the MDD patients exhibited an abnormal shift of eci in the frontal cortex, the parietal cortex, the temporal cortex, and the limbic system (Table 5) after correction for the interaction of si on eci (see method part of the si correction). Testing Modeling To further examine whether the increased/reduced eci, ei, and ci of brain regions found in patients were the effect of coinciding increments/reductions in si in these regions or whether they were resulted from local disorganization, a graph modeling approach was conducted. Significant decreases in eci and ei occurred in the left REC (P ¼ 0.045), the right TPOmid (P ¼ 0.034), the left SMA (P ¼ 0.043) and the left MTG (P ¼ 0.037), the right ANG (P ¼ 0.04), and the left CUN (P ¼ 0.048). Furthermore, the increase of ci was in the left INS (P ¼ 0.031). But no significant differences in graph metrics were found in the other reported brain regions that showed increases or decreases of eci, ei, and ci in patients with MDD. These results were quite different from those obtained with actual patients and control groups. It suggested that these between-group differences reflected disorganized effects rather than a loss of connectivity strength. Clinical Effects The correlations between clinical scores (including the sum score of 17-item Hamilton value and the duration of disease) and the graph organizational characteristics that survived FDR correction were computed, respectively. We found four significant correlations as follows: (i) the eigenvector centrality value in the right MFG (r ¼ 0.7; P ¼ 0.0003) and the right REC (r ¼ 0.44; P ¼ 0.04863) were negatively related with the duration of disease; (ii) the positive correlation between eigenvector centrality and the sum score of the 17-item Hamilton in the right IFGtriang (r ¼ 0.45; P ¼ 0.0425); 3) and the nodal global efficiency value in the right ORBsup (r ¼ 0.51; P ¼ 0.0174) was positively correlated with the sum score of 17-item Hamilton value (Fig. 3).

Table 4 Regions Showing Disrupted ci in the Depressed Patients Compared with the Healthy Groupa

Compared with the healthy controls, the MDD patients revealed a reduction of ei in many default model network (DMN) -related regions (Table 3) after correction for the interaction of si on ei (see method part of the si correction).

Brain regions Decrease Right superior frontal gyrus (orbital part) Left hippocampus Right insula Increase Left insula

Between-group differences in local clustering coefficient ci Compared with the healthy controls, the MDD patients revealed an aberrant change of ci in the frontal cortex, the insula (INS), and hippocampus (HIP) (Table 4) after correction for the interaction of si on ci (see method part of the si correction).

a

P values after Si correction 0.00001* 0.00048* 0.00018* 0.0008*

Regions were considered disrupted in MDD group under the circumstance of their ci exhibiting significant between-group differences (P < 0.05, corrected). Decrease/Increase denoted that a ci of MDD group significantly reduced/rose compared with the healthy group. * indicated that the region survived critical FDR threshold for multiple comparisons.

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Table 5 Regions Showing Disrupted eci in MDD Group Compared with the Healthy Groupa Brain regions Decrease Left inferior frontal gyrus (triangular part) Left/Right gyrus rectus Left precental gyrus Right superior frontal gyrus (orbital part) Left inferior frontal gyrus (opercular part) Left middle temporal gyrus Right temporal pole (middle temporal gyrus) Left postcentral gyrus Right superior frontal gyrus (medial) Right middle frontal gyrus Left inferior temporal gyrus Right middle temporal gyrus Left insula Left anterior cingulate and paracingulate gyri Left rolandic operculum Increase Right superior frontal gyrus (dorsolateral) Left middle frontal gyrus Left precuneus

P values after Si correction 0.00002* 0.00001*L,#2; 0.00003*R, #2 0.00033*,#2 0.00001*,#3 0.00001* 0.00004*,#3 0.0024*,#2 0.00069* 0.0025* 0.00001* 0.0007*,#3 0.00049*,#2 0.003* 0.00003* 0.00002* 0.0017*,#3 0.00009* 0.0025*

a

Regions were considered disrupted in the MDD group under the circumstance of their eci exhibiting significant between-group differences (P < 0.05, corrected). Decrease/Increase denoted that eci of the MDD group significantly reduced/rose compared with the healthy group. * indicated that the region survived critical FDR threshold for multiple comparisons. # denoted that the region was a hub node, while the number following the # was the region’s hub score.

DISCUSSION Combining the results in Tables 3, 4, and 5, Figure 4 depicts the abnormalities on eci, ei, and ci topological organization properties. As shown, the key finding of this study was the disruption of the topological organization of the anatomical brain network in the cognitiveemotional circuitry and frontoparietal circuitry in MDD patients. Generally, the network metrics (eci, ei, and ci) express the capability of networks in the aspect of transfer efficiency, local information processing, and centrality. Thus, our findings suggested an impaired capability of these two circuitries in MDD patients. Moreover, the affected regions shown in Figure 4 with bigger node size were identified as key anatomical network hubs, suggesting that these regions were likely to have a strong impact on information interaction and integration in depressed patients. These findings tended to suggest an aberrant hub role of these regions in the brain anatomical network in MDD patients. Emotion regulation and cognition difficulties have been considered to play an important role in the development and maintenance of MDD. Functional neuroimaging studies had found abnormalities of the cognitiveemotional circuitry in MDD patients. Specifically, the regions including the orbitofrontal cortex (OFC), the SFGdor, and the dorsal ACC showed lower strength functional connectivity under sad stimuli (30); the

regions involving the ACC, the MFG, the inferior and superior frontal cortex, the superior temporal gyrus, and the insular were altered in the resting-state functional connectivity (31); the regions involving the DMN-related brain regions were disrupted based on functional network analysis (15,16). In parallel, our findings were supported by recent DTI studies reporting on reduced integrity of specific white matter in MDD patients, including the bilateral frontal and temporal regions, and the cingulate gyri. Furthermore, the DTI studies provided evidence of abnormal brain regions, including left middle frontal gyrus (32), DMN-related regions (13), and the most discriminating connections related to corticallimbic network (14) in depressed patients. Accordingly, our finding of an affected cognitive-emotional circuitry is in line with these previous studies. This might suggest that MDD impacted the capability of cognitive-emotional circuitry to efficiently regulate the flow and integrate information across the network. Our result of the abnormalities on the frontoparietal circuitry is consistent with previous studies. The frontoparietal circuitry plays an important role in the processes involving in general intelligence. Recent neuroimaging studies demonstrated that some damage to frontoparietal circuitry impaired the cognitive function in depressed patients (4), and the PFC is thought to play a key role in the suppression of a negative affective state by means of an inhibitory connection from regions of the PFC, probably the OFC, to the amygdala (AMYG) (33). Moreover, a high capability of information interaction and integration had been associated with cognitive fitness of the brain (34). Therefore, we might infer that the reduced capability of information interaction in the frontoparietal circuitry should be associated with the decrease of cognitive function and the weak suppression of a negative emotion in MDD patients. Preliminary findings showed that local clustering coefficients of left HIP and right INS were reduced among depressed patients. In addition, we also present preliminary evidence of increased local clustering coefficients, but lower eigenvector centrality, for the left INS among such patients. The HIP belongs to the limbic system and is responsible for the consolidation of information from short-term memory to long-term memory and spatial navigation (35). Several recent neuroimaging studies in depression had revealed reduced gray matter volumes in the HIP (36), and elucidated the role of inhibition in the regulation of mood (37). Additionally, the severity of depressive symptoms was directly increased by such hippocampal structural disturbances (38). The INS had consistently been shown to be involved in processing stimuli that evoke the emotional response of disgust (39) and was a key part of abnormal brain areas of the limbiccortical-striatal-pallidal-thalamic circuit in depression (40). The local clustering coefficient provides an index of the ability of local information processing, and the decrease ci of the left HIP and the right INS suggested that these regions and their neighbors were less network independent and segregated. Moreover, an increased local clustering coefficient of the left INS implied that such a region was less integrated into the network as a whole, the left INS and its neighborhood retained greater

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FIG. 3. Brain regions showing impaired eci and ei in brain anatomical networks and their relationships with clinical variables (the sum score of 17-item Hamilton and the duration of disease) in MDD patients. The contour lines indicated the bootstrapped Mahalanobis distance from the bivariate mean, and filled circles indicated data included in the correlation, while open circles indicated outliers. Pi is the correlation coefficient. The solid black line denoted a linear regression over the data after outlier removal. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

local connectivity. This pattern of decreased global integration with higher local integration might make the left INS tend toward isolation. Simultaneously, decreased

FIG. 4. Abnormal changes (mainly in cognitive-emotional circuitry and frontoparietal circuitry) on ci, ei, and eci topological organization exhibiting significant betweengroup differences (FDR corrected, P ¼ 0.05). The nodes’ color showed what changed in the regions (e.g., decrease or increase) within the MDD patients in comparison to the healthy controls. Nodal size denoted the hub score (see method for the defining hub). [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

eigenvector centrality of the left INS suggests that the shortest path lengths between other nodes in the network transverse this node less, which suggests that the

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left INS might play a weak role in disgust emotion processing and regulation. Together, we presume that the memory impairment and weakened processing in a disgust emotion might contribute to the pathogenesis of MDD. We present preliminary evidence of increased eigenvector centrality, but reduced nodal global efficiency of the left PCUN, reduced nodal global efficiency of the right ANG, decreased eigenvector centrality of left ACC, as well as several frontal nodes among the depressed patients. It was notable that all these nodes were involved in the DMN. The DMN was mainly responsible for self-referential thought and attention-orienting. It was well documented that the functional connectivity altered within DMN in depressed patients (4). Furthermore, converging findings from previous studies had suggested the DMN as a potentially useful biomarker, and key aspects of depression-related cognitive impairment underpinned neurobiological risk of MDD (41). Recently, GadElkarim et al (13) applied a community analysis method on anatomical brain networks also found that brain regions related to DMN exhibited significant differences in MDD. Our result of disrupted DMN-related brain regions supports the idea that the structural basis underlies the brain functional states and structural connectivity abnormalities reflect functional deficits in the MDD patients. Further study might be required to directly explore the association between functional connectivity and anatomical connectivity in DMN among MDD patients. By means of correlation analysis, we found that the eigenvector centrality in the right MFG and the right REC were negatively correlated with the duration of disease severity. Some studies demonstrated that REC and MFG correlated with depression severity. Egger et al had conducted research on elderly patients with depression, indicating a negative correlation between the bilateral REC’s volume and the score of the geriatric depression scale (42). Furthermore, Smoski et al conducted a functional MRI task study using a wheel of fortune task and found that the depression severity was predicted by the activations of the bilateral MFG during reward selection (43). Thus, we speculated that the lesion on the capability of information interaction in these regions might indicate the development status of MDD. In addition, the eigenvector centrality in the right IFGtriang and nodal global efficiency in the right ORBsup were positively related to HAMD value. The PFC had been considered to be important for the regulation of the negative emotion (33). These results might be interpreted that the more severe the depression was, the more efforts from these regions were required to suppress the negative stimuli induced activities. Interestingly, all the correlation results were in the right PFC. According to the emotional valence theory, the left hemisphere is dominant for positive emotion, whereas the right hemisphere is dominant for negative emotion (44). These correlation results might suggest that the regulation function of negative emotion is impaired in MDD patients. Several other issues need to be further addressed when interpreting our results. First, subcortical (caudate, putamen, pallidum, and thalamus) and cerebellar regions in the AAL template were omitted in this study. Hence,

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the current study was engaged in modeling corticocortical connectivity networks. Second, different nodal size was likely to have an impact on the degree of the node. Based on this point, we checked such effect in the group level with a conclusion that the different nodal size did not cause a bias in the group results (Supp. Fig. S1). Third, as for sparsity of anatomical networks based on deterministic tractography, the current mainstream approach is under the same threshold (i.e., fiber number), resulting in different sparsity of the anatomical networks between groups. The sparsity effect was excluded from consideration in this study, because the intersubject fluctuation of sparsity was slight. However, further study should be conducted to address the effect of sparsity on topological analysis in the near future. Also, it is worthwhile to note in the testing model that the simulated samples, which were constructed from depressed samples and control samples together, were not actual samples. Considering that the graph characteristics of these simulated samples inherited mostly from those of the depressed samples, we assumed that the randomization of simulated depressive samples could not alter the original matching situation of these samples (shown as in Table 1). On the other hand, because the gender distribution of actual samples in this study was not one-to-one matched between groups, gender differences may influence the results. Further study is required on a larger sample size to achieve a better gender distribution. CONCLUSIONS In summary, we applied DTI to probe the change of anatomical topological organization between MDD patients and healthy controls and found that the main abnormalities were in the cognitive-emotional and frontoparietal circuitry. These results might suggest that MDD impacted the capability of such two circuitries to efficiently regulate the flow and integrate information across the whole network. In addition, we found the centrality and efficiency of several regions in the right frontal cortex were significantly correlated with the clinical metrics of patients, respectively. These correlation results might suggest that the regulation function of negative emotion is impaired in MDD patients. Together, the disrupted anatomical network organization involved in affective and cognitive processing procedures might contribute to the pathogenesis of MDD. Network studies on DTI can contribute to progress in understanding the pathophysiological processes of MDD. ACKNOWLEDGMENT We thank Mr. Hao Tang, who discussed our results and gave suggestions. REFERENCES 1. American Psychiatric Association Press. DSM-IV: Diagnostic and statistical manual of mental disorders. 4th ed. Washington, DC: American Psychiatric Association Press; 1994. 2. Diener C, Kuehner C, Brusniak W, Ubl B, Wessa M, Flor H. A metaanalysis of neurofunctional imaging studies of emotion and cognition in major depression. Neuroimage 2012;61:677–685.

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Abnormal brain anatomical topological organization of the cognitive-emotional and the frontoparietal circuitry in major depressive disorder.

Despite the increasing understanding of major depressive disorder (MDD) using neuroimaging techniques, the topological organization of anatomical netw...
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