Structural alterations of the brain preceded functional alterations in major depressive disorder patients: Evidence from multimodal connectivity

Accepted Manuscript

Structural alterations of the brain preceded functional alterations in major depressive disorder patients: evidence from multimodal connectivity Zhijun Yao , Ying Zou , Weihao Zheng , Zhe Zhang , Yuan Li , Yue Yu , Zicheng Zhang , Yu Fu , Jie Shi , Wenwen Zhang , Xia Wu , Bin Hu PII: DOI: Reference:

S0165-0327(18)33166-5 https://doi.org/10.1016/j.jad.2019.04.064 JAD 10725

To appear in:

Journal of Affective Disorders

Received date: Revised date: Accepted date:

18 December 2018 11 March 2019 8 April 2019

Please cite this article as: Zhijun Yao , Ying Zou , Weihao Zheng , Zhe Zhang , Yuan Li , Yue Yu , Zicheng Zhang , Yu Fu , Jie Shi , Wenwen Zhang , Xia Wu , Bin Hu , Structural alterations of the brain preceded functional alterations in major depressive disorder patients: evidence from multimodal connectivity, Journal of Affective Disorders (2019), doi: https://doi.org/10.1016/j.jad.2019.04.064

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT

Highlights 

This is the first study to combine the functional network and structural network of the whole brain to investigate major depressive disorder.



Functional network has greater flexibility in the early stages of depression development.



MDD patients show alterations in both functional and structural networks, and

CR IP T

some of the alterations may induce the clinical manifestations in patients with

AC

CE

PT

ED

M

AN US

depression.

1

ACCEPTED MANUSCRIPT

Structural alterations of the brain preceded functional alterations in major depressive disorder patients: evidence from multimodal connectivity Zhijun Yaoa†, Ying Zoua†, Weihao Zhenge, Zhe Zhanga, Yuan Lib, Yue Yua, Zicheng

a

CR IP T

Zhanga, Yu Fua, Jie Shia, Wenwen Zhangc, Xia Wud,*, Bin Hua,* School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province,

730000, P.R.China.

School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong

AN US

b

Province, 250358, P.R.China.

Department of Radiology, Gansu Provincial Hospital, Lanzhou, Gansu Province, 730000, P.R.China.

d

College of Information Science and Technology, Beijing Normal University, Beijing, 100000,

M

c

P.R.China.

College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 730000,

ED

e

Background: Recent studies showed that major depressive disorder (MDD) has been

AC

1

CE

Abstract

PT

P. R. China.

involved in abnormal functional and structural connections in specific brain regions. However, comprehensive researches on MDD-related alterations in the topological organization of brain functional and structural networks are still limited. Methods: Functional network (FN) was constructed from resting-state functional * Correspondence to: Xia Wu, College of Information Science and Technology, Beijing Normal University, Beijing, China. P.O. Box 100000. Phone: (+8610) 5880-0427. E-mail: [email protected] * Correspondence to: Bin Hu, School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu province, China. P.O. Box 730000. Phone: (+860931) 8912-779. E-mail: [email protected] † These authors contributed to the work equally and should be regarded as co-first authors. 2

ACCEPTED MANUSCRIPT

MRI temporal series correlations and structural network (SN) was established by Diffusion tensor imaging (DTI) data in 58 MDD patients and 71 healthy controls (HC). The measurements of the network properties were calculated for two networks respectively. Correlations were conducted between altered network parameters and Hamilton depression scale (HAMD) score. Additionally, network resilient analysis were conducted on FN and SN.

CR IP T

Results: The losses of small-worldness charateristics and the decline of nodal efficiency across FN and SN were found in MDD patients. Based on network-based statistic (NBS) approach, the decreased connections in MDD patients were mainly found in the superior occipital gyrus, superior temporal gyrus for FN and SN, while

AN US

the increased connections were distributed in putamen, superior frontal gyrus only for SN. Compared with the FN, the SN showed less resilient to targeted or random node failure. Besides, altered edges in NBS and regions with decreased nodal efficiency were negatively associated with HAMD score in MDD patients.

antidepressant medications.

M

Limitations: The samples size is small and most of the MDD patients take different

ED

Conclusions: Alterations of SN in the brain of MDD patients preceded that of FN to some extent, and reorganization of the brain network was a mechanism which

PT

compensated for functional and structural alterations during disease progression. Keywords: Major depressive disorder, Functional network, Structural network, Graph

CE

theory analysis

AC

1. Introduction Major depressive disorder (MDD) is a mental illness characterized by persistent,

general grief, guilt and worthlessness that can lead to greater suicide rate (Hu et al., 2015; North et al., 2017). With development of tools and procedures for assessing the human brain, research on MDD neural mechanism has experienced tremendous growth over the past two decades. Resting state functional magnetic resonance 3

ACCEPTED MANUSCRIPT

imaging (rs-fMRI) is a non-invasive imaging technique used to measure spontaneous brain activity as low-frequency fluctuations in blood oxygen level-dependent(BOLD) signals, which can assist the treatment decisions of depression (Tadayonnejad et al., 2015). Documented by prior research, MDD is associated with widespread local activation abnormalities in many brain regions, such as the hippocampus (Lui et al., 2009), posterior cingulate gyrus (Mah et al., 2007), orbitofrontal cortex (Gilbert et al.,

CR IP T

2010), prefrontal cortex (Feng et al., 2012; Frodl et al., 2009), and occipital regions (Gilbert et al., 2010). Diffusion tensor imaging (DTI), a noval MRI technique, can study both the orientation and the diffusion characteristics of white matter (WM) tracts by quantifying the water diffusion rate of a given voxel (Yi et al., 2015).

AN US

Regarded as an important indicator of structural connectivity (Nobuhara et al., 2006b), fractional anisotropy (FA)measures the directionality of water diffusion. Reduced FA in the absence of pathological findings may indicate that microstructural abnormalities can destroy the integrity of the WM tracts (Coplan et al., 2010; W.-b.

M

Guo, Liu, Xue, et al., 2012). WM abnormalities, particularly in the limbic- and cortical-subcortical neural circuits, may contribute to the pathogenesis of MDD

ED

(Ajilore et al., 2014).

A major challenge in multimodal research is to identify methodological

PT

approaches for unifying disparate types of data (Wang et al., 2018). Graph theory provides a mathematical framework for describing interactions between system

CE

entities and is particularly useful in this context (Sun et al., 2017). Previous studies reported topological abnormalities in human brain connectome in MDD patients,

AC

including loss of small-worldness and major reorganizaiton of community structures (M. Zhang et al., 2018). Through graph theory analysis in rs-fMRI, abnormal connections of cerebrum in MDD patients are observed, such as increased medial prefrontal cortex and anterior cingulate cortex connectivity (Zhu et al., 2012), increased subgenual cingulate-thalamic connectivity (Ajilore et al., 2015), and reduced bilateral dorsal lateral prefrontal cortex and right superior parietal lobule connectivity (Wei et al., 2014). Several studies used DTI tractography to research the structural connectivity (SC) of MDD patients. MS et al. (2012) reported that some 4

ACCEPTED MANUSCRIPT

inter-cortical connections acted as biomarkers for the best classification between treatment-free depression patients and healthy controls (HC). Korgaonkar et al. (2014) proposed decreased SC within the DMN and network which included the frontal cortex, thalamus, and caudal regions in a large number of depression samples. Multimodal techniques have provided a holistic characterization of neurological diseases inaccessible from either modality alone. Therefore, we inferred that

pathophysiology and consequences of cerebral disease.

CR IP T

combining rs-fMRI and DTI data could obtain a better understanding of the

The aim of this study is threefold. First, compared with the HC, the MDD patients are likely to have altered regional and global topological parameters of the

AN US

functional network (FN) and structural network (SN), which may be related to clinical manifestations. Second, compared with SN, FN is considered to be relatively stable. Third, alterations in FN and SN of MDD patients are not synchronized in the early stages of major depression disorder. In conclusion, we hope that this study could

M

promote the general understanding of the abnormal functional and structural network

ED

in MDD.

PT

2. Materials and methods

CE

2.1. Participants

58 MDD patients and 71 age and gender matched healthy control subjects were

AC

involved in this study. The MDD patients were recruited from the Gansu Provincial Hospital, while the subjects in the healthy control group were recruited through newspaper advertisements from August, 2017 to July, 2018. The details of the participants were listed in the Supplementary materials. 2.2. Data acquisition All MR images were performed using a 3.0 T Siemens Trio scanner (Siemens Erlangen, Germany). The details of the data acquisition were shown in the 5

ACCEPTED MANUSCRIPT

Supplementary materials. 2.3 Data Processing Using the Statistical Parametric Mapping (SPM8) based toolkit Data Processing Assistant Resting-State fMRI (DPARSFA; http://www.restfmri.net )(Chao-Gan &

CR IP T

Yu-Feng, 2010), all rs-fMRI data were preprocessed. To allow signal stabilization and subjects' adaptation to the scanner's noise, the first 10 volumes of the functional images were removed. Slice timing, head-motion correction, and realignment were applied to the remaining volumns. The participants who had head motion>2 mm maximum displacement in any of the x, y, or z directions or >2° of rotation in any

AN US

direction were excluded (J et al., 2014), which leaved 71 healthy subjects and 58 patients for the further analysis. The functional images were spatially normalized to Montreal Neurological Institute (MNI) space by applying the parameters of structural image normalization and were resampled to a voxel size of 3mm×3mm×3mm

M

resolution. Resulting images were spatially smoothed with a Gaussian kernel of 8 mm full width at half maximum (FWHM) (F. Liu et al., 2013). Subsequently, all the

ED

confounding variables, including six head motion parameters, averaged global, white matter signals and cerebrospinal fluid, were regressed out (Zeng et al., 2014). Linear

PT

de-trend was then performed, followed by temporal band-pass filtering (0.01–0.08 Hz), which was used to reduce the effects of low-frequency drift and high-frequency

CE

physiological noise (L. Wang et al., 2014). Using PANDA (Cui et al., 2013) (http://www.nitrc.org/projects/panda) in

AC

MATLAB2014a, the DTI data were preprocessed as the following steps: First, The format of images was converted from DICOM into NifTI, and the b0 value was determined. Then, the nonbrain sections were removed, and the images were cropped. Subsequently, through correcting for the eddy current effect and head motions, averaging multiple acquisitions, the DTI metrics were calculated, followed by spatial standardization, Gaussian smoothing and calculation of FA.

6

ACCEPTED MANUSCRIPT

2.4 Construction of Brain Networks The nodes of the network were demarcated according to the automated anatomical labeling (AAL) algorithm (Khazaee et al., 2015). Except the cerebellum, the algorithm parcelled the entire cerebral cortex into 90 anatomical regions (AAL-90), which formed 90 cortical and subcortical regions (45 for each

CR IP T

hemisphere)(Table 1). For each subjects, the network was used for further graph analyses, in which each row or column represented a brain region of the AAL template.

AN US

2.4.1 Functional Network Construction

Using GRETNA (www.nitrc.org/projects/gretna/), a graph theoretical network analysis toolbox for connectome, we constructed a functional brain network. The Pearson correlation coefficients for each pair of regions were calculated for the mean

M

time series of each region. Fisher's z-transform was performed to convert the data into z-values for normal distribution. The values of the interregional correlation

ED

coefficients were taken as the weights of the edges. Thus, we constructed a weighted symmetric functional connectivity (FC) matrix for each subject and calculated the

PT

topological properties of the network by graph theoretic analyses.

CE

2.4.2 Structural Network Construction Using the PANDA toolbox, a structural network was constructed using

AC

deterministic tractography. Meanwhile, generated by PANDA, the FA-weighted matrixs (90*90) were divided into different levels and created a symmetric matrix. For each subject, the FA-weighted matrix was used for further graph analysis, which represented the white matter network of cerebral cortex in AAL template. 2.5 Threshold selection The network topologies depend on the density of the network. Therefore, the 7

ACCEPTED MANUSCRIPT

difference of connectivity strength may affect the comparison of the network. When constructing a brain network, the same number of nodes and edges were set across subjects. To investigate high correlation coefficients of the remaining connections, we operated network parameters over a range of threshold values. As in rs-fMRI, using the concept of sparsity to analyze the network, and since there is no definite method for selecting a single threshold, we have calculated a series of thresholds for rs-fMRI

CR IP T

networks over a range of network sparsity (26%~50%) with a step of 1% (H. Guo et al., 2014). In DTI, we used the value of FA (0.2~0.42) with the a step of 0.2 as the threshold, which has been selected based on previous study (W. Jiang et al., 2017). Since the unfully connected networks and highly linked networks could not show

AN US

small-worldness properties any more (Braun et al., 2012), we used these densities to provide a reasonable trade-off between sparsity. 2.6 Graph Analysis

M

2.6.1 Small-worldness properties

ED

Clustering coefficients(𝐶𝑃 ), and shortest path length (𝐿𝑝 ) are two important elements of small-worldness network parameters. For the small-worldness properties,

PT

𝐶𝑝 and 𝐿𝑃 of the brain networks are compared with those of random networks which have the same number of nodes, edges, and degree distribution as the real networks.

CE

𝑟𝑎𝑛𝑑 The normalized shortest path length (λ), λ=𝐿𝑟𝑒𝑎𝑙 , and the normalized clustering 𝑝 /𝐿𝑝

AC

coefficient (γ), γ=𝐶𝑝𝑟𝑒𝑎𝑙 /𝐶𝑝𝑟𝑎𝑛𝑑 are defined, where 𝐿𝑟𝑎𝑛𝑑 and 𝐶𝑝𝑟𝑎𝑛𝑑 represent the 𝑝 mean clustering coefficient and the mean shortest path length of matched random networks. If a network meet the conditions that γ> 1 and λ≈ 1, orδ= γ/λ > 1, the network has small-worldness properties. 2.6.2 Network efficiency The global efficiency 𝐸𝑔𝑙𝑜𝑏 (𝐺) measures the efficiency of the parallel information transfer in the network (Latora & Marchiori, 2001). The local efficiency 8

ACCEPTED MANUSCRIPT

𝐸𝑙𝑜𝑐 (𝐺) is defined as the mean of the global efficiency of subgraphs composed of the immediate neighbors of a particular node (Achard & Bullmore, 2007). Detailed information of each property are shown in Supplementary materials Table S1. 2.6.3 Regional nodal characteristics and Hub identification

CR IP T

The nodal efficiency(𝐸𝑛𝑜𝑑 ) describes the nodal (regional) characteristics of the WM structural network (Achard & Bullmore, 2007). 𝐸𝑛𝑜𝑑 quantifies the ability of a node to communicate with other nodes in a network, which is the inverse of the harmonic mean of the minimum path length between an index node and all other nodes in the network. In the study, A node(𝑖) can be defined as a brain hub if

AN US

𝐸𝑛𝑜𝑑 (𝑖) is at least greater than 1 SD plus average nodal efficiency of the network (Tian et al., 2011; Y. Zhang et al., 2017). Detailed information of each property are shown in Supplementary materials Table S1.

M

2.7 Network Resilience Analysis

Network resilience referring to the network stability and plasticity in case of

ED

losing nodes is a crucial parameter of a complex network (Albet et al., 2001). In the functional or structural brain networks, the network resilience could be evaluated by

PT

removing the nodes in targeted and random patterns at the fixed sparsity or FA value (Bernhardt et al., 2011). In the “random pattern analysis”, the basic design principle

CE

was to remove nodes in a random order. In the “targeted pattern analysis”, we first

AC

computed the betweenness value of each node in the networks for the two groups respectively. Next, the nodes were deleted in decreasing sequence of their betweenness value. The global efficiency was computed for the resulting damaged network and compared with the initial value of unchanged network. Global efficiency is chosen to investigate resilience, as suggested in (Rubinov & Sporns, 2010). 2.8 Statistical Analysis Statistical analyses were performed by using IBM SPSS statistics (version 21). A 9

ACCEPTED MANUSCRIPT

two-sample t-test was performed to analyze group differences in age, HAMD scores and HAMA scores. The Chi square test was employed to compare gender distributions between groups, and p-value less than 0.05 was considered statistically significant. Between-group differences of graph metrics and network resilience measures were determined through non-parametric permutation tests with 5000 iterations, with

CR IP T

a significance threshold of p < 0.05 (Yao et al., 2010). This permutation test procedure was repeated over the range of sparsity threshold (26%~50%) for FN and over the different FA value (0.2~0.42) for SN. And as to 𝐸𝑛𝑜𝑑 and network resilience, the procedure was repeated at a fixed sparsity (sp=26%) and a FA value (FA=0.42). And

AN US

we used FDR correction for these results.

The NBS approach (Zalesky et al., 2010) was used to identify the altered connectivity in functional and structural networks. The details for NBS in present study as follows: Before determining the significant altered connected network in the

M

NBS analysis, a nonparametric one-tailed t test was performed in each group to detect significant non-zero connections. Then NBS method was utilized to define all

ED

connected components with significant differences (p=0.05, FDR correction). At last, Pearson correlation analyses were performed to assess the correlations in

PT

MDD patients between altered edges in NBS analysis, the nodes with significant difference in 𝐸𝑛𝑜𝑑 and HAMD scores.

CE

The same experiment and statistical analysis were performed between drug-naïve and medicated patients with MDD, detailed information were shown in

AC

Supplementary materials.

3. Results 3.1 Demographic characteristics As shown in Table 2, two groups were matched for age (p=0.663), and gender (2 =1.282, p=0.258). There were significant difference in the HAMA (p<0.001) and 10

ACCEPTED MANUSCRIPT

HAMD (p<0.001) score between the two groups. 3.2 Global Topology of Functional and Structural networks For the rs-fMRI datasets, compared with the HC, MDD patients showed a lower clustering coefficient (γ) (Figure 1A, 38% < sparsity <45%) and unchanged shortest

CR IP T

path length (λ)(Figure1B), which resulted in reduced small-worldness(σ) (Figure 1C,41%
AN US

For the DTI datasets, compared with the HC, MDD patients showed increased shortest path length (λ)(Figure2B,FA=0.24,0.26) and decreased Eloc (Figure2E,FA=0.40,0.42), In addition, the clustering coefficient (γ), small-worldness( σ ) and Eglob were not changed significantly in MDD patients (Figures 2A,C,D). 3.3 Between-group differences in connectivity

M

By using NBS approach (p<0.05, 5000 permutation test, FDR corrected, Table 3, Figure 3), the sub-networks were identified, in which decreased connections were

ED

found in both FN and SN of patients with MDD (Figure 3A and Figure 3B), whereas increased connections were only found in SN of the MDD cohort (Figure 3B).

PT

Specifically, the decreased connections within FC were mainly between SOG.L,

CE

bilateral STG, bilateral ROL, INS.R, PUT.R, SPG.L, PAL.R; and the decreased connections in SC were the connections that linked with bilateral HIP, bilateral INS,

AC

PUT.L, PreCG.L, DCG.L, SFGdor.L. In addition, the SN connections between SPG.L, PUT.L, PoCG.L, bilateral SMA, SFGdor.R were significantly increased. 3.4 Regional efficiency and Identification of network hubs Compared with HC, the MDD patients showed significantly decreased nodal efficiency in FN and SN in several regions (p<0.05, after 5000 permutation test)( Table 4,Figure 4). For FN, there were 7 regions in PreCG.R, MFG.R, SFGmed.R, bilateral PoCG, ANG.R, PCL.R. As to SN, the 8 regions mainly were 11

ACCEPTED MANUSCRIPT

distributed in ORBmid.R, SMA.L, PHG.L, MOG.L, IPL.R, PCUN.L, PCL.R, HES.L. In this study, the nodal characteristics of each cortical region in functional and structural network were examined by nodal efficiency. The hub nodes in each group which greater than 1 SD plus mean efficiency are shown in Supplementary materials

3.5 The comparison of network resilience

CR IP T

Table S2, Table S3 and Figure S1.

As seen in Figure 5, the global efficiency was decreased as the deletion ratios growing in the two groups. In both networks, compared with HC, MDD patients have

AN US

a faster rate of decline in global efficiency over a wide percentage of removal. As seen in Figure 5A and Figure 5D , the target node attack led to significant difference (p< 0.05) across almost the entire range of removed nodes for FN, and as to SN only the random node attack led to difference (p<0.05) across a small scale. Furthermore, the SN was more vulnerable than FN in the same percentage of removal. We also noted

M

that the performances of the two groups were interwoven near the end of the removal

ED

processes in both patterns. Responding to the failures and crashes, the functional or structural network resilience in HC subjects are more stable than the MDD subjects.

PT

3.6 Network properties correlation with depression clinical measurements

CE

We analyzed the correlations between HAMD score and altered edges with significant difference in NBS, and the nodes with significant 𝐸𝑛𝑜𝑑 difference. For the

AC

FN, edges between INS.R and SOG.L (r=-0.262,p=0.047); SOG.L and PAL.R (r=-0.260,p=0.049); and SPG.L and PAL.R (r=-0.284,p=0.031) were negatively correlated with the HAMD score (Figure 6). For the SN, the nodal efficiency, MOG.L (r=-0.265, p=0.044), PCUN.L (r=-0.268,p=0.042), and HES.L (r=-0.344,p=0.008) were negatively correlated with the HAMD score(Figure 6).

12

ACCEPTED MANUSCRIPT

4. Discussion In this study, we explored different topological organizations of FN and SN in MDD patients and HC. There were 3 main findings: (1) Compared with HC, MDD patients displayed losses of small-worldness, changed global topological organization

CR IP T

and network connections; (2) regions with significant decreased 𝐸𝑛𝑜𝑑 were mainly distributed in frontal region, parietal region and central region in MDD patients; (3) FN would be more stable than SN across MDD patients and HC in the early stages of depression. The MDD patients showed vulnerable network resilience in FN and SN. Besides, clinical manifestations had negative correlation with the altered edges in

AN US

NBS and regions with decreased 𝐸𝑛𝑜𝑑 in MDD patients. 4.1 Network Properties

As shown in Figure 1, MDD patients exhibited decreased clustering coefficients

M

and local efficiency in FN. Previous studies proved that lower clustering coefficient

ED

suggested weakened interconnections between local brain regions (Horwitz, 2003; Y. Liu et al., 2008). Another graph theory study of neuron sclerosis in the dentate gyrus documented that the clustering coefficient increased during the sclerosis process and

PT

decreased in the final phase (Dyhrfjeldjohnsen et al., 2007). Small-worldness property

CE

reflects the best balance between local specialization and global integration in FN(J. Zhang et al., 2011). The aforementioned abnormalities in small-worldness may

AC

indicate that the organization balance of FN in patients with MDD was disrupted, where the FN is turning to more randomly organized. Since FN is associated with emotional process and cognitive function (Ye et al., 2015), the abnormal cognition performance in MDD patients may result from the dysregulated rs-fMRI network, especially the randomized organization of FN. Li et al. (2015) revealed older depressed individuals who had different degree of cognitive impairment showed reduced local efficiency, which reflected deficient segregation of functional neural processing and a lower level of local connectivity. 13

ACCEPTED MANUSCRIPT

MDD patients show increased shortest path length and decreased local efficiency in SN (Figure 2). Shortest path length ensures the efficiency of information transfer in the brain network, which is one of the main properties that constitutes the basis of cognitive process (Olaf Sporns & Zwi, 2004). The increased shortest path length associated with MDD may be attributed to the degeneration of the fiber bundle for information transmission, which are consistent with previous studies of WM

CR IP T

disconnection (Bai et al., 2009; Yuan et al., 2010). The results of small-worldness losses reflected an undesired topological organization in SN, which may indicate that the deficits of cognitive and emotional processing in MDD patients may result from network damages. A prior study (Shu et al., 2011) reported that local efficiency was

AN US

predominantly associated to the short-range connections between neighboring regions, indicating that local microstructural damage may occur in WM structures due to long-term depression in patients. Another study reported that subtle changes in white matter tract volume were determined to be due to atrophy and transneuronal

M

degeneration (Vaessen et al., 2012). Aforementioned convergent evidences revealed that MDD is associated with disrupted global network organization and patients with

PT

ED

higher levels of cognitive impairment demonstrated greater alterations.

4.2 Difference of connectivity between-group

CE

Significantly decreased FCs were mainly within SOG.L, STG.L, PAL.R (Figure

3A). Depression is associated with both functional and structural abnormalities in

AC

occipital regions, such as decreased cerebral blood flow in the lingual gyrus (Ito et al., 1996) and decreased gray matter volume in the cuneus (Haldane et al., 2008). In rs-fMRI study, Y. Yuan et al. (2008)reported that abnormal activity of occipital cortex was involved in the pathophysiology in remitted geriatric depression. The abnormal changes of internal pallidum disrupting striatal output may induce alteration of functional connectivity between pallidum and occipital regions(Dombrovski et al., 2012). Moreover, evidence from first-episode, treatment-naive MDD patients show 14

ACCEPTED MANUSCRIPT

decreased white matter integrity associated with occipital regions (Ning et al., 2007). The temporal lobe may be associated with depression in persons without a concomitant neurological condition (Richardson et al., 2007). Meanwhile, in the FN study of MDD patients, abnormal FCs existed in both cognitive and emotional task paradigms (Matthews et al., 2008), which provided insights into interregional dysfunction of brain and behavioral abnormalities in MDD patients.

CR IP T

Simultaneously, the decreased SC distributed in HIP.L, INS.L and DCG.L (Figure 3B). With significant attention in study of mood disorders, the stress-sensitive and highly plastic hippocampus region may play a central role in depression (Campbell & MacQueen, 2004; Shi et al., 2010). The ventral anterior region of insula

AN US

is rostrally contiguous with the caudal orbitofrontal cortex and closely related with emotion (J. Jiang et al., 2015). More precisely, Mcgrath et al. (2013) indicated that ventral anterior insula metabolism might be regarded as a predictive measure of response to different treatment methods for depression. The abnormal activation in

M

paracingulate gyrus is regarded as a biomarker of treatment response in several literatures (Dichter et al., 2010; Ressler & Mayberg, 2007) and this region is

ED

noteworthy in development of depression. Moreover, the increased SC were located in PUT.L, SMA.L, and SFGdor.R (Figure 3B). Involved in affect, executive, motivation

PT

and motor function, the putamen and caudate exhibit deficits in older depressed adults (Butters et al., 2004; Seger, 2008). The SMA regulates volitional motor function and

CE

interacted with the primary motor cortex (Tanji et al., 1980). The increased SC in MDD patients might suggest that local nerve fibers reconstructed in response to the

AC

reduction in long-distance connections. The compensatory response of the SN is activated for maintaining brain functional integrity in the early stages of the neurodegeneration (Kaiser et al., 2015). Besides, compared with FN, SN have been altered more widely. We speculated that structural network was a more sensitive system in the early stage of disease and structural network changed earlier than functional network. As shown in Figure 6, negative relationships between the FCs within SOG.L, PAL.R, SPG.L, INS.L and HAMD score of MDD patients were obtained, suggesting 15

ACCEPTED MANUSCRIPT

that participants with higher HAMD score may have more disrupted network connections due to their poor mental status and depression severity. Our findings provided new evidence that abnormal network connections are related to severity of MDD patients. Therefore, we speculated that the severity of clinical symptoms exhibited by MDD patients might be related to the levels of brain impairment.

CR IP T

4.3 Regional nodal parameters

In this study, we observed decreased 𝐸𝑛𝑜𝑑 in FN and SN of MDD patients. MDD patients showed decreased nodal efficiency in PreCG.R, MFG.R, SFGmed.R, bilateral PoCG, ANG.R and PCL.R in FN (Figure 4). Corresponded anatomically to

AN US

the right dorsolateral prefrontal cortex, the middle frontal gyrus involved functionally in cognition and emotional regulation (Phillips et al., 2003) has been found to show hypometabolism in depressed subjects before treatment (Mayberg et al., 1999). The patients with early onset depression show abnormal amplitude of low-frequency

M

fluctuations (ALFF) in the superior frontal gyrus, provoking strong feeling of hate to self or others at rest (W. B. Guo et al., 2013). Consistently, previous imaging studies

ED

have shown increased activation towards negative stimuli in the paracentral lobule, which could result in a negative sensory enhancement of MDD patients (Peng et al.,

PT

2016). The PoCG have been found altered FC in late-life depression (Kenny et al., 2010) and lower regional homogeneity (ReHo) in remitted geriatric depression (Y

CE

Yuan et al., 2008). Observed by Bambini et al. (2011), ANG.L was crucial for social communication and language understanding, which might be related to the integration

AC

of linguistic materials and world knowledge. de Kwaasteniet et al. (2015) show that decreased FC between ANG.L and the frontal lobe might indicate the poor neurocog-nitive function in treatment-resistant depression patients. Furthermore, the decreased 𝐸𝑛𝑜𝑑 were primarily located in frontal cortices, parietal cortices, occipital cortices, temporal cortices, SMA.L, PHG.L and PCUN.L in SN of MDD patients. As reported previously, FA in superior frontal gyrus was decreased in late-life depression (Taylor et al., 2004). The depressed elderly had the 16

ACCEPTED MANUSCRIPT

significantly decreased FA in the right anterior cingulate gyrus, bilateral superior frontal gyrus and left mid-frontal gyrus (Nobuhara et al., 2006a). Meanwhile, frontal and parietal white matter abnormalities identified by DTI may play an important role in the development of MDD pathophysiology (Yang et al., 2007). By using distinct analysis techniques, Yang et al. (2007)found WM microstructural abnormalities in frontal and temporal structures which were related to other regions, such as parietal

CR IP T

region, occipital region, caudate and putamen. The MDD-related decreases in nodal centralities have been observed in occipital regions, including the calcarine fissure, cuneus, and lingual gyrus (J. Zhang et al., 2011). In addition, anatomical connections in the inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, posterior

AN US

thalamic radiation, and corpus callosum demonstrated altered connections in multiple neuronal circuits in MDD patients (Jae Nam et al., 2006). The study (Salomons et al., 2012) provided evidence for the relationship between SMA cortical thickness and helplessness feeling: it has been proposed that SMA can regulate helpless perception

M

of motor behavior in chronic and poorly controlled stressors. Such evidences, along with the results of this study indicated that reduction of white matter anisotropy and

ED

abnormal network properties in MDD patients might cause symptoms of depression, including cognitive impairments, functional disability and depressed mood (Y. Zhang

PT

et al., 2017).

As shown in Figure 6, the negative correlations were examined between HAMD

CE

scores and brain regions with decreased 𝐸𝑛𝑜𝑑 which included MOG.L, PCUN.L and HES.L in SN. Both decreased node efficiency and higher HAMD score may indicate

AC

impaired cognitive control combined with abnormal affective processing in MDD patients (W.-b. Guo, Liu, Chen, et al., 2012; Ye et al., 2016). This negative relationship underlined the function of SN in depression research (Nobuhara et al., 2006b), thus, aberrant nodal efficiency in MOG.L, PCUN.L and HES.L might serve as efficient biomarker for clinical diagnosis of depression.

17

ACCEPTED MANUSCRIPT

4.4 The difference of network resilience By deleting the nodes and computing the global efficiency in the FN and SN, we assessed network resilience quantitatively (Figure 5). Compared with other network properties, global efficiency would be preferable as a measure of network integration (Rubinov & Sporns, 2010). As shown in Figure 5, both networks in MDD patients

CR IP T

were more vulnerable to perturbations or attacks. This finding enhanced the conclusion that the networks of MDD patients were less resilient than that of HC (Ajilore et al., 2014). This pattern have been found in other neurological diseases such as Alzheimer’s disease and temporal lobe epilepsy (Bernhardt et al., 2011). Owing to

AN US

the power-law distribution, brain networks of the two groups are almost constant when deletion ratios is low (Friedman & Landsberg, 2013). As the deletion ratios reached 50%, the performance of both groups amalgamated. It is indicated that the topological structure of brain networks is extremely disrupted and thus cannot support integrality (Yao et al., 2017). From perspective of brain networks, the impact of

M

deleting nodes could indicate the decline in cognition for participants. Similar with

ED

these findings in these studies (Damoiseaux et al., 2006; W. Jiang et al., 2017), the FN was more resilient than the SN in our study. Based on assortativity, functional

PT

networks were more permeable and more robust to node removal (Ajilore et al., 2014). Honey et al. (2009) reported that there was commonly a strong FC between regions

CE

that have no direct SC, which might mean that FC is more widely distributed in brain network. Meanwhile, this was corroborated by two preliminary studies in

AC

structure-function connections which reported a comparable pattern that resting-state FC reflects, to a large degree, the underlying SC (Greicius et al., 2009; Michelle et al., 2003). Previous findings show that FC could form SC through selective event-driven plastic changes, occurring during development and evolution (O Sporns et al., 2000). As mentioned above, SC is a more fragile system than FC in human brain. With our experimental results, we could initially speculate that facing the same disease invasion, alterations of SN in MDD patients and HC preceded that of FN to some extent.

18

ACCEPTED MANUSCRIPT

5. Limitation This study was cross-sectional, so it limited the ability of exploring the causal relationship between changes in network topology and the severity of depression. Another limitation was that the samples size was small and most of the MDD patients

CR IP T

took different antidepressant medications. In addition to use different drugs for individuals, there were also examples of different drugs used by the same patient. Changes in medications may affect results, and we have not attempted to counteract the effects medication effect. Finally, this study lacks joint analysis of two modules data in a holistic framework. We will use different modules data to conduct fusion

AN US

study in the further.

6. Conclusion

M

Our approach uncovers the effects of depression on brain functional and structural network through the use of an integrated, multimodal analysis. MDD

ED

patients show the small-worldness losses, abnormal global topological organization in FN and SN. We also find that alterations in functional connectivity are closely related

PT

to morphological abnormalities in the specific brain regions. Moreover, in the early stages of depression, the alterations in structural networks would be earlier than that

CE

of functional networks to some extent. Discovery of functional and structural networks may elucidate the pathological mechanisms of MDD for further prospective

AC

studies.

Funding: This study was supported by the National Basic Research Program of China (973 Program) (No.2014CB744600), the National Natural Science Foundation of China (Grant, No.61632014), the Program of Beijing Municipal Science & Technology Commission (No.Z171100000117005), the National Key Research and 19

ACCEPTED MANUSCRIPT

Development Program of China (No.2016YFC1307203) and the Fundamental Research Funds for the Central Universities (lzujbky-2017-kb08).

CR IP T

Acknowledgments: We thank the staff of the Department of Radiology of the Gansu Provincial Hospital for their assistance in collecting the data. We thank all participants for their contributions to this article.

AN US

Author Contributions: Conceived and designed the experiments: Zhijun Yao, Ying Zou ， Bin Hu. Analyzed the data: Zhijun Yao, Ying Zou. Contributed reagents/materials/analysis tools: Zhijun Yao, Ying Zou, Weihao Zheng, Zheng Zhang, Yuan Li, Yue Yu, Zicheng Zhang, Yu Fu, Jie Shi, Wenwen Zhang, Xia Wu, Bin Hu. Wrote the paper: Zhijun Yao，Ying Zou. All authors contributed to and have approved the final manuscript. Conflict of Interest: All authors declared no conflict of interest.

M

References

Achard, S., & Bullmore, E. (2007). Efficiency and Cost of Economical Brain Functional Networks: PLoS Comput Biol.

ED

Ajilore, O., et al. (2014). Graph theory analysis of cortical-subcortical networks in late-life depression. Am J Geriatr Psychiatry, 22(2), 195-206. Ajilore, O., et al. (2015). Connectome signatures of neurocognitive abnormalities in euthymic bipolar I

PT

disorder. Journal of Psychiatric Research, 68, 37-44. Albet, R., et al. (2001). Error and attack tolerance of complex networks (vol 406, pg 378, 2000). Bai, F., et al. (2009). Abnormal integrity of association fiber tracts in amnestic mild cognitive

CE

impairment. Journal of the Neurological Sciences, 278(1), 102-106.

Bambini, V., et al. (2011). Decomposing metaphor processing at the cognitive and neural level through functional magnetic resonance imaging. Brain Research Bulletin, 86(3-4), 203-216.

AC

Bernhardt, B. C., et al. (2011). Graph-Theoretical Analysis Reveals Disrupted Small-World Organization of Cortical Thickness Correlation Networks in Temporal Lobe Epilepsy. Cerebral Cortex, 21(9), 2147-2157.

Braun, U., et al. (2012). Test–retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures. Neuroimage, 59(2), 1404-1412. Butters, M. A., et al. (2004). The nature and determinants of neuropsychological functioning in late-life depression. Arch Gen Psychiatry, 61(6), 587-595. Campbell, S., & MacQueen, G. (2004). The role of the hippocampus in the pathophysiology of major depression. J Psychiatry Neurosci, 29(6), 417-426. Chao-Gan, Y., & Yu-Feng, Z. (2010). DPARSF: a MATLAB toolbox for "pipeline" data analysis of resting-state fMRI. Frontiers in Systems Neuroscience, 4(13), 13. 20

ACCEPTED MANUSCRIPT

Coplan, J. D., et al. (2010). The role of early life stress in development of the anterior limb of the internal capsule in nonhuman primates. Neuroscience Letters, 480(2), 93-96. Cui, Z., et al. (2013). PANDA: a pipeline toolbox for analyzing brain diffusion images. Frontiers in Human Neuroscience, 7(42), 42. Damoiseaux, J. S., et al. (2006). Consistent resting-state networks across healthy subjects. Proceedings of the National Academy of Sciences of the United States of America, 103(37), 13848-13853. de Kwaasteniet, B. P., et al. (2015). Decreased Resting-State Connectivity between Neurocognitive Networks in Treatment Resistant Depression. Frontiers in Psychiatry, 6, 28. Dichter, G. S., et al. (2010). The effects of Brief Behavioral Activation Therapy for Depression on

CR IP T

cognitive control in affective contexts: An fMRI investigation. Journal of Affective Disorders, 126(1), 236-244.

Dombrovski, A. Y., et al. (2012). The temptation of suicide: striatal gray matter, discounting of delayed rewards, and suicide attempts in late-life depression. Psychological Medicine, 42(6), 1203-1215.

Dyhrfjeldjohnsen, J., et al. (2007). Topological determinants of epileptogenesis in large-scale structural Neurophysiology, 97(2), 1566-1587.

AN US

and functional models of the dentate gyrus derived from experimental data. Journal of Feng, L., et al. (2012). Abnormal regional spontaneous neural activity in first-episode, treatment-naive patients

with

late-life

depression:

a

resting-state

fMRI

study.

Progress

in

Neuropsychopharmacology & Biological Psychiatry, 39(2), 326-331.

Friedman, E. J., & Landsberg, A. S. (2013). Hierarchical networks, power laws, and neuronal avalanches. Chaos, 23(1), 187-203.

M

Frodl, T., et al. (2009). Neuronal correlates of emotional processing in patients with major depression. World Journal of Biological Psychiatry, 10(3), 202-208. Gilbert, A. M., et al. (2010). Grey matter volume reductions in the emotion network of patients with

ED

depression and coronary artery disease. Psychiatry Research Neuroimaging, 181(1), 9-14. Greicius, M. D., et al. (2009). Resting-state functional connectivity reflects structural connectivity in the default mode network. Cerebral Cortex, 19(1), 72-78.

PT

Guo, H., et al. (2014). Resting-state functional connectivity abnormalities in first-onset unmedicated depression. Neural Regeneration Research, 9(2), 153-163.

CE

Guo, W.-b., et al. (2012). Altered white matter integrity of forebrain in treatment-resistant depression: a diffusion tensor imaging study with tract-based spatial statistics. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 38(2), 201-206.

AC

Guo, W.-b., et al. (2012). Altered white matter integrity in young adults with first-episode, treatment-naive, and treatment-responsive depression. Neuroscience Letters, 522(2), 139-144.

Guo, W. B., et al. (2013). Reversal alterations of amplitude of low-frequency fluctuations in early and late onset, first-episode, drug-naive depression. Prog Neuropsychopharmacol Biol Psychiatry, 40(1), 153-159. Haldane, M., et al. (2008). Structural brain correlates of response inhibition in Bipolar Disorder I. Journal of Psychopharmacology, 22(2), 138. Honey, C. J., et al. (2009). Predicting human resting-state functional connectivity from structural connectivity. Proc Natl Acad Sci U S A, 106(6), 2035-2040. Horwitz, B. (2003). The elusive concept of brain connectivity. Neuroimage, 19(2), 466-470. 21

ACCEPTED MANUSCRIPT

Hu, Q., et al. (2015). Predicting depression of social media user on different observation windows. Paper presented at the 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). Ito, H., et al. (1996). Hypoperfusion in the limbic system and prefrontal cortex in depression: SPECT with anatomic standardization technique. Journal of Nuclear Medicine, 37(3), 410-414. J, W., et al. (2014). Graph theoretical analysis reveals disrupted topological properties of whole brain functional networks in temporal lobe epilepsy. Clinical Neurophysiology, 125(9), 1744-1756. Jae Nam, B., et al. (2006). Dorsolateral prefrontal cortex and anterior cingulate cortex white matter alterations in late-life depression. Biological Psychiatry, 60(12), 1356-1363.

CR IP T

Jiang, J., et al. (2015). An insula-frontostriatal network mediates flexible cognitive control by adaptively predicting changing control demands. Nature Communications, 6, 8165.

Jiang, W., et al. (2017). Disrupted Structural and Functional Networks and Their Correlation with Alertness in Right Temporal Lobe Epilepsy: A Graph Theory Study. Frontiers in Neurology, 8, 179.

Kaiser, R. H., et al. (2015). Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity. Jama Psychiatry, 72(6), 603.

AN US

Kenny, E. R., et al. (2010). Functional connectivity in late-life depression using resting-state functional magnetic resonance imaging. American Journal of Geriatric Psychiatry Official Journal of the American Association for Geriatric Psychiatry, 18(7), 643-651.

Khazaee, A., et al. (2015). Identifying patients with Alzheimer’s disease using resting-state fMRI and graph theory. Clinical Neurophysiology, 126(11), 2132-2141.

Korgaonkar, M. S., et al. (2014). Abnormal Structural Networks Characterize Major Depressive Disorder:

M

A Connectome Analysis. Biological Psychiatry, 76(7), 567-574.

Latora, V., & Marchiori, M. (2001). Efficient Behavior of Small-World Networks. Physical Review Letters, 87(19), 198701.

ED

Li, W., et al. (2015). Disrupted small world topology and modular organisation of functional networks in late-life depression with and without amnestic mild cognitive impairment. Journal of Neurology Neurosurgery & Psychiatry, 86(10), 1097.

PT

Liu, F., et al. (2013). Abnormal amplitude low-frequency oscillations in medication-naive, first-episode patients with major depressive disorder: a resting-state fMRI study. Journal of Affective

CE

Disorders, 146(3), 401-406. Liu, Y., et al. (2008). Disrupted small-world networks in schizophrenia. Brain, 131(4), 945-961. Lui, S., et al. (2009). Depressive disorders: focally altered cerebral perfusion measured with arterial

AC

spin-labeling MR imaging. Radiology, 251(2), 476.

Mah, L., et al. (2007). Regional cerebral glucose metabolic abnormalities in bipolar II depression. Biological Psychiatry, 61(6), 765-775.

Matthews, S. C., et al. (2008). Decreased functional coupling of the amygdala and supragenual cingulate is related to increased depression in unmedicated individuals with current major depressive disorder. Journal of Affective Disorders, 111(1), 13-20. Mayberg, H. S., et al. (1999). Reciprocal limbic-cortical function and negative mood: converging PET findings in depression and normal sadness. Am J Psychiatry, 156(5), 675-682. Mcgrath, C. L., et al. (2013). Toward a Neuroimaging Treatment Selection Biomarker for Major Depressive Disorder. Jama Psychiatry, 70(8), 821-829.

22

ACCEPTED MANUSCRIPT

Michelle, Q., et al. (2003). Role of the corpus callosum in functional connectivity. Ajnr American Journal of Neuroradiology, 24(2), 208. MS, K., et al. (2012). Mapping inter-regional connectivity of the entire cortex to characterize major depressive disorder: a whole-brain diffusion tensor imaging tractography study. Neuroreport, 23(9), 566. Ning, M., et al. (2007). White matter abnormalities in first-episode, treatment-naive young adults with major depressive disorder. Am J Psychiatry, 164(5), 823-826. Nobuhara, K., et al. (2006a). Frontal white matter anisotropy and symptom severity of late-life depression: a magnetic resonance diffusion tensor imaging study. Journal of Neurology

CR IP T

Neurosurgery & Psychiatry, 77(1), 120.

Nobuhara, K., et al. (2006b). Frontal white matter anisotropy and symptom severity of late‐life depression: a magnetic resonance diffusion tensor imaging study. Journal of Neurology Neurosurgery & Psychiatry, 77(1), 120.

North, C. S., et al. (2017). Prevalence and predictors of postdisaster major depression: Convergence of evidence from 11 disaster studies using consistent methods. Journal of Psychiatric Research, 102, 96.

AN US

Peng, W., et al. (2016). Essential brain structural alterations in major depressive disorder: A voxel-wise meta-analysis on first episode, medication-naive patients. J Affect Disord, 199, 114-123. Phillips, M. L., et al. (2003). Neurobiology of emotion perception II: implications for major psychiatric disorders. Biol Psychiatry, 54(5), 515-528.

Ressler, K. J., & Mayberg, H. S. (2007). Targeting abnormal neural circuits in mood and anxiety disorders: from the laboratory to the clinic. Nature Neuroscience, 10(9), 1116.

M

Richardson, E. J., et al. (2007). Structural and functional neuroimaging correlates of depression in temporal lobe epilepsy. Epilepsy & Behavior, 10(2), 242-249. Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and

ED

interpretations. Neuroimage, 52(3), 1059-1069. Salomons, T. V., et al. (2012). Perceived helplessness is associated with individual differences in the central motor output system. European Journal of Neuroscience, 35(9), 1481-1487.

PT

Seger, C. A. (2008). How do the basal ganglia contribute to categorization? Their role in generalization, response selection, and learning via feedback. Neuroscience & Biobehavioral Reviews, 32(2),

CE

265-278.

Shi, F., et al. (2010). Hippocampal volume and asymmetry in mild cognitive impairment and Alzheimer's disease: Meta-analyses of MRI studies. Hippocampus, 19(11), 1055-1064.

AC

Shu, N., et al. (2011). Diffusion tensor tractography reveals disrupted topological efficiency in white matter structural networks in multiple sclerosis. Cerebral Cortex, 21(11), 2565.

Sporns, O., et al. (2000). Theoretical Neuroanatomy: Relating Anatomical and Functional Connectivity in Graphs and Cortical Connection Matrices. Cerebral Cortex, 10(2), 127.

Sporns, O., & Zwi, J. D. (2004). The small world of the cerebral cortex. Neuroinformatics, 2(2), 145-162. Sun, D., et al. (2017). Structural covariance network centrality in maltreated youth with posttraumatic stress disorder. Journal of Psychiatric Research, 98, 70-77. Tadayonnejad, R., et al. (2015). Clinical, cognitive, and functional connectivity correlations of resting-state intrinsic brain activity alterations in unmedicated depression. J Affect Disord, 172, 241-250.

23

ACCEPTED MANUSCRIPT

Tanji, J., et al. (1980). Supplementary motor area: neuronal response to motor instructions. Journal of Neurophysiology, 43(1), 60-68. Taylor, W. D., et al. (2004). Late-life depression and microstructural abnormalities in dorsolateral prefrontal cortex white matter. Am J Psychiatry, 161(7), 1293-1296. Tian, L., et al. (2011). Hemisphere- and gender-related differences in small-world brain networks: a resting-state functional MRI study. Neuroimage, 54(1), 191-202. Vaessen, M. J., et al. (2012). White matter network abnormalities are associated with cognitive decline in chronic epilepsy. Cerebral Cortex, 22(9), 2139. Wang, et al. (2018). Structural and functional abnormalities of the insular cortex in trigeminal

CR IP T

neuralgia: a multimodal magnetic resonance imaging analysis. Pain, 159.

Wang, L., et al. (2014). Overlapping and segregated resting-state functional connectivity in patients with major depressive disorder with and without childhood neglect. Human Brain Mapping, 35(4), 1154-1166.

Wei, X., et al. (2014). Altered resting-state connectivity in college students with nonclinical depressive symptoms. Plos One, 9(12), e114603.

Yang, Q., et al. (2007). White matter microstructural abnormalities in late-life depression.

AN US

International Psychogeriatrics, 19(4), 757-766.

Yao, Z., et al. (2017). Learning Metabolic Brain Networks in MCI and AD by Robustness and Leave-One-Out Analysis: An FDG-PET Study. American Journal of Alzheimer S Disease & Other Dementias, 33(3), 153331751773153.

Yao, Z., et al. (2010). Abnormal cortical networks in mild cognitive impairment and Alzheimer's disease. Plos Computational Biology, 6(11), e1001006-e1001006.

M

Ye, M., et al. (2016). Altered network efficiency in major depressive disorder. Bmc Psychiatry, 16(1), 450.

Ye, M., et al. (2015). Changes of Functional Brain Networks in Major Depressive Disorder: A Graph

ED

Theoretical Analysis of Resting-State fMRI. Plos One, 10(9), e0133775. Yi, L., et al. (2015). A study of brain white matter plasticity in early blinds using tract-based spatial statistics and tract statistical analysis. Neuroreport, 26(18), 1151-1154.

PT

Yuan, Y., et al. (2010). Abnormal Integrity of Long Association Fiber Tracts Is Associated With Cognitive Deficits in Patients With Remitted Geriatric Depression: A Cross-Sectional, Case-Control Study.

CE

Journal of Clinical Psychiatry, 71(10), 1386-1390. Yuan, Y., et al. (2008). Abnormal neural activity in the patients with remitted geriatric depression: a resting-state functional magnetic resonance imaging study. Journal of Afferctive Disorders,

AC

111(2), 145-152.

Yuan, Y., et al. (2008). Abnormal neural activity in the patients with remitted geriatric depression: a resting-state functional magnetic resonance imaging study. Journal of Afferctive Disorders, 111(2), 145-152.

Zalesky, A., et al. (2010). Whole-brain anatomical networks: does the choice of nodes matter? Neuroimage, 50(3), 970-983. Zeng, L. L., et al. (2014). Unsupervised classification of major depression using functional connectivity MRI. Human Brain Mapping, 35(4), 1630-1641. Zhang, J., et al. (2011). Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biological Psychiatry, 70(4), 334-342.

24

ACCEPTED MANUSCRIPT

Zhang, M., et al. (2018). Randomized EEG functional brain networks in major depressive disorders with greater resilience and lower rich-club coefficient. Clinical Neurophysiology, 129(4), 743-758. Zhang, Y., et al. (2017). Abnormal brain white matter network in young smokers: a graph theory analysis study. Brain Imaging & Behavior, 12(2), 1-12. Zhu, X., et al. (2012). Evidence of a dissociation pattern in resting-state default mode network connectivity in first-episode, treatment-naive major depression patients. Biological Psychiatry,

AC

CE

PT

ED

M

AN US

CR IP T

71(7), 611-617.

25

ACCEPTED MANUSCRIPT

Table 1 The abbreviations of AAL regions except the cerebellum. PreCG SFGdor ORBsup MFG ORBmid IFGoperc IFGoperc ORBinf ROL SMA OLF SFGmed ORBsupmed REC INS ACG DCG PCG HIP

Parahippocampal gyrus Amygdala Calcarine fissure and surrounding cortex Cuneus

PHG AMYG CAL CUN

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 Caudate nucleus Lenticular nucleus, putamen Lenticular nucleus, pallidum Thalamus Heschl gyrus Superior temporal gyrus Temporal pole: superior temporal gyrus

CR IP T

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 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

AAL Regions

AN US

Abbreviation

Middle temporal gyrus Temporal pole: middle temporal gyrus Inferior temporal gyrus

AC

CE

PT

ED

M

AAL Regions

26

Abbreviation LING SOG MOG IOG FFG PoCG SPG IPL SMG ANG PCUN PCL CAU PUT PAL THA HES STG TPOsup MTG TPOmid ITG

ACCEPTED MANUSCRIPT

Table 2 Demographic and clinical characteristics of subjects.

Variables(MeanSD) Gender(M:F) Age(years)

Major depression disorder (n=58) 27:31 33.89  12.14

Healthy controls(n=71)

p-value

41:30 34.84  12.39

0.258# 0.663*

17.31  5.93

_

_

HAMA

17.12  7.38

_

_

CR IP T

HAMD

AC

CE

PT

ED

M

AN US

Duration of _ 6.83  7.95 depression Abbreviations: SD=standard deviation, HAMD= Hamilton depression rating scale and HAMA=Hamilton Anxiety Scale. # and * indicate p value for chi-square test and two-sample t-test, respectively.

27

_

ACCEPTED MANUSCRIPT

Table 3 Altered network connectivity in FN and SN. The t-test statistical values for the FN and SN differences between MDD and HC(FDR correction). For the abbreviations of the regions, see Table 1 Network

Test statistic

Network

Test statistic

connectivity

(t-value)

connectivity

(t-value)

fmri

DTI

MDD
MDD
HIP.L- INS.L

SOG.L- STG.L

4.18

SFGdor.L- DCG.L

3.96

SOG.L- PUT.R

4.17

HIP.R-INS.R

3.72

SOG.L- ROL.R

3.96

HIP.L- PUT.L

SOG.L- STG.R

3.77

PreCG.L- DCG.L

ROL.L- STG.L

3.5

SPG.L- PAL.R

3.28

SOG.L- PAL.R

3.15

HC
AN US

SMA.L- PUT.L

4.43

CR IP T

SOG.L -INS.R

3.56 3.46 4.43 4.38

SPG.L- PUT.L

4.37

SMA.L- SMA.R

3.55

PoCG.L -PUT.L

3.27

AC

CE

PT

ED

M

SMA.L- SFGdor.R

28

ACCEPTED MANUSCRIPT

Table 4 Brain regions with significant group effect in the nodal efficiency between MDD patients and HC for functional and structural network. The P-value indicated significance difference between groups obtained from 5000 resampled groups (p <0.05, FDR correction). For the abbreviations of the regions, see Table 1. Regions

Control

MDD

P-value

PoCG.L

0.37210.0632

0.34080.0502

0.0028

PoCG.R

0.37280.0620

0.34500.0487

0.0066

SFGmed.R

0.38490.0528

0.35990.0526

0.0085

ANG.R

0.36480.0457

0.34460.0402

0.0099

MFG.R

0.35400.0507

0.33350.0447

0.0183

PreCG.R

0.36020.0538

0.33970.0581

0.0254

PCL.R

0.29250.0506

0.27140.0546

0.0254

HES.L

0.12620.0191

0.11540.0308

0.0169

0.15650.0275

0.14500.0239

0.0146

0.18580.0269

0.17520.0251

0.0247

PHG.L

0.15470.0200

0.14640.0219

0.0264

IPL.R

0.14890.0269

0.13910.0211

0.0266

ORBmid.R

0.13230.0250

0.12470.0295

0.0270

PCUN.L

0.21230.0263

0.20590.0283

0.0289

SMA.L

0.15120.0354

0.14530.0339

0.0318

AN US

AC

CE

PT

ED

MOG.L

M

DTI

PCL.R

CR IP T

fMRI

29

AC

CE

PT

ED

M

AN US

CR IP T

ACCEPTED MANUSCRIPT

Figure 1 Functional connectivity network at different sparsity for MDD patients (the red line) and controls (the gray line) and their statistical comparison results (p<0.05 5000 permutation test, FDR correction). (A) Gamma, (B) lambda, (C) sigma, (D) local effciency, (E) global effciency. The black triangles indicate a significant group difference.

30

AC

CE

PT

ED

M

AN US

CR IP T

ACCEPTED MANUSCRIPT

Figure 2 Structural connectivity network at different FA threshold for MDD (the red line) and controls (the gray line) and their statistical comparison results (p<0.05 5000 permutation test, FDR correction). (A) Gamma, (B) lambda, (C) sigma, (D) local effciency, (E) global effciency. The black triangles indicate a significant group difference.

31

AN US

CR IP T

ACCEPTED MANUSCRIPT

AC

CE

PT

ED

M

Figure 3 The MDD patients relative to the HC showed decreased connections in (A)FN, (B)SN; The 90 ROIs extracted from the eight regions(Gray font) of cerebrum are represented on one wheel. Red lines represent increased connectivity, and blue lines represent decreased connectivity (p = 0.05 after 5000 permutation test, FDR correction). For the abbreviations of the regions, see Table 1.

32

ED

M

AN US

CR IP T

ACCEPTED MANUSCRIPT

AC

CE

PT

Figure 4 Regions with significant differences in nodal efficiency between MDD patients and HC. Nonparametric permutation tests were applied to nodal efficiency of all 90 cortical regions (p < 0.05 5000 permutation test, FDR correction) (Red for FN, blue for SN and yellow for both). L=left;R=right. For the abbreviations of the regions, see Table 1

33

M

AN US

CR IP T

ACCEPTED MANUSCRIPT

AC

CE

PT

ED

Figure 5 Network resilience under random and target analysis. The alterations of global efficiency under removing node at random (right panel) and targeted pattern (left panel). The red line corresponded to the performance of HC, blue line for MDD, and the black triangles indicate significance difference between groups obtained from 5000 resampled groups (p <0.05, FDR correction).

34

AN US

CR IP T

ACCEPTED MANUSCRIPT

AC

CE

PT

ED

M

Figure 6 The nodal efficiency of several regions were negatively correlated with the HAMD score for SN (left). The altered edges were negatively correlated with the HAMD score for FN (right). The left brain map showed regions with decreased 𝐸𝑛𝑜𝑑 (Blue for HES.L, yellow for PUNC.L and red for MOG.L). The right brain map showed altered edges in NBS (Yellow plots were brain regions connected altered connections; Red lines were altered connections). For the abbreviations of the regions, see Table 1

35