Abnormal functional connectivity density in first-episode, drug-naive adult patients with major depressive disorder

Author’s Accepted Manuscript Abnormal functional connectivity density in firstepisode, drug-naive adult patients with major depressive disorder Zou Ke, Gao Qing, Long Zhiliang, Xu Fei, Sun Xiao, Chen Huafu, Sun. Xueli www.elsevier.com/locate/jad

PII: DOI: Reference:

S0165-0327(15)31102-2 http://dx.doi.org/10.1016/j.jad.2015.12.081 JAD7972

To appear in: Journal of Affective Disorders Received date: 13 October 2015 Revised date: 27 December 2015 Accepted date: 31 December 2015 Cite this article as: Zou Ke, Gao Qing, Long Zhiliang, Xu Fei, Sun Xiao, Chen Huafu and Sun. Xueli, Abnormal functional connectivity density in first-episode, drug-naive adult patients with major depressive disorder, Journal of Affective Disorders, http://dx.doi.org/10.1016/j.jad.2015.12.081 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 galley proof before it is published in its final citable 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.

Abnormal functional connectivity density in first-episode, drug-naive adult patients with major depressive disorder Zou Ke1#; Gao Qing2#; Long Zhiliang3; Xu Fei2; Sun Xiao4; Chen Huafu3*; Sun Xueli5* 1

Neurobiological laboratory, West China Hospital of Sichuan University, Chengdu, Sichuan, China 2

School of Mathematical Sciences, University of Electronic Science and

Technology of China, Chengdu, Sichuan, China 3

Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology,

University of Electronic Science and Technology of China, Chengdu, Sichuan, China 4

Department of Orthopedic Surgery, West China Hospital of Sichuan University, Chengdu, Sichuan, China 5

Mental Health Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China

*Correspondence to Dr. Sun Xueli (Mental Health Center, West China Hospital of Sichuan University,

South

Dianxin

Road

#28

Chengdu,

610041,

Sichuan,

China;

E-mail:

[email protected].) and Ph.D. Chen Huafu (Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan, China. E-mail: [email protected].) #

Zou Ke and Gao Qing contributed equally to this article.

Abstract Previous studies have found evidence of brain functional connectivity (FC) changes with pre-selected region-of-interest (ROI) method in major depressive disorder (MDD). However, these studies couldn’t completely exclude personal inequality when drawing ROIs manually and didn’t measure the total number of FC for each voxel. Here, we firstly applied functional connectivity density (FCD) mapping, a voxel-based analysis to locate the hubs with amount changes of FC between twenty-two first-episode, drug-naive adult MDD patients and twenty-two healthy control (HC) subjects. Both short-range (local) FCD and long-range (distal) FCD were measured. The relationships of FCD changes with Hamilton Depression Rating Scale (HAMD) scores and illness duration were also explored. Compared with the HC group, MDD patients showed significantly decreased short-range FCD in the left superior temporal gyrus (STG), the right orbital frontal cortex (OFC) and bilateral precuneus, while significantly decreased long-range FCD was found in bilateral middle occipital gyrus (MOG), superior occipital gyrus (SOG) and right calcarine. These results firstly demonstrated both local and distal alterations of connection amount at voxel level, and highlighted that the OFC, the precuneus, the STG and the visual cortex were important brain network hubs for first-episode, drug-naive adult MDD patients. Our findings were complementary for previous structural and functional studies in MDD patients, and provided new evidence of the dysfunction of connection hubs in the pathophysiology of MDD at voxel level. Key words: major depressive disorder, first-episode, drug naïve, functional connectivity density, hub, resting-state fMRI

Abnormal functional connectivity density in first-episode, drug-naive adult patients with major depressive disorder Zou Ke, Gao Qing, Long Zhiliang, Xu Fei, Sun Xiao, Chen Huafu, Sun Xueli

Introduction Major depressive disorder (MDD) is a consequential public health problem, with a lifetime prevalence as high as 20% worldwide (Kessler et al., 2005). It was the foremost contributor to the global burden of disease as measured by years of health lost to disability, according to the World Health Organization. It is known to all that brain function depends on the normal work of series networks. Brain networks are defined in graph theory as a set of nodes or vertices and the edges or lines between them, and those nodes with high degree or high centrality are hubs (Bullmore and Sporns, 2009). Hubs play essential roles in the interconnection of distributed functionally specified regions and in coordinating performance across the brain. Research showed that brain networks appeared to have few and well localized hubs for fast integration of neural processing, and their dysfunction could contribute to neuropsychiatric diseases (Tomasi and Volkow, 2011). Over the last two decades, sophisticated brain-imaging techniques have made us a deep understanding of brain networks. One of the most effective techniques for detecting network function was resting-state functional magnetic resonance imaging (fMRI). It is an established imaging modality specific for investigating the integration of neural networks at rest (e.g. lying still with eyes closed) when no task is performed. To date, resting-state fMRI has been widely

used in neuroimaging studies of MDD. In a recent systematic review of resting-state fMRI studies in MDD, it was summarized that though findings of different research groups were somewhat confusing, the current evidence largely suggested abnormal resting functional connectivity (FC) in the cortico-limbic mood regulating circuits (MRC) and the default-mode network (DMN) to be contributing to the pathophysiology of MDD (Wang et al., 2012a). Because depressive episodes and antidepressant medication might have possible influence to the brain function, to study first-episode, medication-free patients maybe better understand the primary functional changes of MDD. There were a few resting state fMRI research focus on first-episode or medication-naive adult patients with MDD so far, the approaches they used including region-of-interest (ROI) analysis (Anand et al., 2009; Cao et al., 2012; Ma et al., 2012; Tao et al., 2013; Zhang et al., 2011), regional homogeneity (ReHo) approach (Guo et al., 2011), independent component analysis (ICA) (Veer et al., 2010; Zhu et al., 2012), and amplitude of low-frequency fluctuation (ALFF) (Wang et al, 2012b). These methods were constrained by the fact that they relied strongly on a priori selection of specific seed regions rather than allowing for the characteristics of the network to identify and locate the node regions; what’s more, these methods were also computationally demanding (Tomasi and Volkow, 2010). And, previous methods calculated FC between brain regions, but they did not measure the total number of FC per voxel. Thus, it reminded us to use a new method to overcome these limitations. Functional connectivity density (FCD) mapping is such a new method. It is a voxel-wise data-driven method for identifying brain hubs and is an ultrafast technique that can speed up the computation of the number of functional connections (Tomasi and Volkow, 2010). Namely, it can measure the amount of FC for each voxel. FCD mapping can calculate short-range FCD and long-range FCD

respectively. The short-range FCD was considered to indicate central roles of voxels in the functional specialization, and mainly located in the posterior cingulate/ventral precuneus; the long-range FCD, representing functional integration of the whole-brain networks and mostly distributed in the visual cortex (Tomasi and Volkow, 2010; Tomasi and Volkow, 2012). Compared with previous FC approaches, it is a good method for identifying hubs with connection number changes at voxel level, and, it avoids artificial factors to a great extent. As far as we know, no study to date has uncovered FC changes at voxel level in first-episode, drug-naive adult MDD patients. Therefore, we performed FCD mapping method with resting-state fMRI data to locate the hubs with amount changes of FC between first-episode, drug-naive adult MDD patients and healthy controls. Then, correlation analysis was performed to explore the relationship between the neuronal connectivity changes and symptom severities and illness duration in patient group. According to Tomasi and Volkow’s studies, we hypothesized that FCD changes might be located in certain regions of the DNM and the visual cortex.

Materials and Methods Participants Twenty-five first-episode, drug-naive patients were recruited from the Mental Health Center, West China Hospital of Sichuan University. They all met DSM-IV criteria for MDD according to the diagnostic assessment by the Structured Clinical Interview for DSM-IV-Patient Edition (SCID-P) and were with scores of 18 or greater on the 17-item Hamilton Depression Scale (HAMD). Patients comorbid with other Axis I and Axis II psychiatric disorders such as schizophrenia, bipolar affective disorder, personality disorders and substance abuse or dependence

were excluded according to the SCID-I and SCID-II assessment. Twenty-four healthy controls (HC) were also recruited from the 5 city districts of Chengdu, China. They were screened through a diagnostic interview, the Structured Clinical Interview for DSM-IV Nonpatient Edition (SCID-NP), to rule out current or past DSM-IV Axis I disorders. They were also interviewed to affirm that there was no history of psychiatric illness in their first-degree relatives. All subjects were right-handed and without severe or acute medical conditions physically based on clinical evaluations and medical records. The ethical committee of the West China Hospital of Sichuan University approved this study, and all participants provided written informed consent.

MRI acquisition Participants underwent scanning using a GE Signa EXCITE 3-T MR system (GE Healthcare, Milwaukee) with an 8-channel phased array head coil. Foam padding was used to minimize head motion for all subjects. First, high-resolution T1-weighted images were acquired using a 3D spoiled gradient-recalled (SPGR) sequence, producing 156 contiguous coronal slices with a slice thickness of 1.0 mm (TR=8.5 msec, echo time=3.4 msec, flip angle=12º). The final matrix size of T1-weighted images was automatically interpolated in-plane to 512×512, which yielded an in-plane resolution of 0.47×0.47 mm2. Then, BOLD signal levels (TR=2000 msec, echo time=30 msec, flip angle=90º) were obtained via a gradient-echo echo-planar imaging (EPI) sequence. Five dummy scans were collected before fMRI scans were performed, and the first two volumes of fMRI time series were discarded for magnetization stabilization (slice thickness=5 mm [no slice gap]; matrix=64×64; field of view=240×240 mm2; voxel size=3.75×3.75×5 mm3). Each brain volume comprised 30 axial slices, and each functional run contained 200 image volumes finally. During the scan, subjects were asked to lie still with their eyes closed and to avoid falling asleep.

After scanning, all subjects were asked whether they fell asleep during the scan, and all subjects confirmed that they were awake.

Data processing and analysis Data preprocessing was performed using the Statistical Parametric Mapping software (SPM8, http://www.fil.ion.ucl.ac.uk/spm). First, the resting-state fMRI images were first corrected by the acquisition time delay among different slices, and then realigned to the first volume for head-motion correction. The dataset with translational or rotational parameters exceeding ±1.5 mm or ±1.5° would be excluded, so two HC subjects and three patients have been excluded for head-motion. Accordingly, twenty-two MDD patients and twenty-two HC subjects were remained for further analysis. Second, the images were spatially normalized into a standard stereotaxic space at 3×3×3mm3, using the Montreal Neurological Institute (MNI) template in SPM8. No spatial smoothing was applied in order to avoid artificially introducing local spatial correlation (Achard and Bullmore, 2007). Since functional connectivity analysis is sensitive to gross head motion effects, we further evaluated the framewise displacement (FD) (Power et al., 2012) with the suggested threshold of 0.5 to express instantaneous head motion. The largest FD of all subjects was less than 0.2 mm. Two-sample t-test showed there was no significant difference of FD between the two groups (0.057±0.031 for HC and 0.048±0.022 for MDD; p = 0.301). Images were then corrected by linear regression to remove the possible spurious variances including six head motion parameters, the white matter (WM), and the ventricular signals averaged from a WM mask and a ventricular mask respectively. The residuals of these regressions were temporally band-pass filtered (0.01 < f < 0.08Hz) to reduce low-frequency

drifts and physiological high-frequency respiratory and cardiac noise, and linearly detrended for further analysis.

Whole brain FCD mapping We limited the procedure within a whole brain gray matter mask including cerebellum (Nvoxels = 54837) that was created based on the automated anatomical labeling (AAL) atlas. The Pearson’s correlation rij was calculated on a voxel-based level to build the whole brain FC network for each subject. A Fisher’s r-to-z transformation was then applied to the correlation matrices to improve normality. To ensure that differences between groups could not be attributed to differences in network sparsity (cost), we thresholded network matrices by cost. To do this, the correlation matrices were firstly thresholded into sparse and strong FC networks using Bonferroni multiple correction with p=0.05. The minimal cost of the FC networks of all the subjects was then chosen as the threshold cost, and was used to correct the threshold for the FC networks of all the subjects. The short-range FCD and long-range FCD were computed following the steps below. Briefly, the long-range FCD at a given voxel i was computed using the following equation (Tomasi and volkow, 2012):

long-range FCDi =gFCDi  short-range FCDi The gFCDi representing the global FCD (gFCD) at voxel i was calculated by the following equation (van den Heuvel et al, 2008):

gFCDi =

1  zij , rij >r0 N j¹i

where rij was the correlation coefficient between voxel i and voxel j; zij was the Fisher’s z-transformation of rij; r0 represented the correlation threshold corresponding to the threshold cost

aforementioned, and N represented the number of voxels survived after thresholding. The computation of short-range FCD at voxel i was similar with the gFCD calculation, but restricted within its local cluster. The number of voxels for calculating short-range FCD at voxel i here was the number of voxels within its local cluster. To determine the local cluster of voxel i, three-dimensional searching algorithm developed in the interactive data language (IDL by ITT Visual Information Solutions) was employed. The algorithm is mainly by looking for voxel coordinates of a given voxel i, and then look for the other neighboring voxel of three-dimensional space according to the voxel coordinates of voxel i. The voxel i and all the neighboring voxel composed the original local cluster of voxel i. Subsequently, a spatial smoothing filter was employed for each volume by convolving with an isotropic Gaussian kernel (FWHM= 8 mm) before group comparing. Two-sample t-test was then used to obtain the regions with different FCD between two groups, with age, sex and FD as covariates. To investigate whether FCD values were correlated with symptom severity and course of disease in MDD patients, relationships between FCD values in regions showing significant group differences and HAMD scores and illness duration were further detected by correlation analysis.

Results There were no significant differences in age and sex between groups (Table 1).

Kolmogorov-Smirnov test was used to test if the FCD distributions follow the normal distributions. All the p values were larger than 0.01, therefore the assumption of normal distributions were acceptable. The short-range FCD results

Figure 1 depicts the average short-range FCD pattern of HC group (first row) and MDD group (second row), respectively. The corresponding correlation threshold is R= 0.45, cost= 0.03. Both groups showed relatively higher short-range FCD values in distributed brain regions including the orbital frontal cortex (OFC), precuneus, postcentral, superior temporal cortex (SPG) and occipital cortex. The third row in Fig. 1 shows the significantly different short-range FCD between the groups. Statistical significance of difference in normalized FCD map between the two groups was based on the AlphaSim program in the REST Software for multiple comparisons with p<0.005 (single voxel threshold of p < 0.01 and cluster size > 88). Compared with HC group, MDD group showed significantly decreased short-range FCD in the left superior temporal gyrus (STG), the frontal lobe encompassing right OFC and bilateral precuneus. Significantly increased short-range FCD in MDD group than in HC group was not found in the present study. Table 2 illustrated the details of the brain regions with significantly reduced short-range FCD between the two groups. The positive t-values represented that MDD group had lower short-range FCD values than HC group.

The long-range FCD results The average long-range FCD pattern of HC group and MDD group are presented in figure 2. The spatial distributions of brain regions with relatively high long-range FCD were similar between the two groups, which were primarily located in the STG and MTG, bilateral medial frontal and parietal regions. The third row in Fig. 2 shows the significantly reduced long-range FCD between the two groups (p<0.005, single voxel threshold of p < 0.01 and cluster size > 88 Alphasim correction). MDD group showed significantly decreased long-range FCD in bilateral occipital cortex including bilateral middle occipital gyrus (MOG), bilateral superior occipital

gyrus (SOG) and right calcarine. None significantly increased long-range FCD was found. Table 3 presents the details of the brain regions with significantly reduced long-range FCD between HC and MDD group.

Relationships between FCD and Clinical Variables We performed the correlation analysis on a whole brain voxels level and then consider overlap with the results from the patient group comparison. In patient group, no significant correlation between short-range FCD values and illness duration (p< 0.005, cluster size = 32, Alphasim correction) or HAMD scores (p< 0.005, cluster size = 28, Alphasim correction) was observed. There was no significant correlation between long-range FCD values and illness duration (p< 0.005, cluster size = 30, Alphasim correction) or HAMD scores (p< 0.005, cluster

size = 24, Alphasim correction) in patient group, either.

Discussion In this study, we explored the hubs with amount changes of functional connectivity between first-episode, drug-naive adult MDD patients and healthy controls by using voxel-wise data-driven method firstly. Our results demonstrated that compared with HC group, MDD patients had decreased local and distal FCD, implying a deficit of both specialization and integration of brain network.

Frontal cortex In this study, MDD patients showed significantly decreased short-range FCD values in the left OFC, suggesting that OFC was a very important hub for first-episode, drug-naïve MDD patients. Structural neuroimaging studies have observed reduction of OFC volumes in MDD patients (Bremner et al., 2002; Lacerda et al., 2004), suggesting a role of the OFC in the

pathophysiology of MDD. Using meta-analytic connectivity modeling (MACM), Zald et al (2014) reported that OFC showed connectivity with prefrontal regions, default mode and limbic regions, these brain areas were known to be associated with cognitive function and emotional processing, and in a review, Kringelbach concluded that OFC is a key region in the network of brain structures implementing the functional neuroanatomy of emotion in humans (Kringelbach et al, 2004). Further, Drevets et al (2007) summarized that OFC modulated the effects of the amygdala in organizing emotional expression via direct projections to the amygdala and to the hypothalamic and brain stem structures. And, OFC belongs to the reward systems of the brain (Schultz et al, 2006), significantly reduced reward-learning signals have been found in MDD patients (Kumar et al, 2008), and diminished responses in the OFC to rewarding stimuli were found in subjects with a parental history of depression (McCabe et al, 2012). So the reduction of FC number in OFC certainly would affect the network function and then lead to cognitive and emotional syndromes. Our results first reported the total number changes of FC in OFC with first-episode, drug-naive adult MDD patients, and again conformed that OFC plays a crucial role in neural circuits in MDD.

Parietal cortex We also found decreased short-range FCD values in bilateral precuneus, mainly in the dorsal part. It was generally believed that the precuneus took part in the DMN (van den Heuvel and Hulshoff Pol, 2010). Further, a large sample study included 225 subjects segmented the precuneus into three sub-regions, and accurately revealed that the ventral part of it was more preferably ascribed to the DMN, the dorsal anterior and dorsal posterior precuneus were involved in spatially guided behaviors, mental imagery, and episodic memory as well as self-related processing (Zhang and Li, 2012). Structurally, the precuneus has a wide-spread network to cortical and subcortical

structures such as the posterior cingulate, retrosplenial cortices, the caudal parietal operculum, the inferior, superior parietal lobules, the frontal lobes, the thalamus, the dorsolateral caudate nucleus, the putamen and so on; so, the precuneus was involved in visuo-spatial imagery, episodic memory retrieval and self-processing, as a review summarized (Cavanna and Trimble, 2006). And these dysfunctions have been reported in MDD patients. i. e. drug-free MDD patients were impaired significantly in visuo-spatial learning (Porter et al, 2003), significant correlations between depression severity and episodic memory were found in MDD patients (McDermott et al, 2009), emotional imagery-avoidance occurred in depressive rumination (Fresco et al, 2002). In previous study, Tomasi and Volkow (2011) have proposed that the precuneus was a key local FCD hub; then, the decreased connection number of any part for precuneus surely would impair corresponding cognitive function. So our findings certified the important role of precuneus in brain network for first-episode, drug-naïve MDD at voxel level.

Temporal cortex In our study, decreased short-range FCD values were observed in left STG, suggesting it was a vulnerable hub for MDD. In previous studies, significant reduction of grey matter volumns/ density was found in STG with middle aged MDD (Abe et al., 2010; Mwangi et al., 2012), late-onset MDD (Hwang et al, 2010) and first-episode MDD patients (Peng et al, 2011). In familial risk for developing MDD subjects (including children), cortical thickness were significantly thinner in high familial risk group than in low familial risk group, and significant inverse correlations with symptom severity were evident in the left STG in both risk groups (Petersona et al, 2009). These studies indicated that STG was involved in the pathogenesis of MDD. Friederici et al (2003) found that STG took part in semantic and syntactic processes, and

impaired semantic fluency have been found in MDD at the acute phase and recurrent stage of illness (Fossati et al., 2003; Schmid et al., 2011); Leff (Leff et al, 2009) suggested that left STG was a shared substrate for auditory short-term memory and speech comprehension, and auditory memory difficulties have been found in MDD patients (Considine et al, 2011). Thus our results of the reduction of connections amount here shed new light on dysfunction of auditory memory and language processing in MDD.

Occipital cortex In this study, decreased long-range FCD was found in bilateral middle occipital gyrus (MOG), superior occipital gyrus (SOG) and right calcarine, suggesting that the visual cortex was a key hub for MDD. Structurally, significant gray matter volume reduction in left occipital lobe has been reported in MDD patients (Abe et al., 2010), significantly thinner MOG cortex was found in high familial risk group than in low familial risk group, and significant inverse correlations with symptom severity were evident in MOG in both risk groups (Petersona et al., 2009). Lately, Maller et al (2014) found occipital bending is more common among patients with MDD than healthy subjects. So, it was indisputable that the primary visual cortex had a role in pathophysiology of MDD. And, decreased early visually evoked potentials (VEP) amplitudes were reported in patients with MDD (Normann et al, 2007), significantly lower cone electroretinogram (ERG) maximal amplitude and lower rod sensitivity was found in depressed patients (Lavoie et al, 2009), the electro-oculographic findings suggested that depressed patients had lower dark trough and light peak values in comparison to controls (Fountoulakis et al, 2005), and using the pattern electroretinogram (PERG), Bubl et al (2010) found that both unmedicated and medicated depressed patients displayed dramatically lower retinal contrast gain. These studies objectively

confirmed that there was dysfunction in the visual system of MDD patients, this might have much to do with the reduction of connection number here. Besides, a working memory task-related fMRI study documented that the non-psychotic depressed groups showed greater activation in the SOG than the healthy comparison (Garrett et al, 2011), suggesting a part in cognition for SOG in MDD. The long-range FCD was distributed maximally in the visual cortex (Tomasi and Volkow, 2012), it was just here that we found the total number of long-range connections were reduced, so our findings highlighted the important role of the visual cortex damage in the pathogenesis of MDD. Because it was a cross-section designed study, we were unable to precisely explore the dynamic changes of Short-range FCD and Long-range FCD values, the relation between those changes and HAMD, and whether these altered neural connectivity are the consequences of disease development or an inherent biomarker for MDD.

Conclusion In summary, we have firstly using voxel-based FCD mapping to demonstrated both local and distal alterations of connection amount in first-episode, drug-naive adult MDD patients. Our findings highlighted that the OFC, the precuneus, the left STG and the visual cortex were important brain network hubs for MDD. Our findings were complementary for previous structural and functional studies in MDD patients, and provided new evidence of the dysfunction of connection hubs in the pathophysiology of MDD at voxel level. A larger sample size and follow-up study that combined with morphometric analysis is required to explore both structural and functional changes of MDD, and to explore the dynamic relationship between these changes and the symptom severity, treatment response, and even gene expression.

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Figure captions Fig. 1: The Short-range FCD maps within groups and statistical differences between groups (p＜ 0.005, Alphasim correction).

Fig. 2: The Long-range FCD maps within groups and statistical differences between groups (p＜ 0.005, Alphasim corrected).

Table 1. Demographics and Clinical Characteristics of the Subjects Variables(Mean±SD)

HC

MDD

p value

N (male/female) Age (year) HAMD Illness duration (month)

22(9/13) 36.7±10.9 NA NA

22(9/13) 38.4±11.1 25.1±4.3 7.4±4.3

/ 0.6148a / /

Data are presented as the range of minimum–maximum (mean± SD). HAMD: Hamilton Depression Scale; HC: Healthy controls; MDD: major depressive disorder; NA: not applicable; a

The p value was obtained by two-sample two-tailed t test.

Table 2. Regions of significantly different short-range FCD between HC group and MDD group Coordinates

Region name

Hem

BA

X

Y

Z

cluster size

Peak t-value

L

40

-60

-30

18

93

4.03

R

32

0

33

-9

508

3.23

L R

5 5

-9 2

-45 -42

57 57

57 71

2.90 3.15

Temporal

Superior temporal gyrus Frontal

Orbital frontal cortex Parietal

Precuneus

BA: Brodmann Area; Hem: Hemisphere Table 3. Regions of significantly different long-range FCD between HC group and MDD group Coordinates Region name

Hem

BA

X

Y

Z

cluster size

Peak t-value

L R L R R

17 / 18 19 17

-15 27 -18 21 18

-90 -84 -99 -102 -87

-3 9 12 12 0

54 51 58 34 45

3.10 3.62 3.52 2.60 2.85

Occipital

Middle occipital gyrus Superior occipital gyrus Calcarine

Highlights ·Firstly performed voxel-based FCD mapping in MDD. ·Both short-range FCD and long-range FCD were measured. ·The OFC, precuneus, STG and visual cortex were important hubs for MDD.