Topological reorganization of the default mode network in patients with poststroke depressive symptoms: A resting-state fMRI study

Journal of Affective Disorders 260 (2020) 557–568

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Research paper

Topological reorganization of the default mode network in patients with poststroke depressive symptoms: A resting-state fMRI study

T

Liang Yana,b, Yao Yong-Chengc, Zhao Leic, Shi Linc, Chen Yang-Kund, Mok Vincent CTe, ⁎ Gabor S. Ungvarif,g, Chu Winnie CWc, Tang Wai-Kwongb,h, a

Department of Neurology, the First Affiliated Hospital, Jinan University, Guangzhou, Guangdong, China Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, SAR, China c Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, SAR, China d Department of Neurology, Dongguan People's Hospital, Dongguan, Guangdong, China e Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, SAR, China f University of Notre Dame Australia, Fremantle, Australia g Division of Psychiatry, School of Medicine, University of Western Australia, Perth, Australia h The Chinese University of Hong Kong, Shenzhen Research Institute, Shenzhen, Guangdong, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Stroke Poststroke depressive symptoms Default mode network Functional magnetic resonance imaging Graph theoretical analysis

Objective: This study mapped the topological configuration of the default mode network (DMN) in patients with depressive symptoms after acute ischemic stroke. Methods: The study sample comprised 63 patients: 36 with poststroke depressive symptoms (PSD) and 37 without PSD matched according to age, gender and the severity of stroke. PSD was defined by a cutoff of ≥ 7 on the 15-item Geriatric Depression Scale (GDS). Resting-state functional magnetic resonance imaging (fMRI) was used to examine functional connectivity (FC) to reconstruct the DMN. Network based statistics estimated the FC differences of the DMN between the PSD and non-PSD groups. Graph theoretical approaches were used to characterize the topological properties of this network. Results: The study sample mainly comprised patients with mild to moderate stroke. A widespread hyper-connected configuration of the functional DMN was characterized in PSD group. The orbital frontal, dorsolateral prefrontal, dorsal medial prefrontal and, ventromedial prefrontal corticis, the middle temporal gyrus and the inferior parietal lobule were the functional hubs related to PSD. The nodal topology in inferior parietal lobule and superior frontal gyrus, overlapping with dorsal medial prefrontal and, ventromedial prefrontal cortices, tended to be functionally integrated in patients with PSD. After False Discovery Rate correction, no significant difference between the PSD and non-PSD groups was found with respect to the global and nodal metrics of the DMN. However, the correlations between these altered network metrics and severity of PSD were lacking. Limitations: The diagnosis of PSD was based on the GDS score rather than established with a structured clinical interview. Conclusions: The DMN in PSD was functionally integrated and more specialized in some core hubs such as the inferior parietal lobule and dorsal prefrontal cortex. The configuration of the subnetwork like DMN may be more essential in the pathogenesis of PSD than single stroke lesions.

1. Introduction Poststroke depression is one of the most common neuropsychiatric sequelae after stroke. Poststroke depression occurs in approximately one third of stroke survivors (Hackett and Pickles, 2014). Poststroke depression adversely affects stroke patients’ physical recovery (Kutlubaev and Hackett, 2014), quality of life (Shi et al., 2016), and

⁎

survival (Bartoli et al., 2013). While previous studies emphasized the role of stroke lesions (locations or size) in the pathogenesis of PSD, recent meta-analyses failed to confirm a relationship between the locations of stroke lesions and the risk of PSD (Carson et al., 2000; Wei et al., 2015), implicating that single lesions only contribute but may not account for the development of depression following stroke. There is growing evidence that poststroke depressive symptoms (PSD) is related

Corresponding author at: Shatin Hospital, 7B, Shatin Hospital, Shatin, N.T, Hong Kong, SAR, China. E-mail address: [email protected] (W.-K. Tang).

https://doi.org/10.1016/j.jad.2019.09.051 Received 31 January 2019; Received in revised form 2 July 2019; Accepted 8 September 2019 Available online 11 September 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.

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history of any major psychiatric disorders; (4)severe neurological deficits such as severe aphasia, auditory, visual or cognitive impairments, grave comorbid medical conditions, or physical frailty that impeded the completion of assessments; (5) recurrent stroke before assessments; and (6) non-Chinese ethnicity. The study protocol was approved by the Joint Chinese University of Hong Kong-New Territories East Cluster Clinical Research Ethics Committee. All participants signed a consent form.

to dysfunctional neural networks (Gong and He, 2015; Lassalle-Lagadec et al., 2012; Yang et al., 2015). Resting-state functional magnetic resonance imaging (fMRI) enables noninvasive examinations of the network-based pathogenesis beyond stroke lesions (Veldsman et al., 2015). In fMRI studies examining PSD, the default mode network (DMN) has been gaining much attention (Balaev et al., 2018; Lassalle-Lagadec et al., 2012; Vicentini et al., 2016). DMN refers to a network distributed throughout the brain and spontaneously activated at rest while deactivated during cognitive tasks (Raichle, 2015). The core components of the DMN include the medial prefrontal cortex (mPFC), the posterior cingulate cortex (PCC), the precuneus, and the inferior parietal lobule (IPL) (Raichle, 2015). Activation of the DMN mediates intrinsic attention, emotional processes, self-reference, and memory (Whitfield-Gabrieli and Ford, 2012). Lassalle-Lagadec et al. (2012) reported first the correlation between altered functional connectivity (FC) within the DMN and depressive symptoms at 3-month after stroke. Subsequent studies replicated this finding that patients with PSD have altered DMN FC pattern compared with those without depression and non-stroke patients examined at both the acute and subacute stages of stroke (Vicentini et al., 2016; Zhang et al., 2018). Further, the aberrant across-network FC between the DMN and salience network also correlates with the severity of poststroke depression (Balaev et al., 2018). Yet, impaired FC alone cannot fully account for the network-based pathogenesis of PSD either. Topological analysis using strategies such as graph theory and network-based statistics (NBS) are powerful mathematical methods to localize network-based pathogenesis underlying neuropsychiatric diseases (Bullmore and Sporns, 2009). Graph theory mathematically models the brain into a graph comprising a series of nodes (brain regions) and edges (the connectivity between a pair of nodes), whose arrangement refers to the network topology (Bullmore and Sporns, 2009). A growing number of network-based studies have found association between depressive disorder and disrupted topological architectures of the brain network. For instance, patients with major depressive disorder (MDD) have reduced global efficiency whereas have increased global betweenness-centrality of the whole brain functional network compared with healthy subjects (Meng et al., 2014). Furthermore, patients with poststroke depression show decreased local efficiency of a depression-related subnetwork (Yang et al., 2015). However, to the best of our knowledge, the topological reorganization of DMN in patients with PSD has not been investigated. The main hypothesis of this study was that patients with PSD would demonstrate altered topological organization of the DMN compared with those without PSD. A further hypothesis was that the altered network properties would correlate with the severity of depressive symptoms after stroke. To test these hypotheses, a resting-state fMRI study was conducted to map the topological configuration of the DMN in patients with acute ischemic stroke, and to explore the relationship between the altered network measures and depressive symptoms at three months after stroke.

2.2. Collection of demographic and clinical data A research assistant collected patients’ basic demographic data (age, gender, years of education, and handedness) and vascular risk factors (history of hypertension, hyperlipidaemia, diabetes mellitus, and smoking). On admission, stroke severity was assessed with the National Institutes of Health Stroke Scale (NIHSS) by a nurse in the stroke unit (Brott et al., 1989). 2.3. Neuropsychiatric assessments Three months after stroke, a trained research assistant who was blind to the patients’ clinical and radiological data assessed the patients’ depressive symptoms with the Hong Kong version of the 15-item Geriatric Depression Scale (GDS-15) (Lim et al., 2000). The GDS-15 had good sensitivity (89%) and specificity (73%) for screening for PSD in an elderly Chinese population (Tang et al., 2004a). In this study, PSD was diagnosed if the GDS score was of ≥7 (Tang et al., 2004b). Patients’ social support and interactions were assessed with the Lubben Social Network Scale (LSNS) (Lubben, 1988). 2.4. MRI acquisition Within three months of the index stroke, all patients were scanned with a 3.0-T MRI system (Achieva, Philips Medical System) with the following sequences: resting-state functional (rs-fMRI) images, diffusion-weighted imaging (DWI), susceptibility weighted imaging (SWI), 3D-T1- and T2-weighted imaging, and fluid attenuated inversion recovery (FLAIR). Rs-fMRI images [TR = 2045 ms, TE = 25 ms, flip angle = 90°, FOV = 205 mm, slice thickness/gap = 3.2 mm/0.0 mm, and acquisition time = 361 s, number of time points = 170, number of slices = 47, matrix = 64 × 64]; DWI [TR = 2500 ms, TE = 53 ms, flip angle = 90°, FOV = 230 mm; slice thickness/gap = 5 mm/0.5 mm, acquisition time = 52 s, number of slices = 25, and matrix = 256 × 256]; SWI [TR = 17 ms, TE = 24 ms, flip angle = 15°, FOV = 230 mm, slice thickness/gap = 2.5 mm/0.0 mm, acquisition time = 177 s, number of slices = 50, and matrix = 256 × 256]; 3D-T1 weighted images [TR = 25 ms, TE = 2 ms, flip angle = 30°, FOV = 230 mm, slice thickness/gap = 5.0 mm/0.5 mm, acquisition time = 163 s, number of slices = 25, matrix = 256 × 256]; Axial T2 weighted images [TR = 2600 ms, TE = 80 ms, flip angle = 90°, FOV = 230 mm, slice thickness/gap = 5 mm/0.5 mm, acquisition time = 142 s, number of slices = 25, and matrix = 1024 × 1024]; and Axial FLAIR [TR = 1100 ms, TE = 125 ms, flip angle = 90°, FOV = 230 mm, slice thickness/gap = 5 mm/0.5 mm, and acquisition time = 198 s, number of slices = 25, and matrix = 704 × 704].

2. Materials and methods 2.1. Participants The study included 63 patients: 36 patients with PSD and 37 age( ± 5 years), gender-, and stroke severity- (NIHSS score: ± 1) matched patients without PSD. Patients were recruited from the Stroke Unit, Prince of Wales Hospital, Hong Kong, between January 2014 to December 2016. The study included patients with (1) first-ever, clinically defined acute ischemic stroke (Sacco et al., 2013); (2) 45 to 80 years of age; and (3) MRI examination on admission. Exclusion criteria were:(1) refusal to participate, or inability to give written consent; (2) history of neurological diseases, such as brain tumor, brain trauma, dementia or Parkinson's disease; (3) history of psychiatric disorders, such as schizophrenia, mood disorders, or substance abuse; or family

2.5. Acute infarcts and SVD markers assessments Two qualified neurologists (Y.K.C and Y.L) blind to all clinical data other than age and gender assessed vascular lesions on DWI, SWI, T2weighted, and FLAIR images. Twenty-three of the 27 and 25 of the 36 patients from the non-PSD and PSD groups, respectively had an acute infarct. These lesions were delineated by one of the two, above-mentioned neurologists (Y.L). The maps of the lesions were then normalized to the Montreal Neurological Institute (MNI)−152 space with an image registration software 558

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threshold affects the statistical inference of NBS, researchers are encouraged to try different primary thresholds (Liu et al., 2016; Marques et al., 2015; Zalesky et al., 2010). Three thresholds were separately applied to NBS: (1) t = 2.39 (P < 0.01, one-tail, df = 61); (2) t = 2.69 (P < 0.005, one-tail, df = 61), and (3) t = 3.23 (P < 0.001, one-tail, df = 61).

package named RegLSM (Zhao et al., 2018). Subsequently, the normalized lesion maps were overlapped and overlaid on the 1 mm MNI152 template to generate the infarct prevalence maps. SVD markers: The diagnoses of SVD markers including lacune, white matter hyperintensities, and cerebral microbleeds corresponded to the STRIVE consensus (Wardlaw et al., 2013). The number of lacune and cerebral microbleeds were counted. The severity of white matter hyperintensities were measured with the Fazekas scale (Fazekas et al., 1987), while the perivascular spaces in the basal ganglia were assessed with a qualitative scoring method (Potter et al., 2015). Any disagreement would invite a consensus from a third, qualified neurologist (Y.L.L). Interrater reliability for all he above assessments was determined by rating 30 randomly selected MRI scans. The results indicated acceptable interrater reliability: the kappa coefficient values were 0.76 for lacunae, 0.63 for periventricular WMH, 0.62 for deep WMH; 0.78 for cerebral microbleeds, 0.80 for enlarged perivascular spaces; and 0.66 for both acute and old infarcts.

2.8. Graph analysis Global and nodal metrics were calculated using the Brain Connectivity Toolbox (Rubinov and Sporns, 2010). A thresholding procedure was first applied to exclude the confounding effects of spurious connections in adjacency matrices. Significant correlations (p < 0.05, FDR corrected) were retained whereas non-significant correlations were set to zeros. The topological architecture of the DMN was delineated with the global metrics, integrative descriptions of whole network, and the nodal metrics describing the local topology of the nodes (Rubinov and Sporns, 2010). Their definitions and calculations are summarized as follows: Degree and betweenness. Degree Kw and betweenness Bw of network are measures of centrality. Degree of a weighted-network Kw was calculated by averaging nodal degree kiw across all nodes,

2.6. Functional network analysis 2.6.1. Images pre-processing The fMRI data were pre-processed with a Matlab-based toolbox (DPARSF) (Yan et al., 2016; Yan and Zang, 2010) as follows: the first 10 volumes were discarded for signal equilibrium. The remaining volumes were corrected for the inter-slice differences in acquisition time and head motions. Data of 3 patients from non-PSD group and 2 patients from PSD group were excluded due to severe head motions of more than 1.5 mm of translation or 1.5° of rotation or 0.5 mm of mean framewise displacement (Power et al., 2012). Subsequently, the images were spatially normalized to the MNI standard space and resampled to 3 mm isotropic voxels. Systematic drift was then removed using a linear model, and the normalized images were spatially smoothed by Gaussian filter (FWHM = 6 mm). To alleviate the effects of head motion and other non-neuronal signal fluctuation, 24 head motion parameters, mean white matter (WM) signal, and mean cerebrospinal fluid (CSF) signal were regressed using a general linear model. Finally, signals were bandpass filtered (0.01 ∼ 0.1 Hz) to reduce the effects of super-lowfrequency and high-frequency physiological noise.

Kw =

1 n

∑ Kiw = i∈N

1 n

∑

wij

i, j ∈ N , i ≠ j

where K iw denotes nodal degree of node i, wij denotes the weight of connection between node i and j, n denotes the number of nodes in the network, and N denotes the set of all nodes. The degree is a sensitive network metric of the network with nonhomogeneous degree distribution. Betweenness of a network Bwwas defined as the average of nodal betweenness Bw (Freeman, 1978). Betweenness of node i was defined as the fraction of shortest path between each pair of other nodes in the network that passes through node i:

Bw =

Biw =

2.6.2. Network construction Network nodes definition. A two-criteria cross-validating procedure was employed to localize DMN. First, the whole brain was segmented into 210 cortical and 36 subcortical regions-of-interest (ROIs) based on the Brainnetome Atlas (BNA) (Fan et al., 2016). The ROIs significantly correlated with the seed-region in PCC, a core component of the DMN, were regarded as the putative hubs of the DMN (AndrewsHanna et al., 2010, 2014). These ROIs were eventually selected as components of the DMN if they were concurrently verified as part of the DMN mask devised by Thomas Yeo et al. (2011). Eventually a DMN atlas comprising 46 ROIs was created (Fig. 1 and Table S1). Nodes in the functional network were defined as the 46 ROIs in the abovementioned BNA-based DMN Atlas. Network connections definition. Average fMRI time-series were calculated across every voxel in each ROI. The absolute value of Fisher's z-transformed Pearson's correlation coefficient between each pair of time-series was defined as the connection strength.

1 n

∑ Biw i∈N

1 (n − 1)(n − 2)

∑ h, j ∈ N i≠j≠h

ρhj (i) ρhj

where ρhj denotes the number of shortest path between node h and j, ρhj(i) denotes the number of shortest path between node h and j that pass-through node i. 2.9. Characteristic path length and clustering coefficient Initially, characteristic path length Lw and clustering coefficient Cw of a network were defined to characterize network integration and segregation, respectively (Watts and Strogatz, 1998). Characteristic path length of a network Lw was defined as the average shortest path length between all pairs of nodes in the network:

Lw =

dijw =

2.7. Network-Based statistics (NBS)

1 n

∑ Liw = i∈N

∑

1 n

∑ i∈N

∑j ∈ N , j ≠ i dijw n−1

−1 wuv

wuv ∈ giw ↔j

where Liw denotes the characteristic path length of node i, dijw denotes the shortest path length between node i and j, and wuv denotes the weight between node u and v which are in the shortest path between node i and j giw↔ j . Clustering coefficient of a network Cw was defined as the average fraction of node's neighbors that were also neighbors of each other:

NBS estimates the connection differences in the brain network (Zalesky et al., 2010). First, the two-sample t-tests were applied to test each pair of connection. A primary threshold was then used to identify the supra-threshold connections, in which the topological clusters were further identified. Next, permutation tests (permutation times = 5000, FWER-corrected p < 0.05) were performed. Because the primary 559

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Fig. 1. The components of the Default Mode Network. Forty-six ROIs were defined as nodes in Default Mode Network. They were color-coded by their physiological locations. L: left, R: right, A: anterior, P: posterior.

Cw =

1 n

∑ Ciw = i∈N

1 n

∑

∑j, h ∈ N , j ≠ h ≠ i 3 wij wih wjh

2.11. Hubs identification

ki (ki − 1)

i∈N

Network hubs refer to nodes occupying central positions in the organization of a network, which are essential for communication in the network (van den Heuvel and Sporns, 2013). Hubs are generally conw sidered to have high nodal degree Kw, global efficiency Eglob and betweenness Bw (Shi et al., 2013; Sporns et al., 2007; Tian et al., 2011), therefore a selection scheme taking these three metrics into account was used in this study: (1) the mean nodal metrics across all subjects in the same group were calculated; (2) one point was added to the hubscore of a node if one mean nodal metric ranked in the top 10%; (3) nodes with a hub-score of 2 or higher were identified as hubs (Fig. 3).

Ciw

where denotes the clustering coefficient of node i and ki denotes the number of neighbor nodes of node i. From the equations, characteristic path length and clustering coefficient of the whole network and each node is defined for further analysis of topological properties of the graph as a whole and each node. 2.10. Global efficiency and local efficiency To address the issue that disconnection between two nodes could result in infinite Lw which is physiologically meaningless, the concept of efficiency of a network was proposed (Latora and Marchiori, 2001). w Global efficiency of a network Eglob was defined as the average inverse harmonic mean of shortest path length between each pair of nodes: w Eglob =

1 n

∑ Eiw_ glob = i∈N

1 n

∑ i∈N

2.12. Statistical analysis The statistical analyses were conducted using IBM SPSS Statistics for Windows, Version 25.0 (IBM Corp, Armonk, NY, USA) or R (Version 3.3.3) as appropriate. Data normality was assessed with the ShapiroWilk test. The data are presented as means ± standard deviations (SD), medians [interquartile ranges (IQR) or ranges] or proportions, as appropriate. Chi-square test, Fisher's exact test, Wilcoxon-Mann Whitney U test or Students’ t-test were used to compare the demographic, clinical, and imaging characteristics between the patients with and without PSD, as appropriate. The Wilcoxon-Mann–Whitney U test with a False Discovery Rate (FDR) correction (p < 0.05, corrected) compared the nodal metrics including clustering coefficient, characteristic path length, global and local efficiency, degree, and betweenness between the two groups. Subsequently, partial correlations were established to estimate the relationship between the severity of PSD and the network metrics showing significant between-group difference in graph theoretical analysis or NBS. The age, gender, years of education, level of social support, laterality of stroke, stroke severity, number of acute infarcts, and cognitive function (MMSE) that were significantly different between the PSD and non-PSD groups were entered as covariates in partial correlations analysis. Bonferroni corrections were further employed to avoid type-I error occurring in the multiple correlations. All the results of partial correlations and two-sample comparisons were regarded significant at a p value of <0.05 (two-tailed).

∑j ∈ N , j ≠ i (dijw )−1 n−1

where Eiw_ glob denotes global efficiency of node i and dijw denotes the w shortest path length between node i and j. Eglob is a measure of the efficiency of information communication in network. Similarly, local w was defined as the average of local effiefficiency of a network Eloc ciency of a node Eiw_ loc , while Eiw_ loc was calculated as the global efficiency of the subgraph of its neighbors: w Eloc =

1 n

∑ Eiw_ loc = i∈N

1 n

∑ i∈N

w −1 ) ∑u, v ∈ Ni (duv

ki (ki − 1)

where Ni denotes the set of node i’s neighbors and ki denotes the w number of neighbor nodes of node i. Eloc is a measure of the degree to which the network is fault-tolerant, which means that it shows how efficient the information communication is between the neighbors of node i when node i was removed. The global efficiency and characteristic path length measure the network integration, while local efficiency, clustering coefficient, and betweenness describe the segregation or specialization of the network (Rubinov and Sporns, 2010). 560

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prescribed with antidepressants prior to the rs-fMRI examinations.

Table 1 Comparison of demographic, clinical, and MRI characteristics between the PSD and non-PSD groups at the 3-month follow-up. non-PSD (N = 31)

P†

62.3 ± 8.2 11 (34.4) 8.8 ± 3.3 32 (100)

62.8 ± 8.5 12 (35.5) 8.8 ± 3.8 31 (100)

0.803* 0.926 0.687‡ NA

15 (51.7) 5 (17.2) 8 (27.6) 15 (48.4) 3 (1–6)

14 (46.7) 3 (10.0) 9 (30.0) 13 (41.9) 3 (1–6)

0.698 0.417 0.838 0.610 0.890‡

1.8 ± 1.7 2.6 ± 4.0

1.0 ± 09 2.7 ± 5.8

0.044* 0.355‡

4 (13.8) 15 (51.7) 6 (20.7)

4 (16.7) 10 (40.0) 3 (12.0)

0.820 0.389 0.480 0.007

7 (24.1) 22 (75.9) 0 (0.0)

17 (56.7) 11 (36.7) 1 (6.7)

1 (0–3)

2 (1–4)

0.138

1 (0–2) 2 (8.0) 1(3.1)

1 (0–2) 2 (8.7) 1(3.2)

0.542 0.931 0.982

9 (8–11)

1 (0–2)

<0.001‡

20.4 ± 9.5

26.8 ± 10.5

0.010‡

26.3 ± 2.0 2 (6.3)

27.7 ± 1.6 0

0.005‡ NA

PSD (N = 32)

Demographic characteristics Age, years, mean ± SD Female sex, n(%) Education, years, mean ± SD Right-sided Handedness, n(%) Vascular risk factors, n (%) Hypertension Diabetes mellitus hyperlipidaemia Current or previous smoker Stroke severity (NIHSS), median (IQR) Imaging characteristics Number of acute infarcts Volume of acute infarcts, ml Acute infarcts in, n (%) Cortical Subcortical Infratentorial Laterality of stroke, n (%) Left hemisphere Right hemisphere Bilateral Cerebral small vessel diseases WMH, Fazekas score, median (IQR) Perivascular spaces, median (IQR) Lacune, n (%) Cerebral microbleeds, n (%) Neuropsychiatric Assessments Depressive symptom (GDS), median (IQR) Social support (LSNS), mean ± SD Cognition (MMSE), mean ± SD Antidepressant treatments, n(%)

3.2. Topological properties of the DMN in PSD 3.2.1. Functional hubs in DMN Four ROIs were identified as functional hubs in patients with PSD (Fig. 3a) including left middle temporal gyrus (MTG.L.4.1), right superior frontal gyrus (SFG.R.7.6), and bilateral precuneus (PCun.L.4.1 and PCun.R.4.1). Six ROIs including MTG.L.4.1, MTG.L.4.4, MTG.R.4.4, SFG.R.7.3, PCun.R.4.1, and PCun.L.4.1 were characterized as functional hubs in patients without PSD (Fig. 3b). Collectively, the functional hubs of both groups were mutually located in MTG, SFG, and PCun. 3.2.2. Global properties No statistically significant difference between the two groups was found with respect to global metrics (Fig. S1). 3.2.3. Nodal properties Compared with the non-PSD group, patients in the PSD group demonstrated increased nodal degree (56.44 ± 21.50 vs. 70.17 ± 23.26, uncorrected p = 0.008, FDR-corrected p = 0.360), increased global efficiency (0.37 ± 0.06 vs. 0.41 ± 0.07, uncorrected p = 0.004, FDR-corrected p = 0.203), and increased clustering coefficient (0.13 ± 0.03 vs. 0.15 ± 0.04, uncorrected p = 0.027, FDRcorrected p = 0.995). However, PSD patients also showed decreased characteristic path length (2.81 ± 0.44 vs. 2.51 ± 0.37, uncorrected p = 0.004, FDR-corrected p = 0.203) in the right inferior parietal lobule (IPL.R.6.5, referring to BA39, PGa), and increased betweenness (80.32 ± 95.78 vs. 136.66 ± 105.58, uncorrected p = 0.004, FDRcorrected p = 0.175), increased global efficiency (0.40 ± 0.08 vs. 0.45 ± 0.10, uncorrected p = 0.035, FDR-corrected p = 0.814) and decreased characteristic path length (2.60 ± 0.54 vs. 2.33 ± 0.50, uncorrected p = 0.035, FDR-corrected p = 0.814) in SFG.R.7.6 overlapped with dorsolateral prefrontal cortex (dlPFC) and dorsal medial prefrontal cortex (dmPFC) (Table S2 & Fig. 4).

GDS = Geriatric Depression Scale; IQR = Interquartile Range; LSNS = Lubben Social Network Scale; MMSE = Mini-Mental State Examination; MRI = Magnetic resonance imaging; NA = Not applicable; NIHSS = National Institutes of Health Stroke Scale; PSD = Poststroke depression; SD = Standard deviation; WMH = White matter hyperintensities. † chi-square test. ‡ Mann–Whitney U test.; ⁎ t-test.

3.2.4. Increased functional connections of the DMN in PSD In NBS, three primary thresholds were applied to identify the altered functional connections at different levels of sensitivity. A more stringent threshold led to fewer connections identified but with stronger group-difference effect. Three hyper-connected subnetworks were thus generated: the first sub-network generated with the least conservative primary threshold (p < 0.01) comprised 26 nodes and 44 connections (Fig. 5a & Fig.S2a), the second with the primary threshold of p < 0.005 had 18 nodes and 24 connections (Fig. 5a & Fig.S2b), and the third with the most conservative primary threshold (p < 0.001) had 6 nodes and 5 connections (Fig. 5a & Fig.S2c). The connections in these three subnetworks overlapped with each other like a pyramid: significant FCs identified under a stringent primary threshold were all statistically significant in NBS analysis under less stringent primary threshold (Fig. 5a-b). Specifically, the following five pairs of increased FCs (PSD>non-PSD) were identified in all three hyper-connected subnetworks indicating strong between-group difference effect: between OrG.R.6.2 and SFG.L.7.6 (0.30 ± 0.23 vs. 0.61 ± 0.24), between OrG.R.6.2 and SFG.R.7.6 (0.38 ± 0.28 vs. 0.64 ± 0.27), between MTG.R.4.4 and SFG.R.7.6 (0.29 ± 0.20 vs. 0.51 ± 0.26), between IPL.R.6.5 and SFG.R.7.6 (0.40 ± 0.26 vs. 0.65 ± 0.21), and between OrG.R.6.2 and SFG.L.7.7 (0.27 ± 0.19 vs. 0.48 ± 0.27) (Fig. 5a, Table S3 & Fig.S2c). These anatomical regions with increased FCs overlapped with, or belonged to the orbital frontal cortex (OFC), dlPFC, dmPFC, ventromedial prefrontal cortex (vmPFC), MTG and IPL (Table S1). The connections’ strength of all increased FCs is listed in Table S3. Nodes involved in these sub-networks were classified into three types: (1) present at three sub-networks, or (2) two sub-networks, or only (3) one sub-network (Fig. 5c). SFG.L.7.6, SFG.R.7.6, OrG.R.6.2, IPL.R.6.5,

3. Results 3.1. Demographic, clinical, and imaging data Table 1 displays the comparisons of the demographic, clinical, and imaging characteristics between patients with PSD (PSD group) and without PSD (non-PSD group). Both groups mainly comprised of patients with mild to moderate stroke (NIHSS score: 3; IQR, 1–6). The distribution of age, gender, education, handedness, vascular risk factors, stroke severity, and severity of cerebral small vessel diseases were similar between the two groups. The infarct prevalence map displays the distribution of acute infarcts in the two groups (Fig. 2). The voxels marked in color indicate infarcts that were damaged in at least one patient. Infarcts were more likely to be located in the basal ganglia in all the patients. Compared with the non-PSD group, in the PSD group there was a higher number of acute infarcts (1.8 ± 1.7 vs. 1.0 ± 09, p = 0.044) on admission. However, the size of acute infarcts was similar between the two groups (Fig. 2& Table 1). In addition, patients in the PSD group had lower level of social support (20.4 ± 9.5 vs. 26.8 ± 10.5, p = 0.010) and cognitive function (26.3 ± 2.0 vs. 27.7 ± 1.6, p = 0.005). Only two of the patients with PSD were 561

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Fig. 2. Infarct topology of non-PSD and PSD groups. Voxels that are damaged in at least one patient are projected on the 1 mm MNI-152 template (Z coordinates: −37, −26, −17, −9, −1, 7, 15, 23, 31, 39). Bar indicates the number of patients with a lesion for each voxel.

Fig. 3. (a) Distribution of functional hubs of PSD group and their corresponding nodal metrics. (b) Distribution of functional hubs of non-PSD group and their corresponding nodal metrics. Nodes with hub-score larger than 2 were defined as hubs. For each bar chart, orange bars represent hubs while gray bars represent nonhubs. L: left, R: right.

metrics of SFG.R.7.6 were also significantly different between two groups. Collectively, these results pictured a widespread hyper-connected configuration of the functional DMN in PSD and depicted several sensitive ROIs related to PSD.

MTG.R.4.4, and SFG.L.7.7 were sensitive ROIs related PSD since they were mutual nodes of three sub-networks. More importantly, these six ROIs could be considered as intrinsic functional hubs in PSD-related sub-network indicated by relatively higher nodal degree. SFG.R.7.6 was simultaneously identified as functional hub of DMN in PSD group and functional hub in hyper-connected sub-network. In addition, nodal 562

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Fig. 3. (continued)

4.2. Global and nodal topological architecture in DMN and PSD

3.2.5. Lack of correlation between the network metrics and severity of PSD Only the significantly altered network metric in previous analyses were further estimated on their correlations with the severity of PSD (GDS scores) adjusting for age, gender, years of educations, level of social support, laterality of stroke, stroke severity, number of acute infarcts, and cognitive function. Partial correlations showed that the relationship between the nodal metrics (Table S4) altered FCs (Table S5), and severity of PSD were insignificant.

The global topology was identical between patients with and without PSD. The results regarding topological changes related to depressive disorders remain contradictory in the literature. Two fMRI studies endorsed the results of this study by finding similar changes of global efficiency, characteristic path length and clustering coefficient in the whole functional network between patients with unipolar depression (Lord et al., 2012) or late life depression (Bohr et al., 2013) and healthy controls. More evidence came from the finding that brain networks are functionally organized in more randomized fashion in major depressive disorder (MDD) than in healthy controls: a shift to increased network integration characterized as greater global efficiency (Li et al., 2017a; Zhang et al., 2011) and a shorter characteristic path length were both confirmed (Li et al., 2017b; Zhang et al., 2011). However, Meng et al. (2014) found reduced global efficiency in functional network in MDD. To date, no study has performed a network analysis in PSD except a diffusion tensor imaging (DTI) study that showed that low efficient depression-related local network predicted the risk of PSD (Yang et al., 2015). A balance of the topological integration and specialization in the brain network ensures that the information is both efficiently and economically transported; any disturbance in this balance may result in neuropsychiatric disorders, such as depressive disorders, cognitive impairments or schizophrenia (Sporns, 2013). However, the nodal topology in IPL and SFG, overlapping with dlPFC and dmPFC, tended to be functionally enhanced in PSD relative to non-PSD stroke controls, although these alterations disappeared after

4. Discussion 4.1. Summary of the main findings This study examined the topological reconfiguration of functional DMN in clinically significant PSD patients three months after stroke using NBS and graph theory. To the best of our knowledge, this was the first study to delineate the functional DMN in patients with PSD that demonstrated a trend to be more locally integrated in the cored hubs, such as mPFC, IPL, OFC, and MTG, whereas the global architecture of DMN was identical to that of patients without PSD. Alternatively, compared with patients without PSD, PSD patients exhibited a wide range of increased FCs between regions (mPFC, OFC, IPL, and MTG) in DMN, suggesting a functionally hyperconnected pattern in PSD. However, this study failed to reveal significant correlations between these altered network metrics and the severity of PSD.

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Fig. 4. Aberrant nodal metrics in patients with PSD. PSD group demonstrated increased nodal degree (p < 0.01, uncorrected), increased clustering coefficient (p < 0.05, uncorrected), increased global efficiency (p < 0.01, uncorrected) and decreased characteristic path length (p < 0.01, uncorrected) in right inferior parietal lobule (IPL.R.6.5). Increased betweenness (p < 0.01, uncorrected), increased global efficiency (p < 0.05, uncorrected) and decreased characteristic path length (p < 0.05, uncorrected) were observed in superior frontal gyrus (SFG.R.7.6). K: Degree, B: Betweenness, L: Characteristic Path Length, C: Clustering Coefficient, Eglob: Global Efficiency.

4.3. Increased FC in the DMN related to PSD

FDR corrections. Specifically, the IPL (BA 39) and SFG (BA 9, dlPFC and dmPFC) more efficiently coordinated information within the whole DMN as indicated by increased nodal degree, greater global efficiency or shorter path length, whereas their increased nodal betweenness centrality also suggested their role in uniting local subnetworks. These results imply that the DMN in PSD was not only more intimately united but also more specialized locally. The results are further supported by the findings that increased nodal centralities, predominately in the DMN regions (IPL, mPFC, or hippocampus) were found in first-episode, drug-naive MDD (Zhang et al., 2011), and increased inter-modular crosstalk in the superior frontal and parietal regions in unipolar depression presenting with mixed episodes (Lord et al., 2012). Unlike the results in MDD, the study of patients with late life depression failed to show significant between-group differences in both global and nodal topology (Bohr et al., 2013). These conflicting results possibly suggest that the functional network reorganized differently according to the subtypes of depression. IPL is thought to subserve the attention control on previously perceived memory representations (Kizilirmak et al., 2015), while the dmPFC and dlPFC are involved in self-referential activities and evaluating the emotional and mental state or intentions of others (Murray et al., 2011). Thus, their role of these structures in assembling the information within DMN possibly enables patients to excessively focus on the negative experiences and mediates the negative self-reflection and interpretations of others’ intentions observed in MDD (Zhou et al., 2010). Findings on the nodal network of this study should be interpreted with caution as the results became non-significant after correction for multiple comparisons.

There is compelling evidence that the hyperconnected configuration of the functional DMN is an essential feature of any types of depression in all age groups (Lassalle-Lagadec et al., 2012; Li et al., 2013, 2017b; Vicentini et al., 2016; Zhang et al., 2018; Zhu et al., 2012). In this study there was increased FC between the prefrontal cortex (PFC) including the left vmPFC (Li et al., 2013; Zhu et al., 2012), bilateral dmPFC (Li et al., 2013; Zhu et al., 2012) and dlPFC (Li et al., 2017a), right OFC (Zhu et al., 2012), middle and inferior temporal cortex (LassalleLagadec et al., 2012; Zhang et al., 2018), and inferior parietal lobule (Li et al., 2017b; Vicentini et al., 2016; Zhang et al., 2018) in PSD at the chronic stage of stroke. The DMN has often been modeled as two, functionally-distinct subsytems: (1) the anterior DMN with PFC as core hub subserving the emotional processing (vmPFC) and self-referential mental functioning (dmPFC), and (2) the posterior DMN with the pivotal regions including the posterior cingulate cortex (PCC), precunes, and inferior parietal lobule (IPL) being responsible for retrospective memory and decision-making (Andrews-Hanna et al., 2010; Buckner et al., 2008). In brief, the present study revealed that hyperconnections are located mainly within the anterior DMN and partly across the anterior and posterior DMN in PSD. The PCC was found hyperconnected to the IPL in depressive or anxiety symptoms one month after stroke in seed-based functional connectivity analysis (Vicentini et al., 2016). Hyperactivity was found in both the anterior and posterior DMN (dlPFC and precunes) in late-onset PSD (Egorova et al., 2017). Moreover, similar findings were also reported in adult MDD patients. Greater FC was consistently observed within the anterior DMN (dmPFC, vmPFC, ACC, and OFC) in fMRI studies applying independent component analysis (Mulders et al., 2015). By contrast, FC 564

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Fig. 5. Three hyper-connected sub-networks (PSD>non-PSD) identified by NBS analysis under different primary thresholds. (a) Three significant sub-networks showing increased FCs in PSD group compared with non-PSD group under primary threshold p < 0.01, p < 0.005, and p < 0.001. (b) Connections-based summary of these three sub-networks. FCs present at all three thresholds, or two liberal thresholds (p < 0.01 and p < 0.005), or only the most liberal threshold (p < 0.01) were shown in red, or blue, or green lines. (c) Nodes-based summary of these three sub-networks. Degree of nodes involved in three sub-networks, or two subnetworks (p < 0.01 and p < 0.005), or only one sub-network (and p < 0.01) were shown in red, or blue, or green bars. 565

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that compared with a single lesion, dysfunction of brain network is superior to account for the development of PSD (Gong and He, 2015; Lassalle-Lagadec et al., 2012; Yang et al., 2015).

in the posterior DMN (left MTC and precunes) examined 10 days after stroke was associated with the severity of PSD assessed at 3-month after stroke (Lassalle-Lagadec et al., 2012). Heterogeneity in analyzing strategies, composition of the study samples, post-stroke interval, and subtypes of depression may account for the above inconsistent findings. Contrary to the above-mentioned findings, in this study, a wide range of depression-related FC was found in the chronic phase of stroke. The discrepancy between this study and the literature may stem from heterogenous methods and the assessment time after stroke. This study identified the DMN components preliminarily based on BNA enabling the finer parcellation of the whole brain (Fan et al., 2016), whereas the ICA employed by Lassalle-Lagadec et al. (2012) extracted several main components of the DMN. The seed-based analysis applied by Zhang et al. (2018) was a hypothesis-driven strategy possibly leading to the omission of some ROIs. Furthermore, at the early stage of stroke, the neurofunctions of certain lesion-connected regions may be temporarily interrupted (called ‘diaschisis’) (Fornito et al., 2015). Along with stroke recovery, neuroplasticity enables the widespread recruitment of the connected or remote regions to compensate for the impaired functions caused by focal stroke lesions, a process called ‘dedifferentiation’ (Fornito et al., 2015). As a consequence, network integration increases due to the newly formed, hyperconnected configuration.

4.6. Limitations and future directions The findings of the present study should be considered with caution due to the following limitations. First, the preponderance of patients included with mild to moderate stroke restricts the generalization of the findings to the whole stroke population. The etiological subtypes of ischemic stroke were not classified due to the limited clinical information. Two PSD patients received antidepressant medications, which might have biased the sample. Moreover, the relatively small sample size may result in insufficient statistical power in examining between-group differences. Examination of a larger cohort of stroke patients with the whole spectrum of stroke severity is warranted to validate the current findings. In addition, the detrimental effects of clinically meaningful depressive symptoms on stroke outcomes (Souza et al., 2013), the lack of standardized clinical diagnosis of PSD may limit the clinical significance of the present findings. Nonetheless, the GDS was validated as a sensitive and specific tool for screening for PSD in elderly stroke patients in Hong Kong (Tang et al., 2004a,b). Last, despite of a similar distributing pattern of infarctions between groups, the infarcts located sparsely over the whole brain. Stroke lesions may affect the network topology. However, to date, the methods and strategies used to examine such effects of lesions are far from advanced. In this study, the lesions were most overlapped in the basal ganglia far inferior to the nodes of the DMN, which may reduce the impacts of the variation of infarct locations on the network measures. Nevertheless, this limitation reduced the generatability of the findings of the current study. Future studies are expected to included patients with homogenous infarct locations.

4.4. Lack of correlations between altered network metrics and severity of PSD This study failed to find correlation between the extent of the altered FC or nodal metrics within DMN and the severity of PSD after correction for the confounding factors. Inconsistently, Zhang et al. (2018) revealed that FC between IPL and precunes correlated with severity of PSD at the first month following stroke. However, similar to the results of this study, other studies also reported lack of significant correlations between FC in the brain networks related to MDD and the severity of MDD (Lai et al., 2017). The data concerning the relationship between nodal metrics and severity of depression are inconsistent due to differences in nodes and types of metrics across studies. For instance, Guo et al. (2014) found correlation between higher nodal degree and efficiency in the hippocampus and less severe MDD, while others (Zhang et al., 2011) reported that higher nodal centrality in hippocampus but lower centrality in the precuneus and caudate nucleus were correlated with less severe MDD. Meng et al. (2014) failed to replicate the findings in hippocampus; instead, these authors demonstrated that the nodal degree and efficiency in the frontal lobe and nodal betweenness in the parietal lobe were positively correlated with the severity of MDD. The lack of correlation between severity of PSD and network metrics including FC and nodal metrics in the current study may indicate a distinct topological pattern of DMN in PSD, differing from that in MDD (Guo et al., 2014; Meng et al., 2014; Zhang et al., 2011) or from the PSD-related network at the subacute stage of stroke (Lai et al., 2017; Zhang et al., 2018). The preponderance of patients with less severe stroke and PSD in this study may have resulted in negative findings.

4.7. Conclusions and implications The DMN, in patients with PSD at the chronic stage of stroke showed a trend to be more functionally integrated as marked by multiple increased FCs and locally integrated hubs, such as mPFC, IPL, and OFC within this network. These findings that stroke-induced interruption of the functional DMN topology may be associated with increased risk of PSD, provided potentially important insight into the networkbased pathogenesis of PSD. Exploring the topological alterations in functional DMN following stroke may help identify stroke patients with elevated risk for PSD. Disclosures None. CRediT authorship contribution statement Liang Yan: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Writing - original draft. Yao Yong-Cheng: Data curation, Formal analysis, Software, Supervision. Zhao Lei: Data curation, Formal analysis. Shi Lin: Data curation, Formal analysis, Software, Supervision. Chen Yang-Kun: Data curation, Formal analysis. Mok Vincent CT: Software, Supervision. Gabor S. Ungvari: Writing - review & editing. Chu Winnie CW: . Tang Wai-Kwong: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration.

4.5. Stroke lesions and PSD In line with the literature, initial stroke severity (De Ryck et al., 2014) and acute infarct burden measured with their total number (Zhang et al., 2012) were associated with PSD in the present study. A recent meta-analysis of 43 studies reported that the acute infarcts were more frequently located in left hemisphere (Zhang et al., 2017). In accordance with the conclusion of a meta-analysis of 48 studies (Carson et al., 2000), this study did not find relationship between a specific location of acute infarcts and PSD. There are two possible reasons for this: first, the low frequency of acute infarcts and relatively small sample size in this study did not allow to draw a more comprehensive lesion map associated with PSD. Second, it has been suggested,

Declaration of Competing Interest None. 566

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Acknowledgments

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