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NeuroImage : Clinical logoLink to NeuroImage : Clinical
. 2023 Feb 24;37:103359. doi: 10.1016/j.nicl.2023.103359

Resting-state functional connectivity of the raphe nuclei in major depressive Disorder: A Multi-site study

Yajuan Zhang a,b, Chu-Chung Huang c,d,⁎, Jiajia Zhao a, Yuchen Liu a, Mingrui Xia e,f,g, Xiaoqin Wang h,i, Dongtao Wei h,i, Yuan Chen j, Bangshan Liu k,l, Yanting Zheng m, Yankun Wu n, Taolin Chen o,p, Yuqi Cheng q, Xiufeng Xu q, Qiyong Gong o,p, Tianmei Si n, Shijun Qiu m, Jingliang Cheng j, Yanqing Tang r, Fei Wang r, Jiang Qiu h,i, Peng Xie s,t,u, Lingjiang Li k,l, Yong He e,f,g,v, Ching-Po Lin w; DIDA-Major Depressive Disorder Working Group1, Chun-Yi Zac Lo x,⁎
PMCID: PMC9999207  PMID: 36878150

Highlight

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    We evaluated the resting-state network related to raphe nuclei with a large healthy cohort.

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    We characterized the abnormal patterns of raphe nuclei functional connectivity in major depressive disorder.

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    The regions with abnormal connectivity of the raphe nuclei were independent of clinical status of MDD patients.

Keywords: Serotonin, Dorsal and median raphe nuclei, Seed-based analysis, Resting-state fMRI data, Functional connectivity, Multicenter

Abstract

Accumulating evidence showed that major depressive disorder (MDD) is characterized by a dysfunction of serotonin neurotransmission. Raphe nuclei are the sources of most serotonergic neurons that project throughout the brain. Incorporating measurements of activity within the raphe nuclei into the analysis of connectivity characteristics may contribute to understanding how neurotransmitter synthesized centers are involved in the pathogenesis of MDD. Here, we analyzed the resting-state functional magnetic resonance imaging (RS-fMRI) dataset from 1,148 MDD patients and 1,079 healthy individuals recruited across nine centers. A seed-based analysis with the dorsal raphe and median raphe nuclei was performed to explore the functional connectivity (FC) alterations. Compared to controls, for dorsal raphe, the significantly decreased FC linking with the right precuneus and median cingulate cortex were found; for median raphe, the increased FC linking with right superior cerebellum (lobules V/VI) was found in MDD patients. In further exploratory analyzes, MDD-related connectivity alterations in dorsal and median raphe nuclei in different clinical factors remained highly similar to the main findings, indicating these abnormal connectivities are a disease-related alteration. Our study highlights a functional dysconnection pattern of raphe nuclei in MDD with multi-site big data. These findings help improve our understanding of the pathophysiology of depression and provide evidence of the theoretical foundation for the development of novel pharmacotherapies.

1. Introduction

Major depressive disorder (MDD) is a mood disorder that can affect a person at any point in life (Pae et al., 2015). Individuals affected by MDD generally exhibit persistently depressed mood, loss of interest, cognitive impairment, and neurovegetative symptoms. In addition to emotional and physical problems in the individual, this disease can also lead to a heavy economic burden on family and society (Fakhoury, 2016, Holt et al., 2016). It has been confirmed that antidepressant medications could alleviate depression symptoms, but only 50–60% of patients respond to treatment and only approximately 35% remit (Trivedi et al., 2006). Currently, antidepressants are not chosen based on neural pathologies but by trial and error, which can intensify patient distress and increase costs (Greenberg et al., 2015). Therefore, elucidating the pathophysiology of MDD will help provide more targeted and efficient treatment for patients.

Evidence from clinical trials supports that monoamine neurotransmitters (serotonin, noradrenaline, and dopamine) have a potential role in the pathogenesis of MDD (Malhi and Mann, 2018). Although dopamine and norepinephrine deficits are believed to contribute to the corresponding features of MDD, most studies have focused on the serotonergic system (El Mansari et al., 2010). Postmortem studies and meta-analyses of molecular imaging studies have observed a reduced availability of serotonin transporters in MDD, although not in all studies (Spies et al., 2015). Genetic, neuroimaging and pharmacological studies of 5-HT receptors or transporters also noted the critical role of serotonin in depression (Olivier et al., 2008, Schreiber and De Vry, 1993). The serotonin (5-hydroxytryptamine, 5-HT) neurotransmitter is diffusely projected to areas of the brain such as the cerebral cortex, subcortical structures, and the cerebellum (Bianciardi et al., 2016), and is involved in many functions such as regulation of emotion, cognition, and behavior (Meneses, 1999, Meneses and Liy-Salmeron, 2012). It has been proposed that down-regulation of serotonergic function was considered the most influential and studied treatment strategy for MDD (Albert and Benkelfat, 2013). For instance, a class of compounds termed selective serotonin reuptake inhibitors (SSRI) is typically used as antidepressants. They can increase the extracellular level of 5-HT by inhibiting the 5-HT reuptake, leading to better neurotransmission and relieved mood symptoms (Carr and Lucki, 2011, Zhou et al., 2009). Therefore, the serotonin system may be a therapeutic target for the symptoms of MDD.

Despite previous clear evidence demonstrating the relevance between the serotonin system and MDD, the role of serotonergic dysfunction on brain functional activity in MDD is not fully understood. Brainstem raphe nuclei encompass the serotoninergic dorsal raphe nuclei (DRN) and median raphe nuclei (MRN), which are the main source of serotonin (Hornung, 2003, Pollak Dorocic et al., 2014). The effects of serotonin signaling on brain function and behavior in depressed patients largely depend on proper communication between the two serotonergic cell groups and other brain regions (Beliveau et al., 2015). Furthermore, a recent study revealed the association between functional connectivity (FC) of raphe nuclei and serotonin transporter binding, supporting a contribution of serotonergic signaling to resting-state brain activity (Beliveau et al., 2015). Therefore, modeling serotonin-related connectivity directly based on raphe nuclei at rest would provide evidence of how serotonin signaling shapes intrinsic brain connectivity (Bär et al., 2016, Beliveau et al., 2015). Resting-state functional magnetic resonance imaging (RS-fMRI) is a valid way to assess neural circuitry functions, as it has the advantage of avoiding potential performance confounds associated with task activation paradigms (Liu et al., 2021, Lu et al., 2022, Zhang et al., 2018). Recent RS-fMRI studies have reported the dysfunctional connectivity between raphe nuclei and brain networks in mental disorders, such as bipolar disorder, depression, and tobacco use disorder (Faulkner et al., 2018, Han et al., 2019). In MDD, an overall decreased FC was found between the DRN and cortical and subcortical regions (Han et al., 2019). Nonetheless, a consistent conclusion about the pattern of RSFC alterations in MDD remains unclear due to the absence of large samples or cross-validated multicenter datasets. The elucidation of the FC of raphe nuclei will advance our understanding of the neurobiological underpinnings of serotonergic pharmacotherapy in this disorder and will help pave the way for the development of new pharmacotherapies.

Using a large cohort of RS-fMRI data from 1,148 MDD patients and 1,079 healthy individuals recruited in nine centers, we performed a seed-based analysis to characterize the abnormal FC of the raphe nuclei in MDD. First, we evaluated the resting-state network related to raphe nuclei with a large healthy cohort. Then we examine the differences in DRN/MRN RSFC between MDD and healthy controls at rest. We hypothesized that i) the resting-state FCs of raphe nuclei is altered in MDD compared to the healthy controls; ii) the clinical factors could affect depression-related differences, such as receiving medication or suffering from first-episode patients.

2. Materials and methods

2.1. Participants

All fMRI data from 2407 participants (1274 MDD patients and 1133 healthy controls (HCs)) were collected from nine research centers in China (China Medical University, CMU; Central South University, CSU; Guangzhou University of Chinese Medicine, GCMU (two datasets); Kunming Medical University, KMU; Peking University Sixth Hospital, PKU; Sichuan University, SCU; Southwest University, SWU; National Yang-Ming University, YMU; and Zhengzhou University, ZZU). All MDD patients were recruited from the outpatient clinic or inpatient in the psychiatric departments of each study institution. All MDD patients were diagnosed by experienced psychiatrists using the structured clinical interview of the Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV) criteria for MDD (First, 1997). The exclusion criteria for the patients were the presence of a comorbid Axis I disorder, an Axis II personality disorder, intellectual disabilities, and a personal or family history of bipolar disorder. The severity of clinical symptoms of MDD patients was evaluated using the 17-item Hamilton depression rating scale (HDRS) by interview (798 MDDs from seven sites) (Williams, 1988). The duration of the disease of each MDD patient was collected (N = 1059). Controls were recruited from people with no history of Axis I disorders. Furthermore, all participants do not have physical disease (brain trauma, concomitant major medical disorders), history of drug or alcohol abuse, or MRI contraindications. The research was approved by the ethics committees of each of the research centers and written informed consent was obtained from all participants. Clinical and imaging data were quality controlled. The final sample included 1,148 MDD patients and 1,079 HCs. A detailed flowchart for subject exclusion is shown in Figure S1.

2.2. Image acquisition and preprocessing

All RS-fMRI data were acquired using 3 T MRI system with gradient echo-planar imaging sequences. All participants were instructed to remain awake with their eyes closed, not think systematically, and move as little as possible during the scanning procedure. Table 1 provides details of the scanning parameters for each center. RS-fMRI images were preprocessed with Statistical Parametric Mapping 12 (www.fil.ion.ucl.ac.uk/spm/) and SeeCAT (www.nitrc.org/projects/seecat/). The procedure includes the first ten time points discarded (for the CSU, GCMU1, and ZZU datasets, the first five-time points were deleted due to their short scan times), slice-timing correction, head-motion correction, normalized to the standard space using the EPI template, resampled to 3-mm isotropic voxels, smoothed with a 6-mm full width at half-maximum Gaussian kernel, and linear detrending. The nuisance variables were then regressed as covariates of the time series for all voxels using multiple linear regression, including Friston-24 head motion parameters, cerebrospinal fluid signals, and white matter. Subsequently, temporal bandpass filtering (0.01–0.1 Hz) was applied. Finally, a 'scrubbing' procedure was performed to remove outlier data due to head motion (Power et al., 2012). Specifically, we 'scrubbed' the frames that occur one frame before and two frames after the frames with high FD (>0.5 mm) with linear interpolated data.

Table 1.

Scan parameters of R-fMRI data in each center.

Center Scanner TR TE FA FOV Matrix Resolution Slices Thickness Gap Volume
(ms) (ms) (°) (mm2) (mm2) (mm) (mm)
CMU GE HDxT 3 T 2000 40 90 240 × 240 64 × 64 3.75 × 3.75 35 3 0 200
CSU GE HDxT 3 T 2000 30 90 220 × 220 64 × 64 3.44 × 3.44 33 4 0.6 180
GCMU1 GE HDxT 3 T 2000 30 90 220 × 220 64 × 64 3.44 × 3.44 36 3 1 185
GCMU2 GE HDxT 3 T 2000 30 90 240 × 240 64 × 64 3.75 × 3.75 33 4 0 250
KMU PHILIPS Achieva 3 T 2200 35 90 230 × 230 128 × 128 1.80 × 1.80 50 3 0 240
PKU Siemens Trio 3 T 2000 30 90 210 × 210 64 × 64 3.28 × 3.28 30 4 0.8 210
SCU GE EXCITE 3 T 2000 30 90 220 × 220 64 × 64 3.44 × 3.44 30 5 0 200
SWU Siemens Trio 3 T 2000 30 90 220 × 220 64 × 64 3.44 × 3.44 32 3 1 242
YMU Siemens Trio 3 T 2500 27 77 220 × 220 64 × 64 3.44 × 3.44 43 3.4 0 200
ZZU GE MR750 3 T 2000 40 90 220 × 220 64 × 64 3.44 × 3.44 32 4 0.5 180
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2.3. Seed-based functional connectivity analysis

The seed-based FC approach was applied to the RS-fMRI data of each subject. The regions of interest (DRN and MRN) masks were provided by the authors of Beliveau et al., who defined the masks on structural MRI and refined them using positron emission tomography (PET) images at the single-subject level and further registered them in the standard space (MNI) (Beliveau et al., 2015). The center coordinates of DRN in MNI were ×  = 2, y = -30, z = -11; the center coordinates of MRN in MNI were ×  = 1, y = –33, z = -21 (Figure S2). Pearson correlation coefficients between the average time series of DRN/MRN and the other cerebrum voxels were calculated for each individual. In order to avoid the signal outside the target region corrupting the waveform and constructing the seed-based FC map, the DRN and MRN time series were extracted before spatial smoothing. Then, we adapted the ComBat harmonization model to correct center effects on the seed-based FC map (Johnson et al., 2007). Based on multivariate linear mixed-effects regression, the ComBat model could remove the variability associated with site/scanner while preserving biological variability. The method was initially designed to correct batch effects in genomic studies (Johnson et al., 2007) and was successfully applied in multisite fusion tensor imaging (Yu et al., 2018), cortical thickness (Fortin et al., 2018), and fMRI analysis (Xia et al., 2022, Xia et al., 2019).

Statistical analysis of the seed-based FC maps was performed. First, we investigate the replicability of RSFC patterns of DRN and MRN. Specifically, we performed the same group analysis as the study by Beliveau et al. that examined the areas where the group means of the DRN/MRN RSFC were significantly different from zero. Group analysis was performed using a one-sample t-test with age and sex as covariates. A cluster-wise correction with the Gaussian random field (GRF) approach was performed to correct multiple comparisons (GRF: voxel-level P < 0.001, cluster-level P < 0.05) (Eklund et al., 2016). Second, the differences between groups in FC map comparisons were evaluated using a two-sample t-test with age and sex as covariates. For the global mean FC, the statistical significance threshold was set at P < 0.05. For the voxel-wise FC maps, the significance threshold was set to P < 0.001 at the voxel level, followed by GRF correction at the cluster level of P < 0.05.

2.4. Correlation analysis

To assess the associations between RSFC and the duration of the illness and the severity of symptoms in MDD, clusters showing altered RSFC between the MDD and HC groups were defined as regions of interest (ROI). The RSFC values of the ROIs were extracted and averaged. Partial correlation analyzes were performed to examine the relationship between the average ROI RSFC and the duration and HDRS scores, with age and sex as covariates (P < 0.05, Bonferroni correction).

To explore whether other regions were associated with clinical symptoms, we investigated the correlation between severity of symptoms and DRN/MRN FC maps. Briefly, a partial correlation analysis was performed to examine the relationship between the raphe nuclei FC maps and the duration and HDRS scores, with age and sex as covariates. The significance threshold was set at P < 0.001 at the voxel level, followed by GRF correction at the cluster level of P < 0.05.

2.5. Effects of the clinical factors

To examine the effects of clinical factors on between-group differences, we classified the patients into different pairs of subgroups, including medicated (MDD_Med, N = 275) vs. non-medicated patients (MDD_NoMed, N = 622), patients suffering from their first episode (MDD_FE, N = 512) vs. recurrent episode (MDD_Recurrent, N = 80), patients with an onset age lower than or equal to 21 years (MDD_Onset < = 21, N = 298) vs. older than 21 years (MDD_Onset > 21, N = 291) (Xia et al., 2022). Then we calculated the averaged RSFC values from the clusters showing altered RSFC between MDD and HC groups and compared these RSFC values between HC and subgroups, as well as between each corresponding pair of subgroups. The statistical significance threshold was set to P < 0.05/3 = 0.016 (Bonferroni-corrected).

2.6. Validation analysis

First, we assessed the site effect on the FC of raphe nuclei and whether the ComBet analysis strategy could reduce site effect, referring to Xia et al (Xia et al., 2019). Briefly, to estimate the site effect at each voxel, we performed Kruskal-Wallis tests across centers. The obtained P-value map was converted to a Z-value map. The significance level was set at a height threshold of P < 0.001 with an extent GRF correction at the cluster level of P < 0.05. Then, the ComBat model was used to correct site effects, and Kruskal-Wallis tests were performed again on the processed metric maps to check whether site effects had been reduced. Second, several potential confounders were considered in the validation process. i) we use the Liptak-Stouffer z-score method to correct for site effects (Cheng et al., 2016, Xia et al., 2019) and identify the reproducibility of MDD-related seed-based FC alterations. A detailed description of the Liptak-Stouffer z-score method is provided in the Supplementary Information; ii) only adult participants were included in the repeated statistical analysis (age > 18, 1,000 MDD patients versus 1,029 HCs), which would exclude confounding factors in brain development; iii) we repeated the between-group comparisons with the mean framewise displacement (FD) as an additional covariate to further control for the effect of head motion on RS-fMRI connectivity measures; iv), a leave-one-site-out cross-validation strategy was performed to confirm that specific sites did not influence the findings. Briefly, using seed-to-voxel analysis, we repeated the between-group comparisons ten times, each time including nine centers and leaving one site out.

2.7. Data availability

The core analysis code and result data are publicly available at https://github.com/zhangyj0430/2022-Functional-Connectivity-of-Raphe-Nuclei-in-MDD.

3. Results

3.1. Demographic

Table 2 and Fig. 1 show the demographic and clinical characteristics of each center. In general, the contribution of the sample size in each center is 107.9 ± 80.1 MDD patients (ranges from 34 to 254), and 114.8 ± 80.7 HC (ranges from 34 to 282). There were no differences between MDD patients and HC in terms of age (MDD: 33.83 ± 14.97 years; HC: 33.96 ± 13.87 years; P = 0.83) and sex (MDD (male/female): 496/652; HC (male/female): 495/584; χ2/P = 0.21). A total of 897 MDD patients had medication information including 275 medicated MDD (MDD_Med) and 622 who did not receive medication (MDD_NoMed). Notably, none of the patients was taking any other psychotropic drugs than antidepressants. The 512 patients with first-episode MDD patients (MDD_FE) included 374 drug-naïve first-episode MDD patients and 131 patients receiving treatment (medication status unavailable for 7). Of 80 with recurrent MDD (MDD_Recurrent), 60 were scanned while receiving antidepressants and 18 were not being treated with medication (medication status unavailable for 1). Due to differences in data management practices in different centers, episodicity (first or recurrent) and medication status were not available for 242 MDD patients. Furthermore, there were 298 patients with an onset in adolescence (age ≤ 21 years, N = 298) and 291 patients who had an onset older than 21 years (age > 21 years, N = 291).

Table 2.

Demographic, clinical and imaging quality characteristics.

Center Group Age, mean (SD), yr Sex (M/F) Education, mean (SD), yr DOI, mean (SD), yr Medication (Yes/No) HDRSa, Mean FD,
mean (SD) mean (SD), mm
CMU, Shenyang Healthy (n = 248) 27.25 (8.21) 103/145 14.83 (3.22) 1.10 (1.68) 0.11 (0.06)
Patient (n = 125) 27.91 (9.70) 39/86 12.16 (3.06) 1.65 (3.17) 49/76 21.44 (8.67) 0.11 (0.07)
Statistics T or χ2/P 0.70/0.493 3.76/0.052 7.65/<0.001 33.71/<0.001 1.08/0.278
CSU, Changsha Healthy (n = 108) 32.31 (7.96) 62/46 11.84 (3.40) 0.62 (0.88) 0.13 (0.06)
Patient (n = 177) 36.28 (10.21) 77/100 10.16 (3.43) 2.52 (3.83) N.A. 31.39 (7.82) 0.14 (0.07)
Statistics T or χ2/P 3.45/0.001 5.19/0.023 4.02 < 0.00 36.52/<0.001 0.88/0.382
GCMU1,Guangzhou Healthy (n = 34) 30.09 (10.88) 10/24 13.68 (3.07) 0.10 (0.03)
Patient (n = 34) 29.41 (8.27) 9/25 13.00 (3.44) 0.65 (0.70) 0/34 21.85 (2.25) 0.09 (0.03)
Statistics T or χ2/P 0.29/0.774 0.07/0.787 0.86/0.395 0.32/0.750
GCMU2,Guangzhou Healthy (n = 66) 29.33 (10.12) 31/35 12.47 (2.53) 0.09 (0.04)
Patient (n = 66) 29.48 (9.91) 25/41 12.18 (3.09) 0.76 (1.00) 0/66 22.30 (3.57) 0.09 (0.06)
Statistics T or χ2/P 0.29/0.774 1.12/0.291 0.59/0.559 0.29/0.770
KMU, Kunming Healthy (n = 46) 39.02 (12.19) 26/20 16.00 (3.78) 0.17 (0.06)
Patient (n = 41) 34.20 (9.37) 20/21 11.73 (4.35) 1.13 (1.28) N.A. 23.61 (4.64) 0.19 (0.08)
Statistics T or χ2/P 2.05/0.043 0.47/0.52 4.06/<0.001 1.26/0.211
PKU, Beijing Healthy (n = 73) 31.90 (9.01) 42/31 15.23 (2.28) 0.18 (0.07)
Patient (n = 75) 31.51 (7.86) 44/31 13.76 (3.02) 0.52 (0.47) 0/75 25.35 (4.77) 0.18 (0.06)
Statistics T or χ2/P 0.29/0.775 0.02/0.889 3.39/0.001 0.91/0.363
SCU, Chengdu Healthy (n = 41) 34.83 (17.69) 17/24 0.12 (0.07)
Patient (n = 48) 35.75(12.21) 23/25 16.02 (4.24) 1.13 (1.49) 23/25 22.88 (4.34) 0.11 (0.07)
Statistics T or χ2/P 0.29/0.77 0.54/0.37 0.72/0.473
SWU, Chongqing Healthy (n = 254) 39.65 (15.80) 88/166 12.80 (4.25) 0.13 (0.06)
Patient (n = 282) 38.74 (13.65) 99/183 11.83 (3.72) 4.20 (5.52) 124/125 20.78 (5.88) 0.13 (0.05)
Statistics T or χ2/P 0.72/0.472 0.01/0.911 2.84/0.005 1.68/0.094
YMU, Taibei Healthy (n = 109) 51.12 (11.70) 69/40 14.83 (3.64) 0.13 (0.06)
Patient (n = 105) 57.05 (16.21) 63/42 11.44 (4.36) 1.21 (1.54) 79/26 11.66 (6.99) 0.14 (0.08)
Statistics T or χ2/P 3.06/0.003 0.25/0.619 6.15/<0.001 1.17/0.243
ZZU, Zhengzhou Healthy (n = 100) 22.43 (4.49) 47/53 15.02 (3.71) 0.09 (0.04)
Patient (n = 195) 18.40 (5.54) 97/98 1.28 (1.48) 0/195 22.43 (5.71) 0.10 (0.04)
Statistics T or χ2/P 6.29/<0.001 0.20/0.655 2.16/0.032
All data Healthy (n = 1079) 33.96(13.87) 495/584 14.0 (3.65) 0.123 (0.063)
Patient (n = 1148) 33.83(14.97) 496/652 12.0 (3.81) 0.125 (0.067)
Statistics T or χ2/P −0.21/0.832 −1.60/0.21 −12.06/<0.001 0.80/0.423
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Abbreviations: SD, standard deviation; HDRS, Hamilton depression rating scale; FD, framewise displacement; DOI: Duration of illness, N.A., not available.

a

17-item HDRS was used in the research centers of CMU, GCMU1, GCMU2, PKU, SCU, SWU, and ZZU while the 21-item HDRS was used in the research center of YMU and the 24-item HDRS was used in the research center of CSU.

Fig. 1.

Fig. 1

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Participants characteristics. (A) Total number of participants per group for each site. (B) Number of male subjects and female subjects per group for each site. (C) Age (in years) distribution for the MDD and HC groups for each site. G1: GCMU1; G2: GCMU2.

3.2. The RSFC pattern of DRN and MRN in healthy controls

The results of two different statistical analysis strategies (ComBat model and Liptak-Stouffer z-score method) were generally consistent (Fig. 2, Figure S3). The result of the one-sample t-test showed that the DRN in HC group was functionally connected with multiple cortical and subcortical regions, and the RSFC pattern for MRN in HC group was largely similar to the DRN results (Fig. 2A and 2B, GRF correction, voxel P < 0.001, cluster P < 0.05). We observed the connectivity of DRN and MRN both showing significant positive RSFC located in the hippocampal, parahippocampal gyrus, insula, anterior and middle cingulate, putamen, caudate, pallidum, amygdala, thalamus and cerebellum. These overlapped clusters showing significant negative RSFC were observed in cortical surfaces, including the bilaterally prefrontal, temporal, occipital, and pre-and postcentral gyrus (Fig. 2C).

Fig. 2.

Fig. 2

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Group-level RSFC map for DRN and MRN seeds in the healthy subject cohort. (A) and (B) Cortical surface and subcortical volume maps show statistically significant FC in DRN and MRN nuclei seeds in the healthy cohort. Color scales reflect the Z statistic. Results were visualized at voxel level P < 0.001 and corrected by GRF at the cluster level of P < 0.05. (C) Overlap of significant FC of DRN and MRN. Orange is the overlap of positive FC between DRN and MRN. Blue is the overlap of negative FC between DRN and MRN. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

3.3. Alterations of Seed-based RSFC in MDD

In the global mean FC of DRN and MRN, there was no significant difference between HC and MDD (Figure S4, P > 0.05). In the voxel-wise analysis, compared to HC, patients with MDD showed decreased connectivity of the DRN with the right precuneus and the right median cingulate cortex (MCC), and increased connectivity of the MRN with the right superior cerebellum (lobules V/VI) and extended to the right fusiform (Fig. 3, GRF correction, voxel P < 0.001, cluster P < 0.05).

Fig. 3.

Fig. 3

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Statistical comparison of the connectivity of the raphe nuclei. (A) Voxel-wise statistical comparisons between patients with MDD and healthy controls in RSFC of DRN and MRN seeds. The MDD group showed decreased RSFC between DRN nuclei and precuneus and increased RSFC between MRN nuclei and the right cerebellum. Color scales reflect the Z statistic. The significance threshold was set to P < 0.001 at the voxel level, followed by Gaussian random field correction at the cluster level of P < 0.05. (B) The average connection strengths for each voxel within the two regions are depicted in histograms.

3.4. Correlation analysis

In the correlation analysis, abnormal RSFC clusters in MDD did not have a significant relationship with the duration of the disease and HDRS scores. Furthermore, HDRS in MDD patients had a positive correlation with DRN connectivity with the occipital lobe, including mainly the bilateral lingual, calcarine and middle occipital gyrus, and a negative correlation with DRN connectivity with the inferior frontal gyrus (Figure S5, GRF correction, voxel P < 0.001, cluster P < 0.05).

3.5. Effects of the clinical factors

As shown in Fig. 4A, medicated MDD patients have significantly lower HDRS scores than non-medicated MDD patients (P < 0.001). Similarly, recurrent MDD patients have significantly lower HDRS scores than patients in their first episode (P = 0.03). Additionally, both medicated and non-medicated MDD patients had a significantly decreased DRN FC compared to HC, and medicated and non-medicated MDD patients did not differ significantly. Similar results were found in patients in their first episode and recurrent and patients with onset in adolescence or adulthood (P < 0.016, Bonferroni-corrected) (Fig. 4B). In comparison of MRN, we found that all subgroups except MDD_Recurrent showed a significantly increased RSFC of MRN in the right superior cerebellum, and no significant differences between each corresponding pair of subgroups (Fig. 4C, Table S1).

Fig. 4.

Fig. 4

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Effects of the clinical factors on the connectivity of the raphe nuclei. (A) Differences in HDRS scores between paired of subgroups. (B) the statistical comparison between HC and subgroup of MDD in the mean FC from the significant cluster of DRN. (C) the statistical comparison between HC and subgroup of MDD in the mean FC from the significant cluster of MRN. The statistical significance threshold was set **P < 0.05/3 = 0.016, Bonferroni-corrected.

3.6. Validation results

First, we observed a significant site effect on the raw FC map, suggesting that differences in scanning protocols can affect results, and it is necessary to perform site effect correction in multicenter imaging studies. After performing the ComBat method, these site effects no longer existed (Figure S6). Second, compared to HC, the between-group difference connectivity of DRN and MRN in the different validation strategies (controlled mean FD, only adult participants included, and Liptak-Stouffer z-score method) is highly similar to our main findings (Figure S7). In the leave-one-site-out validation strategy, MDD-related alterations remained highly similar to the main findings (Figure S8), and the CSU center has a more significant contribution to MDD-related alterations in DRN (Figure S9).

4. Discussion

With the large cohort of the RS-fMRI dataset, we performed a seed-based probe of intrinsic serotonin-related resting-state activity in MDD. Compared to healthy controls, MDD patients showed a decrease in RSFC between DRN and the precuneus and an increase in RSFC between MRN and the right cerebellum (lobules V/VI). In further exploratory analyzes, MDD-related connectivity alterations in DRN and MRN in different clinical factors remained highly similar to the main findings, indicating that the regions with abnormal connectivity of the raphe nuclei were independent of clinical status of MDD patients. Studying functional connectivity of the raphe nuclei can help improve our understanding of the neurobiological mechanism underlying the disruption of the serotonin system and provides a potential target for the development of novel MDD pharmacotherapies.

Previous studies using test–retest evaluation have established robust DRN and MRN delineations, as well as FC maps for raphe nuclei (Beliveau et al., 2015). In this study, we performed a seed-based analysis for a comprehensive survey of FC of raphe nuclei with a large healthy cohort based on the seed mask provided by Beliveau et al. Consistent with the previous study, our findings showed a widespread and overlapped RSFC pattern of DRN and MRN in the large healthy cohort. The extensive innervation between the raphe nuclei and the shared afferent projections from the common brain regions may provide an explanation for connectivity overlaps between the two RSFC maps of the raphe nuclei (Beck et al., 2004, Vertes and Linley, 2007, Vertes and Linley, 2008). Although the small volume of DRN and MRN may contribute to a greater variability compared to larger brain structures, for the seeds may capture signal from the neighboring voxels, the large multisite healthy datasets and reproducibility of the DRN and MRN FC maps may eliminate the effects of variability to some extent. Current research also detects a more widely distributed negative FC between the seeds of the raphe nuclei and the cortical surface, including the bilateral prefrontal, temporal, occipital and parietal lobes (Beliveau et al., 2015). This discrepancy can be traced to the source of the differences in sample sizes. The large healthy cohort in this study can improve statistical power in the analysis and avoid biased sampling in small datasets. Furthermore, we observed that the range of RSFC in MRN is smaller than in DRN. Vertes et al. demonstrated that MRN fibers mainly distribute to forebrain structures lying on or close to the midline/paramidline, while DRN fibers distribute to most of the regions of the forebrain (Vertes and Linley, 2007, Vertes and Linley, 2008). According to this observation, MRN projections to the cortex are narrower relative to DRN, supporting our findings of discrepancies in RSFC patterns between them.

Resting-state FC is a powerful means of characterizing the network architecture of the human brain (Cole et al., 2014). Inclusion of activities measures within the raphe nuclei in the analysis of whole brain connectivity characteristics may contribute to understanding how neurotransmitter synthesizing centers interact with coordinated operations through functional connectivity. In MDD, a reduction in 5-HT level increases the risk of being affected by depression (Albert and Benkelfat, 2013). As a result, decreased DRN functional connectivity with precuneus in MDD could be traced back to a low level of serotonin concentrations or greater serotonin receptor binding (Mann, 1999). Previous findings support this hypothesis by revealing that abnormal RSFC in the precuneus within the default mode network (DMN) in MDD patients can be modulated by acute intravenous serotonergic antidepressant medication (Dutta et al., 2019). Additionally, the precuneus, as part of DMN, plays a central role in self-referential processing, especially sad memories, sense of self, and low self-esteem in depression (Cheng et al., 2018, Yan et al., 2019). The rich connections between the precuneus and the posterior cingulate cortex provide a pathway to the hippocampal memory system and the prefrontal cortex, which can facilitate the uncontrollable rumination of sad memories in depressed patients (Li et al., 2022, Rolls, 2018, Rolls and Wirth, 2018). Previous fMRI research confirmed that the precuneus is a specific region in patients with MDD, where abnormal neural activity and FC can be normalized by regulating serotonin levels in the brain (Kraus et al., 2014, Li et al., 2013). Therefore, the decrease in the FC of DRN in the precuneus in MDD is of great interest, as some of the key typical symptoms of depression are self-rumination and especially feelings of sadness. In the context of previous findings, we speculated that the low level of serotonin concentrations in MDD may lead to inadequate regulation of RSFC between DRN and precuneus, thus contributing to fewer happy memories and bias toward a mood of rumination in sad memories in MDD. Furthermore, a previous study showed that MDD patients had a general decrease in DRN FC, including cortical areas and subcortical regions (Han et al., 2019). Inconsistencies may reflect limited statistical power from small samples, various imaging protocols (e.g., MRI scanners and imaging parameters), and different patient recruitment criteria (e.g., cultural background and diagnostic criteria).

The topographic organization of the cerebellum is largely segregated into the anterior (lobules I-V) and posterior lobes (lobules VI-IX) that represent its “motor” (motor and somatosensory) and “non-motor” (cognitive, affective, and social cognition) functions, respectively (Koziol et al., 2014, Sathyanesan et al., 2019, Schmahmann et al., 2019, Sokolov et al., 2017). In the current study, our findings of damaged serotoninergic pathways between MRN and cerebellar lobules V/VI could imply that the cerebellum is not only limited to motor coordination and motor behavior but also related to high-order cognitive and emotional functions in MDD. Previous findings showed that cerebellar lesions do not affect the ability to experience unpleasant emotions but are associated with a reduced pleasant experience in response to happiness-evoking stimuli, similar to the main symptom of depression (Turner et al., 2007). Additional support for the involvement of the cerebellum in emotional processes is that chronic cerebellar stimulation can produce decreased anxiety and improved mood in neurologic disorders, especially in patients with depression (Heath et al., 1981, Riklan et al., 1977).

Serotonin is widely present in the brain, including in the cerebellum. The cerebellum is richly innervated by serotonin, as serotonergic fibers are the third major afferent fiber to the cerebellum Oostland and van Hooft, 2016. Furthermore, serotonin neuron projection into the cerebellum has been reported to be located in the raphe nuclei (Bishop and Ho, 1985, Chan-Palay, 1975, Oostland and van Hooft, 2013). These fiber-tracts between MRN and cerebellum provide a brain structural basis for FC and can constrain and shape FC patterns. Using diffusion-tractography, the right cerebellum showed damaged white matter integrity (i.e., decreased fractional anisotropy) in patients with MDD (Peng et al., 2013). We speculated that axonal damage within cerebellar fiber-tracts may alter serotoninergic transmission and possibly lead to functional reorganization of cortical brain networks. Additionally, antidepressant treatment (e.g., SSRIs) could normalize local metabolic abnormalities in the cerebellar hemisphere in MDD, providing a direct effect of the dysfunctional serotoninergic neurotransmission on the cerebellum (Chen et al., 2014). Given the above, our results of abnormal FC between the cerebellum and MRN shed light on the understanding of the neurophysiological mechanism of depression and may provide more scientific evidence for emotion dysregulation in MDD.

In the subgroup analysis, both medicated and non-medicated MDD patients had a significantly decreased DRN FC and increased MRN FC compared to HC, and medicated and non-medicated MDD patients did not differ significantly. Due to the non-medicated MDD patients both including first-episode and recurrent status, we further compared the group difference between the 374 drug-naïve first-episode MDD patients and HC. The drug-naïve first-episode MDD patients also showed a decreased DRN FC and increased MRN FC compared to HC (RSFC between DRN and precuneus: t = -2.07, P = 0.035; RSFC between MRN and cerebellum: t = 3.05, P = 0.002). Furthermore, the HDRS scores of MDD patients on medication were significantly lower than those on non-medication, suggesting a significant difference in disease severity between medicated and non-medicated MDD patients. However, these differences in disease severity were not reflected in MDD-related changes in FC raphe nuclei. Similar results were found in patients in their first episode and recurrent, indicating that abnormal FC of the raphe nuclei may be MDD-related alterations. Evidence from many lines of research (postmortem and genetic, neurochemical, neuroimaging, etc.) indicates that MDD is commonly characterized by a variety of functional deficits of 5-HT neurotransmission in brain circuits that regulate emotions, whether primary or secondary (Belmaker and Agam, 2008). High-risk relatives of MDD patients tend more sensitive to 5-HT challenge procedures (Benkelfat et al., 1994, van der Veen et al., 2007), and the altered serotonergic function is still present in MDD patients in remission (Bhagwagar et al., 2004). Collectively, the abnormal serotonin system may represent a risk factor that increases vulnerability to MDD, and the dysfunctional serotonin system in MDD is independent of clinical status (Southwick et al., 2005). In contrast to DRN, no changes in MRN connectivity were found in recurrent patients. The relatively small sample size of the recurrent patients may contribute to the unsignificant differences. It is necessary to investigate the results in recurrent patients with a larger sample size, which gives a reliable sample mean, eliminates outliers, and avoids errors from atypical samples (Biau et al., 2008).

Furthermore, correlation analysis showed that there was no significant correlation between clinical scores and these abnormal DRN FC in MDD subjects, while HDRS of MDD patients was found to have a negative correlation with the connectivity of DRN with the inferior frontal gyrus. MDD is known to have very high rates of relapse. It is estimated that>50% of first-episode MDD patients will relapse at least once (Shea et al., 1992). Therefore, it is reasonable to suspect that regions with abnormal connectivity of the raphe nuclei may be mismatched brain regions related to clinical symptoms in MDD. This phenomenon has also been reported in the previous literature (Elliott et al., 2012, Farb et al., 2011, Kanske et al., 2012, Li et al., 2013, Phillips et al., 2012).

Several limitations need to be addressed. First, the raphe nuclei are small with no clear contrast on T1-weighted image, especially acquired with MRI at 3 Tesla. We replicated the FC results of DR and MR nuclei with previous study by our large multisite dataset. Nonetheless, the precise segmentation method on raphe nuclei is needed in the future study. Second, since the fMRI study does not allow to examine serotonin function per se, the role of serotonin observed in MDD is only inferred. To clarify the role of serotonin in the relationship between MDD and the connectivity of the raphe nuclei, future studies are needed to examine the effects of serotonergic medications on MDD symptoms and associated changes in brain function. Finally, due to differences in data management practices in different centers, not all patients recorded clinical information, and detailed information about their treatment was not collected. These issues may limit our power to interpret functional alterations. In addition, the subtypes characterized by central depression and anxiety of MDD by HDRS are not available. Therefore, no subtype correlation analysis was performed.

CRediT authorship contribution statement

Yajuan Zhang: Conceptualization, Methodology, Software, Investigation, Formal analysis, Validation, Writing – original draft, Writing – review & editing, Visualization. Chu-Chung Huang: Conceptualization, Investigation, Resources, Data curation, Validation, Writing – review & editing, Visualization, Supervision. Jiajia Zhao: Methodology, Software, Investigation. Yuchen Liu: Methodology, Software, Investigation. Mingrui Xia: Data curation, Investigation, Resources. Xiaoqin Wang: Data curation, Investigation, Resources. Dongtao Wei: Data curation, Investigation, Resources. Yuan Chen: Data curation, Investigation, Resources. Bangshan Liu: Data curation, Investigation, Resources. Yanting Zheng: Data curation, Investigation, Resources. Yankun Wu: Data curation, Investigation, Resources. Taolin Chen: Data curation, Investigation, Resources. Yuqi Cheng: Data curation, Investigation, Resources. Xiufeng Xu: Data curation, Investigation, Resources. Qiyong Gong: Data curation, Investigation, Resources. Tianmei Si: Data curation, Investigation, Resources. Shijun Qiu: Data curation, Investigation, Resources. Jingliang Cheng: Data curation, Investigation, Resources. Yanqing Tang: Data curation, Investigation, Resources. Fei Wang: Data curation, Investigation, Resources. Jiang Qiu: Data curation, Investigation, Resources. Peng Xie: Data curation, Investigation, Resources. Lingjiang Li: Data curation, Investigation, Resources. Yong He: Data curation, Investigation, Resources. Ching-Po Lin: Data curation, Investigation, Resources. Chun-Yi Zac Lo: Conceptualization, Methodology, Software, Investigation, Resources, Formal analysis, Data curation, Validation, Writing – original draft, Writing – review & editing, Visualization, Supervision.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

Acknowledgments

This work was supported by Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01), ZJ Lab, and Shanghai Center for Brain Science and Brain-Inspired Technology, the 111 Project (No.B18015),  STI2030–Major Projects (2022ZD0213400), the National Natural Science Foundation of China (82201720; 82071998; 81671767; 82021004; 81620108016; 91432115; 81171286; 91232714; 31771231; 31271087; 81571331; 81271499; 81571311; 81920108019; 91649117; 81771344; 81471251; 81630031; 81621003; 81660237), Shanghai Science and Technology Innovation Plan (17JC1404105 and 17JC1404101), the Beijing Nova Program (Z191100001119023), Fundamental Research Funds for the Central Universities (2020NTST29), the National Key R&D Program of China (2018YFA0701400), the Changjiang Scholar Professorship Award (T2015027), the National Science and Technologic Program of China (2015BAI13B02), the National Basic Research Program of China (2013CB835100), the Natural Science Foundation of Chongqing (cstc2019jcyj-msxmX0520), the National High Tech Development Plan (863) (2015AA020513), the Medical Science and Technology Research Project of Henan Province (201701011), Science and Technology Plan Project of Guangzhou (2018-1002-SF-0442) and Guangzhou Key Laboratory (09002344).

DIDA-MDD Working Group: Yong He, Lingjiang Li, Jingliang Cheng, Qiyong Gong, Ching-Po Lin, Jiang Qiu, Shijun Qiu, Tianmei Si, Yanqing Tang, Fei Wang, Peng Xie, Xiufeng Xu & Mingrui Xia.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.nicl.2023.103359.

Contributor Information

Chu-Chung Huang, Email: czhuang@psy.ecnu.edu.cn.

Chun-Yi Zac Lo, Email: zaclocy@gmail.com.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

Supplementary data 1
mmc1.docx (20.9MB, docx)

Data availability

Data will be made available on request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary data 1
mmc1.docx (20.9MB, docx)

Data Availability Statement

The core analysis code and result data are publicly available at https://github.com/zhangyj0430/2022-Functional-Connectivity-of-Raphe-Nuclei-in-MDD.

Data will be made available on request.

Articles from NeuroImage : Clinical are provided here courtesy of Elsevier

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