Skip to main content
Download PDF

Influence and interaction of resting state functional magnetic resonance and tryptophan hydroxylase-2 methylation on short-term antidepressant drug response

BMC Psychiatry volume 22, Article number: 218 (2022) Cite this article

Abstract

Background

Most antidepressants have been developed on the basis of the monoamine deficiency hypothesis of depression, in which neuronal serotonin (5-HT) plays a key role. 5-HT biosynthesis is regulated by the rate-limiting enzyme tryptophan hydroxylase-2 (TPH2). TPH2 methylation is correlated with antidepressant effects. Resting-state functional MRI (rs-fMRI) is applied for detecting abnormal brain functional activity in patients with different antidepressant effects. We will investigate the effect of the interaction between rs-fMRI and TPH2 DNA methylation on the early antidepressant effects.

Methods

A total of 300 patients with major depressive disorder (MDD) and 100 healthy controls (HCs) were enrolled, of which 60 patients with MDD were subjected to rs-fMRI. Antidepressant responses was assessed by a 50% reduction in 17-item Hamilton Rating Scale for Depression (HAMD-17) scores at baseline and after two weeks of medication. The RESTPlus software in MATLAB was used to analyze the rs-fMRI data. The amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), fractional ALFF (fALFF), and functional connectivity (FC) were used, and the above results were used as regions of interest (ROIs) to extract the average value of brain ROIs regions in the RESTPlus software. Generalized linear model analysis was performed to analyze the association between abnormal activity found in rs-fMRI and the effect of TPH2 DNA methylation on antidepressant responses.

Results

Two hundred ninety-one patients with MDD and 100 HCs were included in the methylation statistical analysis, of which 57 patients were included in the further rs-fMRI analysis (3 patients were excluded due to excessive head movement). 57 patients were divided into the responder group (n = 36) and the non-responder group (n = 21). Rs-fMRI results showed that the ALFF of the left inferior frontal gyrus (IFG) was significantly different between the two groups. The results showed that TPH2–1–43 methylation interacted with ALFF of left IFG to affect the antidepressant responses (p = 0.041, false discovery rate (FDR) corrected p = 0.149).

Conclusions

Our study demonstrated that the differences in the ALFF of left IFG between the two groups and its association with TPH2 methylation affect short-term antidepressant drug responses.

Peer Review reports

Background

Major depressive disorder(MDD)is a serious mental disorder with high morbidity, disability, recurrence rate, and suicide rate [1]. According to the World Health Organization (WHO), 322 million people suffer from MDD worldwide, which is about 4.4% of the global population [2, 3]. Depression is considered a global health issue and a major contributor to the global burden of disease (GBD). MDD is expected to top the GBD list by 2030 [4].

Many treatments for patients with MDD have been developed in recent years, such as physical and psychological therapies (including repetitive transcranial magnetic stimulation [rTMS] and electroconvulsive therapy [ECT]) and cognitive-behavioral therapy (CBT) [5,6,7]. However, use of antidepressants is the gold standard for treating depression [8]. Globally, two-thirds of patients with MDD fail to experience adequate response or remission after antidepressant treatment and develop treatment-resistant depression (TRD) [9, 10].

To improve the effectiveness of antidepressants, researchers have been identifying biomarkers that can predict antidepressant responses so as to develop personalized and optimal clinical therapies. Several biomarkers have been developed as predictors of antidepressant treatment responses, such as brain imaging [11, 12], genetic information [13,14,15] and levels of inflammatory molecules in the peripheral blood [16, 17].

Most antidepressants were developed on the basis of the monoamine deficiency hypothesis of depression [18, 19]. These drugs increase serotonin (5-HT) levels in the synaptic cleft through prohibitive binding to 5-HT transporters (5-HTTs), consequently enhancing 5-HT neurotransmission and causing an antidepressant effect [20, 21]. Thus, 5-HT is critical for 5-HT synthesis and antidepressant effects. Tryptophan hydroxylase-2 (TPH2) is a rate-limiting enzyme for 5-HT synthesis in the central nervous system [22, 23]. Previous studies have reported that TPH2 genetic variants play an important role in antidepressant responses, and the TPH2 methylation status is related to antidepressant effects [24,25,26].

In addition to epigenetic studies on the effects of antidepressants, previous studies have shown that magnetic resonance imaging (MRI), particularly functional MRI (fMRI), is highly useful in studying antidepressant responses as this technique helps in analyzing changes in the brain structure and functional activity [27, 28]. MRI monitors functional brain activities and can directly measure how different brain regions are involved in various brain activities [29]. As the fMRI is capable of showing very early synaptic changes after antidepressant exposure, it can be used to observe antidepressant responses [30, 31].

Resting-state fMRI (rs-fMRI), a type of fMRI, has been used to evaluate spatial functional correlations within neural networks in the resting state [32]. Recently, rs-fMRI has been widely used to investigate abnormal brain function in antidepressant responders and non-responders [33]. Aizenstein et al. found that compared with non-responders, responders showed increased functional connectivity (FC) of the emotion-related region of the brain [34]. Emam1 et al. reported that compared to the responder group, the non-responder group showed significantly less amplitude of frequency fluctuations (ALFF) in the dorsomedial prefrontal cortex (dmPFC) [35]. Thus, rs-fMRI is an important technique that can be used to explore antidepressant effects.

Wheater et al. reported that a combination of DNA methylation status and fMRI data could better explain the changes in disease phenotypes [36]. Until now, the few studies have used this combination approach to study depression [37,38,39]. However, none of these studies explored the effects of antidepressants using this combination approach. These studies have the following limitations: 1. they focused on FC in particular brain regions without considering abnormal functional activities in other brain regions; 2. the correlation between DNA methylation status and rs-fMRI has been studied, but the interaction between them has not been studied has not been lucidated; 3. these studies only focused on two common genes (FKBP5 and SLC6A4) and not TPH2.

Therefore, in this study, we hypothesized that the functional activities in the brain regions between responders and non-responders to antidepressant medication were different, and the relationship between these activities and TPH2 methylation could affect short-term antidepressant responses. Hence, we analyzed the differences in the rs-fMRI data between the responder and non-responder groups, explored the interaction between rs-fMRI data and TPH2 methylation status, and determined the effect of this relationship on antidepressant effects.

Methods

Participants and clinical evaluation

In this study, we enrolled 300 patients with MDD at the Zhongda Hospital and 100 healthy controls (HCs). All the patients with MDD met the criteria mentioned in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) [40]. Blood samples were collected from all the participants before they were administered antidepressants. In addition, one-fifth of the MDD patients (60) underwent fMRI scanning before starting antidepressant treatment.

All the recruited participants belonged to the Han Chinese ancestry. The criteria for selecting patients to be included in the MDD group were as follows: (1) Age between 18 and 65 years; (2) duration of depressive disorder persisting for more than two weeks; (3) 17-item Hamilton Rating Scale for Depression (HAMD-17) scores of ≥ 17 at baseline; (4) no history of any disease that could affect rs-fMRI results, such as cardiovascular, liver, and kidney diseases; (5) newly diagnosed or recently relapsed patients were treatment-free for over two weeks. The patients with depression were diagnosed by two senior psychiatrists independently and confirmed by a third psychiatrist blinded to the previous evaluation.

The exclusion criteria of this study were as follows: (1) Participants with a history of brain organic mental disorder, endocrine and primary organic disorder, or other medical conditions that could disrupt mental assessments; (2) a history of substance abuse (drugs, caffeine, nicotine, alcohol, or other), head injury, or disturbances of consciousness; (3) patients who had received ECT 6 months prior to the study; (4) pregnant and lactating women. (5) a history of manic episodes within the preceding 12 months.

The study was performed in accordance with the Declaration of Helsinki and approved by the Zhongda Hospital ethics committee (2016ZDSYLL100-P01). Informed consent was obtained from all the participants.

Antidepressant treatment and its outcome evaluation

All the patients received antidepressants for more than two weeks without additional psychological intervention and followed up for one year to monitor any changes in the diagnosis. The psychiatrists prescribed the most appropriate single antidepressant to the MDD patients (selective serotonin reuptake inhibitors (SSRIs): n = 177 and non-SSRIs: n = 114, which included serotonin and norepinephrine reuptake inhibitors (SNRI): n = 94, noradrenergic and selective serotonergic antidepressants (NaSSAs): n = 13, and serotonin antagonists and reuptake inhibitors (SARI): n = 7) prohibiting the use of mood stabilizers and antipsychotics or other antidepressants. However, in a few clinical situations, low doses of benzodiazepines were allowed. The patients received the defined dosage of the antidepressants throughout the study period. A meeting was convened for standardizing the assessment and treatment protocols used by the participating psychiatrists before study commencement. Patients received no extra psychological intervention except supportive medical interviews. The HAMD-17 was used to assess the severity of depressive symptoms after two weeks from baseline [41]. The treatment response was defined as ≥ 50% reduction in the baseline HAMD-17 scores after treatment according to World Federation of Societies of Biological Psychiatry guidelines (WFSBP) [42].

DNA methylation analysis

Blood samples were collected from the participants at 8:00 a.m. after fasting for at least 8 h before receiving antidepressant treatment. Venous blood samples were collected from the patients and control group for methylation analysis in 5 mL EDTA vacutainer tube and stored at − 80 °C for later use. The amount of methylated DNA was analyzed by MethylTarget® (Genesky Biotechnologies Inc., Shanghai, China) by NGS-based, multiple-targeted CpG methylation analysis. Polymerase chain reaction (PCR) primers for the target regions were designed using the Methylation Primer software. The samples were treated with sodium bisulfite using the EZDNA Methylation Kit (Zymo Research, Irvine, CA, USA) and according to the manufacturer’s instructions. A PCR mixture was prepared for each reaction (Takara, Tokyo, Japan) and consisted of Mg2+, dNTP, each primer, HotStaraq polymerase (Takara, Tokyo, Japan), appropriate buffer, and the template DNA. PCR amplicons (170–270 bp) were purified using the QIAquick Gel Extraction Kit (QIAGEN). The products were sequenced on an Illumina HiSeq platform using a pair-end 150 bp mode by following the manufacturer’s protocol. For all samples of the target regions, the average sequencing depth reached 600x; for over 80% of the samples, the depth was more than 400X, and for 90% of the samples, the depth was more than 10X.

Based on our previous studies [43, 44], only 11 single nucleotide polymorphisms (SNPs), meeting the standards for the methylation status of the sequence, were detected. Primers were designed to cover the upstream 100 bp and downstream 100 bp of 11 TPH2 SNP sites, as well as the GC sequence content from cytosine-phosphate-guanine (CpG) sites > 20% after methylation. Methylation levels at 38 CpG sites of TPH2 were determined along with the ratio of methylated cytosines over total cytosines. These values were tested and exported to Excel for statistical analysis.

MRI analysis

MRI data were obtained using a 3.0-Tesla Siemens Trio MRI scanner using a 12-channel head coil. At baseline, the MRI scans were obtained at Zhongda Hospital of Southeast University. Each participant lay down supine with the head stabilized with cushions to minimize head movement. Additionally, the participants were provided with earplugs to lower the noise caused by the MRI scanner. High-resolution 3-dimensional T1-weighted images were obtained using a magnetization-prepared rapid gradient echo sequence: repetition time (TR) = 1900 ms; echo time (TE) = 2.48 ms; flip angle (FA) = 9°; acquisition matrix = 256 × 256; field of view (FOV) = 250 × 250 mm2; slice thickness = 1.0 mm, gap = 0; 176 sagittal slices; duration = 4 min 18 s. The rs-fMRI was used with the following sequence parameters: slices = 36; TR = 2000 ms; TE = 25 ms; FA = 90; acquisition matrix = 64 × 64; FOV = 240 mm × 240 mm; thickness = 3.0 mm; gap = 0 mm and 3.75 mm × 3.75 mm in-plane resolution parallel to the anterior commissure-posterior line. The scan time for one measurement lasted for about 8 min. During the fMRI scans, all participants were asked to be awake, to keep their eyes closed, relax, and refrain from any specific thinking [45].

The rs-fMRI data were preprocessed using the software MATLAB 2014a and Rs-fMRI Data Analysis Toolkit (REST; http://restfmri.net) [46]. Preprocessing included the following steps: (1) The first ten time points were discounted to ensure stable-state longitudinal magnetization and to allow subjects acclimate to the scanning environment; (2) functional images were corrected for differences in image acquisition time between slices; (3) all the imaging data were realigned for head movement correction; (4) the participant’s anatomical images were segmented, and the deformation field maps were applied to the functional images to normalize them into the standard Montreal Neurological Institute (MNI) space, with a resampled voxel of size 3 × 3 × 3 mm3 [47]; (5) the functional volumes were spatially smoothed by means of an isotropic Gaussian kernel of 6 mm full-width at half-maximum. (6) the linear trend of the time course was removed; (7) the regression of nuisance covariates including Friston 24 head-motion parameters, cerebrospinal fluid signals, global mean signals and white matter signals from the fMRI data were performed [48]; (8) Data were band-pass filtered to retain frequencies between 0.01 and 0.08 Hz [49]; (9) For ReHo, the calculation was carried out first and then spatial smoothing was performed [52].

The rs-fMRI data were analyzed using ALFF, regional homogeneity (ReHo), and fractional ALFF (fALFF) to measure the local spontaneous activity of individual regions or voxels, and seed-based FC was used to measure functional relationship with the seed region in the whole brain. For ALFF, the fast Fourier transform was used to transform the time series for each voxel to the frequency domain to obtain the power spectrum, and the square root of the power spectrum was calculated and averaged across 0.01–0.08 Hz [50]. FALFF data was computed with the ratio of the power spectrum of a given frequency range to the entire frequency range, which significantly improved the sensitivity and specificity in detecting the regional spontaneous brain activity [51]. ReHo was used for assessing the local temporal synchronizations by calculating Kendall’s coefficient of concordance between the time series of a given voxel and its nearest 26 neighbors [52].

Based on the ALFF, fALFF, and ReHo results, we performed seed-based resting-state FC (rs-FC) analysis to identify the changes in the brain networks that were centered in the regions showing abnormal local spontaneous activity in the responder and non-responder groups [53]. The above results were used as regions of interest (ROIs) for further research.

The Exsignal method in the RESTPlus software in MATLAB 2014a was used to extract the abnormal activity values of the brain regions with differences in the above four rs-fMRI processing methods, which were then converted into Excel for statistical analysis.

Statistical analysis

All statistical analyses were performed using the IBM SPSS Statistical software package version 25 (IBM, Chicago, IL, USA). Means and standard deviations were considered continuous variables. P-values < 0.05 indicated statistical significance. T-test and Mann–Whitney U tests were used to identify differences in demographic characteristics between groups. For determining the CpG-site methylation status between groups, since 12 CpG sites were distributed normally (Shapiro–Wilk test, all p-values > 0.05), the remaining 26 CpG sites (Shapiro–Wilk test, all p-values < 0.05) were compared with t-tests and Mann–Whitney U tests separately. The false discovery rate (FDR) was applied to correct a large number of individually tested CpG sites. The differences in the above rs-fMRI parameters between the responder and non-responder groups were assessed using the two-sample test (p < 0.05). All regression analyses mentioned previously were controlled for age, gender, and education. Gaussian random field (GRF) corrections were used for all multiple comparison corrections (a voxel level of p < 0.005 and a cluster-level of p < 0.05). Based on previous results of our research team, the TPH2 gene methylation sites associated with depression and antidepressant effects (TPH2–7–184, TPH2–10–60, TPH2–1–43, TPH2–9–178, TPH2–5–203, TPH2–7–142 and TPH2-8–237) were included in the statistical analyses [26]. Median values were used as a cut-off point to divide the above sites into hypomethylation and hypermethylation groups [26]. Participants were divided into two categories according to the cut-off value of an average of mean ALFF (mALFF) ROI, which mALFF ROI high group and mALFF ROI low group [54]. The generalized linear model was used to test the effects of the mALFF ROI, TPH2 gene methylation and mALFF ROI-TPH2 gene methylation interaction on the antidepressant effects. The classification variables mALFF ROI and TPH2 gene methylation were taken as independent variables to construct a model of the relationship between mALFF ROI and TPH2 gene methylation as well as their interaction with antidepressant effects, while age and gender were taken as covariables. R programming language (Version 1.2.1335, Vienna, Austria) was used to perform the false discovery rate (FDR) test with a statistical significance of p < 0.05.

Results

All the recruited patients with MDD had completed the two-week antidepressant treatments. During the 2-week follow-up period, one participant did not appear for the blood testing, one patient with depression withdrew from the study, and seven were retrospectively excluded at the one-year follow-up after diagnostic modification. 291 patients with MDD and 100 healthy controls were included in the methylation statistical analysis. Demographic and clinical characteristics of the 291 MDD patients and the 100 controls are detailed in Shen's article [26]. A total of 57 patients were included in the further analysis (3 patients were excluded due to excessive head movement). According to the reduction ratio of HAMD-17, 57 patients with MDD were divided into two groups: 37 patients who responded to antidepressant treatments (responders) and 21 patients who failed to respond to the antidepressant treatments (non-responders). There were no statistical significances in age (p = 0.552) and gender (p = 0.333) in these 57 patients with MDD (Table 1).

Table 1 Demographic characteristics between responders and non-responders
Full size table

Our rs-fMRI results showed differences in the ALFF of the left inferior frontal gyrus (IFG) between the responders and non-responders (Fig. 1 A, Fig. 1 B, Fig. 1 C, Table 2), which coordinate is (-27 30 -6). There was no difference in the brain regions between the two groups according to ReHo and fALFF analyses. Based on the regional differences between groups found in the mALFF analysis, the FC analyses of the rs-fMRI data revealed that no difference in the brain regions was observed between the two groups.

Fig.1
figure 1

Different ALFF in Left IFG between responders and non-responders. ALFF–Amplitude of low-frequency fluctuation.; IFG- Inferior frontal gyrus; L-Left; R-Right

Full size image
Table 2 Brain areas with significantly different ALFF values between responders and non-responders
Full size table

The result showed that low left IFG ALFF significantly interacted with TPH2-1–43 low methylation levels in affecting antidepressant responses(B value = 0.190, Wald Chi-Square = 4.168, p = 0.041, standard error(SE) = 0.931, 95% Wald Confidence Interval, Lower = 0.008, Upper = 0.373, FDR corrected p = 0.149)(Fig. 2, Table 3). MDD patients with TPH2-1–43 low methylation level and low mALFF in the left IFG could have significantly better antidepressant responses. The results further suggested that antidepressant effects in MDD patients with low left IFG mALFF is significantly lower than that those with high mALFF (B value = -0.186, Wald Chi-Square = 8.103, p = 0.004, SE = 0.653, 95% Wald Confidence Interval, Lower = -0.314, Upper = -0.058, FDR corrected p = 0.149). Analysis showed no significant differences in the TPH2-1–43 DNA methylation levels between the two groups (B value = -0.139, Wald Chi-Square = 3.758, p = 0.053, SE = 0.071, 95% Wald Confidence Interval, Lower = -0.280, Upper = -0.002,FDR corrected p = 0.149)(Table 3).

Fig.2
figure 2

Effect of mALFF ROI and TPH2-1–43 interaction on 2-week antidepressant responses. mALFF–mean Amplitude of low-frequency fluctuation; ROI-Regions of interest; HAMD17-17-item Hamilton Rating Scale for Depression; TPH2-Tryptophan hydroxylase2

Full size image
Table 3 Interaction effect of TPH2 methylation and ALFF ROI on HAMD 2-week reduction rate. mALFF mean Amplitude of low-frequency fluctuation, ROI Regions of interest, *p < 0.05, TPH2 Tryptophan hydroxylase2, SE standard error
Full size table

Discussion

Differences in ALFF results of the left IFG between the responders and non-responders were observed in our study. Yang et al. reported that the therapeutic effects of antidepressants could be achieved by changing the FC in the hypothalamic regions, mainly in the inferior frontal, cingulate gyrus thalamus, and cerebellum [55]. Ichikawa et al. reported that antidepressants affect the FC between the left dorsolateral prefrontal cortex (DLPFC)/ IFG and posterior cingulate cortex (PCC)/precuneus to produce different effects of antidepressant treatments [56]. Our findings are comparable to those of previous studies that have reported functional abnormalities in IFG. However, these findings suggest that FC of the IFG may influence the antidepressant effects.

The left IFG is a key region for language comprehension and production. It also plays an important role in language processing [57, 58]. However, previous studies have demonstrated that left IFG is also a site of convergence of cognitive and emotional information, involved in the affective aspects of language processing, semantics, and visual memory [59]. A previous study showed the left IFG as an important area for emotional processing and belongs to a top-down cognitive control network during emotion processing that is responsible for understanding and controlling emotions [60]. Previous studies demonstrated that MDD patients had increased ALFF in the frontal lobe after antidepressant treatments [61]. These results indicated that a return to normal frontal lobe activity is associated with improvement in antidepressant efficacy. So, we speculate that for the patients with the higher spontaneous brain activity in the left IFG, the brain area activity may be more easily affected by the top-down modulatory network, so as to restore the normal emotional processing.

Previous studies have documented that FC of the right IFG and the orbitofrontal cortex was related to antidepressant effects [62]. Additionally, other studies have reported the FC of bilateral IFG and inferior frontal gyrus was related to antidepressant responses [55, 56]. Our results are inconsistent with those of previous studies. It is possible that the sample size of our study was small, which may reduce the effect of FC in the left IFG on antidepressant effects. Further studies are required to explore the relationship between the FC in the left IFG and antidepressant responses.

Moreover, our study explained the interaction between ALFF in the left IFG and TPH2 methylation affecting antidepressant effects. Although there are no studies investigating the effect of interaction with rs-fMRI and DNA methylation on antidepressant responses. So far, only a few studies had focused on the relationship between them and MDD. Julian Chiarella et al. reported that FKBP5 methylation, which is associated with depression, was related to the FC between the left orbitofrontal cortex (OFC) and the frontal lobe-limbic cortex [37]. Another study reported a strong positive correlation between level of SLC6A4 methylation, which is associated with depression, and FC in the amygdala in a healthy population [63]. Meanwhile, Elmira Ismaylovaer et al. had displayed that blood-derived SLC6A4 methylation was positively associated with right lateral parietal area (RLP)-frontal pole regional rs-FC [64]. Additionally, Hedi Foo et al. had analyzed and summarized the above research results, then proposed that epigenetic variation could affect the FC of brain regions which may cause disease resilience/susceptibility [65]. According to the above results, it is proved that FC of brain regions is closely correlated with DNA methylation. Therefore, we have speculated that the functional activities of local brain regions may be correlated with DNA methylation. However, there is no studies have explored the relationship between functional activities of local brain regions and DNA methylation status. Based on the assumption, our research has demonstrated that the interaction existed between DNA methylation and functional activities of local brain regions, and the interaction will eventually affect the efficacy of antidepressants. Thus, it is crucial to focus on the relation between DNA methylation and functional activities of local brain regions in future studies.

In this study, we found that the interaction between hypomethylation levels of TPH2-1–43 and low ALFF level in the left IFG can lead to the better antidepressant responses. However, we also found that antidepressant responses could be reduced in MDD patients who had low spontaneous activity compared with high spontaneous activity. Moreover, previous research of our group demonstrated that the lower the methylation level of TPH2-1–43, the worse the antidepressant effect [26]. Hence, our results suggested a mutually exclusive interaction between the methylation level of TPH2-1–43 and the spontaneous activity of the left IFG. It may explain that the low methylation level of TPH2-1–43 can antagonize the worse antidepressant efficacy caused by low spontaneous activity in the left IFG. The susceptibility of MDD is influenced by the interactions between genomic variants and environmental factors [66]. Particularly, DNA methylation stands for the main mediators of the impact of the environment in increasing the vulnerability risk to develop MDD [67]. DNA methylation could play an important role in brain development and function [68]. Hence, it may eventually lead to influence on the antidepressant effects. In addition, our results also suggested that researchers wanting to identify the biomarkers of antidepressant effects could take into account the interaction between DNA methylation and rs-fMRI. Hence, it may eventually lead to influence on the antidepressant effects. In addition, our results also suggested that researchers wanting to identify the biomarkers of antidepressant effects could take into account the interaction between DNA methylation and rs-fMRI. Based on our results, we speculated that TPH2 methylation may cause abnormal functional activity in the brain regions and ultimately lead to differences in antidepressant effects (such as ALFF). Unfortunately, we needed to point out that this result did not pass the FDR threshold at p = 0.041. The reasons may be that lack of strong interaction between DNA methylation status and brain function and the small sample size.

Limitations of the present study are worth noting. The sample sizes in this study were relatively small; Moreover, the TPH2 methylation status-related information and rs-fMRI data were obtained only at baseline and not after 2-week antidepressant treatment. Therefore, future studies with larger sample sizes are necessary to detect gene DNA methylation and rs-fMRI to confirm our findings. Finally, the fact that our study failed to show relationship between types and doses of antidepressant drugs and the identification of the difference regions. Future studies need to consider the types and dosages of antidepressant drugs to reduce the effect of the heterogeneity and doses difference of drugs on recognition of abnormal brain regions.

Conclusions

Our study suggests that the differences in the ALFF in the left IFG and its association with TPH2 methylation status affect short-term antidepressant drug response. Further research is needed to explore how the relationship between DNA methylation and functional changes in the brain regions affect antidepressant responses. It may help in better guiding treatment decisions and hastening clinical response.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

ALFF:

Amplitude of low-frequency fluctuation

CBT:

Cognitive-behavioral therapy

CpG:

Cytosine-phosphate-guanine

DLPFC:

Dorsolateral prefrontal cortex

DmPFC:

Dorsomedial prefrontal cortex

DSM-IV:

Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition

ECT:

Electroconvulsive therapy

FA:

Flip angle

FALFF:

Fractional ALFF

FC:

Functional connectivity

FDR:

False discovery rate

FMRI:

Functional MRI

FOV:

Field of view

GBD:

Global burden of disease

GRF:

Gaussian random field

HCs:

Healthy controls

HAMD-17:

17-Item Hamilton Rating Scale for Depression

IFG:

Inferior frontal gyrus

L:

Left

mALFF:

Mean ALFF

MDD:

Major depressive disorder

MNI:

Montreal Neurological Institute

MRI:

Magnetic resonance imaging

NaSSAs:

Noradrenergic and selective serotonergic antidepressants

OFC:

Orbitofrontal cortex

PCC:

Posterior cingulate cortex

PCR:

Polymerase chain reaction

Qiagen:

QIAquick Gel extraction kit

R:

Right

ReHo:

Regional homogeneity

RLP:

Right lateral parietal

ROIs:

Regions of interest

Rs-FC:

Resting-state FC

Rs-fMRI:

Resting-state functional MRI

RTMS:

Repetitive transcranial magnetic stimulation

SARI:

Serotonin antagonists and reuptake inhibitors

SD:

Standard deviation

SE:

Standard error

SNRI:

Serotonin and norepinephrine reuptake inhibitors

SNPs:

Single nucleotide polymorphisms

SSRIs:

Selective serotonin reuptake inhibitors

TE:

Echo time

TPH2 :

Tryptophan hydroxylase-2

TR:

Repetition time

TRD:

Treatment-resistant depression

WFSBP:

World Federation of Societies of Biological Psychiatry guidelines

WHO:

World Health Organization

5-HT:

Serotonin

5-HTTs:

5-HT transporters

References

  1. Vink JJT, Mandija S, Petrov PI, van den Berg CAT, Sommer IEC, Neggers SFW. A novel concurrent TMS-fMRI method to reveal propagation patterns of prefrontal magnetic brain stimulation. Hum Brain Mapp. 2018;39:4580–92.

    PubMed  PubMed Central  Google Scholar 

  2. Kim YE, Lee B. The psychometric properties of the patient health questionnaire-9 in a sample of Korean university students. Psychiatry Investig. 2019;16:904–10.

    PubMed  PubMed Central  Google Scholar 

  3. Bowman MA, Vitela M, Clarke KM, Koek W, Daws LC. Serotonin transporter and plasma membrane monoamine transporter are necessary for the antidepressant-like effects of ketamine in mice. Int J Mol Sci. 2020;21:20.

    Google Scholar 

  4. Scerri J, Saliba T, Saliba G, Scerri CA, Camilleri L. Illness perceptions, depression and anxiety in informal carers of persons with depression: a cross-sectional survey. Qual Life Res. 2019;28:451–60.

    PubMed  Google Scholar 

  5. Lee S, Jang KI, Yoon S, Chae JH. The efficacy of miniaturized repetitive transcranial magnetic stimulation in patients with depression. Clin Psychopharmacol Neurosci. 2019;17:409–14.

    PubMed  PubMed Central  Google Scholar 

  6. Qiu H, Li X, Luo Q, Li Y, Zhou X, Cao H, Zhong Y, Sun M. Alterations in patients with major depressive disorder before and after electroconvulsive therapy measured by fractional amplitude of low-frequency fluctuations (fALFF). J Affect Disord. 2019;244:92–9.

    PubMed  Google Scholar 

  7. Rizvi SJ, Zaretsky A, Schaffer A, Levitt A. Is immediate adjunctive CBT more beneficial than delayed CBT in treating depression? A Pilot Study J Psychiatr Pract. 2015;21:107–37.

    PubMed  Google Scholar 

  8. Branchi I, Santarelli S, Capoccia S, Poggini S, D’Andrea I, Cirulli F, Alleva E. Antidepressant treatment outcome depends on the quality of the living environment: a pre-clinical investigation in mice. PLoS ONE. 2013;8: e62226. https://doi.org/10.1371/journal.pone.0062226.

  9. Kircanski K, Williams LM, Gotlib IH. Heart rate variability as a biomarker of anxious depression response to antidepressant medication. Depress Anxiety. 2019;36:63–71.

    CAS  PubMed  Google Scholar 

  10. Yang LP, Jiang FJ, Wu GS, Deng K, Wen M, Zhou X, Hong XC, Zhu MX, Luo HR. Acute treatment with a novel TRPC4/C5 channel inhibitor produces antidepressant and anxiolytic-like effects in mice. PLoS ONE. 2015;10: e0136255. https://doi.org/10.1371/journal.pone.0136255.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Jiang B, Petkova E, Tarpey T, Ogden RT. A Bayesian approach to joint modeling of matrix-valued imaging data and treatment outcome with applications to depression studies. Biometrics. 2020;76:87–97.

    PubMed  Google Scholar 

  12. Lyon M, Welton T, Varda A, Maller JJ, Broadhouse K, Korgaonkar MS, Koslow SH, Williams LM, Gordon E, Rush AJ, et al. Gender-specific structural abnormalities in major depressive disorder revealed by fixel-based analysis. Neuroimage Clin. 2019;21:101668.

    PubMed  PubMed Central  Google Scholar 

  13. Lin E, Kuo PH, Liu YL, Yu YW, Yang AC, Tsai SJ. A Deep learning approach for predicting antidepressant response in major depression using clinical and genetic biomarkers. Front Psychiatry. 2018;9:290.

    PubMed  PubMed Central  Google Scholar 

  14. Papakostas GI, Fava M. Predictors, moderators, and mediators (correlates) of treatment outcome in major depressive disorder. Dialogues Clin Neurosci. 2008;10:439–51.

    PubMed  PubMed Central  Google Scholar 

  15. Basu A, Chadda RK, Sood M, Kaur H, Kukreti R. Association of serotonin transporter (SLC6A4) and receptor (5HTR1A, 5HTR2A) polymorphisms with response to treatment with escitalopram in patients with major depressive disorder: A preliminary study. Indian J Med Res. 2015;142:40–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Tadic A, Wagner S, Gorbulev S, Dahmen N, Hiemke C, Braus DF, Lieb K. Peripheral blood and neuropsychological markers for the onset of action of antidepressant drugs in patients with major depressive disorder. BMC Psychiatry. 2011;11:16.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Vojvodic J, Mihajlovic G, Vojvodic P, Radomirovic D, Vojvodic A, Vlaskovic-Jovicevic T, Peric-Hajzler Z, Matovic D, Dimitrijevic S, Sijan G, et al. The impact of immunological factors on depression treatment - relation between antidepressants and immunomodulation agents. Open Access Maced J Med Sci. 2019;7:3064–9.

    PubMed  PubMed Central  Google Scholar 

  18. Rein T. Is autophagy involved in the diverse effects of antidepressants? Cells. 2019;8:1.

    Google Scholar 

  19. Camacho MB, Anastasio TJ. Computational model of antidepressant response heterogeneity as multi-pathway neuroadaptation. Front Pharmacol. 2017;8:925.

    PubMed  PubMed Central  Google Scholar 

  20. Carr GV, Lucki I. The role of serotonin receptor subtypes in treating depression: a review of animal studies. Psychopharmacology. 2011;213:265–87.

    CAS  PubMed  Google Scholar 

  21. Althof SE, McMahon CG, Waldinger MD, Serefoglu EC, Shindel AW, Adaikan PG, Becher E, Dean J, Giuliano F, Hellstrom WJ, et al. An update of the international society of sexual medicine’s guidelines for the diagnosis and treatment of premature ejaculation (PE). Sex Med. 2014;2:60–90.

  22. Shishkina GT, Kalinina TS, Dygalo NN. Up-regulation of tryptophan hydroxylase-2 mRNA in the rat brain by chronic fluoxetine treatment correlates with its antidepressant effect. Neuroscience. 2007;150:404–12.

    CAS  PubMed  Google Scholar 

  23. Kulikova EA, Kulikov AV. Tryptophan hydroxylase 2 as a therapeutic target for psychiatric disorders: focus on animal models. Expert Opin Ther Targets. 2019;23:655–67.

    CAS  PubMed  Google Scholar 

  24. Wigner P, Czarny P, Synowiec E, Bijak M, Bialek K, Talarowska M, Galecki P, Szemraj J, Sliwinski T. Association between single nucleotide polymorphisms of TPH1 and TPH2 genes, and depressive disorders. J Cell Mol Med. 2018;22:1778–91.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Tzvetkov MV, Brockmoller J, Roots I, Kirchheiner J. Common genetic variations in human brain-specific tryptophan hydroxylase-2 and response to antidepressant treatment. Pharmacogenet Genomics. 2008;18:495–506.

    CAS  PubMed  Google Scholar 

  26. Shen T, Li X, Chen L, Chen Z, Tan T, Hua T, Chen B, Yuan Y, Zhang Z, Kuney L, et al. The relationship of tryptophan hydroxylase-2 methylation to early-life stress and its impact on short-term antidepressant treatment response. J Affect Disord. 2020;276:850–8.

    CAS  PubMed  Google Scholar 

  27. Korgaonkar MS, Rekshan W, Gordon E, Rush AJ, Williams LM, Blasey C, Williams LM, Blasey C, Grieve SM. Magnetic Resonance Imaging Measures of Brain Structure to Predict Antidepressant Treatment Outcome in Major Depressive Disorder. EBioMedicine. 2015;2:37–45.

    PubMed  Google Scholar 

  28. Hultman R, Ulrich K, Sachs BD, Blount C, Carlson DE, Ndubuizu N, Bagot RC, Parise EM, Vu MT, Gallagher NM, et al. Brain-wide Electrical Spatiotemporal Dynamics Encode Depression Vulnerability. Cell. 2018;173(166–180):e114.

    Google Scholar 

  29. Cheng Y, Xiao S. Recent research about mild cognitive impairment in China. Shanghai Arch Psychiatry. 2014;26:4–14.

    PubMed  PubMed Central  Google Scholar 

  30. Bigos KL, Pollock BG, Aizenstein HJ, Fisher PM, Bies RR, Hariri AR. Acute 5-HT reuptake blockade potentiates human amygdala reactivity. Neuropsychopharmacology. 2008;33:3221–5.

    CAS  PubMed  Google Scholar 

  31. Rawlings NB, Norbury R, Cowen PJ, Harmer CJ. A single dose of mirtazapine modulates neural responses to emotional faces in healthy people. Psychopharmacology. 2010;212:625–34.

    CAS  PubMed  Google Scholar 

  32. Wu YY, Wang SF, Zhu PW, Yuan Q, Shi WQ, Lin Q, Li B, Min YL, Zhou Q, Shao Y. altered intrinsic functional connectivity of the primary visual cortex in patients with neovascular glaucoma: a resting-state functional magnetic resonance imaging study. Neuropsychiatr Dis Treat. 2020;16:25–33.

    PubMed  PubMed Central  Google Scholar 

  33. Dichter GS, Gibbs D, Smoski MJ. A systematic review of relations between resting-state functional-MRI and treatment response in major depressive disorder. J Affect Discord. 2015;172:8–17.

    Google Scholar 

  34. Aizenstein HJ, Khalaf A, Walker SE, Andreescu C. Magnetic resonance imaging predictors of treatment response in late-life depression. J Geriatr Psychiatry Neurol. 2014;27:24–32.

    PubMed  Google Scholar 

  35. Emam H, Steffens DC, Pearlson GD, Wang L. Increased ventromedial prefrontal cortex activity and connectivity predict poor sertraline treatment outcome in late-life depression. Int J Geriatr Psychiatry. 2019;34:730–7.

    PubMed  PubMed Central  Google Scholar 

  36. Wheater ENW, Stoye DQ, Cox SR, Wardlaw JM, Drake AJ, Bastin ME, Boardman JP. DNA methylation and brain structure and function across the life course: A systematic review. Neurosci Biobehav Rev. 2020;113:133–56.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Chiarella J, Schumann L, Pomares FB, Frodl T, Tozzi L, Nemoda Z, Yu P, Szyf M, Khalid-Khan S, Booij L. DNA methylation differences in stress-related genes, functional connectivity and gray matter volume in depressed and healthy adolescents. J Affect Disord. 2020;271:160–8.

    CAS  PubMed  Google Scholar 

  38. Tozzi L, Farrell C, Booij L, Doolin K, Nemoda Z, Szyf M, Pomares FB, Chiarella J, O’Keane V, Frodl T. Epigenetic changes of fkbp5 as a link connecting genetic and environmental risk factors with structural and functional brain changes in major depression. Neuropsychopharmacology. 2018;43:1138–45.

  39. Frodl T, Szyf M, Carballedo A, Ly V, Dymov S, Vaisheva, et al. DNA methylation of the serotonin transporter gene (SLC6A4) is associated with brain function involved in processing emotional stimuli. J Psychiatry Neurosci. 2015;40:296–305.

    PubMed  PubMed Central  Google Scholar 

  40. Heaton P, Davis RE, Happe FG. Research note: exceptional absolute pitch perception for spoken words in an able adult with autism. Neuropsychologia. 2008;46:2095–8.

    PubMed  Google Scholar 

  41. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960;23:56–62.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Bauer M, Whybrow PC, Angst J, Versiani M, Moller HJ. World federation of societies biological psychiatry task force on treatment guidelines for unipolar depressive, d., world federation of societies of biological psychiatry (WFSBP) guidelines for biological treatment of unipolar depressive disorders, Part 1: acute and continuation treatment of major depressive disorder. World J Biol Psychiatry. 2002;3:5–43.

    PubMed  Google Scholar 

  43. Xu Z, Reynolds GP, Yuan Y, Shi Y, Pu M, Zhang Z. TPH-2 polymorphisms interact with early life stress to influence response to treatment with antidepressant drugs. Int J Neuropsychopharmacol. 2016;19:11.

    Google Scholar 

  44. Xu Z, Zhang Z, Shi Y, Pu M, Yuan Y, Zhang X, et al. Influence and interaction of genetic polymorphisms in the serotonin system and life stress on antidepressant drug response. J Psychopharmacol. 2012;26:349–59.

    CAS  PubMed  Google Scholar 

  45. Zhu J, Cai H, Yuan Y, Yue Y, Jiang D, Chen C, Zhang W, Zhuo C, Yu Y. Variance of the global signal as a pretreatment predictor of antidepressant treatment response in drug-naive major depressive disorder. Brain Imaging Behav. 2018;12:1768–74.

    PubMed  PubMed Central  Google Scholar 

  46. Cai C, Huang C, Yang C, Zhang X, Peng Y, Zhao W, Hong X, Ren F, Hong D, Xiao Y, et al. Altered patterns of phase position connectivity in default mode subnetwork of subjective cognitive decline and amnestic mild cognitive impairment. Front Neurosci. 2020;14:185.

    PubMed  PubMed Central  Google Scholar 

  47. Feng C, Wang L, Li T, Xu P. Connectome-based individualized prediction of loneliness. Soc Cogn Affect Neurosci. 2019;14:353–65.

    PubMed  PubMed Central  Google Scholar 

  48. Ran Q, Yang J, Yang W, Wei D, Qiu J, Zhang D. The association between resting functional connectivity and dispositional optimism. PLoS ONE. 2017;12: e0180334. https://doi.org/10.1371/journal.pone.0180334.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Dai T, Guo Y. Alzheimers disease neuroimaging initiative predicting individual brain functional connectivity using a Bayesian hierarchical model. Neuroimage. 2017;147:772–87.

    PubMed  Google Scholar 

  50. Nguyen DT, Ryu S, Qureshi MNI, Choi M, Lee KH, Lee B. Hybrid multivariate pattern analysis combined with extreme learning machine for Alzheimer’s dementia diagnosis using multi-measure rs-fMRI spatial patterns. PLoS ONE. 2019;14: e0212582. https://doi.org/10.1371/journal.pone.0212582.

  51. Yu Y, Li Z, Lin Y, Yu J, Peng G, Zhang K, et al. Depression Affects Intrinsic Brain Activity in Patients with Mild Cognitive Impairment. Front. 2019;13:1333.

    Google Scholar 

  52. Dai R, Huang Z, Tu H, Wang L, Tanabe S, Weng X, et al. Interplay between heightened temporal variability of spontaneous brain activity and task-evoked hyperactivation in the blind. Front Hum Neurosci. 2016;10:632 Front Hum Neurosci. 2020; 14: 244.

    PubMed  PubMed Central  Google Scholar 

  53. Bu X, Hu X, Zhang L, Li B, Zhou M, Lu L, Hu X, Li H, Yang Y, Tang W, et al. Investigating the predictive value of different resting-state functional MRI parameters in obsessive-compulsive disorder. Transl Psychiatry. 2019;9:17.

    PubMed  PubMed Central  Google Scholar 

  54. Iacobucci D, Steven S, Posavac F, Kardes FR, Matthew J, et al. The median split: robust refined and revived. J Consum Psychol. 2015;25:690–704.

    Google Scholar 

  55. Yang R, Zhang H, Wu X, Yang J, Ma M, Gao Y, Liu H, Li S. Hypothalamus-anchored resting brain network changes before and after sertraline treatment in major depression. Biomed Res Int. 2014;2014:915026.

    PubMed  PubMed Central  Google Scholar 

  56. Ichikawa N, Lisi G, Yahata N, Okada G, Takamura M, Hashimoto RI, Yamada T, Yamada M, Suhara T, Moriguchi S, et al. Primary functional brain connections associated with melancholic major depressive disorder and modulation by antidepressants. Sci Rep. 2020;10(1):3542.

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Schipul SE, Keller TA, Just MA. Inter-regional brain communication and its disturbance in autism. Front Syst Neurosci. 2011;5:10.

    PubMed  PubMed Central  Google Scholar 

  58. Buckner RL. Memory and executive function in aging and AD: multiple factors that cause decline and reserve factors that compensate. Neuron. 2004;44:195–208.

    CAS  PubMed  Google Scholar 

  59. Beacher FD, Radulescu E, Minati L, Baron-Cohen S, Lombardo MV, Lai MC, Walker A, Howard D, Gray MA, Harrison NA, et al. Sex differences and autism: brain function during verbal fluency and mental rotation. PLoS ONE. 2012;7: e38355. https://doi.org/10.1371/journal.pone.0038355.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Pan W, Wang T, Wang X, Hitchman G, Wang L, Chen A. Identifying the core components of emotional intelligence: evidence from amplitude of low-frequency fluctuations during resting state. PLoS ONE. 2014;9(10): e111435. https://doi.org/10.1371/journal.pone.0111435.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Lai CH. Increases in amplitude of low-frequency fluctuations in left fronto-parietal area after duloxetine therapy in first-episode, drug-naive, major depressive disorder with panic disorder patients. J Neuropsychiatry Clin Neurosci. 2012;24:E24–5.

    PubMed  Google Scholar 

  62. Rolls ET, Cheng W, Du J, Wei D, Qiu J, Dai D, et al. Functional connectivity of the right inferior frontal gyrus and orbitofrontal cortex in depression. Soc Cogn Affect Neurosci. 2020;15:75–86.

    PubMed  PubMed Central  Google Scholar 

  63. Muehlhan M, Kirschbaum C, Wittchen HU, Alexander N. Epigenetic variation in the serotonin transporter gene predicts resting state functional connectivity strength within the salience-network. Hum Brain Mapp. 2015;36:4361–71.

    PubMed  PubMed Central  Google Scholar 

  64. Ismaylova E, Di Sante J, Szyf M, Nemoda Z, Yu WJ, Pomares FB, Turecki G, Gobbi G, Vitaro F, Tremblay RE, et al. Serotonin transporter gene promoter methylation in peripheral cells in healthy adults: neural correlates and tissue specificity. Eur Neuropsychopharmacol. 2017;27:1032–41.

    CAS  PubMed  Google Scholar 

  65. Foo H, Mather KA, Jiang J, Thalamuthu A, Wen W, Sachdev PS. Genetic influence on ageing-related changes in resting-state brain functional networks in healthy adults: a systematic review. Neurosci Biobehav Rev. 2020;113:98–110.

    CAS  PubMed  Google Scholar 

  66. Hung YY, Huang YL, Chang C, Kang HY. Deficiency in androgen receptor aggravates the depressive-like behaviors in chronic mild stress model of depression. Cells. 2019;9:2.

    Google Scholar 

  67. Lopizzo N, Bocchio Chiavetto L, Cattane N, Plazzotta G, Tarazi FI, Pariante CM, et al. Gene-environment interaction in major depression: focus on experience-dependent biological systems. Front Psychiatry. 2015;6:68.

    PubMed  PubMed Central  Google Scholar 

  68. Kuehner JN, Bruggeman EC, Wen Z, Yao B. Epigenetic regulations in neuropsychiatric disorders. Front Genet. 2019;10:268.

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors appreciate the assistance and cooperation of the participants of this study.

Funding

This work was funded by Natural Science Foundation of Jiangsu Province (No. BK20181272), Jiangsu Provincial Medical Youth Talent (No. QNRC2016825), National Natural Science Foundation of China (No. 81301167, 81971277).

Author information

Author notes

  1. Tingting Tan and Zhi Xu are co-first author.

Authors and Affiliations

  1. Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009, People’s Republic of China

    Tingting Tan, Zhi Xu, Chenjie Gao, Tian Shen, Zimu Chen, Lei Chen & Yonggui Yuan

  2. Key Laboratory of Developmental Genes and Human Diseases, Ministry of Education, School of Medicine, Southeast University, Nanjing, 210009, People’s Republic of China

    Tingting Tan, Zhi Xu, Chenjie Gao, Zimu Chen & Yonggui Yuan

  3. Department of Psychiatric Rehabilitation, Wuxi Mental Health Center, Nanjing Medical University, WuXi, 214123, People’s Republic of China

    Tian Shen

  4. School of Medicine, Southeast University, Nanjing, 210009, People’s Republic of China

    Lei Li

  5. Department of Psychology and Psychiatry, School of Medicine, Jinling Hospital, Nanjing University, Nanjing, 210018, People’s Republic of China

    Lei Chen

  6. Department of Anatomy, Medical School, Southeast University, Nanjing, 210009, People’s Republic of China

    Min Xu

  7. Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, 210009, People’s Republic of China

    Bingwei Chen

  8. Department of Nuclear Medicine, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009, People’s Republic of China

    Jiacheng Liu

  9. Department of Neurology, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009, People’s Republic of China

    Zhijun Zhang

Authors
  1. Tingting Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhi Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chenjie Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tian Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zimu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Min Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Bingwei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jiacheng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Zhijun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yonggui Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

TT was a major contributor in the formal analysis, writing—original draft, writing—review & editing and validation. CG, TS, LL and MX conducted the data curation and formal analysis. ZC and LC carried out the data curation. BC and JL conducted the statistical analysis. ZZ and YY contributed to the conceptualization. ZX was a major contributor in conception, investigation and project administration. All authors contributed to the article and approved the submitted version.

Corresponding author

Correspondence to Zhi Xu.

Ethics declarations

Ethics approval and consent to participate

The studies involving human participants were reviewed and approved by the ethics committee of Zhongda Hospital (2016ZDSYLL100-P01). The patients/participants provided their written informed consent to participate in this study. All procedures of this study were based on the Helsinki Declaration.

Consent for publication

Not applicable.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tan, T., Xu, Z., Gao, C. et al. Influence and interaction of resting state functional magnetic resonance and tryptophan hydroxylase-2 methylation on short-term antidepressant drug response. BMC Psychiatry 22, 218 (2022). https://doi.org/10.1186/s12888-022-03860-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12888-022-03860-z

Keywords

BMC Psychiatry

ISSN: 1471-244X

Contact us