Skip to main content Accessibility help
×
Home
Hostname: page-component-558cb97cc8-rx7pk Total loading time: 1.866 Render date: 2022-10-07T10:52:12.322Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "displayNetworkTab": true, "displayNetworkMapGraph": false, "useSa": true } hasContentIssue true

Brain grey matter abnormalities in medication-free patients with major depressive disorder: a meta-analysis

Published online by Cambridge University Press:  21 March 2014

Y.-J. Zhao
Affiliation:
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, People's Republic of China
M.-Y. Du
Affiliation:
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, People's Republic of China
X.-Q. Huang
Affiliation:
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, People's Republic of China
S. Lui
Affiliation:
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, People's Republic of China
Z.-Q. Chen
Affiliation:
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, People's Republic of China
J. Liu
Affiliation:
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, People's Republic of China
Y. Luo
Affiliation:
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, People's Republic of China
X.-L. Wang
Affiliation:
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, People's Republic of China
G. J. Kemp
Affiliation:
Magnetic Resonance and Image Analysis Research Centre (MARIARC) and Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK
Q.-Y. Gong*
Affiliation:
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, People's Republic of China
*
*Address for correspondence: Q.-Y. Gong, M.D., Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guo Xue Xiang Chengdu, 610041, People's Republic of China. (Email: qiyonggong@hmrrc.org.cn)
Rights & Permissions[Opens in a new window]

Abstract

Background

Because cerebral morphological abnormalities in major depressive disorder (MDD) may be modulated by antidepressant treatment, inclusion of medicated patients may have biased previous meta-analyses of voxel-based morphometry (VBM) studies. A meta-analysis of VBM studies on medication-free MDD patients should be able to distinguish the morphological features of the disease itself from those of treatment.

Method

A systematic search was conducted for the relevant studies. Effect-size signed differential mapping was applied to analyse the grey matter differences between all medication-free MDD patients and healthy controls. Meta-regression was used to explore the effects of demographics and clinical characteristics.

Results

A total of 14 datasets comprising 400 medication-free MDD patients and 424 healthy controls met the inclusion criteria. The pooled meta-analysis and subgroup meta-analyses showed robustly reduced grey matter in prefrontal and limbic regions in MDD. Increased right thalamus volume was only seen in first-episode medication-naive patients, and increased grey matter in the bilateral anterior cingulate cortex only in medication wash-out patients. In meta-regression analyses the percentage of female patients in each study was negatively correlated with reduced grey matter in the right hippocampus.

Conclusions

By excluding interference from medication effects, the present study identified grey matter reduction in the prefrontal–limbic network in MDD. The subgroup meta-analysis results suggest that an increased right thalamus volume might be a trait directly related to MDD, while an increased anterior cingulate cortex volume might be an effect of medication. The meta-regression results perhaps reveal the structural underpinning of the sex differences in epidemiological and clinical aspects of MDD.

Type
Original Articles
Creative Commons
Creative Common License - CCCreative Common License - BY
The online version of this article is published within an Open Access environment subject to the conditions of the Creative Commons Attribution licence .
Copyright
Copyright © Cambridge University Press 2014

Introduction

Major depressive disorder (MDD) (lifetime prevalence 13–16%) is a complex constellation of disturbances in cognition, behaviour and physical functioning (Rizvi & Kennedy, Reference Rizvi and Kennedy2012). MDD is the third leading cause of global disease burden and a leading cause of disability worldwide (Mathers et al. Reference Mathers, Fat and Boerma2008). Depression is clinically and aetiologically heterogeneous (Hasler, Reference Hasler2010). A major research effort aims to elucidate its biological underpinnings, and thus to develop more effective treatments.

Numerous structural magnetic resonance imaging (MRI) studies have sought to identify the key brain areas involved in the pathogenesis of depressive symptoms. Voxel-based morphometry (VBM) is a fully automated method for analysing neuromorphological MRI data which allows unbiased investigation of differences in brain structure between patients and healthy subjects (Ashburner & Friston, Reference Ashburner and Friston2001). It has accuracy comparable with manual volumetry (Davies et al. Reference Davies, Scahill, Graham, Williams, Graham and Hodges2009) and overcomes the technical limitations of region-of-interest approaches. VBM has been widely used in characterizing subtle changes in brain structure in a variety of neurological and psychiatric diseases. VBM studies have reported that MDD is associated with widespread local abnormalities in many brain regions, such as the frontal gyrus (Wagner et al. Reference Wagner, Koch, Schachtzabel, Reichenbach, Sauer and Schlösser2008; Scheuerecker et al. Reference Scheuerecker, Meisenzahl, Koutsouleris, Roesner, Schopf, Linn, Wiesmann, Bruckmann, Moller and Frodl2010; Salvadore et al. Reference Salvadore, Nugent, Lemaitre, Luckenbaugh, Tinsley, Cannon, Neumeister, Zarate and Drevets2011), anterior cingulate gyrus (Tang et al. Reference Tang, Wang, Xie, Liu, Li, Su, Liu, Hu, He and Blumberg2007; Wagner et al. Reference Wagner, Koch, Schachtzabel, Reichenbach, Sauer and Schlösser2008; Salvadore et al. Reference Salvadore, Nugent, Lemaitre, Luckenbaugh, Tinsley, Cannon, Neumeister, Zarate and Drevets2011) and hippocampus (Wagner et al. Reference Wagner, Koch, Schachtzabel, Reichenbach, Sauer and Schlösser2008; Zou et al. Reference Zou, Deng, Li, Zhang, Jiang, Huang and Sun2010; Arnone et al. Reference Arnone, McKie, Elliott, Juhasz, Thomas, Downey, Williams, Deakin and Anderson2012). However, these reports are inconsistent in detail, probably because of small sample sizes, heterogeneous subjects and methodological differences. Identifying consistent results from VBM studies of MDD patients through meta-analysis is therefore of particular importance.

New analytical tools have made possible whole-brain meta-analyses using the spatial coordinates of reported structural imaging findings to produce consensus neuroanatomical maps of abnormalities. Three recent meta-analyses of VBM studies found that MDD was characterized by volume reductions in the bilateral anterior cingulate cortex (ACC) (Bora et al. Reference Bora, Fornito, Pantelis and Yucel2012; Du et al. Reference Du, Wu, Yue, Li, Liao, Kuang, Huang, Chan, Mechelli and Gong2012; Lai, Reference Lai2013), right middle and inferior frontal gyrus (Du et al. Reference Du, Wu, Yue, Li, Liao, Kuang, Huang, Chan, Mechelli and Gong2012), parahippocampal gyrus and hippocampus (Du et al. Reference Du, Wu, Yue, Li, Liao, Kuang, Huang, Chan, Mechelli and Gong2012). However, most of the patients included in these were receiving antidepressant drugs at the time of scanning. It is recognized that the inclusion of medicated patients may bias meta-analyses and limit the interpretability and generalizability of the results.

Animal and human studies yield primary evidence on the effect of therapy on grey matter. For example, 3 weeks of agomelatine administration affects neurogenesis in the dorsal and ventral hippocampus of adult rats (Banasr et al. Reference Banasr, Soumier, Hery, Mocaer and Daszuta2006); similarly, 4 weeks of tianeptin administration protects against the decreased hippocampal volume produced by stress in tree shrews (Czeh et al. Reference Czeh, Michaelis, Watanabe, Frahm, de Biurrun, van Kampen, Bartolomucci and Fuchs2001). Medication-related differences in regional brain volumes have been reported in patients with mood disorders (Brambilla et al. Reference Brambilla, Nicoletti, Harenski, Sassi, Mallinger, Frank, Kupfer, Keshavan and Soares2002; Chang et al. Reference Chang, Karchemskiy, Barnea-Goraly, Garrett, Simeonova and Reiss2005; Lavretsky et al. Reference Lavretsky, Roybal, Ballmaier, Toga and Kumar2005; Savitz et al. Reference Savitz, Nugent, Bogers, Liu, Sills, Luckenbaugh, Bain, Price, Zarate, Manji, Cannon, Marrett, Charney and Drevets2010; Smith et al. Reference Smith, Chen, Baxter, Fort and Lane2013). Adult patients with MDD show increased grey matter in the left dorsolateral prefrontal cortex (DLPFC) after 12 weeks of selective serotonin reuptake inhibitor (SSRI) treatment (Smith et al. Reference Smith, Chen, Baxter, Fort and Lane2013), while in geriatric MDD patients antidepressant exposure is associated with increased orbitofrontal grey matter (Lavretsky et al. Reference Lavretsky, Roybal, Ballmaier, Toga and Kumar2005). In addition, a meta-analysis has reported that the greater the proportion of medicated individuals in the depressed sample, the larger is the abnormality in amygdala volume (Hamilton et al. Reference Hamilton, Siemer and Gotlib2008). Furthermore, there is growing evidence that decreased levels of brain-derived neurotrophic factor and other neurotrophic factors in MDD could contribute to the atrophy of limbic structures such as the hippocampus and prefrontal cortex, and that the neurotrophic actions of antidepressants could reverse neuronal atrophy and cell loss (Duman & Monteggia, Reference Duman and Monteggia2006). It is therefore conceivable that antidepressant medication itself could cause regional grey matter volume changes in MDD. Thus studies of unmedicated patients could better elucidate brain abnormalities that are directly related to the disease itself.

Direct study of unmedicated depressed patients faces substantial practical and ethical problems (Blease, Reference Blease2011). Although published VBM studies on medication-free MDD patients have recently become sufficiently numerous to warrant specific meta-analysis, no such meta-analysis of grey matter volumes has yet been published. Therefore, the goals of this study were twofold: first, to establish the most prominent and replicable grey matter abnormalities in medication-free MDD patients; and, second, to examine the effects of demographics and clinical characteristics on grey matter volumes in MDD, using a new coordinate-based meta-analytic technique called effect-size signed differential mapping (ES-SDM). ES-SDM combines several useful features of earlier methods, such as activation likelihood estimation and multilevel kernel density analysis. It also introduces some novel improvements: to avoid any voxel appearing significant in opposite directions, both positive and negative differences are reconstructed in the same signed differential map; the use of effect sizes allows combination of reported peak coordinates with statistical parametric maps, thus allowing more detailed and accurate meta-analyses; lastly, complementary analyses such as jack-knife, subgroup and meta-regression analyses are readily used to assess the robustness and heterogeneity of the results (Radua et al. Reference Radua, Mataix-Cols, Phillips, El-Hage, Kronhaus, Cardoner and Surguladze2012b ).

Method

Inclusion of studies

A systematic strategy was used to search for relevant studies published in Pubmed, Embase, Web of Science and Science Direct up to March 2013, using the keywords ‘depression’ or ‘depressive disorder’ or ‘unipolar depression’ plus ‘voxel-based morphometry’ or ‘VBM’ or ‘voxel*’ or ‘morphometry’. The reference lists of these studies were checked to identify further studies for inclusion. Studies were included according to the following criteria: (1) used VBM to analyse whole-brain grey matter changes in MDD patients; (2) compared MDD patients with healthy control subjects; and (3) investigated medication-free MDD patients, who either had never received antidepressive medications (i.e. were medication-naive), or who underwent a wash-out period before MRI scanning. Findings based on small volume correction were excluded. Theoretical papers and reviews were excluded, as well as studies that reported adolescent or late-life depression. For studies where multiple independent patient samples were compared with healthy controls, the appropriate coordinates were included as separate datasets. For studies using overlapping samples, the study with the most subjects was included.

Recorded variables

For each included study we recorded: numbers, sex and mean age of subjects; illness duration, depression symptom severity and mean numbers of episodes; drug status; the percentage of patients who had ever received psychotherapy; the statistical threshold of the main findings, and the method employed to correct whole-brain results for multiple comparisons.

Meta-analysis of regional differences in grey matter

Regional differences in grey matter were analysed using ES-SDM (http://www.sdmproject.com), a voxel-based meta-analytic approach that uses the reported peak coordinates to re-create maps of the effect size of group differences in grey matter. For peak coordinates, the re-creation is based on first converting the peak t value to Hedges' effect size and then applying a non-normalized Gaussian kernel to the voxels close to the peak. In ES-SDM both positive and negative differences are reconstructed in the same map, which prevents a particular voxel appearing to be significant in opposite directions. Importantly, negative studies are also included in the meta-analysis. Jack-knife sensitivity analysis was used to test the replicability of results and meta-regression methods to characterize the impact of key clinical variables on brain structure (Radua et al. Reference Radua, Mataix-Cols, Phillips, El-Hage, Kronhaus, Cardoner and Surguladze2012b ).

ES-SDM has been described in detail elsewhere (Radua et al. Reference Radua, Via, Catani and Mataix-Cols2011, Reference Radua, Borgwardt, Crescini, Mataix-Cols, Meyer-Lindenberg, McGuire and Fusar-Poli2012a , Reference Radua, Mataix-Cols, Phillips, El-Hage, Kronhaus, Cardoner and Surguladze b ) and applied to meta-analysis studies on neuropsychiatric disorders including schizophrenia and bipolar disorder (Bora et al. Reference Bora, Fornito, Radua, Walterfang, Seal, Wood, Yucel, Velakoulis and Pantelis2011; Palaniyappan et al. Reference Palaniyappan, Balain, Radua and Liddle2012). Here we summarize its main features. First, we ensured that the same threshold was used throughout the whole brain in each included study (although not necessarily the same in different studies), in order to avoid biases towards liberally thresholded brain regions (Radua & Mataix-Cols, Reference Radua and Mataix-Cols2009). Second, an effect-size signed map of the differences in grey matter was separately recreated for each study. Third, the mean map was obtained by calculating the mean of the study maps, weighted by the inverse of each study variance and accounting for inter-study heterogeneity. Statistical significance was assessed by a permutation test (Radua et al. Reference Radua, Mataix-Cols, Phillips, El-Hage, Kronhaus, Cardoner and Surguladze2012b ).

Sensitivity analysis

A systematic whole-brain voxel-based jack-knife sensitivity analysis was used to test the replicability of the results. This involves repeating the main analysis 14 times, discarding a different study each time: if a brain region remains significant in all or most of the combinations of studies it can be concluded that this finding is highly replicable.

Subgroup analysis

We conducted a subgroup analysis of first-episode medication-naive MDD patients. Strategically, recruiting such individuals helps to reduce confounders such as illness duration and previous antidepressant treatment that may dilute the neuropathological findings in MDD. Because medication withdrawal will not immediately reverse any medication-related grey matter changes, we compared these results with a subgroup analysis of MDD patients who underwent a wash-out period before MRI scanning.

Meta-regression analysis

The following variables were explored by meta-regression: the percentage of female MDD patients (i.e. in the whole MDD group in each study); mean age of patients; illness duration; and the mean number of illness episodes. Depression symptom severity could not be explored by meta-regression due to a lack of consistent depression-scale data. The percentage of MDD patients who had ever received psychotherapy could not be explored by meta-regression because data were available for fewer than nine studies (Radua & Mataix-Cols, Reference Radua and Mataix-Cols2009). As described in previous meta-analyses, in order to minimize the detection of spurious relationships, we decreased the probability threshold to 0.0005 required abnormalities to be detected both in the slope and in one of the extremes of the regressor, and discarded findings in regions other than those detected in the main analyses (Radua & Mataix-Cols, Reference Radua and Mataix-Cols2009). Furthermore, regression plots were visually inspected to discard fittings driven by too few studies (Radua & Mataix-Cols, Reference Radua and Mataix-Cols2009).

Results

Included studies and sample characteristics

The search strategy identified 1144 studies, of which 11 studies (Tang et al. Reference Tang, Wang, Xie, Liu, Li, Su, Liu, Hu, He and Blumberg2007; Wagner et al. Reference Wagner, Koch, Schachtzabel, Reichenbach, Sauer and Schlösser2008; Cheng et al. Reference Cheng, Xu, Chai, Li, Luo, Yang, Li, Shan, Xu and Xu2010; Jia et al. Reference Jia, Huang, Wu, Zhang, Lui, Zhang, Amatya, Kuang, Chan, Kemp, Mechelli and Gong2010; Lai et al. Reference Lai, Hsu and Wu2010; Scheuerecker et al. Reference Scheuerecker, Meisenzahl, Koutsouleris, Roesner, Schopf, Linn, Wiesmann, Bruckmann, Moller and Frodl2010; Zou et al. Reference Zou, Deng, Li, Zhang, Jiang, Huang and Sun2010; Salvadore et al. Reference Salvadore, Nugent, Lemaitre, Luckenbaugh, Tinsley, Cannon, Neumeister, Zarate and Drevets2011; Arnone et al. Reference Arnone, McKie, Elliott, Juhasz, Thomas, Downey, Williams, Deakin and Anderson2012; Ma et al. Reference Ma, Ding, Li, Guo, Long, Liu, Gao, Zeng, Zhao and Chen2012; Zhang et al. Reference Zhang, Yao, Zhu, Wang and Zhong2012), including 14 datasets, ultimately met inclusion criteria, with 400 unmedicated MDD patients (142 male and 258 female; mean age 33.8 years) and 424 healthy controls (163 male and 261 female; mean age 32.4 years). Of two studies using overlapping samples, the one with the most subjects was included after email discussion with the authors (Arnone et al. Reference Arnone, McKie, Elliott, Juhasz, Thomas, Downey, Williams, Deakin and Anderson2012). Fig. 1 shows a flow diagram of the identification and attrition of studies. Table 1 summarizes clinical and demographic data from all included studies. In no study was there any significant difference in age and sex between the MDD group and the healthy controls.

Fig. 1. Identification and attrition of studies. Search terms were ((depression OR depressive disorder OR unipolar depression) AND (voxel-based morphometry OR VBM OR voxel* OR morphometry)). MDD, Major depressive disorder; VBM, voxel-based morphometry; HC, healthy controls.

Table 1. Demographic and clinical characteristics of subjects in the 14 voxel-based morphometry datasets included in the meta-analysis

MDD, Major depressive disorder; HC, healthy control; dMDD, currently depressed subjects; n.a., not available; MADRS, Montgomery–Asberg Depression Rating Scale; FWE, familywise error correction; rMDD, subjects currently in remission; HAMD, Hamilton Depression Rating Scale; FE, first episode; nMDD, depressed patients without a history of suicide attempts; sMDD, depressed patients with a history of suicide attempts; ASC, AlphaSim correction; CES-D, Center for Epidemiological Studies Depression Scale.

Pooled meta-analysis of all included medication-free studies

A group comparison of MDD patients with healthy controls across the 14 datasets revealed grey matter volume reductions in the left inferior frontal gyrus, the right middle frontal gyrus extending to the inferior frontal gyrus, the right superior frontal gyrus, the left superior frontal gyrus extending to the middle frontal gyrus, and the bilateral parahippocampal gyrus and hippocampus (Table 2 and Fig. 2). No grey matter volume increases were found.

Fig. 2. Regions showing reduced grey matter in medication-free major depressive disorder patients compared with healthy controls. L, Left; R, right; SFG, superior frontal gyrus; IFG, inferior frontal gyrus; MIFG, middle frontal gyrus; PG, parahippocampal gyrus; HP, hippocampus.

Table 2. Regional differences in grey matter volume between MDD patients and HC subjects identified by the present meta-analyses (voxelwise p < 0.005 and FWHM 20 mm)

MDD, Major depressive disorder; FWHM, full width at half maximum; SDM, signed differential mapping; HC, healthy control; BA, Brodmann area.

Jack-knife sensitivity analysis

Whole-brain jack-knife sensitivity analysis showed that grey matter reduction in the right middle frontal gyrus and the right superior frontal gyrus was highly replicable, being preserved throughout all 14 combinations of datasets. The results in the left inferior and superior frontal gyrus remained significant in all but one combination, those in the left parahippocampal gyrus and the hippocampus in all but two combinations, and those in the right parahippocampal gyrus and hippocampus in all but three combinations.

Subgroup meta-analysis of first-episode medication-naive studies

In six datasets recruiting first-episode medication-naive MDD patients the subgroup analysis revealed grey matter volume increase relative to controls in the right thalamus, and decrease in the right superior and middle frontal gyrus, the right parahippocampal gyrus and hippocampus extending to the amygdala, and the left parahippocampal gyrus and hippocampus (Table 2).

Subgroup meta-analysis of medication wash-out studies

The wash-out subgroup analysis of eight datasets showed grey matter volume increase relative to controls in the bilateral anterior cingulate cortex, and decrease in the left inferior and middle frontal gyrus extending to the superior frontal gyrus, the right middle and superior frontal gyrus, and the right parahippocampal gyrus (Table 2).

Meta-regression analysis

The percentage of female patients with MDD (i.e. in the whole MDD group in each study) was negatively associated with grey matter volume in the right hippocampus (r = 0.814, permutation-derived p < 0.0001) (Fig. 3). However, this result should be interpreted with some caution, being driven only by four studies (Tang et al. Reference Tang, Wang, Xie, Liu, Li, Su, Liu, Hu, He and Blumberg2007; Wagner et al. Reference Wagner, Koch, Schachtzabel, Reichenbach, Sauer and Schlösser2008; Lai et al. Reference Lai, Hsu and Wu2010; Scheuerecker et al. Reference Scheuerecker, Meisenzahl, Koutsouleris, Roesner, Schopf, Linn, Wiesmann, Bruckmann, Moller and Frodl2010). The mean age of patients, illness duration, and the mean number of episodes were not associated with MDD-related grey matter changes, at least linearly.

Fig. 3. Meta-regression results show that the percentage of female major depressive disorder patients is negatively correlated with grey matter in the right hippocampus (RHP). In the graphs, the effect sizes needed to create this plot have been extracted from the peak of maximum slope significance, and each study is represented as a dot, whose size reflects sample size. Large dots indicate samples with >40 patients; medium dots, 20–40 patients; and small dots, < 20 patients. The regression line (meta-regression signed differential mapping slope) is shown.

Discussion

This is to our knowledge the first meta-analysis of VBM studies in medication-free MDD patients, examining how age, sex and clinical characteristics affect grey matter volumes without the interference of medication effects. The medication-free MDD patients, compared with healthy control subjects, displayed significantly and robustly reduced grey matter in the prefrontal and limbic regions. Notably, increasing percentage of female patients correlated with decreasing grey matter volumes in the right hippocampus. Although most of the results of the two subgroup meta-analyses were consistent, there were some differences. Increased grey matter volume in the right thalamus compared with controls was only seen in the first-episode medication-naive studies, which might suggest that increased right thalamus volume is a trait for MDD, i.e. directly related to MDD per se. Conversely, increased grey matter volume in the bilateral ACC compared with controls was only seen in the subgroup meta-analysis of wash-out studies, which is at least consistent with the idea that increased ACC volume is an effect of medication. To our knowledge, there are no prospective randomized controlled studies of the long-term effects of antidepressants on brain structure, and this finding in the ACC merits further investigation. This result differs from that of previous meta-analyses which showed decreased grey matter volume in the bilateral ACC (Bora et al. Reference Bora, Fornito, Pantelis and Yucel2012; Du et al. Reference Du, Wu, Yue, Li, Liao, Kuang, Huang, Chan, Mechelli and Gong2012; Lai, Reference Lai2013) and decreased grey matter in the left thalamus (Du et al. 2012), compared with controls, which might be explained by the fact that all of these studies included both medicated and unmedicated MDD patients.

Findings in the hippocampus and parahippocampus

The reduction in grey matter volume in the bilateral hippocampus and parahippocampal gyrus in medication-free MDD patients, compared with controls, which we identified in the present study is consistent with a recent meta-analysis of traditional MRI and computed tomography studies on depression (Kempton et al. Reference Kempton, Salvador, Munafo, Geddes, Simmons, Frangou and Williams2011). The hippocampus and parahippocampus are key regions of the limbic system that play a central role in the regulation of emotions, motivation, memory and affective dimension of pain (Fang et al. Reference Fang, Jin, Wang, Li, Kong, Nixon, Zeng, Ren, Tong, Wang and Hui2009; Fossati, Reference Fossati2012); they are extensively connected with other cortical and subcortical structures in the frontal and temporal lobes and play an important role in cognitive processes of MDD (Price & Drevets, Reference Price and Drevets2010). Thus grey matter damage in these regions may be related to memory function impairment and emotion processing, which is the main clinical manifestation of MDD. Furthermore, in a 3-year longitudinal study, hippocampal grey matter volume declined significantly more in MDD patients than in controls, and also significantly more in patients who did not remit during the period than those who did, suggesting that depression may lead to changes in hippocampal morphologic structure (Frodl et al. Reference Frodl, Koutsouleris, Bottlender, Born, Jager, Scupin, Reiser, Moller and Meisenzahl2008).

Findings in the prefrontal cortex

Our finding of decreased grey matter in medication-free MDD patients in the superior, middle and inferior frontal gyrus compared with controls corresponds to the right DLPFC and left DLPFC and ventrolateral prefrontal cortex. Postmortem studies have demonstrated reductions in glial cell density and neuronal size in the deeper cortical layers of the PFC in MDD which may underlie these grey matter abnormalities detected by neuroimaging (Rajkowska et al. Reference Rajkowska, Miguel-Hidalgo, Wei, Dilley, Pittman, Meltzer, Overholser, Roth and Stockmeier1999; Rajkowska, Reference Rajkowska2000; Cotter et al. Reference Cotter, Mackay, Chana, Beasley, Landau and Everall2002). Furthermore, there is evidence that increased glutamatergic transmission in depression may cause excitotoxic damage and cellular loss, dendritic reshaping and consequent grey matter morphometric abnormalities (Price & Drevets, Reference Price and Drevets2010). The lateral prefrontal lobe has been theoretically identified as important in the pathophysiology of MDD, being associated with cognition dysfunction and emotion regulation in depression (Levesque et al. Reference Levesque, Eugene, Joanette, Paquette, Mensour, Beaudoin, Leroux, Bourgouin and Beauregard2003; Ochsner et al. Reference Ochsner, Ray, Cooper, Robertson, Chopra, Gabrieli and Gross2004; Phan et al. Reference Phan, Fitzgerald, Nathan, Moore, Uhde and Tancer2005; Matsuo et al. Reference Matsuo, Glahn, Peluso, Hatch, Monkul, Najt, Sanches, Zamarripa, Li, Lancaster, Fox, Gao and Soares2007; Walter et al. Reference Walter, Wolf, Spitzer and Vasic2007; Price & Drevets, Reference Price and Drevets2010). Moreover a recent longitudinal therapeutic study on SSRI-treated MDD patients has shown grey matter changes in the left DLPFC, which is associated with top-down neuronal modulation of emotion (Smith et al. Reference Smith, Chen, Baxter, Fort and Lane2013); a defect in the regulation of negative affect due to DLPFC dysfunction is a plausible mechanism for DLPFC involvement in depression (Koenigs & Grafman, Reference Koenigs and Grafman2009). Therefore, the identified grey matter abnormality in the prefrontal gyrus may be part of the structural underpinning of the prefrontal dysfunction in MDD patients.

Sex effects

The percentage of female MDD patients was negatively correlated with grey matter volume in the right hippocampus, which indicates that female medication-free MDD patients tend to have smaller right hippocampus. Other structural imaging studies have shown significant sex effects consistent with the present results: greater hippocampal volume in male compared with female MDD patients (Steffens et al. Reference Steffens, Byrum, McQuoid, Greenberg, Payne, Blitchington, MacFall and Krishnan2000; Frodl et al. Reference Frodl, Meisenzahl, Zetzsche, Born, Groll, Jager, Leinsinger, Bottlender, Hahn and Moller2002; Kronmuller et al. Reference Kronmuller, Schroder, Kohler, Gotz, Victor, Unger, Giesel, Magnotta, Mundt, Essig and Pantel2009), and specifically greater right hippocampal volumes in male than female psychotropic-naive patients with familial MDD (MacMaster et al. Reference MacMaster, Mirza, Szeszko, Kmiecik, Easter, Taormina, Lynch, Rose, Moore and Rosenberg2008). Sex differences are seen in epidemiological and clinical aspects of MDD: rates of MDD are higher in females than males (Kessler et al. Reference Kessler, Chiu, Demler, Merikangas and Walters2005); most studies report greater sensitivity in women to the depressogenic effect of stressful life events (Post et al. Reference Post, Weiss, Smith, Rosen and Frye1995); female MDD patients show higher rates of suicide attempts and co-morbid anxiety disorders, while male MDD patients are more likely to show psychomotor agitation and have co-morbid substance abuse disorders (Roeloffs et al. Reference Roeloffs, Fink, Unutzer, Tang and Wells2001; Marcus et al. Reference Marcus, Young, Kerber, Kornstein, Farabaugh, Mitchell, Wisniewski, Balasubramani, Trivedi and Rush2005). The sex difference in right hippocampus volume in MDD may contribute to differences in epidemiological and clinical aspects of depression. However, other reports (Vakili et al. Reference Vakili, Pillay, Lafer, Fava, Renshaw, Bonello-Cintron and Yurgelun-Todd2000; MacQueen et al. Reference MacQueen, Campbell, McEwen, Macdonald, Amano, Joffe, Nahmias and Young2003; Neumeister et al. Reference Neumeister, Wood, Bonne, Nugent, Luckenbaugh, Young, Bain, Charney and Drevets2005) found no such sex difference. The heterogeneous findings of structural imaging studies can perhaps be explained by the highly variable female:male ratio among studies.

Limitations and conclusions

This study has several limitations. First, peak-based meta-analyses are based on summarized (i.e. coordinates from published studies) rather than raw statistical brain maps, and this approach may result in less accurate results (Nakao et al. Reference Nakao, Radua, Rubia and Mataix-Cols2011). Second, the main meta-analysis included primary studies enrolling medicated MDD patients who underwent a wash-out period before MRI scanning and the results of the two subgroups were to some extent different. Clearly the best way to minimize the effects of illness duration and antidepressant treatment is to focus on drug-naive MDD patients. However, the sample size of our drug-naive subgroup analysis is necessarily small, which limits the generalizability of the results. Meta-analyses of first-episode drug-naive MDD are needed when the number of published VBM studies on such patients permits. Third, the meta-regression finding that the percentage of female MDD patients was negatively correlated with grey matter volume in the right hippocampus was driven by only four studies.

In summary, our findings indicate that medication-free MDD patients have significantly and robustly reduced grey matter mainly in prefrontal and limbic brain regions excluding the confounding influences of medication status. The subgroup meta-analysis results suggest that increased right thalamus volume may be directly related to MDD, while increased ACC volume may be an effect of medication. Meta-regression results suggest that female medication-free MDD patients may have smaller right hippocampus, which may constitute the structural underpinnings of the sex differences observed in both epidemiological and clinical aspects of MDD. Future studies will benefit from the use of a longitudinal approach to track patients from disease onset and through follow-up and examine the dynamic effect of medication on brain structure in MDD patients. Longitudinal studies that investigate the dynamic brain structure change of competent MDD patients who reject all therapies are also encouraged.

Acknowledgements

This work was supported by the National Natural Science Foundation (grant nos. 81030027, 81227002 and 81220108013), the National Key Technologies R&D Programme (programme no. 2012BAI01B03), and the Programme for Changjiang Scholars and Innovative Research Team in University (PCSIRT, grant no. IRT1272) of China.

Declaration of Interest

None.

References

Arnone, D, McKie, S, Elliott, R, Juhasz, G, Thomas, EJ, Downey, D, Williams, S, Deakin, JF, Anderson, IM (2012). State-dependent changes in hippocampal grey matter in depression. Molecular Psychiatry 6, 12651272.Google Scholar
Ashburner, J, Friston, KJ (2001). Why voxel-based morphometry should be used. NeuroImage 14, 12381243.CrossRefGoogle Scholar
Banasr, M, Soumier, A, Hery, M, Mocaer, E, Daszuta, A (2006). Agomelatine, a new antidepressant, induces regional changes in hippocampal neurogenesis. Biological Psychiatry 59, 10871096.CrossRefGoogle ScholarPubMed
Blease, C (2011). Deception as treatment: the case of depression. Journal of Medical Ethics 37, 1316.CrossRefGoogle Scholar
Bora, E, Fornito, A, Pantelis, C, Yucel, M (2012). Gray matter abnormalities in major depressive disorder: a meta-analysis of voxel based morphometry studies. Journal of Affective Disorders 138, 918.CrossRefGoogle ScholarPubMed
Bora, E, Fornito, A, Radua, J, Walterfang, M, Seal, M, Wood, SJ, Yucel, M, Velakoulis, D, Pantelis, C (2011). Neuroanatomical abnormalities in schizophrenia: a multimodal voxelwise meta-analysis and meta-regression analysis. Schizophrenia Research 127, 4657.CrossRefGoogle ScholarPubMed
Brambilla, P, Nicoletti, MA, Harenski, K, Sassi, RB, Mallinger, AG, Frank, E, Kupfer, DJ, Keshavan, MS, Soares, JC (2002). Anatomical MRI study of subgenual prefrontal cortex in bipolar and unipolar subjects. Neuropsychopharmacology 27, 792799.CrossRefGoogle ScholarPubMed
Chang, K, Karchemskiy, A, Barnea-Goraly, N, Garrett, A, Simeonova, DI, Reiss, A (2005). Reduced amygdalar gray matter volume in familial pediatric bipolar disorder. Journal of the American Academy of Child and Adolescent Psychiatry 44, 565573.CrossRefGoogle ScholarPubMed
Cheng, YQ, Xu, J, Chai, P, Li, HJ, Luo, CR, Yang, T, Li, L, Shan, BC, Xu, XF, Xu, L (2010). Brain volume alteration and the correlations with the clinical characteristics in drug-naïve first-episode MDD patients: a voxel-based morphometry study. Neuroscience Letters 480, 3034.CrossRefGoogle ScholarPubMed
Cotter, D, Mackay, D, Chana, G, Beasley, C, Landau, S, Everall, IP (2002). Reduced neuronal size and glial cell density in area 9 of the dorsolateral prefrontal cortex in subjects with major depressive disorder. Cerebral Cortex 12, 386394.CrossRefGoogle ScholarPubMed
Czeh, B, Michaelis, T, Watanabe, T, Frahm, J, de Biurrun, G, van Kampen, M, Bartolomucci, A, Fuchs, E (2001). Stress-induced changes in cerebral metabolites, hippocampal volume, and cell proliferation are prevented by antidepressant treatment with tianeptine. Proceedings of the National Academy of Sciences USA 98, 1279612801.CrossRefGoogle ScholarPubMed
Davies, RR, Scahill, V, Graham, A, Williams, G, Graham, K, Hodges, J (2009). Development of an MRI rating scale for multiple brain regions: comparison with volumetrics and with voxel-based morphometry. Neuroradiology 51, 491503.CrossRefGoogle ScholarPubMed
Du, MY, Wu, QZ, Yue, Q, Li, J, Liao, Y, Kuang, WH, Huang, XQ, Chan, RC, Mechelli, A, Gong, QY (2012). Voxelwise meta-analysis of gray matter reduction in major depressive disorder. Progress in Neuro-Psychopharmacology and Biological Psychiatry 36, 1116.CrossRefGoogle ScholarPubMed
Duman, RS, Monteggia, LM (2006). A neurotrophic model for stress-related mood disorders. Biological Psychiatry 59, 11161127.CrossRefGoogle ScholarPubMed
Fang, J, Jin, Z, Wang, Y, Li, K, Kong, J, Nixon, EE, Zeng, Y, Ren, Y, Tong, H, Wang, P, Hui, KK (2009). The salient characteristics of the central effects of acupuncture needling: limbic–paralimbic–neocortical network modulation. Human Brain Mapping 30, 11961206.CrossRefGoogle ScholarPubMed
Fossati, P (2012). Neural correlates of emotion processing: from emotional to social brain. European Neuropsychopharmacology 22, S487-S491.CrossRefGoogle ScholarPubMed
Frodl, T, Meisenzahl, EM, Zetzsche, T, Born, C, Groll, C, Jager, M, Leinsinger, G, Bottlender, R, Hahn, K, Moller, HJ (2002). Hippocampal changes in patients with a first episode of major depression. American Journal of Psychiatry 159, 11121118.CrossRefGoogle ScholarPubMed
Frodl, TS, Koutsouleris, N, Bottlender, R, Born, C, Jager, M, Scupin, I, Reiser, M, Moller, HJ, Meisenzahl, EM (2008). Depression-related variation in brain morphology over 3 years: effects of stress? Archives of General Psychiatry 65, 11561165.CrossRefGoogle ScholarPubMed
Hamilton, JP, Siemer, M, Gotlib, IH (2008). Amygdala volume in major depressive disorder: a meta-analysis of magnetic resonance imaging studies. Molecular Psychiatry 13, 9931000.CrossRefGoogle ScholarPubMed
Hasler, G (2010). Pathophysiology of depression: do we have any solid evidence of interest to clinicians? World Psychiatry 9, 155161.CrossRefGoogle ScholarPubMed
Jia, Z, Huang, X, Wu, Q, Zhang, T, Lui, S, Zhang, J, Amatya, N, Kuang, W, Chan, RC, Kemp, GJ, Mechelli, A, Gong, Q (2010). High-field magnetic resonance imaging of suicidality in patients with major depressive disorder. American Journal of Psychiatry 167, 13811390.CrossRefGoogle ScholarPubMed
Kempton, MJ, Salvador, Z, Munafo, MR, Geddes, JR, Simmons, A, Frangou, S, Williams, SC (2011). Structural neuroimaging studies in major depressive disorder. Meta-analysis and comparison with bipolar disorder. Archives of General Psychiatry 68, 675690.CrossRefGoogle ScholarPubMed
Kessler, RC, Chiu, WT, Demler, O, Merikangas, KR, Walters, EE (2005). Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry 62, 617627.CrossRefGoogle ScholarPubMed
Koenigs, M, Grafman, J (2009). The functional neuroanatomy of depression: distinct roles for ventromedial and dorsolateral prefrontal cortex. Behavioural Brain Research 201, 239243.CrossRefGoogle ScholarPubMed
Kronmuller, KT, Schroder, J, Kohler, S, Gotz, B, Victor, D, Unger, J, Giesel, F, Magnotta, V, Mundt, C, Essig, M, Pantel, J (2009). Hippocampal volume in first episode and recurrent depression. Psychiatry Research 174, 6266.CrossRefGoogle ScholarPubMed
Lai, CH (2013). Gray matter volume in major depressive disorder: a meta-analysis of voxel-based morphometry studies. Psychiatry Research 211, 3746.CrossRefGoogle ScholarPubMed
Lai, CH, Hsu, YY, Wu, YT (2010). First episode drug-naïve major depressive disorder with panic disorder: gray matter deficits in limbic and default network structures. European Neuropsychopharmacology 20, 676682.CrossRefGoogle ScholarPubMed
Lavretsky, H, Roybal, DJ, Ballmaier, M, Toga, AW, Kumar, A (2005). Antidepressant exposure may protect against decrement in frontal gray matter volumes in geriatric depression. Journal of Clinical Psychiatry 66, 964967.CrossRefGoogle ScholarPubMed
Levesque, J, Eugene, F, Joanette, Y, Paquette, V, Mensour, B, Beaudoin, G, Leroux, JM, Bourgouin, P, Beauregard, M (2003). Neural circuitry underlying voluntary suppression of sadness. Biological Psychiatry 53, 502510.CrossRefGoogle ScholarPubMed
Ma, C, Ding, J, Li, J, Guo, W, Long, Z, Liu, F, Gao, Q, Zeng, L, Zhao, J, Chen, H (2012). Resting-state functional connectivity bias of middle temporal gyrus and caudate with altered gray matter volume in major depression. PLOS ONE 7, 24.Google ScholarPubMed
MacMaster, FP, Mirza, Y, Szeszko, PR, Kmiecik, LE, Easter, PC, Taormina, SP, Lynch, M, Rose, M, Moore, GJ, Rosenberg, DR (2008). Amygdala and hippocampal volumes in familial early onset major depressive disorder. Biological Psychiatry 63, 385390.CrossRefGoogle ScholarPubMed
MacQueen, GM, Campbell, S, McEwen, BS, Macdonald, K, Amano, S, Joffe, RT, Nahmias, C, Young, LT (2003). Course of illness, hippocampal function, and hippocampal volume in major depression. Proceedings of the National Academy of Sciences USA 100, 13871392.CrossRefGoogle ScholarPubMed
Marcus, SM, Young, EA, Kerber, KB, Kornstein, S, Farabaugh, AH, Mitchell, J, Wisniewski, SR, Balasubramani, GK, Trivedi, MH, Rush, AJ (2005). Gender differences in depression: findings from the STAR*D study. Journal of Affective Disorders 87, 141150.CrossRefGoogle ScholarPubMed
Mathers, C, Fat, DM, Boerma, J (2008). The Global Burden of Disease: 2004 Update. World Health Organization: Geneva.CrossRefGoogle Scholar
Matsuo, K, Glahn, DC, Peluso, MA, Hatch, JP, Monkul, ES, Najt, P, Sanches, M, Zamarripa, F, Li, J, Lancaster, JL, Fox, PT, Gao, JH, Soares, JC (2007). Prefrontal hyperactivation during working memory task in untreated individuals with major depressive disorder. Molecular Psychiatry 12, 158166.CrossRefGoogle ScholarPubMed
Nakao, T, Radua, J, Rubia, K, Mataix-Cols, D (2011). Gray matter volume abnormalities in ADHD: voxel-based meta-analysis exploring the effects of age and stimulant medication. American Journal of Psychiatry 168, 11541163.CrossRefGoogle ScholarPubMed
Neumeister, A, Wood, S, Bonne, O, Nugent, AC, Luckenbaugh, DA, Young, T, Bain, EE, Charney, DS, Drevets, WC (2005). Reduced hippocampal volume in unmedicated, remitted patients with major depression versus control subjects. Biological Psychiatry 57, 935937.CrossRefGoogle ScholarPubMed
Ochsner, KN, Ray, RD, Cooper, JC, Robertson, ER, Chopra, S, Gabrieli, JD, Gross, JJ (2004). For better or for worse: neural systems supporting the cognitive down- and up-regulation of negative emotion. NeuroImage 23, 483499.CrossRefGoogle ScholarPubMed
Palaniyappan, L, Balain, V, Radua, J, Liddle, PF (2012). Structural correlates of auditory hallucinations in schizophrenia: a meta-analysis. Schizophrenia Research 137, 169173.CrossRefGoogle ScholarPubMed
Phan, KL, Fitzgerald, DA, Nathan, PJ, Moore, GJ, Uhde, TW, Tancer, ME (2005). Neural substrates for voluntary suppression of negative affect: a functional magnetic resonance imaging study. Biological Psychiatry 57, 210219.CrossRefGoogle ScholarPubMed
Post, RM, Weiss, SR, Smith, M, Rosen, J, Frye, M (1995). Stress, conditioning, and the temporal aspects of affective disorders. Annals of the New York Academy of Sciences 771, 677696.CrossRefGoogle ScholarPubMed
Price, JL, Drevets, WC (2010). Neurocircuitry of mood disorders. Neuropsychopharmacology 35, 192216.CrossRefGoogle ScholarPubMed
Radua, J, Borgwardt, S, Crescini, A, Mataix-Cols, D, Meyer-Lindenberg, A, McGuire, PK, Fusar-Poli, P (2012 a). Multimodal meta-analysis of structural and functional brain changes in first episode psychosis and the effects of antipsychotic medication. Neuroscience and Biobehavioral Reviews 36, 23252333.CrossRefGoogle ScholarPubMed
Radua, J, Mataix-Cols, D (2009). Voxel-wise meta-analysis of grey matter changes in obsessive–compulsive disorder. British Journal of Psychiatry 195, 393402.CrossRefGoogle ScholarPubMed
Radua, J, Mataix-Cols, D, Phillips, ML, El-Hage, W, Kronhaus, DM, Cardoner, N, Surguladze, S (2012 b). A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps. European Psychiatry 27, 605611.CrossRefGoogle ScholarPubMed
Radua, J, Via, E, Catani, M, Mataix-Cols, D (2011). Voxel-based meta-analysis of regional white-matter volume differences in autism spectrum disorder versus healthy controls. Psychological Medicine 41, 15391550.CrossRefGoogle ScholarPubMed
Rajkowska, G (2000). Postmortem studies in mood disorders indicate altered numbers of neurons and glial cells. Biological Psychiatry 48, 766777.CrossRefGoogle ScholarPubMed
Rajkowska, G, Miguel-Hidalgo, JJ, Wei, J, Dilley, G, Pittman, SD, Meltzer, HY, Overholser, JC, Roth, BL, Stockmeier, CA (1999). Morphometric evidence for neuronal and glial prefrontal cell pathology in major depression. Biological Psychiatry 45, 10851098.CrossRefGoogle ScholarPubMed
Rizvi, SJ, Kennedy, SH (2012). Emerging drugs for major depressive disorder: an update. Expert Opinion on Emerging Drugs 17, 285295.CrossRefGoogle ScholarPubMed
Roeloffs, CA, Fink, A, Unutzer, J, Tang, L, Wells, KB (2001). Problematic substance use, depressive symptoms, and gender in primary care. Psychiatric Services 52, 12511253.CrossRefGoogle ScholarPubMed
Salvadore, G, Nugent, AC, Lemaitre, H, Luckenbaugh, DA, Tinsley, R, Cannon, DM, Neumeister, A, Zarate, CA Jr, Drevets, WC (2011). Prefrontal cortical abnormalities in currently depressed versus currently remitted patients with major depressive disorder. NeuroImage 54, 26432651.CrossRefGoogle ScholarPubMed
Savitz, J, Nugent, AC, Bogers, W, Liu, A, Sills, R, Luckenbaugh, DA, Bain, EE, Price, JL, Zarate, C, Manji, HK, Cannon, DM, Marrett, S, Charney, DS, Drevets, WC (2010). Amygdala volume in depressed patients with bipolar disorder assessed using high resolution 3 T MRI: the impact of medication. NeuroImage 49, 29662976.CrossRefGoogle Scholar
Scheuerecker, J, Meisenzahl, EM, Koutsouleris, N, Roesner, M, Schopf, V, Linn, J, Wiesmann, M, Bruckmann, H, Moller, HJ, Frodl, T (2010). Orbitofrontal volume reductions during emotion recognition in patients with major depression. Journal of Psychiatry and Neuroscience 35, 311320.CrossRefGoogle ScholarPubMed
Smith, R, Chen, K, Baxter, L, Fort, C, Lane, RD (2013). Antidepressant effects of sertraline associated with volume increases in dorsolateral prefrontal cortex. Journal of Affective Disorders 146, 414419.CrossRefGoogle ScholarPubMed
Steffens, DC, Byrum, CE, McQuoid, DR, Greenberg, DL, Payne, ME, Blitchington, TF, MacFall, JR, Krishnan, KR (2000). Hippocampal volume in geriatric depression. Biological Psychiatry 48, 301309.CrossRefGoogle ScholarPubMed
Tang, Y, Wang, F, Xie, G, Liu, J, Li, L, Su, L, Liu, Y, Hu, X, He, Z, Blumberg, HP (2007). Reduced ventral anterior cingulate and amygdala volumes in medication-naïve females with major depressive disorder: a voxel-based morphometric magnetic resonance imaging study. Psychiatry Research 156, 8386.CrossRefGoogle ScholarPubMed
Vakili, K, Pillay, SS, Lafer, B, Fava, M, Renshaw, PF, Bonello-Cintron, CM, Yurgelun-Todd, DA (2000). Hippocampal volume in primary unipolar major depression: a magnetic resonance imaging study. Biological Psychiatry 47, 10871090.CrossRefGoogle ScholarPubMed
Wagner, G, Koch, K, Schachtzabel, C, Reichenbach, JR, Sauer, H, Schlösser, RG (2008). Enhanced rostral anterior cingulate cortex activation during cognitive control is related to orbitofrontal volume reduction in unipolar depression. Journal of Psychiatry and Neuroscience 33, 199208.Google ScholarPubMed
Walter, H, Wolf, RC, Spitzer, M, Vasic, N (2007). Increased left prefrontal activation in patients with unipolar depression: an event-related, parametric, performance-controlled fMRI study. Journal of Affective Disorders 101, 175185.CrossRefGoogle ScholarPubMed
Zhang, X, Yao, S, Zhu, X, Wang, X, Zhong, M (2012). Gray matter volume abnormalities in individuals with cognitive vulnerability to depression: a voxel-based morphometry study. Journal of Affective Disorders 136, 443452.CrossRefGoogle ScholarPubMed
Zou, K, Deng, W, Li, T, Zhang, B, Jiang, L, Huang, C, Sun, X (2010). Changes of brain morphometry in first-episode, drug-naive, non-late-life adult patients with major depression: an optimized voxel-based morphometry study. Biological Psychiatry 67, 186188.CrossRefGoogle Scholar
Figure 0

Fig. 1. Identification and attrition of studies. Search terms were ((depression OR depressive disorder OR unipolar depression) AND (voxel-based morphometry OR VBM OR voxel* OR morphometry)). MDD, Major depressive disorder; VBM, voxel-based morphometry; HC, healthy controls.

Figure 1

Table 1. Demographic and clinical characteristics of subjects in the 14 voxel-based morphometry datasets included in the meta-analysis

Figure 2

Fig. 2. Regions showing reduced grey matter in medication-free major depressive disorder patients compared with healthy controls. L, Left; R, right; SFG, superior frontal gyrus; IFG, inferior frontal gyrus; MIFG, middle frontal gyrus; PG, parahippocampal gyrus; HP, hippocampus.

Figure 3

Table 2. Regional differences in grey matter volume between MDD patients and HC subjects identified by the present meta-analyses (voxelwise p < 0.005 and FWHM 20 mm)

Figure 4

Fig. 3. Meta-regression results show that the percentage of female major depressive disorder patients is negatively correlated with grey matter in the right hippocampus (RHP). In the graphs, the effect sizes needed to create this plot have been extracted from the peak of maximum slope significance, and each study is represented as a dot, whose size reflects sample size. Large dots indicate samples with >40 patients; medium dots, 20–40 patients; and small dots, < 20 patients. The regression line (meta-regression signed differential mapping slope) is shown.

You have Access Open access
126
Cited by

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Brain grey matter abnormalities in medication-free patients with major depressive disorder: a meta-analysis
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

Brain grey matter abnormalities in medication-free patients with major depressive disorder: a meta-analysis
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

Brain grey matter abnormalities in medication-free patients with major depressive disorder: a meta-analysis
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *