Gray matter abnormalities in Major Depressive Disorder: A meta-analysis of voxel based morphometry studies

Journal of Affective Disorders 138 (2012) 9–18

Contents lists available at ScienceDirect

Journal of Affective Disorders j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / j a d

Review

Gray matter abnormalities in Major Depressive Disorder: A meta-analysis of voxel based morphometry studies Emre Bora a,⁎, Alex Fornito a, Christos Pantelis a, Murat Yücel a,b a b

Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Victoria, Australia Orygen Youth Health Research Centre, Centre for Youth Mental Health, University of Melbourne, Parkville, Victoria, Australia

a r t i c l e

i n f o

Article history: Received 11 January 2011 Received in revised form 29 March 2011 Accepted 29 March 2011 Available online 20 April 2011

Keywords: Major Depressive Disorder MRI Voxel-based-morphometry Gray matter

a b s t r a c t Background: Voxel-based morphometry (VBM) has been widely used to quantify structural brain changes associated with Major Depressive Disorder (MDD). While some consistent findings have been reported, individual studies have also varied with respect to the key brain regions affected by the illness, and how these abnormalities are related to patients' clinical characteristics. Here, we aimed to identify those brain regions that most consistently showed gray matter anomalies in MDD, and their clinical correlates, using meta-analytic techniques. Methods: A systematic search of VBM studies was applied in MDD. Signed differential mapping, a new coordinate based neuroimaging meta-analysis technique, was applied to data collated from a total of 23 studies comparing regional gray matter volumes of 986 MDD patients and 937 healthy controls. Results: Gray matter was significantly reduced in a confined cluster located in the rostral anterior cingulate cortex (ACC). There were also gray matter reductions in dorsolateral and dorsomedial prefrontal cortex and decrease in the latter region was evident in patients with multiple-episodes. Amygdala and parahippocampal gray matter volumes were significantly reduced in studies including patients with comorbid anxiety disorders, as well as in firstepisode/drug free samples. Conclusions: Gray matter reduction in rostral ACC was the most consistent finding in VBM studies of MDD. The evidence for reductions in other regions within fronto-subcortical and limbic regions was less consistent. The associations between these gray matter anomalies and clinical characteristics, particularly measures relating to illness duration, suggest that chronic MDD has a robust and deleterious, albeit spatially focal, effect on brain structure. © 2011 Elsevier B.V. All rights reserved.

Contents 1. 2.

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Introduction . . . . . . . . . . . . . . . Methods and materials . . . . . . . . . . 2.1. Inclusion of studies . . . . . . . . . 2.2. Statistical analyses . . . . . . . . . 2.2.1. Meta-analysis of VBM studies Results . . . . . . . . . . . . . . . . . . 3.1. Regional differences in gray matter . 3.2. Analyses of confounding factors . . .

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⁎ Corresponding author at: Alan Gilbert Building NNF level 3, Carlton 3053, Australia. Tel.: + 61 3 8345 5611; fax: + 61 3 8345 5610. E-mail addresses: [email protected], [email protected] (E. Bora). 0165-0327/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.jad.2011.03.049

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E. Bora et al. / Journal of Affective Disorders 138 (2012) 9–18

3.2.1. First-episode vs multiple-episodes 3.2.2. Co-morbidity . . . . . . . . . . 3.2.3. Current depression . . . . . . . 4. Discussion . . . . . . . . . . . . . . . . . . . Role of funding source . . . . . . . . . . . . . . . . Conflict of interest . . . . . . . . . . . . . . . . . . Acknowledgement . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . .

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1. Introduction Major Depressive Disorder (MDD) is a prevalent mental health concern associated with significant disability and suffering (Kessler et al., 2003; Hasin et al., 2005). Trauma, stress, psychological and social factors undoubtedly play a role in the etiology of MDD, but neuroimaging, neuropathological and familial studies also point to a role for biological factors (Hasler, 2010; Lorenzetti et al., 2009; Rajkowska et al., 1999; Sheline et al., 2003; Sullivan et al., 2000). In particular, magnetic resonance imaging (MRI) has been widely applied to identify the key brain regions implicated in the pathophysiology of MDD, and has revealed functional abnormalities in a distributed network of brain regions known to play a role in mood regulation (Drevets, 2001; Fitzgerald et al., 2008; Rigucci et al., 2010). These regions principally involve the cingulate cortex, dorsomedial frontal cortex, amygdala, basal ganglia, and dorsolateral prefrontal cortex. Based on this work, several neurobiological models have been proposed to account for MDD pathogenesis. For example, a number of authors have emphasized the role of dysfunctional cortico-limbic networks in MDD (Drevets et al., 2008; Mayberg, 1997; Pizzagalli, 2011). Mayberg (1997, 2003) conceptualize MDD as a disorder involving abnormalities in a network including dorsal (i.e., dorsomedial frontal cortex, dorsolateral prefrontal cortex, dorsal ACC, posterior cingulate cortex), and ventral (i.e., subgenual anterior cingulate, amygdala) components as well as the rostral ACC (pre-genual or peri-genual), which connects these two components. Other authors suggest a role for dysfunctional cortico-subcortical abnormalities including limbic-corticalstriatal-pallidal-thalamic circuits and also medial and orbitofrontal cortex and their extended cortical circuits (Drevets et al., 2008; Marchand, 2010). Neuroimaging has also been used to guide the selection of candidate brain regions for novel treatments (Mayberg et al., 2005). One factor often complicating theoretical models and novel treatments of the disorder concerns the variability in findings across studies. Different studies tend to implicate key brain regions to varying degrees. This heterogeneity of findings may be partially explained by differences in the clinical characteristics of the sample such as illness duration, severity, comorbidity and medication. Meta-analytic techniques can help to mitigate these effects to some extent, by identifying the most robust and consistent brain anomalies in MDD, in addition to any moderating (e.g., clinical or demographic) factors. Several meta-analyses of region-of-interest structural MRI studies in MDD have been reported, and found evidence of volume reductions in the hippocampus, orbitofrontal cortex,

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ACC, as well as caudate and putamen (Campbell et al., 2004; Hamilton et al., 2008; Koolschijn et al., 2009; Videbech and Ravnkilde, 2004). However, such work is biased towards the study of brain regions that are either of primary theoretical importance, or which are easy to delineate on anatomical scans. Accordingly, most of this research has focused on the amygdala and hippocampus, with the number of available studies for other brain regions being very small (Koolschijn et al., 2009). Voxel-based morphometry (VBM) is a semi-automated whole-brain technique that allows regionally unbiased interrogation of differences in brain tissue composition between groups (Ashburner and Friston, 2000). The highly multivariate nature of the data (many thousands of volumetric measures) has complicated attempts to undertake metaanalyses of this work, and has made estimation of effect sizes difficult. Nonetheless, recent advances have enabled the identification of the most spatially consistent brain changes within the literature through the use of the coordinate information reported in each study (Radua and Mataix-Cols, 2009; Turkeltaub et al., 2002). These methods have successfully been applied to identify the most consistent structural brain changes in psychiatric disorders like schizophrenia, bipolar disorder and obsessive–compulsive disorder (Bora et al., 2010; Ellison-Wright and Bullmore, 2010; Fornito et al., 2009; Radua and Mataix-Cols, 2009) but, to our knowledge, have never been used to study brain changes in MDD. Here, we use a new coordinate-based meta-analytic technique, signed differential mapping (SDM, detailed below), to identify the most consistent gray matter reductions found in VBM studies of MDD. Furthermore, by combining these techniques with meta-regression methods, we were able to characterize the impact of key clinical variables on brain structure. 2. Methods and materials 2.1. Inclusion of studies Meta-analysis was conducted according to the PRISMA guidelines (Moher et al., 2009). Potential studies were identified through an extensive literature search in PUBMED, Scopus and PsychINFO between January 1995 and November 2010. Keywords selected in the literature search were: major depression, voxel, morphometry. Reference lists of published studies were also cross-checked for additional studies. Studies were included if they: 1) compared a sample with MDD to a healthy control group using T1-weighted imaging; 2) reported stereotactic coordinates for whole-brain comparisons of gray matter volume/density; 3) used significance

E. Bora et al. / Journal of Affective Disorders 138 (2012) 9–18

thresholds for multiple comparisons. Studies with no group differences were also included, as were studies that used patients experiencing comorbid anxiety disorders. Findings based on small volume correction (SVC) were not included. Studies examining physical illness and depression comorbidity were also excluded. Given evidence suggesting that lateonset MDD (old age) may have a different etiology to earlieronset MDD, studies in this patient group were also excluded as there was an insufficient number of these studies to conduct a separate meta-analysis. For those studies with overlapping samples, the study with the largest sample size was included. The Flowchart (Fig. 1) summarizes the study inclusion process. Twenty-three studies (including 27 MDDcontrol group comparisons) (Abe et al., 2010; Arnone et al., 2009; Bergouignan et al., 2009; Cheng et al., 2010; Frodl et al., 2008a; Inkster et al., 2010; Kim et al., 2008; Koolschijn et al., 2010; Lai et al., 2010; Leung et al., 2009; Li et al., 2010; Peng et al., 2011; Salvadore et al., 2011; Scheuerecker et al., 2010; Shah et al., 1998; Soriano-Mas et al., 2011; Tang et al., 2007; Treadway et al., 2009; Van Tol et al., 2010; Vasic et al., 2008; Wagner et al., 2011; Zhang et al., 2009; Zou et al., 2010) comparing 986 patients with MDD and 937 healthy controls were included in the final meta-analysis (Table 1). 2.2. Statistical analyses

Eligibility

Screening

Identification

2.2.1. Meta-analysis of VBM studies Demographic characteristics of MDD and control groups were compared using meta-analysis software, MIX (Bax et al.,

Unique records identified through database searching (n = 158 )

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2006). For coordinate based meta-analysis, we used a newly developed method called signed differential mapping (SDM; www.sdmproject.com/software/) software to analyze gray matter abnormalities in MDD, which has been described in detail elsewhere (Radua and Mataix-Cols, 2009). Briefly, a map of gray matter differences, comprising the reported stereotactic coordinates for each significant group difference, was generated for each study. In SDM, unlike other coordinate based meta-analytic methods, both positive and negative differences are reconstructed in the same map (signed map), which prevents a particular voxel appearing to be significant in opposite directions. Importantly, when using SDM, negative studies are also included in the meta-analyses. Then meta-analytic statistical maps were obtained by calculating the corresponding statistics from the study maps, weighted by the square root of the sample size of each study so that studies with large sample sizes contributed more. The statistical significance of each voxel was determined using randomization tests (p b 0.001). In our previous meta-analysis of VBM studies in bipolar disorder, this threshold gave more conservative and consistent results compared to ALE analysis with FDR p b 0.05 in the same dataset (Bora et al., 2010). Jackknife sensitivity analysis was used to test the replicability of results. In the current analysis, the meta-analysis was repeated 27 times by excluding one of the samples each time. Descriptive analysis of quartiles was used to describe the actual proportion of studies contributing to significant findings. This method was used to calculate the proportion of studies

Additional records identified through other sources (n =12)

Records screened (n =170)

Records excluded (n =118)

Records assesed for Inclusion criteria (n =52)

Records not meeting criteria (n =14)

Articles meeting inclusion criteria (n =38)

Full-text articles excluded Sample overlap=7 Late-onset=4 Physical illness=4

Included

Studies included in quantitative synthesis (meta-analysis) (n =23)

Fig. 1. Flow diagram for meta-analysis of voxelwise gray matter studies in MDD. From: Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting /Items for Systematic Reviews and Meta-Analysis: The PRISMA Statement. Plos Med 6(6): e1000097. doi:10.1371/journal.pmed1000097. For more information, visit www.prisma-statement.org.

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Table 1 Studies included in the meta-analysis. Study

Age

Duration

Anxiety disorder

State

Slice thickness

Tesla

21(10)/42(20)

19/21 on AD

48.1

6 years

Not excluded

1.5

1.5

Arnone et al. (2009) Bergouignan et al. (2009) Cheng et al. (2010)

25(16)/35(23) 21(17)/21(14) 68(47)/68(47)

Medication free 21/21 AD Drug naive

33.2 33.2 29.9

8.5 years FE

Not excluded Excluded

1.5 0.9

1.5 1.5 1.5

Frodl et al. (2008a)

77(35)/77(35)

61/77 on AD

42.6

5.4

Excluded

HDRS = 9.2 7/21 depressed 13/25 depressed 21/21 depressed HDRS = 22.3 68/68 depressed Inpatients HDRS = 22.8

1.5

1.5

119/145 on AD , last 6 months 12/22 no medication 17/28 on AD

49.4 38.5 64

14.3 17.4 31 years

Not excluded Excluded Excluded

1.5

1.5 1.5 1.5

Drug naive 17/17 on AD 44/44 on AD 5/22 on AD Medication free Drug free

37.9 45.5 45.1 46.7 39.3 37.9

FE 7 9.2 FE 17.3 4.4

Not excluded Excluded Not excluded Excluded Not excluded Excluded

1 3 1.5 1 1.2 1.4

3 1.5 1.5 3 3 3

31/40 on AD 50/70 on AD Drug naive Drug free 58/156 on SSRI 15/15 on AD

9.8 10.4 FE 12.9 years 13 3.6 years 6 10.3

Not excluded Excluded Not excluded Not excluded Not excluded Excluded Excluded Excluded

1.87 2.5 1.6 1.2

15/15 on AD TRD

48.3 61.6 29.5 35.2 37.2 37.4 37.5 33.5

1.0

1 1.5 1.5 3 3 1.5 1.5 3

Drug naïve

31.1

FE

Not excluded

Outpatients 8/28 depressed MADRS = 18.3 16/16 depressed 17/17 depressed 25/44 depressed HDRS = 18.5 58/85 depressed 13/13 depressed HDRS = 20.5 20/40 depressed 70/70 depressed 14/14 depressed 19/19 depressed 33/156 remitted 15/15 depressed 30/30 depressed HDRS = 21.1 15/15 depressed HDRS = 24.4 23/23 depressed

1.0

3

Inkster et al. (2010) Kim et al. (2008) Koolschijn et al. (2010) Lai et al. (2010) Leung et al. (2009) Li et al. (2010) Peng et al. (2011) Salvadore et al. (2011) Scheuerecker et al. (2010) Shah et al. (1998) Soriano-Mas et al. (2011) Tang et al. (2007) Treadway et al. (2009) Van Tol et al. (2010) Vasic et al. (2008) Wagner et al. (2011) Zhang et al. (2009) Zou et al. (2010)

MDD/HC (# females)

145(94)/183(110) 22(22)/25(25) 28(28)/38(38) 16(11)/15(11) 17(17)/17(17) 44(33)/25(19) 22(14)/30(19) 85(58)/107(60) 13(10)/15(10) 40(26)/20(13) 70(41)/40(23) 14(14)/13(13) 19(10)/19(10) 156(103)/65(41) 15(6)/14(6) 30(25)/30(25) 15(5)/15(5) 23(13)/23(13)

AD = Antidepressants, TRD = Treatment resistant depression, HDRS = Hamilton Depression Rating Scale, FE = First-episode.

1.0

E. Bora et al. / Journal of Affective Disorders 138 (2012) 9–18

Antidepressant use

Abe et al. (2010)

E. Bora et al. / Journal of Affective Disorders 138 (2012) 9–18

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Fig. 2. Gray matter reductions in MDD compared to healthy controls (p b 0.001).

that reported volume reduction in at least 25%, 50% and 75% of the studies and provides a semi-quantitative summary of variability in findings. The possibility of publication bias was examined by extracting SDM values in peak voxels of significant clusters using the Egger test and funnel plots. Meta-regression analyses and subgroup analyses were conducted to examine the moderator variables contributing to heterogeneity of the findings. Potential confounding factors examined were: 1) inclusion/exclusion of co-morbid anxiety disorders; 2) duration of illness (first-episode vs multiple episodes); 3) current depression (studies in which all patients were currently depressed); and 4) Percentage of females in MDD group (dichotomized based on mean percentage).

3. Results The mean age of patients (mean age = 42.4) and controls (mean age = 41.4) did not differ significantly (d = 0.08, CI = − 0.04–0.20, Z = 1.2, p = 0.22). Both groups were well

matched for percentage of females (64.7% vs 62.8%) (RR = 1.04,CI = 0.96–1.12, Z = 0.32, p = 0.59). 3.1. Regional differences in gray matter Meta-analysis of 23 studies identified a single cluster of gray matter reduction encompassing bilateral rostral ACC (BA24 and BA32) (Fig. 2; Table 2). Whole-brain jackknife sensitivity analysis revealed that the gray matter decrease in the ACC was highly replicable, as this finding was observed in all 27 combinations of studies/analyses. There was no evidence for a publication bias for this cluster. When we repeated this analysis with a less conservative statistical threshold (p b 0.005), gray matter reduction in rostral ACC was extending to subgenual and rostral parts of dorsal ACC and there was also additional gray matter reductions in bilateral dorsomedial frontal cortex (BA 6/8/9) and right dorsolateral frontal cortex (BA9) (Fig. 3; Table 2). In the analysis of quartiles, the ACC cluster was found to be significantly reduced in 25–50% of the studies included in the analyses. In addition to the finding in the ACC, there were also

Table 2 Gray matter reductions in MDD compared to healthy controls. Region

Talairach coordinates

Cluster breakdown (# of voxels)

Gray matter decrease (p b 0.001) Bilateral ACC

4, 36, 14

Left BA 32 (19) Left BA 24 (14) Right BA 32 (35) Right BA 24 (19)

Gray matter decrease (p b 0.005) Bilateral ACC and dorsomedial frontal

4, 36, 14

Right dorsolateral/precentral frontal

38,12,34

Left BA 32 (122) Left BA 24 (92) Left BA 8 (13) Left BA 9 (27) Right BA 32 (130) Right BA 24 (92) Right BA 8 (151) Right BA 9 (41) Right BA 6 (24) Right BA 9

BA = Brodmann Area, SDM = Signed differential mapping, R = Right, L = Left.

Number of voxels

SDM

p value

87

− 0.15

0.0003

707

− 0.15

0.0003

25

− 0.12

0.002

14

E. Bora et al. / Journal of Affective Disorders 138 (2012) 9–18

Fig. 3. Gray matter reductions in MDD compared to healthy controls using a less conservative threshold (p b 0.005).

gray matter reductions in other regions such as the bilateral dorsomedial frontal cortex, right middle frontal gyrus (BA9) extending into precentral gyrus, bilateral putamen, caudate, and right anterior insula/inferior frontal cortex (1989 voxels). 3.2. Analyses of confounding factors Out of 4 variables all but differences in percentage of females had significant effects on observed findings. 3.2.1. First-episode vs multiple-episodes Patients in multi-episode samples had decreased gray matter in ACC (4, 36, 20, SDM = − 0.17, p = 0.0002, 131 voxels) (BA32) and dorsomedial frontal cortex (8, 28, 44, SDM = − 0.15, p = 0.0005, 68 voxels) (BA8, BA9 and BA6) compared to controls. First-episode patients had a significant reduction in a cluster including right superior temporal gyrus, and parahippocampal gyrus/amygdala (40, 4, −14, SDM = −0.43, p = 0.0001, 224 voxels). Only 5 of these 153 patients included in first-episode studies were on antidepressants and most were medication naive. In multi-episode vs. firstepisode comparison, multi-episode patients had less gray matter in the dorsomedial frontal cortex bilaterally (8, 28, 44, SDM = − 0.15, p = 0.00002, 254 voxels). 3.2.2. Co-morbidity Studies including multi-episode samples (excluding 5 first-episode studies) without co-morbid anxiety disorders had significantly less gray matter in the right middle frontal gyrus/precentral gyrus (38, 2, 42, SDM = − 0.24, p = 0.0006, 24 voxels) and also in the ACC (4, 34, 22, SDM = −0.31,

p = 0.00004, 178 voxels). In contrast, studies of patients with co-morbid anxiety disorders had significantly less gray matter in the right amygdala/parahippocampal gyrus extending to the putamen (18, 4, − 12, SDM = −0.23, p = 0.0002, 122 voxels). 3.2.3. Current depression Patients in currently depressed samples (17 studies) had gray matter reduction in bilateral dorsomedial frontal cortex (BA 8) (8, 28, 44, SDM = − 0.22, p = 0.0002, 173 voxels). This finding was only evident in multi-episode samples (8, 28, 44, SDM =− 0.33, p = 0.00004, 634 voxels, extending to ACC). 4. Discussion To our knowledge, this is the first co-ordinate based metaanalysis of VBM studies in MDD. The most robust gray matter reductions were identified in a relatively focal region in rostral ACC. This contrasts with the often widespread and spatially distributed abnormalities reported in VBM metaanalyses of schizophrenia (Bora et al., 2011; Ellison-Wright and Bullmore, 2010; Fornito et al., 2009). Notably, longer illness duration was associated with greater gray matter reduction in this region. Gray matter anomalies in other regions were also affected by certain moderating clinical features. For example, first-episode patients (who were mostly drug free) had decreased gray matter volume in the amygdala compared with chronic patients and controls, while patients with no comorbid anxiety disorder showed reduced middle frontal gyrus volume compared to those with a comorbidity, who had volume reduction in medial temporal regions. Samples including patients with multiple episodes

E. Bora et al. / Journal of Affective Disorders 138 (2012) 9–18

had gray matter reduction dorsomedial and dorsolateral prefrontal cortex. Together, these findings point to a robust role for anterior cingulate regions in the pathophysiology of MDD, and suggest that volumetric changes in other regions may depend on the particular clinical characteristics of the sample being studied. Our finding of robust ACC gray matter reductions in MDD is consistent with the findings of region of interest and neuropathological studies (Koolschijn et al., 2009; Ongür et al., 1998). Abnormality in these regions is not surprising since ACC have a significant role in emotion regulation (Fan et al., 2011; Phillips et al., 2008). In Mayberg's model, pregenual ACC has reciprocal connections with both dorsal and ventral compartments and facilitates interactions between cognitive and emotional processes (Mayberg, 1997). Functional imaging studies suggest that baseline hyperactivity in this region predicts treatment response in acutely depressed patients (Pizzagalli, 2011). Interestingly, there was also evidence for gray matter reduction in dorsomedial and dorsolateral frontal cortex which are part of dorsal compartment in Mayberg's model. These regions are hypoactive in MDD (Mayberg, 2003) and there is also neuropathological and neurochemical evidence showing abnormality in these regions (Hasler et al., 2007; Khundakar and Thomas, 2009; Rajkowska et al., 1999). Subgenual ACC is connected with regions that include the amygdala and anterior insula, and has an important role in the generation of negative mood states and associated internal somatic changes (Mayberg et al., 1999). Subgenual overactivity has been associated with depressive states and there is some evidence that treatment ameliorates this hyperactivity (Drevets et al., 2008; Mayberg, 1997, 2003; Pizzagalli, 2011). Few studies examined the relationship between structural and functional abnormalities in MDD (Scheuerecker et al., 2010). Notably, volumetric and functional changes in these regions, and their interaction with the amygdala, are observed in carriers of the putative 5-HTTPR risk allele for MDD (Pezawas et al., 2005) and has been targeted for deep brain stimulation studies to treat intractable depression (Mayberg et al., 2005). Future studies are needed to examine the relationship between volumetric and functional abnormalities of ACC in MDD. Gray matter reduction in ACC and dorsomedial frontal cortex were only observed in samples including multi-episode patients suggesting a possible progression of abnormalities in these regions over time, a postulate which has received some support from longitudinal work (Frodl et al., 2008b). Chronic stress and toxic effects of recurrent depressive episodes might lead to progressive ACC and dorsomedial frontal changes in MDD. It is also possible that gray matter reduction in ACC and dorsomedial frontal cortex might be related to recurring hypoactivity in depressive episodes in these regions given that these regions were decreased in currently depressed samples in our meta-analysis. However, the dearth of longitudinal imaging studies in MDD means that further work is required to conclusively test these hypotheses. First-episode MDD was associated with volume reductions in amygdala and parahippocampus which were not observed in chronic patients. Most of the patients included in these studies were medication-free, which is in line with previous region of interest studies showing that the amygdala is only reduced in unmedicated MDD patients (Hamilton et al., 2008). It is therefore possible that antidepressant treatment normalizes

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the amygdala volume reductions observed in MDD. However, other factors like age could potentially account for such differences. Medial temporal regional volumes were also reduced in studies that include patients with comorbid anxiety disorders. Anxiety disorders are frequently comorbid in MDD, leading some to suggest that they may be part of the same syndrome or spectrum (Gorman, 1996; Kessler et al., 2005; Ressler and Mayberg, 2007; Roy-Byrne et al., 2000). Our findings indicate that differences between MDD patients with and without comorbid anxiety can be identified at the level of brain structure. Specifically, right precentral/dorsolateral frontal gray matter was significantly reduced only in the pure depressive samples (i.e., with no comorbid anxiety disorder). Previous functional imaging and neuropathologic studies have found evidence for abnormalities of this region in MDD, which may play an important role in patients' cognitive control of emotional regulation deficits (Drevets, 2001; Mayberg, 1997). The relative preservation of prefrontal volume in patients with anxiety may be related to other reports of an association between anxiety symptoms and relatively increased prefrontal gray matter (Fornito et al., 2008). In contrast, amygdala and parahippocampal regions were significantly reduced in studies of comorbid patients. Similar abnormalities in amygdala and parahippocampal gyrus have also been reported in panic disorder (Hayano et al., 2009; Massana et al., 2003a, 2003b), suggesting that these abnormalities may be overlaid on a background of MDDrelated changes. As such, patients presenting with mixed anxiety-depressive symptoms might have neurobiologically distinct characteristics, which may partly explain the heterogeneity of findings reported across previous studies. It is important to emphasize that there were MDD-related gray matter abnormalities in other regions including putamen, caudate, right precentral and dorsolateral prefrontal cortex and right fronto-insular cortex. However, these reductions were only reported in 25–50% of the studies. These regions and the dorsomedial frontal cortex and the ACC are part of a frontalsubcortical circuit and it is reasonable to expect that these regions play a role in MDD (Cummings, 1992; Pantelis and Maruff, 2002). For example, depression is commonly observed in neuropsychiatric disorders such as Parkinson's disease, as well as following cerebrovascular insult to fronto-subcortical connectivity (Cummings, 1992; Hama et al., 2007; Hickie et al., 1995). This is also consistent with findings of small-tomoderate effect sized volume reductions for caudate, putamen and prefrontal cortex in previous region-of-interest studies of MDD (Koolschijn et al., 2009). Therefore, it is possible that frontal and basal ganglia abnormalities play a role in a subgroup of adult MDD patients. Our meta-analysis did not find any significant volume reduction in the hippocampus, whereas previous metaanalyses of region of interest studies have reported small (d = 0.4) but significant reductions in this region (Koolschijn et al., 2009; McKinnon et al., 2009). It has been suggested that that these subtle hippocampal reductions reflect stressrelated changes associated with recurrent depressive episodes (MacQueen et al., 2003; Warner-Schmidt and Duman, 2006). One explanation of our negative findings might be an insensitivity of voxel based methods to detect tissue differences in relatively circumscribed regions. A potential

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reason for different sensitivity of analyses in hippocampus and neocortex is the regional differences in the quality of the segmentation into gray and white matter. Segmentation in VBM may be less accurate in regions like hippocampus where gray and white matter sheets are convoluted into each other. Variations in clinical characteristics of the samples may also have been a factor, as previous findings suggest that medial temporal changes are sensitive to factors such as illness severity, and may also be affected by antidepressant treatment (Duman et al., 2001; Sahay and Hen, 2007). The power to detect differences might also be another factor. While the number of ROI studies available for vast majority of the regions are less than number of VBM studies, hippocampus and amygdala are the only exceptions for this. Our analysis of moderating variables was limited by the data reported in individual studies, so we were unable to examine the impact of many other potentially important factors, such as melancholic features and history of psychosis. Other factors like presence or absence of early life stress might also play a role. Therefore, further studies which will take clinical heterogeneity of MDD into account are important for a more complete understanding of brain abnormalities observed in this syndrome. Second, most of the patients were using antidepressants and the analyses in first-episode and drug free samples had limited power. Thus, our findings may represent a conservative estimate of the true extent of gray matter changes in these patient groups. Third, while VBM is a relatively consistent methodology, there are many analysis parameters, such as smoothing kernel size, slice thickness, statistical threshold and the use of Jacobian modulation, which are tailored to individual studies and which may affect the results derived from individual studies. There was not a sufficient number of studies employing these different techniques to allow an assessment of their impact on the findings. In summary, our meta-analysis of VBM studies in MDD indicates that the most robust gray matter changes observed in the disorder are reductions in rostral ACC gray matter. Brain changes in other regions were more sensitive to variations in the clinical characteristics of the sample. As such, our findings strongly implicate an abnormality of the ACC as central to the pathophysiology of MDD, possibly interacting with changes in other brain regions depending on the precise clinical circumstances of the patient. Role of funding source There is no role of any funding source for this paper. Conflict of interest The authors reported no biomedical financial interests or potential conflicts of interest.

Acknowledgement MY was supported by a National Health and Medical Research Council (NHMRC) clinical career development award (ID: 509345). AF was supported by a National Health and Medical Research Council CJ Martin Fellowship (ID: 454797). CP was supported by a NHMRC Senior Principal Research Fellowship (ID: 628386) and by NHMRC Program Grant (ID: 566529).

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