Magnetic resonance imaging studies in unipolar depression: Systematic review and meta-regression analyses

European Neuropsychopharmacology (2012) 22, 1–16

www.elsevier.com/locate/euroneuro

REVIEW

Magnetic resonance imaging studies in unipolar depression: Systematic review and meta-regression analyses D. Arnone a, c,⁎, A.M. McIntosh b , K.P. Ebmeier b, c , M.R. Munafò d , I.M. Anderson a a

Neuroscience and Psychiatry Unit, University of Manchester and MAHSC, Manchester, UK University Division of Psychiatry, Edinburgh, UK c University Department of Psychiatry, Oxford, UK d Department of Experimental Psychology, University of Bristol, Bristol, UK b

Received 14 October 2010; received in revised form 28 March 2011; accepted 11 May 2011

KEYWORDS Depression; Bipolar disorder; Meta-analysis; MRI

Abstract Previous meta-analyses of structural MRI studies have shown diffuse cortical and sub-cortical abnormalities in unipolar depression. However, the presence of duplicate publications, recruitment of particular age groups and the selection of specific regions of interest means that there is uncertainty about the balance of current research. Moreover, the lack of systematic exploration of highly significant heterogeneity has prevented the generalisability of finding. A systematic review and random-effects meta-analysis was carried out to estimate effect sizes. Possible publication bias, and the impact of various study design characteristics on the magnitude of the observed effect size were systematically explored. The aim of this study was 1) to include structural MRI studies systematically comparing unipolar depression with bipolar disorder and healthy volunteers; 2) to consider all available structures of interest without specific age limits, avoiding data duplication, and 3) to explore the influence of factors contributing to the measured effect sizes systematically with meta-regression analyses. Unipolar depression was characterised by reduced brain volume in areas involved in emotional processing, including the frontal cortex, orbitofrontal cortex, cingulate cortex, hippocampus and striatum. There was also evidence of pituitary enlargement and an excess of white matter hyperintensity volume in unipolar depression. Factors which influenced the magnitude of the observed effect sizes were differences in methods, clinical variables, pharmacological interventions and sample age. © 2011 Elsevier B.V. and ECNP. All rights reserved.

⁎ Corresponding author at: Neuroscience and Psychiatry Unit, University of Manchester and Manchester Academic Health Sciences Centre, G810 Stopford Building, Oxford Road, Manchester, M13 9PT, UK. Tel.: + 44 161 2751731; fax: + 44 161 2757429. E-mail address: [email protected] (D. Arnone). 0924-977X/$ - see front matter © 2011 Elsevier B.V. and ECNP. All rights reserved. doi:10.1016/j.euroneuro.2011.05.003

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1. Introduction Several neuroimaging studies have investigated brain structure in individuals with unipolar depression and bipolar disorder, but uncertainty remains about the regions involved in the pathogenesis of both disorders. Meta-analysis is a tool to combine quantitative data from individual studies, increasing the power to detect small effects and investigate causes of heterogeneity (Thompson et al., 1997; Wright et al., 2000). Meta-analyses of volumetric magnetic resonance imaging (MRI) studies in patients with unipolar depression in comparison with healthy controls have shown volume reductions in the hippocampus (Campbell et al., 2004; Videbech and Ravnkilde, 2004; McKinnon et al., 2009), the subgenual cortex (Hajek et al., 2008), and the amygdala in un-medicated patients (Hamilton et al., 2008), and an excess of white matter hyperintense lesions (Videbech, 1997). A more recent meta-analysis (Koolschijn et al., 2009) found volume reduction in frontal regions, hippocampus, putamen and caudate nucleus. In patients with bipolar disorder in comparison with healthy controls mild ventricular enlargement and the presence of white matter hyperintensities are among the most consistently reported abnormalities, although volume reductions in whole brain, prefrontal cortex and increases in the volume of the globus pallidus have also been described (McDonald et al., 2004; Kempton et al., 2008; Arnone et al., 2009). Treatment with lithium and mood stabilisers has been associated with volume increases in bipolar disorder (Kempton et al., 2008; Arnone et al., 2009) and similar increases have been found in unipolar depression, in association with antidepressants (Hamilton et al., 2008). In patients with unipolar depression, a negative association has been shown between volume of the hippocampus and both the number of episodes (Videbech and Ravnkilde, 2004; McKinnon et al., 2009) and longer than 2 years duration of illness (McKinnon et al., 2009). In this meta-analysis we sought to update and extend previous meta-analyses (Videbech, 1997; Campbell et al., 2004; Videbech and Ravnkilde, 2004; Hamilton et al., 2008; Hajek et al., 2008; McKinnon et al., 2009; Koolschijn et al., 2009) by systematically including MRI studies of patients with unipolar depression and all the possible available structures of interest, with no specific age limits. Moreover, we searched for studies comparing not only patients with unipolar depression and healthy controls, but also patients with bipolar disorder, in an attempt to identify diagnosisspecific morphometric differences. We sought to avoid data duplication in the analyses and to quantify and explain between-study heterogeneity by using meta-regression to examine the influence of key clinical and methodological variables. We predicted volume reduction in unipolar depression in brain areas including the frontal regions, cingulate cortex, and hippocampus, and expected an increase in the overall volume of white matter lesions in unipolar depression and bipolar disorder.

D. Arnone et al. cross-referencing. Key words used to identify the studies were: MAGNETIC RESONANCE IMAGING, MRI, DEPRESSION, BIPOLAR DISORDER, MANIA, and MOOD DISORDERS. Studies were included if they presented original clinical data, were published by March 2011, matched patients with unipolar depression with healthy controls and/or patients with bipolar disorder, used comparable diagnostic criteria and methodology and reported mean volumetric measures of brain regions (with standard deviations, SDs) or mean area measures (with SDs) for corpus callosum only. Study authors were contacted if this information was not readily available. If the results of a particular study were reported more than once, the study with the largest sample size was preferred whenever possible in order to avoid repeated inclusion of data from the same individual. Information systematically extracted from the studies included: diagnosis (with diagnostic criteria), volumetric measurements and number of participants in order to calculate effect sizes, and other potentially critical variables including sample demographics (age, gender), illness variables (age of onset, duration of illness, number of episodes, illness severity, mood state, presence of psychosis, family history, pharmacological treatment), year of publication, scanner strength and slice thickness. Conference abstracts and letters were included only if there were no other publications from the same study that had been published in full as peer reviewed articles. Studies were excluded when there was a comorbid diagnosis of learning disability, or chromosomal or genetic disorder. Studies were not included when the healthy controls were related to affected probands. Statistical analyses was conducted using STATA 9.0 (Stata Corp, College Station, Texas) supplemented by ‘Metan’ software (Centre for Statistics in Medicine, Oxford, UK). Standardised mean differences were calculated using Cohen's d X 1 −X 2 statistic: Cohen's d = , where X 1 and X 2 are the mean SDp volumes from the first and second groups respectively and SDp is the pooled standard deviation estimated from both groups: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi ðn1 −1ÞSD12 + ðn2 −1ÞSD22 , where ni and SDi are the mean and SDp = ðn1 + n2 −2Þ standard deviation of the ‘ith’ group. Standardised effect sizes were then combined using the inverse variance method. The variance of qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi d2 Cohen's d is estimated as: SD(d) = n1Nn2 + 2ðN−2 Þ, where N is the total sample size for the study, d is Cohen's d and n1 and n2 are as defined above. Random effects analyses (DerSimonian and Laird, 1986) were used throughout to weight each study. The presence of heterogeneity was tested using the Q-test and its magnitude estimated using I2, which can be interpreted as the proportion of effect size variance due to heterogeneity (Higgins et al., 2003). Conventionally, I2 values of 0.25, 0.50 and 0.75 are considered low, moderate and high, respectively (Higgins et al., 2003). When the Q-test was significant, we used a Galbraith plot to identify those studies contributing the greatest amount to that heterogeneity, in order to investigate potential causes. Small study bias, which describes the tendency of small studies to report large effect sizes (for example, due to publication bias), was examined using Egger's test (Egger et al., 1997). To further investigate causes for heterogeneity, meta-regression analyses were performed. The STATA programme“metareg.ado” was used throughout, and the REML (restricted maximum likelihood) method used to estimate the model parameters.

3. Results 3.1. Literature search

2. Experimental procedures A systematic search was conducted using a range of electronic databases, including the Cochrane Library, EMBASE, PsycINFO, OVID and PubMED, complemented by a manual search with bibliographic

A total of over 220,000 reports were identified and 101 met criteria for inclusion (see Fig. 1 and Table 1 for details). The only brain region which allowed comparison between unipolar depression and bipolar disorder was the pituitary gland. All

Magnetic resonance imaging studies in unipolar depression

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~220.000 potentially relevant articles were identified and screened for retrieval. Non-structural MRI studies were excluded.

385 articles retrieved for full text evaluation. Non-structural MRI studies with a region of interest approach, not quantitative studies and reports with sub-optimal comparators were excluded.

192 potentially appropriate articles were selected and 91 were excluded e.g. repeat publication, raw data were not reported or there were insufficient studies for a given region, etc.

101 articles were included in the meta-analysis.

Figure 1

Study flow and reasons for exclusion.

other studies compared patients with unipolar depression and healthy controls. Studies used comparable diagnostic criteria and tested a total 4118 patients with unipolar depression, 159 with bipolar disorder, and 3545 healthy controls. Basic demographic characteristics were generally well reported unlike clinical details such as medication status, number of episodes, duration of illness, and age at first presentation. Most studies included male and female participants, but only a few presented separate analyses by sex. Men were equivalent to approximately 32% of the total number of participants. Age ranged from 12 to 74 years with a mean of 46 (SD = 17).

3.2. Unipolar depression in comparison with healthy controls Summary results are presented in Tables 2 and 3a–d. There were no differences in global volumetric brain measures including intracranial volumes, total cerebral volume, whole brain, whole brain grey/white matter and cerebrospinal fluid (CSF). Regions of interest which showed no significant differences also included the thalamus, left and right temporal lobes, amygdala, amygdala–hippocampus and corpus callosum. The volume of the total frontal cortex was significantly reduced in patients with unipolar depression in comparison with healthy controls with a trend to moderate heterogeneity but no evidence of publication bias (N = 4; d = −0.57, 95% CI = −0.93, −0.21; I2 = 0.56, p = 0.079; pEgger = 0.47). Left and right orbito-frontal cortices were reduced in volume with no evidence of publication bias (N = 4; dleft = −0.57, 95% CI= −0.95, −0.19; pEgger = 0.15 and dright = −0.44, 95% CI = −0.73, −0.15; pEgger = 0.17). The analysis in the left orbitofrontal cortex showed significant evidence of moderate heterogeneity (I2 = 0.69, p= 0.021) which was systematically explored. Meta-regression suggested larger effect size with increasing slice thickness (C = 0.18, Z = 2.99, p = 0.003). Grey but not white matter contributed to the volume reduction in the orbito-frontal cortex bilaterally in the absence of publication bias (N = 4; dleft = −0.85,

95% CI = −1.43, −0.27; pEgger = 0.41 and dright = −0.71, 95% CI = −1.17, −0.26; pEgger = 0.32). These analyses showed substantial heterogeneity (I2left = 0.76, p= 0.006 and I2right = 0.62, p = 0.049). Metaregression indicated larger effect size with increasing slice thickness (Cleft = 0.27, Z = 3.22, p= 0.001 and Cright = 0.21, Z = 2.53, p = 0.011) and proportion of depressed participants taking mood stabilisers (Cleft = 4.48, Z = 3.22, p= 0.001 and Cright = 3.52, Z = 2.54, p = 0.011). The effect size of the right orbito-frontal cortex grey matter was larger with increasing proportion of participants taking antidepressants (C = 1.24, Z = 2.54, p= 0.011) and antipsychotics (C = 7.05, Z = 2.54, p = 0.011). Conversely, the fraction of time lapsed since initial diagnosis was associated with smaller effect size (C = −2.96, Z = −2.64, p = 0.008). Volumes of the amygdalae did not significantly differ in all the depression vs. control comparisons in the absence of publication bias. The analyses of left and right amygdalae included 19 studies. A sensitivity analysis indicated that the study by Burke et al. (2011) provided a much larger effect size, resulting in significant evidence of small study bias in the analyses of left amygdala (pEgger b 0.001). By excluding this study, results did not change (left, d: 0.09; 95% CI: − 0.15, 0.33 and right, d: 0.08; 95% CI: − 0.15, 0.31) but there was no further evidence of small study bias (pEgger = 0.057 and 0.14, respectively). Heterogeneity was however highly significant and substantial in all analyses (I 2s 0.64–0.93, ps b 0.001). Meta-regressions suggested larger differences in the effect size with increasing proportion of prescribed antidepressants (C = 4.7, Z = 4.71, p b 0.001) and smaller differences with increasing slice thickness (C = − 1.34, Z = − 3.04, p = 0.002) in the amygdalae. In the left amygdala increasing age was associated with a smaller effect size (C = − 0.018, Z = − 2.09, p = 0.037). All analyses of the hippocampus (bilateral hippocampi, left and right hippocampi, and hippocampal grey matter) showed a morphometric reduction of this structure in unipolar depression. The analyses of left and right hippocampi included the highest number of studies (N = 37). There was evidence of volume reduction in the left (d: −0.26; 95% CI: −0.39, −0.13)

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D. Arnone et al. Table 1 Details of the studies included in the meta-analysis; Comparison Groups: BP: Bipolar Disorder; HC: Healthy Controls; K-SADS: Schedule for Affective and Schizophrenic Disorders; K SADS-E: Schedule for Affective and Schizophrenic Disorders, epidemiologic version; K-SADS-PL: Schedule for Affective and Schizophrenic Disorders for School Age Children: Present and Life Time Version; ICD 10: International Classification of Mental and Behavioural Disorders (10th Ed.); DSM III, IIIR and IV: Diagnostic and Statistical Manual of Mental Disorders version III, III Research Version and IV; MINI: Mini International Neuropsychiatric Inventory. Brain regions: WB: whole brain; ICV: intra-cranial volume; WBG: whole brain grey matter; WBW: whole grey white matter; CV: cerebral volume; FC: frontal cortex; OFC: orbito-frontal cortex; TL: temporal lobe; A: amygdala; A–H: amygdala– hippocampus complex; H: hippocampus; C: caudate; P: putamen; T: thalamus; ACC: anterior cingulate cortex; SGPFC: subgenual prefrontal cortex; Pit: pituitary region; WML: white matter lesions; CSF: cerebro-spinal fluid; CC: corpus callosum. Study

N

Diagnostic system

Males Mean HC (%) age (years)

BP Magnet (T)

Slice Brain region thickness (mm)

Husain et al.(1991a) Husain et al.(1991b) Krishnan et al.(1991) Krishnan et al.(1992) Axelson et al.(1993) Krishnan et al.(1993) Wu et al.(1993) Dupont et al.(1995) Pantel et al.(1997) Pillay et al.(1997) Kumar et al.(1998) Parashos et al.(1998) Sheline et al.(1998) Ashtari et al.(1999) Lenze and Sheline(1999) Sheline et al.(1999) Bremner et al.(2000) Kumar et al.(2000) Mervaala et al.(2000) Vakili et al.(2000) (36) von Gunten et al.(2000) Caetano et al.(2001) Rusch et al.(2001) Sassi et al.(2001) Botteron et al.(2002) Brambilla et al.(2002) Bremner et al.(2002) Frodl et al.(2002) Naismith et al.(2002) Nolan et al.(2002) Salokangas et al.(2002) Steingard et al.(2002) Frodl et al.(2003) Lacerda et al.(2003) MacMillan et al.(2003) MacQueen et al.(2003) Posener et al.(2003) Sheline et al.(2003) Baldwin et al.(2004) Ballmaier et al.(2004a) Ballmaier et al.(2004b) Caetano et al.(2004) Hastings et al.(2004) Janssen et al.(2004) Lacerda et al.(2004) Lange and Irle(2004) Lloyd et al.(2004)

41 20 19 50 19 25 20 30 19 38 34 32 20 40 24 24 16 51 34 38 14 17 25 13 48 18 15 30 47 22 37 19 57 25 23 37 27 38 50 24 24 31 18 28 31 17 51

DSM III DSM III R DSM III DSM III DSM III DSM III DSM III R SADS/DSM III R DSM III R DSM III R DSM IV DSM III R DSM IV DSM IIIR DSM IV DSM IV DSM IV DSM IV DSM III R DSM IV ICD-10 DSM IV DSM IV DSM IV DSM IV DSM IV DSM IV DSM IV DSM IV K-SAD-PL DSM IV K-SADS DSM IV DSM IV KSAD DSM IV DSM IV DSM IV DSM IV DSM IV DSM IV DSM IV DSM III R DSM IV DSM IV DSM IV DSM IV

NS 25 27 46 100 32 45 30 22 45 30 44 0 30 0 0 63 30 48 45 43 6 44 8 0 6 67 44 32 46 44 11 48 84 44 36 45 0 40 25 25 23 45 0 23 0 20

0 0 0 0 0 0 0 36 0 0 0 0 0 0 0 0 0 0 0 0 0 25 0 23 0 27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

5 3–5 3–5 7 5 5 5 5 1.25 NS 5 5 1.25 3.1 1.25 1.25 NS 5 3 NS 1.5 1.5 1.2 1.5 NS 1.5 3 3 1.5 1.5 5.4 1.5–4 1.5–3 1.5 NS 1.2 NK 1.25 3 1.4 1.4 1.5 1.5 5 5 1.3 5

55 54 55 48 47 74 33 39 72 38 74. 54 54 74 53 53 43 74 42 38 58 43 33 42 30 42 43 40 52 12 36 15 44 41 14 32 33 51 74 66 66 39 39 64 39 34 74

44 20 19 50 30 29 16 26 13 20 30 32 20 46 24 24 16 30 17 20 14 39 15 34 17 38 20 30 20 22 19 38 57 48 23 37 42 38 35 19 19 31 18 41 34 17 39

1.5 T Signa GE 1.5 T Signa GE 1.5 Signa GE 1.5 T 1.5 T Signa GE 1.5 T Signa GE 1.5 T GE 1.5 T GE 1.5 T Siemens 1.5 T Signa GE 1.5 T GE 1.5 T Signa GE 1.5 T Siemens 1.0 T Siemens NS 1.5 T Siemens NS 1.5 T GE 1.5 T Siemens 1.5 T Signa GE 1.5 T Signa GE 1.5 T Signa GE 1.5 T Signa GE 1.5 Signa GE 1.5 T Siemens 1.5 Signa GE 1.5 T Signa GE 1.5 T Siemens NS 1.5 T GE Signa 1.5 T Siemens 1.5 T Signa GE 1.5 T Siemens 1.5 T Signa GE 1.5 T Signa GE 1.5 T Signa GE 1.5 T Siemens 1.5 T Siemens 1.5 T Phillips 1.5 T Signa GE 1.5 T Signa GE 1.5 T Signa GE 1.5 T Signa GE 1.5 T Philips 1.5 T Signa GE 1.5 T Philips 1.0 T Siemens

CV, P CC Pit CV, C A–H, P, T CC WB, WBG, WBW, CV, C, CSF WB, ICV, TL, A–H, CSF CV, WBG, WBW, C WB, ICV, FC WB, FC, OFC, C, P, T WB, A WB, A–H, H C, P WB, H WB, FC, TL, A, C, H WB, FC, WML A, H H ICV, A, H T H ICV, Pit ICV, SGPFC SGPFC WB, OFC, SGPFC WB, WBG, WBW, H WB, C, ICV, FC FC WB, WBG, WBW, FC, CSF A C, P A, H H WB, H CV CSF OFC, ACC WBG, WBW, CSF ICV, TL CV, SGPFC WB, ICV, OFC, H OFC WB, H ICV, A

Magnetic resonance imaging studies in unipolar depression

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Table 1 (continued) Study

N

MacMaster and Kusumakar (2004) 17 O'Brien et al.(2004) 61 Vythilingam et al.(2004) 38 Coryell et al.(2005) 10 Hickie et al.(2005) 66 Lacerda et al.(2005) 22 Neumeister et al.(2005) 31 Pariante et al.(2005) 13 Rosso et al.(2005) 20 Taylor et al.(2005) 135 Caetano et al.(2006) 31 Frodl et al.(2006) 34 Hannestad et al.(2006) 182 MacMaster et al.(2006) 35 Saylam et al.(2006) 24 Velakoulis et al.(2006) 19 Weniger et al.(2006) 21 Yoshikawa et al.(2006) 11 Colla et al.(2007) 24 Hickie et al.(2007) 16 Lavretsky et al.(2007) 43 Maller et al.(2007) 45 Monkul et al.(2007) 17 Munn et al.(2007) 26 Taylor et al.(2007) 226 Andreescu et al.(2008) 71 Ballmaier et al.(2008) 46 Eker et al.(2008) 34 Frodl et al.(2008) 78 Greenberg et al.(2008) 124 Keller et al.(2008) 42 Lenze et al. (2008) 31 MacMaster et al. (2008a) 10 MacMaster et al.(2008b) 32 Matsuo et al.(2008) 27 Walterfang et al.(2008) 54 Yucel et al.(2008) 65 Zhao et al.(2008) 61 Dalby et al.(2009) 22 Delaloye et al.(2010) 41 Kronenberg et al.(2009) 24 Kronmüller et al.(2009) 57 Lorenzetti et al.(2009) 56 Pan et al.(2009) 170 40 van Eijndhoven et al.(2009) Yucel et al.(2009) 40 Eker et al.(2010) 25 Kaymak et al.(2010) 20 Lorenzetti et al.(2010) 56 Malykhin et al.(2010) 39 Meisenzahl et al.(2010) 92 Burke et al.(2011) 91 Kanellopoulos et al.(2011) 33 Steffens et al.(2011) 90 4118

Diagnostic system

Males Mean HC (%) age (years)

KSADS-PL 48 DSM IV 22 DSM IV 40 DSM III R 60 DSM IV 34 DSM IV NS DSM IV 26 ICD-10 39 K-SADS 15 DDES 34 DSM IV 23 DSM IV 56 DSM IV 30 KSADS-PL 43 DSM IV 25 DSM III R 48 DSM IV 0 DSM IV 0 DSM IV 38 DSM IV 44 DSM IV 24 DSM IV 49 DSM IV 0 DSM IV 0 DSM IV 34 DSM IV 31 DSM IV 27 DSM IV 24 DSM IV 49 DSM IV 32 DSM IV 46 DSM IV 0 KSADS-PL 40 SADS-PL 38 K-SADS-PL 38 DSM IV 23 DSM IV 54 DSM IV 40 DSM IV/ICD 10 32 MINI/DSM IV 24 DSM IV 38 DSM IV 43 DSM IV 13 DSM IV 35 DSM IV 33 DSM IV 33 DSM IV 28 DSM IV 0 DSM IV 13 DSM IV 26 DSM IV 49 DSM IV 91 DSM-IV-TR 36 DSM-IV 39 3545

16 73 41 22 53 41 40 30 15 70 39 45 70 14 33 22 34 48 54 52 71 37 34 20 70 72 71 32 45 70 36 50 17 14 14 34 29 66 57 67 54 43 34 69 35 69 32 32 34 34 45 67 72 69 159

17 40 40 40 20 40 57 40 40 83 40 40 40 40 40 40 40 40 14 40 40 40 40 40 40 43 40 40 40 40 40 40 40 40 40 40 93 40 22 30 14 40 33 83 20 40 22 15 31 34 138 31 23 72

BP Magnet (T)

Slice Brain region thickness (mm)

0 0 0 0 0 0 0 16 0 0 0 0 0 0 0 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1.45 1 1.5 NS 1.5 3 1.1 1.5 4 3 1.5 1.5 3 1.5 2 1.5 1.3 1.5 1.05 1.5 1.4 NS 1.5 NS 3 1.5 1.4 2 3 3 1.5 1.25 1.4 1.5 1.5 NS 1.2 NS 1.2 NS 1.05 1.5 1 3 1 1.2 2 1 1 1.5 3 1.25 1.5 3

1.5 T Siemens 1.0 T Siemens 1.5 T Signa GE 1.5 T 1.5 T Signa GE 1.5 T Signa GE 3 T GE 1.5 T Signa GE 1.5 T Signa GE 1.5 T Signa GE 1.5 T Signa GE NS 1.5 T Signa GE 1.5 T GE 1.5 T Mgnetom Vision 1.5 T 1.5 Philips 1.5 T Signa GE 1.5 T Siemens 1.5 T GE 1.5 T Signa GE 1.5 T Signa GE 1.5 T Signa GE 1.5 T Signa GE 1.5 T Signa GE 1.5 T Signa GE 1.5 T Signa GE 1.5 T Siemens 1.5 T Siemens 1.5 T Signa 3 T Signa GE 1.5 T Siemens 1.5 T Siemens 1.5 T Signa GE 1.5 T Signa GE 1.5 T Siemens 1.5 T Signa GE 3 T Siemens 3 T Signa GE 3T 1.5 Siemens 1.5 T Siemens 1.5 T 1.5 T Signa GE 1.5 T Siemens 1.5/3 T Signa GE 1.5 T Siemens 3 T Siemens 1.5 T 1.5 T Siemens 1.5 T Siemens 1.5 T Signa GE 1.5 T Siemens 1.5 GE

ICV, Pit WB, H WB, TL, H SGPFC, ACC H CC WB, H Pit WB, A, H H SGPFC, ACC ICV, WB, C, WML ICV, Pit ICV, H WB, ICV, A, H WB, ICV, A, H A, H ICV, H ICV, A, C, P ICV, FC, OFC, ACC, SGPFC, CSF WB, WBG, ICV, H, CSF OFC, A, H, SGPFC, ACC A WB, WML, OFC WB WB, H ICV, Pit SGPFC, ACC H A, H WB, ICV, Pit A, H C, P, WB CC SGPFC CV, H WML H, A, ICV A H Pit CV, P WB, WBG, WBW, A, H SGPFC H WB WB, A H, ICV H A, CV H, ICV H, CV

6 and right (d: −0.27; 95% CI: − 0.40, −0.13) hippocampus, with no strong evidence of publication bias (psEgger = 0.1 and 0.09 respectively). In these analyses there was significant evidence of moderate between-study heterogeneity (I 2left = 0.62, p b 0.001 and I 2right = and 0.63, p b 0.001). Meta-regression indicated larger effect size with increasing proportion of participants currently depressed in the left (C = 0.52, Z = 2.12, p = 0.034) and right (C = 0.52, Z = 2.06, p = 0.04) hippocampus. The volume of the right anterior cingulate cortex was reduced in patients with unipolar depression without significant heterogeneity or publication bias (d: −0.35; 95% CI: −0.63, −0.08; I2 = 0.38, p = 0.15; pEgger = 0.21). The analyses in left cingulate cortex and subgenual prefrontal cortex (SGPFC) did not indicate any difference between groups, but suggested substantial heterogeneity. Meta-regression showed larger effect size for the left SGPFC with increasing illness severity (C = 0.11, Z = 4.68, p b 0.001), fraction of patients treated with antidepressants (C = 1.4, Z = 4.85, p b 0.001), or with antipsychotics and mood stabilisers (C = 10.29, Z = 4.85, p b 0.001), and smaller effect size with progressive fraction of time lapsed since diagnosis (C = −4.52, Z = −2.61, p = 0.009). In the total SGPFC the effect size was larger with increasing scanner strength (C = 0.46, Z = 2.69, p = 0.007) and time lapsed since diagnosis (C = 11.41, Z = 2.86, p = 0.004). In the left anterior cingulate cortex a larger effect size was associated with increasing severity of illness (C = 0.13, Z = 3.74, p b 0.001) and later age of onset (C = 0.054, Z = 2.08, p = 0.037). The analysis of the caudate indicated a significant volume reduction with marginal evidence of moderate heterogeneity with no evidence of small study bias (N = 5, d: −0.33; 95% CI: −0.62, −0.05; I 2 = 0.52, p = 0.08; pEgger = 0.22). This reduction was not significant in the left and right caudate when these were analysed separately. Similarly, the putamen showed significant volume reduction (N = 4, d: −0.65; 95% CI: −0.91, −0.38; I 2 = 0.03, p = 0.38; pEgger = 0.74). In the left and right putamen the reduction did not reach a level of significance but there was statistically significant evidence of moderate heterogeneity (I2left = 0.62, p = 0.034 and I2right = 0.66, p = 0.02). Meta-regression indicated larger effect size in both regions with increasing year of publication (Cleft = 0.033, Z = 2.36, p = 0.019 and Cright = 0.037, Z = 2.65, p = 0.008). In the right putamen increasing slice thickness was associated with a smaller effect size (C = −0.20, Z = −2.04, p = 0.041). The volume of the pituitary gland was increased in depression with marginal evidence of moderate heterogeneity and evidence of small study bias (N = 8, d: 0.49; 95% CI: 0.20, 0.77; I 2 = 0.48, p = 0.059; pEgger = 0.021). A sensitivity analysis showed that two studies by Pariante et al. (2005) in psychotic depression and by Krishnan et al. (1991) which included bipolar disorder participants were responsible. Removing these two reports did not alter these results (d = 0.35; 95% CI: 0.05, 0.64) with only a slight reduction in the statistical significance of small study bias (I 2 = 0.38, p = 0.15; pEgger = 0.062). We also found evidence for an excess of white matter lesions in depression (N = 4; d = 0.31; 95% CI: 0.04, 0.57; I 2 = 0.55, p = 0.082; pEgger = 0.98). The exclusion of the study by Kumar et al. (2000), which used a voxel based methodology to calculate the volume of white matter lesions, did not alter these results (d = 0.23; 95% CI: 0.04, 0.42; I 2 = 0.17, p = 0.3; pEgger = 0.36). Older age was associated with an increasing excess of white matter lesions (C = 0.047, Z = 2.20, p = 0.028). Analysis of the CSF only showed a trend towards a volume increase in depression in the presence of significant and moderate heterogeneity (I 2 = 0.67, p = 0.006). Meta-regression sug-

D. Arnone et al. gested larger effect size with increasing slice thickness (C = 0.36, Z = 2.94, p = 0.003). Conversely, more recently published reports were associated with a smaller effect size (C = −0.15, Z = − 3.65, p b 0.001).

3.3. Unipolar depression in comparison with bipolar disorder The pituitary gland was the only region which included a sample of patients with bipolar disorder and allowed comparison with patients with unipolar depression. This indicated a trend towards a volume reduction in bipolar disorder which did not reach statistical significance (N = 3; d = −0.35; 95% CI: −1.10, 0.41; I 2 = 0.65, p = 0.058; pEgger = 0.55).

4. Discussion This comprehensive meta-analysis of morphometric studies suggests that individuals with unipolar depression are characterised by volume reductions in the frontal cortex, orbitofrontal cortex, hippocampus, right cingulate cortex, caudate and putamen, and an excess of white matter lesion volumes, in comparison with healthy controls. We also found evidence of increased pituitary volume, with no difference compared with patients with bipolar disorder for this comparison. In contrast with bipolar disorder and schizophrenia (Arnone et al., 2008a,b) the area of the corpus callosum does not appear to be affected in unipolar depression. Global brain volume preservation and concordant lack of CSF increase suggests that volume reductions in unipolar depression tend to be regional rather than diffuse. Alternatively, differences in method e.g. different spatial resolution in the studies included may be responsible for the absence of global volume differences in the effect sizes. For instance meta-regression in the CSF suggested a larger effect size with increasing slice thickness whereas more recently published reports were associated with a smaller effect size. Elkis et al. (1995) demonstrated the presence of mild but significant increase in the volume of the lateral ventricles and sulcal prominence in mood disorders. This effect may be related to bipolar disorder rather than unipolar depression, as recently shown in a meta-analysis of MRI studies suggesting whole brain volume reduction and ventricular enlargement in bipolar disorder, similarly to schizophrenia but to a lesser degree (Arnone et al., 2009). Frontal and orbito-frontal volume reductions in depression are in agreement with the meta-analysis of Koolschijn et al. (2009), and are consistent with the importance of this structure in relaying sensory and affective neurotransmission (Ongur et al., 1998; Drevets et al., 2008). Functional studies have reported abnormal metabolic activity in prefrontal areas in depression which tend to be followed by normalisation of metabolic function as a marker of response to antidepressant treatment (Mayberg et al., 2000; Kennedy et al., 2001; Brody et al., 2001; Drevets et al., 2002; Goldapple et al., 2004). Similarly, fMRI studies on brain activity have consistently shown abnormal neural responses to sad and happy stimuli in frontal areas including ventro-medial, dorsolateral and orbito-frontal cortices (e.g. Keedwell et al., 2005; Lee et al., 2007).

Magnetic resonance imaging studies in unipolar depression

7

Table 2 Comparison of regional brain volumes of patients with unipolar depression vs. healthy controls and unipolar depression vs. bipolar disorder. Brain region

Intracranial volume Cerebral volume Whole brain Whole brain grey Whole brain white Frontal cortex, total Frontal grey matter, total Frontal white matter, total Frontal cortex, left Frontal cortex, right Orbito-frontal cortex, total Orbito-frontal cortex, left Orbito-frontal cortex, right Orbito-frontal cortex grey, left Orbito-frontal cortex grey, right Orbito-frontal cortex white, left Orbito-frontal cortex white, right Temporal lobe, left Temporal lobe, right Amygdalae Amygdala, left Amygdala, right Hippocampi Hippocampus left Hippocampus right Hippocampus, grey matter, left Hippocampus, grey matter, right Amygdala–hippocampus, left Amygdala–hippocampus, right SGPFC, total SGPFC, left SGPFC, right Anterior cingulate cortex, left Anterior cingulate cortex, right Thalamus Caudate Caudate, left Caudate, right Putamen, total Putamen, left Putamen, right Pituitary, total Pituitary, without BP and psychosis White matter lesions CSF Corpus callosum (area)

No. of Studies

No. of subjects

Effect size

Heterogeneity

UD

Controls

Estimate

95% CI

I ,p

Egger (p)

25 10 30 7 6 4 3 3 3 3 5 4 4 4 4 3 3 4 4 5 18 18 9 37 37 5 5 3 3 4 7 7 6 6 3 5 7 7 4 5 5 8 6 4 7 4

839 627 1334 226 196 159 97 97 57 57 346 323 323 111 111 94 94 104 104 123 489 489 407 1512 1512 144 144 78 78 160 220 220 200 200 74 339 359 359 143 287 287 197 165 481 209 116

861 425 1083 183 157 133 97 97 51 51 271 238 238 118 118 101 101 93 93 87 484 484 301 1340 1340 137 137 89 89 190 209 209 192 192 96 188 218 218 112 225 225 265 168 260 195 107

−0.08 −0.08 −0.07 −0.04 −0.15 −0.57 −0.07 −0.35 −0.13 −0.16 −0.33 −0.57 − 0.44 −0.85 −0.71 −0.28 −0.28 −0.22 −0.16 −0.95 0.09 0.08 −0.45 −0.26 −0.27 −0.71 −0.60 −0.07 −0.08 0.05 −0.37 −0.21 −0.43 −0.35 −0.16 −0.33 −0.09 −0.10 −0.65 −0.26 −0.28 0.49 0.35 0.31 0.25 0.09

−0.21, 0.06 −0.23, 0.08 −0.16, 0.014 −0.24, 0.16 −0.46, 0.16 −0.93, −0.21 −0.43, 0.30 −0.86, 0.16 −0.51, 0.25 −0.57, 0.25 −0.69, 0.04 −0.95, −0.19 −0.73, −0.15 −1.43, −0.27 −1.17, −0.26 −0.66, 0.09 −0.56, 0.01 −0.65, 0.21 −0.48, 0.17 −2.05, 0.15 −0.15, 0.33 −0.15, 0.31 −0.68, −0.22 −0.39, −0.13 −0.40, −0.13 −1.03, −0.39 −0.91, −0.28 −0.38, 0.24 −0.39, 0.23 −0.33, 0.44 −0.82, 0.08 −0.48, 0.07 −1.02, 0.16 −0.63, −0.08 −0.53, 0.22 −0.62, −0.05 −0.27, 0.09 −0.29, 0.09 −0.91, −0.38 −0.58, 0.05 −0.61, 0.06 0.20, 0.77 0.05, 0.64 0.04, 0.57 −0.11, 0.60 −0.30, 0.49

0.44, p = 0.009 0.30, p = 0.17 0.04, p = 0.40 0, p = 0.99 0.42, p = 0.14 0.56, p = 0.079 0.34, p = 0.22 0.65, p = 0.057 0, p = 0.50 0.12, p = 0.32 0.72, p = 0.007 0.69, p = 0.021 0.50, p = 0.11 0.76, p = 0.006 0.62, p = 0.049 0.40, p = 0.19 0, p = 0.93 0.55, p = 0.086 0.23, p = 0.27 0.93, p b 0.001 0.68, p b 0.001 0.64, p b 0.001 0.41, p = 0.1 0.62, p b 0.001 0.63, p b 0.001 0.45, p = 0.12 0.46, p = 0.11 0, p = 0.83 0, p = 0.78 0.65, p = 0.035 0.78, p b 0.001 0.40, p = 0.12 0.86, p b 0.001 0.38, p = 0.15 0.45, p = 0.23 0.52, p = 0.08 0, p = 0.62 0.09, p = 0.36 0.03, p = 0.38 0.62, p = 0.034 0.66, p = 0.02 0.48, p = 0.059 0.38, p = 0.15 0.55, p = 0.082 0.67, p = 0.006 0.5, p = 0.11

0.82 0.9 0.053 0.46 0.62 0.47 0.77 0.79 0.21 0.36 0.79 0.15 0.17 0.41 0.32 0.2 0.3 0.74 0.79 0.22 0.057 0.14 0.25 0.1 0.09 0.62 0.71 0.9 0.89 0.67 0.19 0.17 0.094 0.21 0.66 0.22 0.5 0.44 0.74 0.61 0.65 0.021 0.062 0.98 0.062 0.19

172

265

−0.35

−1.10, 0.41

0.65, p = 0.058

0.55

2

Publication bias

Unipolar depression vs. bipolar disorder Pituitary

3

Bold indicates statistically significant comparisons.

In the analysis of the amygdalae, in contrast to Hamilton et al. (2008), left and right raw amygdala volumes were considered separately to avoid the possibility of Type I or II error. Our findings of no significant difference are in agreement with Campbell et al. (2004), Koolschijn et al. (2009)

and Hamilton et al. (2008). Hamilton et al. (2008) conducted further meta-analyses for medicated and un-medicated patients. To avoid the risk of sampling bias, separate analyses were not considered. Instead we used meta-regression analyses to explore causes of between-study heterogeneity.

8 Table 3 Meta-regression analyses of the effect of illness characteristics and demographic variables on volume differences of brain structures which showed statistically significant levels of heterogeneity. Results are expressed as regression coefficient (C), Z statistic and p value.

a. ICV Orbito-frontal cortex, total Orbito-frontal cortex, left Orbitofrontal cortex grey matter, left Orbitofrontal cortex grey matter, right SGPFC, total SGPFC, left Anterior cingulate cortex, left Amygdalae Left amygdala Right amygdala Left hippocampus Right hippocampus Putamen left Putamen right CSF

Age

Time fraction since diagnosis (duration of illness/age)

Number of episodes

C = 0.003, Z = 0.32, p = 0.75 C = −0.11, Z = −0.07, p = 0.95 C = 3.41, Z = 0.94, p = 0.35 C = 3.07, Z = 0.87, p = 0.38 C = −1.21, Z = −0.57, p = 0.56 C = −1.41, Z = −1.32, p = 0.19 C = 0.96, Z = 1.07, p = 0.28 C = 1.65, Z = 1.16, p = 0.25 C = 0.03, Z = 0.39, p = 0.70 C = 0.38, Z = 0.61, p = 0.54 C = 0.42, Z = 0.72, p = 0.47 C = 0.41, Z = 0.89, p = 0.37 C = 0.44, Z = 0.95, p = 0.34 C = −0.38, Z = −0.87, p = 0.38 C = −0.096, Z = −0.22, p = 0.82 C = −0.41, Z = −0.31, p = 0.75

C = −0.003, Z = −1.02, p = 0.31 C = −0.002, Z = −0.10, p = 0.92 C = −0.008, Z = −0.45, p = 0.65 C = 0.004, Z = 0.17, p = 0.87 C = 0.014, Z = 0.75, p = 0.45 C = 0.005, Z = 0.35, p = 0.72 C = −0.018, Z = −0.65, p = 0.51 C = 0.009, Z = 0.49, p = 0.62 C = 0.053, Z = 1.34, p = 0.18 C = −0.018, Z = −2.09, p = 0.037 C = −0.014, Z = −1.58, p = 0.11 C = 0.0004, Z = 0.10, p = 0.92 C = 0.0004, Z = 0.09, p = 0.92 C = 0.0035, Z = 0.36, p = 0.72 C = 0.0013, Z = 0.12, p = 0.90 C = −0.003, Z = −0.26, p = 0.79

C = −0.61, Z = −1.03, p = 0.30 C = −4.17, Z = −1.48, p = 0.14 C = −3.07, Z = −0.96, p = 0.34 C = −4.12, Z = −1.53, p = 0.13 C = −2.96, Z = −2.64, p = 0.008 C = 11.41, Z = 2.86, p = 0.004 C = −4.52, Z = −2.61, p = 0.009 C = −3.59, Z = −1.68, p = 0.093 C = 8.4, Z = 0.64, p = 0.52 C = 0.65, Z = 0.49, p = 0.62 C = −0.29, Z = −0.22, p = 0.83 C = 0.15, Z = 0.23, p = 0.82 C = 0.19, Z = 0.29, p = 0.77 NA NA NA

C = −0.093, Z = −1.89, p = 0.058 NA NA C = 0.02, Z = 0.27, p = 0.79 C = −0.04, Z = − 0.74, p = 0.46 C = 0.098, Z = 0.52, p = 0.60 C = 0.042, Z = 0.49, p = 0.63 C = −0.059, Z = −0.49, p = 0.62 NA C = −0.015, Z = −0.30, p = 0.76 C = −0.027, Z = −0.71, p = 0.48 C = 0.062, Z = 1.26, p = 0.21 C = 0.067, Z = 1.28, p = 0.2 NA NA NA

HAM-D score

Mood state

Psychotic symptoms

Family history

C = −0.015, Z = −0.74, p = 0.46 C = 0.018, Z = 0.60, p = 0.55 C = 0.074, Z = − 0.43, p = 0.67 C = 0.036, Z = 0.37, p = 0.71 C = 0.08, Z = 1.13, p = 0.26 C = 0.097, Z = − 1.29, p = 0.20 C = 0.11, Z = 4.68, p b 0.001 C = 0.13, Z = 3.74, p b 0.001 C = 0.32, Z = 1.34, p = 0.18 C = 0.019, Z = 0.84, p = 0.4 C = 0.0004, Z = −0.02, p = 0.99 C = −0.004, Z = −0.25, p = 0.8 C = −0.005, Z = −0.28, p = 0.78 NA NA C = 0.029, Z = 0.66, p = 0.51

C = 0.46, Z = 1.98, p = 0.047 C = 0.57, Z = 0.95, p = 0.34 C = −0.79, Z = −0.58, p = 0.56 C = 3.07, Z = 0.87, p = 0.38 C = 1.89, Z = 0.94, p = 0.35 C = −0.011, Z = −0.02, p = 0.98 C = 1.54, Z = 0.61, p = 0.54 C = 1.5, Z = 0.54, p = 0.59 NA C = −0.087, Z = −0.22, p = 0.83 C = −0.22, Z = −0.59, p = 0.56 C = 0.52, Z = 2.12, p = 0.034 C = 0.52, Z = 2.06, p = 0.04 C = −0.16, Z = −0.13, p = 0.89 C = −0.59, Z = −0.49, p = 0.63 C = −0.13, Z = −0.19, p = 0.85

C = −0.11, Z = −0.28, p = 0.78 NA NA NA NA NA NA NA NA C = 0.42, Z = 0.7, p = 0.48 C = 0.48, Z = 0.96, p = 0.34 C = 0.32, Z = 1.01, p = 0.31 C = 0.32, Z = 1.01, p = 0.31 NA NA NA

NA NA NA NA NA NA NA NA NA C = 0.017, Z = 0.05, p = 0.96 C = −0.09, Z = − 0.28, p = 78 C = 0.57, Z = 0.95, p = 0.34 C = 0.57, Z = 0.95, p = 0.34 NA NA NA

D. Arnone et al.

b. ICV Orbito-frontal cortex, total Orbito-frontal cortex, left Orbitofrontal cortex grey matter, left Orbitofrontal cortex grey matter, right SGPFC, total SGPFC, left Anterior cingulate cortex, left Amygdalae Left amygdala Right amygdala Left hippocampus Right hippocampus Putamen left Putamen right CSF

Fraction of men

d. ICV Orbito-frontal cortex, total Orbito-frontal cortex, left Orbitofrontal cortex grey matter, left Orbitofrontal cortex grey matter, right SGPFC, total SGPFC, left Anterior cingulate cortex, left Amygdalae Left amygdala Right amygdala Left hippocampus Right hippocampus Putamen left Putamen right CSF

Antidepressant treatment

Antipsychotics

Mood stabilisers

C = − 0.003, Z = −0.68, p = 0.5 C = − 0.019, Z = −0.59, p = 0.55 C = 0.0016, Z = −0.06, p = 0.95 C = 0.009, Z = 0.48, p = 0.63

C = − 0.12, Z = −0.50, p = 0.62 NA NA C = 1.55, Z = 1.50, p = 0.13

C = −0.074, Z = − 0.12, p = 0.9 NA NA C = 8.77, Z = 1.50, p = 0.13

C = −1.97, Z = −0.94, p = 0.35 NA NA C = 4.48, Z = 3.22, p = 0.001

C = 0.018, Z = 1.15, p = 0.25

C = 1.24, Z = 2.54, p = 0.011

C = 7.05, Z = 2.54, p = 0.011

C = 3.52, Z = 2.54, p = 0.011

C = 0.003, Z = 0.14, p = 0.89 C = 0.002, Z = 0.08, p = 0.93 C = 0.054, Z = 2.08, p = 0.037

C = 0.13, Z = 0.23, p = 0.82 C = 1.4, Z = 4.85, p b 0.001 C = 1.21, Z = 1.55, p = 0.12

NA C = 10.29, Z = 4.85, p b 0.001 C = 8.91, Z = 1.55, p = 0.12

NA C = 10.29, Z = 4.85, p b 0.001 C = 8.91, Z = 1.55, p = 0.12

C = 0.11, Z = 1.54, p = 0.12 C = − 0.003, Z = −0.19, p = 0.85 C = 0.008, Z = 0.65, p = 0.52 C = 0.001, Z = 0.17, p = 0.87 C = 0.001, Z = 0.16, p = 0.87 NA NA NA

C = 4.7, Z = 4.71, p b 0.001 C = 0.61, Z = 1.76, p = 0.079 C = 0.49, Z = 1.69, p = 0.092 C = 0.03, Z = 0.15, p = 0.88 C = 0.03, Z = 0.18, p = 0.86 NA NA C = 0.34, Z = 0.58, p = 0.56

C = 13.45, Z = 0.34, p = 0.73 C = 0.56, Z = 0.68, p = 0.50 C = 0.69, Z = 0.96, p = 0.33 C = 0.74, Z = 1.14, p = 0.25 C = 0.76, Z = 1.11, p = 0.26 NA NA C = 0.54, Z = 0.19, p = 0.85

NA C = 0.1, Z = 0.02, p = 0.98 C = −2.15, Z = −0.63, p = 0.53 C = 0.82, Z = 0.49, p = 0.63 C = 0.87, Z = 0.5, p = 0.62 NA NA NA

Slice thickness

Scanner strength

Year of publication

C = −0.003, Z = −0.04, p = 0.97 C = 0.20, Z = 1.94, p = 0.052 C = 0.18, Z = 2.99, p = 0.003 C = 0.27, Z = 3.22, p = 0.001 C = 0.21, Z = 2.53, p = 0.011 C = −0.16, Z = −0.49, p = 0.62 NA C = −1.85, Z = −0.25, p = 0.81 C = −1.34, Z = −3.04, p = 0.002 C = −0.11, Z = −0.64, p = 0.52 C = −0.15, Z = −0.96, p = 0.34 C = 0.12, Z = 1.65, p = 0.1 C = 0.12, Z = 1.63, p = 0.1 C = −0.17, Z = −1.67, p = 0.094 C = −0.20, Z = −2.04, p = 0.041 C = 0.36, Z = 2.94, p = 0.003

C = −0.16, Z = −0.79, p = 0.43 NA NA NA NA C = 0.46, Z = 2.69, p = 0.007 NA NA C = 0.56, Z = 0.39, p = 0.70 C = −0.039, Z = −0.11, p = 0.91 C = −0.19, Z = −0.58, p = 0.56 C = −0.21, Z = −1.53, p = 0.12 C = −0.23, Z = −1.62, p = 0.11 NA NA NA

C = 0.013, Z = 0.64, p = 0.52 C=−0.078, Z=−1.52, p=0.13 C = 0.01, Z = 0.09, p = 0.93 C = −0.07, Z = −0.29, p = 0.77 C = −0.12, Z = −0.69, p = 0.49 C = 0.062, Z = 0.72, p = 0.47 C = −0.045, Z = −0.44, p = 0.66 C = 0.29, Z = 1.68, p = 0.093 C = 0.11, Z = 0.32, p = 0.75 C = 0.01, Z = 0.27, p = 0.78 C = 0.007, Z = − 0.20, p = 0.84 C = 0.015, Z = 0.69, p = 0.5 C = 0.013, Z = 0.6, p = 0.55 C = 0.033, Z = 2.36, p = 0.019 C = 0.037, Z = 2.65, p = 0.008 C = −0.15, Z = −3.65, p b 0.001

Magnetic resonance imaging studies in unipolar depression

c. ICV Orbito-frontal cortex, total Orbito-frontal cortex, left Orbitofrontal cortex grey matter, left Orbitofrontal cortex grey matter, right SGPFC, total SGPFC, left Anterior cingulate cortex, left Amygdalae Left amygdala Right amygdala Left hippocampus Right hippocampus Putamen left Putamen right CSF

Age of onset

9

10 Heterogeneity could be explained by differences in slice thickness, suggesting that the variability in the demarcation of anatomical boundaries, in combination with technical differences, might explain such discrepancies. We also found that antidepressant treatment and biological variables such as age might influence reported effect sizes. Consistent with Campbell et al. (2004), we did not find volume differences in the amygdala–hippocampal complex. Lack of volume differences in the amygdala does not exclude the involvement of this structure in depression. The amygdala has been shown to play a role in affective recognition bias associated with depression. Functional MRI studies in conjunction with affect recognition tasks have repeatedly reported abnormal BOLD signal responses in the amygdala (e.g. Thomas et al., 2001; Dannlowski et al., 2007). In combination with facial recognitions tasks, fMRI has shown normalisation of amygdala hyper- or hypoactivation at baseline in comparison with controls in relation to response to treatment (e.g. Sheline et al., 2001; Fu et al., 2004; Canli et al., 2005; Anand et al., 2007). Drevets et al. (2002), for instance, reported increased metabolism in the left amygdala in depression at baseline, which decreased following six months treatment with sertraline. Our finding of hippocampal volume reduction in depression is consistent with animal studies, including primate studies (Sapolsky et al., 1990), previous meta-analyses (Campbell et al., 2004; Videbech and Ravnkilde, 2004; McKinnon et al., 2009), post-mortem evidence (Rajkowska et al., 1999), positron emission tomography studies (Videbech et al., 2002) and clinical evidence of memory impairment and hypothalamic–pituitary–adrenal (HPA) axis hyperactivity. Depressed individuals show dexamethasone non-suppression and increased glucocorticoid levels (Carroll, 1982; Young et al., 1994) and decreased availability of glucocorticoid receptors in the hippocampus following HPA overactivity might reduce hippocampal negative feedback affecting the hypothalamic release of corticotropin releasing factor (Bremner, 1999). We found evidence of right cingulate cortex volume reduction in the right anterior cingulate cortex in unipolar depression but, in contrast to Hajek et al. (2008) and Koolschijn et al. (2009), we found no difference in volumes of the left cingulate and subgenual cingulate cortex. Such discrepancies may be explained by study selection and differences in methods. For instance, we avoided sample duplication — for example, Botteron et al. (2002) included participants from Drevets et al. (1997). However, our findings are consistent with cingulate cortex functional impairment in depression as indicated by metabolic and fMRI studies (Brody et al., 2001; Drevets et al., 1997, 2002; Gotlib et al., 2005; Frodl et al., 2007; Lee et al., 2007), metabolic changes following response to antidepressant treatment and clinical improvement (Mayberg et al., 1999, 2000; Kennedy et al., 2001; Brody et al., 2001; Drevets et al., 2002; Davidson et al., 2003; Goldapple et al., 2004; Fu et al., 2004; Keedwell et al., 2009). The substantial heterogeneity in studies of the left anterior cingulate was explained by a number of clinical variables, which could in turn explain the variability of results in the published literature. We found evidence of an increased pituitary volume in depression. This finding is in keeping with the literature which supports the role of the hypothalamic–adrenal–pituitary (HPA) axis in the pathogenesis of affective disorders. In our analysis, the inclusion of the studies by Pariante et al. (2005)

D. Arnone et al. and Krishnan et al. (1991), which recruited patients with psychotic symptoms, was associated with increased significance of evidence for small study bias. Therefore, due to the larger variance of these two studies, it is not possible to be confident about the contribution of psychotic depression to pituitary enlargement. In depression, levels of ACTH correlate with circulating cortisol levels (Krishnan et al., 1991; Pariante et al., 2005). Hypothalamic corticotropin releasing hormone (CRH) or stress might be responsible for activating a cascade of biological events leading to the activation of pituitary corticotrope cells and HPA axis hyperactivity (e.g. Axelson et al., 1992; Pariante et al., 2005). In agreement with Videbech (1997), we found evidence for a volume excess of white matter hyperintense lesions in unipolar depression. We also found that older age was associated with an increasing excess of white matter lesions. The mean age of 68 years (SD = 7) of the depressed participants in these analyses suggests this finding may be particularly found in geriatric unipolar depression and may not apply to younger patients. However Kempton et al. (2008) found evidence of an increased rate of white matter hyperintensities in bipolar disorder and it is possible that this finding may generalise to affective disorder as a whole. The volumes of the total putamen and total caudate were decreased in depression in our analysis, suggesting the possibility that structural abnormalities in the striatum might be linked with symptoms of depression as indicated by basal ganglia degenerative diseases and focal lesions (Nauta and Domesick, 1984; Folstein et al., 1985; Drevets et al., 2008). Functional studies have also confirmed the presence of metabolic abnormalities in depression in the whole striatum (Mayberg et al., 2000) and caudate nucleus (Brody et al., 2001) whereas fMRI studies have reported abnormal neural responses (Surguladze et al., 2005) and reduction in aberrant neural responses following treatment with fluoxetine (Fu et al., 2004) in the putamen. One of the major innovations of this meta-analysis was a systematic appraisal of the influence of factors contributing to the effect sizes of volume differences measured in unipolar depression by using meta-regression. Of the several variables considered a few proved to significantly contribute to the results. Pharmacological interventions such as antidepressants, antipsychotics and mood stabilisers were associated with larger effect sizes in the orbito-frontal cortex, amygdala, and SGPFC. This finding suggests a differential susceptibility of brain regions to pharmacological treatment. We generally found that a more recent year of publication was predictably associated with smaller slice thickness and higher scanner strength contributing in some brain regions to larger (e.g. putamen, amygdala and SGPFC) or smaller (e.g. orbito-frontal cortex and CSF) effect sizes. Among clinical variables, time lapsed since diagnosis, proportion of participants currently depressed and age of onset were particularly associated with the measurements of volumes in the hippocampus, SGPFC, orbitofrontal cortex, and ICV. Age was the only biological variable associated with variation in the measurement of the volume of the amygdala. Overall results from meta-regression suggest that at least some of the inconsistencies in the findings of MRI studies can be explained on the basis of methodological, clinical and biological heterogeneity. These associations could be taken into consideration when designing future studies.

Magnetic resonance imaging studies in unipolar depression Findings from this meta-analysis need to be interpreted with caution. The number of studies included in some of the comparisons was limited; samples were often heterogeneous in terms of clinical variables, data acquisition and analysis; there was anatomical variability in the regions of interest considered. These factors are particularly relevant in case– control studies (such as those included in this meta-analysis) because the specific selection process for imaging procedures, the relatively small number of matched participants, and the absence of randomisation are likely to generate greater between-study heterogeneity. For these reasons a random effect model was adopted. The effect of heterogeneity is to reduce the precision of the summary effect sizes and in general the statistical significance of any findings. Significant and moderate or large heterogeneity was investigated using meta-regression, in order to provide some clarification and guide future research. Selective reporting and publication of positive results is a potential limitation to all meta-analyses. Small study bias (such as might be caused by publication bias against non-significant results) was only detected in the pituitary region. However, this limitation cannot be definitively excluded, especially in the context of small studies with similar, relatively small sample sizes. In this respect, a post-hoc pEgger calculation based on the pooled effect size estimate for the right hippocampus and the median number of participants recruited in these studies, suggests an overall achieved power (1-β error probability) of approximately 18%. According to this analysis, the a priori estimated number of participants necessary to achieve α = 0.05 (1:1 allocation ratio) is over 300 in each group, suggesting that studies to date may lack sufficient statistical power. International collaborative mega-analysis of published and unpublished data, such as the work published recently by Hallahan et al. (2011) in bipolar disorder, could contribute to overcoming the limitation of low statistical power and offer the opportunity to systematically investigate the contribution of sources of heterogeneity. Future research would benefit from careful and inclusive reporting of clinical variables and the use of longitudinal designs to clarify the contribution of illness or its treatment on observed brain regions. Disease-specific morphometric differences between unipolar depression and bipolar disorder are largely unknown because they are currently underinvestigated. A cross-nosological approach in the design of future studies is necessary to provide further clarification. Consideration could be given to using brain atlases and analyses at voxel level to increase the accuracy of spatial localization with more precise mapping of brain regions with regional specificities of significant anatomical differences. In addition, results from the areas identified here need to be systematically reported in all studies in order to minimise publication bias through the non-reporting of negative findings. In summary, we found that unipolar depression is associated with brain volume reductions localised in the frontal cortex, orbitofrontal cortex, cingulate cortex, hippocampus and striatum. There is also evidence of pituitary enlargement and an excess of white matter hyperintensities in depression. However, apart from the hippocampus and amygdala, the small number of studies contributing to our results means that publication bias cannot be excluded.

11

Role of the funding source AM is currently supported by the Health Foundation and by NARSAD (Independent investigator Award). DA was in a post funded by the Medical Research Council UK.

Contributors DA was involved in literature searches, data extraction and analyses, and in writing the first draft of the report. AM, KE and MM provided supervision, statistical expertise, and offered guidance in the interpretation of the results. IA had a role in overall supervision and final drafting of the report. All the authors contributed to and have approved the final manuscript.

Conflict of interest Nothing to declare.

Acknowledgement DA would like to thank colleagues who supplemented their published work with detailed auxiliary information or generously provided unpublished data: Rikke B Dalby, Christophe Delaloye, Cagdas Eker, Dora Kanellopoulos, Klaus-Thomas Kronmüller, Helen Lavretsky, Valentina Lorenzetti, Frank P MacMaster, Glenda M MacQueen, Alexander Neumeister, Carmine Pariante, Isabelle M Rosso, David C Steffens, Warren D Taylor, Murat Yücel, and Poul Videbech.

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