Meta-analysis of volumetric abnormalities in cortico-striatal-pallidal-thalamic circuits in major depressive disorder

Psychological Medicine (2012), 42, 671–681. f Cambridge University Press 2011 doi:10.1017/S0033291711001668

REVIEW ARTICLE

Meta-analysis of volumetric abnormalities in cortico-striatal-pallidal-thalamic circuits in major depressive disorder E. Bora1*, B. J. Harrison1, C. G. Davey1,2, M. Yu¨cel1,2 and C. Pantelis1 1 2

Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, VIC, Australia Orygen Youth Health Research Centre, Melbourne, The University of Melbourne, VIC, Australia

Background. Abnormalities in cortico-striatal-pallidal-thalamic (CSPT) circuits have been implicated in major depressive disorder (MDD). However, the robustness of these findings across studies is unclear, as is the extent to which they are influenced by demographic, clinical and pharmacological factors. Method. With the aim of clarifying these questions, we conducted a meta-analysis to map the volumetric abnormalities that were most robustly identified in CSPT circuits of individuals with MDD. A systematic search identified 41 studies meeting our inclusion criteria. Results. There were significant volume reductions in prefrontal (especially orbitofrontal) and anterior cingulate cortices, and also in subcortical structures such as the caudate nucleus and putamen, with effect sizes ranging from small to moderate. The subgenual anterior cingulate and orbitofrontal cortices were significantly smaller in antidepressant-free samples compared to medicated patients. Late-life depression (LLD) tended to be associated with smaller volumes in circumscribed frontal and subcortical structures, with the most robust differences being found in thalamic volume. Conclusions. Individuals with major depression demonstrate volumetric abnormalities of CSPT circuits. However, these observations may be restricted to certain subgroups, highlighting the clinical heterogeneity of the disorder. On the basis of this meta-analysis, CSPT abnormalities were more prominent in those with LLD whereas antidepressant use seemed to normalize certain cortical volumetric abnormalities. Received 24 February 2011 ; Revised 17 May 2011 ; Accepted 1 August 2011 ; First published online 13 September 2011 Key words : Anterior cingulate cortex, frontal lobe, major depressive disorder, MRI, striatum, thalamus.

Introduction The DSM-IV diagnostic criteria for major depressive disorder (MDD) encapsulate a group of heterogeneous conditions that include psychotic/melancholic depression, reactive depression and personality-based depression (Parker, 2000). Thus, it follows that multiple aetiological factors probably contribute to the pathogenesis of MDD. Although psychological and social factors are clearly relevant to MDD, neurobiological abnormalities, either secondary to such factors or operating as causal influences (‘ endophenotypes ’), also have a role. However, for such knowledge to contribute to the clinical definition of MDD, consensus as to the abnormalities that are most common is needed.

* Address for correspondence : Dr E. Bora, Alan Gilbert Building NNF level 3, Carlton 3053, Australia. (Email : [email protected])

Magnetic resonance imaging (MRI) has been widely used to examine neurobiological correlates of MDD and has provided converging evidence in support of prevailing pathophysiological models of this disorder (Mayberg et al. 1997 ; Drevets et al. 2008 ; Marchand, 2010 ; Marchand & Yurgelun-Todd, 2010 ; Pizzagalli, 2011). According to these models, the characteristic clinical symptoms and associated cognitive deficits of MDD may arise, in part, through the corresponding dysfunction of cortico-striatal-pallidal-thalamic (CSPT) brain circuits. These circuits are composed of distributed neuroanatomical loops that connect subregions of the prefrontal cortex (PFC) and the anterior cingulate cortex (ACC), in particular, with the basal ganglia and thalamus in a highly organized and integrated manner to support diverse motor, cognitive and emotional processes (Alexander et al. 1986, 1990 ; Haber & Calzavara, 2009). In MDD patients, structural MRI studies indicate that significant volumetric alterations of CSPT regions are common in this

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disorder, a general observation that is substantiated by other neuropsychological and functional neuroimaging evidence (Rogers et al. 1998 ; Marchand & Yurgelun-Todd, 2010). Neurodegenerative and vascular conditions affecting CSPT regions have been associated with an increased prevalence of depression (Lauterbach et al. 1998 ; Marchand, 2010 ; Walterfang et al. 2011), and late-onset depression (LOD) has also been associated with vascular abnormalities in CSPT regions (Herrmann et al. 2008). Therefore, it is likely that volumetric changes in CSPT, despite having different neurobiological causes, might be one of the common markers of MDD. However, the precise nature of these regional volumetric changes in MDD patients has varied between studies, meaning that a consensus view of such CSPT alterations has yet to be reached. The substantial clinical heterogeneity of MDD and the different characteristics of patients included in imaging studies, such as differences in age of onset and duration of illness, makes it difficult to form a common description of CSPT volumetric changes in MDD. In this regard, meta-analytical techniques can help to overcome some of these issues. Although several meta-analyses have examined various brain regions affected by MDD (Campbell et al. 2004 ; Hajek et al. 2008 ; Hamilton et al. 2008), only one has included most of the regions that encompass the CSPT circuits (Koolschijn et al. 2009). That study found volume reductions in the frontal lobes, especially in the anterior cingulate cortex (ACC) and the orbitofrontal cortex (OFC), and moderate volume reductions in the caudate and putamen. However, the study did not examine the effect of factors such as late-life depression (LLD) or antidepressant use on the observed findings. The effects of depression that starts later in life (geriatric depression) should be considered because a later onset of depression has been associated with more severe cognitive deficits and structural MRI abnormalities (Schweitzer et al. 2001 ; Herrmann et al. 2007, 2008). In addition, antidepressant treatment might influence volumetric findings in MDD as antidepressant-using patients have been reported to have larger OFC volumes compared to medicationnaive patients (Lavretsky et al. 2005). In this work, we systematically reviewed the evidence for volumetric abnormalities in CSPT regions in MDD patients. This was achieved by using a metaanalytical method suitable for the analysis of published structural MRI studies. We also performed meta-regression and subgroup analyses to test the influence of confounding factors on the patterns of CSPT alterations described in the literature ; namely age, gender, duration, medication and current severity of illness.

Method Study selection Meta-analyses were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Moher et al. 2009). Potential articles were identified by a comprehensive literature search in PubMed, Scopus and PsycINFO in the period from January 1980 to December 2010. The following keywords were used : major depression, major depressive disorder, MRI. The reference lists of identified published studies were also cross-checked for additional studies. Studies were included if they : (1) compared an MDD sample to a healthy control group using a region-of-interest (ROI) approach ; (2) reported sufficient data to calculate effect sizes (means and standard deviations or standard errors) ; and (3) examined ROIs in the PFC, ACC, caudate, putamen, thalamus or globus pallidus. Regions were only included in meta-analytical calculations if there were at least four studies available for each ROI. Studies that examined physical illness and depression co-morbidity were excluded. For those studies that examined the same ROI with overlapping samples, the study with the largest sample size was included. Overlapping samples were only included if they examined different ROIs. The flowchart shown in the online supplementary figure (Fig. S1) summarizes the study inclusion process. Data from three studies (Bremner et al. 2002 ; Yucel et al. 2008, 2009) were not included in the meta-analysis of the ACC because the reported ROIs were limited to only part of this structure. There was an insufficient number of studies of the globus pallidus volume. The OFC (in the PFC) and subgenual ACC were the only subregions for which there were sufficient numbers of studies to be included in the meta-analysis. Forty-one studies comparing patients with MDD and healthy controls were included in the final meta-analysis (Table 1). Although previous ROI meta-analyses in MDD have reported their sample sizes of patients and controls by summing the sample sizes of the studies included (1845 patients versus 1527 controls in our analysis), this is misleading because different ROIs were examined using the same or overlapping samples. In the analysis, the ranges for numbers of patients included for each region were 185–605 for MDD patients and 129–453 for healthy controls. Statistical analysis For each ROI an effect size (Cohen’s d) and a standard error were calculated based on the reported means and standard deviations of volumetric change. In the event that standard errors were reported, these were

MRI in MDD

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Table 1. Characteristics of the studies included in the meta-analysis

Study

MDD/HC (no. females)

Almedia et al. 2003 EOD LOD Andreescu et al. 2010 Ballmaier et al. 2004 Bilgi et al. 2010 Botteron et al. 2002

51 (41)/37 (10) 24 (23) 27 (18) 71 (49)/32 (17) 24 (18)/19 (15) 23 (18)/28 (17) 48 (48)/17 (17)

Brambilla et al. 2002

18 (17)/38 (14)

Bremner et al. 2000 Bremner et al. 2002 Caetano et al. 2001 Caetano et al. 2006 Chen et al. 2008 Coryell et al. 2005 Delaloye et al. 2010 Dupont et al. 1995 Elderkin-Thompson et al. 2009 Frodl et al. 2006 Frodl et al. 2008 Hannestad et al. 2006 Hastings et al. 2004 Hickie et al. 2007 Husain et al. 1991 Jannsen et al. 2004 Krishnan et al. 1992 Krishnan et al. 1993 LOD EOD Kumar et al. 1998 Lacerda et al. 2003 Lacerda et al. 2004

16 (6)/16 (6) 15 (5)/20 (9) 17 (16)/39 (14) 31 (24)/31 (24) 27 (17)/26 (12) 10 (4)/10 (4) 11 (7)/30 (22) 30 (21)/26 ( ?) 26 (21)/23 (11) 34 (15)/34 (15) 78 (40)/78 (40) 182 (118)/64 (40) 18 (10)/18 (10) 45 (30)/16 (9) 41 ( ?)/44 ( ?) 28 (28)/41 (41) 50 (27)/50 (27) 25 (17)/20 (11) 14 (9) 11 (8) 35 (25)/30 (23) 25 (21)/48 (19) 31 (24)/34 (22)

Lavretsky et al. 2005 Lenze & Sheline, 1999 Matsuo et al. 2008

43 (33)/41 (20) 24 (24)/24 (24) 27 (17)/26 (14)

Monkul et al. 2007

17 (17)/17 (17)

Pan et al. 2009 Pantel et al. 1997 Parashos et al. 1998 Pillay et al. 1998 Salokangas et al. 2002 Psychotic Non-psychotic Steingard et al. 2002 Taylor et al. 2007 Weber et al. 2010 Yucel et al. 2008 Yucel et al. 2009

Antidepressant use

LOD

72.8 75.5 72.2 65.9 30.4 26.1

ROIs F

12/71 0/24 0/21 Nearly all no medication, past >4 weeks 0/18 16/16 15/15 0/17 0/31 0/27, all naive 8/11 13/31 0/26, most naive 31/34 70/78 0/18 29/45 11/28

0/31

42 44 43 42.8 39.2 14.4 21.9 75.8 39 70.0 45.5 44.7 70.2 38.9 52 55.3 64 48.3 76.9 70.6 70.9 41.0 39.3

33.6 3.1 19.9 30.9 0.9

MADRS=30.2 MADRS=30.8 HAMD=18.3 HAMD=17.3 HAMD=25.5

No Yes Mixed No No No

C, P, F, OFC, ACC ACC, OFC F SG

9.0

8/18 remitted HAMD=14.0 Remission Remission 8/17 remitted HAMD=11.8 HAMD=20.1

No

SG

No No No No No No Yes No Mixed ?

F, C OFC, SG T ACC OFC SG ACC C ACC, OFC

No No No No No

F ACC, SG C F, OFC C, P P OFC C C, P, T

11.3 4.7 1.7 20.2 6.8 5.5 26.5 15.9 15.6 31

1.5 11.9 11.4

0/43, 60 % naive 70.7 Most on medication 0/27, all 14.4 drug naive 0/17 34.4

21.1

69.4 72.4

24.6 7.5

170 (112)/83 (61) 19 (11)/13 (10) 72 (45)/32 (18) 38 (21)/20 (11)

Age Duration (years) (years) State

18/38 prior treatment

37 (21)/19 (7) 20 (12) 17 (9) 19 (16)/38 (25) 0/19 226 (150)/144 (100) 38 (31)/62 (48) 18/38 65 (30)/93 (56) 0/65 40 (25)/40 (25)

3.2 12.1

55.1 38.5

34 38.4 15.4 70 66.1 28 41.4

2.2 24.6 28.5 8.2 14.4

Remission HAMD=12.0 HAMD=17.9 HAMD=24.8 HAMD=23.5 MADRS=27.7 HAMD=26.8 Depressed MADRS=18.3

HAMD=19.7 HAMD=10.2 12/31 euthymic HAMD=14.1 HAMD=17.7 Depressed HAMD=12.1 5/17 euthymic MADRS=27 HAMD=26.7 MADRS=34.6 Depressed HAMD=20.6

HAMD=17.3 MADRS=27.2 Remitted HAMD=13.3 13/40 remitted HAMD=11.1

No No Yes No Yes No No

F C, P OFC

Mixed ? OFC No C, P No C, P No

ACC, OFC

Mixed Yes

P F

No No

F, C, P, T, OFC C

No

F

No

F OFC ACC SG SG

No No No

MDD, Major depressive disorder ; HC, healthy control ; LOD, late-onset depression ; EOD, early-onset depression ; ROI, region of interest ; ACC, anterior cingulated cortex ; F, prefrontal cortex ; P, putamen ; SG, subgenual ACC ; OFC, orbitofrontal cortex ; C, caudate ; T, thalamus ; HAMD, Hamilton Depression Rating Scale ; MADRS, Montgomery–Asberg Depression Rating Scale.

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converted to standard deviations. A failure to do this in a previous meta-analysis that included a study by Botteron et al. (2002) led to an exaggerated effect size estimate for the ACC region (Koolschijn et al. 2009). For preliminary analyses, separate effect sizes for grey matter (GM), white matter (WM) and left and right ROIs were also calculated. Effect sizes were weighted using the inverse variance method. We used a random effects model (DerSimonian–Laird estimate) because the distributions of effect sizes are likely to be heterogeneous. The Q test was used to measure the heterogeneity of the distribution of effect sizes. When the Q test was significant, I2, a measure of the degree of inconsistency in the studies’ results, was used to quantify heterogeneity (Higgins & Thompson, 2002). I2 describes the percentage of total variation across studies that is due to heterogeneity rather than chance. I2 values between 0 and 0.25 suggest small magnitudes of heterogeneity, I2 values in the range 0.25–0.50 suggest medium magnitudes, and those >0.50 indicate large magnitudes. Publication bias was assessed by Egger’s test and meta-analyses were performed using MIX software version 1.7 on a Windows platform (Bax et al. 2006). We also calculated homogeneity statistics using Qbet to test for differences between LLD and early-onset depression (EOD), and between antidepressant-free [AD(x)] and medicated [AD(+)] groups. The LLD group comprised elderly subjects whose age of onset was in later life (onset after 50–65 years, depending on the study). In some studies, samples of both EOD and LLD patients were reported. These samples were analysed separately if they provide data for both groups. For studies that reported both EOD and LLD in elderly patients without providing separate data for each group, the study was classified as LLD. As studies did not report separate data for their antidepressant-free and medicated patients, studies in which more than 80 % of the patients were not taking antidepressants were classified as AD(x). Meta-regression analyses were used to estimate the impact of demographic (age, gender) and clinical (age at onset, duration of illness, severity of current depression) variables on between-group differences. Meta-regression analyses that are based on group rather than individual subject data were necessary because the vast majority of the individual studies did not report correlations with volumetric findings and these variables. The severity of current depression was based on reported Hamilton Depression Rating Scale (HAMD) and Montgomery Asberg Depression Rating Scale (MADRS) scores. For this analysis, studies were classified according to patient status, as euthymic (HAMD 30). Meta-regression analyses (weighted generalized least squares regressions) were conducted using SPSS version 11.0 (SPSS Inc., USA). Meta-regression analyses performed with a random effects model were conducted using the restricted-information maximum likelihood method with a significance level set at p