Great interest has been focused on studying brain structure in individuals with bipolar disorder and schizophrenia. Several neuroimaging studies have been published, but there is still uncertainty about the key areas involved in the pathogenesis of these conditions. Meta-analysis as a technique is a tool to combine quantitative data from individual studies, increasing power to detect anatomical differences and investigate causes of heterogeneity. Reference Thompson, Smith and Sharp1,Reference Wright, Rabe-Hesketh, Woodruff, David, Murray and Bullmore2
In schizophrenia and bipolar disorder meta-analyses of volumetric magnetic resonance imaging (MRI) studies have indicated that differences exist between affected individuals and healthy controls. In schizophrenia, meta-analyses have found evidence of reductions in the volumes of thalamus, hippocampus, anterior cingulate cortex Reference Nelson, Saykin, Flashman and Riordan3–Reference Baiano, David, Versace, Churchill, Balestrieri and Brambilla5 and in the area of the corpus callosum. Reference Woodruff, McManus and David6,Reference Arnone, Mcintosh, Tan and Ebmeier7 Wright et al broadly replicated the above findings but also showed reductions in cerebral volume with an enlarged ventricular system, particularly lateral ventricles. Reference Wright, Rabe-Hesketh, Woodruff, David, Murray and Bullmore2 A more recent voxel-based meta-analysis also showed similar volumetric reductions in schizophrenia. Reference Ellison-Wright, Glahn, Laird, Thelen and Bullmore8 In bipolar disorder, mild ventricular enlargement and the presence of white matter hyper-intensities Reference McDonald, Zanelli, Rabe-Hesketh, Ellison-Wright, Sham and Kalidindi9,Reference Kempton, Geddes, Ettinger, Williams and Grasby10 are among the most consistently reported abnormalities. Kempton et al also reported larger lateral and third ventricles and smaller hippocampi in individuals with schizophrenia compared with those with bipolar disorder. Kempton et al considered a limited number of confounders and lithium prescribing was also associated with volumetric grey matter increases in bipolar disorder. Reference Kempton, Geddes, Ettinger, Williams and Grasby10
In this meta-analysis we sought to update the work carried out by McDonald et al (2004) Reference McDonald, Zanelli, Rabe-Hesketh, Ellison-Wright, Sham and Kalidindi9 by focusing only on MRI studies. We extended our search strategy to: systematically include studies comparing people with bipolar disorder with those with schizophrenia apart from controls in an attempt to identify diagnosis-specific differences; and sought to quantify and explain between-study heterogeneity using meta-regression to examine the influence of key clinical and methodological variables.
Method
Search strategy
A systematic search was conducted from a range of electronic databases, including The Cochrane Library, EMBASE, PsycINFO, OVID and PubMED and complemented by a manual search with bibliographic cross-referencing. Key words used to identify the studies were: magnetic resonance imaging, MRI, bipolar disorder, mania, mood disorders and schizophrenia. Studies were included if they presented original data and were published by March 2008, compared individuals with schizophrenia, bipolar disorder and/or healthy controls, reported volumetric measures of brain areas according to the international system of units (SI units) as means and standard deviations. If standard deviations were missing from the published articles, these were conservatively estimated from the largest standard deviation of other studies that measured the same structure in the same volumetric units. Studies that included participants with unipolar depression were included provided participants with bipolar disorder made up more than 79% of the sample. Researchers were contacted if this information was not readily available. Studies were excluded if data were subsumed in more recent larger studies. Information systematically extracted from the studies included diagnosis according to diagnostic criteria, volumetric measurements and number of participants essential to calculate effect sizes, but also a number of potentially critical confounding variables. These included demographics (age, gender), illness variables (age at onset, duration of illness, presence of euthymia, medication, chronicity of the condition), year of publication, magnetic field strength of the scanner 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. Where a single study was published in several journal articles, the article reporting the largest group size for that volume of interest was used. When multiple publications were identified, disagreement was resolved by consensus between the authors. Studies were excluded when there was a comorbid diagnosis of intellectual disability, chromosomal or genetic disorder. Studies were included irrespective of slice thickness, although these factors were recorded as potential sources of heterogeneity. Studies were not included when the control group were genetically related to affected probands.
Statistical analysis
Statistical analysis was conducted using STATA 8.0 for Windows (Stata Corp, College Station, Texas) supplemented by Metan software (www.stata.com/stb/stb44/sbe24/metan.hlp; downloadable from the Centre for Statistics in Medicine, Oxford, UK). Standardised mean differences were calculated using Cohen's d statistic.
Random effects analyses Reference DerSimonian and Laird11 were used throughout to weight each study. The presence of heterogeneity was tested using the Q-test and its magnitude estimated using I Reference Wright, Rabe-Hesketh, Woodruff, David, Murray and Bullmore2 and can be interpreted as the proportion of variance in effect size due to heterogeneity. Reference Higgins, Thompson, Deeks and Altman12 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. Publication bias, which describes the tendency of small studies to report large effect sizes, was examined using the Egger's test. Reference Egger, Davey Smith, Schneider and Minder13 The significance level was set at P<0.05.
To further investigate causes for heterogeneity, meta-regression analyses were performed for the following variables: age at onset, duration of illness, presence of euthymia, chronicity, mean age, scanner strength, slice thickness, year of publication, and current medication, including mood stabilisers, antipsychotics and antidepressants. The STATA program ‘metareg.ado’ was used throughout and the REML (restricted maximum likelihood) method used to estimate the model parameters.
Results
Systematic search
Seventy-two reports Reference Ahn, Breeze, Makris, Kennedy, Hodge and Herbert14–Reference Zipursky, Bury, Langevin, Wortzman and Katz85 from 180 studies identified met criteria for inclusion in the meta-analysis and are described in detail in online Table DS1. Sixty-five compared people with bipolar disorder with controls. Of these 65, 18 articles included a further comparison group of individuals diagnosed with schizophrenia, Reference Altshuler, Bartzokis, Grieder, Curran and Mintz15,Reference Altshuler, Bartzokis, Grieder, Curran, Jimenez and Leight16,Reference Dasari, Friedman, Jesberger, Stuve, Findling and Swales36,Reference Harvey, Ron, Baker and Murray46,Reference Hirayasu, McCarley, Salisbury, Tanaka, Kwon and Frumin49,Reference Kasai, Shenton, Salisbury, Hirayasu, Lee and Ciszewski52–Reference Kasai, Shenton, Salisbury, Hirayasu, Onitsuka and Spencer54,Reference Lim, Rosenbloom, Faustman, Sullivan and Pfefferbaum56,Reference McDonald, Marshall, Sham, Bullmore, Schulze and Chapple58,Reference McIntosh, Forrester, Lawrie, Byrne, Harper and Kestelman59,Reference Pearlson, Barta, Powers, Menon, Richards and Aylward65,Reference Roy, Zipursky, Saint-Cyr, Bury, Langevin and Seeman68,Reference Schlaepfer, Harris, Tien, Peng, Lee and Federman75,Reference Strasser, Lilyestrom, Ashby, Honeycutt, Schretlen and Pulver80–Reference Swayze, Andreasen, Alliger, Yuh and Ehrhardt82,Reference Zipursky, Bury, Langevin, Wortzman and Katz85 one article added a third comparison group, of schizoaffective disorder Reference Velakoulis, Wood, Wong, McGorry, Yung and Phillips83 and one compared with schizoaffective disorder but not with schizophrenia. Reference Getz, DelBello, Fleck, Zimmerman, Schwiers and Strakowski45 Only two reports compared bipolar disorder with schizophrenia without the comparator of healthy controls. Reference Frazier, Hodge, Breeze, Giuliano, Terry and Moore44,Reference Rossi, Stratta, Di Michele, Gallucci, Splendiani and de Cataldo66
Thirty-six reports did not meet inclusion criteria mainly because authors used alternative or qualitative measurements of brain areas, reports were superseded by subsequent inclusive publications, volumes were not retrievable or there were fewer than three studies available for a given brain region. Years of publication ranged from 1990 to 2008. Studies were exclusively published in English. Studies used comparable diagnostic criteria and tested a total of 1823 participants with bipolar disorder, 670 with schizophrenia, 29 with schizoaffective disorder, 106 with unipolar depression (not included in the analysis) and 1940 healthy controls. This number includes duplicate publications investigating different brain regions. Same samples were considered only once in each individual analysis.
Studies generally considered individuals with recurrent episodes of illness who were treated with one or more mood stabilisers. Basic demographic characteristics were generally well reported but clinical details such as medication status, number of episodes, duration of illness, age at first presentation and illness subtype were not. Most studies included male and female participants but only a few offered separated analyses according to gender. There were 926 male participants with bipolar disorder, equivalent to 51% of the total sample. Age ranged from 10.6 to 58.8 years with a mean of 29.4 (s.d. = 11.8).
Bipolar disorder in comparison with healthy controls
Comparisons of regional brain volumes between people with bipolar disorder and healthy controls are described in detail in online Table DS2. There was a small but significant reduction in whole brain volume in bipolar disorder (n = 661) compared with healthy controls (n = 723) with an estimated standardised effect size of −0.15 (95% CI −0.27 to −0.02) and without significant heterogeneity (I 2 = 0.23, P = 0.15) or publication bias (Egger's P = 0.9). Left and right lateral ventricles were significantly enlarged in bipolar disorder (n = 157, healthy controls n = 179) with an effect size estimate of 0.27 (95% CI 0.05–0.49) with no heterogeneity (I 2 = 0, P = 0.7) or publication bias (Egger's P = 0.07). A larger number of studies (n = 11) measured lateral ventricles separately, suggesting a significant contribution of the left but not the right lateral ventricle, with no significant heterogeneity or publication bias. An analysis of five studies that measured the volume of the globus pallidus bilaterally (n = 135, healthy controls n = 106) showed a significantly increased volume in participants with bipolar disorder (estimate 0.57, 95% CI 0.03–1.11) with a significant level of heterogeneity (I 2 = 0.74, P = 0.004) and publication bias (Egger's P = 0.02). This effect was not evident in the analysis of the three studies which measured the volume of the left and right globi pallidi separately (Table DS2).
Bipolar disorder in comparison with schizophrenia
Table DS2 shows that in comparison with schizophrenia, people with bipolar disorder showed an increased right amygdala volume (n = 115 v. n = 200), effect size estimate 0.47, 95% CI 0.21–0.73, I 2 = 0, Egger's P = 0.59. Lateral ventricles in bipolar disorder appeared bilaterally smaller than in schizophrenia (n = 126 v. n = 158). The effect size estimate for the left lateral ventricle was: −0.35, 95% CI −0.59 to −0.11, I 2 = 0.007, Egger's P = 0.11; for right lateral ventricle it was: −0.26, 95% CI −0.49 to −0.02, I 2 = 0; Egger's P = 0.06. This effect was not present in the analysis of the three studies that measured the cumulative volume of left and right lateral ventricles (Table DS2).
Heterogeneity and publication bias
Heterogeneity and publication bias were not detected in structures that showed significant volumetric differences, except for whole brain grey matter and globus pallidus in the comparison of bipolar participants with healthy controls. Heterogeneity was, however, detected in a larger number of structures (as shown in Table DS2) and it is likely that methodological differences and clinical sample variation (including the effect of medication) are accountable for such an effect. With the limitation of selective reporting of relevant variables across the included studies we systematically investigated causes of heterogeneity in multiple meta-regression analyses. The results are displayed in Tables 1 and 2 and the main findings described below.
Mean age, years | Duration of illness, years | Euthymia | Age at onset, years | Chronicity | Male participants | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Structure | C | Z | P | C | Z | P | C | Z | P | C | Z | P | C | Z | P | C | Z | P |
Bipolar disorder in comparison with healthy controls | ||||||||||||||||||
Whole brain (grey matter) | 0.0045 | 0.21 | 0.83 | 0.09 | 2.38 | 0.02 | 0.66 | 1.08 | 0.28 | -0.23 | -1.29 | 0.2 | NA | -1.19 | -1.49 | 0.14 | ||
Amygdalae | 0.033 | 1.42 | 0.16 | 0.05 | 1.04 | 0.30 | 0.17 | 0.20 | 0.84 | 0.006 | 1.13 | 0.26 | NA | 2.16 | 1.64 | 0.1 | ||
Left amygdala | 0.032 | 1.91 | 0.056 | 0.08 | 2.21 | 0.03 | -0.01 | -0.02 | 0.98 | 0.022 | 0.74 | 0.46 | 0.04 | 0.07 | 0.94 | 0.08 | 0.03 | 1 |
Right amygdala | 0.025 | 1.49 | 0.14 | 0.06 | 1.07 | 0.30 | -0.16 | -0.30 | 0.76 | 0.02 | 0.38 | 0.7 | 0.11 | 0.21 | 0.83 | -1.32 | -0.53 | 0.6 |
Hippocampi | -0.017 | -1.53 | 0.13 | 0.024 | 0.75 | 0.45 | 0.66 | 1.53 | 0.13 | 0.063 | 2.96 | 0.003 | 0.12 | 0.32 | 0.75 | 0.63 | 0.65 | 0.51 |
Globi pallidi | 0.022 | 0.64 | 0.52 | 0.15 | 0.91 | 0.36 | -0.76 | -0.80 | 0.42 | 0.02 | 0.67 | 0.5 | NA | 6.23 | 1.55 | 0.12 | ||
Right globus pallidus | -0.05 | -1.16 | 0.25 | -0.08 | -2.39 | 0.02 | -2.07 | -2.76 | 0.006 | NA | NA | 6.85 | 1.08 | 0.28 | ||||
Left anterior cingulate cortex | 0.13 | 1.58 | 0.12 | 0.028 | 0.83 | 0.41 | -8.82 | -1.66 | 0.1 | NA | NA | -0.04 | -1.23 | 0.22 | ||||
Thalami | 0.004 | 0.34 | 0.73 | 0.28 | 1.98 | 0.047 | -0.13 | -0.26 | 0.8 | NA | NA | 2.0 | 1.21 | 0.228 | ||||
Temporal lobes | 0.03 | 2.92 | 0.004 | 0.054 | 2.87 | 0.004 | 0.89 | 0.75 | 0.45 | NA | NA | 0.54 | 0.48 | 0.63 | ||||
Left temporal lobe | 0.012 | 0.70 | 0.48 | 0.07 | 1.09 | 0.3 | -1.34 | -1.46 | 0.14 | -0.0071 | -0.09 | 0.92 | NA | 0.74 | 0.97 | 0.33 | ||
Right temporal lobe | -0.012 | -0.66 | 0.51 | 0.076 | 2.22 | 0.03 | 0.83 | 0.50 | 0.62 | 0.091 | 1.04 | 0.3 | NA | 0.90 | 1.12 | 0.3 | ||
Pituitary gland | -0.014 | -1.58 | 0.11 | NA | -1.09 | -1.65 | 0.1 | NA | NA | -5.80 | -2.37 | 0.02 | ||||||
Bipolar disorder in comparison with schizophrenia | ||||||||||||||||||
Intracranial volume | 0.072 | 0.97 | 0.33 | NA | NA | NA | NA | 2.66 | 1.32 | 0.19 | ||||||||
Whole brain (white matter) | 0.15 | 1.64 | 0.1 | NA | NA | NA | NA | 1.94 | 2.55 | 0.011 | ||||||||
Left amygdala | 0.016 | 0.58 | 0.6 | NA | 0.98 | 2.10 | 0.035 | NA | NA | 2.30 | 1.41 | 0.16 | ||||||
Left hippocampus | 0.043 | 4.29 | <0.001 | NA | 1.62 | 3.43 | 0.001 | 0.043 | 1.28 | 0.2 | NA | 1.3 | 0.97 | 0.33 | ||||
Right hippocampus | 0.039 | 4.04 | <0.001 | NA | 1.73 | 3.45 | 0.001 | 0.04 | 1.19 | 0.23 | NA | 1.58 | 1.25 | 0.21 |
Mood stabilisers | Antipsychotics | Antidepressants | Scanner strength, T | Slice thickness, mm | Year of publication, years | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Structure | C | Z | P | C | Z | P | C | Z | P | C | Z | P | C | Z | P | C | Z | P |
Bipolar disorder in comparison with healthy controls | ||||||||||||||||||
Whole brain (grey matter) | -0.40 | -0.41 | 0.68 | -1.13 | -2.46 | 0.014 | -0.93 | -1.28 | 0.2 | 0.22 | 0.98 | 0.32 | 0.006 | 0.05 | 1 | 0.005 | 0.13 | 0.9 |
Amygdalae | 2.28 | 2.33 | 0.02 | -0.8 | -0.48 | 0.63 | -2.02 | -1.64 | 0.1 | -0.51 | -0.83 | 0.41 | -0.16 | -0.31 | 0.75 | -0.29 | -3.31 | 0.001 |
Left amygdala | 0.19 | 0.14 | 0.89 | -0.63 | -1.26 | 0.21 | -1.2 | -1.83 | 0.067 | -0.4 | -0.98 | 0.33 | -0.04 | -0.43 | 0.70 | -0.03 | -0.71 | 0.50 |
Right amygdala | -0.19 | -0.23 | 0.82 | -1.28 | -2.38 | 0.017 | -1.67 | -2.08 | 0.038 | -0.50 | -1.49 | 0.13 | 0.01 | 0.13 | 0.90 | -0.05 | -1.31 | 0.20 |
Hippocampi | 0.98 | 1.31 | 0.19 | -1.05 | -1.85 | 0.065 | 0.18 | 0.20 | 0.84 | 0.21 | 0.53 | 0.60 | 0.03 | 0.11 | 0.91 | -0.06 | -0.72 | 0.50 |
Globi pallidi | 2.32 | 0.70 | 0.48 | 0.67 | 0.58 | 0.56 | -0.46 | -0.44 | 0.66 | NA | -0.16 | -0.72 | 0.47 | 0.022 | 0.64 | 0.52 | ||
Right globus pallidus | 3.22 | 2.72 | 0.007 | 1.48 | 1.30 | 0.19 | 2.3 | 0.40 | 0.69 | NA | -1.29 | -0.78 | 0.43 | -0.006 | -0.03 | 1.0 | ||
Left anterior cingulate cortex | -7.91 | -0.93 | 0.35 | 0.25 | 0.04 | 0.97 | -2.59 | -0.69 | 0.49 | -3.28 | -0.47 | 0.64 | NA | 0.33 | 0.42 | 0.70 | ||
Thalami | 1.27 | 1.08 | 0.28 | -0.86 | -1.82 | 0.069 | -0.77 | -1.23 | 0.22 | -0.07 | -0.21 | 0.83 | -0.46 | -2.38 | 0.02 | -0.15 | -2.92 | 0.003 |
Temporal lobes | 2.23 | 2.69 | 0.007 | -1.62 | -1.06 | 0.3 | -1.48 | -0.73 | 0.46 | -0.29 | -0.61 | 0.54 | -0.07 | -0.29 | 0.8 | -0.11 | -1.59 | 0.11 |
Left temporal lobe | -1.76 | -0.37 | 0.71 | NA | -0.32 | -0.37 | 0.71 | 0.09 | 0.29 | 0.77 | 0.012 | 0.25 | 0.81 | -0.044 | -1.71 | 0.09 | ||
Right temporal lobe | -2.15 | -0.15 | 0.88 | NA | -0.39 | -0.15 | 0.88 | 0.24 | 0.74 | 0.5 | -0.053 | -1.03 | 0.3 | -0.044 | -1.55 | 0.12 | ||
Pituitary gland | -1.58 | -0.98 | 0.32 | NA | NA | NA | -3.65 | -0.97 | 0.33 | 0.18 | 1.39 | 0.17 | ||||||
Bipolar disorder in comparison with schizophrenia | ||||||||||||||||||
Intracranial volume | NA | NA | NA | NA | NA | 0.16 | 0.54 | 0.6 | ||||||||||
Whole brain (white matter) | NA | NA | NA | NA | NA | 0.016 | 0.58 | 0.56 | ||||||||||
Left amygdala | NA | NA | NA | NA | NA | 0.0076 | 0.009 | 0.93 | ||||||||||
Left hippocampus | NA | NA | NA | 0.44 | 0.69 | 0.49 | -0.34 | -1.63 | 0.1 | -0.024 | -0.58 | 0.56 | ||||||
Right hippocampus | NA | NA | NA | 0.44 | 0.73 | 0.5 | -0.062 | -1 | 0.31 | -0.02 | -0.45 | 0.65 |
Bipolar disorder in comparison with healthy controls
In the comparison between people with bipolar disorder and healthy controls significant heterogeneity was found for several regions. The effect size for whole grey matter volume correlated with duration of illness (C = 0.09, Z = 2.38, P = 0.02) and use of antipsychotic medication (C = −1.13, Z = −2.46, P = 0.014), suggesting larger bipolar v. control differences in people with longer durations of illness and larger reductions with use of antipsychotics, perhaps a proxy for a more severe illness. The differences in the right globus pallidus correlated with length of illness (C = −0.08, Z = −2.39, P = 0.02), percentage of participants with euthymia (C = −2.07, Z = −2.76, P = 0.006) and mood stabilisers (C = 3.22, Z = 2.72, P = 0.007), suggesting smaller differences with increasing illness duration and euthymia and increased differences with the use of mood stabilisers. In the amygdalae, volumetric differences tended to increase with the use of mood stabilisers (C = 2.28, Z = 2.33, P = 0.02) and to decrease with increasing year of publication (C = −0.29, Z = −3.31, P = 0.001). In the left amygdala, volumes increased with duration of illness (C = 0.08, Z = 2.21, P = 0.03). In the right amygdala, volumes decreased in relation to use of antipsychotic and antidepressant medication (C = −1.28, Z = −2.38, P = 0.017 and C = −1.67, Z = −2.08, P = 0.038 respectively). The hippocampi differences between participants with bipolar disorder and controls correlated positively with age at onset (C = 0.063, Z = 2.96, P = 0.003), suggesting larger bipolar v. control hippocampal differences in individuals with a later age at onset. The effect size with respect to the thalami correlated negatively with year of publication (C = −0.15, Z = −2.92, P = 0.003) and slice thickness (C = −0.46, Z = −2.38, P = 0.02), and positively with duration of illness (C = 0.28, Z = 1.98, P = 0.047). These findings suggest that the thalami may have been smaller in individuals with bipolar disorder in more recently published studies and in studies where larger voxel sizes were used. However, the observed non-significant reductions in participants with bipolar disorder may be less evident in those with longer durations of illness. Pituitary volumes were negatively associated with percentage of male participants (C = −5.80, Z = −2.37, P = 0.02). Volumetric differences in the temporal lobes correlated positively with mean age (C = 0.03, Z = 2.92, P = 0.004), duration of illness (C = 0.054, Z = 2.87, P = 0.004) and use of mood stabilisers (C = 2.23, Z = 2.69, P = 0.007). These results suggest smaller volumetric differences in this region in bipolar disorder with increasing age, duration of illness and the use of mood stabilisers. Duration of illness was positively associated with bipolar–control differences in right temporal lobe volume (C = 0.076, Z = 2.22, P = 0.03).
Bipolar disorder in comparison with schizophrenia
In the bipolar disorder v. schizophrenia comparison, whole brain volume was positively associated with percentage of male participants (C = 1.94, Z = 2.55, P = 0.011). Left amygdala volume was positively associated with number of participants with euthymia (C = 0.98, Z = 2.10, P = 0.035). Volume differences in the left and right hippocampus were positively associated with age (C = 0.043, Z = 4.29, P<0.001 and C = 0.039, Z = 4.04, P<0.001 respectively) and number of participants with euthymia (C = 1.62, Z = 3.43, P = 0.001 and C = 1.73, Z = 3.45, P = 0.001 respectively).
Discussion
Findings from this report suggest that individuals with bipolar disorder in comparison with healthy controls are characterised by significant whole brain and prefrontal lobe reductions and by enlargement of the lateral ventricles and globus pallidus. These findings did not separate bipolar disorder from schizophrenia however, although schizophrenia was characterised by a greater degree of ventricular enlargement and by amygdala volume reduction.
The finding of a global brain volume reduction reported in this meta-analysis is a relatively novel one and is in keeping with a mild but significant increase in the volume of the lateral ventricles and sulcal prominence in mood disorders as demonstrated by Elkis et al. Reference Elkis, Friedman, Wise and Meltzer86 The fact that two previous meta-analyses, by Hoge et al Reference Hoge, Friedman and Schulz87 and McDonald et al, Reference McDonald, Zanelli, Rabe-Hesketh, Ellison-Wright, Sham and Kalidindi9 which included 7 and 11 studies respectively did not find such a reduction suggests the presence of a small effect which might require a larger pool of studies (n = 25, 661 patients and 723 controls) to allow detection. Our finding is supported by an absence of publication bias and lack of significant heterogeneity. Interestingly, we found that people with schizophrenia are characterised by a greater ventricular enlargement compared with those with bipolar disorder. Wright et al reported decreased mean cerebral volume in participants with schizophrenia in comparison with controls and a concordant ventricular system enlargement particularly evident in the left lateral ventricle. Reference Wright, Rabe-Hesketh, Woodruff, David, Murray and Bullmore2 The significance of an enlarged lateral ventricular system in the pathophysiology of these two conditions and the more pronounced effect in schizophrenia could be attributable to a similar disease process with a different intensity or two separate processes with a similar outcome. Elkis et al found a similar effect in their meta-analysis of 11 studies but extended inclusion criteria to more generic mood disorder rather than bipolar disorder and to the whole ventricular system. Reference Elkis, Friedman, Wise and Meltzer86
No volumetric changes were detected in the amygdala in bipolar disorder compared with controls and this structure was significantly larger than in individuals with schizophrenia. Although current evidence indicates that the amygdala may be implicated in the aetiology and pathogenesis of unipolar depression, this role is not entirely established in bipolar disorder. In unipolar depression evidence from both structural and functional MRI studies suggests a possible volumetric reduction in the amygdala Reference Phillips, Drevets, Rauch and Lane88 mirrored functionally by a biased emotional response in the recognition of different emotional states, particularly fear. Reference LaBar, Gatenby, Gore, LeDoux and Phelps89 With reference to bipolar disorder, the literature reports both increased Reference Altshuler, Bartzokis, Grieder, Curran, Jimenez and Leight16,Reference Davis, Kwon, Cardenas and Deicken37,Reference Strakowski, DelBello, Sax, Zimmerman, Shear and Hawkins78 and decreased volumetric changes in the amygdala, almost exclusively in adolescents with bipolar disorder. Reference Blumberg, Kaufman, Martin, Whiteman, Zhang and Gore21,Reference Chang, Karchemskiy, Barnea-Goraly, Garrett, Simeonova and Reiss30 Although this discrepancy can be explained as an abnormal development trajectory in bipolar disorder, Reference McEwen and Milner90 it is also possible that other variables/and or confounders might play a role. Abnormal activation of the amygdala has been reported in several functional MRI (fMRI) studies of schizophrenia using fearful faces. Reference Fahim, Stip, Mancini-Marie, Mensour, Boulay and Leroux91–Reference Baas, Aleman, Vink, Ramsey, de Haan and Kahn93 Our findings extend previous reports that suggest a mechanistic role for the amygdala in schizophrenia, by suggesting that volumetric deficits may be disease specific. Reference Soares and Mann94
Consistent with our finding of globus pallidus enlargement, several strands of evidence suggest that the basal ganglia are implicated in the aetiology of mood disregulation in bipolar disorder. Reference Goldman, Pezawas, Mattay, Fischl, Verchinski and Zoltick95 Caligiuri et al, by using a motor reaction time task, showed that individuals with euthymia or hypomania exhibited increased caudate activity bilaterally and in the left globus pallidus whereas an increase in severity of depression was associated with a decrease in activity in the external segment of the right globus pallidus. Reference Caligiuri, Brown, Meloy, Eberson, Niculescu and Lohr96 In an earlier study with a similar design, the same group found that either people with mania or depression exhibited abnormally elevated blood oxygen level-dependent (BOLD) responses in cortical and subcortical areas. Individuals with mania and bipolar disorder had significantly higher BOLD responses in the left globus pallidus and significantly lower BOLD responses in the right globus pallidus compared with people with depression and bipolar disorder. Reference Caligiuri, Brown, Meloy, Eberson, Kindermann and Frank97 Malhi et al (2004) in an fMRI experiment also found that people with bipolar disorder in the depressed phase shown pictures designed to evoke affective change recruited prefrontal and anterior cingulate cortices and additional subcortical limbic systems when compared with healthy individuals, in particular in the amygdala, thalamus, hypothalamus and medial globus pallidus. Patients and comparison participants displayed differential sensitivity to affective change with negative and positive affect induction producing converse patterns of activation. Reference Malhi, Lagopoulos, Ward, Kumari, Mitchell and Parker98
The finding of increased volumes in the globus pallidus is in keeping with several reports suggesting that antipsychotic drugs affect this structure, although this result was not confirmed in the left and right analysis of the structure, and the presence of publication bias and statistically significant unexplained heterogeneity limits the validity of results. Whether these changes are related to alterations in gamma-aminobutyric acid (GABA)/dopamine neurotransmission or possibly the result of artefacts in the presence of ventricular enlargement will require further investigation.
In agreement with Kempton et al's meta-analysis, Reference Kempton, Geddes, Ettinger, Williams and Grasby10 in the bipolar disorder v. healthy controls comparison we found evidence of lateral ventricular enlargement in the absence of heterogeneity or publication bias. We also found evidence of whole brain and prefrontal lobe volume reductions, and globus pallidus volumetric increase. Similarly, in comparison with schizophrenia, bipolar disorder was associated with smaller lateral ventricular volume and enlarged amygdala volume. Our analysis did not confirm volumetric differences affecting the third ventricle or the hippocampi. Extensive meta-regression analyses confirmed the effect of mood stabilisers and other pharmacological compounds such as antipsychotics and antidepressants on morphometric differences. Finally several clinical and demographic variables exercise an effect on brain volumes. It is likely that the ability of meta-regression analyses to detect small differences is relatively limited. This observation, together with a different methodology can explain discrepancies in findings, although this meta-analysis largely confirms Kempton et al's findings. Reference Kempton, Geddes, Ettinger, Williams and Grasby10
Some of the analyses reveal modest effect sizes. Whether these are clinically significant or scientifically important is, however, difficult to judge, as a very small change in the volume of a particular structure may have significant effects on behaviour. There is evidence of significant heterogeneity in several analyses. We predicted this would be the case and used random effects analyses that take heterogeneity into account when calculating all summary effect sizes. The effect of heterogeneity is to reduce the precision of the summary effect sizes and in general this reduces the significance of any findings. We have gone to considerable lengths to investigate this heterogeneity using meta-regression that we think has provided some much needed clarification for the considerable inconsistency in the published literature. Although this meta-analysis is limited by an inconsistency in the published literature, heterogeneity can provide important aetiological and methodological insights that can guide future research. We found several effect size associations with a number of both methodological and clinical variables (e.g. course/phase of bipolar disorder, medication status and treatment modalities) that should be borne in mind when designing future studies.
Another further potential limitation is the lack of studies that included a longitudinal perspective, such that it is difficult to exclude the possibility that the observed brain changes occur as a consequence of illness or its treatment. More neuroimaging studies with a longitudinal perspective could help clarify the natural evolution of brain abnormalities. Moorhead et al for instance found that people with bipolar disorder tend to lose hippocampal, fusiform and cerebellar grey matter at an accelerated rate compared with healthy controls and that tissue loss is associated with deterioration in cognitive function and illness course. Reference Moorhead, McKirdy, Sussmann, Hall, Lawrie and Johnstone99 Further understanding could also emerge from clear reporting of clinical variables such as duration of illness, age at onset and number of previous episodes. Finally, the selective reporting and publication of positive results is a further potential limitation to all meta-analyses. Although this limitation cannot be definitively excluded, we found evidence of publication bias only in a very limited number of structures.
In summary, we found that bipolar disorder is associated with global and prefrontal volumetric brain reductions, enlarged lateral ventricles and an enlarged globus pallidus. Compared with individuals with schizophrenia, people with bipolar disorder presented with a reduced right amygdala volume and smaller lateral ventricles.
Acknowledgements
D.A. would like to thank Drs Pariante and MacMaster for supplementing their work with unpublished data to complete this meta-analysis.
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