The British Journal of Psychiatry (2009)
195, 194–201. doi: 10.1192/bjp.bp.108.059717
Review article
Magnetic resonance imaging studies in bipolar
disorder and schizophrenia: meta-analysis
D. Arnone, J. Cavanagh, D. Gerber, S. M. Lawrie, K. P. Ebmeier and A. M. McIntosh
Background
Several magnetic resonance imaging (MRI) studies have
identified structural abnormalities in association with bipolar
disorder. The literature is, however, heterogeneous and there
is remaining uncertainty about which brain areas are pivotal
to the pathogenesis of the condition.
Aims
To identify, appraise and summarise volumetric MRI studies
of brain regions comparing bipolar disorder with an unrelated
control group and individuals with schizophrenia.
Method
A systematic review and random-effects meta-analysis was
carried out to identify key areas of structural abnormality in
bipolar disorder and whether the pattern of affected areas
separated bipolar disorder from schizophrenia. Significant
heterogeneity was explored using meta-regression.
Conclusions
There appear to be robust changes in brain volume in
bipolar disorder compared with healthy volunteers,
although most changes do not seem to be diagnostically
specific. Age and duration of illness appear to be key
issues in determining the magnitude of observed effect
sizes.
Results
Participants with bipolar disorder are characterised by whole
Declaration of interest
None.
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.1,2
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 cortex3–5 and in the area of the corpus
callosum.6,7 Wright et al broadly replicated the above findings
but also showed reductions in cerebral volume with an enlarged
ventricular system, particularly lateral ventricles.2 A more recent
voxel-based meta-analysis also showed similar volumetric
reductions in schizophrenia.8 In bipolar disorder, mild ventricular
enlargement and the presence of white matter hyper-intensities9,10
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.10
In this meta-analysis we sought to update the work carried
out by McDonald et al (2004)9 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
194
brain and prefrontal lobe volume reductions, and also by
increases in the volume of the globus pallidus and lateral
ventricles. In comparison with schizophrenia, bipolar disorder
is associated with smaller lateral ventricular volume and
enlarged amygdala volume. Heterogeneity was widespread
and could be partly explained by clinical variables and year
of publication, but generally not by differences in image
acquisition.
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
MRI studies in bipolar disorder and schizophrenia
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.
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).
Statistical analysis
Bipolar disorder in comparison
with healthy controls
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 analyses11 were used throughout to weight
each study. The presence of heterogeneity was tested using the
Q-test and its magnitude estimated using I 2 and can be interpreted
as the proportion of variance in effect size due to heterogeneity.12
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.13 The significance
level was set at P50.05.
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.
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 70.15 (95% CI 70.27 to 70.02) and without significant
heterogeneity (I2 = 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
(I2 = 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 (I2 = 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
Results
Systematic search
Seventy-two reports14–85 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,15,16,36,46,49,52–54,56,58,59,65,68,75,80–82,85 one article added a
third comparison group, of schizoaffective disorder83 and one
compared with schizoaffective disorder but not with schizophrenia.45 Only two reports compared bipolar disorder with
schizophrenia without the comparator of healthy controls.44,66
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
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,
I2 = 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, I2 = 0.007, Egger’s P = 0.11;
for right lateral ventricle it was: 70.26, 95% CI 70.49 to
70.02, I2 = 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
195
196
0.072
0.15
0.016
0.043
0.039
Bipolar disorder in comparison with schizophrenia
Intracranial volume
Whole brain (white matter)
Left amygdala
Left hippocampus
Right hippocampus
0.97
1.64
0.58
4.29
4.04
0.21
1.42
1.91
1.49
71.53
0.64
71.16
1.58
0.34
2.92
0.70
70.66
71.58
C
Z
0.33
0.1
0.6
50.001
50.001
NA
NA
NA
NA
NA
0.02
0.30
0.03
0.30
0.45
0.36
0.02
0.41
0.047
0.004
0.3
0.03
P
Duration of illness, years
0.83
0.09
2.38
0.16
0.05
1.04
0.056
0.08
2.21
0.14
0.06
1.07
0.13
0.024
0.75
0.52
0.15
0.91
0.25 70.08 72.39
0.12
0.028
0.83
0.73
0.28
1.98
0.004
0.054 2.87
0.48
0.07
1.09
0.51
0.076 2.22
0.11
NA
P
NA
NA
0.98
1.62
1.73
0.66
0.17
70.01
70.16
0.66
70.76
72.07
78.82
70.13
0.89
71.34
0.83
71.09
C
2.10
3.43
3.45
1.08
0.20
70.02
70.30
1.53
70.80
72.76
71.66
70.26
0.75
71.46
0.50
71.65
Z
Euthymia
C
Z
0.035
0.001
0.001
NA
NA
NA
0.043
0.04
1.28
1.19
0.2
0.23
0.92
0.3
0.2
0.26
0.46
0.7
0.003
0.5
P
Age at onset, years
0.28 70.23 71.29
0.84
0.006
1.13
0.98
0.022
0.74
0.76
0.02
0.38
0.13
0.063
2.96
0.42
0.02
0.67
0.006
NA
0.1
NA
0.8
NA
0.45
NA
0.14 70.0071 70.09
0.62
0.091
1.04
0.1
NA
P
NA
NA
NA
NA
NA
NA
NA
0.04
0.11
0.12
NA
NA
NA
NA
NA
NA
NA
NA
C
0.07
0.21
0.32
Z
Chronicity
0.94
0.83
0.75
P
2.66
1.94
2.30
1.3
1.58
71.19
2.16
0.08
71.32
0.63
6.23
6.85
70.04
2.0
0.54
0.74
0.90
75.80
1.32
2.55
1.41
0.97
1.25
71.49
1.64
0.03
70.53
0.65
1.55
1.08
71.23
1.21
0.48
0.97
1.12
72.37
Z
NA
NA
NA
NA
NA
Bipolar disorder in comparison with schizophrenia
Intracranial volume
Whole brain (white matter)
Left amygdala
Left hippocampus
Right hippocampus
70.41
2.33
0.14
70.23
1.31
0.70
2.72
70.93
1.08
2.69
70.37
70.15
70.98
Z
NA, information not sufficiently available or varied to allow meta–regression analyses.
a. Results in bold are significant.
70.40
2.28
0.19
70.19
0.98
2.32
3.22
77.91
1.27
2.23
71.76
72.15
71.58
C
Bipolar disorder in comparison with healthy controls
Whole brain (grey matter)
Amygdalae
Left amygdala
Right amygdala
Hippocampi
Globi pallidi
Right globus pallidus
Left anterior cingulate cortex
Thalami
Temporal lobes
Left temporal lobe
Right temporal lobe
Pituitary gland
Structure
P
0.68
0.02
0.89
0.82
0.19
0.48
0.007
0.35
0.28
0.007
0.71
0.88
0.32
Mood stabilisers
NA
NA
NA
NA
NA
71.13
70.8
70.63
71.28
71.05
0.67
1.48
0.25
70.86
71.62
NA
NA
NA
C
72.46
70.48
71.26
72.38
71.85
0.58
1.30
0.04
71.82
71.06
Z
Antipsychotics
0.014
0.63
0.21
0.017
0.065
0.56
0.19
0.97
0.069
0.3
P
NA
NA
NA
NA
NA
70.93
72.02
71.2
71.67
0.18
70.46
2.3
72.59
70.77
71.48
70.32
70.39
C
71.28
71.64
71.83
72.08
0.20
70.44
0.40
70.69
71.23
70.73
70.37
70.15
NA
Z
P
0.2
0.1
0.067
0.038
0.84
0.66
0.69
0.49
0.22
0.46
0.71
0.88
Antidepressants
NA
NA
NA
0.44
0.44
0.22
70.51
70.4
70.50
0.21
NA
NA
73.28
70.07
70.29
0.09
0.24
NA
C
0.49
0.5
0.64
0.83
0.54
0.77
0.5
70.47
70.21
70.61
0.29
0.74
0.69
0.73
0.32
0.41
0.33
0.13
0.60
P
0.98
70.83
70.98
71.49
0.53
Z
Scanner strength, T
72.38
70.29
0.25
71.03
70.97
0.05
70.31
70.43
0.13
0.11
70.72
70.78
Z
NA
NA
NA
70.34 71.63
70.062
71
0.006
70.16
70.04
0.01
0.03
70.16
71.29
NA
70.46
70.07
0.012
70.053
73.65
C
0.1
0.31
0.02
0.8
0.81
0.3
0.33
1
0.75
0.70
0.90
0.91
0.47
0.43
P
Slice thickness, mm
P
0.16
0.016
0.0076
70.024
70.02
0.005
70.29
70.03
70.05
70.06
0.022
70.006
0.33
70.15
70.11
70.044
70.044
0.18
C
0.54
0.58
0.009
70.58
70.45
0.13
73.31
70.71
71.31
70.72
0.64
70.03
0.42
72.92
71.59
71.71
71.55
1.39
Z
0.6
0.56
0.93
0.56
0.65
0.9
0.001
0.50
0.20
0.50
0.52
1.0
0.70
0.003
0.11
0.09
0.12
0.17
P
Year of publication, years
0.19
0.011
0.16
0.33
0.21
0.14
0.1
1
0.6
0.51
0.12
0.28
0.22
0.228
0.63
0.33
0.3
0.02
Male participants
C
Table 2 Meta-regression analyses of the effect of medication, scanner strength and year of publication on volumetric differences of brain structures that showed statistically significant
levels of heterogeneity. a Results are expressed as regression coefficient ( C ), Z-statistic and P
NA, information not sufficiently available or varied to allow meta–regression analyses.
a. Results in bold are significant.
0.0045
0.033
0.032
0.025
70.017
0.022
70.05
0.13
0.004
0.03
0.012
70.012
70.014
Z
Mean age, years
C
Bipolar disorder in comparison with healthy controls
Whole brain (grey matter)
Amygdalae
Left amygdala
Right amygdala
Hippocampi
Globi pallidi
Right globus pallidus
Left anterior cingulate cortex
Thalami
Temporal lobes
Left temporal lobe
Right temporal lobe
Pituitary gland
Structure
Table 1 Meta-regression analyses of the effect of illness characteristics and demographic variables on volumetric differences of brain structures that showed statistically significant levels
of heterogeneity. a Results are expressed as regression coefficient ( C ), Z-statistic and P
Arnone et al
MRI studies in bipolar disorder and schizophrenia
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.
(C = 1.62, Z = 3.43, P = 0.001 and C = 1.73, Z = 3.45, P = 0.001
respectively).
Discussion
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 = 71.13, Z = 72.46, 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 = 70.08, Z = 72.39, P = 0.02), percentage of participants with euthymia (C = 72.07, Z = 72.76, 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 = 70.29,
Z = 73.31, 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 = 71.28, Z = 72.38, P = 0.017
and C = 71.67, Z = 72.08, 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 = 70.15, Z = 72.92, P = 0.003) and slice
thickness (C = 70.46, Z = 72.38, 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 = 75.80, Z = 72.37, 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, P50.001 and C = 0.039, Z = 4.04, P50.001
respectively) and number of participants with euthymia
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.86 The fact that two previous metaanalyses, by Hoge et al 87 and McDonald et al,9 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.2 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.86
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 amygdala88 mirrored functionally by a biased emotional
response in the recognition of different emotional states,
particularly fear.89 With reference to bipolar disorder, the
literature reports both increased16,37,78 and decreased volumetric
changes in the amygdala, almost exclusively in adolescents with
bipolar disorder.21,30 Although this discrepancy can be explained
as an abnormal development trajectory in bipolar disorder,90 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.91–93 Our findings extend previous reports that
suggest a mechanistic role for the amygdala in schizophrenia, by
suggesting that volumetric deficits may be disease specific.94
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.95 Caligiuri et al, by using a motor reaction time task,
showed that individuals with euthymia or hypomania exhibited
197
Arnone et al
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.96 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.97 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.98
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,10 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.10
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
198
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.99 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.
D. Arnone, DM, MRCPsych, Neuroscience and Psychiatry Unit, University of
Manchester, Manchester and University Department of Psychiatry, Oxford;
J. Cavanagh, MD, FRCPsych, Division of Community Based Sciences, Faculty of
Medicine, University of Glasgow, Glasgow; D. Gerber, MRCPsych, Gartnavel Royal
Hospital, Glasgow; S. M. Lawrie, MD, FRCPsych, Division of Psychiatry, University
of Edinburgh, Royal Edinburgh Hospital, Edinburgh; K. P. Ebmeier, MD, Division
of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh
and University Department of Psychiatry, Oxford; A. M. McIntosh, MD,
MRCPsych, Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital,
Edinburgh, UK
Correspondence: Danilo Arnone, Neuroscience and Psychiatry Unit, University
of Manchester, G810 Stopford Building, Oxford Road, Manchester M13 9PT, UK.
Email: [email protected]
First received 28 Sep 2008, final revision 2 Jan 2009, accepted 4 Mar 2009
Funding
D.A. is currently supported by the UK Medical Research Council. A.M.M. is currently
supported by the Health Foundation.
Acknowledgements
D.A. would like to thank Drs Pariante and MacMaster for supplementing their work with
unpublished data to complete this meta-analysis.
References
1
Thompson SG, Smith TC, Sharp SJ. Investigating underlying risk
as a source of heterogeneity in meta-analysis. Stat Med 1997; 16:
2741–58.
2
Wright IC, Rabe-Hesketh S, Woodruff PW, David AS, Murray RM, Bullmore ET.
Meta-analysis of regional brain volumes in schizophrenia. Am J Psychiatry
2000; 157: 16–25.
3
Nelson MD, Saykin AJ, Flashman LA, Riordan HJ. Hippocampal volume
reduction in schizophrenia as assessed by magnetic resonance imaging: a
meta-analytic study. Arch Gen Psychiatry 1998; 55: 433–40.
4
Konick LC, Friedman L. Meta-analysis of thalamic size in schizophrenia. Biol
Psychiatr 2001; 49: 28–38.
MRI studies in bipolar disorder and schizophrenia
5 Baiano M, David A, Versace A, Churchill R, Balestrieri M, Brambilla P.
Anterior cingulate volumes in schizophrenia: a systematic review and a
meta-analysis of MRI studies. Schizophr Res 2007; 93: 1–12.
6 Woodruff PW, McManus IC, David AS. Meta-analysis of corpus callosum in
schizophrenia. J Neurol Neurosurg Psychiatry 1995; 58: 457–61.
7 Arnone D, Mcintosh AM, Tan GMY, Ebmeier KP. Meta-analysis of magnetic
resonance imaging studies of the corpus callosum in schizophrenia.
Schizophr Res 2008; 101: 124–32.
8 Ellison-Wright I, Glahn DC, Laird AR, Thelen SM, Bullmore E. The anatomy of
first-episode and chronic schizophrenia: an anatomical likelihood estimation
meta-analysis. Am J Psychiatry 2008; 165: 1015–23.
9 McDonald C, Zanelli J, Rabe-Hesketh S, Ellison-Wright I, Sham P,
Kalidindi S, et al. Meta-analysis of magnetic resonance imaging
brain morphometry studies in bipolar disorder. Biol Psychiatry 2004;
56: 411–7.
10 Kempton MJ, Geddes JR, Ettinger U, Williams SC, Grasby PM. Meta-analysis,
database, and meta-regression of 98 structural imaging studies in bipolar
disorder. Arch Gen Psychiatry 2008; 65: 1017–32.
11 DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials
1986; 7: 177–88.
12 Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in
meta-analyses. BMJ 2003; 327: 557–60.
13 Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis
detected by a simple, graphical test. BMJ 1997; 315: 629–34.
14 Ahn MS, Breeze JL, Makris N, Kennedy DN, Hodge SM, Herbert MR, et al.
Anatomic brain magnetic resonance imaging of the basal ganglia in pediatric
bipolar disorder. J Affect Disord 2007; 104: 147–54.
15 Altshuler LL, Bartzokis G, Grieder T, Curran J, Mintz J. Amygdala enlargement
in bipolar disorder and hippocampal reduction in schizophrenia: an MRI study
demonstrating neuroanatomic specificity. Arch Gen Psychiatry 1998; 55:
663–4.
16 Altshuler LL, Bartzokis G, Grieder T, Curran J, Jimenez T, Leight K, et al. An
MRI study of temporal lobe structures in men with bipolar disorder or
schizophrenia. Biol Psychiatry 2000; 48: 147–62.
17 Atmaca M, Ozdemir H, Cetinkaya S, Parmaksiz S, Belli H, Poyraz AK,
et al. Cingulate gyrus volumetry in drug free bipolar patients and
patients treated with valproate or valproate and quetiapine. J Psychiatr
Res 2007; 41: 821–7.
18 Aylward EH, Roberts-Twillie JV, Barta PE, Kumar AJ, Harris GJ, Geer M, et al.
Basal ganglia volumes and white matter hyperintensities in patients with
bipolar disorder. Am J Psychiatry 1994; 151: 687–93.
19 Beyer JL, Kuchibhatla M, Payne M, Moo-Young M, Cassidy F, MacFall J, et al.
Caudate volume measurement in older adults with bipolar disorder. Intern J
Geriatr Psychiatry 2004; 19: 109–14.
20 Beyer JL, Kuchibhatla M, Payne ME, Moo-Young M, Cassidy F, Macfall J, et al.
Hippocampal volume measurement in older adults with bipolar disorder.
Am J Geriatr Psychiatry 2004; 12: 613–20.
30 Chang K, Karchemskiy A, Barnea-Goraly N, Garrett A, Simeonova DI,
Reiss A. Reduced amygdalar gray matter volume in familial pediatric
bipolar disorder. J Am Acad Child Adolesc Psychiatry 2005; 44:
565–73.
31 Chen BK, Sassi R, Axelson D, Hatch JP, Sanches M, Nicoletti M,
et al. Cross-sectional study of abnormal amygdala development in
adolescents and young adults with bipolar disorder. Biol Psychiatry 2004;
56: 399–405.
32 Chen HH, Nicoletti M, Sanches M, Hatch JP, Sassi RB, Axelson D,
et al. Normal pituitary volumes in children and adolescents with
bipolar disorder: a magnetic resonance imaging study. Depress Anxiety
2004; 20: 182–6.
33 Chen HH, Nicoletti MA, Hatch JP, Sassi RB, Axelson D, Brambilla P, et al.
Abnormal left superior temporal gyrus volumes in children and adolescents
with bipolar disorder: a magnetic resonance imaging study. Neurosci Lett
2004; 363: 65–8.
34 Chiu S, Widjaja F, Bates ME, Voelbel GT, Pandina G, Marble J, et al. Anterior
cingulate volume in pediatric bipolar disorder and autism. J Affect Disord
2008; 105: 93–9.
35 Cousins D, Moore PB, Harrison L, Ferrier IN, Young AH, Lloyd AJ.
Third ventricle width and pituitary volumes in euthymic bipolar subjects.
J Psychopharmacology 2007; 21 (suppl): A14.
36 Dasari M, Friedman L, Jesberger J, Stuve TA, Findling RL, Swales TP, et al.
A magnetic resonance imaging study of thalamic area in adolescent patients
with either schizophrenia or bipolar disorder as compared to healthy
controls. Psychiatry Res 1999; 91: 155–62.
37 Davis KA, Kwon A, Cardenas VA, Deicken RF. Decreased cortical gray and
cerebral white matter in male patients with familial bipolar I disorder. J Affect
Disord 2004; 82: 475–85.
38 DelBello MP, Strakowski SM, Zimmerman ME, Hawkins JM, Sax KW. MRI
analysis of the cerebellum in bipolar disorder: a pilot study.
Neuropsychopharmacology 1999; 21: 63–8.
39 DelBello MP, Zimmerman ME, Mills NP, Getz GE, Strakowski SM.
Magnetic resonance imaging analysis of amygdala and other subcortical
brain regions in adolescents with bipolar disorder. Bipolar Disord 2004;
6: 43–52.
40 Dupont RM, Jernigan TL, Heindel W, Butters N, Shafer K, Wilson T, et al.
Magnetic resonance imaging and mood disorders. Localization of white
matter and other subcortical abnormalities. Arch Gen Psychiatry 1995; 52:
747–55.
41 El-Badri SM, Cousins DA, Parker S, Ashton HC, McAllister VL, Ferrier IN, et al.
Magnetic resonance imaging abnormalities in young euthymic patients with
bipolar affective disorder. Br J Psychiatry 2006; 189: 81–2.
42 Frazier JA, Chiu S, Breeze JL, Makris N, Lange N, Kennedy DN, et al.
Structural brain magnetic resonance imaging of limbic and thalamic volumes
in pediatric bipolar disorder. Am J Psychiatry 2005; 162: 1256–65.
21 Blumberg HP, Kaufman J, Martin A, Whiteman R, Zhang JH, Gore JC, et al.
Amygdala and hippocampal volumes in adolescents and adults with bipolar
disorder. Arch Gen Psychiatry 2003; 60: 1201–8.
43 Frazier JA, Breeze JL, Makris N, Giuliano AS, Herbert MR, Seidman L,
et al. Cortical gray matter differences identified by structural magnetic
resonance imaging in pediatric bipolar disorder. Bipolar Disord 2005;
7: 555–69.
22 Botteron KN, Vannier MW, Geller B, Todd RD, Lee BC. Preliminary study of
magnetic resonance imaging characteristics in 8- to 16-year-olds with mania.
J Am Acad Child Adolesc Psychiatry 1995; 34: 742–9.
44 Frazier JA, Hodge SM, Breeze JL, Giuliano AJ, Terry JE, Moore CM, et al.
Diagnostic and sex effects on limbic volumes in early-onset bipolar disorder
and schizophrenia. Schizophr Bull 2008; 34: 37–46.
23 Brambilla P, Harenski K, Nicoletti MA, Mallinger AG, Frank E, Kupfer DJ, et al.
Anatomical MRI study of basal ganglia in bipolar disorder patients. Psychiatry
Res 2001; 106: 65–80.
45 Getz GE, DelBello MP, Fleck DE, Zimmerman ME, Schwiers ML, Strakowski
SM. Neuroanatomic characterization of schizoaffective disorder using MRI: a
pilot study. Schizophr Res 2002; 55: 55–9.
24 Brambilla P, Harenski K, Nicoletti M, Mallinger AG, Frank E, Kupfer DJ, et al.
MRI study of posterior fossa structures and brain ventricles in bipolar
patients. J Psychiatr Res 2001; 35: 313–22.
46 Harvey I PR, Ron MA, Baker G, Murray RM. Volumetric MRI measurements in
bipolars compared with schizophrenics and healthy controls. Psychol Med
1994; 24: 689–99.
25 Brambilla P, Harenski K, Nicoletti M, Mallinger AG, Frank E, Kupfer DJ, et al.
Differential effects of age on brain gray matter in bipolar patients and healthy
individuals. Neuropsychobiology 2001; 43: 242–7.
47 Hauser P, Matochik J, Altshuler LL, Denicoff KD, Conrad A, Li X, et al.
MRI-based measurements of temporal lobe and ventricular structures in
patients with bipolar I and bipolar II disorders. J Affect Disord 2000; 60:
25–32.
26 Brambilla P, Nicoletti MA, Harenski K, Sassi RB, Mallinger AG, Frank E, et al.
Anatomical MRI study of subgenual prefrontal cortex in bipolar and unipolar
subjects. Neuropsychopharmacology 2002; 27: 792–9.
27 Brambilla P, Harenski K, Nicoletti M, Sassi RB, Mallinger AG, Frank E, et al.
MRI investigation of temporal lobe structures in bipolar patients. J Psychiatr
Res 2003; 37: 287–95.
48 Haznedar MM, Roversi F, Pallanti S, Baldini-Rossi N, Schnur DB, Licalzi EM, et
al. Fronto-thalamo-striatal gray and white matter volumes and anisotropy of
their connections in bipolar spectrum illnesses. Biol Psychiatry 2005; 57:
733–42.
28 Caetano SC, Sassi R, Brambilla P, Harenski K, Nicoletti M, Mallinger AG, et al.
MRI study of thalamic volumes in bipolar and unipolar patients and healthy
individuals. Psychiatry Res 2001; 108: 161–8.
49 Hirayasu Y, McCarley RW, Salisbury DF, Tanaka S, Kwon JS, Frumin M, et al.
Planum temporale and Heschl gyrus volume reduction in schizophrenia: a
magnetic resonance imaging study of first-episode patients. Arch Gen
Psychiatry 2000; 57: 692–9.
29 Chang K, Barnea-Goraly N, Karchemskiy A, Simeonova DI, Barnes P, Ketter T,
et al. Cortical magnetic resonance imaging findings in familial pediatric
bipolar disorder. Biol Psychiatry 2005; 58: 197–203.
50 Hwang J, Lyoo IK, Dager SR, Friedman SD, Oh JS, Lee JY, et al. Basal
ganglia shape alterations in bipolar disorder. Am J Psychiatry 2006;
163: 276–85.
199
Arnone et al
51 Javadapour A, Malhi GS, Ivanovski B, Chen X, Wen W, Sachdev P. Increased
anterior cingulate cortex volume in bipolar I disorder. Aust N Z Journal
Psychiatry 2007; 41: 910–6.
73 Sassi RB, Brambilla P, Hatch JP, Nicoletti MA, Mallinger AG, Frank E,
et al. Reduced left anterior cingulate volumes in untreated bipolar patients.
Biol Psychiatry 2004; 56: 467–75.
52 Kasai K, Shenton ME, Salisbury DF, Hirayasu Y, Lee CU, Ciszewski AA,
et al. Progressive decrease of left superior temporal gyrus gray matter
volume in patients with first episode schizophrenia. Am J Psychiatry 2003;
160: 156–64.
74 Sax KW, Strakowski SM, Zimmerman ME, DelBello MP, Keck PE, Jr, Hawkins
JM. Frontosubcortical neuroanatomy and the continuous performance test in
mania. Am J Psychiatry 1999; 156: 139–41.
53 Kasai K, Shenton ME, Salisbury DF, Onitsuka T, Toner SK, Yurgelun-Todd D,
et al. Differences and similarities in insular and temporal pole MRI gray
matter volume abnormalities in first-episode schizophrenia and affective
psychosis. Arch Gen Psychiatry 2003; 60: 1069–77.
54 Kasai K, Shenton ME, Salisbury DF, Hirayasu Y, Onitsuka T, Spencer MH,
et al. Progressive decrease of left Heschl gyrus and planum temporale
gray matter volume in first-episode schizophrenia: a longitudinal
magnetic resonance imaging study. Arch Gen Psychiatry 2003;
60: 766–75.
55 Kaur S, Sassi RB, Axelson D, Nicoletti M, Brambilla P, Monkul ES, et al.
Cingulate cortex anatomical abnormalities in children and adolescents with
bipolar disorder. Am J Psychiatry 2005; 162: 1637–43.
56 Lim KO, Rosenbloom MJ, Faustman WO, Sullivan EV, Pfefferbaum A.
Cortical gray matter deficit in patients with bipolar disorder. Schizophr
Res 1999; 40: 219–27.
57 Lopez-Larson MP, DelBello MP, Zimmerman ME, Schwiers ML,
Strakowski, SM. Regional prefrontal gray and white matter abnormalities
in bipolar disorder. Biol Psychiatry 2002; 52: 93–100.
58 McDonald C, Marshall N, Sham PC, Bullmore ET, Schulze K, Chapple B,
et al. Regional brain morphometry in patients with schizophrenia or
bipolar disorder and their unaffected relatives. Am J Psychiatry 2006;
163: 478–87.
59 McIntosh AM, Forrester A, Lawrie SM, Byrne M, Harper A, Kestelman JN,
et al. A factor model of the functional psychoses and the relationship of
factors to clinical variables and brain morphology. Psychol Med 2001; 31:
159–71.
60 Mills NP, Delbello MP, Adler CM, Strakowski SM. MRI analysis
of cerebellar vermal abnormalities in bipolar disorder. Am J Psychiatry
2005; 162: 1530–2.
61 MacMaster FP, Leslie R, Rosenberg DR, Kusumakar V. Pituitary gland volume
in adolescent and young adult bipolar and unipolar depression. Bipolar
Disord 2008; 10: 101–4.
62 Monkul ES, Nicoletti MA, Spence D, Sassi RB, Axelson D, Brambilla P, et al.
MRI study of thalamus volumes in juvenile patients with bipolar disorder.
Depress Anxiety 2006; 23: 347–52.
63 Noga JT, Vladar K, Torrey EF. A volumetric magnetic resonance imaging
study of monozygotic twins discordant for bipolar disorder. Psychiatry Res
2001; 106: 25–34.
64 Pariante CM, Dazzan P, Danese A, Morgan KD, Brudaglio F, Morgan C, et al.
Increased pituitary volume in antipsychotic-free and antipsychotic-treated
patients of the AEsop first-onset psychosis study. Neuropsychopharmacology
2005; 30: 1923–31.
65 Pearlson GD, Barta PE, Powers RE, Menon RR, Richards SS, Aylward EH, et al.
Ziskind-Somerfeld Research Award 1996. Medial and superior temporal gyral
volumes and cerebral asymmetry in schizophrenia versus bipolar disorder.
Biol Psychiatry 1997; 41: 1–14.
76 Sharma V, Menon R, Carr TJ, Densmore M, Mazmanian D, Williamson PC.
An MRI study of subgenual prefrontal cortex in patients with familial and
non-familial bipolar I disorder. J Affect Disord 2003; 77: 167–71.
77 Strakowski SM, Wilson DR, Tohen M, Woods BT, Douglass AW, Stoll AL.
Structural brain abnormalities in first-episode mania. Biol Psychiatry 1993;
33: 602–9.
78 Strakowski SM, DelBello MP, Sax KW, Zimmerman ME, Shear PK, Hawkins
JM, et al. Brain magnetic resonance imaging of structural abnormalities in
bipolar disorder. Arch Gen Psychiatry 1999; 56: 254–60.
79 Strakowski SM, DelBello MP, Zimmerman ME, Getz GE, Mills NP, Ret J,
et al. Ventricular and periventricular structural volumes in first- versus
multiple-episode bipolar disorder. Am J Psychiatry 2002; 159: 1841–7.
80 Strasser HC, Lilyestrom J, Ashby ER, Honeycutt NA, Schretlen DJ,
Pulver AE, et al. Hippocampal and ventricular volumes in psychotic
and nonpsychotic bipolar patients compared with schizophrenia patients
and community control subjects: a pilot study. Biol Psychiatry 2005;
57: 633–9.
81 Swayze VW 2nd, Andreasen NC, Alliger RJ, Ehrhardt JC, Yuh WT. Structural
brain abnormalities in bipolar affective disorder. Ventricular enlargement and
focal signal hyperintensities. Arch Gen Psychiatry 1990; 47: 1054–9.
82 Swayze VW 2nd, Andreasen NC, Alliger RJ, Yuh WT, Ehrhardt JC. Subcortical
and temporal structures in affective disorder and schizophrenia: a magnetic
resonance imaging study. Biol Psychiatry 1992; 31: 221–40.
83 Velakoulis D, Wood SJ, Wong MT, McGorry PD, Yung A, Phillips L, et al.
Hippocampal and amygdala volumes according to psychosis stage and
diagnosis: a magnetic resonance imaging study of chronic schizophrenia,
first-episode psychosis, and ultra-high-risk individuals. Arch Gen Psychiatry
2006; 63: 139–49.
84 Yucel K, Taylor VH, McKinnon MC, Macdonald K, Alda M, Young LT, et al.
Bilateral hippocampal volume increase in patients with bipolar disorder and
short-term lithium treatment. Neuropsychopharmacology 2008; 33: 361–7.
85 Zipursky RB SM, Bury A, Langevin R, Wortzman G, Katz R. Deficits in gray
matter volume are present in schizophrenia but not bipolar disorder.
Schizophr Res 1997; 26: 85–92.
86 Elkis H, Friedman L, Wise A, Meltzer HY. Meta-analyses of studies of
ventricular enlargement and cortical sulcal prominence in mood disorders.
Comparisons with controls or patients with schizophrenia. Arch Gen
Psychiatry 1995; 52: 735–46.
87 Hoge EA, Friedman L, Schulz SC. Meta-analysis of brain size in bipolar
disorder. Schizophr Res 1999; 37: 177–81.
88 Phillips ML, Drevets WC, Rauch SL, Lane R. Neurobiology of emotion
perception II: Implications for major psychiatric disorders. Biol Psychiatry
2003; 54: 515–28.
66 Rossi A, Stratta P, Di Michele V, Gallucci M, Splendiani A, de Cataldo S, et al.
Temporal lobe structure by magnetic resonance in bipolar affective disorders
and schizophrenia. J Affect Disord 1991; 21: 19–22.
89 LaBar KS, Gatenby JC, Gore JC, LeDoux JE, Phelps EA. Human amygdala
activation during conditioned fear acquisition and extinction: a mixed-trial
fMRI study. Neuron 1998; 20: 937–45.
67 Rosso IM, Killgore WD, Cintron CM, Gruber SA, Tohen M, Yurgelun-Todd DA.
Reduced amygdala volumes in first-episode bipolar disorder and correlation
with cerebral white matter. Biol Psychiatry 2007; 61: 743–9.
90 McEwen BS, Milner TA. Hippocampal formation: shedding light on
the influence of sex and stress on the brain. Brain Res Rev. 2007;
55: 343–55.
68 Roy PD, Zipursky RB, Saint-Cyr JA, Bury A, Langevin R, Seeman MV.
Temporal horn enlargement is present in schizophrenia and bipolar disorder.
Biol Psychiatry 1998; 44: 418–22.
91 Fahim C, Stip E, Mancini-Marie A, Mensour B, Boulay LG, Leroux JM,
et al. Brain activity during emotionally negative pictures in schizophrenia
with and without flat affect: an FMRI Study. Psychiatry Res 2005;
140: 1–15.
69 Sanches M, Sassi RB, Axelson D, Nicoletti M, Brambilla P, Hatch JP, et al.
Subgenual prefrontal cortex of child and adolescent bipolar patients: a
morphometric magnetic resonance imaging study. Psychiatry Res 2005; 138:
43–9.
92 Schneider F, Weiss U, Kessler C, Salloum JB, Posse S, Grodd W, et al.
Differential amygdala activation in schizophrenia during sadness. Schizophr
Res 1998; 34: 133–42.
70 Sanches M, Roberts RL, Sassi RB, Axelson D, Nicoletti M, Brambilla P,
et al. Developmental abnormalities in striatum in young bipolar patients:
a preliminary study. Bipolar Disord 2005; 7: 153–8.
93 Baas D, Aleman A, Vink M, Ramsey NF, de Haan EH, Kahn RS. Evidence
of altered cortical and amygdala activation during social decision-making
in schizophrenia. Neuroimage 2008; 40: 719–27.
71 Sassi RB, Nicoletti M, Brambilla P, Harenski K, Mallinger AG, Frank E, et al.
Decreased pituitary volume in patients with bipolar disorder. Biol Psychiatry
2001; 50: 271–80.
94 Soares JC, Mann JJ. The functional neuroanatomy of mood disorders.
J Psychiatr Res 1997; 31: 393–432.
72 Sassi RB, Nicoletti M, Brambilla P, Mallinger AG, Frank E, Kupfer DJ, et al.
Increased gray matter volume in lithium-treated bipolar disorder patients.
Neurosci Lett 2002; 329: 243–5.
200
75 Schlaepfer TE, Harris GJ, Tien AY, Peng LW, Lee S, Federman EB, et al.
Decreased regional cortical gray matter volume in schizophrenia.
Am J Psychiatry 1994; 151: 842–8.
95 Goldman AL, Pezawas L, Mattay VS, Fischl B, Verchinski BA, Zoltick B, et al.
Heritability of brain morphology related to schizophrenia: a large-scale
automated magnetic resonance imaging segmentation study. Biol Psychiatry
2008; 63: 475–83.
MRI studies in bipolar disorder and schizophrenia
96 Caligiuri MP, Brown GG, Meloy MJ, Eberson S, Niculescu AB, Lohr JB.
Striatopallidal regulation of affect in bipolar disorder. J Affect Disord 2006;
91: 235–42.
98 Malhi GS, Lagopoulos J, Ward PB, Kumari V, Mitchell PB, Parker GB, et al.
Cognitive generation of affect in bipolar depression: an fMRI study. Eur J
Neurosci 2004; 19: 741–54.
97 Caligiuri MP, Brown GG, Meloy MJ, Eberson SC, Kindermann SS, Frank LR,
et al. An fMRI study of affective state and medication on cortical and
subcortical brain regions during motor performance in bipolar disorder.
Psychiatry Res 2003; 123: 171–82.
99 Moorhead TW, McKirdy J, Sussmann JE, Hall J, Lawrie SM, Johnstone EC, et al.
Progressive gray matter loss in patients with bipolar disorder. Biol Psychiatry
2007; 62: 894–900.
Why I chose
psychiatry
Ian Rowbotham
Part one – In the Beginning
‘Most of you will become GPs’, said the smooth lecturer, himself a consultant, a word which once meant Great Beast of the Swamplands. But
gene dilution diminished its potency; thus it begat consultant nurse, consultant physiotherapist, consultant (car sales) and consultant hospital
cardiac specialist (drug rep.), consultant accountant (turf) and subspecies too numerous and complicated to spell.
With DNA weakened, having neither Awayday awareness certificate nor distance learning MSc to protect against the approaching cataclysm,
they were hit hardest. General practitioner (prosapiamedicus), no longer ‘slacker’ and ‘also ran’, walked over the Earth, terrorising governments. These intelligent, well-adjusted creatures mated early, saw their progeny grow up and drank in the bounteous new dawn.
The old, lumbering predators grew scarce, further handicapped by the twin eruptions of ‘Emmemsi’ and ‘Emtasse’ in 2007. Their descendants
ran amok panicking, stricken by shortages, blinded by fallout, even charging round on 7-hour ward rounds at night and weekends, as their
dominion shrivelled and the more nimble-footed opt-out doctors (Cotidieopus) joined the ascendancy.
Part two – Apres le Déluge (moi)
A mature student of mature years, I had watched in wonderment from the first rumblings of Tomorrow’s Doctors. I had seen two great eras:
Surgery and Medicine. Next came the Paediatrics and the Age of Endoctrinement, then, as a meteor paints the sky with its single daub of
brilliance, Psychiatry. Seven weeks of revelation followed. I could understand its language, follow the puzzling, yet attractive logic and
uncertainty; there were also many people. The full tide of human existence was not, as Samuel Johnson put it, at Charing Cross but right here
in the vortex of my medical school career. These were not bags of symptoms to be diagnosed, treated and pressure-hosed through the
rotating door of MAU, but human lives; part of the joyful, tragic, brutal, desperate web of experience.
Our course organiser, and head of year, made sure Psychiatry formed a good chunk of exams (wake up!). We visited a secure hospital,
attended interactive lectures and clinics, saw child, liaison, eating disorders and drug and alcohol psychiatry culminating with a forensic
flourish in a mock murder trial.
Part three – flying solo
FY1 colleagues asked, ‘What did you do before medicine?’ (I made musical noises on a piece of wood) and, ‘Are you going to do General
Practice? (I’ll do anything, make the tea). But was I a GP? It was an obvious choice, but Psychiatry’s small flame persisted, illuminating the
hallways and passages of my mind.
Psychiatry at FY2 (getting serious) was another positive experience. Then came Orthopaedics (deeply enjoyed but never intended as a career).
I set 4 months to decide, but the Damascene moment came during the second week, when I was asked to review a frail elderly man,
debilitated with Parkinson’s disease, who had been given a new hip. He had become agitated 3 days post op and had ‘punched’ the
physiotherapist (how?) and ‘throttled’ a nurse (serious cricoid trauma was, fortunately, avoided). What I subsequently found may have decided
my choice of career. Clearly frightened, he believed patients were being systematically murdered (they were being discharged), addressed me
as Dr Death (justifiable given my spirited drilling technique) and claimed police were outside to interview me about the killings. He was plainly
terrified and, after further investigation, plainly psychotic. Owing to his severe Parkinson’s, I prescribed Quetiapine 25 mg, had a further rootle
through his notes and found he was already on it, a fact recorded in the A&E admitting notes but never entered on his chart. A simple mistake
was corrected and this man was allowed freedom from his demons. Psychiatry’s die was, if not fully set, cast.
Ian Rowbotham, CT1 in Psychiatry, St Mary’s Kettering, East Midlands Deanery
The British Journal of Psychiatry (2009)
195, 201. doi: 10.1192/bjp.195.3.201
201
The British Journal of Psychiatry (2009)
195, 194–201. doi: 10.1192/bjp.bp.108.059717
Data supplement
Table DS1
Details of the studies included in the meta-analysis
Participants with bipolar disorder
Study, first author (year)
Swayze (1990)
81
n
Diagnostic
system
Comparison group(s)
Males
n
Age,
years:
mean
Healthy
controls
Methods
Schizophrenia
Unipolar
depression/
schizoaffective
disorder
Slice
Magnet thickness,
T
mm
48
DSM–III
29
33.9
47
54
0
0.5
Rossi (1991)66
16
DSM–III
NK
47
0
10
0
0.5
10
5
Swayze (1992)82
48
DSM–III
29
33.9
47
54
0
0.5
10
Strakowski (1993)77
17
DSM–III–R
7
28.4
16
0
0
1.5
6
Aylward (1994)18
30
DSM–III–R
16
39.2
30
0
0
1.5
5
Harvey (1994)46
26
DSM–III–R
10
35.6
34
48
0
0.5
5
Schlaepfer (1994)75
27
DSM–III–R
16
34.9
60
46
0
1.5
5
Botteron (1995)22
10
DSM–III–R
5
11.3
5
0
0
1.5
4–5
Dupont (1995)40
36
DSM–III–R
24
36.6
26
0
30a
1.5
5
65
27
DSM–III–R
16
34.9
60
46
0
1.5
5
Zipursky (1997)85
14
DSM–III–R
3
35.9
17
23
0
1.5
5
Altshuler (1998)15
12
DSM–III–R
12
50.8
18
14
0
1.5
1.4
Roy (1998)68
14
DSM–III–R
3
35.9
15
22
0
1.5
5
Dasari (1999)36
15
DSM–III–R
8
15.3
16
20
0
1.5
5
DelBello (1999)38
30
DSM–III–R
18
26.3
15
0
0
1.5
1
Pearlson (1997)
Lim (1999)56
9
DSM–III–R
NK
44.4
16
9
0
1.5
5
Sax (1999)74
17
DSM–III–R
10
27
12
0
0
1.5
1
Strakowski (1999)78
24
DSM–III–R
17
27
22
0
0
1.5
1
Altshuler (2000)16
24
DSM–III–R
NK
50.2
18
20
0
1.5
1.4
Hauser (2000)47
47
RDC
20
40.7
19
0
0
0.5
5
Hirayasu (2000)49
24
DSM–III–R
18
23.6
22
20
0
1.5
1.5
Brambilla (2001)23
22
DSM–IV
13
36
22
0
0
1.5
1.5
Brambilla (2001)24
22
DSM–IV
13
36
22
0
0
1.5
1.5
Brambilla (2001)25
22
DSM–IV
13
36
22
0
0
1.5
1.5
Caetano (2001)28
25
DSM–IV
15
34.4
39
0
17a
1.5
1.5
McIntosh (2001)59
14
OPCRIT
4
40.2
29
16
9a
1
1.9
6
DSM–III–R
1
34.5
11
0
0
1.5
2
23
DSM–IV
16
34.7
34
0
13a
1.5
1.5
27
DSM–IV
15
35
38
0
18a
1.5
1.5
29
DSM–IV
17
NK
46
0
0
1.5
1.5
12
DSM–IV
8
29.2
12
0
12b
1.5
1
17
DSM–IV
11
29
12
0
0
1.5
1
Strakowski (2002)79
35
DSM–IV
17
23.5
32
0
0
1.5
1.5
Blumberg (2003)21
36
DSM–IV
16
31
56
0
0
1.5
1.2
Brambilla (2003)27
24
DSM–IV
15
35
36
0
0
1.5
1.5
Kasai (2003)52
15
DSM–IV
14
21.8
14
13
0
1.5
1.5
Kasai (2003)53
26
DSM–IV
21
23.2
29
27
0
1.5
1.5
Kasai (2003)54
15
DSM–IV
14
21.8
22
13
0
1.5
1.5
Sharma (2003)76
12
DSM–III–R
6
38.3
8
0
0
4
Beyer (2004)19
36
DSM–IV
12
58.8
35
0
0
1.5
3
Beyer (2004)20
36
DSM–IV
13
58.2
29
0
0
1.5
3
Chen (2004)31
16
DSM–IV/K–SADS
8
16
21
0
0
1.5
1.5
Chen (2004)32
16
DSM–IV K–SAD– PL
8
15.5
21
0
0
1.5
1.5
16
DSM–IV/K–SADS–PL
8
15.5
21
0
0
1.5
1.5
Davis (2004)37
22
DSM–IV
22
43.1
32
0
0
1.5
3
Del Bello (2004)39
23
DSMIV/K–SADS
14
16.3
20
0
0
1.5
1.5
Sassi (2004)73
27
DSM–IV
15
35.1
39
0
0
1.5
1.5
Chang (2005)29
20
DSM–IV/K–SADS
16
14.6
20
0
0
3
1.5
Chang (2005)30
20
DSM–IV/K–SADS
16
14.6
20
0
0
3
1.5
43
DSM–IV K–SADS–E
23
11.3
20
0
0
1.5
3
3
Noga (2001)63
Sassi (2001)71
Brambilla (2002)
26
Sassi (2002)72
Getz (2002)45
Lopez Larson (2002)
Chen (2004c)
Frazier (2005)
33
42
57
3.3
Frazier (2005)43
32
DSM–IV K–SADS–E
21
11.2
15
0
0
1.5
Kaur (2005)55
16
DSM–IV/KSADS–PL
8
15.5
21
0
0
1.5
1.5
Haznedar (2005)48
40
DSM–IV
NK
42.2
36
0
0
1.5
1.2
continued
1
Table DS1
(continued)
Participants with bipolar disorder
Study, first author (year)
McDonald (2006)
58
n
Diagnostic
system
Males,
n
Comparison group(s)
Age,
years:
mean
Methods
Healthy
controls
Schizophrenia
Unipolar
depression/
schizoaffective
disorder
Slice
Magnet thickness,
T
mm
38
DSM–IV/DSM–III–R
15
41
54
42
0
1.5
1.5
Mills (2005)60
39
DSM–IV
19
23.6
32
0
0
1.5
1
Pariante (2005)64
16
ICD–10
7
28.2
78
0
0
1.5
1.5
Sanches (2005)69
15
DSM–IV K–SADS–PL
8
15.5
21
0
0
1.5
1.5
Sanches (2005)70
15
DSM–IV K–SADS–PL
7
15.9
21
0
0
1.5
1.5
Strasser (2005)80
38
DSM–III R/DSM–IV/RDC
16
38.1
44
33
0
1.5
1.5
El-Badri (2006)41
50
DSM–IV
15
30.2
26
0
0
0.5
NK
Hwang (2006)50
49
DSM–IV
19
32.4
37
0
0
1.5
1.5
Monkul (2006)62
16
DSM–IV/K–SADS–PL
8
15.5
21
0
Velakoulis (2006)83
22
DSM–IV/DSM–III–R
11
21.7
87
120
Atmaca (2007)17
30
DSM–IV
13
24.7
10
0
0
1.5
2.4
Ahn (2007)14
46
DSM–IV/K–SADS–E
26
11.3
22
0
0
1.5
1.5
Cousin (2007)35
49
DSM–IV
23
44.5
47
0
0
1.5
1.7
Javadapour (2007)51
24
DSM–IV
6
38.2
24
0
0
1.5
1.6
Rosso (2007)67
20
DSM–IV
13
23
23
0
0
1.5
NK
Chiu (2008)34
16
K–SADS
12
10.6
15
0
0
1.5
1.5
Frazier (2008)44
54
DSM–IV; KSADS–E
29
10.8
0
20
0
1.5
NK
MacMaster (2008)61
10
DSM–IV
4
17.2
10
0
0
1.5
1.45
Yucel (2008)84
28
DSM–IV
13
25.2
30
0
0
1.5
1.2
0
1.5
1.5
19/17
1.5
1.5
RDC, Research Diagnostic Criteria; OPCRIT, Operationalised Criteria Checklist; K–SADS, Schedule for Affective and Schizophrenic Disorders; K–SADS–PL, Schedule for Affective and
Schizophrenic Disorders for School Age Children – present and life time version; K–SADS–E, Schedule for Affective and Schizophrenic Disorders – epidemiologic version;
NK, not known.
a. Unipolar depression.
b. Schizoaffective disorder.
2
3
9
7
Right temporal lobe
Amygdalae
3
3
8
7
7
6
Left globus pallidus
Right globus pallidus
Thalamus
Left thalamus
Right thalamus
Left and right lateral ventricle
8
167
348
348
157
162
162
199
69
69
135
197
197
77
273
273
255
114
73
73
233
325
325
179
187
187
177
64
64
106
183
183
74
279
279
209
190
70
70
109
109
71
71
499
499
221
102
102
383
383
168
290
290
68
87
87
97
228
228
723
403
Healthy
controls
0.15
0.13
0.18
0.27
0.17
0.18
70.12
0.24
0.43
0.57
0.07
0.04
0.38
70.03
0.01
0.03
0.17
70.21
70.06
0.12
71.19
70.20
70.06
0.07
0.11
0.13
70.19
70.21
70.02
70.03
70.42
70.12
70.04
70.29
70.21
70.17
70.42
70.12
70.19
70.15
0.02
Estimate
70.13 to 0.43
70.02 to 0.29
0.02 to 0.33
0.05 to 0.49
70.11 to 0.46
70.05 to 0.42
70.44 to 0.20
70.45 to 0.92
70.03 to 0.88
0.03 to 1.11
70.13 to 0.27
70.17 to 0.24
70.10 to 0.87
70.20 to 0.14
70.16 to 0.18
70.21 to 0.27
70.43 to 0.77
70.83 to 0.40
70.50 to 0.38
70.30 to 0.54
72.44 to 0.07
70.56 to 0.15
70.48 to 0.36
70.11 to 0.25
70.05 to 0.27
70.48 to 0.22
70.50 to 0.13
70.53 to 0.10
70.38 to 0.33
70.40 to 0.34
71.04 to 0.20
70.41 to 0.17
70.31 to 0.23
70.85 to 0.27
70.63 to 0.22
70.54 to 0.21
70.70 to 70.15
70.34 to 0.10
70.51 to 0.13
70.27 to 70.02
70.15 to 0.18
95% CI
Effect size
0.42, P = 0.1
0, P = 0.89
0, P = 0.7
0, P = 0.7
0.42, P = 0.11
0.16, P = 0.31
0.55, P = 0.03
0.74, P = 0.02
0.42, P = 0.18
0.74, P = 0.004
0, P = 0.92
0, P = 0.7
0.50, P = 0.13
0, P = 0.46
0, P = 0.5
0.38, P = 0.11
0.81, P50.001
0.66, P = 0.054
0.36, P = 0.21
0.56, P = 0.06
0.94, P50.001
0, P = 0.76
0.27, P = 0.25
0.39, P = 0.07
0.23, P = 0.2
0.7, P = 0.001
0.06, P = 0.36
0.07, P = 0.36
0.79, P50.001
0.80, P50.001
0.86, P50.001
0.61, P = 0.008
0.56, P = 0.02
0.66, P = 0.03
0.43, P = 0.15
0.28, P = 0.24
0, P = 0.67
0.16, P = 0.3
0.57, P = 0.012
0.23, P = 0.15
0.07, P = 0.37
I2, P
0.8
0.39
0.6
0.07
0.43
0.42
0.89
0.93
0.49
0.02
0.95
0. 58
0.36
0.09
0.06
0.16
0.25
0.6
0.34
0.36
0.02
0.23
0.72
0.48
0.43
0.86
0.06
0.25
0.3
0.92
0.63
0.91
0.48
0.92
0.2
0.2
0.2
0.9
0.6
0.9
0.9
Egger’s P
Heterogeneity Publication bias
Bipolar disorder v. healthy controls
SGPFC, subgenual prefrontal cortex; AHC, amygdala–hippocampus complex; NA, not available.
Third ventricle
11
5
Globus pallidus
11
6
Right putamen
Right lateral ventricle
6
Left putamen
Left lateral ventricle
3
Putamen
10
9
Caudate
Right caudate
5
10
3
Right posterior cingulate cortex
Pituitary gland
Left caudate
3
Left posterior cingulate cortex
113
113
5
5
Left anterior cingulate cortex
Right anterior cingulate cortex
55
3
Right superior temporal gyri
55
386
3
14
Left superior temporal gyri
Right hippocampus
247
386
9
Hippocampi
83
83
290
290
182
264
264
90
78
78
122
195
195
661
301
Bipolar
disorder
14
4
Right AHC
Left hippocampus
4
Left AHC
11
9
Left temporal lobe
Right amygdala
4
Temporal lobes
11
4
Right SGPFC
Left amygdala
4
10
Whole brain (white)
Left SGPFC
10
Whole brain (grey)
6
25
Frontal cortex
14
Intracranial volume
Studies
n
Participants, n
4
4
4
3
NA
NA
NA
NA
NA
NA
NA
NA
NA
3
3
NA
NA
NA
NA
NA
NA
NA
NA
7
7
NA
NA
NA
4
4
NA
6
6
NA
NA
NA
NA
3
3
7
3
Studies
n
64
126
126
61
NA
NA
NA
NA
NA
NA
NA
NA
NA
116
116
NA
NA
NA
NA
NA
NA
NA
NA
239
239
NA
NA
NA
115
115
NA
143
143
NA
NA
NA
NA
50
50
249
61
Bipolar
disorder
84
93
158
158
73
NA
NA
NA
NA
NA
NA
NA
NA
NA
89
89
NA
NA
NA
NA
NA
NA
NA
NA
328
328
NA
NA
NA
200
200
NA
187
187
NA
NA
NA
NA
78
78
212
70.05
70.26
70.35
70.23
NA
NA
NA
NA
NA
NA
NA
NA
NA
70.18
70.25
NA
NA
NA
NA
NA
NA
NA
NA
0.13
0.1
NA
NA
NA
0.47
0.22
NA
0.19
0.18
NA
NA
NA
NA
0.38
0.41
0.01
0.49
Estimate
70.52 to 0.43
70.49 to 70.02
70.59 to 70.11
70.70 to 0.23
NA
NA
NA
NA
NA
NA
NA
NA
NA
70.53 to 0.16
70.56 to 0.06
NA
NA
NA
NA
NA
NA
NA
NA
70.24 to 0.51
70.31 to 0.51
NA
NA
NA
0.21 to 0.73
70.38 to 0.82
NA
70.04 to 0.41
70.05 to 0.40
NA
NA
NA
NA
70.27 to 1.13
70.15 to 0.96
70.20 to 0.21
70.39 to 1.36
95% CI
Effect size
0.49, P = 0.12
0, P = 0.55
0.007, P = 0.4
0.36, P = 0.21
NA
NA
NA
NA
NA
NA
NA
NA
NA
0.26, P = 0.26
0.11, P = 0.33
NA
NA
NA
NA
NA
NA
NA
NA
0.76, P<0.001
0.79, P<0.001
NA
NA
NA
0, P = 0.59
0.79, P = 0.002
NA
0, P = 0.75
0, P = 0.5
NA
NA
NA
NA
0.71, P = 0.031
0.5, P = 0.14
0, P = 0.73
0.81, P = 0.005
I2, P
0.14
0.06
0.11
0.48
NA
NA
NA
NA
NA
NA
NA
NA
NA
0.41
0.28
NA
NA
NA
NA
NA
NA
NA
NA
0.25
0.39
NA
NA
NA
0.28
0.3
NA
0.87
0.48
NA
NA
NA
NA
0.63
0.24
0.15
0.051
Egger’s P
Heterogeneity Publication bias
Bipolar disorder v. schizophrenia
Schizophrenia
Participants, n
Comparison of regional brain volumes of participants with bipolar disorder v. healthy controls and bipolar disorder v. schizophrenia
Whole brain
Brain region
Table DS2
Magnetic resonance imaging studies in bipolar disorder and
schizophrenia: meta-analysis
D. Arnone, J. Cavanagh, D. Gerber, S. M. Lawrie, K. P. Ebmeier and A. M. McIntosh
BJP 2009, 195:194-201.
Access the most recent version at DOI: 10.1192/bjp.bp.108.059717
Supplementary
Material
References
Reprints/
permissions
You can respond
to this article at
Downloaded
from
Supplementary material can be found at:
http://bjp.rcpsych.org/content/suppl/2009/09/01/195.3.194.DC1
This article cites 99 articles, 5 of which you can access for free at:
http://bjp.rcpsych.org/content/195/3/194#BIBL
To obtain reprints or permission to reproduce material from this paper, please
write to [email protected]
/letters/submit/bjprcpsych;195/3/194
http://bjp.rcpsych.org/ on June 16, 2017
Published by The Royal College of Psychiatrists
To subscribe to The British Journal of Psychiatry go to:
http://bjp.rcpsych.org/site/subscriptions/