Division of Psychological Medicine, Institute of Psychiatry, London
Brain Mapping Unit, University of Cambridge, Department of Psychiatry, Addenbrookes Hospital, Cambridge
Division of Psychological Medicine, Institute of Psychiatry, London
Brain Mapping Unit, University of Cambridge, Department of Psychiatry, Addenbrookes Hospital, Cambridge
Division of Psychological Medicine, Institute of Psychiatry, London.
Correspondence: Dr Colm McDonald, Division of Psychological Medicine, Box 63, Institute of Psychiatry, de Crespigny Park, London SE5 8AF, UK.Tel: +44 (0)20 7848 0057; fax: ++444 (0)2 (0)20 7701 9044; e-mail: c.mcdonald{at}iop.kcl.ac.uk
Funding detailed in Acknowledgement.
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ABSTRACT |
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Aims To assess volumetric abnormalities of grey and white matter throughoutthe entire brain in individuals with schizophrenia or with bipolar disorder compared with the same control group.
Method Brain scans were obtained by magnetic resonance imaging from 25 people with schizophrenia, 37 with bipolar disorder who had experienced psychotic symptoms and 52 healthy volunteers. Regional deviation in grey and white matter volume was assessed using computational morphometry.
Results Individuals with schizophrenia had distributed grey matter deficit predominantly involving the fronto-temporal neocortex, medial temporal lobe, insula, thalamus and cerebellum, whereas those with bipolar disorder had no significant regions of grey matter abnormality. Both groups had anatomically overlapping white matter deficits in regions normally occupied by major longitudinal and interhemispheric tracts.
Conclusions Schizophrenia and psychotic bipolar disorder are associated with distinct grey matter deficits but anatomically coincident white matter abnormalities.
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INTRODUCTION |
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METHOD |
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Patients and healthy volunteers were assessed using the same clinical scales. Structured diagnostic interviews were performed using the Schedule for Affective Disorders and Schizophrenia Lifetime Version (SADSL) (Spitzer & Endicott, 1978) and additional information regarding the timing and nature of psychopathology was collected to enable DSMIV diagnoses to be made. Socio-economic status based on details of parental occupation at birth was derived from the Office of Population Censuses and Surveys Standard Occupational Classification (Office of Population Censuses and Surveys, 1991).
Acquisition and pre-processing of magnetic resonance imaging data
For each participant a set of 1.5 mm thick contiguous coronal
T1-weighted magnetic resonance images encompassing the
whole brain was acquired using a three-dimensional spoiled gradient recall
echo sequence running on a GE N/Vi Signa System scanner (General Electric,
Milwaukee, Wisconsin, USA) operating at 1.5 T with the following parameters:
time to repetition=13.1 ms, inversion time=450 ms, echo time=5.8 ms, number of
excitations=1, flip angle=20° and acquisition matrix=
256x256x128.
Optimised voxel-based morphometry (Good et al, 2001) was used to segment MRI data and to record probabilistic maps of grey matter and white matter volume density for each participant in a standard anatomical space. These pre-processing steps were implemented in Matlab version 6.0 (MathWorks, Natick, Massachusetts, USA) using SPM99 statistical parametric mapping software (Wellcome Department of Imaging Neuroscience, 2003). Each MRI scan was segmented into grey, white and cerebrospinal fluid (CSF) tissue classes in native space, and global tissue volumes were estimated. This and each other segmentation step used a modified mixture model cluster analysis technique with correction for non-uniformity of image intensity, combined with prior probabilistic knowledge of the spatial distribution of tissues, and included an automated procedure to remove non-brain tissue such as skull, scalp and venous sinus (Good et al, 2001). Customised study-specific grey, white and CSF template images in standard stereotactic space were then created from the control group, in order to minimise any scanner-specific bias and provide a template matched to the sample. The tissue maps of controls were smoothed using an 8 mm full-width at half-maximum (FWHM) isotropic Gaussian kernel and then spatially normalised using parameters derived from applying a 12-parameter affine transformation of each unsmoothed grey matter map to the standard SPM T1 grey matter template and applying these to the smoothed segmented images. The images were then averaged to create customised grey, white and CSF tissue templates in standard stereotactic space. The original brain scan of each participant was then normalised to the customised grey matter template, thus removing any contribution of non-brain tissue or other tissue types to this spatial normalisation step. The spatial normalisation used residual sum of squared differences to match images and both an affine transformation and linear combination of smooth cosine basis functions to model global non-linear shape differences. These normalisation parameters were applied back onto the original brain image to produce an image optimally normalised for grey matter segmentation and the images were resliced at a final voxel size of 1.5 mm3. All images were checked visually to confirm that they were well matched to the template. The images were then resegmented, using the customised tissue templates as probability maps, and the grey matter maps retained. These grey matter maps were thus in standard stereotactic (Talairach) space. This procedure was repeated using parameters derived from normalising each white matter map to the white matter template and reapplying to the original image, in order to derive white matter tissue maps for each participant. The grey and white matter images were then modulated through multiplying voxel values by the Jacobian determinants from the spatial normalisation to correct for volume changes introduced at this step (Ashburner & Friston, 2000; Good et al, 2001). Finally, all normalised, segmented, modulated grey and white matter tissue maps were smoothed at 4 mm using a FWHM isotropic Gaussian kernel.
Statistical analysis of MRI data
Differences in grey matter and white matter volume between each patient
group and the healthy volunteer group, and differences between the two patient
groups, were estimated by fitting an analysis of covariance (ANCOVA) model at
each intracerebral voxel in standard space, with age, gender and global tissue
volume as covariates. We tested the null hypothesis by permutation at the
level of spatially contiguous voxel clusters, as described in detail elsewhere
(Bullmore et al,
1999). Briefly, a map of the standardised ANCOVA model coefficient
of interest (ß) at each voxel was thresholded such that if ß>1.96
(approximately, null probability of ß<0.05) the voxel value was set to
ß-1.96, otherwise the voxel value was set to 0. This procedure generated
a set of suprathreshold voxel clusters in three dimensions, each described by
its mass or the sum of suprathreshold voxel statistics it
comprised. The mass of each cluster was tested against a null distribution
ascertained by repeatedly re-estimating and thresholding the ß
coefficient of the ANCOVA model at each voxel after repeated random
permutations of group membership; the results of ten permutations at each
voxel were pooled over all intracerebral voxels to sample the permutation
distribution of three-dimensional cluster mass under the null hypothesis of no
differences in brain structure between the two groups. A critical value for
statistical significance of cluster mass was derived from this permutation
distribution. For each between-group comparison, we used probability
thresholds for cluster level testing such that the expected number of
false-positive tests for each map was less than one; typically one-tailed
cluster-wise P<0.01. Significant clusters were anatomically
labelled using the standard atlas of Talairach and Tournoux
(Talairach & Tournoux,
1988). The principal advantages of cluster-level testing are that
it confers greater sensitivity by incorporating information from more than one
voxel in the test statistic, and it also substantially reduces the search
volume or number of tests required for a whole brain analysis, thereby
mitigating the multiple comparisons problem. Parametric tests for spatial
extent statistics in brain mapping may be over-conservative; hence our
preferred use of a relatively assumption-free non-parametric permutation test
based on data resampling (Bullmore et
al, 1999; Hayasaka &
Nichols, 2003).
The mass of each cluster for each individual was transferred to a spreadsheet and, where multiple clusters were present, principal components analysis without rotation was used to explore the extent of correlation between discrete clusters and to reduce the dimensionality of the data prior to further analyses. Multiple linear regression with principal components scores as the dependent variable, and age, gender and global tissue volume as covariates, was used to test for a pathoplastic effect of gender on casecontrol differences in brain structure. The two-tailed probability threshold for significance was set at P=0.05.
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RESULTS |
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Grey matter differences between those with schizophrenia and healthy volunteers
Compared with healthy volunteers, those with schizophrenia had spatially
distributed regions of grey matter volume deficit in 12 three-dimensional
voxel clusters (Fig. 1,
Table 2). These deficits were
predominantly bilateral and included the hemispheres and vermis of the
cerebellum, orbitofrontal cortex and temporal pole (more prominently on the
right) extending to the lateral temporal cortex, anterior cingulate gyrus,
basal ganglia, thalamus, medial temporal lobe, insula, dorsolateral prefrontal
cortex (more prominently on the left), right postcentral gyrus and inferior
parietal lobule, and the precuneus. Principal components analysis showed that
deficits were highly correlated between regions. All clusters of grey matter
volume deficit loaded positively on the first principal component, which
explained 74% of the total variance. Schizophrenia was strongly associated
with reduced scores on this first component (B=-1.07, P<0.001, 95%
CI -1.49 to -0.66) and there was no significant interaction between diagnostic
group and gender (B=0.27, P=0.55, 95% CI -0.61 to 1.14), indicating
that this pattern of grey matter deficit was not differentially expressed by
males and females with schizophrenia. There were no significant regions of
relative grey matter excess in those with schizophrenia compared with healthy
volunteers.
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Grey matter differences between those with bipolar disorder and healthy volunteers
Compared with healthy volunteers, those with bipolar disorder demonstrated
no significant abnormalities (neither deficits nor excesses) of grey matter
structure.
Grey matter differences between those with bipolar disorder and schizophrenia
Participants with schizophrenia also demonstrated a distributed pattern of
grey matter deficit when compared with those with bipolar disorder rather than
with healthy volunteers (Fig.
2, Table 3). These
deficits were located in several of the regions identified as abnormal in the
casecontrol comparison for schizophrenia, including bilateral superior
temporal neocortex, basal ganglia, insula, prefrontal cortex and precuneus,
and right medial temporal lobe and thalamus. There were no regions of
significant grey matter excess in those with schizophrenia compared with those
with bipolar disorder.
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White matter differences between those with schizophrenia and healthy volunteers
Compared with healthy volunteers, those with schizophrenia had deficits of
white matter volume in two spatially extensive three-dimensional voxel
clusters (Fig. 3,
Table 4). These included parts
of prefrontal, temporal and parietal lobes normally occupied by the long white
matter tracts of the superior longitudinal fasciculus and occipitofrontal
fasciculus bilaterally and the left inferior longitudinal fasciculus, as well
as anterior and posterior parts of the corpus callosum. White matter volumes
were highly correlated between clusters (r=0.90,
P<0.001); mean cluster volume was therefore used to examine gender
interactions. Those with schizophrenia had significantly reduced mean white
matter volumes compared with controls (B=-1.59, P<0.001, 95% CI
-2.46 to -0.72) and there was no significant interaction between diagnostic
group and gender (B=0.64, P=0.49, 95% CI -1.18 to 2.46).
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White matter differences between those with bipolar disorder and healthy volunteers
Compared with healthy volunteers, those with bipolar disorder had
distributed regional deficits of white matter in four voxel clusters
(Fig. 3,
Table 5). These included parts
of the brain-stem, prefrontal, temporal and parietal lobes normally occupied
by the long white matter tracts of the superior longitudinal fasciculus and
occipitofrontal fasciculus bilaterally, as well as anterior and posterior
parts of the corpus callosum. All regions of white matter volume deficit
loaded positively on the first principal component, which explained 72.4% of
the total variance. Bipolar disorder was strongly associated with reduced
scores on the first component (B=-0.79, P<0.001, 95% CI -1.19 to
-0.39) and there was no significant interaction between diagnostic group and
gender (B=-0.37, P=0.37, 95% CI -1.18 to 0.44).
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White matter differences between those with bipolar disorder and schizophrenia
Notably, in both patient groups compared with healthy volunteers, there
were extensive areas of white matter abnormality in anatomically coincident
regions of bilateral frontal and temporo-parietal cortex
(Fig. 3). Hence, the anatomical
profile of white matter deficit was much more consistent between types of
psychosis than the profile of grey matter deficit, which was highly specific
to schizophrenia. We found no evidence for a significant difference in white
matter structure between those with schizophrenia and those with bipolar
disorder.
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DISCUSSION |
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The presence of grey matter deficit in prefrontal regions, thalamus and cerebellum provided the basis for the concept of cognitive dysmetria in schizophrenia (Andreasen et al, 1998), i.e. the hypothesis that distributed pathology throughout key information processing nodes underlies the deficits in integrating and coordinating information associated with schizophrenia. We also found volume deficit of the basal ganglia in those with schizophrenia compared with healthy volunteers; this is at variance with several other studies which have reported increased basal ganglia volume. The basal ganglia is rich in dopaminergic input, and increased volume in patients with schizophrenia has usually been attributed to conventional or typical anti-psychotic drugs, which potently block dopamine D2 receptors; thus, basal ganglia volume increase has not been found in patients who have had minimal exposure to typical antipsychotics or purely atypical antipsychotic drug treatment (Lang et al, 2001). In this context, we note that the majority of those with schizophrenia in our sample (18 out of 25) were taking atypical antipsychotic drugs; this may have disclosed disease-related reductions in basal ganglia volume that could be obscured by the volume-increasing effects of typical antipsychotics.
In sharp contrast to those with schizophrenia, grey matter volume was relatively well preserved (statistically indistinguishable from normal) in those with bipolar disorder, despite the fact that they were chosen to be closely akin to those with schizophrenia in terms of severity of illness and experience of positive psychotic symptoms. Brain structural changes in bipolar disorder are arguably under-researched, although some groups have reported volumetric abnormality within grey matter structures, such as enlargement of the amygdala (Altshuler et al, 2000) and reduction of the subgenual cingulate gyrus (Drevets et al, 1997); these were not found in the present study. Variable sample size and heterogeneity may account for some of these discrepancies. Structural neuroimaging studies of bipolar disorder have often been conducted using broad diagnostic categories such as affective disorder or affective psychosis which encompass a heterogeneous sample of patients. Since brain structural abnormalities in unipolar depression may differ from those in bipolar disorder (e.g. hippocampal volume is reportedly reduced in unipolar depression (Frodl et al, 2002) but preserved in bipolar disorder (Altshuler et al, 2000)), it is arguable that adoption of broad diagnostic categories may have obscured brain structural differences specifically related to bipolar disorder. The possibility that medication could reverse or prevent grey matter volume deficit in bipolar disorder also cannot be excluded. Most of our patients were taking lithium, which is neurotrophic and has been reported to increase grey matter volume in vivo (Moore et al, 2000).
To the best of our knowledge, no prior neuroimaging study has specifically compared individuals with familial bipolar I disorder and a history of psychotic symptoms with those with schizophrenia. When compared directly in this way, individuals with schizophrenia demonstrated distributed regions of grey matter volume deficit which involved most of the regions identified in the comparison of those with schizophrenia and healthy volunteers, namely bilateral fronto-temporal cortex, insula, basal ganglia, precuneus and right medial temporal lobe and thalamus. These findings further emphasise the specificity to schizophrenia of regional grey matter volume deficits in these areas. Previous studies that compared individuals with schizophrenia and bipolar disorder (or affective psychosis) either with each other or with the same control group have reported conflicting findings. Some found that grey matter or medial temporal lobe volume deficit was specific to schizophrenia (Harvey et al, 1994; Pearlson et al, 1997; Zipursky et al, 1997; Altshuler et al, 2000; Hirayasu et al, 2001), whereas others found evidence for deficit in both disorders (Friedman et al, 1999; Lim et al, 1999; Velakoulis et al, 1999). A recent study reports that insular cortex reduction is specific to schizophrenia, whereas both schizophrenia and affective psychosis share volume reduction of the left temporal pole (Kasai et al, 2003). However, there are multiple methodological differences between these studies conducted over the course of a decade, including changes in scanner technology and data analysis as well as variation in sample size and diagnostic inclusion criteria. In a previous voxel-based morphometry study of individuals with a first episode of psychosis, Kubicki et al (2002) found distributed regional grey matter deficit in schizophrenia but not in affective psychosis, which is consistent with the present study (although a subsequent analysis confined to limited areas revealed mild volume reduction in the insula among patients with affective psychosis).
Our study has provided clear evidence for greater salience of grey matter abnormalities in those with schizophrenia compared with matched individuals with bipolar disorder, suggesting that schizophrenia may generally be associated with more severe and extensive disorganisation of cortical and subcortical grey matter. Our negative finding, that there is no significant grey matter abnormality in those with bipolar disorder, should probably be evaluated more cautiously in the light of the moderate sample size and the necessarily conservative nature of multiple hypothesis testing entailed by whole brain morphometry.
White matter differences
There was evidence for white matter abnormalities in both patient groups;
moreover, there was a striking degree of anatomical coincidence in the
distribution of white matter deficits in schizophrenia and bipolar disorder.
In both groups of individuals with psychotic disorder, white matter volume was
significantly reduced in the frontal and temporo-parietal territory of major
longitudinal tracts and in the corpus callosum.
Reduction of regional white matter volume has been less comprehensively studied than grey matter in schizophrenia, partly because methods for morphometric subdivision of white matter have only recently been developed. Earlier studies focused on area or shape measurements of the corpus callosum and most found reduced callosal area of distorted shape (Woodruff et al, 1995).
There have also been neuroradiological reports of qualitatively diagnosed white matter hyperintensities in schizophrenia, especially among elderly subjects with late onset of psychotic symptoms (Davis et al, 2003). More recently studies using computational morphometry have identified regional white matter volume deficit in schizophrenia within fronto-temporal and parietal regions and anterior corpus callosum (Sigmundsson et al, 2001; Spalletta et al, 2003). This evidence is in accordance with that from magnetic transfer imaging and diffusion transfer imaging, which have identified white matter abnormalities in those with schizophrenia compared with controls, predominantly involving fronto-temporal regions; this is also in accordance with evidence from neurocytochemistry, neuropathology and gene expression studies implicating white matter dysfunction in schizophrenia (Davis et al, 2003).
In bipolar disorder, increased rates of hyperintense white matter lesions in subcortical and periventricular regions are among the most consistently reported anatomical abnormalities (Bearden et al, 2001) but regional morphometry of white matter has rarely been studied. A recent twin study reported reduced white matter volume in frontal regions bilaterally in those with bipolar disorder, which is consistent with the present study (Kieseppa et al, 2003); another study found no white matter volume change in prefrontal subregions (Lopez-Larson et al, 2002).
Our finding that schizophrenia and bipolar disorder are both characterised by white matter volume deficit in frontal and parietal regions is in accordance with the hypothesis that both major types of psychosis represent a disorder of anatomical connectivity between components of large-scale neurocognitive networks (Bullmore et al, 1997; Wright et al, 1999). It is also in accordance with recent evidence from gene expression profiling studies of frontal cortical tissue, which have identified specific downregulation of genes related to myelination and oligodendrocyte function in both schizophrenia and bipolar disorder (Hakak et al, 2001; Tkachev et al, 2003). Ultrastructural abnormalities and reduced density of oligodendroglial cells in the prefrontal cortex have also been reported in both disorders (Uranova et al, 2001, 2004).
Methodological issues
Strengths of this study include the moderately large numbers of carefully
characterised participants who were selected to optimise the clinical
homogeneity of the groups with psychotic disorder and to ensure that the
comparison between groups was reasonably well controlled for illness duration
and severity. The same group of healthy volunteers was used for both
casecontrol comparisons and was well matched for key socio-demographic
variables to both patient groups. We used contemporary computational tools for
fully automated whole-brain morphometric analysis and non-parametric
hypothesis testing, sourcing and combining relevant software from different
laboratories to construct a customised image-processing
pipeline.
The study also had a number of limitations besides the general issue of type 2 error already discussed. In common with many previous studies, the bipolar and schizophrenia groups were not well matched for gender, owing to an excess of males within the schizophrenia group. However, gender was included as a covariate in all casecontrol and casecase comparisons, and there was no evidence for a significant interaction between gender and diagnostic group, implying that there was no major modulatory effect of gender on volumetric deficits due to disorder. Both groups of patients were recruited on the basis of having other family members with a similar illness. It is therefore possible that the results of this study may not be generally applicable to those with non-familial forms of psychosis. Both groups of patients had chronic illnesses with many years of exposure to psychotropic medication, and thus it is theoretically possible that the common white matter morphometric deficit resulted from such exposure. However, medication exposure differed between the two groups, with most individuals with bipolar disorder only exposed to anti-psychotic medication during exacerbation of illness. Although the morphometric analysis of white matter volume deficit suggested involvement of certain longitudinal and interhemispheric tracts, the anatomical labelling of tracts on the basis of Talairach coordinates should be regarded as heuristic. A more compelling demonstration that specific tracts are involved in both psychotic disorders, and that anatomical connectivity between frontal and temporo-parietal cortex is compromised as a result, could be provided by future studies incorporating diffusion tensor imaging and tractography techniques.
The Kraepelinian dichotomy
Our findings neither wholly support nor wholly negate the Kraepelinian
dichotomy of psychosis. Support for the Kraepelinian position comes from the
fact that schizophrenia was characterised by a distinctive pattern of
distributed grey matter deficit in fronto-temporal, subcortical and cerebellar
regions, whereas psychotic bipolar disorder was not associated with
significant grey matter abnormality. However, the classic dichotomy is
partially subverted by our demonstration of common white matter abnormalities
in the two disorders, suggesting that anatomical disconnectivity between
frontal and temporo-parietal cortex may be important for emergence of
psychotic syndromes in general.
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Clinical Implications and Limitations |
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LIMITATIONS
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ACKNOWLEDGMENTS |
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Received for publication March 4, 2004. Revision received November 5, 2004. Accepted for publication November 16, 2004.