BK Life Sciences, Seoul National University
Department of Nuclear Medicine, Seoul National University College of Medicine
Department of Biomedical Engineering, Hanyang University College of Medicine
Department of Radiology, Seoul National University College of Medicine
Department of Psychiatry, Seoul National University College of Medicine & Neuroscience Institute, SNU-MRC, Korea
Correspondence: Profesor Jun Soo Kwon, Department of Psychiatry and BK Life Sciences, Seoul National University Hospital, 28 Yongon-dong, Chongno-gu, Seoul, Korea 110-744
Declaration of interest This study was supported by the Korean Research Foundation (1998-003-F00172).
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ABSTRACT |
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Aims To investigate grey matter abnormalities in patients with OCD by employing a novel voxel-based analysis of magnetic resonance images.
Method Statistical parametric mapping was utilised to compare segmented grey matter images from 25 patients with OCD with those from 25 matched controls.
Results Increased regional grey matter density was found in multiple cortical areas, including the left orbitofrontal cortex, and in subcortical areas, including the thalamus. On the other hand, regions of reduction were confined to posterior parts of the brain, such as the left cuneus and the left cerebellum.
Conclusions Increased grey matter density of frontalsubcortical circuits, consonant with the hypermetabolic findings from functional imaging studies, seems to exist in patients with OCD, and cerebellar dysfunction may be involved in the pathophysiology of OCD.
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INTRODUCTION |
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METHOD |
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The mean ages for the OCD and control groups were 27.4 (s.d.=7.0) and 27.0 (s.d.=6.2) years (t=0.21, d.f.=4.8, P=0.83), respectively, and the mean periods of education were 14.2 (s.d.=2.2) and 15.3 (s.d.=1.8) years (t=1.97, d.f.=48, P=0.06), respectively. Composition of handedness was identical in both groups; 24 were right-handed and 1 was left-handed. At the time of the study, the patients had a mean duration of illness of 8.4 (s.d.=6.7) years, ranging from 1 to 23 years. Eight patients were drug-naïve and 17 had a history of anti-obsessional medication (four included a history of combined therapy with neuro-leptics), but they all remained psychotropic-free for at least 4 weeks. Clinical assessment included the YaleBrown Obsessive Compulsive Scale (YBOCS; Goodman et al, 1989) for measuring OCD symptom severity (mean score for obsessive symptoms=13.0, s.d.=3.3; mean score for compulsive symptoms=11.2, s.d.=5.0; mean total score=24.2, s.d.=8.0).
Magnetic resonance imaging acquisition and image processing
Three-dimensional T1-weighted spoiled gradient echo magnetic resonance
images were acquired on a 1.5-T GE SIGNA scanner (GE Medical Systems,
Milwaukee, USA). Imaging parameters were as follows: 1.5-mm sagittal slices,
echo time=5.5 ms, repetition time=14.4 ms, number of excitations=1, rotation
angle=20°, field of view=21 x 21 cm and a matrix of 256 x
256.
Magnetic resonance images were processed using an image-processing software package, ANALYZE (version 3.0, Mayo Foundation, USA). Images were resampled to 1.0-mm3 voxels, reoriented to the conventional position and spatially normalised so that the anteriorposterior axis of the brain was aligned parallel to the inter-commissural line and the other two axes were aligned along the inter-hemispheric fissure. The data sets then were filtered using anisotropic diffusion methods to improve the signal-to-noise ratio. Images of tissues exterior to the brain were removed by the semi-automated region growing method. The extracted brain images were segmented into grey matter, white matter and cerebrospinal fluid, employing the fuzzy C-means algorithm (Cannon et al, 1986). This method was chosen because it did not require a priori probability. Grey matter data were flipped to reverse right and left (to comply with neurological convention) and then reconstructed into binary grey matter images; grey matter voxels were set to a uniform intensity value of unity and others were set to a value of zero.
Processing of grey matter images for the regional analysis was performed using Statistical Parametric Mapping (SPM) 99 (Institute of Neurology, University College of London, UK) implemented in Matlab (Mathworks Inc., USA) (Friston et al, 1995). Binary grey matter images were smoothed using an 8-mm full width at half-maximum (FWHM) Gaussian kernel to assign a weighted sum of grey matter values to each voxel for voxel-based statistical analysis of grey matter differences. These images were spatially normalised to remove variability due to differences in head size and to facilitate a voxel-based analysis. This process consisted of the following two steps. First, to determine the transformation parameters, filtered T1-weighted imaged were spatially normalised into the MNI (Montreal Neurological Institute, McGill University, CA) standard T1 template at the Standard Talairach spaces (Talairach & Tournoux, 1988), and affine transformation was performed to determine the 12 optimal parameters to register the brain on the template. Second, by applying the parameters produced in the first step, the smoothed grey matter images were spatially transformed. Spatially normalised grey matter again were smoothed by convolution with an isotropic Gaussian kernel with 12-mm FWHM. Parameters for these two separate smoothing process steps were applied following those of Wright et al (1995).
Statistical analysis
Total intracranial volume and global grey matter volume prior to the
processing for regional analyses were calculated and compared between the
patients and the controls using Student's t-test. P values
less than 0.05 were considered significant.
Voxel-based regional analyses of the processed grey matter images were performed using SPM. The effects of the global grey matter intensity were removed by proportional scaling in which the count of each voxel was normalised relative to the total count of the brain. Any significant changes of regional grey matter density in the patients then were estimated by comparing their pre-processed grey matter images with those of the controls using t-statistics at every voxel. For ease of interpretation, t values were transformed to Z scores in the standard Gaussian distribution. The clusters consisting of a minimum of 50 contiguous voxels with a threshold of uncorrected P<0.001 (Z=3.09) were considered to have significant differences and were displayed on three orthogonal planes.
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RESULTS |
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Regional analysis
The SPM results for the grey matter differences between the two groups are
summarised in Table 1. As shown
in Fig. 1a, regions where the
patient group had significantly increased grey matter were observed to be
distributed over multiple areas: left side of the orbitofrontal cortex,
superior temporal gyrus, inferior parietal lobule and thalamus, right side of
the insula, middle temporal gyrus and inferior occipital cortex and both sides
of the hypothalamus.
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On the other hand, as shown in Fig. 1b, regions where the patient group had significantly reduced grey matter appeared to be confined to the posterior part of the brain. Areas of reduction were distributed in a large circumscribed region in the left cerebellum and in a small region in the left cuneus.
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DISCUSSION |
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On the other hand, a novel voxel-based approach using SPM seems to have promise in the detection of subtle changes. Without artificial delineation, changes are searched for in the whole brain rather than in preselected regions. This approach has been verified as being useful to find subtle structural abnormalities in schizophrenia (Sowell et al, 2000), as well as maturational changes in childhood and adolescence (Paus et al, 1999).
It should be noted that the regional grey matter differences that we detected may have arisen by chance (type I error) following multiple comparisons. We attempted, therefore, to apply a strict criterion: clusters of more than 50 contiguous voxels with a threshold of P<0.001. On the contrary, consideration should be given to the fact that changes in variably located area or areas with highly variable volume cannot be detected. Because of the possibility of these type II errors, the regional differences detected in the current study need to be viewed as representing foci of maximal change rather than regions that are exclusively affected.
Increased grey matter density in the orbitofrontal cortex
The voxel-by-voxel analyses of grey matter density in patients with OCD
revealed regional brain abnormalities that were generally consistent with our
a priori hypothesis based on previous results from functional imaging
studies. Abnormality in the left orbitofrontal cortex is particularly
impressive. It is noteworthy that the orbitofrontal cortex might play a
potential role in inhibitory motor control
(Rubia et al, 2001).
This may be of special importance given the compulsions presented in OCD and
the patients' attempts to resist performing these actions.
Increased regional grey matter density in the orbitofrontal cortex, together with those in the thalamus and hypothalamus, is in agreement with the hyperfunctioning of the frontalsubcortical circuits in OCD, although the mechanism of the relationship between them is still unclear. One study also identified increased volume of the anterior cingulate gyrus in paediatric patients with OCD (Rosenberg & Keshavan, 1998). This concurs with the surgical interruption of frontal, cingulate or related fibres that has been found effective in the treatment of severe OCD (Jenike et al, 1991), probably by interfering with these inappropriately overactive circuits.
Hyperfunctional frontalsubcortical circuits
Hyperfunctioning in these circuits may lead to increased grey matter
density, in common with results regarding the exposure to neuroleptics among
patients with schizophrenia. Neuroleptic treatment has been shown repeatedly
to be related to enlarged basal ganglia structures, possibly resulting from
the compensatory overactivities secondary to dopamine blockade
(Gur et al, 1998).
Likewise, long-standing metabolic and perfusional increases in
frontalsubcortical circuits that have been provoked by OCD symptoms
also might result in increased grey matter density.
It is possible also that increased grey matter density in frontalsubcortical circuits is a result of neurodevelopmental abnormality. Some failure in normally programmed cell death during brain development may contribute to greater amounts of cerebral cortex in patients with OCD. Frontalsubcortical circuits consisting of transient juvenile projections that would have been extinguished eventually may be retained abnormally and evoke obsessional symptoms. Alternatively, these circuits may stem from impaired neuronal pruning during development.
Parietal and cerebellar involvements in OCD
In contrast to the orbitofrontal cortex, the left cuneus in the current
study was observed to be of a reduced area. This finding is consistent with a
previous one where the retrocallosal region was reduced in patients with OCD
(Breiter et al, 1994).
Several functional imaging studies also have suggested that similar areas have
reduced metabolic activity, although such reductions have not been important
in their own consideration (Nordahl et
al, 1989; Lucey et
al, 1995). Given that these extrastriate areas have been
activated with cognitive tasks related to visual imagery
(Mellet et al, 1996),
the defective features of these areas found in both functional and structural
imaging studies may be related to visuospatial processing and visual memory
deficits in some patients with OCD
(Purcell et al,
1998).
It is noteworthy that cerebellar grey matter is characteristically reduced in the left upper part. In fact, the cerebellum has not been an area of interest in psychiatric imaging studies, so it has not been investigated frequently as an ROI in previous studies. Cerebellar involvement in the symptomatology of OCD might be negligible because the binding site for serotonergic drugs such as paroxetine is nearly absent from the cerebellum (Cumming & Gjedde, 1993). However, consideration should be given to the important role that the cerebellum plays in coordinating complex mental and non-motor higher cognitive functions (Kim et al, 1999), as well as in controlling motor functions through abundant corticalsubcorticalcerebellar connections (Andreasen et al, 1998). The cerebellar reduction identified in the current study, therefore, might be related to some cognitive deficits in OCD, including executive and visual memory dysfunction, although this is somewhat speculative. Previous researchers have interpreted abnormal neuropsychological performances in OCD as reflecting dysfunction of frontalsubcortical circuits (Galderisi et al, 1995). Our finding indicates that such an interpretation needs to be extended to the abnormal circuits, including the cerebellum. Further research is necessary to verify cerebellar involvement in the pathophysiology of OCD.
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Clinical Implications and Limitations |
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LIMITATIONS
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Received for publication November 24, 2000. Revision received April 24, 2001. Accepted for publication April 27, 2001.
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