Shape deformity of the corpus striatum in obsessive–compulsive disorder

Shape deformity of the corpus striatum in obsessive–compulsive disorder

Psychiatry Research: Neuroimaging 155 (2007) 257 – 264 www.elsevier.com/locate/psychresns Shape deformity of the corpus striatum in obsessive–compuls...

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Psychiatry Research: Neuroimaging 155 (2007) 257 – 264 www.elsevier.com/locate/psychresns

Shape deformity of the corpus striatum in obsessive–compulsive disorder Jung-Seok Choia , Sun Hyung Kimc , So Young Yooa , Do-Hyung Kanga , Chi-Won Kima , Jong-Min Leec , In Young Kimc , Sun I. Kimc , Young Youn Kimb , Jun Soo Kwona,b,⁎ a

Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea b Neuroscience Institute, SNU-MRC, Seoul, Republic of Korea c Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea Received 21 October 2006; received in revised form 21 January 2007; accepted 15 February 2007

Abstract Volumetric changes of striatal structures based on magnetic resonance imaging (MRI) have been inconsistent in patients with obsessive–compulsive disorder (OCD) due to methodological limitations. The purpose of this study was to investigate shape deformities of the corpus striatum in patients with OCD. We performed 3-D shape deformation analysis of the caudate nucleus, the putamen, and the globus pallidus in 36 patients with OCD and 36 healthy normal subjects. Shape analysis showed deformity of the striatal structures, especially the caudate nucleus. Outward deformities in the superior, anterior portion of the bilateral caudate were observed in patients with OCD. In addition, an outward deformity in the inferior, lateral portion of the left putamen was also detected. These results suggest that patients with OCD have shape deformities of the corpus striatum, especially the caudate nucleus, compared with healthy normal subjects, and that shape analysis may provide an important complement to volumetric MRI studies in investigating the pathophysiology of OCD. © 2007 Elsevier Ireland Ltd. All rights reserved. Keywords: Obsessive–compulsive disorder; Corpus striatum; Shape analysis

1. Introduction Many studies performed with various methodologies have suggested that symptoms of obsessive–compulsive disorder (OCD) are due to dysfunction of the fronto– subcortical circuitry originating from the prefrontal cortex and the anterior cingulate cortex (ACC), projecting into the caudate nucleus, and finally reaching the

⁎ Corresponding author. Department of Psychiatry, Seoul National University College of Medicine, 28 Yongon-dong, Chongno-gu, Seoul, 110-744, Republic of Korea. Tel.: +82 2 2072 2972; fax: +82 2 747 9063. E-mail address: [email protected] (J.S. Kwon).

thalamic relay. The role of the basal ganglia is to integrate the various inputs arriving from the cortex and to use this information to select certain motor and/or cognitive programs. Of the structures of the basal ganglia, the caudate nucleus is the area that has been implicated most consistently in the pathophysiology of OCD. Functional imaging studies have detected hyperactivities of the bilateral (Baxter et al., 1987, 1988) or right (Molina et al., 1995) head of the caudate nucleus, which might be consistent; however, volumetric changes of the striatal structures based on magnetic resonance imaging (MRI) have been inconsistent in patients with OCD. Robinson et al. (1995) reported that bilateral caudate volumes were reduced in patients with OCD,

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while Scarone et al. (1992) found increased right caudate volumes in patients with OCD. Riffkin et al. (2005) did not reveal any statistical significant differences in OCD, using manual region of interest method and automated voxed based morphometry method. Our group did not also find significant volume differences of the bilateral caudate nucleus or the putamen between patients with OCD and normal controls (Kang et al., 2004). The differences between these volumetric studies with MRI could have been caused by methodological limitations. Recently, interest has been growing in measuring changes in the shapes of specific brain regions. In the shape analysis study with bipolar disorder patients, drugnaïve bipolar disorder patients had shape differences of the striatum, relative to healthy comparison subjects, and that these differences might be modulated by treatment (Hwang et al., 2006). Based on a hypothesis suggested by Van Essen (1997), the shapes of specific brain structures may be determined by the physical properties of neural tissue combined with the patterns of neural connectivity. Therefore, a shape analysis method would be more sensitive for detecting morphological changes of brain regions in patients with OCD, in which abnormalities of the fronto–subcortical circuitry may be involved, compared to conventional volumetric measurements. In addition, a shape analysis method might reliably detect subtle volume changes of small brain regions such as the striatal structures (Levitt et al., 2004). No previous study has reported structural abnormalities of the basal ganglia using a shape analysis method in patients with OCD. In the current study, we hypothesized that patients with OCD would show characteristic differences in the shapes of structures of the basal ganglia compared to normal subjects. 2. Methods 2.1. Subjects Thirty-six (28 men and 8 women) patients and healthy normal subjects were included in this study. The subjects were the same as in a previous report by our group (Kang et al., 2004). All participants were right-handed. And no differences existed in age or socioeconomic status (SES) (Hollingshead and Redlich, 1958) between the patients and controls. The mean ages of the patients with OCD and the controls were 26.33 years (S.D. = 6.18) and 26.50 years (S.D. = 7.58), respectively. The mean parental SES of the patients with OCD and the controls were 3.00 (S.D. = 0.71) and 2.86 (S.D. = 0.73), respectively. However, the OCD and control groups were significantly different in terms of educational level (13.86 years [S.D. =

2.04] and 15.17 years [S.D. = 1.89], respectively; t = 2.814, df = 70, P = 0.006) and IQ (109.20 [S.D. = 8.41] and 116.64 [S.D. = 10.88], respectively; t = 3.215, df = 70, P = 0.002). At the time of the study, the patients had a mean illness duration of 8.90 years (S.D. = 7.00). Four patients with OCD had a major depressive disorder as a comorbidity, but no other DSM-IVaxis I disorder. Eleven patients were drug naïve, and 25 had a history of antiobsessional medication (six patients had a history of antipsychotic medication). However, all were psychotropic drug-free for at least 4 weeks prior to the measurement of clinical symptoms. In addition, an interval of about 2 weeks had passed between the measurement of clinical symptoms and MRI scanning. Clinical assessment was conducted using the Yale– Brown Obsessive Compulsive Scale (Y–BOCS; Goodman et al., 1989). The mean scores for obsessive symptoms, compulsive symptoms, and total score were 13.31 (S.D. = 3.54), 11.22 (S.D. = 4.26), and 24.17 (S.D. =5.94), respectively. To obtain an IQ estimate, the Vocabulary, Arithmetic, Block Design, Picture Arrangement, and Digit Span subscales, which were included in the Korean version of the Wechsler Adult Intelligence Scale (WAIS), were administered to all subjects. This study was performed according to regulations on the use of human subjects established by our institutional review board. After complete description of the study to the subjects, written informed consent was obtained. 2.2. Image acquisition and processing We performed MRI scanning of the entire brain, and acquired 3-D T1-weighted spoiled gradient echo MR images using a 1.5-T General Electric SIGNA system (GE Medical Systems, Milwaukee, WI, USA) with imaging parameters of 123 1.5-mm sagittal slices, echo time = 5.5 ms, repetition time = 14.4 ms, number of excitation = 1, rotation angle = 20°, field of view = 21 × 21 cm, and matrix = 256 × 256. MRI data were then processed with the ANALYZE software package (version 4.0, Mayo Foundation, Rochester, Minesota, USA). Images were re-sampled to 1.0 mm3 voxels, reoriented to the conventional position, and spatially realigned so that the anterior–posterior axis of the brain was aligned parallel to the inter-commissural line, and the other two axes were aligned along the interhemispheric fissure. The data sets were then filtered using anisotropic diffusion methods (Perona and Malik, 1990) to improve the signal to noise ratio. In order to extract the brain, tissues exterior to the brain were removed by the semi-automated region growing method. Employing the fuzzy C-means algorithm, the extracted

J.-S. Choi et al. / Psychiatry Research: Neuroimaging 155 (2007) 257–264

brain images were segmented into gray matter, white matter and cerebrospinal fluid. Intracranial volume was calculated by summing up the subtotal volumes of these three components.

may be defined generally as a weighted sum of Nt term, each of which may be thought of as a model term: OðSÞ ¼

2.4. Preprocessing, parameterized shape model, and alignment process

Nt X

X

w1 Timage þ w2 Tstretch

þ w3 TdistXfield þ w4 Tcurvature

ð1Þ

Where wk is a weighting factor and Tk represents one of the terms defined in the following sentences. The image term, Timage, represents the proximity of the surface to edges in a particular volume images. This term decreases as the surface approaches the edges in the image: X

wk Timage ¼

nv X

wk ðV ðxi Þ−tÞ2

ð2Þ

i¼1

Where t is the threshold denoting the image value that best defines the image edges, nv is the number of vertices in the polyhedral mesh. V(xi) is the intensity of the volume image on the vertex position. In order to increase the power of locating image boundaries that are relatively far from the current surface position, a supplementary image term, the distance field term, is introduced. X

wk TdistXfield ¼

nv X

wk ðdðxi Þ−Dthreshold Þ2

ð3Þ

i¼1

Distance field map, d, is calculated to set the zero value on the boundary of the volume image and the gradient value far from the boundary. The stretch term, Tstetch, is the constrain condition to avoid self-intersection between the vertices. X

wk Tstretch

¼

nj nv X X i¼1 j¼1

Several procedures were required to measure shape deformity of the basal ganglia. After pruning all the extra volume except the basal ganglia volume in the entire brain volume, the smoothed and clipped volume was converted into binary to discriminate the boundary. An active, flexible, and deformable shape model, which was originally developed by MacDonald et al. (2000), was used for the cortical surface parameterization. The overall preprocessing flow is displayed in Fig. 1. The deformation process was performed based on an objective function, which was the weighted sum of several different terms such as image, stretch, distance field and curvature terms. The objective function, O(S) ,

wk Tk ¼

k¼1

2.3. Boundary definition and manual delineation We traced manually the caudate nucleus, putamen, and globus pallidus (both right and left) on coronal slices in the ANALYZE ROI module. The tracing of the caudate nucleus started at a plane where the anterior horns of the lateral ventricles were first visualized and ended when the caudate nucleus was no longer discernible. The ventral boundary was the nucleus accumbens. In addition, the medial and the lateral boundary were the lateral ventricle and the internal capsule, respectively. The boundary definitions of the putamen were as follows: the medial boundary was the internal capsule and the lateral boundary was defined by the white matter intercalated between the putamen and claustrum. In case of the globus pallidus, the superior and medial boundaries corresponded to the internal capsule. The lateral boundary was same as the putamen, and the last measured slice was when the globus pallidus disappeared medially to the putamen. Anatomical landmarks and the boundaries for the basal ganglia were determined with the aid of the human brain atlas (Duvernoy et al., 1999). Inter-rater reliability for each regional brain volume was evaluated in a randomly selected subset of ten MR scans by two raters. The intra-class correlation coefficients were as follows: the caudate nucleus (0.85, 0.86 [right and left, respectively]), the putamen (0.76, 0.82) and the globus pallidus (0.80, 0.82). Raters were unaware of the names and diagnoses of the subjects.

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0qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 12 ðxi −xj Þ2 þ ðyi −yj Þ2 þ ðzi −zj Þ2 −L @ A wk L ð4Þ

Where nj is the number of neighbor vertex in ith vertex and L is the ideal length of an edge, which is empirically defined. The other constrain term, Tcurvature, is provided to conserve the smoothness of shape. The curvature at a vertex on a surface is usually represented by mean curvature, which is based on the minimum and maximum curvatures defined by flat and over-sharp at a point. The optimal parameterized model was obtained by minimizing this objective function, by using the Powell algorithm, with the basal ganglia triangulated surface

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Fig. 1. Overview of the data processing. The corpus striatum data were extracted using the ANALYE 4.0 program, and clipped and smoothed for further processing. The extracted and smoothed caudate was rendered to a 3D volume. The surface of the caudate was parameterized, and consisted of 2562 points. The putamen and the globus pallidus were processed using the same procedure.

consisting of 2562 points per object. All the parameterized shape models were obtained for each basal ganglia data set by the same algorithm. This procedure is described in detail in our previous study (Lee et al., 2004). 2.5. Alignment and statistical process An alignment procedure was applied to the rigid transformation process to optimize the accuracy of our determinations of the corresponding points. The alignment process consisted of two steps, the coarse and fine alignments. The coarse alignment was based on the main axis, and the fine alignment was based on the mean squared difference. The 3D surface model was aligned by translating and rotating in relation to these optimized angles. The vertex of the source, through re-indexing, had the same index as the target, which was generate by averaging the control group. The vertex index of source

was defined by the shortest distance to the vertex index of target In this process, as the vertex index of the source was not exactly matched to that of the target. Each subject had the same single template domain and homologous points. The details of the alignment process have been described in our previous study (Lee et al., 2004). Before the t-test, we performed diffusion smoothing with a 6-mm FWHM Diffusion Gaussian kernel for 1000 iterations (Chung et al., 2003). 2.6. Statistical analysis All measures of regional brain volumes of the caudate nucleus, the putamen and the globus pallidus were subjected to multivariate analysis of variance with the group (OCD, normal subjects) as the between-subject factor, and the intracranial volume as a covariate. On the other hand, after alignment step, we obtained the

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features, that is, shape differences, as the distance between a homologous vertex of source surface model and the vertex of average template which is generated in control group. The significant differences in shapes between the two groups were calculated with t-tests and subsequently mapped on the average basal ganglia template (Fig. 2). In the exploratory analysis, correlations of shape deformity with clinical measurements (illness duration, Y– BOCS scores) were assessed using the Pearson correlation method. The mean deformities of shape were calculated by averaging deformities in statistical significant areas. All analyses were two-tailed, and the significance level was set at α = 0.05. 3. Results In this study, no significant volumetric differences were observed between the two groups, as we previously reported (Kang et al., 2004). Volumes of the caudate nucleus and the putamen were same as in our

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previous study (Kang et al., 2004), and volumes of the globus pallidus in patients with OCD and normal subjects were as follows: right side (0.78 ml [0.15] and 0.76 ml [0.11], respectively; F = 0.532, P = 0.468), left side (0.84 ml [0.16] and 0.84 ml [0.12], respectively; F = 0.126, P = 0.724). Shape analysis showed deformity of the striatal structures, especially the caudate nucleus (Fig. 2). The most prominent surface deformity, as determined both by the size of the colored area and by the depth of its color, was observed in the caudate nucleus in patients with OCD. That is, outward deformities in the superior, anterior portion of the bilateral caudate (right N left) were found in patients with OCD (left P = 0.01435, t = − 2.5139 and right P = 0.00572, t = − 2.8550). In addition, an outward deformity in the inferior, lateral portion of the left putamen was also observed (P = 0.01462, t = − 2.5066). However, no significant correlations were detected between the shape deformity and clinical measurements in patients with OCD.

Fig. 2. Regional shape deformities of the basal ganglia structures in patients with OCD. Statistically significant differences (P b 0.05) of shape based on the t tests are mapped. Red and blue colors represent outward and inward deformity, respectively. OCD indicates obsessive–compulsive disorder. Cd: caudate nucleus, Pt: putamen, GP: globus pallidus.

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4. Discussion 4.1. Abnormalities in fronto–subcortical circuitry The present study suggests that patients with OCD have shape deformities of the striatal structures, especially the caudate nucleus, and that shape analysis may provide an important complement to volumetric MRI studies in investigating the pathophysiology of OCD. To our knowledge, this is the first study to elucidate the shape alterations of the corpus striatum using shape analysis in patients with OCD. In this study, the most prominent shape deformities were found in the superior, anterior part of the caudate nucleus (right side N left side) in patients with OCD. Fronto–subcortical circuitry appear to be relatively distinct. In particular, the dorsal caudate has interconnections with the dorsolateral prefrontal cortex (DLPFC), while the ventral prefrontal cortex projects to the ventromedial caudate (Fuster, 1989). There was a report that patients with the dorsal or ventral caudate nucleus lesions exhibited deficits in memory, attention, and set shifting (Mendez et al., 1989). Especially, lesions of the dorsal portion of the caudate nucleus may induce impairments in tasks, such as spatial delayed and delayed alternation tasks, which are also found after DLPFC lesions (Butters and Rosvold, 1968; Iversen, 1979). The dorsal caudate is a key structure in an anatomical– functional network in combination with the DLPFC which contributes to executive functions (Levy and Dubois, 2006). On the other hand, based on the report of Lehericy et al. (2004), human striatum is divided functionally into anterior (associative), posterior (sensorimotor), and ventral (limbic) components. Lehericy et al. (2004) observed that the associative compartment of the striatum (anterior striatum) was connected to the prefrontal cortex, the frontal pole, and the pre-supplementary motor area (SMA). Considering these interconnections of the caudate nucleus with the prefrontal cortex, especially the DLPFC, shape deformities of the superior, anterior part of the caudate nucleus observed in this study may be associated with the expression of the cognitive deficits in OCD. In addition, an outward deformity in the inferior, lateral portion of the left putamen was also noted. The ventral striatum is connected with limbic areas, including the orbitofrontal cortex, the amygdala, and the hippocampus (Russchen et al., 1985; Alheid and Heimer, 1988; Haber et al., 1995; Friedman et al., 2002; Fudge et al., 2002; Lehericy et al., 2004), whereas the posterior part of the putamen is connected to the primary sensory and motor areas and to the posterior part of the SMA, which has a primary

executive role in the control of movement (Jueptner et al., 1997; Lehericy et al., 1998). Thus, shape deformities of the putamen may be related to the generation of clinical symptoms observed in patients with OCD. 4.2. Correlation of symptoms with shape deformity We used a value of the mean deformity to correlate the differences in shapes with clinical measurements in an exploratory analysis and obtained the mean deformity within the brain regions in which significant shape differences were observed. In this study, however, no significant correlations were detected between the mean deformity and Y–BOCS scores and illness duration in patients with OCD. These results suggest that the observed shape deformities of the corpus striatum may be more related to the fundamental neurobiology of OCD than to the clinical state at the time of scanning. 4.3. Neurodevelopmental aspects of OCD The basal ganglia are a collection of subcortical neuronal groups in the forebrain located beneath the anterior portion of the lateral ventricles. The three main subdivisions are the caudate nucleus, the putamen, and the globus pallidus (corpus striatum). The caudate nucleus and the putamen are referred to as the neostriatum because they are phylogenetically the most recent of the basal ganglia to appear and are developmentally related (Gazzaniga et al., 2002). Some research has supported the possible neurodevelopmental origins of OCD. As many as 80% of all cases of OCD have their onset in childhood and adolescence (Pauls et al., 1995). Another observation supporting the neurodevelopmental origins of OCD is its comorbidity with Tourette's syndrome, which is a neurodevelopmental disorder. In addition, some neuroimaging studies have reported findings associated with the neurodevelopmental model of OCD. Rosenberg et al. (1997) found that children with OCD who had not been exposed to psychotropic drugs had significantly smaller striatal volumes (the caudate, and the putamen) and their striatal volumes were inversely correlated with OCD symptom severity but not illness duration. Using singlevoxel proton magnetic resonance spectroscopy, caudate glutamatergic concentrations were significantly greater in treatment-naïve pediatric patients with OCD than in controls (Rosenberg et al., 2000). The shapes of brain structures may be linked to neurodevelopmental influences. Therefore, deformations in the shapes of brain structures may reflect abnormalities in neurodevelopment. According to a tension-based theory of morphogenesis of the central nervous system proposed by Van Essen (1997),

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shape deformations may be associated with the physical properties of morphogenetic mechanisms that directly impact the particular shapes of brain structures during neurodevelopment. As noted above, the caudate nucleus and the putamen are developmentally related, and the shapes of these structures might be distorted in developmental disorders. Therefore, shape analysis with MRI can be useful studies investigating abnormalities of the basal ganglia involved in the pathophysiology of OCD. 4.4. Limitations Our study had several limitations. Firstly, alignment process to get the homologous points was performed with linear rigid transformation. Non-linear transformation, surface registration, was suitable for more precisely corresponding points. However, it is difficult to extract the features such as geodesic depth and curvatures for nonlinear transformation because subcortical structures had generally smooth shape without irregularities. We, therefore, believed that sub-sampling with 2562 vertices in small structures, rigid transformation with six parameters and Gaussian diffusion smoothing on Riemannian space were sufficient to generate the homologous points. Secondly, we could not correct the random chances resulted from many vertices in contrast to small sample. However, our study showed good result to reveal general pattern of difference between the healthy control and OCD group. In addition, patients with OCD who participated in this study were not drug naïve, and 25 of 36 patients had a history of anti-obsessional medication, although all were psychotropic drug-free for at least 4 to 6 weeks prior to the time of MRI scanning. Thus, we could not completely exclude the possibility of the effects of medications on the basal ganglia shape. Our study was cross-sectional, and we could not examine the longitudinal changes of brain shape abnormalities in patients with OCD. Further studies are needed to confirm our present findings. Acknowledgments This paper was supported by a grant (M103KV010007 04K2201 00710) from Brain Research Center of the 21st Century Frontier Research Program funded by the Ministry of Science and Technology of Republic of Korea. References Alheid, G.F., Heimer, L., 1988. New perspectives in basal forebrain organization of special relevance for neuropsychiatric disorders: the striatopallidal, amygdaloid, and corticopetal components of substantia innominata. Neuroscience 27, 1–39.

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