Brain Advance Access published online on October 24, 2008
Brain, doi:10.1093/brain/awn267
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
The major symptom dimensions of obsessive-compulsive disorder are mediated by partially distinct neural systems
1Department of Psychiatry, 2Department of Anatomy and Neurosciences, VU University Medical Centre, Amsterdam, The Netherlands, 3King's College London, Institute of Psychiatry, London, UK, 4Department of Radiology, VU University Medical Center, Amsterdam and 5Department of Psychiatry and Neuropsychology, Division Brain and Cognition, School for Mental Health and Neuroscience, University of Maastricht, Maastricht, The Netherlands
Correspondence to:
Odile A. van den Heuvel, MD PhD, Department of Psychiatry, VU University Medical Center, PO Box 7057, 1007 MB, Amsterdam, The Netherlands E-mail: oa.vandenheuvel{at}vumc.nl
 |
Summary |
|---|
 Top Summary IntroductionMethodsResultsDiscussionReferences |
|---|
Obsessive–compulsive disorder (OCD) is a clinically heterogeneous disorder characterized by multiple, temporally stable symptom dimensions. Preliminary functional neuroimaging studies suggest that these symptom dimensions may have distinct neural substrates. Whole-brain voxel-based morphometry was used to examine the common and distinct neuroanatomical (structural) substrates of the major symptom dimensions of OCD. First, we compared 55 medication-free patients with OCD and 50 age-matched healthy control subjects. Multiple regression analyses were then used to examine the relationship between global and regional grey matter (GM) and white matter (WM) volumes and symptom dimension scores within the patient group. OCD patients showed decreased GM volume in left lateral orbitofrontal (BA47), left inferior frontal (BA44/45), left dorsolateral prefrontal (BA9) and right medial prefrontal (BA10) cortices and decreased bilateral prefrontal WM volume. Scores on the ‘symmetry/ordering’ dimension were negatively correlated with ‘global’ GM and WM volumes. Scores on the ‘contamination/washing’ dimension were negatively correlated with ‘regional’ GM volume in bilateral caudate nucleus and WM volume in right parietal region. Scores on the ‘harm/checking’ dimension were negatively correlated with regional GM and WM volume in bilateral temporal lobes. Scores on the ‘symmetry/ordering’ dimension were negatively correlated with regional GM volume in right motor cortex, left insula and left parietal cortex and positively correlated with bilateral temporal GM and WM volume. The results remained significant after controlling for age, sex, educational level, overall illness severity, global WM and GM volumes and excluding patients with comorbid depression. The reported symptom dimension-specific GM and WM alterations support the hypothesis that OCD is an etiologically heterogeneous disorder, with both overlapping and distinct neural correlates across symptom dimensions. These results have clear implications for the current neuroanatomical model of OCD and call for a substantial revision of such model which takes into account the heterogeneity of the disorder.
Key Words: obsessive-compulsive; neuroimaging; VBM; symptom dimensions
Abbreviations: BA, Brodmann's area; GM, grey matter; OCD, Obsessive–compulsive disorder; ROI, regions of interest; VBM, voxel-based morphometry; WM, white matter
Received April 2, 2008. Revised September 8, 2008. Accepted September 22, 2008.
|
Introduction |
|---|
|
TopSummaryIntroductionMethodsResultsDiscussionReferences |
|---|
Current neuroanatomical models of obsessive–compulsive disorder (OCD) propose that specific frontal-striatal and limbic circuits are involved in the mediation of its symptoms (Saxena et al., 1998
; Remijnse et al., 2005; Mataix-Cols and van den Heuvel, 2006). Whereas the findings of functional neuroimaging studies have been relatively consistent with this view, structural neuroimaging studies have been far less consistent. For example, the volume of the caudate nucleus, a key structure thought to be involved in OCD, was found to be decreased (Luxenberg et al., 1988; Robinson et al., 1995), normal (Kellner et al., 1991; Stein et al., 1993, 1997; Aylward et al., 1996; Rosenberg et al., 1997; Bartha et al., 1998), and even increased (Scarone et al., 1992) in OCD patients compared with controls. The same variability applies to other regions of interest (ROIs), including the amygdala (Rosenberg and Keshavan, 1998; Szeszko et al., 1999; Kwon et al., 2003b), thalamus (Gilbert et al., 2000), and the orbitofrontal (Szeszko et al., 1999; Choi et al., 2004; Kang et al., 2004), anterior cingulate (Rosenberg and Keshavan, 1998; Szeszko et al., 2004) and temporal/hippocampal (Kwon et al., 2003) cortices. This obvious lack of replicability among structural neuroimaging studies in OCD can be partially attributed to methodological differences between studies. Small sample sizes have been the norm and some studies have not excluded patients on medication and with comorbid psychopathology. Most morphometric studies in OCD have used manual or semi-automated methods to measure the volumes of brain regions defined a priori as being implicated in OCD, therefore preventing the exploration of other brain regions potentially implicated in the disorder.
The recent use of fully-automated, whole-brain, voxel-based morphometry (VBM) methods (Ashburner and Friston, 2000, 2001; Mechelli et al., 2005), which overcome some of the limitations of the ROI approach, have also produced mixed results. Kim et al. (2001) were the first to report structural abnormalities in OCD using VBM. They compared 25 medication-free OCD patients and 25 healthy controls and reported increased grey matter (GM) volumes in the orbitofrontal, superior and middle temporal, inferior parietal and occipital cortices, thalamus, hypothalamus and insula. They did not investigate differences in white matter (WM) volume. Since the publication of this initial study, three further VBM studies in OCD have appeared (Pujol et al., 2004; Valente et al., 2005; Carmona et al., 2007). In the largest of these studies (n = 72), Pujol et al. (2004) found decreased GM volume in the medial orbitofrontal cortex, dorso-medial prefrontal cortex and the insulo-opercular region, as well as increased GM volume in the ventral putamen and cerebellum. No WM differences were found. Valente et al. (2005) also found decreased medial prefrontal GM volume in their smaller sample of mostly medicated, and comorbid depressive, OCD patients (n = 19). However, they reported increased rather than decreased orbitofrontal and insular GM volumes. Also, the parahippocampal gyri were larger compared with those of healthy subjects. No WM measurements were performed. Finally, in a pediatric OCD sample (n = 18), Carmona et al. (2007) showed decreased GM in dorsolateral prefrontal, inferior frontal, medial prefrontal and anterior cingulate cortices, as well as decreased WM in bilateral frontal and right parietal regions.
Based on the current frontal-striatal model of OCD, one might predict abnormalities in the WM tracts that connect the prefrontal cortex with the basal ganglia but only a handful of VBM studies have examined WM abnormalities in OCD. The results of recent diffusion tensor imaging studies, showing decreased fractional anisotropy (a measure of WM connectivity) in the anterior cingulate region and the internal capsule (Szeszko et al., 2005; Cannistraro et al., 2007; Yoo et al., 2007), are consistent with the current model.
In summary, VBM studies in OCD have shown frontal-striatal and limbic GM alterations, although the implicated regions and the direction of the differences between patients and healthy controls have been inconsistent so far. Again, these discrepant findings may be partially attributable to a number of methodological issues, such as insufficient power [with the exception of the Pujol et al. (2004) study], comorbidity, and medication confounds. Another important source of variability is the clinical heterogeneity of OCD. It is becoming increasingly clear that OCD is not a unitary disorder and that it consists of multiple potentially overlapping symptom dimensions (Mataix-Cols et al., 2005; Leckman et al., 2007), which are temporally (Mataix-Cols et al., 2002; Rufer et al., 2005) and transculturally (Matsunaga et al., 2008) stable. Several preliminary functional neuroimaging studies have suggested that these symptom dimensions may be mediated by partially distinct neural systems (Mataix-Cols et al., 2004; Saxena et al., 2004; Lawrence et al., 2007; An et al., 2008). It is therefore plausible that the above inconsistencies in structural neuroimaging studies of OCD can be partially attributable to the clinical heterogeneity of the recruited samples. In support of this idea, Pujol et al. (2004) found that patients with elevated scores on the ‘aggressive/checking’ dimension had significantly reduced GM volumes in the right amygdala. Similarly, Valente et al. (2005) showed a distinct pattern of correlations between various symptom dimension scores and GM volumes, although these analyses were probably underpowered. Clearly, more research is needed in large patient samples to identify the structural neuroanatomical correlates of the major symptom dimensions of OCD employing validated instruments.
The present VBM study aimed to build upon the existing functional neuroimaging literature by examining the common as well as distinct structural (GM and WM) correlates of the major symptom dimensions of OCD (‘contamination/washing’, ‘harm/checking’ and ‘symmetry/ordering’) in a large sample of unmedicated patients (n = 55). If the hypothesis that different symptom dimensions have distinct neuroanatomical substrates is confirmed, the results would have profound implications for the current neuroanatomical model of OCD.
|
Methods |
|---|
|
TopSummaryIntroductionMethodsResultsDiscussionReferences |
|---|
Participants
Fifty-five unmedicated patients meeting DSM-IV criteria for OCD and 50 age-matched healthy controls participated in the study. OCD patients were recruited from the outpatient clinic for anxiety disorders of Stichting Buitenamstel Geestgronden in Amsterdam, the outpatient clinic for anxiety disorders of GGZ Nijmegen, the Netherlands Anxiety, OCD & Phobia Foundation, and by advertisements on the internet. Exclusion criteria were the presence of major somatic disorders, other major psychiatric disorders (except depression) and use of psychotropic medication. Subjects had to be off antidepressive and antipsychotic medication for at least 4 weeks prior to the scan and were asked not to use benzodiazepines during the 2 weeks prior to the scan.
Fifty healthy controls were recruited among hospital and university staff and by advertisements on the Internet. They were interviewed to exclude any psychiatric and somatic disorders. The ethical review board of the VU University Medical Center approved the study and all participants provided written informed consent.
Measures
Diagnoses were established using the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I/P) (First et al., 1996). The severity of OCD symptoms was assessed with the 10-item clinician-administered Yale Brown Obsessive-Compulsive Scale (Y-BOCS) (Goodman et al., 1989a, b).
Two complementary methods were employed to ascertain the presence and severity of the most prevalent symptom dimensions in this sample (contamination/washing, harm/checking and symmetry/ordering). First, all patients and controls were asked to complete the Dutch version of the Padua Inventory-revised (Padua-IR) (Sanavio, 1988; van Oppen et al., 1995), a widely used and reliable self-administered measure of obsessive-compulsive symptoms. Here, we were interested in three of its sub-scales: ‘washing’ (10 items; score range 0–40), ‘checking’ (7 items; score range 0–28), and ‘precision’ (6 items: score range 0–24), corresponding to the three major symptom domains under study.
Second, each of the major categories of the Y-BOCS symptom checklist was assigned a score of 0 (absent symptom), 1 (symptom present but not major reason for concern) or 2 (prominent symptom). The major symptom dimension scores were then computed using the algorithm described by Mataix-Cols et al. (1999). Briefly, the ‘contamination/washing’ score was the sum of ‘contamination obsessions’ and ‘washing/cleaning compulsions’ divided by 2; the ‘harm/checking’ score was the sum of ‘aggressive obsessions’ and ‘checking compulsions’ divided by 2; and the ‘symmetry/ordering’ score was the sum of ‘symmetry obsessions’, ‘ordering compulsions’, ‘repeating compulsions’ and ‘counting compulsions’ divided by 4. Dividing by the number of items in each dimension ensured comparable score ranges across dimensions. Because very few patients in our sample endorsed hoarding or sexual/religious symptoms these dimensions were not computed in this study.
Due to administrative problems, Y-BOCS and Padua-IR data were unavailable for eight and five subjects, respectively.
MRI acquisition and processing
All images were acquired using a 1.5 T MRI system (Magnetom Sonata, Siemens, Erlangen, Germany) with a standard radiofrequency receiver head coil. The anatomical scans included 160 coronal slices (slice thickness = 1.5 mm) acquired with a 3D gradient-echo T1-weighted sequence (flip angle = 8°; repetition time, TR = 2700 ms; echo time, TE = 4 ms; inversion time, TI = 950 ms; bandwidth, BW = 190 Hz/pixel). In-plane resolution was 256 x 192 pixels (pixel size 1 mm2).
Prior to volumetric analyses, the integrity of the acquired MR images was visually checked using MRIcro (Chris Rorden, http://www.sph.sc.edu/comd/rorden/mricro.html). Images were processed and analysed using SPM5 (Wellcome Department of Cognitive Neurology, London, UK). The origin of each MR volume was aligned on the anterior commissure.
VBM
First, DICOM images were converted to Analyzeanalyse format, followed by cropping to remove the neck using a registration-based approach employing tools from FSL (FMRIB's Software Library). Using SPM5 with default priors, images were then segmented to generate, for each subject, modulated GM, WM and CSF probability maps in standard Montreal Neurological Institute (MNI)-152 space and resampled to 2 x 2 x 2 mm3 voxels. These maps were smoothed using a Gaussian kernel of 12 mm full width at half maximum as is customary in VBM, given that the accuracy of cortical registration between subjects is about 1 cm (Ashburner and Friston, 2001), and an absolute minimum threshold of 0.05 was applied. For each tissue type (WM or GM), analyses were restricted to voxels included in a mask obtained by thresholding the corresponding prior probability map at 0.1. In addition to the MNI-152 segments, the GM, WM and CSF probability maps in native space obtained in the same segmentation process were also stored and used to calculate total GM and WM volumes for each individual.
Statistical analyses
Comparisons between OCD patients and controls were conducted separately for regional GM and WM using (1 x 2) ANOVA as implemented in SPM5. To correct for global GM and WM differences, total GM and WM volumes were added as a regressor (covariate) in the models. The associations between symptom dimension scores within the OCD group and GM/WM volumes were examined using whole-brain multiple regression analyses with the scores of the three major symptom dimensions and total GM/WM volumes as regressors. In addition, to control for potentially confounding variables, we repeated all analyses including age, sex and total YBOCS scores as covariates. Since cluster-based statistics are invalid due to non-stationarity of VBM data (Mechelli et al., 2005), we adopted an a priori voxel-based threshold of P < 0.05 corrected for multiple comparisons, unless indicated otherwise. For our regions of interest (striatum, orbitofrontal cortex, lateral and medial prefrontal cortex, posterior parietal cortex and anterior and medial temporal cortex) we employed an initial threshold of P < 0.001 uncorrected with an extent threshold of 25 voxels, using the small volume correction option implemented in SPM5 to establish whether the observed differences were also significant at a corrected level.
|
Results |
|---|
|
TopSummaryIntroductionMethodsResultsDiscussionReferences |
|---|
Sample characteristics
There were no statistically significant differences in age, sex and handedness between patients and controls (Table 1). Controls had a slightly but significantly higher educational level. The mean total Y-BOCS scores were 22.83 (SD = 6.13), corresponding to moderately severe OCD. Ten out of 55 patients with OCD had a comorbid depressive disorder at the time of the scan. As expected, patients had significantly higher scores than controls on all the Padua-IR subscales. All patients endorsed more than one symptom type on the Y-BOCS Symptom Checklist (Table 2). Age of onset negatively correlated with the scores on the symmetry/ordering dimension of the Padua-IR (Spearman's
= –0.353, P < 0.05) and the Y-BOCS (Spearman's = –0.506, P < 0.01), not with the other symptom dimensions.
|
|
All patients were unmedicated at the time of the scan. Twenty-four (43.6%) patients were medication naïve, and the rest had been medication-free for at least 4 weeks prior to participation in the study. Mean washout period was 26 months (range 1–96 months). Past medication history was as follows: nine (16.4%) paroxetine, five (9.1%) fluoxetine, two (3.6%) fluvoxamine, two (3.6%) venlafaxine and nine (16.4%) used more than one drug in the past, two of whom used antipsychotic medication as an augmentation strategy). Medication history was unavailable from four (7.3%) patients.
Global GM and WM volumes
Patients with OCD and healthy control subjects did not significantly differ in global GM and WM volumes (GM: 685 ± 74 and 708 ± 72 ml, respectively, P = 0.11; WM: 494 ± 59 and 509 ± 64 ml, respectively, P = 0.21). However, the ‘symmetry/ordering’ dimension of the Padua-IR was negatively correlated with global GM volume (partial correlation coefficient –0.42, t = –2.84, P = 0.007), with a trend for global WM volume (partial correlation coefficient –0.35, t = –1.96, P = 0.057). This association was independent from age, sex and disease severity (total Y-BOCS scores), which were also included in the models. The correlation between age of onset and global GM or WM volume was not significant and multiple regression analyses showed that the association between symmetry/ordering symptoms and global GM volume remained significant after controlling for the illness onset. Finally, we repeated these analyses excluding the 10 patients with comorbid depression and the results remained unchanged. Scores on the other symptom dimensions did not correlate with global GM or WM volumes.
Regional GM and WM alterations in OCD versus controls
Compared with healthy controls, patients with OCD showed significantly decreased regional volume in the left lateral orbitofrontal cortex [Brodmann's area (BA) 47], left inferior frontal cortex (BA44/45), left dorsolateral prefrontal cortex (BA9) and bilateral medial prefrontal cortex (BA10). No regions of increased GM volume were found in patients with OCD (Table 3 and Fig. 1). WM volume was decreased in the bilateral prefrontal lobes in patients with OCD (Table 4 and Fig. 2). No regions of increased WM volume were found. These results were independent from age, sex, educational level and global GM/WM volumes, which were included as covariates in the ANOVAs. To control for the effect of comorbid depressive symptoms, we repeated all analyses with comorbid depression (dummy-coded as present/absent) as an extra covariate and yielding nearly identical results. In fact, the exclusion of the 10 depressed OCD patients resulted in even more strongly significant results (increased cluster sizes and t-values).
|
|
|
|
We next conducted a whole-brain regression analysis to examine the relationship between overall OCD symptom severity and regional GM/WM volumes. Total Y-BOCS scores were inversely correlated with GM volume of the left (MNI coordinates x, y, z = –18, –78, –54, t = 5.19, cluster size = 753 voxels) and right (x, y, z = 22, –84, –50, t = 4.05, cluster size = 548 voxels) cerebellar cortex.
Specific neural correlates of OCD symptom dimensions
Multiple regression analyses using the symptom dimension scores of the Padua-IR (n = 50) and Y-BOCS symptom checklist (n = 47) and controlling for global GM/WM volumes, demonstrated that each of the studied symptom dimensions had a clearly distinct neural substrate. The results using the Padua-IR and YBOCS symptom checklist were remarkably similar (Tables 5 and 6).
Scores on the ‘contamination/washing’ dimension were negatively correlated with GM volume in the bilateral dorsal caudate nucleus (Table 5 and Fig. 3A) and WM volume in the right parietal region (Table 6 and Fig. 4). Scores on the ‘harm/checking’ dimension were negatively correlated with GM and WM volume of the bilateral temporal lobes (Tables 5 and 6, Figs 3B and 4). Scores on the ‘symmetry/ordering’ dimension were negatively correlated with GM volume of the bilateral parietal cortex (Table 5 and Fig. 3C) and positively correlated with bilateral medial temporal GM and WM volume (Table 6 and Fig. 4). At a slightly lower threshold of P < 0.001 uncorrected (extent threshold 25 voxels), we also found that scores on the ‘symmetry/ordering’ dimension negatively correlated with GM volume in the right motor and left insular cortices.
|
|
|
|
In an additional analysis with age, sex (dummy-coded as man/woman) and total Y-BOCS scores as extra covariates the results appeared to be largely independent from these variables. To control for the effect of comorbid depressive symptoms, we repeated all analyses excluding the 10 OCD patients with comorbid depression and similar results were found.
|
Discussion |
|---|
|
TopSummaryIntroductionMethodsResultsDiscussionReferences |
|---|
To our knowledge this is the first study to explore the structural GM and WM correlates of the major symptom dimensions of OCD in a large unmedicated patient sample. Previous efforts were limited by the inclusion of small sample sizes (Valente et al., 2005
) or a substantial proportion of patients on medication (Pujol et al., 2004; Valente et al., 2005). Furthermore, we addressed this question using two different measures of each symptom dimension to ensure that the results were robust and replicable. In our analyses, care was taken to control for global illness severity (YBOCS severity scores) and a range of potentially confounding variables, which allowed us to separate the common as well as distinct neural substrates of the major symptom dimensions of OCD.
Common neuroanatomical substrates in OCD compared with healthy controls
Overall, patients with OCD showed significantly decreased GM volume in the left lateral orbitofrontal (BA47), left inferior frontal (BA44/45), left dorsolateral prefrontal (BA9) and bilateral medial prefrontal (BA10) cortices compared with healthy control subjects. GM volume reduction in the orbitofrontal and inferior frontal cortex was also found by Pujol et al. (2004) and Carmona et al. (2007), whereas two other VBM studies reported increased instead of decreased volume in the orbitofrontal cortex (Kim et al., 2001; Valente et al., 2005). Whereas Pujol et al. (2004) described medial orbitofrontal volume reduction (gyrus rectus, BA11), in the present study the orbitofrontal volume reduction was more laterally localized. Recently, Shin et al. (2007) investigated cortical thickness in OCD and reported cortical thinning of the medial orbitofrontal (BA11), lateral orbitofrontal (BA47), inferior frontal (BA45) and dorsolateral prefrontal (BA10) cortices of the left hemisphere. This left-right asymmetry is consistent with our results.
Decreased GM volume of the medial prefrontal cortex (BA 9/10) has consistently been found by others (Pujol et al., 2004; Valente et al., 2005). Volume reduction of the dorsolateral prefrontal cortex has been described both in adults (Shin et al., 2007) and in children (Carmona et al., 2007) with OCD. Dorsolateral prefrontal involvement in OCD corresponds with results from recent functional neuroimaging studies, showing decreased recruitment of the dorsolateral prefrontal cortex during neuro-cognitive testing (van den Heuvel et al., 2005; Remijnse et al., 2006). Medial and dorsolateral prefrontal regions are also involved in emotion regulation and cognitive control processes. Theoretical models of emotion perception suggest a reciprocal interaction between ventral and dorsal circuits in the brain underlying emotional processing (Phillips et al., 2003a). Although this model needs to be tested experimentally in more detail, it is a useful model for psychiatric disorders (Phillips et al., 2003b) and OCD in particular (Mataix-Cols and van den Heuvel, 2006). Decreased volume of these dorsal regions may underlie the impaired cognitive control during emotional processing and cognitive functioning found in OCD (Remijnse et al., 2005).
Reduction of prefrontal GM volume was accompanied with bilateral prefrontal WM volume reduction. At a lower statistical threshold, these regions of decreased WM volume were found to extend into the internal capsule. Although most previous VBM studies did not investigate WM volumes (Kim et al., 2001; Valente et al., 2005) or did not observe WM volume alterations (Pujol et al., 2004), Carmona et al. (2007) also found prefrontal WM volume reduction in their pediatric OCD patients. Decreased WM volume may underlie altered cortico-cortical and cortico-subcortical connectivity in OCD but direct evidence from the few published diffusion tensor imaging studies to date is limited (Szeszko et al., 2005; Cannistraro et al., 2007; Yoo et al., 2007).
There was an inverse correlation between disease severity and bilateral cerebellar GM volume. Although the cerebellum is traditionally considered to be essential for the coordination of movement and motor learning, recent studies have indicated that the cerebellum is also involved in cognitive and emotional processes (Middleton and Sherman, 1998; Schmahmann and Caplan, 2006). In OCD cerebellar involvement has been reported often, but the direction of the effect is inconclusive so far since both decreased (Nabeyama et al., 2008) and increased (Tolin et al., 2008) activation and decreased (Kim et al., 2001) and increased (Pujol et al., 2004) volume has been described. The cerebellar correlation with disease severity found in the present study parallels the recent finding that decreased cerebellar activation normalized with symptom improvement after 12 weeks of cognitive behaviour treatment (Nakao et al., 2005; Nabeyama et al., 2008).
Taken together, these results are consistent with the view that there are some global neural abnormalities present in OCD that may reflect the loss of normal inhibitory processes (Chamberlain et al., 2005; Menzies et al., 2007). These abnormalities would be common to most patients with OCD and, according to recent work, even their unaffected first-degree relatives (Chamberlain et al., 2007; Menzies et al., 2007). However, the diagnostic specificity of these findings still remains to be established as difficulties in inhibitory processes and alterations in the corresponding brain regions may not be exclusive to OCD. Most notably, attention deficit and hyperactivity disorder is also characterized by such abnormalities (Rubia et al., 2005; Smith et al., 2006). It is therefore plausible that these are general vulnerability factors for a number of neuropsychiatric problems including OCD. This is supported by the fact that overall symptom severity of OCD did not correlate with any of the above regions but with the cerebellum bilaterally instead.
Distinct neural substrates across symptom dimensions
The most important contribution of the present study is that different symptom dimensions appear to have distinct neural substrates.
High scores on the ‘contamination/washing’ dimension were negatively correlated with the volume of the dorsal parts of the bilateral dorsal caudate nucleus. Many previous morphometric and functional neuroimaging studies have implicated the caudate nucleus in OCD, although the direction of the findings has been inconsistent (Remijnse et al., 2005). Because the contamination/washing symptom dimension is one of the most prevalent in OCD (Rasmussen and Eisen, 1992; Mataix-Cols et al., 1999) it may be assumed that previous studies consistently included a large proportion of these patients. Furthermore, contamination-related anxiety is particularly amenable to symptom provocation procedures and this probably played a role in the selective recruitment of these patients in such studies.
At this stage, the functional implications of decreased volume of the dorsal caudate nucleus in patients with prominent washing symptoms remain speculative. However, a possible hypothesis is that the lack of control on compulsive behaviour due to dorsal striatal dysfunction results in conditionally reinforced washing rituals accompanied by relatively increased ventral striatal involvement. The dorsal striatum has been implicated in habit learning and action initiation (Yin et al., 2004). Considering the phenomenological overlap between OCD and addiction (Hollander et al., 2007), these addiction studies may be relevant to understand the striatal role in cleaning behaviour in contamination-related OCD. The progression from initial drug use to habitual drug use and ultimately to compulsive drug seeking behavior corresponds with a transition at the neural level from prefrontal cortical to striatal control and from ventral to dorsal striatal involvement (Everitt and Robbins, 2005; Volkow et al., 2006; Everitt et al., 2008). In Huntington's disease, a neurological frontal-striatal disease with often co-morbid obsessive-compulsive behaviour, the striatal atrophy also shows a dorsal-ventral gradient (Douaud et al., 2006). Initially, the atrophy mainly affects the dorsal caudate nucleus relatively sparing the ventral striatum.
Turning to the ‘harm/checking’ symptom dimension, we found that the scores on this dimension were strongly negatively correlated with both GM and WM volume in the bilateral anterior temporal poles. The anterior parts of temporal lobes including the amygdala and parahippocampal cortices have close connections with the hippocampal formation, the medial and orbitofrontal prefrontal areas and the ventral striatum (Kondo et al., 2005; Munoz and Insausti, 2005). This finding is consistent with that of Pujol et al. (2004) who found a significant inverse correlation between scores in this dimension and right amygdala volume. Checking rituals are most often associated with obsessions about harm and aggression (Mataix-Cols et al., 2005; Leckman et al., 2007). In OCD, patients with high scores on this symptom are at elevated risk from having comorbid anxiety and mood disorders, including panic disorder (Hasler et al., 2005; Rosario-Campos et al., 2006). Consistently, volume reduction in similar regions has been described in panic disorder (Massana et al., 2003a, b) which, like OCD patients with harm/checking symptoms, is characterized by the overestimation of threat.
Interestingly, temporal lobe atrophy in patients with frontotemporal dementia appears to mediate complex compulsive behaviour such as checking rituals (Rosso et al., 2001). Rosso et al. (2001) found that atrophy in frontal and subcortical regions was not associated with the development of such compulsive behaviours. Temporal lobe epilepsy is another condition associated with OCD symptoms, with compulsions being more frequently observed than obsessions (Isaacs et al., 2004). The involvement of the anterior temporal lobes in OCD has often been overlooked so far and our results suggest that this might be partially due to the heterogeneity of the disorder. Remarkably, in the current study, GM and WM volumes were inversely correlated with scores on the ‘harm/checking’ dimension and positively correlated with scores on the ‘symmetry/ordering’ dimension. This indicates that recruiting different proportions of patients with these predominant symptom presentations may result in different results or even non-significant results, as these may cancel each other out.
The temporal lobe volume reductions may also be viewed with respect to the neuropsychological hypothesis of altered memory function in OCD patients with predominantly checking rituals. One of the proposed aetiologies for checking behaviour is the inability to accurately recall whether an activity is completed correctly (Rachman, 2002). Paradoxically, repeating checking results in even more distrust in one's own memory (van den Hout and Kindt, 2003, 2004). Although the confidence in memory seems to be more impaired than memory per se, there is some evidence for this so-called memory-deficit theory, with checkers showing more non-verbal memory deficits than non-checkers (Cha et al., 2008).
Finally, scores on the ‘symmetry/ordering’ dimension were inversely correlated with global GM volume. There was also a trend in the same direction for global WM volume. No other symptom dimensions were associated with global GM/WM volumes. Patients with high scores in this dimension are known to have an earlier age of onset of their OCD (Leckman et al., 2003; Mataix-Cols et al., 2005) and also an increased risk of having an affected family member (Alsobrook et al., 1999; Hanna et al., 2005a, b). Therefore, OCD patients with high scores on this symptom dimension may have a more neurodevelopmental and familial form of the disorder. Consistently, the only paediatric VBM study in OCD to date found reduced global GM volumes in patients compared with controls (Carmona et al., 2007). Even though Carmona et al. (2007) did not describe their sample in detail, younger samples tend to include a substantial proportion of patients endorsing symmetry/ordering symptoms (Stewart et al., 2007).
After controlling for global GM and WM volumes, scores on the ‘symmetry/ordering’ dimension were inversely correlated with regional GM volume in motor, parietal and insular cortices and positively correlated with regional GM and WM volume in the bilateral anterior temporal poles. However, the correlations with motor and insular cortices need to be interpreted with caution given that these were only significant at a lower statistical threshold. Correlations with motor and somatosensory regional abnormalities are consistent with the known association between the symmetry/ordering dimension of OCD and comorbid tics or Tourette's Syndrome (Leckman et al., 1997; Mataix-Cols et al., 1999). The positive correlations with grey and white volume in the anterior temporal pole in OCD patients with predominantly symmetry/ordering symptoms, is interesting in this respect: Peterson et al. (2007) recently reported increased volume in anterior temporal structures such as the amygdala and hippocampus in a large sample of patients with Tourette's Syndrome. The negative correlation between GM volume of the motor cortex and the symmetry/ordering dimension as found in the present study is inconsistent with the results of Gilbert et al. (2008), who found motor cortex volume negatively correlating with the contamination/washing dimension. However, in the Gilbert et al. (2008) study, correlation analyses were only conducted for regions that showed significant volumetric differences between patients and controls. In the present study we performed whole-brain correlations between symptom dimension scores and regional GM volume, which limits comparability between the two studies.
Although frontal-striatal and limbic brain regions have long been implicated in OCD, less attention has been paid to the possible involvement of the parietal cortex in the pathophysiology of the disorder (Menzies et al., 2008). Previous structural (Szeszko et al., 2005; Valente et al., 2005; Kitamura et al., 2006; Carmona et al., 2007), resting state (Kwon et al., 2003a), and activation (van den Heuvel et al., 2005) neuroimaging found abnormalities in this brain region. Because the parietal cortex is known to be involved in attention and visuospatial processes (Posner and Petersen, 1990; Cabeza and Nyberg, 2000) as well as various executive functions, such as task switching (Sohn et al., 2000), planning (van den Heuvel et al., 2003) and working memory (Veltman et al., 2003), parietal dysfunction may contribute to the cognitive impairments found in some OCD patients (Menzies et al., 2008). The present study showed a negative correlation between parietal WM volume and the washing dimension, and parietal GM volume and the symmetry dimension. Although we consider it premature to interpret the functional implications of these findings, they indicate that the parietal cortex is particularly involved in these symptom dimensions. One recent study found that set-switching abilities may be particularly impaired in OCD patients with predominant symmetry/order symptoms (Lawrence et al., 2006), but replication is needed.
Strengths and limitations
The use of two different measures of OCD symptoms is a particular strength of the present study because clinician and self-administered measures of OCD symptoms are not perfectly correlated (Mataix-Cols et al., 2004). The fact that we obtained similar results using both types of scales adds to the robustness of the findings. All patients were medication naïve or medication-free for at least 4 weeks prior to inclusion. Although a washout period of 4 weeks may not be sufficient to mitigate all the effects of long-term medication use, this is a methodological advance compared to most previous VBM studies in OCD (Pujol et al., 2004; Valente et al., 2005; Carmona et al., 2007). The effects of psychotropic medication on brain morphometry have been shown mainly for antipsychotic medication, with for example olanzapine treatment resulting in increased volume of the caudate nucleus (Okugawa et al., 2007). However, the use of selective serotonin reuptake inhibitors may also present a confounder in morphometric studies in patients, given that these drugs stimulate neurogenesis. Both in humans (Becker and Wojtowicz, 2007) and non-human species (Lau et al., 2007; Sairanen et al., 2007) antidepressive treatment results in enhanced cell proliferation in the hippocampus, the subventricular zone and the medial prefrontal cortex.
Another methodological advantage of the present study is the parallel investigation of both GM and WM morphometry. The only previous VBM results showing WM abnormalities in OCD were based on a small paediatric sample (Carmona et al., 2007). The investigation of GM and WM in the same subjects provides a more complete insight into the neural systems involved in the disorder.
The present study is not without limitations, however. A weakness is the lack of quantitative measures of comorbid depressive symptoms. Recent re-analyses of the Pujol et al. (2004) sample showed that OCD patients with comorbid major depressive disorder had larger volume reductions in the medial orbitofrontal cortex than OCD patients without comorbid depression (Cardoner et al., 2007). In the present study, no volume reduction was found in the medial orbitofrontal cortex, which may reflect that only 10 of our 55 OCD patients had a comorbid depression. Whereas we found that the exclusion of these 10 depressed patients did not modify the overall results, we could not completely rule out that subclinical depressive symptoms (measured dimensionally) had an effect on our findings. Our control group was significantly higher educated than the patient group but the inclusion of education level as a covariate in the analyses did not modify the results. We did not assess general intellectual function and did not use a structured instrument to assess handedness. With regard to our statistical method, it should be recognized that although the use of uncorrected thresholds carries an obvious risk of Type I error, adopting whole-brain correction for multiple comparisons may be overly conservative; however, the use of small volume correction may present problems due to non-stationary smoothness of VBM data. Because of the small number of patients endorsing hoarding and sexual/religious obsessions, we had to restrict our analyses to the major, i.e. more prevalent, symptom dimensions of OCD. Preliminary neuropsychological (Lawrence et al., 2006) and functional neuroimaging (Mataix-Cols et al., 2004; An et al., 2008; Tolin et al., 2008) studies suggest that compulsive hoarding may constitute yet another neurobiologically distinct dimension of OCD.
Conclusion and future directions
The current study demonstrates common as well as distinct neuroanatomical substrates for the major symptom dimensions of OCD. Between-group analyses revealed that there are some global neural abnormalities present in OCD that may reflect the loss of normal inhibitory processes (Chamberlain et al., 2005; Menzies et al., 2007). However, the diagnostic specificity of these findings still remains to be established and it is plausible that decreased prefrontal GM and WM volume is a general vulnerability factor for a number of neuropsychiatric problems including OCD. Multiple regression analyses within the patient group revealed that distinct neural systems may be underlying the major symptom dimensions of OCD, although causal relationships cannot be inferred. Our results further confirm the hypothesis that OCD is not a homogeneous disorder and that adopting a quantitative multidimensional approach has great promise in further understanding the set of problems we collectively call OCD (Mataix-Cols et al., 2005; Mataix-Cols, 2006; Mataix-Cols and van den Heuvel, 2006). These results have clear implications for the current psychobiological model of OCD (Saxena et al., 1998; Remijnse et al., 2005; Mataix-Cols and van den Heuvel, 2006) and call for a substantial revision of the model that takes into account the heterogeneity of the disorder. The results of the current study add to a growing neuroimaging literature (Mataix-Cols et al., 2004; Saxena et al., 2004; Lawrence et al., 2007; An et al., 2008; Gilbert et al., 2008) and will hopefully lead to more hypothesis-driven research into the common and specific neural substrates of these symptom dimensions. Multimodal imaging protocols will be necessary to understand the complex relationship between biochemistry, structure and function in relation to each of the major symptom dimensions of OCD.
|
References |
|---|
|
TopSummaryIntroductionMethodsResultsDiscussionReferences |
|---|
Alsobrook JP, Leckman JF, Goodman WK, Rasmussen SA, Pauls DL. Segregation analysis of obsessive-compulsive disorder using symptom-based factor scores. Am J Med Genet (1999) 88:669–75.[CrossRef][Web of Science][Medline]
An SK, Mataix-Cols D, Lawrence NS, Wooderson S, Giampietro V, Speckens A, et al. To discard or not to discard: the neural basis of hoarding symptoms in obsessive-compulsive disorder. Mol Psychiatry (2008) Jan 8. [Epub ahead of print], doi:10.1038/sj.mp.4002129.
Ashburner J, Friston KJ. Voxel-based morphometry - the methods. Neuroimage (2000) 11:805–21.[CrossRef][Web of Science][Medline]
Ashburner J, Friston KJ. Why voxel-based morphometry should be used. Neuroimage (2001) 14:1238–43.[CrossRef][Web of Science][Medline]
Aylward EH, Harris GJ, HoehnSaric R, Barta PE, Machlin SR, Pearlson GD. Normal caudate nucleus in obsessive-compulsive disorder assessed by quantitative neuroimaging. Arch Gen Psychiatry (1996) 53:577–84.
Bartha R, Stein MB, Williamson PC, Drost DJ, Neufeld RW, Carr TJ, et al. A short echo 1H spectroscopy and volumetric MRI study of the corpus striatum in patients with obsessive-compulsive disorder and comparison subjects. Am J Psychiatry (1998) 155:1584–91.
Becker S, Wojtowicz JM. A model of hippocampal neurogenesis in memory and mood disorders. Trends Cogn Sci (2007) 11:70–6.[CrossRef][Web of Science][Medline]
Cabeza R, Nyberg L. Imaging cognition II: an empirical review of 275 PET and fMRI studies. J Cogn Neurosci (2000) 12:1–47.[Web of Science][Medline]
Cannistraro PA, Makris N, Howard JD, Wedig MM, Hodge SM, Wilhelm S, et al. A diffusion tensor imaging study of white matter in obsessive-compulsive disorder. Depress Anxiety (2007) 24:440–6.[CrossRef][Web of Science][Medline]
Cardoner N, Soriano-Mas C, Pujol J, Alonso P, Harrison BJ, Deus J, et al. Brain structural correlates of depressive comorbidity in obsessive-compulsive disorder. Neuroimage (2007) 38:413–21.[CrossRef][Web of Science][Medline]
Carmona S, Bassas N, Rovira M, Gispert JD, Soliva JC, Prado M, et al. Pediatric OCD structural brain deficits in conflict monitoring circuits: a voxel-based morphometry study. Neurosci Lett (2007) 421:218–23.[CrossRef][Web of Science][Medline]
Cha KR, Koo MS, Kim CH, Kim JW, Oh WJ, Suh HS, et al. Nonverbal memory dysfunction in obsessive-compulsive disorder patients with checking compulsions. Depress Anxiety (2008) Oct 11. [Epub ahead of print], doi:10.1002/da.20377.
Chamberlain SR, Blackwell AD, Fineberg NA, Robbins TW, Sahakian BJ. The neuropsychology of obsessive compulsive disorder: the importance of failures in cognitive and behavioural inhibition as candidate endophenotypic markers. Neurosci Biobehav Rev (2005) 29:399–419.[CrossRef][Web of Science][Medline]
Chamberlain SR, Fineberg NA, Menzies LA, Blackwell AD, Bullmore ET, Robbins TW, et al. Impaired cognitive flexibility and motor inhibition in unaffected first-degree relatives of patients with obsessive-compulsive disorder. Am J Psychiatry (2007) 164:335–8.
Choi JS, Kang DH, Kim JJ, Ha TH, Lee JM, Youn T, et al. Left anterior subregion of orbitofrontal cortex volume reduction and impaired organizational strategies in obsessive-compulsive disorder. J Psychiatr Res (2004) 38:193–9.[CrossRef][Web of Science][Medline]
Douaud G, Gaura V, Ribeiro MJ, Lethimonnier F, Maroy R, Verny C, et al. Distribution of grey matter atrophy in Huntington's disease patients: a combined ROI-based and voxel-based morphometric study. Neuroimage (2006) 32:1562–75.[CrossRef][Web of Science][Medline]
Everitt BJ, Belin D, Economidou D, Pelloux Y, Dalley JW, Robbins TW. Review. Neural mechanisms underlying the vulnerability to develop compulsive drug-seeking habits and addiction. Philos Trans R Soc Lond B Biol Sci (2008) 363:3125–35.
Everitt BJ, Robbins TW. Neural systems of reinforcement for drug addiction: from actions to habits to compulsion. Nat Neurosci (2005) 8:1481–9.[CrossRef][Web of Science][Medline]
First MB, Spitzer RL, Gibbon M, Williams JBW. Structured clinical interview for DSM-IV axis I disorders - patient edition. (1996) New York: Biometrics Research Department. (SCID-I/P (version 2.0) edn).
Gilbert AR, Mataix-Cols D, Almeida JR, Lawrence N, Nutche J, Diwadkar V, et al. Brain structure and symptom dimension relationships in obsessive-compulsive disorder: a voxel-based morphometry study. J Affect Disord (2008) 109:117–26.[CrossRef][Web of Science][Medline]
Gilbert AR, Moore GJ, Keshavan MS, Paulson LAD, Narula V, MacMaster FP, et al. Decrease in thalamic volumes of pediatric patients with obsessive-compulsive disorder who are taking paroxetine. Arch Gen Psychiatry (2000) 57:449–56.
Goodman WK, Price LH, Rasmussen SA, Mazure C, Delgado P, Heninger GR, et al. The Yale-Brown Obsessive Compulsive Scale. II. Validity. Arch Gen Psychiatry (1989a) 46:1012–16.
Goodman WK, Price LH, Rasmussen SA, Mazure C, Fleischmann RL, Hill CL, et al. The Yale-Brown Obsessive Compulsive Scale. I. Development, use, and reliability. Arch Gen Psychiatry (1989b) 46:1006–11.
Hanna GL, Fischer DJ, Chadha KR, Himle JA, Van EM. Familial and sporadic subtypes of early-onset obsessive-compulsive disorder. Biol Psychiatry (2005a) 57:895–900.[CrossRef][Web of Science][Medline]
Hanna GL, Himle JA, Curtis GC, Gillespie BW. A family study of obsessive-compulsive disorder with pediatric probands. Am J Med Genet B Neuropsychiatr Genet (2005b) 134:13–9.
Hasler G, LaSalle-Ricci VH, Ronquillo JG, Crawley SA, Cochran LW, Kazuba D, et al. Obsessive-compulsive disorder symptom dimensions show specific relationships to psychiatric comorbidity. Psychiatry Res (2005) 135:121–32.[CrossRef][Web of Science][Medline]
Hollander E, Kim S, Khanna S, Pallanti S. Obsessive-compulsive disorder and obsessive-compulsive spectrum disorders: diagnostic and dimensional issues. CNS Spectr (2007) 12(2 Suppl 3):5–13.[Web of Science][Medline]
Isaacs KL, Philbeck JW, Barr WB, Devinsky O, Alper K. Obsessive-compulsive symptoms in patients with temporal lobe epilepsy. Epilepsy Behav (2004) 5:569–74.[CrossRef][Web of Science][Medline]
Kang DH, Kim JJ, Choi JS, Kim YI, Kim CW, Youn T, et al. Volumetric investigation of the frontal-subcortical circuitry in patients with obsessive-compulsive disorder. J Neuropsychiatry Clin Neurosci (2004) 16:342–9.
Kellner CH, Jolley RR, Holgate RC, Austin L, Lydiard RB, Laraia M, et al. Brain MRI in obsessive-compulsive disorder. Psychiatry Res (1991) 36:45–9.[CrossRef][Web of Science][Medline]
Kim JJ, Lee MC, Kim J, Kim IY, Kim SI, Han MH, et al. Grey matter abnormalities in obsessive-compulsive disorder - statistical parametric mapping of segmented magnetic resonance images. Br J Psychiatry (2001) 179:330–4.
Kitamura H, Shioiri T, Kimura T, Ohkubo M, Nakada T, Someya T. Parietal white matter abnormalities in obsessive-compulsive disorder: a magnetic resonance spectroscopy study at 3-Tesla. Acta Psychiatr Scand (2006) 114:101–8.[CrossRef][Web of Science][Medline]
Kondo H, Saleem KS, Price JL. Differential connections of the perirhinal and parahippocampal cortex with the orbital and medial prefrontal networks in macaque monkeys. J Comp Neurol (2005) 493:479–509.[CrossRef][Web of Science][Medline]
Kwon JS, Kim JJ, Lee DW, Lee JS, Lee DS, Kim MS, et al. Neural correlates of clinical symptoms and cognitive dysfunctions in obsessive-compulsive disorder. Psychiatry Res (2003a) 122:37–47.[CrossRef][Web of Science][Medline]
Kwon JS, Shin YW, Kim CW, Kim YI, Toun T, Han MH, et al. Similarity and disparity of obsessive-compulsive disorder and schizophrenia in MR volumetric abnormalities of the hippocampus-amygdala complex. J Neurol Neurosurg Psychiatry (2003b) 74:962–4.
Lau WM, Qiu G, Helmeste DM, Lee TM, Tang SW, So KF, et al. Corticosteroid decreases subventricular zone cell proliferation, which could be reversed by paroxetine. Restor Neurol Neurosci (2007) 25:17–23.[Web of Science][Medline]
Lawrence NS, An SK, Mataix-Cols D, Ruths F, Speckens A, Phillips ML. Neural responses to facial expressions of disgust but not fear are modulated by washing symptoms in OCD. Biol Psychiatry (2007) 61:1072–80.[CrossRef][Web of Science][Medline]
Lawrence NS, Wooderson S, Mataix-Cols D, David R, Speckens A, Phillips ML. Decision making and set shifting impairments are associated with distinct symptom dimensions in obsessive-compulsive disorder. Neuropsychology (2006) 20:409–19.[CrossRef][Web of Science][Medline]
Leckman JF, Grice DE, Boardman J, Zhang H, Vitale A, Bondi C, et al. Symptoms of obsessive-compulsive disorder. Am J Psychiatry (1997) 154:911–17.[Abstract]
Leckman JF, Pauls DL, Zhang H, Rosario-Campos MC, Katsovich L, Kidd KK, et al. Obsessive-compulsive symptom dimensions in affected sibling pairs diagnosed with Gilles de la Tourette syndrome. Am J Med Genet B Neuropsychiatr Genet (2003) 116:60–8.
Leckman JF, Rauch SL, Mataix-Cols D. Symptom dimensions in obsessive-compulsive disorder: implications for the DSM-V. CNS Spectr (2007) 12:376–400.[Web of Science][Medline]
Luxenberg JS, Swedo SE, Flament MF, Friedland RP, Rapoport J, Rapoport SI. Neuroanatomical abnormalities in obsessive-compulsive disorder detected with quantitative X-ray computed tomography. Am J Psychiatry (1988) 145:1089–93.
Massana G, Serra-Grabulosa JM, Salgado-Pineda P, Gasto C, Junque C, Massana J, et al. Parahippocampal gray matter density in panic disorder: a voxel-based morphometric study. Am J Psychiatry (2003a) 160:566–8.
Massana G, Serra-Grabulosa JM, Salgado-Pineda P, Gasto C, Junque C, Massana J, et al. Amygdalar atrophy in panic disorder patients detected by volumetric magnetic resonance imaging. Neuroimage (2003b) 19:80–90.[Web of Science][Medline]
Mataix-Cols D. Deconstructing obsessive-compulsive disorder: a multidimensional perspective. Curr Opin Psychiatry (2006) 19:84–9.[Web of Science][Medline]
Mataix-Cols D, do Rosario-Campos MC, Leckman JF. A multidimensional model of obsessive-compulsive disorder. Am J Psychiatry (2005) 162:228–38.
Mataix-Cols D, Fullana MA, Alonso P, Menchon JM, Vallejo J. Convergent and discriminant validity of the Yale-Brown Obsessive-Compulsive Scale Symptom Checklist. Psychother Psychosom (2004) 73:190–6.[CrossRef][Web of Science][Medline]
Mataix-Cols D, Rauch SL, Baer L, Eisen JL, Shera DM, Goodman WK, et al. Symptom stability in adult obsessive-compulsive disorder: data from a naturalistic two-year follow-up study. Am J Psychiatry (2002) 159:263–8.
Mataix-Cols D, Rauch SL, Manzo PA, Jenike MA, Baer L. Use of factor-analyzed symptom dimensions to predict outcome with serotonin reuptake inhibitors and placebo in the treatment of obsessive-compulsive disorder. Am J Psychiatry (1999) 156:1409–16.
Mataix-Cols D, van den Heuvel OA. Common and distinct neural correlates of obsessive-compulsive and related disorders. Psychiatr Clin North Am (2006) 29:391–410.[CrossRef][Web of Science][Medline]
Mataix-Cols D, Wooderson S, Lawrence N, Brammer MJ, Speckens A, Phillips ML. Distinct neural correlates of washing, checking, and hoarding symptom dimensions in obsessive-compulsive disorder. Arch Gen Psychiatry (2004) 61:564–76.
Matsunaga H, Maebayashi K, Hayashida K, Okino K, Matsui T, Iketani T, et al. Symptom structure in Japanese patients with obsessive-compulsive disorder. Am J Psychiatry (2008) 165:251–3.
Mechelli A, Price CJ, Friston KJ, Ashburner J. Voxel-based morphometry of the human brain: Methods and applications. Curr Med Imag Rev (2005) 1:105–13.[CrossRef]
Menzies L, Achard S, Chamberlain SR, Fineberg N, Chen CH, Del CN, et al. Neurocognitive endophenotypes of obsessive-compulsive disorder. Brain (2007) 130:3223–36.
Menzies L, Chamberlain SR, Laird AR, Thelen SM, Sahakian BJ, Bullmore ET. Integrating evidence from neuroimaging and neuropsychological studies of obsessive-compulsive disorder: the orbitofronto-striatal model revisited. Neurosci Biobehav Rev (2008) 32:525–49.[CrossRef][Web of Science][Medline]
Middleton FA, Strick PL. Basal ganglia and cerebellar loops: motor and cognitive circuits. Brain Res Brain Res Rev (2000) 31:236–50.[CrossRef][Medline]
Munoz M, Insausti R. Cortical efferents of the entorhinal cortex and the adjacent parahippocampal region in the monkey (Macaca fascicularis). Eur J Neurosci (2005) 22:1368–88.[CrossRef][Web of Science][Medline]
Nabeyama M, Nakagawa A, Yoshiura T, Nakao T, Nakatani E, Togao O, et al. Functional MRI study of brain activation alterations in patients with obsessive-compulsive disorder after symptom improvement. Psychiatry Res: Neuroimaging (2008) 163:236–47.[CrossRef]
Nakao T, Nakagawa A, Yoshiura T, Nakatani E, Nabeyama M, Ysohizato C, et al. Brain activation of patients with obsessive-compulsive disorder during neuropsychological and symptom provocation tasks before and after symptom improvement: a functional magnetic resonance imaging study. Biol Psychiatry (2005) 57:901–10.[CrossRef][Web of Science][Medline]
Okugawa G, Nobuhara K, Takase K, Saito Y, Yoshimura M, Kinoshita T. Olanzapine increases grey and white matter volumes in the caudate nucleus of patients with schizophrenia. Neuropsychobiology (2007) 55:43–6.[CrossRef][Web of Science][Medline]
Peterson BS, Choi HA, Hao X, Amat JA, Zhu H, Whiteman R, et al. Morphologic features of the amygdala and hippocampus in children and adults with Tourette syndrome. Arch Gen Psychiatry (2007) 64:1281–91.
Phillips ML, Drevets WC, Rauch SL, Lane R. Neurobiology of emotion perception I: The neural basis of normal emotion perception. Biol Psychiatry (2003a) 54:504–14.[CrossRef][Web of Science][Medline]
Phillips ML, Drevets WC, Rauch SL, Lane R. Neurobiology of emotion perception II: Implications for major psychiatric disorders. Biol Psychiatry (2003b) 54:515–28.[CrossRef][Web of Science][Medline]
Posner MI, Petersen SE. The attention system of the human brain. Annu Rev Neurosci (1990) 13:25–42.[CrossRef][Web of Science][Medline]
Pujol J, Soriano-Mas C, Alonso P, Cardoner N, Menchon JM, Deus J, et al. Mapping structural brain alterations in obsessive-compulsive disorder. Arch Gen Psychiatry (2004) 61:720–30.
Rachman S. A cognitive theory of compulsive checking. Behav Res Ther (2002) 40:625–39.[CrossRef][Web of Science][Medline]
Rasmussen SA, Eisen JL. The epidemiology and clinical features of obsessive compulsive disorder. Psychiatr Clin North Am (1992) 15:743–58.[Web of Science][Medline]
Remijnse PL, Nielen MM, van Balkom AJ, Cath DC, Van OP, Uylings HB, et al. Reduced orbitofrontal-striatal activity on a reversal learning task in obsessive-compulsive disorder. Arch Gen Psychiatry (2006) 63:1225–36.
Remijnse PL, van den Heuvel OA, Veltman DJ. Neuroimaging in obsessive-compulsive disorder. Curr Med Imag Rev (2005) 1:331–51.[CrossRef]
Robinson D, Wu HW, Munne RA, Ashtari M, Alvir JMJ, Lerner G, et al. Reduced Caudate-Nucleus Volume in Obsessive-Compulsive Disorder. Arch Gen Psychiatry (1995) 52:393–8.
Rosario-Campos MC, Miguel EC, Quatrano S, Chacon P, Ferrao Y, Findley D, et al. The Dimensional Yale-Brown Obsessive-Compulsive Scale (DY-BOCS): an instrument for assessing obsessive-compulsive symptom dimensions. Mol Psychiatry (2006) 11:495–504.[CrossRef][Web of Science][Medline]
Rosenberg DR, Keshavan MS. A.E. Bennett Research Award. Toward a neurodevelopmental model of of obsessive–compulsive disorder. Biol Psychiatry (1998) 43:623–40.[CrossRef][Web of Science][Medline]
Rosenberg DR, Keshavan MS, Ohearn KM, Dick EL, Bagwell WW, Seymour AB, et al. Frontostriatal measurement in treatment-naive children with obsessive-compulsive disorder. Arch Gen Psychiatry (1997) 54:824–30.
Rosso SM, Roks G, Stevens M, de KI, Tanghe HLJ, Kamphorst W, et al. Complex compulsive behaviour in the temporal variant of frontotemporal dementia. J Neurol (2001) 248:965–70.[CrossRef][Web of Science][Medline]
Rubia K, Smith AB, Brammer MJ, Toone B, Taylor E. Abnormal brain activation during inhibition and error detection in medication-naive adolescents with ADHD. Am J Psychiatry (2005) 162:1067–75.
Rufer M, Grothusen A, Mass R, Peter H, Hand I. Temporal stability of symptom dimensions in adult patients with obsessive-compulsive disorder. J Affect Disord (2005) 88:99–102.[CrossRef][Web of Science][Medline]
Sairanen M, O’Leary OF, Knuuttila JE, Castren E. Chronic antidepressant treatment selectively increases expression of plasticity-related proteins in the hippocampus and medial prefrontal cortex of the rat. Neuroscience (2007) 144:368–74.[CrossRef][Web of Science][Medline]
Sanavio E. Obsessions and compulsions: the Padua Inventory. Behav Res Ther (1988) 26:169–77.[CrossRef][Web of Science][Medline]
Saxena S, Brody AL, Maidment KM, Smith EC, Zohrabi N, Katz E, et al. Cerebral glucose metabolism in obsessive-compulsive hoarding. Am J Psychiatry (2004) 161:1038–48.
Saxena S, Brody AL, Schwartz JM, Baxter LR. Neuroimaging and frontal-subcortical circuitry in obsessive-compulsive disorder. Br J Psychiatry (1998) 173:26–37.
Scarone S, Colombo C, Livian S, Abbruzzese M, Ronchi P, Locatelli M, et al. Increased right caudate nucleus size in obsessive-compulsive disorder: detection with magnetic resonance imaging. Psychiatry Res (1992) 45:115–21.[CrossRef][Web of Science][Medline]
Schmahmann JD, Caplan D. Cognition, emotion and the cerebellum. Brain (2006) 129:288–292.
Shin YW, Yoo SY, Lee JK, Ha TH, Lee KJ, Lee JM, et al. Cortical thinning in obsessive compulsive disorder. Hum Brain Mapp (2007) 28:1128–35.[CrossRef][Web of Science][Medline]
Smith AB, Taylor E, Brammer M, Toone B, Rubia K. Task-specific hypoactivation in prefrontal and temporoparietal brain regions during motor inhibition and task switching in medication-naive children and adolescents with attention deficit hyperactivity disorder. Am J Psychiatry (2006) 163:1044–51.
Sohn MH, Ursu S, Anderson JR, Stenger VA, Carter CS. Inaugural article: the role of prefrontal cortex and posterior parietal cortex in task switching. Proc Natl Acad Sci USA (2000) 97:13448–53.
Stein DJ, Coetzer R, Lee MY, Davids B, Bouwer C. Magnetic resonance brain imaging in women with obsessive-compulsive disorder and trichotillomania. Psychiatry Res NI (1997) 74:177–82.[CrossRef]
Stein DJ, Hollander E, Chan S, DeCaria CM, Hilal S, Liebowitz MR, et al. Computed-tomography and neurological soft signs in obsessive-compulsive disorder. Psychiatry Res NI (1993) 50:143–50.
Stewart SE, Rosario MC, Brown TA, Carter AS, Leckman JF, Sukhodolsky D, et al. Principal components analysis of obsessive-compulsive disorder symptoms in children and adolescents. Biol Psychiatry (2007) 61:285–91.[CrossRef][Web of Science][Medline]
Szeszko PR, Ardekani BA, Ashtari M, Malhotra AK, Robinson DG, Bilder RM, et al. White matter abnormalities in obsessive-compulsive disorder: a diffusion tensor imaging study. Arch Gen Psychiatry (2005) 62:782–90.
Szeszko PR, MacMillan S, McMeniman M, Chen S, Baribault K, Lim KO, et al. Brain structural abnormalities in psychotropic drug-naive pediatric patients with obsessive-compulsive disorder. Am J Psychiatry (2004) 161:1049–56.
Szeszko PR, Robinson D, Alvir JMJ, Bilder RM, Lencz T, Ashtari M, et al. Orbital frontal and amygdala volume reductions in obsessive-compulsive disorder. Arch Gen Psychiatry (1999) 56:913–9.
Tolin DF, Kiehl KA, Worhunsky P, Book GA, Maltby N. An exploratory study of the neural mechanisms of decision-making in compulsive hoarding. Psychol Med (2008) May 19. [Epub ahead of print], doi:10.1017/S0033291708003371.
Valente AA, Miguel EC, Castro CC, Amaro E, Duran FLS, Buchpiguel CA, et al. Regional gray matter abnormalities in obsessive-compulsive disorder: a voxel-based morphometry study. Biol Psychiatry (2005) 58:479–87.[CrossRef][Web of Science][Medline]
van den Heuvel OA, Groenewegen HJ, Barkhof F, Lazeron RH, van DR, Veltman DJ. Frontostriatal system in planning complexity: a parametric functional magnetic resonance version of Tower of London task. Neuroimage (2003) 18:367–74.[CrossRef][Web of Science][Medline]
van den Heuvel OA, Veltman DJ, Groenewegen HJ, Cath DC, van Balkom AJ, van HJ, et al. Frontal-striatal dysfunction during planning in obsessive-compulsive disorder. Arch Gen Psychiatry (2005) 62:301–9.
van den Hout M, Kindt M. Repeated checking causes memory distrust. Behav Res Ther (2003) 41:301–16.[CrossRef][Web of Science][Medline]
van den Hout M, Kindt M. Obsessive-compulsive disorder and the paradoxical effects of perseverative behaviour on experienced uncertainty. J Behav Ther Exp Psychiatry (2004) 35:165–81.[CrossRef][Web of Science][Medline]
van Oppen P, Hoekstra RJ, Emmelkamp PM. The structure of obsessive-compulsive symptoms. Behav Res Ther (1995) 33:15–23.[CrossRef][Web of Science][Medline]
Veltman DJ, Rombouts SA, Dolan RJ. Maintenance versus manipulation in verbal working memory revisited: an fMRI study. Neuroimage (2003) 18:247–56.[CrossRef][Web of Science][Medline]
Volkow ND, Wang GJ, Telang F, Fowler JS, Logan J, Childress AR, et al. Cocaine cues and dopamine in dorsal striatum: mechanism of craving in cocaine addiction. J Neurosci (2006) 26:6583–8.
Yin HH, Knowlton BJ, Balleine BW. Lesions of dorsolateral striatum preserve outcome expectancy but disrupt habit formation in instrumental learning. Eur J Neurosci (2004) 19:181–9.[CrossRef][Web of Science][Medline]
Yoo SY, Jang JH, Shin YW, Kim DJ, Park HJ, Moon WJ, et al. White matter abnormalities in drug-naive patients with obsessive-compulsive disorder: a diffusion tensor study before and after citalopram treatment. Acta Psychiatr Scand (2007) 116:211–19.[CrossRef][Web of Science][Medline]
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  B. J. Harrison, C. Soriano-Mas, J. Pujol, H. Ortiz, M. Lopez-Sola, R. Hernandez-Ribas, J. Deus, P. Alonso, M. Yucel, C. Pantelis, et al. Altered Corticostriatal Functional Connectivity in Obsessive-compulsive Disorder Arch Gen Psychiatry, November 1, 2009; 66(11): 1189 - 1200. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. Radua and D. Mataix-Cols Voxel-wise meta-analysis of grey matter changes in obsessive-compulsive disorder The British Journal of Psychiatry, November 1, 2009; 195(5): 393 - 402. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||