White matter abnormalities in pediatric obsessive-compulsive disorder

Psychiatry Research: Neuroimaging ∎ (∎∎∎∎) ∎∎∎–∎∎∎

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White matter abnormalities in pediatric obsessive-compulsive disorder Timothy Silk a,n, Jian Chen a, Marc Seal a,c, Alasdair Vance b a b c

Developmental Imaging, Murdoch Childrens Research Institute, Royal Children's Hospital, Melbourne 3052, Australia Academic Child Psychiatry Unit, Department of Paediatrics, University of Melbourne, Royal Children's Hospital, Melbourne, Australia Department of Paediatrics, University of Melbourne, Melbourne, Australia

art ic l e i nf o

a b s t r a c t

Article history: Received 16 May 2012 Received in revised form 4 December 2012 Accepted 25 April 2013

Diffusion tensor imaging (DTI) has been useful in allowing us to examine the nature and extent of neuronal disruption associated with obsessive-compulsive disorder (OCD). However, little is known about the underlying brain structure in OCD. Diffusion-weighted magnetic resonance imaging was performed in 16 children with OCD and 22 typically developing children. Tract-based spatial statistics (TBSS) was used to compare the microstructure of white-matter tracts of OCD children with those of typically developing children. Correlation/regression analyses were also performed on each diffusion measure in order to detect any correlation of white-matter microstructure with scales of symptom severity. Analysis revealed significantly greater axial diffusivity in both the genu and the splenium of the corpus callosum in the control compared to the OCD group; these regions consecutively connect bilateral medial frontal regions and bilateral parietal regions. Secondly, correlation and voxel-based regression analysis revealed that lower axial diffusion correlated with greater severity of symptoms within the OCD group, as measured by the Child Behaviour Checklist-Obsessive Compulsive Scale (CBCL-OCS). The findings demonstrated a correlation of axial diffusivity with severity of symptoms in children with OCD. DTI may provide novel ways to help reveal the relationships between clinical symptoms and altered brain regions. & 2013 Elsevier Ireland Ltd. All rights reserved.

Keywords: Diffusion tensor imaging (DTI) OCD Axial diffusivity

1. Introduction Obsessive-compulsive disorder (OCD) is one of the most disabling anxiety disorders, involving a recurrent pattern of intrusive thoughts, dysphoric feelings and maladaptive compulsive behaviors that interfere with young people's academic, social and home life. Key neurobiological models of OCD have implicated substructures and connections of a fronto– striatal–thalamic–cortical network, the most widely accepted involving the orbitofrontal cortex (OFC), anterior cingulate, thalamus and and striatum (Modell et al., 1989; Saxena et al., 1998; Graybiel and Rauch, 2000). In general, the model of fronto– striatal–thalamic–cortical involvement in OCD has been well supported by both structural and functional neuroimaging. There are several informative reviews on neuroimaging in adult OCD (e.g. Saxena and Rauch, 2000; Kwon et al., 2009), but few overviews in pediatric OCD (MacMaster et al., 2008; Huyser et al., 2009; MacMaster, 2010). Although morphometric studies of OCD have been somewhat inconsistent, the most consistent findings in OCD are gray matter volumetric abnormalities involving the orbitofrontal cortex and the basal ganglia.

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Corresponding author. Tel.: +61 3 9936 6708. E-mail address: [email protected] (T. Silk).

While conventional structural magnetic resonance imaging (MRI) can provide valuable information about brain regional macrostructure and volume, diffusion tensor imaging (DTI) allows further examination of microstructure of cortical gray and white matter. This technique estimates the direction and extent of diffusion of water within the brain and is particularly sensitive to the organization of white matter tracts. Diffusion properties are typically quantified by measures derived from three principal directions of diffusion; primary (λ1), secondary (λ2), and tertiary (λ3) eigenvalues. The primary eigenvalue (also known as axial diffusivity; λ∥) denotes water diffusivity parallel to nerve axons. The secondary and tertiary eigenvalues correspond to diffusion orthogonal to λ1 and can generate a measure of radial diffusivity (λ⊥). Mean diffusivity (MD) is a composite of the three eigenvalues and represents apparent mobility of water, which is simply the magnitude of diffusion in each measured voxel; and fractional anisotropy (FA) is an index of the ‘directionality’ of diffusion in each voxel. Therefore, because the direction and extent of water diffusion is dependent on the underlying structure of tissue, differences in cellular structure can be inferred from these measures. Diffusion parameters can be biomarkers of changes in these axonal properties with particular measures differentially sensitive to different white-matter development and pathology. For example, radial diffusivity appears to be modulated by dys- or de-myelination (cell membrane and cytoskeleton) in white matter,

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Please cite this article as: Silk, T., et al., White matter abnormalities in pediatric obsessive-compulsive disorder. Psychiatry Research: Neuroimaging (2013), http://dx.doi.org/10.1016/j.pscychresns.2013.04.003i

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whereas the axial diffusivity may be more specific to axonal degeneration (volume and organization) (Harsan et al., 2006). DTI studies of OCD are few; of the 14 studies to date, only two studies have been conducted in adolescents and/or children, and findings have been variable. All studies have reported FA as the main measure of diffusion. A few studies have also included additional measures of diffusivity including MD (or apparent diffusion coefficient) (Saito et al., 2008; Nakamae et al., 2011; Lochner et al., 2012), axial diffusivity (or principal diffusion direction) (Garibotto et al., 2010; Bora et al., 2011; Jayarajan et al., 2012) and radial diffusivity (Bora et al., 2011; Jayarajan et al., 2012). FA changes in adult OCD have been reported in a number of brain regions, including the anterior cingulate (Szeszko et al., 2005; Ha et al., 2009) and the cingulum bundle (Cannistraro et al., 2007; Garibotto et al., 2010; Chiu et al., 2011; Nakamae et al., 2011; Zarei et al., 2011), corpus callosum (CC) (Yoo et al., 2007; Saito et al., 2008; Garibotto et al., 2010; Bora et al., 2011; Nakamae et al., 2011; Zarei et al., 2011), internal capsule (Cannistraro et al., 2007; Yoo et al., 2007; Nakamae et al., 2011), parietal regions (Szeszko et al., 2005; Menzies et al., 2008), medial frontal regions (Menzies et al., 2008; Zarei et al., 2011), the inferior frontooccipital fasciculus (Garibotto et al., 2010; Zarei et al., 2011) and the anterior thalamic radiation (Chiu et al., 2011), bilateral centrum semiovale and sub-insular region (Nakamae et al., 2008), and a region superolateral to the right caudate (Yoo et al., 2007). Two studies also used fiber tracking to examine a priori regions of interest (ROIs) in OCD adults (Garibotto et al., 2010; Chiu et al., 2011). Garibotto et al. (2010) examined the inferior fronto-occipital fasciculus and found decreased fiber tract coherency and directionality in the connections of the orbitofrontal and occipital cortex in OCD. Chiu et al. (2011) found lower generalized FA in the right anterior thalamic radiation and the left anterior segment of the cingulum bundle in adults with OCD compared to healthy control participants. The most consistent finding from DTI studies in OCD is that of diffusion differences in the CC. The CC is the largest whitematter commissure and allows interhemispheric communication. While varying in their approaches and the regions of the CC selected, most studies found lower FA in OCD (Saito et al., 2008; Garibotto et al., 2010; Bora et al., 2011; Nakamae et al., 2011) with some exceptions (Yoo et al., 2007; Zarei et al., 2011). Involvement of the CC is also supported by morphometric studies (Rosenberg et al., 1997; Farchione et al., 2002; Chen et al., in preparation). While previous studies have often used VBM methods for whole-brain analysis, this approach has several limitations when applied to DTI data (Smith et al., 2006). Methodologically, limitations relate to difficulties in accurate inter-subject image registration, which is crucial for DTI data in which large image intensity boundaries exist between white-matter tracts (relatively high FA values) and cortical gray matter or subcortical nuclei (relatively low FA values). White-matter structure is inherently highly variable between individuals, and therefore registration for white matter between individuals at a voxel basis is critical. Tract-based spatial statistics (TBSS) is an analytic method developed specifically for DTI data that restricts analysis to just the center of major white-matter tracts, rather than indiscriminately across the whole-brain volume (Smith et al., 2006). TBSS minimizes intersubject registration problems and problems of multiple comparisons by first determining a mean FA ‘skeleton’, representing only the center of major white-matter fiber tracts, then mapping each participant's DTI data directly onto that skeleton. TBSS methods are therefore highly sensitive to changes in microstructure within the major white-matter fiber pathways of the brain. Two recent studies have used TBSS to analyze DTI results in examining OCD adults (Bora et al., 2011; Nakamae et al., 2011), and two in adolescents/children (Zarei et al., 2011; Jayarajan et al., 2012).

Both adult studies found significantly lower FA in the body of the CC. Nakamae et al. (2011), additionally found a trend toward lower FA across a larger proportion of the CC, the right cingulum, and the left anterior limb of the internal capsule. In adolescents, Zarei et al. (2011) reported higher FA in the splenium and genu of the CC, as well as a number of other tracts including the major and minor forceps and the cingulum. In children, Jayarajan et al. (2012) found no difference between patients and controls for FA. However, there was widespread difference reported in both axial and radial diffusivity in regions including the CC, superior and inferior longitudinal fascicule, cingulum, anterior thalamic radiation and cerebellar peduncle. Significantly, several findings suggest that anomalies in white matter may vary according to symptom severity. Seven of the DTI studies have explored whether there is a correlation between FA and clinical assessments, most commonly the Yale–Brown Obsessive-Compulsive Scale (Y-BOCS) (Szeszko et al., 2005; Saito et al., 2008; Garibotto et al., 2010; Chiu et al., 2011; Nakamae et al., 2011; Zarei et al., 2011; Lochner et al., 2012), the obsession or compulsion subscores from the Y-BOCS (Ha et al., 2009; Chiu et al., 2011) or the children's Y-BOCS (Jayarajan et al., 2012), but also a Depression Scale (Saito et al., 2008), and a battery of tests examining visuospatial ability, working memory, and decision making (Garibotto et al., 2010). Negative correlations (higher scores on the Y-BOCS correlated with lower FA) have been reported in the CC, fronto-occipital fasciculus, superior longitudinal fasciculus (SLF), optic radiation and parietal regions (Szeszko et al., 2005; Saito et al., 2008; Garibotto et al., 2010), positive correlations in the anterior cingulum bundle and the anterior cingulate (Ha et al., 2009; Chiu et al., 2011), the CC and major and minor forceps (Zarei et al., 2011) and also no significant correlation with respects to FA (Nakamae et al., 2011; Lochner et al., 2012). No correlation was found with scores on a depression scale (Saito et al., 2008), but Garibotto et al. (2010) found a negative correlation of FA with performance in a spatial attention and a decision making task in the fronto-occipital fasciculus, the superior longitudinal fasciculus, and the whole CC. Given these inconsistent findings to date, the current study examined if different diffusion parameters in white matter vary with respect to two measures of OCD symptom severity (Children's Yale–Brown Obsessive Compulsive Scale (Children's Y-BOCS) and Child Behavior Checklist-Obsessive Compulsive Scale (CBCL-OCS)) and two depression/anxiety measures (Children's Depression Inventory (CDI) and Child Behavior Checklist-Anxiety and Depression scale (CBCL-AD)). In the present study, we examined the microstructure of white matter in children and adolescents with OCD compared with healthy age-, handedness- and IQ-matched control participants. Utilizing TBSS (Smith et al., 2006), we examined four diffusivity measures (FA, MD, axial and radial diffusivity) within major whitematter pathways throughout the whole brain. Additionally, correlation/regression analyses were performed on each diffusion measure over the white matter of the whole brain in order to detect any correlation of white-matter microstructure with scores on standard cognitive tests and scales of symptom severity.

2. Methods 2.1. Participants Sixteen children fulfilling DSM-IV criteria for OCD (6 males, 10 females), aged 8–18 years (mean 12.7772.83 years), were identified at the Royal Children's Hospital, Melbourne, Australia. OCD is defined categorically, using a semistructured clinical interview with the participant's parent(s) and the young persons themselves, and the Anxiety Disorders Interview Schedule for Children (A-DISC); (Silverman and Albano, 1996), in addition to dimensionally using the

Please cite this article as: Silk, T., et al., White matter abnormalities in pediatric obsessive-compulsive disorder. Psychiatry Research: Neuroimaging (2013), http://dx.doi.org/10.1016/j.pscychresns.2013.04.003i

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clinician rating of OCD (CY-BOCS). Participants had a full scale IQ above 70, according to an age-appropriate Wechsler test (2004: mean performance IQ: 100.3 7 15.8; mean verbal IQ: 95.6 7 17.2); and there were no known neurological or endocrine conditions, mood disorders, autistic disorders, psychotic disorders, attention deficit hyperactivity disorder, combined type, reading/spelling/arithmetic learning disorders, developmental coordination disorder or alcohol/substance abuse/dependence disorders. Three of the patients had comorbid generalized anxiety disorder. All participants were medication-naïve except for two patients; one had been on fluoxetine (20 mg) for 6 months, and one had been on sertraline (50 mg) and ritalin (10 mg) for 12 months. These two participants were withdrawn from medication for 24 h before scanning. Twenty-two healthy control participants (16 males, 6 females) were matched in age (8–18 years; mean 11.247 2.13 years) and IQ (mean performance IQ: 104.77 10.3; mean verbal IQ: 102.7 7 14.8) to the OCD group. All participants were right-handed. After complete description of the study to the subjects, written consent was obtained. All procedures were approved by the Human Research Ethics Committee of the Royal Children's Hospital, Melbourne, Australia.

implemented within FSL software (Oxford, UK) was used to track likely paths extending from the clusters identified as abnormal by the TBSS analysis. Two ROI masks were generated, one for the cluster in the genu of the CC and one for the cluster in the splenium of the CC. These ROI masks were then back-projected to the original FA images for each individual. Probabilistic fiber-tracking, as implemented within FSL, was then conducted using each of these ROI masks as “seed” regions, with a step length of 0.5 mm, a maximum of 2000 steps, and a curvature threshold of 0.2. Probabilistic tracks for each individual are then normalized back to standard space and averaged across the group. Using this method, we obtained probabilistic connectivity maps representing the most likely paths extending from the clusters in which abnormalities in axial diffusivity had been found. Finally, correlation of clinical measures to diffusion measures were conducted using TBSS General Linear Model (GLM) regression. Each demeaned clinical measure was entered as an explanatory variable in the design matrix and tested for positive or negative correlation. Voxel-wise independent t-tests were conducted using Randomize, and the TFCE approach was used to obtain cluster inferences.

2.2. Clinical assessment

3. Results

Children's Yale–Brown Obsessive Compulsive Scale (CY-BOCS): The CY-BOCS is a semi-structured interview assessing the severity of both obsessions and compulsions separately, as well as giving an overall score (Scahill et al., 1997). Child Behavior Checklist (CBCL): The CBCL is an extensively used rating scale questionnaire for parents to detail the frequency and intensity of behavioral and emotional problems exhibited by their child (Achenbach and Edelbrock, 1983). It contains 118 items covering problems that have occurred over the preceding 6 months. A number of subscores are derived for the CBCL including the Anxiety and Depression (CBCL-AD) scale, which is used in the current study. A CBCL Obsessive-Compulsive Scale (CBCL-OCS) was developed (Nelson et al., 2001) using eight items (creating a range between 0 and 16) with the aim of predicting OCD. It has been shown to be both reliable and valid (Geller et al., 2006; Hudziak et al., 2006; Storch et al., 2006). Although there have been various adaptations of the CBCL-OCS tested, with fewer items including versions with six items (Storch et al., 2006), three items (Geller et al., 2006), and even just two items (Ivarsson and Larsson, 2008), we used the original eight-item scale (Nelson et al., 2001) for the purposes of our research. The CBCL-OCS was the main scale of interest. The CBCL-AD was included for validation as the 13-item scale contains four of the eight items in the CBCL-OCS scale. Children's Depression Inventory (CDI): The CDI is a 27-item self-report measure of depressive symptoms in children and adolescents (Kovacs, 1985).

3.1. Between-group TBSS analysis of regional white matter

2.3. Data acquisition and analysis Neuroimaging data were acquired on the 3T Siemens TIM Trio scanner (Siemens, Erlangen, Germany) at the Murdoch Children's Research Institute, Royal Children's Hospital, Melbourne. Participants lay supine with their head supported in a 12-channel head coil. Diffusion weighted echo-planar images (EPI) were acquired along 60 diffusion gradient directions for acquisition of 36 slices through the whole brain (TR¼ 5200 ms, TE ¼ 88 ms, flip angle ¼ 901, b value ¼1000 s/mm2, FOV ¼220 mm2, 128  128 matrix, 1.72 mm in-plane resolution, slice thickness¼ 3 mm, b0 images¼10). Analyses of diffusion-weighted images were done using FMRIB's Software Library (FSL; Oxford, UK): FMRIB's Diffusion Toolbox (FDT). Initially, Eddy Current Correction was run to correct for gradient-coil distortions and small head motions, using affine registration to a b0 reference volume. A diffusion tensor model was fitted to each voxel, generating FA, MD, and diffusivity along the principal λ1, λ2, λ3 directions. Finally, we used TBSS (Smith et al., 2006) to carry out voxel-wise statistical analyses for relevant diffusivity measures (FA, MD and axial and radial diffusivity) along major white matter tracts. The TBSS method minimizes the potential misalignment problems of other voxel-based whole-brain analysis methods by determining a white-matter “skeleton” restricted only to the center of major white-matter tracts, and mapping diffusion values from each individual directly onto this standard skeleton for group comparison. Images from all individuals were aligned to each other using non-linear registration (IRTK local matrix deformation) in order to determine the most representative individual (i.e. the closest to the mean of the group) to be defined as the target image. This target image was then aligned, using affine registration to the Montreal Neurological Institute (MNI) brain template. A white-matter skeleton was then generated representing a single line running down the centers of all the common whitematter fibers. Group statistical analysis was then conducted only on voxels within the white-matter skeleton mask, therefore restricting the voxel-wise analysis only to voxels with high confidence of lying within equivalent major white-matter pathways in each individual. Differences in FA, MD and axial and radial diffusivity between OCD and control groups were assessed using voxel-wise independent two-sample t-tests by Randomize, the nonparametric analysis tool in FSL. The Threshold-Free Cluster Enhancement (TFCE (Smith and Nichols, 2009)) option was employed at po 0.05 to obtain cluster inferences. To further characterize pathways of the white-matter tracts in which differences in axial diffusivity were found, the probabilistic tractography method

TBSS analysis identified several clusters of significantly greater axial diffusivity in the control group compared to OCD after correction for multiple comparisons. Regions of significant differences included fibers in both the genu and the splenium (see Fig. 1a). Mean axial diffusivity measures in the ROIs of the clusters identified from the TBSS analysis were: OCD: 1.51 70.02 (  10−3 mm2/s); Controls: 1.44 70.04 (  10−3 mm2/s). No significant between-group differences were identified in FA, MD, or radial diffusivity after correction for multiple comparisions. Probabilistic tractography was then used to illustrate the pathways of the white-matter tracts by representing the most likely paths extending from the clusters in which abnormalities in axial diffusivity had been found. Fig. 1b shows that the fibers running through the genu of the CC connecting bilateral medial frontal regions, and the fibers running through the splenium of the CC connecting bilateral parietal regions as well as extending down to superior temporal regions. Previous DTI studies have found differential developmental trajectories between males and females during childhood and adolescence (Schmithorst et al., 2008). Given the OCD and control groups were not matched for gender, examination of potential gender differences in diffusivity revealed no significant differences on any of the diffusion measures between males and females in the mean diffusion values in either the whole white-matter skeleton or in ROIs of the clusters identified from the TBSS analysis (difference in axial diffusivity between OCD and control participants). 3.2. Whole brain correlation analysis in OCD Pearson correlation analyses were performed on the mean of each diffusion measure over the whole-brain white-matter skeleton in order to detect any correlation of white-matter microstructure with scores on the clinical assessments within the OCD group. Scores on the CBCL-OCS, showed a significant negative correlation with axial diffusivity (r ¼−0.45, p ¼0.041) (see Fig. 2c). Scores on the CBCL-AD also showed a significant negative correlation (r ¼−0.51, p ¼0.021). There were no significant correlations for scores on the CY-BOCS or the CDI. 3.3. Voxel-based regression analysis in OCD To locate specific regions that correlated scores on the clinical assessment in the OCD group with white-matter microstructure, linear regressions were used to examine correlations between clinical assessment scores and diffusion measures. The threshold of po0.05 was considered significant. Regression analysis of axial diffusivity shows significant negative correlation with scores on the

Please cite this article as: Silk, T., et al., White matter abnormalities in pediatric obsessive-compulsive disorder. Psychiatry Research: Neuroimaging (2013), http://dx.doi.org/10.1016/j.pscychresns.2013.04.003i

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diffusivity in both the genu and the splenium of the CC. Secondly, on the basis of whole-brain correlation and voxel-based regression, lower axial diffusion correlated with greater severity of symptoms within the OCD group. Regions shown to correlate most significantly included the left cingulate and superior longitudinal fasciculus, and bilateral posterior limbs of the internal capsule. 4.1. Between-group difference in diffusion

Fig. 1. (a) White-matter skeleton (green) showing regions of significantly higher axial diffusivity (red) in healthy controls compared to pediatric OCD (tfce_coorp o 0.05). For illustrative purposes, regions showing significant differences are thickened using FSL's tbss_fill script. (b) Average probabilistic tractography within the OCD group (standard space), implemented to illustrate the likely paths extending from the clusters identified by the TBSS analysis. Two ROI masks were generated; the genu (blue) and the splenium (red) of the corpus callosum. Top row is overlaid on the group-averaged FA image; the bottom row is a 3D rendering of the probabilistic tractography. Neurological orientation (R ¼ R). (For interpretation of references to color in this figure legend, the reader is referred to the web version of this article.)

CBCL-OCS (p¼0.018) (see Fig. 2a and b). Regions shown to correlate most significantly included the left cingulate and superior longitudinal fasciculus, and bilateral posterior limbs of the internal capsule. As for the affirmation of the CBCL-OCS, there was also a significant negative correlation with scores on the CBCL-AD (p¼0.016), with the map of significant regions being very similar to that of the CBCL-OCS. Higher scores on both the CBCL-OCS and the CBCL-AD reflect higher levels of anxiety, depression, obsessions and compulsions; therefore, lower axial diffusion correlated with greater severity of symptoms. Scores on the CBCL-OCS and CBCL-AD did not significantly correlate with any other diffusion measures. There were no significant correlations with scores on the CY-BOCS or CDI.

4. Discussion This study examined the microstructure of white-matter pathways in young people with OCD. Compared to the OCD group, healthy control children demonstrated significantly greater axial

To date all published DTI studies of OCD have used FA as the main measure of diffusion. The current study in pediatric OCD, found no group differences in FA or other measures of diffusivity, MD or radial diffusivity but, rather, found significant difference in axial diffusivity. While this study did not identify FA differences, it did confirm differences in diffusion properties in the CC (Yoo et al., 2007; Saito et al., 2008; Garibotto et al., 2010; Bora et al., 2011; Nakamae et al., 2011; Zarei et al., 2011), cingulum (Cannistraro et al., 2007; Garibotto et al., 2010; Chiu et al., 2011; Nakamae et al., 2011; Zarei et al., 2011), and fibers connecting medial frontal areas (Nakamae et al., 2011; Zarei et al., 2011) in OCD. Two studies reporting differences in diffusion properties in the CC (as well as other regions) also measured axial diffusivity. Bora et al. (2011), reported no difference in axial diffusivity in the CC of adults with OCD. Garibotto et al. (2010), noted altered principal diffusion direction (taken here to represent axial diffusion). While not reporting the direction in which it is altered, they suggest the finding to implicate tract disorganization reflected by local changes in fiber directionality. However, these studies were conducted in adults. One DTI study was conducted on adolescents with OCD (Zarei et al., 2011), but only FA was reported. In the only other study conducted in children with OCD, Jayarajan et al. (2012) found no difference between patients and controls for FA, but widespread differences in both axial and radial diffusivity in regions including the CC, superior and inferior longitudinal fasciculus, cingulum, anterior thalamic radiations and cerebellar peduncle. Consistent with Jayarajan et al. (2012) the current results also showed no significant difference in FA in the patient group, found axial diffusivity differences in the genu and the splenium of the CC, and also found the axial diffusivity correlated most significantly in regions including the left cingulate and superior longitudinal fasciculus. However, while the current study found greater axial diffusivity in healthy controls and worse symptom severity associated with lower axial diffusivity in patients, Jayarajan et al. (2012) found great axial diffusivity in OCD patients. While both these results clearly indicate an abnormality in particular whitematter pathways in children with OCD, they are restricted in revealing what the changes represent at a cellular level, and why this may developmentally differ from adults. It is important to note that FA, MD, and axial and radial diffusivity are not independent measures and have a complex inter-relationship. Quite broadly, with respect to FA, for example, FA increases when radial diffusivity decreases and/or axial diffusivity increases (Qiu et al., 2008). To make the matter even more complex, FA has been reported to increase over development (Snook et al., 2005) and both radial and axial diffusivity are reported to decrease over development (Kumar et al., 2012). It has been suggested that radial diffusivity appears to be modulated by dys- or de-myelination, whereas the axial diffusivity may be more specific to the volume and organization of axons (Harsan et al., 2006). It may be the case that children with OCD have lower regional axial diffusivity, but as they develop, the normal decrease in axial diffusivity negates any between-group differences with healthy control participants. At the same time the normal decrease in radial diffusivity over development and its relationship to the axial diffusivity generate

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Fig. 2. (a) Results of voxel-based regression analysis of axial diffusivity showing regions significantly correlating (negatively) with scores on the CBCL-OCS (red) on the white-matter skeleton (green) in the OCD group (tfce_coorp o 0.05). For illustrative purposes, regions showing significant differences are thickened using FSL's tbss_fill script. Neurological orientation (R ¼R). (b) A 3-dimensional rendering of the regression result. (c) Graph of the whole-brain white-matter skeleton correlation of axial diffusivity with scores on the CBCL-OCS in OCD. (For interpretation of references to color in this figure legend, the reader is referred to the web version of this article.)

FA differences in adulthood. Another possibility, put forth by Jayarajan et al. (2012), is that pediatric OCD may be a developmental subtype of OCD differing from adult OCD with respect to white-matter development. A future longitudinal study would help clarify this matter. Probabilistic tractography was used in the current study to represent the paths and endpoints of fibers running through the clusters in which abnormalities in axial diffusivity had been found. Fibers running through the genu of the CC were shown to connect bilateral medial frontal regions, and the fibers running through the splenium of the CC connected bilateral parietal regions. In development, the corpus callosum continues to grow at a rate of 1.3% per year (at least until the early 20s) (Giedd, 2004), and these intercortical associational neurons are the last to myelinate (Yakolev and Lecours, 1967). A reduction in the interhemispheric connectivity, or perhaps a lag in myelination, may impair efficient information processing to medial frontal regions and parietal regions, and may relate to the structural anomalies observed in morphometric studies. Although morphometric studies of OCD have been somewhat inconsistent, one of the most consistent findings in OCD are volumetric abnormalities involving the orbitofrontal cortex and other medial frontal regions (e.g. Szeszko et al., 2005; Carmona et al., 2007), with these regions attributed to deficits in response control and inhibition. While a less common finding, parietal regions have been reported as structurally abnormal in OCD (Carmona et al., 2007; Lazaro et al., 2009). The parietal regions are involved in attention, executive functioning, inhibition and visuospatial ability, with connections with fronto-striatal networks (Mesulam, 1990). A morphometric study of young people with OCD from our own research group also demonstrates lower gray matter volumes in bilateral frontal, cingulate, and parietal regions for OCD, and lower white-matter volume in the cingulate, right frontal and parietal regions, and the CC (Chen

et al., in preparation). The current results support these morphometric findings. 4.2. Clinical correlates The OCD group showed lower axial diffusivity correlated with greater severity of symptoms. Several of the DTI studies to date have explored whether there is a correlation between FA and clinical assessments, most commonly the Y-BOCS (Szeszko et al., 2005; Saito et al., 2008; Garibotto et al., 2010; Chiu et al., 2011; Nakamae et al., 2011; Zarei et al., 2011; Lochner et al., 2012). The current study, in a pediatric population, used the CY-BOCS but did not find a correlation of CY-BOCS scores with any of the diffusion parameters including FA, consistent with Jayarajan et al. (2012). Given that the CY-BOCS is a simple child-friendly version of the YBOCS the lack of a structural correlate with clinical severity in children might therefore be due to the developmentally different pattern of white-matter maturation, rather than differing Y-BOCS assessments between childhood and adulthood. The current study did, however, demonstrate a correlation of axial diffusivity with another measure of symptom severity, the CBCL-OCS. Higher scores on the CBCL-OCS reflect higher levels of anxiety, depression, obsessions and compulsions. While the CY-BOCS and the CBCLOCS both have high validity for assessing symptom severity (Scahill et al., 1997; Storch et al., 2006), it is not known how closely the two tests assess the pattern of symptoms; for example, it has been suggested that the CY-BOCS has stronger associations to obsessional thinking and compulsive symptoms than to measures of general anxiety and depression (Scahill et al., 1997). While the OCD participants in the current study were medication naïve or taken off their medication for 24 h before scanning, the potential long-term effects of medication on white-matter development are unknown. As only two of the participants in the

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current study had a history of medication, and both for a relatively short period (one of 6 and one of 12 months), any potential long-term effects would not be expected to have had a significant impact on these results, but this issue will be important for future research to investigate. 4.3. Conclusions The current study has provided further insight into the organization and the structure of white-matter pathways in OCD in children. While the etiology and the cognitive and behavioral implications of the identified structural abnormalities are unclear, it is apparent that the axial diffusivity anomalies in children with OCD correspond to symptom severity as measured by the CBCLOCS. DTI can potentially be important in allowing us to examine the nature and extent of neuronal disruption associated with OCD that conventional MRI cannot identify, and in the future, providing new avenues to help reveal the relationships between the clinical symptoms and the affected brain regions. In the future, diffusion measures may potentially serve as an important biomarker for OCD.

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