European Psychiatry 30 (2015) 205–213

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Original article

Comparative neuropsychiatry: White matter abnormalities in children and adolescents with schizophrenia, bipolar affective disorder, and obsessive-compulsive disorder T. White a,b,*, C. Langen a,b, M. Schmidt a, M. Hough c, A. James c,d a

Department of Child and Adolescent Psychiatry, Erasmus Medical Centre – Sophia, Rotterdam, The Netherlands Department of Radiology, Erasmus Medical Centre – Sophia, Rotterdam, The Netherlands Department of Psychiatry, Oxford University, Oxford, United Kingdom d Highfield Adolescent Unit, Warneford Hospital, Oxford, United Kingdom b c

A R T I C L E I N F O

A B S T R A C T

Article history: Received 29 July 2014 Received in revised form 8 October 2014 Accepted 12 October 2014 Available online 12 December 2014

Background: There is considerable evidence that white matter abnormalities play a key role in the pathogenesis of a number of major psychiatric disorders, including schizophrenia, bipolar affective disorder, and obsessive-compulsive disorder. Few studies, however, have compared white matter abnormalities early in the course of the illness. Methods: A total of 102 children and adolescents participated in the study, including 43 with early-onset schizophrenia, 13 with early-onset bipolar affective disorder, 17 with obsessive-compulsive disorder, and 29 healthy controls. Diffusion tensor imaging scans were obtained on all children and the images were assessed for the presence of non-spatially overlapping regions of white matter differences, a novel algorithm known as the pothole approach. Results: Patients with early-onset schizophrenia and early-onset bipolar affective disorder had a significantly greater number of white matter potholes compared to controls, but the total number of potholes did not differ between the two groups. The volumes of the potholes were significantly larger in patients with early-onset bipolar affective disorder compared to the early-onset schizophrenia group. Children and adolescents with obsessive-compulsive disorder showed no differences in the total number of white matter potholes compared to controls. Conclusions: White matter abnormalities in early-onset schizophrenia and bipolar affective disorder are more global in nature, whereas children and adolescents with obsessive-compulsive disorder do not show widespread differences in FA. ß 2014 Elsevier Masson SAS. All rights reserved.

Keywords: Diffusion tensor imaging Potholes Early-onset schizophrenia Children Adolescents

1. Introduction The constellation of symptoms associated with both schizophrenia and bipolar affective disorder (BPAD) support processes that involve global brain disruptions. While the classic symptoms of these two disorders show different patterns, there is considerable overlap in specific symptoms, genes associated with the disorders [23,52,16], underlying neurobiology [8], and patterns of cognitive deficits [47]. In addition, both patients with schizophrenia and BPAD benefit from similar treatment strategies, primarily the use of neuroleptics [14,78,15]. The findings that both schizophrenia and BPAD show an array of clinical symptoms (i.e., thought disorder, psychosis), non-focal findings on brain

* Corresponding author. Department of Child and Adolescent Psychiatry, Erasmus MC – Sophia, kamer SP-2869, Postbus 2060, 3000 CB Rotterdam, The Netherlands. E-mail address: [email protected] (T. White). http://dx.doi.org/10.1016/j.eurpsy.2014.10.003 0924-9338/ß 2014 Elsevier Masson SAS. All rights reserved.

MRI, and global cognitive deficits support mechanisms that involve multiple brain regions. This led to the concept of schizophrenia being considered a ‘disconnection syndrome’ involving brain networks [6,20], which later spread to include BPAD as well [11,68,55]. While patients with both schizophrenia and BPAD can show obsessive and compulsive symptoms, individuals with classic obsessive-compulsive disorder (OCD) have symptoms that tend to fit more focal patterns of brain involvement. Functional imaging studies suggest network abnormalities between the caudate nucleus and the orbitofrontal cortex (OFC). Patients with OCD generally do not show severe global cognitive deficits [53,59], have similar symptom profiles, and some individuals with severe OCD have partial relief of symptoms from neurosurgery involving connectivity with the anterior cingulate [80,27]. This raises question whether processes involving global brain processes, such as myelination, would be abnormal in schizophrenia and BPAD

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compared to OCD. Based on the symptoms, cognitive patterns, and studies of the underlying neurobiology of schizophrenia, BPAD, and OCD, we would expect much more similarities between schizophrenia and BPAD compared to OCD. One approach to evaluate global versus local abnormalities in structural brain connectivity is to evaluate the white matter (WM) in the brain. Myelinated neurons in the brain allows for the high-speed transfer of neuronal signals. While the vast majority of myelination takes place prior to four years of age, there is evidence for continued myelination in specific brain regions throughout adolescence and into early adult life, most notably in the association cortices [76]. Since the individual neurons within the major WM tracts tend to travel in parallel bundles, they are well suited to be studied using diffusion tensor imaging (DTI). Once commonly used metric of white matter coherence derived from DTI is fractional anisotropy (FA), which is a rotationally invariant scalar value reflecting the underlying WM microstructure [13,51]. The protracted development of WM into adolescence and young adulthood, coupled with its role to enhance the overall efficiency of brain function, has made it a key target for exploring the underlying neurobiology of major neuropsychiatric disorders, especially since the period of adolescence is also associated with a higher incidence of major mood and psychotic disorders. Multiple studies have found abnormalities in the WM microstructure in patients with schizophrenia [73,57,38], bipolar affective disorder (BPAD) [44], and obsessive-compulsive disorder (OCD) [60,33,18]. While these studies have shown WM abnormalities within disorders, few have directly compared the WM abnormalities across disorders. Early-onset schizophrenia (EOS) reflects the presence of schizophrenia prior to 18 years of age. EOS has been shown to be on a continuum with the adult form of the illness [54], although those with EOS tend to have greater genetic loading [9] and more pronounced negative symptoms [19,34] compared to adult-onset schizophrenia. There have been a number of studies exploring WM

abnormalities in EOS and in early-onset BPAD (EOB) [37,36,72,74,58,24,65,39,25,12,1,28,32]. Similar to studies in adults [73], there is considerable heterogeneity of the WM findings in both EOS and EOB [73]. There are also differences in the WM microstructure based on the age of onset of the illness [40]. One possibility is that different WM tracts or different sections of WM tracts are affected in different individuals. If this is the case, then both ROI and voxel-based approaches will be less able to detect regions that show spatial heterogeneity. In order to circumvent this problem, we have developed an algorithm to assess nonspatially specific WM abnormalities. This approach, known as the ‘pothole approach’, does not rely on the assumption that WM abnormalities are spatially overlapping [74,75]. The underlying tenet of the pothole approach is that disruptions in WM integrity may occur at different ‘‘points of weakness’’ in different individuals, similar to what is seen in tuberous sclerosis [22,43]. Thus, voxel-based or region of interest techniques may miss WM abnormalities that are spatially different between individuals. Thus, the goal of this study was to utilize the pothole approach to examine the specificity of WM abnormalities between EOS, EOB, and children and adolescents with OCD. Our hypothesis was that EOS and EOB would show considerable overlap and have global differences in WM, whereas children and adolescents with OCD would show more focal WM differences. 2. Methods 2.1. Participants The study sample included a total of 102 children and adolescents, 43 with EOS (6 of these with schizophreniform disorder), 13 with EOB with psychotic symptoms, 17 with OCD, and 29 healthy controls (see Table 1 for demographic information).

Table 1 Demographic and clinical characteristics of the children and adolescents.

Demographics Number Age (years, S.D.) Sex (M/F) Hand (R/L/both) IQ Clinical measures Age of onset PANSS (positive) PANNS (negative) Chlorpromazine equivalents

Antipsychotics Aripiprazole Chlorpromazine Clozapine Fluphenazine Olanzapine Quetiapine Risperidone Sulpiride Mood stabilizers Valproate Lithium Serotonin reuptake inhibitors SSRIs Fluoxetine Fluvoxamine Sertraline None

Early-onset schizophrenia

Bipolar affective disorder

Obsessive-compulsive disorder

Controls

43 17.0 (1.8) 24/19 36/6/1 89.0 (16.1)

13 15.4 (2.1) 6/7 10/2/1 94.3 (15.5)

17 16.2 (1.6) 9/8 17/1/0 107.5 (13.0)

29 16.5 (2.0) 15/14 26/2/1 106.9 (15.4)

14.4 (1.6) 21.8 (4.0) 15.3 (4.3) 352 (248) n = 35

14.1 (2.1) 19.5 (3.7) 10.5 (2.9) 210 (133) n = 13

11.2 (3.3) n/a

n/a n/a n/a n/a

2 1 6 1 9 3 6 1

0

1

6 3 1

1 3

1

6

1

5 4 3 4

29

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The patients were recruited from the Oxford regional adolescent unit and surrounding units. Diagnoses were assessed based on the DSM-IV [2] criteria using the Kiddie Schedule for Affective Disorders and Schizophrenia – Present and Lifetime version (KSADS-PL) [30]. In addition, the participants with either EOS or EOB were administered the Positive and Negative Syndrome Scale (PANSS) [31]. Family histories were ascertained using the Family History Research Diagnostic Criteria (FH-RDC) [7]. IQs were measured by a trained psychology assistant using the Wechsler Abbreviated Scale of Intelligence (WASI) [71]. Thirty-five of the 43 patients with EOS and 10 of the patients with EOB were receiving atypical neuroleptics (Table 1). Two patients with EOS were receiving typical neuroleptics. Twelve of the children and adolescents with OCD were receiving selective serotonin reuptake inhibitors (SSRIs) and one adolescent was receiving low dose aripiprazole. A group of matched healthy adolescents were recruited from the community through their general practitioners and were screened using the K-SADS-PL for any psychiatric or substance use problems. In addition, participants were screened for medical problems. Handedness for all participants was assessed using the Edinburgh Handedness Inventory [48]. The exclusion criteria included moderate mental impairment (IQ < 60), a history of pervasive developmental disorder, significant head injury, neurological disorders, or major medical disorders. The Oxford Psychiatric Research Ethics Committee approved the study and written informed consent was obtained from all participants and their parents. 2.2. Imaging protocol MRI images were obtained using a 1.5 Tesla Sonata MR system (Siemens, Erlangen, Germany). A standard quadrature head coil was used with a maximum 40 mT/m gradient capability. Diffusionweighted images were collected using a spin-echo EPI sequence with the following parameters: TE = 89 ms, TR = 8500 ms, 60 axial slices, bandwidth = 1860 Hz/vx, voxel size = 2.5 mm isotropic, five b = 0 images, b = 1000, and 60 non-collinear directions. To increase the signal-to-noise ratio, scanning was repeated three times and the results were averaged. 2.3. Preprocessing Preprocessing of the diffusion-weighted images was performed using FSL [63]. Individual images first underwent eddy-current correction. Second, skull stripping was performed using BET [62]. Next, fractional anisotropy (FA) images were created by fitting a tensor model to the diffusion data using FMRIB’s Diffusion Toolbox (FDT) [63]. All subjects’ FA data were then aligned into common space using the non-linear registration tool FNIRT [4,5], which uses a b-spline representation of the registration warp field [56]. 2.4. Pothole analysis Following the preprocessing of the images, an in-house MATLAB (Mathworks, Natick, MA) program was used to quantify the number and spatial characteristics of WM ‘potholes’ along the major WM tracts [74]. The input to the program involved FA images that have undergone non-linear registration into MNI space using TBSS [64]. No spatial filtering was applied to the images. The first step was to generate a voxel-by-voxel mean and standard deviation (S.D.) image of the control subjects. These group and S.D. images were then used to individually create a voxel-wide z-image for every subject, both patients and controls, with each voxel based on the mean and S.D. of the control group. To

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ensure the search involved only WM regions, each image was masked with the cortical areas defined by the Johns Hopkins University WM atlas [46]. In addition, voxels were included in which all subjects had an FA greater than 0.2. This resulted in 102 WM-masked, z-transformed FA images, one for each participant. The individual z-FA images were used to search for contiguous voxels of WM that fall below a set z-threshold and were greater than a specified pothole size. Potholes were determined by thresholding each image and labeling the three-dimensional connectivities (26 neighboring voxels). This was performed using the Matlab command ‘bwlabeln’, which labeled the z-valuethresholded discrete image based on contiguous voxels. Neighboring voxels that fell below the z-threshold were considered to belong to the same pothole. Two pothole measures were used, including the total number of WM potholes per individual, and the mean size of these potholes. To evaluate the spatial location of the individual WM potholes, the volume of each pothole was evaluated within specific labels of the WM atlas. 2.5. Statistical analyses The comparison between demographic and clinical data was performed using an ANOVA or Chi2 tests. Post hoc analyses of demographic data were followed up with t-tests or Chi2, as appropriate. Pothole analyses were performed using an ANCOVA and controlling for age. Post hoc tests were followed up with ANCOVA between the different groups. Tests of individual WM brain regions were performed using Wilcoxon rank order tests. Spearman correlation coefficients were utilized to evaluate the relationship between the demographic and clinical measures and the FA potholes. Statistical analyses were performed utilizing the SAS statistical package (Cary, NC, USA).

3. Results 3.1. Demographics The demographic and clinical information are shown in Table 1. There was a significant difference in the age between the groups (F3,97 = 2.8, P = 0.04). There were no significant age differences between each of the patient groups and the control group when evaluated separately. The significant difference in age was a result of the patients with EOS being older than children with EOB (F1,53 = 7.7, P = 0.008). Age was used as a covariate in all analyses. There were no significant differences in sex (x2 = 0.4, P = 0.9) or handedness distributions (x2 = 3.3, P = 0.7) between the four groups. There was a significant difference in IQ between the groups (F3,93 = 10.0, P < 0.0001), with both patients with EOS (F1,68 = 21.7, P < 0.0001) and EOB (F1,39 = 5.6, P = 0.02) having lower IQ compared to the controls (Table 1). There was no significant difference in IQ between patients with EOS and EOB. A Wilcoxon rank order test found no difference in chlorpromazine equivalents between patients with EOS versus EOB. There were no significant differences in the age of onset, duration of psychosis, or the extent of positive symptoms between patients with EOS compared to EOB. However, there was a significant difference in negative symptoms, being more prominent in patients with EOS (F1,52 = 13.6, P = 0.0005). 3.2. White matter potholes We found no sex-related differences in the number of potholes, thus sex was not used as a covariate in the analyses. There was a significant effect of age on the number of potholes (F1,95 = 4.1, P = 0.04). Fig. 1 shows the number of potholes within each of the

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Fig. 1. Mean number of white matter potholes at different cluster sizes in children and adolescents with early-onset schizophrenia, early-onset bipolar affective disorder, and obsessive-compulsive disorder compared to controls.

four groups that have a z-score < 2 and with various cluster sizes. The relative differences between groups remain relatively constant irrespective of the cluster size. Using a cluster size of at least 50 voxels and a threshold for contiguous voxels being below zscore < 2, similar to other studies [74,75], an ANCOVA resulted in a significant group difference in the number of potholes (F3,96 = 7.7, P = 0.005). Comparing the individual groups to controls, there was a significant difference between both the schizophrenia group (F1,68 = 5.8, P = 0.02) and bipolar affective group (F1,39 = 8.5, P = 0.006), but no significant difference between children and adolescents with OCD compared to controls (F1,543 = 0, P = 0.98). There was also no significant difference between the patients with schizophrenia and EOB (F1,52 = 3.3, P = 0.07). A measure of pothole volume per participant was obtained by averaging the mean size of all potholes within each individual. Using this measure, we calculated the median pothole volume per group (Fig. 2). An ANCOVA with age as a covariate showed a significant difference in the size of the potholes between groups (F3,96 = 8.6, P < 0.001). The average size of the potholes after residualizing for age was 174 mm3 for EOS, 739 mm3 for EOB, 127 mm3 for OCD, and 136 mm3 for controls. Post hoc between group comparisons showed that size was significantly greater between EOS versus controls (F1,68 = 6.8, P = 0.01); between EOB and controls (F1,39 = 8.5, P = 0.006), but also between EOB and EOS (F1,52 = 10.1, P = 0.002). There was no difference in the size of the WM potholes between children and adolescents with OCD and controls. The spatial location of the WM potholes is shown in Fig. 3. The EOB group shows multiple WM tracts that have significantly greater number of potholes compared to controls (Table 2). These tracts include the right and left internal and external capsule, corona radiata, thalamic radiation, inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus (SLF), and the right uncinate fasciculus. In addition, the corpus callosum shows a significantly larger number of potholes compared to controls. The EOS group,

however, showed differences in the right and left posterior corona radiata, thalamic radiation, ILF, SLF, and the right posterior limb of the internal capsule. In addition, patients with EOS also showed WM potholes in the posterior corpus callosum and the fornix. While the overall number of potholes did not differ between children and adolescents with OCD and controls, as a comparison we also provide regional differences between children and adolescents with OCD and controls. We also evaluated the correlation between the number of potholes when the images were independently assessed between the three runs. The correlation matrix between the total number of potholes between each of the runs were greater than 0.92. Table 3 demonstrates the correlation matrix for the within subject number of potholes for each of the three individual DTI acquisitions. 3.3. Relationship between potholes and clinical measures Spearman correlation coefficients were evaluated between the clinical and IQ measures and the total number of potholes in the patients with EOS and EOB combined. None of the correlations was significant between age of onset (r = 0.25), duration of psychosis (r = 0.01), positive symptoms (r = 0.07), negative symptoms (r = 0.07), or chlorpromazine equivalents (r = 0.01). In addition, we did not find a significant correlation between the number of potholes and verbal IQ (r = 0.18), performance IQ (r = 0.20), or full scale IQ (r = 0.19). When evaluating correlations only within the EOS group, the correlation between the number of potholes and performance IQ was significant (r = 0.36, P < 0.05), however, this does not remain significant after correction for the number of correlation tests performed. There were no significant correlations in any of the clinical or cognitive variables and the number of potholes in the EOB group alone. There was a non-significant negative correlation between age and number of potholes (r = 0.24) in the control group. The results of Spearman correlations coefficients for mean size of the potholes was similar

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Fig. 2. The median volume of potholes within the four groups. This measure was derived by first calculating the average pothole volume per individual, then calculating the median pothole volume from all individuals within each of the four groups.

Fig. 3. The spatial location of the white matter potholes in children and adolescents with early-onset schizophrenia, early-onset bipolar affective disorder, and obsessivecompulsive disorder (OCD). The color bar reflects the percent overlap of potholes within each group.

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Table 2 Regional differences in white matter potholes between patients with early-onset schizophrenia, bipolar affective disorder, and obsessive-compulsive disorder compared to controls. Early-onset schizophrenia Right White matter region Internal capsule (anterior limb) Internal capsule (posterior limb) Internal capsule (retrolenticular) Corona radiata (anterior) Corona radiata (superior) Corona radiata (posterior) Thalamic radiation Inferior longitudinal fasciculus External capsule Cingulate bundle Posterior cingulate/hippocampus Superior longitudinal fasciculus Uncinate fasciculus Midline regions of interest Corpus callosum (genu) Corpus callosum (body) Corpus callosum (splenium) Fornix

Left

**

* * **

* *** ** *

*

**

Bipolar affective disorder

Obsessive-compulsive disorder

Right

Left

Right

** ** ** * ** * * ** * **

** *** * *** *** *** ** ** *** **

*** *

***

Left

*

*** *** **

* *

*P < 0.05, **P < 0.01, ***P < 0.001.

to analyses with the total number, with the relationship within the EOS group showing a significant correlation between size of the potholes and verbal IQ (r = 0.61, P < 0.03) and full scale IQ (r = 0.61, P < 0.03). 4. Discussion We evaluated non-spatially dependent WM microstructural abnormalities between children and adolescents with EOS, EOB, and OCD using the WM pothole approach and found differences only in the EOS and EOB groups compared to controls. The EOS and EOB groups did not differ in the total number of WM potholes, whereas children and adolescents with OCD did not differ from controls. While there were no differences in the total number of potholes between patients with EOS and EOB, children and adolescents with EOB showed more widespread involvement within specific WM tracts (Fig. 3). In fact, the EOB children had significant differences in all WM tracts except for the fornix, left uncinate fasciculus, and the right and left posterior cingulate/ hippocampus regions (Table 2). The children and adolescents with EOS showed differences in the right and left posterior corona radiata, thalamic radiation, ILF, SLF, and the right posterior limb of the internal capsule. The reason why the children and adolescents with EOB show potholes encompassing more anatomical regions was due to differences in the overall size of the potholes, with children and adolescents with EOB have a significantly larger mean pothole sizes. Larger potholes have a greater spatial extent and thus cover more of the boundary areas between anatomic regions. This discrepancy between EOS and EOB does not appear to be related to the severity of disease, as the children with EOB and EOS both have Table 3 Correlation matrix of the number of potholes within the children and adolescents across the three diffusion tensor imaging runs.

Run 1 Run 2 Run 3

Run 1

Run 2

Run 3

1

0.95 1

0.96 0.93 1

approximately equal ratings of psychotic symptoms. Furthermore, the patients with EOS had significantly more negative symptoms compared to the EOB group. In addition, the differences in pothole size does not appear to be a result of antipsychotic exposure, as there was no difference in exposure rates between the two groups. Finally, there were no significant relationships found between clinical symptoms and the number of potholes. While there have been no prior studies of WM microstructure comparing EOS and EOB, there have been several studies in adults [42,66,3,17,41,61]. Whereas most studies found similar differences between EOS and EOB [66,3,17,41,61], some studies did show regional differences [42,3]. Anderson et al. [3] found similar differences in frontal regions, but patients with EOS had lower FA in the superior and medial temporal lobes. Lu et al. [42] found that patients with EOB showed lower FA in the cingulum, internal capsule, posterior corpus callosum, tapetum, and occipital white matter. Interestingly, these areas mesh well with regions we found to be more associated with EOB than EOS (Table 2). In addition, the brain regions that overlap between EOS and EOB in our study mesh well with regions found in adult studies, including decreased factional anisotropy in widespread regions [61], including the corpus callosum [41,61], posterior corona radiata [17,61]. The high amount of overlap in prior studies and our study suggests the similar neurobiological substrates underlying WM involvement in EOS and EOB begin early in life. The constellation of WM tracts overlapping between EOS and EOB in our study is interesting. Both the SLF and the ILF form major association pathways connecting occipital/parietal with prefrontal/temporal regions, respectively. These pathways subserve multiple functions, including directed attention [45,67] and more global cognitive deficits [21]. Thus WM abnormalities along these pathways may reflect the more global nature of symptoms and cognitive deficits seen in the EOS and EOB groups. Specific studies of the SLF in young recent-onset patients with schizophrenia have found lower FA along the entire SLF, with greater abnormalities on the left, with additional relation of these abnormalities in working memory [29]. Studies in EOS populations have also found WM abnormalities using tractography of the ILF [10]. In addition to the SLF and ILF, we also found that patients with EOS have regional abnormalities in the corpus callosum, fornix, and bilateral thalamic

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radiation tracts that are WM tracts show abnormalities in other DTI studies of schizophrenia [32,35,77]. Finally, a meta-analysis of WM development during typically developing adolescents shows that regions with the highest age-related FA during this period of development included the left SLF, ILF, inferior front-occipital fasciculus, and the anterior thalamic radiation [50]. It is interesting that there is a high overlap with regions found in our EOS group and regional differences in typical development, suggesting that WM abnormalities interact with the developmental trajectories of WM. The DTI findings for pediatric OCD are more discrete than either EOS or EO-BAPD, but also variable in showing increased FA in frontostriatal tracts [79], while other studies show no change in FA [26], but alterations in axial as well as radial diffusivities in widespread tracts including the anterior thalamic tract. We only evaluated the presence of WM potholes related to FA in the present study. Thus, we did not evaluate regions with increased FA. We found that only the posterior corona radiata had a significantly lower number of WM potholes in children and adolescents with OCD compared to controls. However, since the overall number of potholes was not different between OCD and controls, this finding disappears when controlling for multiple testing. There are several limitations to the current study. First the sample size of our EOB and OCD groups are relatively small. However, in spite of the relatively small sample size we were able to detect significant differences in the EOB group compared to controls in both the size and the number of potholes compared to both the controls and the OCD group. In addition, the size of the potholes was considerably larger than those in the EOS group. Second, concern has surface recently over potential bias that can be associated with the pothole approach when the sample size for the control group is small [70]. While this bias clearly applies for small cluster sizes, it is not necessarily true for larger cluster sizes of contiguous voxels. If the bias is a result of noise, then it is highly dependent on the amount of spatial filtering that is applied. Using the typical intrinsic smoothing of 5 mm FWHM, the bias would disappear with a cluster size of approximately 20 voxels. However, we agree that this assumption is based on random Gaussian noise, whereas there may be actual developmental differences, or the presence of subthreshold potholes in controls, that may account for these differences. Since a major goal of this study was to compare different psychiatric disorders, each of the patient groups were assessed using the same metrics of the controls, thus eliminating the bias of comparisons between the patient groups. Interestingly, the children and adolescents with OCD and the controls had nearly a perfect overlap, which would not be expected in the presence of bias unless the group of children and adolescents with OCD has, on average, less WM potholes than the controls. When bias correction was applied as suggested by Watts et al. [70], the EOB group had a significantly greater number and volume of potholes, whereas the OCD group had significantly fewer potholes compared to controls. Although we controlled for chlorpromazine equivalents of antipsychotic treatment, we are unable to completely rule out the effect of medication on the presence of WM potholes. A non-dose dependent decrease in WM has been found in studies of short-term antipsychotic use [69], although an increase in WM microstructure has also been reported with the use of clozapine [49]. Finally, another limitation of the data driven nature of the pothole approach makes it prone to the potential for inverse causality in relation to the neurobiological relevance of the findings. In conclusion, we found that EOS and EOB had significantly more WM potholes compared to controls, whereas children and adolescents with OCD showed no difference compared to controls. Furthermore, child and adolescents with EOB and psychosis had significantly larger potholes compared to the other groups. While

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