Integrity of the cingulum bundle as predictor for rTMS effects in OCD patients

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in OCD patients

Veerle Daanen

11277238

Bachelor Psychobiology, University of Amsterdam

Daily supervisor: Sophie Fitzsimmons

Formal supervisor: Chris Vriend

22-01-2021

Department of Psychiatry | Department of Anatomy & Neurosciences

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Integrity of the cingulum bundle as predictor for rTMS effects in OCD patients

Integrity of the cingulum bundle as predictor for rTMS effects

in OCD patients

Repetitive transcranial magnetic stimulation (rTMS) is a brain stimulation technique which leads to significant improvement of symptoms in OCD. However, the variations in rTMS treatment outcome make optimisation of the protocol very challenging. To resolve this problem, we aim to examine whether the integrity of the cingulum bundle (CB) can predict the effect of rTMS on emotion regulation. In addition, we looked into the differences in integrity of the CB between OCD patients and healthy controls (HC).

Thirty-six (36) OCD patients and 37 HC participated in this study. Participants performed an emotion regulation task (ERT) before and after 10 Hz excitatory rTMS to the dorsolateral prefrontal cortex (dlPFC) or vertex. The relative change in distress scores served as the behavioural outcome measure.

Since 90% of white matter (WM) voxels consist of crossing fibres, and voxel-based analysis approaches do not take the orientation of these fibres into account, these methods are poorly interpretable. For that reason, we used a fixel-based analysis (FBA) to investigate the integrity of specific fibres and therefore their ability to relay information. By combining both within-voxel microscopic fibre density (FD) and macroscopic fibre-bundle cross-section (FC), we calculated the fibre density and cross-section (FDC) as a measure of the integrity of the CB.

No significant correlation was found between the relative change in distress and FDC values in OCD patients. Also, no significant differences were found in FDC values between OCD patients and HC.

We can conclude that structural integrity of the CB may not be a good predictor for the effect of rTMS on emotion regulation. Future research to possible predictors of rTMS effects in OCD patients is important to optimise protocols and eventually enhance therapeutic outcomes.

Key words: obsessive-compulsive disorder, repetitive transcranial magnetic stimulation, cingulum bundle,

emotion regulation, dorsolateral prefrontal cortex, fixel-based analysis.

Introduction Background

Leaving the house but having to walk back six times to make sure the door is locked. Washing hands after every contact with someone/something, or arranging crayons, sweets, clothes or books by color. These are just some examples of symptoms people suffering from an obsessive-compulsive disorder (OCD) are dealing with on a daily basis. OCD is, as the name suggests, characterised by obsessions and/or compulsions. Obsessions are defined as repetitive, intrusive and unwanted thoughts which sufferers attempt to neutralise by having other thoughts and/or performing actions. These actions are called compulsions and are defined as uncontrollable, repetitive behaviours in order to reduce anxiety and distress following from the obsessive thoughts (American Psychiatric Association, 2013). OCD has an estimated lifetime prevalence of 2 to 3% worldwide (Weissman, 1998).

Treatment of OCD often consists of cognitive

behavioural therapy (CBT), the use of selective serotonin reuptake inhibitors (SSRIs), or a combination of both (Franklin & Foa, 2011; Cottraux et al., 2005). Nevertheless, 40 to 60% of patients do not benefit sufficiently from these treatments (Pallanti & Quercioli, 2006). Therefore, other treatment methods are needed.

Repetitive transcranial magnetic stimulation (rTMS)

One of these upcoming methods is repetitive transcranial magnetic stimulation (rTMS). It is a non-invasive brain stimulation technique in which a coil with an electric current passing through it is placed on a person’s head. This induces a time-varying magnetic field, and based on the principle of electromagnetic induction, an electric current in the brain is generated. If this pulse of magnetic field is applied multiple times, cortical excitability can be modulated (Rossini et al., 2015). High frequency (HF) stimulation (5-20 Hz) results in an increase in excitability, whereas low frequency (LF) stimulation (1 Hz) results in a decrease of cortical excitability.

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The review of Lefaucheur et al. (2020) shows that rTMS leads to significant improvement of symptoms in several psychiatric disorders, including OCD. These are promising results for the clinical use of this technique. While the Food and Drug Administration (FDA) has already approved rTMS as a treatment for the patients leading from depression who do not respond to antidepressant-drugs (Slotema et al., 2010), the efficacy of rTMS still needs to be proven for other psychiatric disorders.

Nevertheless, previous research shows that rTMS has a positive effect on executive functioning, emotional tasks and the reduction of fear distress in OCD patients, by modulating involved brain regions (De Wit et al., 2015; Guse et al., 2010; Harmer et al., 2001; Van Den Heuvel et al., 2013; van der Werf et al., 2010).

As a treatment for OCD, the way rTMS should be delivered is not clear yet. This includes the exact stimulation site, but also stimulation intensity and duration of series. This might be a result of the interindividual differences in symptom subtypes, symptom severity, and the underlying neural correlates, making OCD a heterogeneous disease (Lochner & Stein, 2003; Mataix-Cols et al., 2004; Okada et al., 2015). Variations in treatment outcome between individuals may be due to these differences, making optimisation of the protocol even more challenging (Rostami et al., 2020).

To resolve this problem, research on predictors of rTMS response should be done in order to optimise protocols and therefore enhance therapeutic outcomes in treatment-resistant OCD patients. It has already been shown that more homogeneity between patients results in higher response rates, indicating the relevance of studying predictors (Dunlop et al., 2016; Pallanti et al., 2016; Rostami et al., 2020; Zhang et al., 2019). Moreover, Barredo et al. (2019) found that structural differences in anterior thalamic radiation (ATR) integrity and varying functional connectivity between the amygdala and the medial prefrontal cortex (mPFC) are predictive for improvement in symptoms after TMS treatment in posttraumatic stress disorder (PTSD) and major depression. In OCD patients, Douw et al. (2020) found that baseline resting-state network characteristics of the target region (dlPFC) can predict treatment outcomes. However, differences in white matter (WM) structures as a predictor for symptom improvement after rTMS in OCD have not been researched before.

Neural correlates of OCD

Patients with OCD tend to show exaggerated emotional responses, which appear to result from impairments in emotion regulation (Mataix-Cols & van den Heuvel, 2006). Both top-down and bottom-up stimulus appraisal are involved in emotion regulation. Bottom-up stimulus appraisal is driven by external factors like properties of a stimulus and involves the ventral pathway with regions like the amygdala, whereas top-down appraisal uses cognitive evaluation of the stimulus and involves regions like the dorsolateral prefrontal cortex (dlPFC) and the dorsomedial prefrontal cortex (dmPFC) (Frijda, 1988; LeDoux, 2009; McRae et al., 2012; K N Ochsner & Gross, 2007). Studies have shown that in healthy participants, the dlPFC and dmPFC can down-regulate the amygdala, in order to reduce the emotional response to a negative stimulus (Ochsner et al., 2012). Patients with OCD appear to have difficulties in this down-regulation due to a lower recruitment of the dlPFC, lower dmPFC-amygdala connectivity (during fear regulation), and higher recruitment of the dmPFC (during OCD-related regulation) relative to healthy participants. These impairments seem to disappear after improvement of symptoms (De Wit et al., 2012; Vriend et al., 2013).

Another region involved in emotion regulation is the anterior cingulate cortex (ACC). This region appears to be structurally and functionally connected with the amygdala (Ghashghaei et al., 2007; Pezawas et al., 2005) and has been related to self-monitoring of negative affect (Ochsner et al., 2004). Besides emotion regulation, the ACC plays a role in attentional control and decision-making, which is abnormal in OCD (Versace et al., 2019). Bilateral lesions of the ACC result in improvement of symptoms in patients with OCD. This appears to result from a reduction of attention to negative emotional thoughts instead of direct effect on reducing the OCD symptom severity (Versace et al., 2019).

Besides using functional magnetic resonance imaging (fMRI) to measure brain activity, WM characteristics are also relevant to examine. This is because of its connections between cortical and subcortical regions of the fronto-striato-thalamic circuitry, contributing to the pathophysiology of OCD.

One of those WM tracts is the cingulum bundle (CB; Fig. 1), that is involved in executive

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functioning, decision-making and emotion (Adnan et al., 2016; Heilbronner & Haber, 2014), and connects all the aforementioned brain regions. It carries fibres from the ACC to the dlPFC and ventrolateral PFC (vlPFC), implicated in attentional control and set shifting. The CB also connects the ACC to the orbitofrontal cortex (OFC), involved in value encoding, as well as to subcortical regions, like the amygdala, primarily involved in mediating the function of the ACC and prefrontal cortical regions via cortico-striato-thalamo-cortical circuits (Goldman-Rakic et al., 1984; Pandya & Seltzer, 1982; Versace et al., 2019; Vogt et al., 1979).

Previous studies applying neurosurgery demonstrated that ablation of the CB can improve OCD symptoms (Dougherty et al., 2002; Gosgrove & Rauch, 1995; Lippitz et al., 1999; Rauch, 2003).

WM tracts can be studied by means of diffusion tensor imaging (DTI), measuring the diffusion of water molecules (Soares et al., 2013). In this way information about WM microstructure can be obtained on a voxel level. Previous research found

decreased fractional anisotropy (FA) in both the left and right CB in OCD patients versus HC, reflecting lower integrity in this tract (Ayling et al., 2012; Fontenelle et al., 2009; Koch et al., 2014; Nakamae et al., 2011; Szeszko et al., 2005; Versace et al., 2019). Versace et al. (2019) found that this decrease in integrity is focal, which may reflect abnormalities in integrity of specific fibres that connect the ACC with other prefrontal cortical regions.

Voxel-wise approaches like voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS) are mostly used to obtain DTI measures like FA (Basser & Pierpaoli, 1996; Buchsbaum et al., 1998; Smith et al., 2006). However, 90% of WM voxels appear to consist of crossing fibres (Jeurissen et al., 2013). In VBM and TBSS, the orientation of these crossing fibres is not taken into account, resulting in diffusivity values being averaged out. FA values of crossing fibres will eventually be lower than the values of individual fibre populations (Winston, 2012). Therefore, voxel-based methods are not fibre-specific and have poor interpretability (Raffelt et al., 2017).

Fig. 1. Sagittal section of the brain. Brain regions involved in impairments in emotion regulation in obsessive-compulsive disorder are

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To investigate the integrity of specific fibres, a novel method was introduced, called fixel-based analysis (FBA; Raffelt et al., 2017).

Fixel-based analysis

Using FBA, fibre orientations within a voxel are estimated in order to obtain fibre-specific measures. Raffelt et al. (2015) defined a fixel as: “a single fibre population within a voxel”, and intra-axonal volume as: “the ability to relay information”. To obtain a measure of this intra-axonal volume and therefore the ability to relay information, both within-voxel microscopic fibre density (FD) and macroscopic fibre-bundle morphology or fibre cross-section (FC) should be combined. This results in a measure called fibre density and cross-section (FDC). Since neurodegeneration can manifest at both microscopic (e.g. axonal loss) and macroscopic (e.g. atrophy) levels, FDC provides a more complete and therefore more reliable measure than FD or FC alone (Raffelt et al., 2017).

Research question and hypothesis

The CB connects brain regions involved in impairments in emotion regulation and appears to have lower integrity in patients with OCD. Since, to our knowledge, differences in WM structures as a predictor for symptom improvement after rTMS in OCD have not been researched before, the aim of this study is to investigate whether the effect of rTMS on emotion regulation in OCD patients can be predicted by the integrity of the CB. Besides, we want to look into the differences in integrity of the CB between OCD patients and healthy controls (HC).

An emotion regulation task (ERT) was used to measure decrease in distress as an indicator for the ability to regulate emotions. We hypothesised that a higher FDC value is positively correlated with a decrease in distress and therefore the effect of rTMS

on emotion regulation. Additionally, we hypothesised that FBA reveals lower integrity (FDC) of the cingulum bundle in OCD patients versus HC.

The hypotheses are based on the involvement of the CB in the ability to regulate emotions and its impairment in OCD. Besides, it is based on the fact that ablation of the CB results in improvement of OCD symptoms, and on previous research which found lower FA values in both the left and right CB in OCD patients versus HC.

Method Participants

Patients were recruited through outpatient clinics within The Netherlands OCD Association (Schuurmans et al., 2012), the Academic Anxiety Center Altrecht (Utrecht, the Netherlands) and online advertisements.

By means of the Structural Clinical Interview for DMS-IV (First et al., 2002), we screened participants for Axis-I psychiatric disorders. The Yale-Brown Obsessive-Compulsive Scale (YBOCS; Goodman et al., 1989) was used to measure OCD severity and the Padua Inventory – Revised (Padua-IR; Van Oppen et al., 1995) to assess symptom dimensions. Using the Montgomery-Åsberg Depression Rating Scale (MADRS; Montgomery & Asberg, 1979), we measured mood. To measure the use of reappraisal and suppression for emotion regulation on a daily basis, we used the Emotion Regulation Questionnaire (ERQ; Gross & John, 2003), and we assessed handedness by means of the Edinburg Handedness Inventory (Oldfield, 1971).

Participants were between 18 and 65 years old and had (corrected to) normal vision. They were excluded in case participants used psychoactive medication, had a current psychosis, were leading from major physical illness, had a history of major neurological illness or had MRI contraindications.

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Patients also had to be medication free for at least 4 weeks and they should have a primary OCD diagnosis (without predominant hoarding). Psychiatric co-morbidity, including tics, was not considered as an exclusion criterion. The control group did not have a DSM-IV Axis-I diagnosis.

All procedures of this study met the ethical standards of the local medical ethical review board on human experimentation and the Helsinki Declaration of 1975, as revised in 2013 and all participants signed an informed consent.

Study design

In this study, participants visited the VU University medical centre (VUmc) three times within a time span of 1 to 4 weeks (Fig. 1). The psychiatric screening was done in the first session. In this session participants also practiced the ERT. The next time they visited the VUmc, they had to perform this ERT while lying in the MRI scanner. In this session the baseline DTI scan was obtained. In the third session, the participants received rTMS, after which they went in the MRI scanner and did the ERT again.

Experimental task

In figure 2 the ERT design is shown. Participants had to look at pictures under two varying instruction conditions. In the ‘attend’ condition, participants were told to view and experience the stimulus naturally (e.g., ‘focus on what the stimulus means to you) and in the ‘regulate’ condition, they had to use mental techniques in order to reappraise the stimulus

such that it elicits less negative affect (e.g., ‘imagine a more positive outcome or interpretation of the portrayed events’ or ‘realise the stimulus is not real-life’). The pictures were either fearful, neutral or OCD-specific. The OCD-specific pictures targeted contamination concerns (OCD wash), checking (OCD check) and ordering (OCD symmetry). Fearful and OCD-specific pictures were shown during both the attend and regulate condition, whereas neutral stimuli were only presented under the attend condition. Attending to neutral stimuli was used as a baseline condition, resulting in a total of 9 conditions (4 stimulus types under 2 different instructions and a baseline condition). Three pictures of the same stimuli type were shown per block. After each individual picture, participants had to indicate how distressed they felt. Because of possible OCD-related connotations, no numbers were used for this indication. Instead, it was a visual analogue scale (VAS) ranging from ‘not distressed’ (left) to ‘maximally distressed’ (right). After each block of 3 pictures participants indicated how well they thought to have performed the task. Each block ended with a fixation, resulting in a total session of 27 blocks with three blocks per condition and a total duration of 26 minutes. For details about the programming and stimulus set selection, see the supplementary method of de Wit et al. (2015).

TMS protocol

The rTMS which participants received in the third session was applied with a hand-held statically cooled figure-of-eight TMS coil (Medtronic MagOption) under guided real-time neuronavigation

Fig. 3. Task design with two instruction conditions. Reprinted from “Emotion regulation before and after transcranial magnetic

stimulation in obsessive compulsive disorder”, De Wit, S. J., Van der Werf, Y. D., Mataix-Cols, D., Trujillo, J. P., Van Oppen, P., Veltman, D. J., & Van Den Heuvel, O. A., 2015, Psychological Medicine, 45(14), 3059-3073

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(Visor v. 1.0, Eemagine GmbH, Germany). Participants received rTMS over either the dlPFC (target region) or the vertex (sham). OCD patients received HF rTMS (10 Hz) and controls received LF rTMS (1 Hz) over either the dlPFC or vertex. Because individual defined stimulation locations by means of MRI are proven to have better behavioural effects than predefined coordinates (Sack et al., 2009), the stimulation coordinate for the active rTMS condition (dlPFC) was based on a hot-spot. This hot-spot was defined as the most significant cluster in the left dlPFC during emotion regulation in the first session. Details of rTMS hot-spot definition and localization of the placebo vertex coordinates can be found in the supplementary method of de Wit et al. (2015). Participants were assigned to the different conditions randomly and were blind to the stimulus location.

Behavioural outcome measure

The distress ratings obtained from the VAS were used to compute the behavioural outcome measure. In order to examine whether there is a correlation between the relative change in distress before and after rTMS and FDC values, we calculated a difference score [relative change in distress = (posttreatment distress – pretreatment distress)/(pretreatment distress + 1)*100]. Because we were only interested in the effect of rTMS on emotion regulation in OCD patients, difference scores were computed for the OCD-specific pictures (wash, check and symmetry) in the regulate condition.

Image acquisition

Imaging was performed on a GE Signa HDxt 3.0-T MRI scanner (General Electric, USA), using an 8-channel head coil. Diffusion weighted (DW) images were acquired using parallel imaging (R = 2) with an echo planar imaging (EPI) sequence. Forty-nine contiguous axial slices of 2.4 mm thickness were acquired with an in-plane resolution of 2.0 mm x 2.0 mm (repetition time [TR] = 14000 ms, echo time [TE] = 85 ms). Five reference volumes (b = 0 s/mm2) and thirty DW volumes (b = 1000 s/mm2) were obtained.

Preprocessing

First, the denoise function in MRtrix3 was used to improve the signal-noise-ratio. Then, the eddy

tool (FSL; the FMRIB Software Library version 6.01, Jenkinson et al., 2012) corrected for motion and eddy-current induced distortions (Rohde et al., 2004). All images were visually inspected and volumes containing artefacts were removed. Because the images needed for unwarping diffusion data were not obtained during scanning, non-linear registration was used to adjust for susceptibility-induced distortions. This was done with Advanced Normalisation Tools (ANTs) (Avants et al., 2008). The ANT Brain Extraction Tool (BET) was used for skull stripping, after which fslmaths inverted the contrast. AntsRegistrationSyNQuick registered the b0 images to the T1 image, and antsApplyTransforms was used to apply the transformation to all the volumes. A binary brain mask was created using the BET and the images were checked.

For the apparent fibre density (AFD) pre-processing steps, the 4D diffusion weighted data, gradient directions (bvecs) and b values (bvals) were first converted into one file. After this, we eliminated low frequency intensity inhomogeneities by means of a bias field correction, after which we created a new mask using BET. To overcome that compartments within WM would influence each other we performed a global intensity normalisation on all subjects using a groupwise registration method.

Fixel-based analysis

For FBA, first tissue response functions were computed. Tournier et al. (2013) proposes a method by means of which single-shell response functions can be obtained. However, single-shell single-tissue constrained spherical deconvolution (SSST-CSD) for Fibre Orientation Distribution (FOD) estimation only uses WM. This leads to distortions when gray matter (GM) and cerebrospinal fluid (CSF) are present. Therefore, multi-shell multi-tissue (MSMT)-CSD, estimates WM, GM and CSF components in order to obtain more accurate results. Because MSMT-CSD requires more than one b-value, it was initially thought that this strategy is not possible for single-shell data. However, it is possible by using a combination of single-shell and b=0 (SS+b0) data (Dhollander et al., 2016, 2019; Dhollander & Connelly, 2016). Because this results in only two unique b-values, we included two tissue compartments, WM and GM, to compute average response functions (b = 0,1000 s/mm2).

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Fig. 4. An overview of the fixel-based analysis method. Reprinted from Home | Jan Daanen Scientific Illustrator, by J. Daanen, 2021,

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We then upsampled the DTI images (voxel size = 1.25 mm), computed an upsampled brain mask from these images and checked whether the masks included our tract of interest. After this, we performed MSMT-CSD to estimate the FODs from DTI images (Fig. 4a). Because of the use of multi-tissue CSD, we corrected for global intensity differences between subjects by means of a joint bias field correction and intensity normalisation.

Next, a study-specific group-average unbiased FOD template was generated from a subset of 40 individuals (20 OCD patients and 20 HCs) to subsequently register all subject FOD images to this template (Fig. 4b). This registration results in estimated warps, which we used to warp all FOD images to template space (Fig. 4c). By defining the position and orientation of all fixels in the FOD template, we computed a WM template fixel mask (Fig. 4d). The warped FOD images were used to estimate FD per fixel (Fig. 4e, f), after which subject fixels were matched to fixels in the template fixel mask (Fig. 4g). FC was calculated by means of the aforementioned estimated warps (Fig. 4h) and the template fixel mask (Fig. 4i). Next, we calculated

FDC for each fixel by a modulation of FD by FC (FDC = FD x FC) (Fig. 4j) (Raffelt et al., 2017).

Using probabilistic fibre tractography, a whole-brain fibre tractogram was generated from the FOD template (Fig. 4k). Next, the JHU voxel-based WM tractography atlas from NeuroVault (2016) was used and registered to the whole-brain tractogram (Fig. 4l). We then warped the atlas, layed it over the tractogram (Fig. 4m, n), and extracted our tract of interest (Fig. 4o). After this, FD, FC and FDC of each subjects’ CB was calculated (Fig. 4p, q), to eventually correlate this with the relative change in distress after rTMS (Fig. 4r).

Statistical analysis

R version 3.6.1 (R Core Team, 2019) and R-studio version 1.3.959 (RR-studio, 2020) was used to perform statistical analysis. Results were considered significant at an alpha of p < 0.05.

Differences in demographics and clinical data were examined using a One-way ANOVA. We used a t-test to examine differences in relative change in distress scores for OCD-specific pictures in the regulate condition.

Table 1

Demographics, clinical measures and behavioural outcome OCD patients

(N = 36)

Healthy controls (N =37) dlPFC (N = 18) vertex (N = 18)

Mean SD Mean SD Mean SD Test-statistic

(p-value) Characteristics Age 38.8 9.7 40.3 10.6 39.4 11.5 0.09 (0.918)b Sex [women:men (% men)] 10:8 (55.6%) 9:9 (50.0%) 18:19 (48.6%) 0.13 (0.939)c Years of educationa _5.9 _1.9 _6.1 _1.7 _5.8 _1.9 _{0.13 (0.879)}b YBOCS 20.4 7.1 22.4 5.6 – – 133 (0.366)d MADRSf _9.6 _6.8 _11.5 _8.4 _0.9 _1.5 _{29.19 (<0.001)}b Relative change in distress -3.1 69.3 4.9 58.0 – – 0.38 (0.707)e

a_{Years of education ranged from 1 (no finished education) to 9 (university training).} b_{One-way ANOVA.}

c_χ2_test.

d_{Mann-Whitney U test.} e_{Two sample t-test.}

f_{OCD dlPFC vs controls significant p < 0.05; OCD vertex vs controls significant p < 0.05.}

dlPFC, dorsolateral prefrontal cortex; YBOCS, Yale-Brown Obsessive Compulsive Symptom Severity Scale; MADRS, Montgomery-Åsberg Depression Rating Scale.

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An independent t-test was used to investigate differences in FDC, FD and FC values of the cingulum bundle between OCD patients and HC. Since the FBA data did not meet parametric assumptions, Spearman’s rho instead of Pearson’s chi-squared test was used to perform correlations between a change in subjective distress ratings before and after rTMS and FDC values of the cingulum bundle.

Results

Subject characteristics and behavioural outcome

Because of distortions in regions of interest (ROI) and signal loss in the obtained DTI scans, and because of outliers in distress ratings, seven OCD patients were excluded from the statistical analysis. This resulted in 36 OCD patients and 37 HC.

Table 1 shows the demographics and clinical group. No significant differences were found between groups in age, sex, years of education, and YBOCS scores. MADRS scores were significantly higher in both OCD dlPFC and OCD vertex versus controls.

The relative change in distress after rTMS is shown in table 1. The OCD-dlPFC group showed a

decrease in distress after rTMS (-3.1 ± 69.3), whereas the OCD-vertex group showed an increase in distress after rTMS (4.9 ± 58.0). We did not find a significant difference between these groups.

Fixel-based analysis of the cingulum bundle

Table 2 shows the mean values and standard deviations (SD) of the FBA measures (FDC, FD and log-FC) of the bilateral, right and left CB. No significant differences were found between OCD patients and HC.

Correlations between FBA measures and behavioural data

No significant correlations were found between the FBA measures (FDC, FD and log-FC) in the right, left and bilateral CB, and the relative change in distress (table 3).

Discussion

The aim of this study was to examine whether the effect of rTMS on emotion regulation can be predicted by the integrity of the CB. We additionally

Table 2

FBA measures of the cingulum bundle

OCD patients (N = 36) Healthy controls (N = 37)

Mean SD Mean SD Test statistic

(p-value) Bilateral CB FDC 0.338 0.059 0.356 0.056 -1.331 (0.188)a FD 0.343 0.033 0.351 0.019 605 (0.507)b log-FC -0.031 0.107 -0.006 0.120 -0.917 (0.362)a Right CB FDC 0.297 0.051 0.319 0.049 -1.883 (0.064)a FD 0.301 0.033 0.312 0.019 -1.767 (0.082)a log-FC -0.028 0.110 0.003 0.114 -1.196 (0.236)a Left CB FDC 0.362 0.069 0.378 0.065 -0.988 (0.326)a FD 0.369 0.036 0.375 0.023 -0.900 (0.371)a log-FC -0.032 0.117 -0.012 0.132 -0.694 (0.490)a

a_{Two sample t-test.} b_{Mann-Whitney U test.}

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investigated the difference in integrity of this tract between OCD patients and HC.

We did not find a correlation between FDC and the relative change in distress before and after rTMS in patients with OCD. This means it was not possible to predict rTMS effects by the integrity of the CB. We also did not find significant differences in FDC values, and therefore the integrity of the CB, between OCD patients and HC.

We expected that the FDC value in the CB would correlate positively with a decrease in distress. This hypothesis was based on the fact that this tract connects the ACC, amygdala and several prefrontal regions. These regions are involved in emotion regulation and appear to contribute to impairments in emotion regulation in patients with OCD (Adnan et al., 2016; S. J. De Wit et al., 2015; Stella J. De Wit et al., 2012; Ghashghaei et al., 2007; Goldman-Rakic et al., 1984; Heilbronner & Haber, 2014; K N Ochsner & Gross, 2007; Kevin N. Ochsner et al., 2004, 2012; Pandya & Seltzer, 1982; Pezawas et al., 2005; Versace et al., 2019; Vogt et al., 1979; Vriend et al., 2013).

Moreover, we expected to find lower integrity in the CB in OCD patients versus HC. This hypothesis was based on previous research which found that ablation of this tract can improve OCD symptoms (Dougherty et al., 2002; Gosgrove & Rauch, 1995;

Lippitz et al., 1999; Rauch, 2003). Previous research also found decreased FA values in both the left and right CB in OCD patients versus HC (Ayling et al., 2012; Fontenelle et al., 2009; Koch et al., 2014; Nakamae et al., 2011; Szeszko et al., 2005; Versace et al., 2019). Nevertheless, because 90% of WM, including (parts of) the CB, consists of crossing fibers, voxel-based methods are poorly interpretable (Jeurissen et al., 2013; Raffelt et al., 2017; Winston, 2012). In this study we used FBA to investigate the integrity of specific fibres.

Possible explanations

The CB in OCD patients has not been studied by means of FBA before. Since we did not find differences in the integrity of this bundle, the question arises whether results of previous voxel-based research can be used to support hypotheses of studies using fixel-based methods. Research has to be done in order to examine the comparability of voxel- and fixel based analysis methods. This is important to study because of the aforementioned advantages of FBA over voxel-based methods like VBM and TBSS.

Another possible explanation of the non-significant results might be the use of MSMT-CSD with single shell data. This was done to undermine Table 3

Correlations between FBA measures and relative change in distress OCD patients (N = 36)

dlPFC (N = 18) vertex (N = 18)

rho p-value rho p-value

Bilateral CB FDC - distress -0.226 0.819 -0.282 0.874 FD - distress -0.195 0.784 -0.375 0.938 log-FC - distress -0.197 0.787 -0.447 0.968 Right CB FDC -0.296 0.886 -0.550 0.990 FD -0.119 0.684 -0.294 0.884 log-FC -0.148 0.724 -0.598 0.995 Left CB FDC -0.127 0.695 -0.253 0.847 FD -0.292 0.882 -0.325 0.908 log-FC -0.205 0.796 -0.381 0.941

dlPFC, dorsolateral prefrontal cortex; CB, cingulum bundle; FDC, fibre density and section; FD, fibre density; FC, fibre cross-section.

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problems like distortions due to the presence of GM and CSF (Dhollander et al., 2016, 2019; Dhollander & Connelly, 2016). By combining single-shell and b=0 data, two tissue compartments could be chosen to include in the CSD to estimate a multi-tissue FOD. We opted for the inclusion of WM and GM. However, even though MSMT-CSD with single-shell data leads to more robust outcomes, Dhollander & Connelly (2016) state that this could lead to an aggressive cleanup of the data. Therefore, WM might not be represented correctly.

To explain our results, we should also take a look at the CB itself. Versace et al. (2019) found lower integrity in the middle section of the CB, suggesting abnormalities of specific fibres that connect the ACC with prefrontal cortical regions. However, because we used an already existing atlas to extract our tract of interest, it was not possible to select a specific part of the CB. Therefore, potential focal effects might be averaged out.

Furthermore, the aforementioned brain regions (ACC, amygdala and prefrontal regions) involved in emotion regulation and responsible for impairments in emotion regulation in OCD patients are connected by the anterior part of the CB as illustrated in figure 1. However, as shown in figure 4p, the extracted CB which we used for the analysis does not cover the anterior part of the tract. This is due to the use of an already existing atlas which was not manually adapted. In order to track down possible focal effects, future studies should use a new atlas, consisting of only the anterior part of the CB.

Lastly, we excluded subjects due to highly deviating responses on the ERT, distortions in our tract of interest and signal loss. Since the sample size might not be big enough, it may not perfectly represent the whole population, resulting in a higher degree of random errors (Akobeng, 2016).

Relevance & further research

Studying the possibility to predict rTMS treatment outcomes is of great importance because of the heterogeneity in OCD. This heterogeneity manifests in differences in symptom subtype, symptom severity and the underlying neural correlates (Lochner & Stein, 2003; Mataix-Cols et al., 2004; Okada et al., 2015). But most importantly, it leads to variations in treatment outcome, making optimisation of rTMS protocols very challenging (Rostami et al., 2020). Previous research found higher response rates in OCD patients with more homogenous neural correlates (Dunlop et al., 2016;

Pallanti et al., 2016; Rostami et al., 2020; Zhang et al., 2019). Therefore, studying possible predictors of rTMS response could eventually help to optimise protocols in order to enhance therapeutic outcomes in treatment-resistant OCD patients.

Future research needs to examine the comparability of voxel- and fixel based methods and new atlases should be used consisting of specific parts of the CB involved in impairments in emotion regulation in OCD patients.

Moreover, the CB is not the only WM tract involved in OCD and in the possible prediction of rTMS treatment effects. As stated in the introduction, Barredo et al. (2019) show that structural differences in the ATR are predictive for improvement of symptoms after TMS treatment in PTSD and major depression. Abnormalities in this tract also contribute to the neuropathology of OCD (Chiu et al., 2011). Other studies found abnormalities in the anterior limb of the internal capsule and the superior longitudinal fasciculus in OCD patients versus HC (Cannistraro et al., 2007; Peng et al., 2012). In future studies it might be interesting to take a look at these bundles regarding the predictiveness of rTMS effects in OCD patients.

Conclusion

In conclusion, this is the first study to examine the integrity of WM tracts as a predictor for improvement in emotion regulation after rTMS in OCD. Despite the fact that we did not find a correlation between emotion regulation improvement and the CB, the insights of this study are of great value to build on in further research. Further research into possible predictors is important to optimise protocols and increase the improvement of OCD symptoms after rTMS treatment.

Acknowledgements

The author would like to thank Sophie Fitzsimmons for her very helpful daily support and guidance during my internship. Besides, I would like to thank Chris Vriend for all the help regarding the analysis and writing. Also, a huge thanks to Odile van den Heuvel and Ysbrand van der Werf for their help during these strange times and the opportunity to continue working in this driven team. Lastly, I would like to thank Jan Daanen for the beautiful and clear illustrations.

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References

Adnan, A., Barnett, A., Moayedi, M., McCormick, C., Cohn, M., & McAndrews, M. P. (2016). Distinct hippocampal functional networks revealed by tractography-based parcellation. Brain Structure

and Function, 221(6), 2999–3012.

https://doi.org/10.1007/s00429-015-1084-x Akobeng, A. K. (2016). Understanding type I and type II

errors, statistical power and sample size. Acta

Paediatrica, 105(6), 605–609.

https://doi.org/10.1111/apa.13384

American Psychiatric Organisation. (2013). Diagnostic and Statistical Manual of Mental Disorders, 5th Edition. In Diagnostic and Statistical Manual of

Mental Disorders, 5th Edition.

https://doi.org/10.1176/appi.books.978089042559 6.893619

Avants, B. B., Epstein, C. L., Grossman, M., & Gee, J. C. (2008). Symmetric diffeomorphic image

registration with cross-correlation: Evaluating automated labeling of elderly and

neurodegenerative brain. Medical Image Analysis,

12(1), 26–41.

https://doi.org/10.1016/j.media.2007.06.004 Ayling, E., Aghajani, M., Fouche, J. P., & Van Der Wee,

N. (2012). Diffusion tensor imaging in anxiety disorders. In Current Psychiatry Reports (Vol. 14, Issue 3, pp. 197–202). Springer.

https://doi.org/10.1007/s11920-012-0273-z Barredo, J., Bellone, J. A., Edwards, M., Carpenter, L. L.,

Correia, S., & Philip, N. S. (2019). White matter integrity and functional predictors of response to repetitive transcranial magnetic stimulation for posttraumatic stress disorder and major depression.

Depression and Anxiety.

https://doi.org/10.1002/da.22952

Basser, P. J., & Pierpaoli, C. (1996). Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. Journal of

Magnetic Resonance - Series B, 111(3), 209–219.

https://doi.org/10.1006/jmrb.1996.0086

Buchsbaum, M. S., Tang, C. Y., Peled, S., Gudbjartsson, H., Lu, D., Hazlett, E. A., Downhill, J., Haznedar, M., Fallon, J. H., & Atlas, S. W. (1998). MRI white matter diffusion anisotropy and PET metabolic rate in schizophrenia. NeuroReport,

9(3), 425–430.

https://doi.org/10.1097/00001756-199802160-00013

Cannistraro, P. A., Makris, N., Howard, J. D., Wedig, M. M., Hodge, S. M., Wilhelm, S., Kennedy, D. N., & Rauch, S. L. (2007). A diffusion tensor imaging study of white matter in obsessive-compulsive disorder. Depression and Anxiety, 24(6), 440–446. https://doi.org/10.1002/da.20246

Chiu, C. H., Lo, Y. C., Tang, H. S., Liu, I. C., Chiang, W. Y., Yeh, F. C., Jaw, F. S., & Tseng, W. Y. I. (2011). White matter abnormalities of

fronto-striato-thalamic circuitry in obsessive-compulsive disorder: A study using diffusion spectrum imaging tractography. Psychiatry Research -

Neuroimaging, 192(3), 176–182.

https://doi.org/10.1016/j.pscychresns.2010.09.009 Cottraux, J., Bouvard, M. A., & Milliery, M. (2005).

Combining pharmacotherapy with cognitive-behavioral interventions for obsessive-compulsive disorder. In Cognitive Behaviour Therapy. https://doi.org/10.1080/16506070510043750

Daanen, J., Scientific Illustrator. (2021, 15 januari). Jan

Daanen Scientific Illustrator. https://www.medilan.eu

De Wit, S. J., Van Der Werf, Y. D., Mataix-Cols, D., Trujillo, J. P., Van Oppen, P., Veltman, D. J., & Van Den Heuvel, O. A. (2015). Emotion regulation before and after transcranial magnetic stimulation in obsessive compulsive disorder.

Psychological Medicine.

https://doi.org/10.1017/S0033291715001026 De Wit, Stella J., De Vries, F. E., Van Der Werf, Y. D.,

Cath, D. C., Heslenfeld, D. J., Veltman, E. M., Van Balkom, A. J. L. M., Veltman, D. J., & Van Den Heuvel, O. A. (2012). Presupplementary motor area hyperactivity during response inhibition: A candidate endophenotype of obsessive-compulsive disorder. American Journal

of Psychiatry, 169(10), 1100–1108.

https://doi.org/10.1176/appi.ajp.2012.12010073 Dhollander, T., & Connelly, A. (2016). A novel iterative

approach to reap the benefits of multi-tissue CSD from just single-shell ( + b = 0 ) diffusion MRI data A novel iterative approach to reap the benefits of multi-tissue CSD. May.

https://www.researchgate.net/publication/3017666 19

Dhollander, T., Mito, R., Raffelt, D., & Connelly, A. (2019). Improved white matter response function estimation for 3-tissue constrained spherical deconvolution. Proc. Intl. Soc. Mag. Reson. Med,

May 11-16, 555.

https://www.researchgate.net/publication/3311651 68_Improved_white_matter_response_function_es

timation_for_3-tissue_constrained_spherical_deconvolution Dhollander, T., Raffelt, D., & Connelly, A. (2016).

Unsupervised 3-tissue response function estimation from single-shell or multi-shell diffusion MR data without a co-registered T1 image. ISMRM Workshop on Breaking the

Barriers of Diffusion MRI, 35(September), 5.

https://www.researchgate.net/publication/3078631

33_Unsupervised_3- tissue_response_function_estimation_from_single-

(14)

shell_diffusion_MR_data_without_a_co-Integrity of the cingulum bundle as predictor for rTMS effects in OCD patients

registered_T1_image

Dougherty, D. D., Baer, L., Cosgrove, G. R., Cassem, E. H., Price, B. H., Nierenberg, A. A., Jenike, M. A., & Rauch, S. L. (2002). Prospective long-term follow-up of 44 patients who received cingulotomy for treatment-refractory obsessive-compulsive disorder. American Journal of

Psychiatry.

https://doi.org/10.1176/appi.ajp.159.2.269 Douw, L., Quaak, M., Fitzsimmons, S. M. D. D., de Wit,

S. J., van der Werf, Y. D., van den Heuvel, O. A., & Vriend, C. (2020). Static and dynamic network properties of the repetitive transcranial magnetic stimulation target predict changes in emotion regulation in obsessive-compulsive disorder. Brain

Stimulation.

https://doi.org/10.1016/j.brs.2019.10.017 Dunlop, K., Woodside, B., Olmsted, M., Colton, P.,

Giacobbe, P., & Downar, J. (2016). Reductions in Cortico-Striatal Hyperconnectivity Accompany Successful Treatment of Obsessive-Compulsive Disorder with Dorsomedial Prefrontal rTMS.

Neuropsychopharmacology, 41(5), 1395–1403.

https://doi.org/10.1038/npp.2015.292

First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. B. W. (2002). Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition (SCID I/P). Biometrics Research, New

York State Psychiatric Institute: New York, NY.

Fontenelle, L. F., Harrison, B. J., Yücel, M., Pujol, J., Fujiwara, H., & Pantelis, C. (2009). Is There Evidence of Brain White-Matter Abnormalities in Obsessive-Compulsive Disorder? Topics in

Magnetic Resonance Imaging, 20(5), 291–298.

https://doi.org/10.1097/RMR.0b013e3181e8f22c Franklin, M. E., & Foa, E. B. (2011). Treatment of

obsessive compulsive disorder. Annual Review of

Clinical Psychology.

https://doi.org/10.1146/annurev-clinpsy-032210-104533

Frijda, N. H. (1988). The Laws of Emotion.

Ghashghaei, H. T., Hilgetag, C. C., & Barbas, H. (2007). Sequence of information processing for emotions based on the anatomic dialogue between prefrontal cortex and amygdala. NeuroImage, 34(3), 905– 923.

https://doi.org/10.1016/j.neuroimage.2006.09.046 Goldman-Rakic, P. S., Selemon, L. D., & Schwartz, M.

L. (1984). Dual pathways connecting the dorsolateral prefrontal cortex with the hippocampal formation and parahippocampal cortex in the rhesus monkey. Neuroscience, 12(3), 719–743.

https://doi.org/10.1016/0306-4522(84)90166-0

Goodman, W. K., Price, L. H., Rasmussen, S. A., Mazure, C., Fleischmann, R. L., Hill, C. L., Heninger, G. R., & Charney, D. S. (1989). The Yale-Brown Obsessive Compulsive Scale: I.

Development, Use, and Reliability. Archives of

General Psychiatry, 46(11), 1006–1011.

https://doi.org/10.1001/archpsyc.1989.0181011004 8007

Gosgrove, G. R., & Rauch, S. L. (1995). Psychosurgery. In Neurosurgery Clinics of North America (Vol. 6, Issue 1, pp. 167–176). W.B. Saunders.

https://doi.org/10.1016/s1042-3680(18)30485-6 Gross, J. J., & John, O. P. (2003). Individual Differences

in Two Emotion Regulation Processes:

Implications for Affect, Relationships, and Well-Being. Journal of Personality and Social

Psychology, 85(2), 348–362.

https://doi.org/10.1037/0022-3514.85.2.348 Guse, B., Falkai, P., & Wobrock, T. (2010). Cognitive

effects of high-frequency repetitive transcranial magnetic stimulation: A systematic review. In

Journal of Neural Transmission.

https://doi.org/10.1007/s00702-009-0333-7 Harmer, C. J., Thilo, K. V., Rothwell, J. C., & Goodwin,

G. M. (2001). Transcranial magnetic stimulation of medial-frontal cortex impairs the processing of angry facial expressions. Nature Neuroscience. https://doi.org/10.1038/82854

Heilbronner, S. R., & Haber, S. N. (2014). Frontal cortical and subcortical projections provide a basis for segmenting the cingulum bundle: Implications for neuroimaging and psychiatric disorders.

Journal of Neuroscience, 34(30), 10041–10054.

https://doi.org/10.1523/JNEUROSCI.5459-13.2014

Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012). FSL.

NeuroImage, 62(2), 782–790.

https://doi.org/10.1016/j.neuroimage.2011.09.015 Jeurissen, B., Leemans, A., Tournier, J. D., Jones, D. K., & Sijbers, J. (2013). Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging.

Human Brain Mapping, 34(11), 2747–2766.

https://doi.org/10.1002/hbm.22099

Koch, K., Reeß, T. J., Rus, O. G., Zimmer, C., & Zaudig, M. (2014). Diffusion tensor imaging (DTI) studies in patients with obsessive-compulsive disorder (OCD): A review. In Journal of Psychiatric

Research.

https://doi.org/10.1016/j.jpsychires.2014.03.006 LeDoux, J. E. (2009). Emotion Circuits in the Brain.

FOCUS, 7(2), 274–274.

https://doi.org/10.1176/foc.7.2.foc274

Lefaucheur, J. P., Aleman, A., Baeken, C., Benninger, D. H., Brunelin, J., Di Lazzaro, V., Filipović, S. R., Grefkes, C., Hasan, A., Hummel, F. C., Jääskeläinen, S. K., Langguth, B., Leocani, L., Londero, A., Nardone, R., Nguyen, J. P., Nyffeler, T., Oliveira-Maia, A. J., Oliviero, A., … Ziemann, U. (2020). Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic

(15)

stimulation (rTMS): An update (2014–2018). In

Clinical Neurophysiology (Vol. 131, Issue 2, pp.

474–528). Elsevier Ireland Ltd.

https://doi.org/10.1016/j.clinph.2019.11.002 Lippitz, B. E., Mindus, P., Meyerson, B. A., Kihlström,

L., & Lindquist, C. (1999). Lesion topography and outcome after thermocapsulotomy or gamma knife capsulotomy for obsessive-compulsive disorder: Relevance of the right hemisphere. Neurosurgery. https://doi.org/10.1097/00006123-199903000-00005

Lochner, C., & Stein, D. J. (2003). Heterogeneity of obsessive-compulsive disorder: A literature review. In Harvard Review of Psychiatry (Vol. 11, Issue 3, pp. 113–132).

https://doi.org/10.1080/10673220303949 Mataix-Cols, D., & van den Heuvel, O. A. (2006).

Common and Distinct Neural Correlates of Obsessive-Compulsive and Related Disorders. In

Psychiatric Clinics of North America.

https://doi.org/10.1016/j.psc.2006.02.006 Mataix-Cols, D., Wooderson, S., Lawrence, N.,

Brammer, M. J., Speckens, A., & Phillips, M. L. (2004). Distinct neural correlates of washing, checking, and hoarding symptom dimensions in obsessive-compulsive disorder. Archives of

General Psychiatry, 61(6), 564–576.

https://doi.org/10.1001/archpsyc.61.6.564 McRae, K., Misra, S., Prasad, A. K., Pereira, S. C., &

Gross, J. J. (2012). Bottom-up and top-down emotion generation: implications for emotion regulation. Social Cognitive and Affective

Neuroscience, 7(3), 253–262.

https://doi.org/10.1093/scan/nsq103 Montgomery, S. A., & Asberg, M. (1979). A new

depression scale designed to be sensitive to change. British Journal of Psychiatry, 134(4), 382–389. https://doi.org/10.1192/bjp.134.4.382 Nakamae, T., Narumoto, J., Sakai, Y., Nishida, S.,

Yamada, K., Nishimura, T., & Fukui, K. (2011). Diffusion tensor imaging and tract-based spatial statistics in obsessive-compulsive disorder.

Journal of Psychiatric Research, 45(5), 687–690.

https://doi.org/10.1016/j.jpsychires.2010.09.016 NeuroVault. (2016, 29 februari). JHU DTI-based

white-matter atlases.

https://neurovault.org/collections/264/ Ochsner, K N, & Gross, J. J. (2007). The neural

architecture of emotion regulation. In Handbook of

emotion regulation.

Ochsner, Kevin N., Ray, R. D., Cooper, J. C., Robertson, E. R., Chopra, S., Gabrieli, J. D. E., & Gross, J. J. (2004). For better or for worse: Neural systems supporting the cognitive down- and up-regulation of negative emotion. NeuroImage.

https://doi.org/10.1016/j.neuroimage.2004.06.030

Ochsner, Kevin N., Silvers, J. A., & Buhle, J. T. (2012). Functional imaging studies of emotion regulation: a synthetic review and evolving model of the cognitive control of emotion. In Annals of the New

York Academy of Sciences.

https://doi.org/10.1111/j.1749-6632.2012.06751.x Okada, K., Nakao, T., Sanematsu, H., Murayama, K.,

Honda, S., Tomita, M., Togao, O., Yoshiura, T., & Kanba, S. (2015). Biological heterogeneity of obsessive-compulsive disorder: A voxel-based morphometric study based on dimensional assessment. Psychiatry and Clinical

Neurosciences, 69(7), 411–421.

https://doi.org/10.1111/pcn.12269

Oldfield, R. C. (1971). The assessment and analysis of handedness: The Edinburgh inventory.

Neuropsychologia, 9(1), 97–113.

https://doi.org/10.1016/0028-3932(71)90067-4 Pallanti, S., Marras, A., Salerno, L., Makris, N., &

Hollander, E. (2016). Better than treated as usual: Transcranial magnetic stimulation augmentation in selective serotonin reuptake inhibitor-refractory obsessive-compulsive disorder, mini-review and pilot open-label trial. Journal of

Psychopharmacology, 30(6), 568–578.

https://doi.org/10.1177/0269881116628427 Pallanti, S., & Quercioli, L. (2006). Treatment-refractory

obsessive-compulsive disorder: Methodological issues, operational definitions and therapeutic lines. Progress in Neuro-Psychopharmacology and

Biological Psychiatry.

https://doi.org/10.1016/j.pnpbp.2005.11.028 Pandya, D. N., & Seltzer, B. (1982). Association areas of

the cerebral cortex. In Trends in Neurosciences (Vol. 5, Issue C, pp. 386–390). Elsevier Current Trends.

https://doi.org/10.1016/0166-2236(82)90219-3

Peng, Z. W., Lui, S. S. Y., Cheung, E. F. C., Jin, Z., Miao, G. D., Jing, J., & Chan, R. C. K. (2012). Brain structural abnormalities in obsessive-compulsive disorder: Converging evidence from white matter and grey matter. In Asian Journal of

Psychiatry (Vol. 5, Issue 4, pp. 290–296). Elsevier

Science B.V.

https://doi.org/10.1016/j.ajp.2012.07.004 Pezawas, L., Meyer-Lindenberg, A., Drabant, E. M.,

Verchinski, B. A., Munoz, K. E., Kolachana, B. S., Egan, M. F., Mattay, V. S., Hariri, A. R., & Weinberger, D. R. (2005). 5-HTTLPR

polymorphism impacts human cingulate-amygdala interactions: A genetic susceptibility mechanism for depression. Nature Neuroscience.

https://doi.org/10.1038/nn1463

Raffelt, D. A., Smith, R. E., Ridgway, G. R., Tournier, J. D., Vaughan, D. N., Rose, S., Henderson, R., & Connelly, A. (2015). Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of

(16)

crossing fibres. NeuroImage, 117, 40–55. https://doi.org/10.1016/j.neuroimage.2015.05.039 Raffelt, D. A., Tournier, J. D., Smith, R. E., Vaughan, D.

N., Jackson, G., Ridgway, G. R., & Connelly, A. (2017). Investigating white matter fibre density and morphology using fixel-based analysis.

NeuroImage, 144, 58–73.

https://doi.org/10.1016/j.neuroimage.2016.09.029 Rauch, S. L. (2003). Neuroimaging and neurocircuitry

models pertaining to the neurosurgical treatment of psychiatric disorders. In Neurosurgery Clinics of

North America.

https://doi.org/10.1016/S1042-3680(02)00114-6

Rohde, G. K., Barnett, A. S., Basser, P. J., Marenco, S., & Pierpaoli, C. (2004). Comprehensive Approach for Correction of Motion and Distortion in Diffusion-Weighted MRI. Magnetic Resonance in

Medicine, 51(1), 103–114.

https://doi.org/10.1002/mrm.10677 Rossini, P. M., Burke, D., Chen, R., Cohen, L. G.,

Daskalakis, Z., Di Iorio, R., Di Lazzaro, V., Ferreri, F., Fitzgerald, P. B., George, M. S., Hallett, M., Lefaucheur, J. P., Langguth, B., Matsumoto, H., Miniussi, C., Nitsche, M. A., Pascual-Leone, A., Paulus, W., Rossi, S., … Ziemann, U. (2015). Non-invasive electrical and magnetic stimulation of the brain, spinal cord, roots and peripheral nerves: Basic principles and procedures for routine clinical and research application: An updated report from an I.F.C.N. Committee. In Clinical Neurophysiology. https://doi.org/10.1016/j.clinph.2015.02.001 Rostami, R., Kazemi, R., Jabbari, A., Madani, A. S.,

Rostami, H., Taherpour, M. A., Molavi, P., Jaafari, N., Kuo, M. F., Vicario, C. M., Nitsche, M. A., & Salehinejad, M. A. (2020). Efficacy and clinical predictors of response to rTMS treatment in pharmacoresistant obsessive-compulsive disorder (OCD): A retrospective study. BMC Psychiatry,

20(1), 372.

https://doi.org/10.1186/s12888-020-02769-9

Schuurmans, J., van Balkom, A. J. L. M., van Megen, H. J. G. M., Smit, J. H., Eikelenboom, M., Cath, D. C., Kaarsemaker, M., Oosterbaan, D., Hendriks, G. J., Schruers, K. R. J., van der Wee, N. J. A., Glas, G., & Van Oppen, P. (2012). The Netherlands Obsessive Compulsive Disorder Association (NOCDA) study: Design and rationale of a longitudinal naturalistic study of the course of OCD and clinical characteristics of the sample at baseline. International Journal of Methods in

Psychiatric Research, 21(4), 273–285.

https://doi.org/10.1002/mpr.1372

Slotema, C. W., Blom, J. D., Hoek, H. W., & Sommer, I. E. C. (2010). Should we expand the toolbox of psychiatric treatment methods to include repetitive transcranial magnetic stimulation (rTMS)? A meta-analysis of the efficacy of rTMS in

psychiatric disorders. Journal of Clinical

Psychiatry.

https://doi.org/10.4088/JCP.08m04872gre Smith, S. M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T. E., Mackay, C. E., Watkins, K. E., Ciccarelli, O., Cader, M. Z., Matthews, P. M., & Behrens, T. E. J. (2006). Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. NeuroImage,

31(4), 1487–1505.

https://doi.org/10.1016/j.neuroimage.2006.02.024 Szeszko, P. R., Ardekani, B. A., Ashtari, M., Malhotra,

A. K., Robinson, D. G., Bilder, R. M., & Lim, K. O. (2005). White matter abnormalities in

obsessive-compulsive disorder: A diffusion tensor imaging study. Archives of General Psychiatry,

62(7), 782–790.

https://doi.org/10.1001/archpsyc.62.7.782 Tournier, J. D., Calamante, F., & Connelly, A. (2013).

Determination of the appropriate b value and number of gradient directions for high-angular-resolution diffusion-weighted imaging. NMR in

Biomedicine, 26(12), 1775–1786.

https://doi.org/10.1002/nbm.3017

Van Den Heuvel, O. A., Van Gorsel, H. C., Veltman, D. J., & Van Der Werf, Y. D. (2013). Impairment of executive performance after transcranial magnetic modulation of the left dorsal frontal-striatal circuit.

Human Brain Mapping.

https://doi.org/10.1002/hbm.21443

van der Werf, Y. D., Sanz-Arigita, E. J., Menning, S., & van den Heuvel, O. A. (2010). Modulating spontaneous brain activity using repetitive transcranial magnetic stimulation. BMC

Neuroscience.

https://doi.org/10.1186/1471-2202-11-145

Van Oppen, P., Hoekstra, R. J., & Emmelkamp, P. M. G. (1995). The structure of obsessive-compulsive symptoms. Behaviour Research and Therapy,

33(1), 15–23.

https://doi.org/10.1016/0005-7967(94)E0010-G

Versace, A., Graur, S., Greenberg, T., Lima Santos, J. P., Chase, H. W., Bonar, L., Stiffler, R. S., Hudak, R., Kim, T., Yendiki, A., Greenberg, B., Rasmussen, S., Liu, H., Haber, S., & Phillips, M. L. (2019). Reduced focal fiber collinearity in the cingulum bundle in adults with obsessive-compulsive disorder. In Neuropsychopharmacology. https://doi.org/10.1038/s41386-019-0353-4 Vogt, B. A., Rosene, D. L., & Pandya, D. N. (1979).

Thalamic and cortical afferents differentiate anterior from posterior cingulate cortex in the monkey. Science, 204(4389), 205–207. https://doi.org/10.1126/science.107587

Vriend, C., de Wit, S. J., Remijnse, P. L., van Balkom, A. J. L. M., Veltman, D. J., & van den Heuvel, O. A. (2013). Switch the itch: A naturalistic follow-up study on the neural correlates of cognitive

(17)

flexibility in obsessive-compulsive disorder.

Psychiatry Research - Neuroimaging, 213(1), 31–

38.

https://doi.org/10.1016/j.pscychresns.2012.12.006 Weissman, M. M. (1998). Cross-National Epidemiology

of Obsessive-Compulsive Disorder. CNS

Spectrums, 3(S1), 6–9.

https://doi.org/10.1017/S1092852900007136 Winston, G. P. (2012). The physical and biological basis

of quantitative parameters derived from diffusion MRI. Quantitative Imaging in Medicine and

Surgery, 2(4), 254–265.

https://doi.org/10.3978/j.issn.2223-4292.2012.12.05

Zhang, K., Fan, X., Yuan, J., Yin, J., Su, H., Hashimoto, K., & Wang, G. (2019). Impact of serotonin transporter gene on rTMS augmentation of SSRIs for obsessive compulsive disorder.

Neuropsychiatric Disease and Treatment, 15,