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Published in final edited form as: Psychiatry Res Neuroimaging. 2020 Apr 22;300:111081. doi: 10.1016/j.pscychresns.2020.111081

Functional disruption in prefrontal-striatal network in obsessive-compulsive disorder

Zhiqiang Sha a,*, Amelia Versace a, E Kale Edmiston a, Jay Fournier a, Simona Graur a, Tsafrir Greenberg a, João Paulo Lima Santos a, Henry W Chase a, Richelle S Stiffler a, Lisa Bonar a, Robert Hudak a, Anastasia Yendiki b, Benjamin D Greenberg c, Steven Rasmussen c, Hesheng Liu b, Gregory Quirk d, Suzanne Haber e, Mary L Phillips a
PMCID: PMC7266720  NIHMSID: NIHMS1589512  PMID: 32344156

Abstract

Obsessive-compulsive disorder (OCD) is characterized by intrusive thoughts and repetitive, compulsive behaviors. While a cortico-striatal-limbic network has been implicated in the pathophysiology of OCD, the neural correlates of this network in OCD are not well understood. In this study, we examined resting state functional connectivity among regions within the cortico-striatal-limbic OCD neural network, including the rostral anterior cingulate cortex, dorsolateral prefrontal cortex, ventrolateral prefrontal cortex, orbitofrontal cortex, ventromedial prefrontal cortex, amygdala, thalamus and caudate, in 44 OCD and 43 healthy participants. We then examined relationships between OCD neural network connectivity and OCD symptom severity in OCD participants. OCD relative to healthy participants showed significantly greater connectivity between the left caudate and bilateral dorsolateral prefrontal cortex. We also found a positive correlation between left caudate-bilateral dorsolateral prefrontal cortex connectivity and depression scores in OCD participants, such that greater positive connectivity was associated with more severe symptoms. This study makes a significant contribution to our understanding of functional networks and their relationship with depression in OCD.

Keywords: functional MRI, connectivity, obsessive-compulsive disorder

1. Introduction

Obsessive compulsive disorder (OCD) is a chronic psychiatric disorder that affects 1–3% of the population worldwide (Rasmussen and Eisen, 1992; Ruscio et al., 2010). OCD is characterized by persistent, intrusive thoughts (obsessions) and repetitive behaviors (compulsions). Identifying neural markers of pathophysiologic processes underlying these core symptoms of OCD is critical to developing novel interventions based on specific neural targets (Chamberlain et al., 2005; Pauls et al., 2014).

Given the wealth of literature emphasizing alterations in executive control and emotional regulation in OCD (Milad and Rauch, 2012; Snyder et al., 2015), prior neural models of OCD focused on identifying abnormalities in prefrontal and subcortical regions implicated in these processes (Milad and Rauch, 2012). Specific prefrontal regions showing functional abnormalities in OCD include the rostral anterior cingulate cortex (rACC) (Fitzgerald et al., 2005; Maltby et al., 2005), orbitofrontal cortex (OFC), ventrolateral prefrontal cortex (vlPFC), dorsolateral prefrontal cortex (dlPFC), and the ventromedial prefrontal cortex (vmPFC) (Milad and Rauch, 2012; Remijnse et al., 2006; van den Heuvel et al., 2005). The rACC supports error monitoring and emotional regulation (Botvinick et al., 2004; Etkin et al., 2006; Forman et al., 2004; Vuilleumier et al., 2001). The rACC is also highly connected with the vmPFC and the amygdala (Beckmann et al., 2009; Lehman et al., 2011), key emotion processing areas (Etkin et al., 2011; Etkin et al., 2006), and with the vlPFC, a region important for emotional regulation and reversal learning (Cools et al., 2002; Gray et al., 2002; Rygula et al., 2010). Additionally, both the rACC and vlPFC are key sites of integration of direct amygdala inputs (Petrides and Pandya, 2002; Yu et al., 2011). The rACC is further connected with the dlPFC (Petrides and Pandya, 1999), important for executive function (Mansouri et al., 2009; Quilodran et al., 2008). The rACC, vlPFC and dlPFC are, in turn, connected with the caudate nucleus (Ferry et al., 2000), important for the balance between goal-directed and habitual behaviors (Balleine and O’Doherty, 2010; Tricomi et al., 2009). The rACC receives thalamic input to transmit information to lateral prefrontal cortex (Paus, 2001). Thus the thalamus and rACC are critical for cortico-subcortical information transfer, placing the rACC in a unique position to translate intention to cognition and action (Paus, 2001).

Many of the processes associated with the above regions, in particular habit formation, error monitoring, and reversal learning, are aberrant in OCD (Gruner et al., 2016; Milad and Rauch, 2012). Thus, abnormalities in and among these regions likely underlie the development of OCD symptoms. Furthermore, given its connectivity with many of the above neural regions, the rACC in particular can be considered a “hub” region connecting key prefrontal cortical (e.g. dlPFC, vlPFC and vmPFC) and subcortical (e.g. amygdala, caudate, thalamus) regions in a distributed neural network (Alexander and Brown, 2011; Heilbronner and Haber, 2014; Pourtois et al., 2010) that is important for a variety of different processes that are aberrant in OCD (Milad and Rauch, 2012).

More recently, neuroimaging studies have focused on examining resting state connectivity to identify connectivity abnormalities in neural networks of interest in psychiatric populations (Fornito et al., 2015), given the utility of this method to identify endogenous abnormalities in connectivity in such networks, which may reflect core, task independent pathophysiological processes. Such studies in OCD reported abnormally elevated connectivity in cortico-striatal-limbic circuitries, and an association between alterations in these circuitries and OCD symptom severity. These findings include greater connectivity between the caudate and amygdala, and between the caudate and prefrontal cortices (e.g. dlPFC, vlPFC, rACC and vmPFC) in OCD versus healthy participants (Hou et al., 2013; Jung et al., 2013; Sakai et al., 2011). Furthermore, these patterns of abnormally elevated connectivity were associated with impaired cognitive flexibility in participants with OCD (Fitzgerald et al., 2011; Vaghi et al., 2017). Participants with OCD also showed abnormally elevated connectivity between the rACC and subcortical regions (e.g. thalamus and caudate) and prefrontal cortical regions (e.g. vmPFC and dlPFC) (Hou et al., 2013; Sakai et al., 2011), with caudate to vmPFC and OFC connectivity being positively correlated with symptom severity in OCD participants (Harrison et al., 2013; Hou et al., 2013). These findings thus highlight specific patterns of abnormally elevated connectivity among prefrontal cortical and subcortical regions in OCD, which, in turn, are related to OCD symptom severity. In contrast, other studies reported lower vlPFC global connectivity strength in OCD relative to healthy participants (Anticevic et al., 2014), as well as lower connectivity with specific regions, such as the caudate (Vaghi et al., 2017). The latter aberrant connectivity pattern was inversely associated with deficits in attentional set-shifting, which is associated with OCD symptoms (Vaghi et al., 2017). Together, these findings provide increasing evidence of abnormally elevated connectivity between neural regions previously implicated in OCD, particularly the caudate and rACC, and reduced connectivity between the vlPFC and other regions in the neural network implicated in OCD. Given the findings above highlighting the rACC as a hub region connecting other prefrontal cortical and subcortical regions in this network, the critical role of the caudate in the balance between goal-directed versus habitual behaviors, and the important role of the vlPFC in set shifting during reversal learning, these patterns of aberrant rACC-, caudate-, and vlPFC-centered connectivity likely underlie the difficulty that many individuals with OCD experience in shifting away from behaviors that have become habitual but that are no longer useful (Gillan et al., 2015; Gillan and Robbins, 2014).

We aimed to examine resting state functional connectivity in the OCD neural network, including the rACC, dlPFC, vlPFC, OFC, vmPFC, amygdala, thalamus and caudate. We also assessed relationships between regions with altered connectivity and OCD symptom severity. We hypothesized in OCD relative to healthy participants: (1) greater functional connectivity among regions in the OCD neural network, in particular the caudate and prefrontal cortical regions within this OCD neural network; (2) significant relationships between the magnitude of connectivity abnormalities and OCD symptom severity.

2. Material and Methods

2.1. Participants

We report findings from an ongoing study which commenced in June, 2015. The study received institutional review board approval from the Department of Psychiatry, University of Pittsburgh School of Medicine. Written informed consent was obtained from each participant. We initially screened 350 participants; 249 participants were excluded at the initial screening assessment because of failure to meet inclusion criteria. The OCD participants were diagnosed by licensed clinical psychologists. All participants were recruited from the Department of Psychiatry, University of Pittsburgh website and from departmentally-affiliated outpatient clinics, and were assessed with the Hamilton Rating Scale for Depression (17-items) (HRSD-17) (Hamilton, 1960), Hamilton Anxiety Scale 14 (HAMA-14) (Hamilton, 1959), and the Yale-Brown Obsessive Compulsive Scale (Y-BOCS) (Goodman et al., 1989). Exclusion criteria for individuals with OCD included: a history of neurological or neurodevelopmental disorders, schizophrenia; current substance use disorder in the last three months; and magnetic resonance imaging contraindications. Individuals with OCD with Y-BOCS scores <16 or predominant hoarding symptoms were excluded. All healthy participants were without history of neurological or psychiatric disorders. Of the 101 participants undergoing neuroimaging assessments, 14 were excluded due to incomplete neuroimaging data, excessive head motion (above 3 mm or 3° in any direction), or distortions in structural images, resulting in a total of eighty-seven participants (44 OCD and 43 healthy). Fifteen participants with OCD were medicated with selective serotonin reuptake inhibitors.

Image acquisition

Participants were scanned using a Siemens Trio Tim 3.0T scanner (11 subjects) and 3.0 T Magnetom Prisma (Siemens, Germany) with a 32-channel phased array head coil in the Magnetic Resonance Research Center, University of Pittsburgh Medical Center Health System. All participants were instructed to keep their eyes open and fixate their gaze on a cross back-projected onto a screen. Each functional scan was 6 min in length. Functional images were obtained using a Multiband scan with the following parameters: repetition time=1500ms; echo time=31ms; flip angle=55°; field of view=220×220mm2; multiband accelerate factor=4; slice thickness=2.0mm; slices=60 and voxel size=2×2×2mm3. Structural images were obtained using a sagittal magnetization-prepared rapid gradient echo three-dimensional T1-weighted sequence with the following parameters: repetition time=1520ms; echo time=3.17ms; flip angle=8°; field of view=256×256mm2; slice thickness=1.0mm; slices=176 and voxel size=1×1×1mm3.

2.2. R-fMRI analysis

The R-fMRI data were preprocessed using Statistical Parametric Mapping (SPM12) (http://www.fil.ion.ucl.ac.uk/spm) and the DPARSF toolbox (Chao-Gan and Yu-Feng, 2010). We (1) realigned to the first volume for head motion correction, (2) co-registered the mean functional image to the structural images using a linear transformation and segmented the image into gray matter, white matter and cerebrospinal fluid using a unified segmentation algorithm (Ashburner and Friston, 2005), (3) normalized functional images to the Montreal Neurological Institute (MNI) space using structural image unified segmentation with estimated transformation parameters and resampled to a 3×3×3mm3 voxel, (4) performed the spatial smoothing with full width at half maximum (FWMH)=7 mm, (5) detrended the linear drift, (6) regressed out the nuisance signals (including white matter, cerebrospinal fluids, global signal and Friston 24 head motion parameters) (Friston et al., 1996) and (7) performed temporal band pass filtering (0.01–0.1Hz). Notably, 3 subjects were excluded due to significant head motion (above 3 mm or 3° in any direction).

2.3. Seed-based resting state functional connectivity analysis

To examine connectivity patterns among neural regions implicated in OCD, connectivity maps were generated for the following regions of interest: left dlPFC (−42, 25, 30), right dlPFC (40, 18, 40), left vlPFC (−42, 45, −2), right vlPFC (43, 49, −2), left OFC (−21, 41, −20), right OFC (24, 45, −15) and midline vmPFC (8, 41, −24) defined by the Power264 Atlas (Power et al., 2011); the (midline) rACC defined by the Glasser360 Atlas (Glasser et al., 2016); and right and left caudate, thalamus and amygdala defined by the Harvard-Oxford Atlas (http://neuro.debian.net/pkgs/fsl-harvard-oxford-atlases.html). The Power264 and Glasser360 atlases were used to define prefrontal regions and the rACC because they include more focal masks for these regions than the Harvard-Oxford Atlas. We defined 14 seeds, including left and right amygdala, caudate, dlPFC, orbitofrontal cortex, thalamus, vlPFC and midline rACC and vmPFC. We also defined 8 targets among these regions by averaging the time series of these bilateral regions. Time series were then extracted from bilateral caudate, thalamus, amygdala and rACC based on the anatomical parcellation of atlases, and from 5mm radius spheres centered on the dlPFC, vlPFC, OFC and vmPFC ROIs in each hemisphere. Connectivity maps were produced by extracting the mean time course from each seed region and computing the correlation coefficient between that time course and the time courses from all other bilateral regions of interest. After removing self-correlations between seed and target regions of interests, there were 98 parallel tests. Between-group (OCD vs. healthy) connectivity comparisons were performed using two-sample t-tests, controlling for age, gender (Anticevic et al., 2014; Posner et al., 2017; Shin et al., 2014) and scanner.

Given our focus on the OCD neural network, we wished to identify significant between-group differences in connectivity among seeds and targets limited to the above regions. Statistical inferences for these analyses were made at p<0.05 after the sequential goodness of fit metatest (SGoF) correction (Carvajal-Rodriguez et al., 2009) to control for 98 parallel between-group tests. In addition, we also performed whole-brain voxel-wise functional connectivity analysis with these regions of interest as an exploratory analysis. Connectivity maps were produced by extracting the time course from each seed region and computing the correlation coefficient between that time course and the time course from all other voxels across the whole brain. Between-group (OCD vs. healthy) connectivity comparisons were performed using two-sample t-tests, controlling for age, gender and scanner. The significance threshold was set at voxel-level p<0.001 and cluster-level p<0.05 with Gaussian Random Field (GRF) correction (Worsley et al., 1992).

2.4. Relationships between connectivity and symptom severity

We wished to determine whether connectivity patterns that significantly differed between OCD and healthy participants were associated with symptom measures: the HRSD-17, HAMA-14 and Y-BOCS total scores, controlling for age, gender and scanner. We first extracted z-scores of connectivity between seed and connected regions centered on the peak coordinates that showed between-group differences. Then, we used Pearson correlation analyses to test for relationships between connectivity measures and scores on each symptom scale in OCD participants.

2.5. Posthoc analysis

First, given that previous studies revealed the effects of global mean connectivity on connectivity differences between patients and healthy participants (van den Heuvel et al., 2017; Vasa et al., 2018), we included global mean function connectivity as a covariate to reanalyze connectivity analysis. Briefly, for each participant, we constructed the whole-brain functional connectivity matrix with the combination of 300 cortical nodes (Schaefer et al., 2018), 14 subcortical nodes of the Harvard-Oxford Atlas (FSL, https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases) and 14 cerebellum nodes (SUIT toolbox, http://www.diedrichsenlab.org/imaging/suit.htm). We then obtained the global mean functional connectivity score by averaging all the functional connectivity for each participant. Functional connectivity analyses were reperformed with the regression of age, gender, scanner and global mean connectivity score. For the statistical analysis, in order to determine if functional connectivity changes in OCD are affected by the distribution of between-group differences under the null hypothesis, we used non-parametric permutation tests. Briefly, for each connectivity, we used 10,000 permutation tests to estimate the distribution of between-group differences by randomly reallocating all the participants into two groups. Then, we determined between-group significances of functional connectivity based on the distribution of the statistical values of 10,000 permutation tests.

Second, to examine whether head motion affected connectivity findings, we employed a “scrubbing” procedure during preprocessing (Power et al., 2012). Briefly, volumes with frame-wise displacement exceeding a threshold of 0.5 mm were removed, and their adjacent volumes (2 forward and 1 back) were replaced with the nearest neighbor interpolated data within the fMRI time series. Functional connectivity analyses were subsequently performed again using these adjusted measures.

Third, to exclude potential medication effects on connectivity patterns, we performed functional connectivity analyses in only the 29 unmedicated OCD participants versus 43 healthy participants.

Fourth, to estimate the reproducibility of connectivity findings with different brain atlases, other atlases were used to define ROIs: the Dosenbach160 Atlas (Dosenbach et al., 2010) for cortical regions, including left dlPFC (−44, 27, 33), right dlPFC (40, 17, 40), left vlPFC (−43, 47, 2), right vlPFC (42, 48, −3) and vmPFC (−6, 50, −1), the Brainnetome Atlas for the rACC, OFC, caudate, thalamus and amygdala (Fan et al., 2016). The Dosenbach160 and Brainnetome Atlases were used to define prefrontal regions and the midline rACC because they include more focal masks for these regions. Connectivity among these atlas-defined regions of interest was then examined using the same statistical analyses as described above.

3. Results

3.1. Demographic and clinical characteristics

There were no significant between-group differences in age, gender and educational level (p>0.05). As expected, OCD participants reported significantly more severe depressive (HRSD-17), anxiety (HAMA-14), and obsessive-compulsive (Y-BOCS) symptoms than healthy participants (p<0.001; Table 1).

Table 1.

Demographic and Clinical Characteristics

OCD (n = 44) Healthy (n = 43) p Value
Age (years) 23.61±4.76 23.51±4.05 0.92
Gender (male/female) 15/29 18/25 0.46
Education level (with/without college) 34/10 38/5 0.17
Scanner (Trio/Prisma) 6/38 5/38 0.78
HRSD-17 10.52±5.80 1.30±1.14 p<0.001
HAMA-14 12.00±6.40 1.05±1.15 p<0.001
Y-BOCS 20.34±3.12 0.12±0.76 p<0.001
Illness duration 14.26±7.19 0
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3.2. Resting state functional connectivity

Participants with OCD had significantly greater connectivity between left caudate and bilateral dlPFC relative to healthy participants (T=2.478, p=0.015), using a SGoF corrected p-value of p<0.0153, to control for 98 tests (Fig. 1A). Observation of the mean parameter estimates revealed that this resulted from participants with OCD having greater positive connectivity than healthy participants (OCD mean=0.1830; standard deviation=0.1802; healthy: mean=0.0991; standard deviation=0.1848). Other connectivity patterns in the OCD neural network that did not meet the statistical threshold are shown in the Supplementary Materials (Table S1). In addition, whole-brain voxel-wise functional connectivity analyses of these regions of interest are shown in the Supplementary Materials (Fig. S1).

Figure 1. Greater functional connectivity between left caudate and dlPFC in OCD relative to healthy participants and relationship with depression severity in OCD.

Figure 1.

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(A) Greater functional connectivity between left caudate and dlPFC in OCD relative to healthy participants; y-axis: resting state functional connectivity parameter estimates; (B) Left caudate-dlPFC functional connectivity was positively correlated with HRSD-17 score in OCD participants, such that such that greater positive connectivity was associated with more severe symptom; x-axis: resting state functional connectivity parameter estimates.

3.3. Relationships with symptoms

Given that there was only one significant region of interest-based between-group difference in connectivity in the OCD neural network and three clinical scales, we used the SGoF-corrected p-value of p<0.024 for three parallel tests. There was a significant positive correlation between left caudate-bilateral dlPFC connectivity and HRSD-17 score in OCD participants, such that greater positive connectivity was associated with more severe symptoms (R=0.352, p=0.024; Fig. 1B).

3.4. Posthoc analysis

These analyses focused on connectivity between left caudate and bilateral dlPFC. Connectivity findings were replicated after regressing out global mean connectivity (T=2.304, p=0.011), and after head motion effects (T=2.409, p=0.018) and after removing medicated participants from analyses (T=2.727, p=0.008). Between-group differences in connectivity also persisted using different atlases (T=2.324, p=0.023).

4. Discussion

To identify neural correlates of the core symptoms of OCD, we examined intrinsic functional coupling of an OCD-related network that included prefrontal regions (rACC, vlPFC, dlPFC, vmPFC, OFC) and subcortical regions (thalamus, amygdala, caudate). Our primary finding was significantly greater positive connectivity between the left caudate and bilateral dlPFC in participants with OCD relative to healthy participants. We also observed a positive correlation between left caudate-bilateral dlPFC connectivity and HRSD-17 score in OCD participants, such that such that greater positive connectivity was associated with more severe symptoms. These findings contribute to the literature implicating striatal-prefrontal alterations in OCD, while also demonstrating a relationship between depression symptoms and differences in this network.

The caudate nucleus is functionally connected with the dlPFC, and is associated with reinforcement learning and task switching (Kehagia et al., 2010). For example, in a task-based meta-analytic connectivity modeling study, the caudate exhibited functional connectivity with dlPFC during cognition, perception and emotion-related tasks (Kehagia et al., 2010). Our finding of greater connectivity between the left caudate and bilateral dlPFC in participants with OCD relative to healthy participants is consistent with previous studies (Harrison et al., 2009; Sakai et al., 2011). These studies showed greater connectivity in a prefrontal-striatal circuit in participants in the OCD relative to healthy participants. Furthermore, the caudate is increasingly co-activated with the dlPFC during symptom provocation in participants with OCD (Mataix-Cols et al., 2004; van den Heuvel et al., 2005). These findings not only suggest that the caudate functionally covaries with the dlPFC both during task performance and at rest, but also that there is greater coupling of these regions in participants with OCD. This greater coupling may contribute to various dimensional symptoms of OCD, such as checking and hoarding symptoms (Mataix-Cols et al., 2004; van den Heuvel et al., 2005), although we did not observe a linear relationship between left caudate-bilateral dlPFC connectivity and OCD symptom severity. Rather, we observed a positive relationship between the magnitude of connectivity between these regions and depressive symptom severity in participants with OCD. Previous analyses have suggested, however, that OCD symptoms typically precede the onset of depressive symptoms (Tibi et al., 2017), and that distress related to obsessional thoughts and concentration problems link depressive and OCD symptom clusters (Jones et al., 2018; McNally et al., 2017). Thus, our finding of a positive association between greater dlPFC-caudate connectivity and greater depressive symptom severity in participants with OCD might reflect a relationship between elevated dlPFC-caudate connectivity and cognitive dysfunction.

Animal models for OCD also support disruptions in the caudate and dlPFC. For example, in a rat model of excessive checking, lesion of the medial prefrontal cortex (homologous to human dlPFC) or dorsal striatum (homologous to human caudate) resulted in increased checking or reduced instrumental lever press behaviors, respectively, indicating that disruption of dlPFC-caudate coupling could lead to an imbalance between checking and instrumental responses, a behavioral pattern that is often observed in OCD (d’Angelo et al., 2017). Furthermore, in a mouse model of OCD-like behaviors, mice with excessive self-grooming showed disrupted corticostriatal spiking activity and alterations in postsynaptic glutamate receptor composition and dendritic complexity, which contribute to deficient corticostriatal neurotransmission (Shmelkov et al., 2010). Given that glutamate plays an important role in regulating functional connectivity (Kapogiannis et al., 2013; Schwarz et al., 2007), these findings suggest that alterations in the caudate, coupled with increased dlPFC activity, may underlie the core symptoms of OCD by mediating transmission of excitatory neurotransmitters along the corticostriatal circuit in OCD participants.

The present study has limitations. The amount of resting-state data was relatively small (6 minutes) in an intermediate range of the time needed for stable resting state fMRI estimates (Birn et al., 2013). Future studies should use other independent datasets with longer scanning duration (e.g. 8–10 minutes) to estimate functional connectivity and validate our findings. Given the symptomatic heterogeneity among participants with OCD, replication of our findings in independent samples is needed. Even though we replicated our results in the subsample of unmedicated participants, we cannot completely discount possible medication effects on functional connectivity in OCD. Future longitudinal studies that are designed to assess treatment effects will help us to distinguish medication effects from the pathophysiology of OCD. We identified a critical role for the caudate and dlPFC in OCD. Other modalities, such as task-based fMRI, positron emission tomography, diffusion tensor imaging, and arterial spin labeling will be needed to further evaluate the contributions of these regions to specific symptoms in OCD. We did not observe connectivity abnormalities between regions other than the caudate and dlPFC in OCD participants, which might reflect the fact that OCD participants in the current study were recruited in different stages of the disorder. Follow-up datasets will help to determine whether abnormalities in the OCD neural network can differentiate different stages of OCD.

In summary, the present study identified functional connectivity abnormalities in the OCD neural network, particularly between the caudate and dlPFC. This study makes a significant contribution to our understanding of cortico-striatal network alterations and their relationship with depression in OCD. Future studies can examine the relationship between functional and structural abnormalities in OCD, in order to clarify the specific role of this cortico-striatal network in the pathophysiology of OCD.

Supplementary Material

1

Highlights:

  • We used resting state data and a priori regions to assess OCD neural networks

  • OCD versus healthy adults had greater dorsolateral prefrontal-caudate connectivity

  • More abnormal connectivity in 2. was associated with more severe depression in OCD

Acknowledgments and conflicts of interest:

We thank Randy L. Buckner for the helpful comments. This study was supported by the National Institute of Mental Health grant P50 MH106435 (PI: Dr. Haber; Project 2 PI Dr. Phillips), and the Pittsburgh Foundation (Dr. Phillips).

Footnotes

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The authors report no biomedical financial interests or potential conflicts of interest.

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