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Original Investigation |

Neuroanatomical Characteristics Associated With Response to Dorsal Anterior Cingulotomy for Obsessive-Compulsive Disorder FREE

Garrett P. Banks, BS1; Charles B. Mikell, MD1; Brett E. Youngerman, MD1; Bryan Henriques, BS2; Kathleen M. Kelly, BS1; Andrew K. Chan, BS1; Diana Herrera1; Darin D. Dougherty, MD3; Emad N. Eskandar, MD4; Sameer A. Sheth, MD, PhD1
[+] Author Affiliations
1Department of Neurological Surgery, Neurological Institute, Columbia University, New York, New York
2College of Physicians and Surgeons, Columbia University, New York, New York
3Department of Psychiatry, Massachusetts General Hospital, Boston
4Department of Neurosurgery, Massachusetts General Hospital, Boston
JAMA Psychiatry. 2015;72(2):127-135. doi:10.1001/jamapsychiatry.2014.2216.
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Published online

Importance  Approximately 10% of patients with obsessive-compulsive disorder (OCD) have symptoms that are refractory to pharmacologic and cognitive-behavioral therapies. Neurosurgical interventions can be effective therapeutic options in these patients, but not all individuals respond. The mechanisms underlying this response variability are poorly understood.

Objective  To identify neuroanatomical characteristics on preoperative imaging that differentiate responders from nonresponders to dorsal anterior cingulotomy, a neurosurgical lesion procedure used to treat refractory OCD.

Design, Setting, and Participants  We retrospectively analyzed preoperative T1 and diffusion magnetic resonance imaging sequences from 15 patients (9 men and 6 women) who underwent dorsal anterior cingulotomy. Eight of the 15 patients (53%) responded to the procedure.

Main Outcomes and Measures  We used voxel-based morphometry (VBM) and diffusion tensor imaging to identify structural and connectivity variations that could differentiate eventual responders from nonresponders. The VBM and probabilistic tractography metrics were correlated with clinical response to the cingulotomy procedure as measured by changes in the Yale-Brown Obsessive Compulsive Scale score.

Results  Voxel-based morphometry analysis revealed a gray matter cluster in the right anterior cingulate cortex, anterior to the eventual lesion, for which signal strength correlated with poor response (P = .017). Decreased gray matter in this region of the dorsal anterior cingulate cortex predicted improved response (mean [SD] gray matter partial volume for responders vs nonresponders, 0.47 [0.03] vs 0.66 [0.03]; corresponding to mean Yale-Brown Obsessive Compulsive Scale score change, −60% [19] vs −11% [9], respectively). Hemispheric asymmetry in connectivity between the eventual lesion and the caudate (for responders vs nonresponders, mean [SD] group laterality for individual lesion seeds, −0.79 [0.18] vs −0.08 [0.65]; P = .04), putamen (−0.55 [0.35] vs 0.50 [0.33]; P = .001), thalamus (−0.82 [0.19] vs 0.41 [0.24]; P = .001), pallidum (−0.78 [0.18] vs 0.43 [0.48]; P = .001), and hippocampus (−0.66 [0.33] vs 0.33 [0.18]; P = .001) also correlated significantly with clinical response, with increased right-sided connectivity predicting greater response.

Conclusions and Relevance  We identified features of anterior cingulate cortex structure and connectivity that predict clinical response to dorsal anterior cingulotomy for refractory OCD. These results suggest that the variability seen in individual responses to a highly consistent, stereotyped procedure may be due to neuroanatomical variation in the patients. Furthermore, these variations may allow us to predict which patients are most likely to respond to cingulotomy, thereby refining our ability to individualize this treatment for refractory psychiatric disorders.

Figures in this Article

Obsessive-compulsive disorder (OCD) is a debilitating and chronic disorder with a lifetime prevalence of 2% to 3%.1 The disorder is characterized by intrusive thoughts and repetitive intentional behaviors that persist despite a desire to suppress them, often accompanied by marked anxiety. Although most patients attain adequate symptomatic relief with medication and cognitive-behavioral therapy, it is estimated that OCD may be refractory to these treatments in 10% to 20% of patients.1,2 Unfortunately, alternative treatment options are limited.

A subset of patients with refractory OCD may be candidates for surgical treatment. Given the risk of morbidity with surgery, candidate patients must be carefully evaluated by a multidisciplinary team consisting of psychiatrists, neurosurgeons, psychologists, and neurologists in consultation with ethicists.36 Typical inclusion criteria for surgical consideration are a diagnosis of persistent severe OCD that is refractory to several adequate pharmacologic trials and cognitive-behavioral therapy, the ability to follow instructions and provide consent, and demonstration of realistic expectations. Typical exclusion criteria are severe medical comorbidities, imminent suicidal intent, comorbid severe psychiatric disorders, and evidence of neurocognitive disorders. Given these necessarily stringent requirements for surgical consideration, the number of appropriate surgical candidates is relatively small.

Stereotactic surgical lesions, originally developed in the mid-20th century, have been successfully used for decades to treat severe refractory OCD and other psychiatric disorders. The necessity for safe, accurate, and reproducible surgical treatment options for psychiatric conditions was a major motivation for the development of stereotactic neurosurgery in the late 1940s.7 The dorsal anterior cingulotomy, one such stereotactic procedure, involves lesioning the dorsal anterior cingulate cortex (dACC), a region believed to play a role in the pathogenesis of the neural network that causes OCD. Clinical series with long-term follow-up have demonstrated a durable response rate of 45% to 70% following cingulotomy.811 This response rate is significant considering that these patients had been refractory to conventional therapy for years or even decades. Response rates of an alternative lesion procedure, the anterior capsulotomy, were fairly similar in open-label series,1215 but data from controlled blinded trials are limited.16,17 Nevertheless, these are invasive procedures with a complication rate of 5% to 10%. Improving our ability to predict which patients will respond to surgical treatment would represent a major advance in our management of OCD.

Whereas our understanding of the role of the dACC in both normal cognitive processes1820 and OCD21,22 is steadily improving, few studies have investigated features that predict outcome after cingulotomy or other neurosurgical procedures.23 Structural differences have been described2427 between patients with OCD and individuals serving as controls in components of the limbic corticobasal ganglia-thalamo-cortical (CBTC) network, including the orbitofrontal, parahippocampal, and cingulate cortices; the striatum; and the medial thalamus. Voxel-based morphometry (VBM) studies24,25 have reported gray matter volume differences in these structures, and diffusion tensor imaging (DTI) studies26,27 have demonstrated hemispheric asymmetries in white matter bundles connecting limbic structures, including the anterior limb of the internal capsule and cingulum. Significant heterogeneities in these metrics exist across studies, and the population of patients with intractable OCD who are surgical candidates could have different organizational patterns from those reported in the previous studies. Nevertheless, existing data suggest that these metrics are reasonable candidate predictors of response.

We therefore hypothesized that preoperative structural or connectivity variations in and between these regions may underlie the variability in patients’ response to cingulotomy. We tested this hypothesis by applying VBM and DTI analyses to preoperative imaging data in patients who received this stereotyped surgical lesion to determine whether neuroanatomical factors can predict response.

Ethics Statement

All study procedures were approved by the Columbia University Medical Center and Massachusetts General Hospital institutional review boards. The requirement for informed consent was waived by both institutions.

Participants

We retrospectively reviewed the records of all patients receiving cingulotomy for severe, intractable OCD between January 1, 2000, and December 31, 2010, at Massachusetts General Hospital. Patients who received preoperative magnetic resonance imaging (MRI) with either a high-resolution T1 or DTI sequence were included in the study. Patients with prior neurosurgical interventions to treat OCD were excluded. Details regarding the clinical outcome of these patients have been described.5 Surgical technique is described in the eAppendix in the Supplement.

Patient Evaluation

Surgical candidacy was determined by a multidisciplinary team as described previously.3,4,9 Patients underwent a psychiatric evaluation prior to cingulotomy and during follow-up visits. The severity of OCD was assessed by the treating psychiatrist (D.D.D.) using the Yale-Brown Obsessive Compulsive Scale (Y-BOCS).28 The Y-BOCS score obtained during the follow-up visit closest to 1 year after intervention was used as the postoperative score. For patients who underwent another neurosurgical procedure within the first 12 months, the score obtained immediately before the second procedure was used. Patients with a 35% or greater reduction in the Y-BOCS score were considered responders.

High-resolution (in-plane resolution, ≤1 mm; out-of-plane spacing, ≤2.5 mm) T1-weighted and diffusion (6 or 25 directions) sequences were acquired on a 1.5T scanner (Signa HDxt; GE Healthcare). Mann-Whitney analyses were performed to assess for scan parameter biases between responders and nonresponders (eTable 1 in the Supplement).

Gray Matter Analysis

Analyses were performed at Columbia University Medical Center. All patients with a preoperative high-resolution T1 MRI sequence were included. We used VBM to identify differences in gray matter between the brains of responders and nonresponders, along with a model-free algorithm to avoid preconceived biases regarding regions of interest. Strict statistical thresholding was used to account for multiple comparisons.

An optimized VBM protocol was performed using the FMRIB Software Library, version 5.0 (FMRIB; University of Oxford), which included brain extraction, segmentation, and linear/nonlinear transformation.29 A left-right, symmetric, study-specific gray matter template was designed from native-space T1 images after affine transformation to the gray matter ICBM 152, version 2009c (McConnell Brain Imaging Center) 2-mm standard template. Native-space gray matter volumes were nonlinearly normalized to the study-specific template. A modulation algorithm, part of the FMRIB Software Library protocol, was used to compensate for distortion from nonlinear transformation. Resultant gray matter volumes were smoothed with an isotropic gaussian kernel of sigma 3.5.

The FMRIB Software Library randomize function was used to perform permutation-based nonparametric inference using a generalized linear model to compare responders and nonresponders.30 In addition to changes in the Y-BOCS score, the generalized linear model included age and sex as nuisance variables. A threshold-free cluster enhancement analysis was used to compare modulated gray matter maps between groups on a voxelwise basis (5000 permutations). The results were subjected to a voxelwise Bonferroni correction to account for familywise errors. An after-correction value of P < .05 was considered significant.

A spherical region-of-interest diameter of 10 mm was constructed using FSLView in standard space, centered on the voxel with the smallest postcorrection P value. For each patient’s modulated gray matter volume map, the mean gray matter fraction in the area of the spherical region of interest was calculated using fslstats and regressed against patient-specific Y-BOCS score changes.

White Matter Analysis

All patients who underwent preoperative DTI were included. We used DTI data to estimate the connectivity between the eventual lesion site and predefined target structures. To delineate the lesioned area, we created patient-specific lesion masks using postoperative T2-weighted images.31 This delineation was performed by 2 investigators (B.H. and D.H.) blinded to patient information. We assessed interrater reliability using the Cohen κ test. The lesion masks were determined for patients individually and then transformed to the diffusion space defined by each patient’s preoperative DTI scan. These lesion masks were the seeds for the subsequent DTI analysis.

To objectively define the target structures, we used the 2-mm cortical and subcortical Harvard-Oxford structural probability atlases, with a threshold set a priori at 25% probability.32 By using these atlases, there were 56 possible seed-target pairs. The atlases were transformed to patient diffusion space using linear/nonlinear transformation.

The tractography analysis was performed in patient diffusion space using the Diffusion Toolbox of the FMRIB Software Library.3335 For each patient we sought to estimate the strength of connectivity between the origin seed (lesion mask) and the atlas-defined target structures. To do so, we calculated probability distributions based on 2 fiber directions using a previously described3335 multiple fiber extension algorithm. We used these calculated streamline distributions to estimate fiber tracts from the origin seeds to ipsilateral target regions. The strength of connectivity from an origin seed to any other area in the brain correlates with the number of seed-originating streamline traces passing through the target.36 To suppress spuriously generated tracts passing through areas unlikely to support white matter connectivity, a patient-specific cerebrospinal fluid termination mask was designed by thresholding the nondirectionally weighted diffusion image. Streamline tracking originated in the seed and stopped after leaving brain space or encountering the cerebrospinal fluid mask. To quantify connections from the origin seed to ipsilateral targets, we calculated the percentage of seed-originating streamlines reaching the targets.36 Left- and right-hemisphere connections were calculated separately. For the primary analysis, we only considered ipsilateral tracts, ignoring any hemisphere-crossing streamlines.36 An analysis allowing hemispheric crossings through the corpus callosum is included in eTable 2 in the Supplement.

To select appropriate seed-target pairs, only pairs wherein at least 1 streamline connecting the origin seed to the ipsilateral target in all patients were used. This requirement prevented unnecessary analysis on spurious seed-target pairs that had no anatomical basis. In addition, dorsal cingulate and paracingulate targets were excluded because they partially overlapped with the origin seed in standard space, and tracts would therefore automatically connect to their target and create spurious results. Target pairs meeting the above criteria were considered in network and were included in the analysis.

We quantified hemispheric asymmetry in seed-target connectivity by calculating a laterality metric (LM). For each seed-target pair, we subtracted the right-sided streamline percentage from the left-sided streamline percentage and divided by the sum. The LM is thus a ratio between −1 and 1 that describes whether the right- or left-sided target was more highly connected to the seed area, with negative values indicating greater right-sided connectivity. A Mann-Whitney test was performed to compare the LM between responders and nonresponders for in-network targets. All results were subjected to a Benjamini-Hochberg false discovery rate correction to account for multiple comparisons.

The primary analysis used each patient’s lesion mask as the seed. As a secondary analysis, we created a standard lesion mask by aggregating the individual lesion masks. All individual lesions were transformed to standard space, and the threshold for the resultant lesion probability distribution was set at P < .05 after threshold-free cluster enhancement and familywise error correction to create the standard lesion mask. This standard lesion mask was transformed linearly and nonlinearly to patient-specific diffusion space and used as the seed for this secondary analysis. In-network targets were determined separately for the standard lesion mask analysis.

Patients

Fifteen patients (9 men [60%]; mean age, 37 years) met the study inclusion criteria. Of these, 14 individuals (93%) had preoperative high-resolution MRI T1 sequences and 13 (87%) had preoperative DTI sequences. Eight of the 14 patients (57%) with high-resolution T1 and 7 of the 13 patients (54%) with DTI data were responders (Table 1). There were no significant differences between responders and nonresponders in sex, age, surgical year, or preoperative Y-BOCS score as determined by a Mann-Whitney test.

Table Graphic Jump LocationTable 1.  Demographic Information and Clinical Severity Scores of Participants
Imaging Parameters

We found no significant differences between responders and nonresponders in voxel volume on T1 images, slice spacing on T1 images, or DTI gradient direction number (eTable 1 in the Supplement). Nonetheless, to address the possible effect of heterogeneity in acquisition parameters, several additional analyses were performed (eTables 3-5 and eFigures 1-3 in the Supplement). No analysis suggested that the distinction between responders and nonresponders was driven by this heterogeneity. Representative images of the postoperative T2 sequences along with the segmented lesions are shown in eFigure 4 in the Supplement.

Gray Matter Results

The VBM analysis of whole-brain gray matter topography demonstrated a gray matter cluster associated with the outcome. The cluster, centered in the right anterior cingulate cortex (x = 2, y = 42, and z = 20) several millimeters anterior to the standard lesion area, demonstrated greater gray matter volume in nonresponders (Figure 1A). Postcorrection significance of the cluster was P = .017. No clusters of gray matter surviving familywise error correction correlated with age or sex. Given the heterogeneity of the scans, the scan parameters were included as nuisance repressors in the generalized linear model; subsequently, correlations in the same area remained statistically significant (eFigure 1 in the Supplement).

Place holder to copy figure label and caption
Figure 1.
Differences in Gray Matter Between Responders and Nonresponders

A, Sagittal (left), coronal (center), and axial (right) views centered on the dorsal anterior cingulate. Shown is the cluster of gray matter that was greater in nonresponders, with a threshold after correction of P < .05 and overlaid on a 1-mm standard brain. Orange/yellow voxels indicate areas that were significantly different between responders and nonresponders. B, Yale-Brown Obsessive Compulsive Scale (Y-BOCS) score changes regressed against the mean partial volume gray matter fraction (ie, number between 0 and 1 without units) in the cluster center region of interest (ROI).

Graphic Jump Location

We quantified the relationship for illustrative purposes by placing a 10-mm diameter seed in the right dACC at the cluster center and plotting gray matter volume against the Y-BOCS score change (Figure 1B). Decreased gray matter in this region of the dACC predicted improved response (mean [SD] gray matter partial volume for responders vs nonresponders, 0.47 [0.03] vs 0.66 [0.03]; corresponding to mean Y-BOCS score change, −60% [19] vs −11% [9], respectively).

White Matter Analysis

Two investigators (B.H. and D.H.) blinded to response status delineated the lesions on postoperative T2 MRIs of individual patients. The Cohen interrater κ statistic was 0.92, indicating a consistent segmentation.

Of the 56 potential seed-target pairs, 18 pairs (32%) were considered in network for the lesion study and included in the analysis (Table 2). After false discovery rate correction, the LM for 4 of the 18 structures (22%) differed significantly between the groups. Connectivity to the thalamus, putamen, pallidum, and hippocampus readily distinguished responders from nonresponders (Table 2). The LMs of these 4 targets were plotted against patient-specific Y-BOCS score changes (Figure 2). Greater right-sided connectivity between the lesioned areas in the dACC and these 4 target areas correlated with better response to cingulotomy.

Table Graphic Jump LocationTable 2.  Statistical Comparison of LM Between Responders and Nonrespondersa
Place holder to copy figure label and caption
Figure 2.
Regression of Yale-Brown Obsessive Compulsive Scale (Y-BOCS) Score Changes Against Laterality Metric Values for Each of the Significant Target Pairs in the Patient-Specific Lesion Study

Lesion connectivity to the pallidum (A), hippocampus (B), putamen (C), and thalamus (D).

Graphic Jump Location

To permit preoperative evaluation of patients in whom the lesion was not yet created, the analysis was repeated using a standardized lesion area (eFigure 5 in the Supplement). In this analysis, 23 structures were considered in network (Table 2). Again, 4 seed-target LM pairs differed significantly between responders and nonresponders. However, in this standardized analysis, the caudate nucleus instead of the putamen LM was significant. Targets of the significant pairs included the thalamus, pallidum, hippocampus, and caudate nucleus. Anatomical targets and tracts are described in eFigures 6 and 7, respectively, in the Supplement. Significant LMs for the standard areas were plotted against Y-BOCS score changes for illustrative purposes (Figure 3), and greater right-sided connectivity predicted better response. Right-sided dominance of at least 2 of the 3 consistent structures (thalamus, pallidum, and hippocampus) had strong positive and negative predictive values for treatment response in this small cohort (eAppendix in the Supplement). Allowing for streamline crossing through the corpus callosum did not significantly alter the results (eTable 2 in the Supplement).

Place holder to copy figure label and caption
Figure 3.
Regression of Yale-Brown Obsessive Compulsive Scale (Y-BOCS) Score Changes Against Laterality Metric Values for Each of the Significant Target Pairs in the Standard Area Study

Standard area connectivity to the pallidum (A), hippocampus (B), caudate nucleus (C), and thalamus (D).

Graphic Jump Location

We identified several neuroanatomical features associated with clinical outcome following a stereotactic surgical lesion procedure for severe intractable OCD. We observed differences in gray matter signal in the anterior cingulate gyrus as well as in connectivity between the dorsal cingulate, caudate, putamen, pallidum, thalamus, and hippocampus that readily distinguished responders from nonresponders to cingulotomy.

These structures are part of the limbic CBTC loop implicated in the neural network dysfunction thought to be responsible for OCD. Other than defining the DTI seed based on the location of the lesion, our analysis was agnostic to candidate brain structures. Even without an a priori region of interest definition, limbic CBTC structures were the only areas to survive stringent significance testing. Previous studies25,37,38 have identified structural differences in components of this circuit between patients with OCD and matched controls. Our results extend these findings by demonstrating that anatomical characteristics of these structures also predict which patients are likely to respond to cingulotomy.

Our findings were strongly lateralized: less gray matter in the right dACC and greater right-sided connectivity in the limbic CBTC circuit predicted improved response. This notion of lateralized anatomical differences in OCD is consistent with the findings of previous studies demonstrating unilateral differences between patients and healthy controls throughout the CBTC circuit,39,40 although bilateral differences have also been identified.25,37 Several pharmacologic and cognitive-behavioral therapy trials have demonstrated unilateral right-sided imaging changes across metabolic4144 and perfusion4547 domains, all of which correlated with response to treatment. Fewer examples exist of left-sided47 and bilateral48,49 imaging-response correlations.

Targeted surgical interventions provide further support for the laterality hypothesis. Although most clinical series and trials of deep brain stimulation (DBS) for OCD have used bilateral stimulation,50 2 reports2,51 described results with unilateral right-sided stimulation of the ventral striatum, with similar response to bilateral stimulation.52 A similar situation holds for the results from ventral capsulotomy, a stereotactic lesion targeting the ventral portion of the anterior limb of the internal capsule, a region very close to the DBS target. In a study by Lippitz et al53 including 29 patients, all 16 patients with a good outcome had lesions that overlapped in the right, but not left, anterior limb of the internal capsule. Again, most capsulotomy studies use bilateral lesions and have reported similar outcomes.1214,54 However, image evaluators in the Lippitz et al study were not blinded to response, and those findings were not reproduced in a later capsulotomy study55 in patients with non-OCD anxiety disorders from the same group. Finally, a recent DBS study56 found that blood oxygen level–dependent functional MRI signal in the right (but not left) nucleus accumbens during a reward anticipation task increased to levels near those of controls during stimulation and exhibited decreased signal during the off state. This intriguing finding suggests that DBS normalizes nucleus accumbens activity, potentially in a lateralized fashion. The confluence of these results therefore permits consideration of whether most of the clinical response to these surgical procedures is derived from the right-sided intervention.

The significance of the potential unilateral (right predominant) aspect of the pathogenesis of OCD remains unclear. It is possible that, just as language and verbal memory are heavily lateralized functions, the planning, decision making, and reward circuitry dysfunction that underlies OCD could also be significantly lateralized. Further work in this area is necessary to explore this possibility.

An important implication of our results pertains to the presurgical evaluation of patients with severe refractory OCD who are being considered for neurosurgical treatment. Although the complication rate of these procedures is relatively low, the procedures are nonetheless invasive and should be offered only to appropriately selected patients. Previous studies57 of cingulotomy lesions suggest that lesion size is not a strong predictor of response. Given this finding as well as the reproducibility of stereotactic lesion procedures, it may be that response variability is more dependent on patients’ neuroanatomical variation5860 than on lesion heterogeneity.9,57 The ability to preoperatively, noninvasively identify patients who are more likely to respond to specific neurosurgical interventions would therefore represent a significant advance in our treatment algorithm. The imaging metrics we identified could be applied prospectively to patients undergoing targeted interventions, such as stereotactic lesions or DBS.

Better preoperative evaluation techniques should ideally lead to a more individualized view of each patient’s disorder. Improved understanding of the neuroanatomical basis of OCD will inevitably lead to more refined and individualized targeting for stereotactic neurosurgical procedures. A more comprehensive appreciation of the underlying circuit will not only help predict which patients are likely to respond but also which aspects of their disorder are likely to improve.

Important limitations of our study are its retrospective design and consequent scan parameter heterogeneity. However, because there were no significant differences between responders and nonresponders when imaging parameters were compared, we believe this limitation was unlikely to have significantly affected the results of our study. Moreover, our tractography analysis used only within-subject comparisons to avoid the difficulty of intersubject comparisons with differing scan parameters. Although the sample size was not large, these procedures are rare, and this study represents, to our knowledge, the largest investigating preoperative imaging predictors of response to a stereotactic lesion.

In this group of patients undergoing dorsal anterior cingulotomy for severe intractable OCD, we found structural and connectivity differences in the limbic CBTC circuit that distinguished responders from nonresponders. Decreasing right anterior cingulate gray matter volume and increasing connectivity between the right cingulate and caudate, putamen, pallidum, thalamus, and hippocampus correlated with improved clinical outcomes. These results emphasize the importance of hemispheric asymmetry in the pathophysiology of OCD as well as the possibility of using neuroanatomical markers to inform treatment decisions and predict outcomes in an individualized manner. Prospective work will help determine the generalizability of these results as well as whether these findings should be considered for clinical decision making.

Submitted for Publication: February 18, 2014; final revision received August 16, 2014; accepted August 25, 2014.

Corresponding Author: Sameer A. Sheth, MD, PhD, Department of Neurological Surgery, Neurological Institute, Room 427, Columbia University, 710 W 168th St, New York, NY 10032 (ss4451@cumc.columbia.edu).

Published Online: December 23, 2014. doi:10.1001/jamapsychiatry.2014.2216.

Author Contributions: Mr Banks and Dr Sheth had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Banks, Mikell, Eskandar, Sheth.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Banks, Mikell, Henriques, Kelly, Herrera, Dougherty, Sheth.

Critical revision of the manuscript for important intellectual content: Banks, Mikell, Youngerman, Chan, Eskandar, Sheth.

Statistical analysis: Banks, Mikell, Henriques, Herrera.

Obtained funding: Sheth.

Administrative, technical, or material support: Banks, Mikell, Youngerman, Chan, Eskandar, Sheth.

Study supervision: Sheth.

Conflict of Interest Disclosures: None reported.

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Milad  MR, Rauch  SL.  Obsessive-compulsive disorder: beyond segregated cortico-striatal pathways. Trends Cogn Sci. 2012;16(1):43-51.
PubMed   |  Link to Article
Rauch  SL, Dougherty  DD, Cosgrove  GR,  et al.  Cerebral metabolic correlates as potential predictors of response to anterior cingulotomy for obsessive compulsive disorder. Biol Psychiatry. 2001;50(9):659-667.
PubMed   |  Link to Article
Valente  AA  Jr, Miguel  EC, Castro  CC,  et al.  Regional gray matter abnormalities in obsessive-compulsive disorder. Biol Psychiatry. 2005;58(6):479-487.
PubMed   |  Link to Article
Radua  J, van den Heuvel  OA, Surguladze  S, Mataix-Cols  D.  Meta-analytical comparison of voxel-based morphometry studies in obsessive-compulsive disorder vs other anxiety disorders. Arch Gen Psychiatry. 2010;67(7):701-711.
PubMed   |  Link to Article
Cannistraro  PA, Makris  N, Howard  JD,  et al.  A diffusion tensor imaging study of white matter in obsessive-compulsive disorder. Depress Anxiety. 2007;24(6):440-446.
PubMed   |  Link to Article
Chiu  C-H, Lo  Y-C, Tang  H-S,  et al.  White matter abnormalities of fronto-striato-thalamic circuitry in obsessive-compulsive disorder. Psychiatry Res. 2011;192(3):176-182.
PubMed   |  Link to Article
Goodman  WK, Price  LH, Rasmussen  SA,  et al.  The Yale-Brown Obsessive Compulsive Scale, I: development, use, and reliability. Arch Gen Psychiatry. 1989;46(11):1006-1011.
PubMed   |  Link to Article
Douaud  G, Smith  S, Jenkinson  M,  et al.  Anatomically related grey and white matter abnormalities in adolescent-onset schizophrenia. Brain. 2007;130(pt 9):2375-2386.
PubMed   |  Link to Article
Nichols  TE, Holmes  AP.  Nonparametric permutation tests for functional neuroimaging. Hum Brain Mapp. 2002;15(1):1-25.
PubMed   |  Link to Article
Tomlinson  FH, Jack  CR  Jr, Kelly  PJ.  Sequential magnetic resonance imaging following stereotactic radiofrequency ventralis lateralis thalamotomy. J Neurosurg. 1991;74(4):579-584.
PubMed   |  Link to Article
Desikan  RS, Ségonne  F, Fischl  B,  et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31(3):968-980.
PubMed   |  Link to Article
Behrens  TEJ, Johansen-Berg  H, Woolrich  MW,  et al.  Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat Neurosci. 2003;6(7):750-757.
PubMed   |  Link to Article
Behrens  TE, Woolrich  MW, Jenkinson  M,  et al.  Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn Reson Med. 2003;50(5):1077-1088.
PubMed   |  Link to Article
Behrens  TEJ, Berg  HJ, Jbabdi  S, Rushworth  MFS, Woolrich  MW.  Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage. 2007;34(1):144-155.
PubMed   |  Link to Article
Bach  DR, Behrens  TE, Garrido  L, Weiskopf  N, Dolan  RJ.  Deep and superficial amygdala nuclei projections revealed in vivo by probabilistic tractography. J Neurosci. 2011;31(2):618-623.
PubMed   |  Link to Article
Radua  J, Mataix-Cols  D.  Voxel-wise meta-analysis of grey matter changes in obsessive-compulsive disorder. Br J Psychiatry. 2009;195(5):393-402.
PubMed   |  Link to Article
Bourne  SK, Eckhardt  CA, Sheth  SA, Eskandar  EN.  Mechanisms of deep brain stimulation for obsessive compulsive disorder: effects upon cells and circuits. Front Integr Neurosci. 2012;6:29.
PubMed   |  Link to Article
Scarone  S, Colombo  C, Livian  S,  et al.  Increased right caudate nucleus size in obsessive-compulsive disorder. Psychiatry Res. 1992;45(2):115-121.
PubMed   |  Link to Article
Tang  W, Li  B, Huang  X,  et al.  Morphometric brain characterization of refractory obsessive-compulsive disorder. Prog Neuropsychopharmacol Biol Psychiatry. 2013;46:126-131.
PubMed   |  Link to Article
Baxter  LR  Jr, Schwartz  JM, Bergman  KS,  et al.  Caudate glucose metabolic rate changes with both drug and behavior therapy for obsessive-compulsive disorder. Arch Gen Psychiatry. 1992;49(9):681-689.
PubMed   |  Link to Article
Swedo  SE, Pietrini  P, Leonard  HL,  et al.  Cerebral glucose metabolism in childhood-onset obsessive-compulsive disorder. Arch Gen Psychiatry. 1992;49(9):690-694.
PubMed   |  Link to Article
Hansen  ES, Hasselbalch  S, Law  I, Bolwig  TG.  The caudate nucleus in obsessive-compulsive disorder. Int J Neuropsychopharmacol. 2002;5(1):1-10.
PubMed   |  Link to Article
Saxena  S, Brody  AL, Maidment  KM,  et al.  Localized orbitofrontal and subcortical metabolic changes and predictors of response to paroxetine treatment in obsessive-compulsive disorder. Neuropsychopharmacology. 1999;21(6):683-693.
PubMed   |  Link to Article
Ho Pian  KL, van Megen  HJGM, Ramsey  NF,  et al.  Decreased thalamic blood flow in obsessive-compulsive disorder patients responding to fluvoxamine. Psychiatry Res. 2005;138(2):89-97.
PubMed   |  Link to Article
Nakatani  E, Nakgawa  A, Ohara  Y,  et al.  Effects of behavior therapy on regional cerebral blood flow in obsessive-compulsive disorder. Psychiatry Res. 2003;124(2):113-120.
PubMed   |  Link to Article
Yamanishi  T, Nakaaki  S, Omori  IM,  et al.  Changes after behavior therapy among responsive and nonresponsive patients with obsessive-compulsive disorder. Psychiatry Res. 2009;172(3):242-250.
PubMed   |  Link to Article
Benkelfat  C, Nordahl  TE, Semple  WE, King  AC, Murphy  DL, Cohen  RM.  Local cerebral glucose metabolic rates in obsessive-compulsive disorder: patients treated with clomipramine. Arch Gen Psychiatry. 1990;47(9):840-848.
PubMed   |  Link to Article
Perani  D, Colombo  C, Bressi  S,  et al.  [18F]FDG PET study in obsessive-compulsive disorder. Br J Psychiatry. 1995;166(2):244-250.
PubMed   |  Link to Article
Blomstedt  P, Sjöberg  RL, Hansson  M, Bodlund  O, Hariz  MI.  Deep brain stimulation in the treatment of obsessive-compulsive disorder. World Neurosurg. 2013;80(6):e245-e253. doi:10.1016/j.wneu.2012.10.006.
PubMed   |  Link to Article
Sturm  V, Lenartz  D, Koulousakis  A,  et al.  The nucleus accumbens: a target for deep brain stimulation in obsessive-compulsive- and anxiety-disorders. J Chem Neuroanat. 2003;26(4):293-299.
PubMed   |  Link to Article
Greenberg  BD, Gabriels  LA, Malone  DA  Jr,  et al.  Deep brain stimulation of the ventral internal capsule/ventral striatum for obsessive-compulsive disorder: worldwide experience. Mol Psychiatry. 2010;15(1):64-79.
PubMed   |  Link to Article
Lippitz  BE, Mindus  P, Meyerson  BA, Kihlström  L, Lindquist  C.  Lesion topography and outcome after thermocapsulotomy or gamma knife capsulotomy for obsessive-compulsive disorder. Neurosurgery. 1999;44(3):452-458.
PubMed   |  Link to Article
Kondziolka  D, Flickinger  JC, Hudak  R.  Results following gamma knife radiosurgical anterior capsulotomies for obsessive compulsive disorder. Neurosurgery. 2011;68(1):28-32.
PubMed   |  Link to Article
Rück  C, Svanborg  P, Meyerson  BA.  Lesion topography in capsulotomy for refractory anxiety—is the right side the right side? Stereotact Funct Neurosurg. 2005;83(4):172-179.
PubMed   |  Link to Article
Figee  M, Luigjes  J, Smolders  R,  et al.  Deep brain stimulation restores frontostriatal network activity in obsessive-compulsive disorder. Nat Neurosci. 2013;16(4):386-387.
PubMed   |  Link to Article
Vogt  BA, Nimchinsky  EA, Vogt  LJ, Hof  PR.  Human cingulate cortex: surface features, flat maps, and cytoarchitecture. J Comp Neurol. 1995;359(3):490-506.
PubMed   |  Link to Article
Fornito  A, Whittle  S, Wood  SJ, Velakoulis  D, Pantelis  C, Yücel  M.  The influence of sulcal variability on morphometry of the human anterior cingulate and paracingulate cortex. Neuroimage. 2006;33(3):843-854.
PubMed   |  Link to Article
Yücel  M, Stuart  GW, Maruff  P,  et al.  Hemispheric and gender-related differences in the gross morphology of the anterior cingulate/paracingulate cortex in normal volunteers. Cereb Cortex. 2001;11(1):17-25.
PubMed   |  Link to Article
Yang  JC, Ginat  DT, Dougherty  DD, Makris  N, Eskandar  EN.  Lesion analysis for cingulotomy and limbic leucotomy. J Neurosurg. 2014;120(1):152-163.
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure 1.
Differences in Gray Matter Between Responders and Nonresponders

A, Sagittal (left), coronal (center), and axial (right) views centered on the dorsal anterior cingulate. Shown is the cluster of gray matter that was greater in nonresponders, with a threshold after correction of P < .05 and overlaid on a 1-mm standard brain. Orange/yellow voxels indicate areas that were significantly different between responders and nonresponders. B, Yale-Brown Obsessive Compulsive Scale (Y-BOCS) score changes regressed against the mean partial volume gray matter fraction (ie, number between 0 and 1 without units) in the cluster center region of interest (ROI).

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.
Regression of Yale-Brown Obsessive Compulsive Scale (Y-BOCS) Score Changes Against Laterality Metric Values for Each of the Significant Target Pairs in the Patient-Specific Lesion Study

Lesion connectivity to the pallidum (A), hippocampus (B), putamen (C), and thalamus (D).

Graphic Jump Location
Place holder to copy figure label and caption
Figure 3.
Regression of Yale-Brown Obsessive Compulsive Scale (Y-BOCS) Score Changes Against Laterality Metric Values for Each of the Significant Target Pairs in the Standard Area Study

Standard area connectivity to the pallidum (A), hippocampus (B), caudate nucleus (C), and thalamus (D).

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1.  Demographic Information and Clinical Severity Scores of Participants
Table Graphic Jump LocationTable 2.  Statistical Comparison of LM Between Responders and Nonrespondersa

References

Greenberg  BD, Rauch  SL, Haber  SN.  Invasive circuitry-based neurotherapeutics: stereotactic ablation and deep brain stimulation for OCD. Neuropsychopharmacology. 2010;35(1):317-336.
PubMed   |  Link to Article
Huff  W, Lenartz  D, Schormann  M,  et al.  Unilateral deep brain stimulation of the nucleus accumbens in patients with treatment-resistant obsessive-compulsive disorder. Clin Neurol Neurosurg. 2010;112(2):137-143.
PubMed   |  Link to Article
Mian  MK, Campos  M, Sheth  SA, Eskandar  EN.  Deep brain stimulation for obsessive-compulsive disorder: past, present, and future. Neurosurg Focus. 2010;29(2):E10. doi:10.3171/2010.4.FOCUS10107.
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Cosgrove  GR, Rauch  SL.  Stereotactic cingulotomy. Neurosurg Clin N Am. 2003;14(2):225-235.
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Dougherty  DD, Baer  L, Cosgrove  GR,  et al.  Prospective long-term follow-up of 44 patients who received cingulotomy for treatment-refractory obsessive-compulsive disorder. Am J Psychiatry. 2002;159(2):269-275.
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Sheth  SA, Neal  J, Tangherlini  F,  et al.  Limbic system surgery for treatment-refractory obsessive-compulsive disorder. J Neurosurg. 2013;118(3):491-497.
PubMed   |  Link to Article
Jung  HH, Kim  C-H, Chang  JH, Park  YG, Chung  SS, Chang  JW.  Bilateral anterior cingulotomy for refractory obsessive-compulsive disorder. Stereotact Funct Neurosurg. 2006;84(4):184-189.
PubMed   |  Link to Article
Bourne  SK, Sheth  SA, Neal  J,  et al.  Beneficial effect of subsequent lesion procedures after nonresponse to initial cingulotomy for severe, treatment-refractory obsessive-compulsive disorder. Neurosurgery. 2013;72(2):196-202.
PubMed   |  Link to Article
Rück  C, Karlsson  A, Steele  JD,  et al.  Capsulotomy for obsessive-compulsive disorder. Arch Gen Psychiatry. 2008;65(8):914-921.
PubMed   |  Link to Article
Sheehan  JP, Patterson  G, Schlesinger  D, Xu  Z.  γ Knife surgery anterior capsulotomy for severe and refractory obsessive-compulsive disorder. J Neurosurg. 2013;119(5):1112-1118.
PubMed   |  Link to Article
Lopes  AC, Greenberg  BD, Norén  G,  et al.  Treatment of resistant obsessive-compulsive disorder with ventral capsular/ventral striatal gamma capsulotomy: a pilot prospective study. J Neuropsychiatry Clin Neurosci. 2009;21(4):381-392.
PubMed   |  Link to Article
D’Astous  M, Cottin  S, Roy  M, Picard  C, Cantin  L.  Bilateral stereotactic anterior capsulotomy for obsessive-compulsive disorder. J Neurol Neurosurg Psychiatry. 2013;84(11):1208-1213.
PubMed   |  Link to Article
Gouvea  F, Lopes  A, Greenberg  B,  et al.  Response to sham and active gamma ventral capsulotomy in otherwise intractable obsessive-compulsive disorder. Stereotact Funct Neurosurg. 2010;88(3):177-182.
PubMed   |  Link to Article
Lopes  AC, Greenberg  BD, Canteras  MM,  et al.  Gamma ventral capsulotomy for obsessive-compulsive disorder: a randomized clinical trial. JAMA Psychiatry. 2014;71(9):1066-1076.
PubMed   |  Link to Article
Carter  CS, van Veen  V.  Anterior cingulate cortex and conflict detection. Cogn Affect Behav Neurosci. 2007;7(4):367-379.
PubMed   |  Link to Article
Sheth  SA, Mian  MK, Patel  SR,  et al.  Human dorsal anterior cingulate cortex neurons mediate ongoing behavioural adaptation. Nature. 2012;488(7410):218-221.
PubMed   |  Link to Article
Shenhav  A, Botvinick  MM, Cohen  JD.  The expected value of control: an integrative theory of anterior cingulate cortex function. Neuron. 2013;79(2):217-240.
PubMed   |  Link to Article
Fitzgerald  KD, Welsh  RC, Gehring  WJ,  et al.  Error-related hyperactivity of the anterior cingulate cortex in obsessive-compulsive disorder. Biol Psychiatry. 2005;57(3):287-294.
PubMed   |  Link to Article
Milad  MR, Rauch  SL.  Obsessive-compulsive disorder: beyond segregated cortico-striatal pathways. Trends Cogn Sci. 2012;16(1):43-51.
PubMed   |  Link to Article
Rauch  SL, Dougherty  DD, Cosgrove  GR,  et al.  Cerebral metabolic correlates as potential predictors of response to anterior cingulotomy for obsessive compulsive disorder. Biol Psychiatry. 2001;50(9):659-667.
PubMed   |  Link to Article
Valente  AA  Jr, Miguel  EC, Castro  CC,  et al.  Regional gray matter abnormalities in obsessive-compulsive disorder. Biol Psychiatry. 2005;58(6):479-487.
PubMed   |  Link to Article
Radua  J, van den Heuvel  OA, Surguladze  S, Mataix-Cols  D.  Meta-analytical comparison of voxel-based morphometry studies in obsessive-compulsive disorder vs other anxiety disorders. Arch Gen Psychiatry. 2010;67(7):701-711.
PubMed   |  Link to Article
Cannistraro  PA, Makris  N, Howard  JD,  et al.  A diffusion tensor imaging study of white matter in obsessive-compulsive disorder. Depress Anxiety. 2007;24(6):440-446.
PubMed   |  Link to Article
Chiu  C-H, Lo  Y-C, Tang  H-S,  et al.  White matter abnormalities of fronto-striato-thalamic circuitry in obsessive-compulsive disorder. Psychiatry Res. 2011;192(3):176-182.
PubMed   |  Link to Article
Goodman  WK, Price  LH, Rasmussen  SA,  et al.  The Yale-Brown Obsessive Compulsive Scale, I: development, use, and reliability. Arch Gen Psychiatry. 1989;46(11):1006-1011.
PubMed   |  Link to Article
Douaud  G, Smith  S, Jenkinson  M,  et al.  Anatomically related grey and white matter abnormalities in adolescent-onset schizophrenia. Brain. 2007;130(pt 9):2375-2386.
PubMed   |  Link to Article
Nichols  TE, Holmes  AP.  Nonparametric permutation tests for functional neuroimaging. Hum Brain Mapp. 2002;15(1):1-25.
PubMed   |  Link to Article
Tomlinson  FH, Jack  CR  Jr, Kelly  PJ.  Sequential magnetic resonance imaging following stereotactic radiofrequency ventralis lateralis thalamotomy. J Neurosurg. 1991;74(4):579-584.
PubMed   |  Link to Article
Desikan  RS, Ségonne  F, Fischl  B,  et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31(3):968-980.
PubMed   |  Link to Article
Behrens  TEJ, Johansen-Berg  H, Woolrich  MW,  et al.  Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat Neurosci. 2003;6(7):750-757.
PubMed   |  Link to Article
Behrens  TE, Woolrich  MW, Jenkinson  M,  et al.  Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn Reson Med. 2003;50(5):1077-1088.
PubMed   |  Link to Article
Behrens  TEJ, Berg  HJ, Jbabdi  S, Rushworth  MFS, Woolrich  MW.  Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage. 2007;34(1):144-155.
PubMed   |  Link to Article
Bach  DR, Behrens  TE, Garrido  L, Weiskopf  N, Dolan  RJ.  Deep and superficial amygdala nuclei projections revealed in vivo by probabilistic tractography. J Neurosci. 2011;31(2):618-623.
PubMed   |  Link to Article
Radua  J, Mataix-Cols  D.  Voxel-wise meta-analysis of grey matter changes in obsessive-compulsive disorder. Br J Psychiatry. 2009;195(5):393-402.
PubMed   |  Link to Article
Bourne  SK, Eckhardt  CA, Sheth  SA, Eskandar  EN.  Mechanisms of deep brain stimulation for obsessive compulsive disorder: effects upon cells and circuits. Front Integr Neurosci. 2012;6:29.
PubMed   |  Link to Article
Scarone  S, Colombo  C, Livian  S,  et al.  Increased right caudate nucleus size in obsessive-compulsive disorder. Psychiatry Res. 1992;45(2):115-121.
PubMed   |  Link to Article
Tang  W, Li  B, Huang  X,  et al.  Morphometric brain characterization of refractory obsessive-compulsive disorder. Prog Neuropsychopharmacol Biol Psychiatry. 2013;46:126-131.
PubMed   |  Link to Article
Baxter  LR  Jr, Schwartz  JM, Bergman  KS,  et al.  Caudate glucose metabolic rate changes with both drug and behavior therapy for obsessive-compulsive disorder. Arch Gen Psychiatry. 1992;49(9):681-689.
PubMed   |  Link to Article
Swedo  SE, Pietrini  P, Leonard  HL,  et al.  Cerebral glucose metabolism in childhood-onset obsessive-compulsive disorder. Arch Gen Psychiatry. 1992;49(9):690-694.
PubMed   |  Link to Article
Hansen  ES, Hasselbalch  S, Law  I, Bolwig  TG.  The caudate nucleus in obsessive-compulsive disorder. Int J Neuropsychopharmacol. 2002;5(1):1-10.
PubMed   |  Link to Article
Saxena  S, Brody  AL, Maidment  KM,  et al.  Localized orbitofrontal and subcortical metabolic changes and predictors of response to paroxetine treatment in obsessive-compulsive disorder. Neuropsychopharmacology. 1999;21(6):683-693.
PubMed   |  Link to Article
Ho Pian  KL, van Megen  HJGM, Ramsey  NF,  et al.  Decreased thalamic blood flow in obsessive-compulsive disorder patients responding to fluvoxamine. Psychiatry Res. 2005;138(2):89-97.
PubMed   |  Link to Article
Nakatani  E, Nakgawa  A, Ohara  Y,  et al.  Effects of behavior therapy on regional cerebral blood flow in obsessive-compulsive disorder. Psychiatry Res. 2003;124(2):113-120.
PubMed   |  Link to Article
Yamanishi  T, Nakaaki  S, Omori  IM,  et al.  Changes after behavior therapy among responsive and nonresponsive patients with obsessive-compulsive disorder. Psychiatry Res. 2009;172(3):242-250.
PubMed   |  Link to Article
Benkelfat  C, Nordahl  TE, Semple  WE, King  AC, Murphy  DL, Cohen  RM.  Local cerebral glucose metabolic rates in obsessive-compulsive disorder: patients treated with clomipramine. Arch Gen Psychiatry. 1990;47(9):840-848.
PubMed   |  Link to Article
Perani  D, Colombo  C, Bressi  S,  et al.  [18F]FDG PET study in obsessive-compulsive disorder. Br J Psychiatry. 1995;166(2):244-250.
PubMed   |  Link to Article
Blomstedt  P, Sjöberg  RL, Hansson  M, Bodlund  O, Hariz  MI.  Deep brain stimulation in the treatment of obsessive-compulsive disorder. World Neurosurg. 2013;80(6):e245-e253. doi:10.1016/j.wneu.2012.10.006.
PubMed   |  Link to Article
Sturm  V, Lenartz  D, Koulousakis  A,  et al.  The nucleus accumbens: a target for deep brain stimulation in obsessive-compulsive- and anxiety-disorders. J Chem Neuroanat. 2003;26(4):293-299.
PubMed   |  Link to Article
Greenberg  BD, Gabriels  LA, Malone  DA  Jr,  et al.  Deep brain stimulation of the ventral internal capsule/ventral striatum for obsessive-compulsive disorder: worldwide experience. Mol Psychiatry. 2010;15(1):64-79.
PubMed   |  Link to Article
Lippitz  BE, Mindus  P, Meyerson  BA, Kihlström  L, Lindquist  C.  Lesion topography and outcome after thermocapsulotomy or gamma knife capsulotomy for obsessive-compulsive disorder. Neurosurgery. 1999;44(3):452-458.
PubMed   |  Link to Article
Kondziolka  D, Flickinger  JC, Hudak  R.  Results following gamma knife radiosurgical anterior capsulotomies for obsessive compulsive disorder. Neurosurgery. 2011;68(1):28-32.
PubMed   |  Link to Article
Rück  C, Svanborg  P, Meyerson  BA.  Lesion topography in capsulotomy for refractory anxiety—is the right side the right side? Stereotact Funct Neurosurg. 2005;83(4):172-179.
PubMed   |  Link to Article
Figee  M, Luigjes  J, Smolders  R,  et al.  Deep brain stimulation restores frontostriatal network activity in obsessive-compulsive disorder. Nat Neurosci. 2013;16(4):386-387.
PubMed   |  Link to Article
Vogt  BA, Nimchinsky  EA, Vogt  LJ, Hof  PR.  Human cingulate cortex: surface features, flat maps, and cytoarchitecture. J Comp Neurol. 1995;359(3):490-506.
PubMed   |  Link to Article
Fornito  A, Whittle  S, Wood  SJ, Velakoulis  D, Pantelis  C, Yücel  M.  The influence of sulcal variability on morphometry of the human anterior cingulate and paracingulate cortex. Neuroimage. 2006;33(3):843-854.
PubMed   |  Link to Article
Yücel  M, Stuart  GW, Maruff  P,  et al.  Hemispheric and gender-related differences in the gross morphology of the anterior cingulate/paracingulate cortex in normal volunteers. Cereb Cortex. 2001;11(1):17-25.
PubMed   |  Link to Article
Yang  JC, Ginat  DT, Dougherty  DD, Makris  N, Eskandar  EN.  Lesion analysis for cingulotomy and limbic leucotomy. J Neurosurg. 2014;120(1):152-163.
PubMed   |  Link to Article

Correspondence

CME


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Multimedia

Supplement.

eAppendix. Surgical Technique and Positive and Negative Predictive Value Analysis

eTable 1. Imaging Parameters for Study Patients

eTable 2. Comparison of Non-crossing and Interhemispheric Crossing Analyses

eTable 3. VBM Leave-One-Out Analyses

eTable 4. Leave-One-Out Rank Sum Analyses

eTable 5. Leave-One-Out Spearman Calculations

eFigure 1. VBM Analysis With Modified GLM

eFigure 2. Relationship of 6 and 25 Gradient Directions in Laterality to Y-BOCS Change Relationship for the Specific Lesion Study

eFigure 3. Relationship of 6 and 25 Gradient Directions in Laterality to Y-BOCS Change Relationship for the Standard Lesion Area Study

eFigure 4. T2 Lesion Segmentations

eFigure 5. Standard Lesion Area

eFigure 6. Tractography Targets

eFigure 7. Probabilistic Tractography Average Patient Tract Maps

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