Neuroanatomical Correlates of Impulsive Action in Excoriation (Skin-Picking) Disorder
Publication: The Journal of Neuropsychiatry and Clinical Neurosciences
Abstract
Excoriation (skin-picking) disorder (SPD) has similarities to obsessive-compulsive disorder (OCD) and is included within the obsessive-compulsive and related disorders (OCRD) diagnostic class in DSM-5. Separate neuroimaging and neurocognitive studies suggest that people affected by SPD find it difficult to inhibit dominant motor responses due to a failure of “top-down” control mechanisms. No study has examined the neural correlates of SPD in participants with varying degrees of impulsive motor behavior. This study correlated cortical thickness and volumes of selected subcortical structures with stop-signal task performance in participants with SPD (N=15) and in healthy control subjects (N=8). All participants were free from current psychiatric comorbidity, including OCD. In volunteers with SPD, longer stop-signal reaction times were correlated with cortical thinning in the right insula and right-inferior parietal lobe and with increased cortical thickness in the left-lateral occipital lobe, though these findings did not withstand correction for multiple comparisons. There were no significant correlations between cortical thickness in these three structures and stop-signal reaction times in the control group. This study suggests that structural abnormalities in the insular cortex and parietal and occipital regions may play a role in the pathophysiology of SPD. Further neuroimaging research is needed to understand the neurobiology of SPD and its relationship with other putative OCRDs.
Between 1.4% and 5.4% of people meet DSM-5 criteria for excoriation (skin-picking) disorder (SPD), a behavior characterized by repetitive and compulsive picking of skin, leading to tissue damage.1,2 Within the class of obsessive-compulsive and related disorders (OCRDs), SPD is also conceptualized as a body-focused repetitive behavior alongside trichotillomania, or hair-pulling disorder, suggesting a possible shared pathophysiology between these conditions.3 Even so, important differences between SPD and trichotillomania, including unique neurocognitive profiles, indicate that skin picking and hair pulling are not phenotypic manifestations of a single grooming disorder.4 One way to better understand SPD and its relationship with other conditions is by probing the association between cognitive findings and neural circuitry involved in the disorder.
Separate neurocognitive and imaging studies implicate frontostriatal circuit dysfunction in SPD. From a cognitive perspective, SPD participants show greater motor impulsivity (as measured by stop-signal tasks) than do both people with trichotillomania and healthy controls.4,5 Neuroimaging studies, likewise, implicate motor control circuits in the pathophysiology of SPD. In a diffusion tensor imaging study, SPD was associated with white matter abnormalities close to the bilateral anterior cingulate cortex,6 which is activated during unsuccessful response inhibition.7 Also, a functional neuroimaging study comparing participants with SPD and healthy controls detected significant hypoactivation of the anterior cingulate cortices as well as of the dorsal striatum and right-medial frontal regions—structures also involved in generation and suppression of movement.8 Finally, a structural study found that SPD volunteers (in comparison with trichotillomania participants and controls) showed altered brain volume and cortical thickness in frontal areas implicated in impulse control, including the inferior frontal gyrus, orbitofrontal cortex, and nucleus accumbens.9 The parahippocampal gyrus was additionally implicated in participants with trichotillomania but not in those with SPD.9 Thus, defects in frontostriatal pathways that exert top-down inhibitory control may contribute to the repetitive picking behavior that characterizes SPD. It is unknown, however, whether variations in these neuroanatomical substrates correlate with deficits in cognitive flexibility, response inhibition, or both.
Because only a few studies have explored the neurobiology of SPD, this study evaluated the association between neurocognitive function and structural abnormalities in the neural structures that appear to be implicated in SPD. On the basis of previous research, we hypothesized that impaired inhibitory control (i.e., worse performance on a stop-signal task) in SPD would be associated with cortical thinning in frontal cortical regions and increased volumes in basal ganglia structures, consistent with a neurobiological model implicating lack of top-down control and excessive subcortical drive in the manifestation of this habit disorder.10
Methods
Participants
Men and women ages 18–65 years with a primary current diagnosis of SPD, based on DSM-5 criteria, were recruited through media advertisements and referrals. A board-certified psychiatrist (J.E.G.), with expertise in SPD and other body-focused repetitive behaviors, conducted a structured clinical interview with each participant to confirm the diagnosis.
Inclusion criteria were as follows: 1) current DSM-5 diagnosis of SPD; 2) a minimum score of >16 on the Yale–Brown Obsessive Compulsive Scale Modified for Neurotic Excoriation (NE-YBOCS); and 3) picking behavior occurring daily for at least 30 minutes consistently over the past 12 months.
Exclusion criteria among those with SPD included the following: 1) unstable medical illness; 2) history of seizures; 3) current pregnancy or inadequate contraception use among women of child-bearing potential; 4) current suicide risk; 5) lifetime history of bipolar disorder, dementia, or any psychotic disorder; 6) illicit substance use within 2 weeks of the study initiation; 7) history of head injury or neurologic disorders; 8) any contraindications to MRI based on safety screening and clinical history; and 9) inability to understand or undertake the procedures or inability to provide written, informed consent.
Age- and gender-matched healthy controls were recruited by word of mouth and through media advertisements. All control group participants had no current or lifetime psychiatric disorder, according to the Structured Clinical Interview for DSM-IV (Patient edition; SCID-I/P),11 the Minnesota Impulsive Disorders Interview,12,13 and DSM-5 criteria for SPD, and no use of psychotropic medications.
All participants were recruited and underwent neuroimaging procedures at either the University of Chicago or University of Minnesota under the same principal investigator. Recruitment criteria and procedures were identical between sites. Participants were recruited from November 2010 through May 2012 at the University of Minnesota and from November 2014 through February 2015 at the University of Chicago.
The institutional review boards of the University of Chicago and University of Minnesota approved the study and consent procedures, which followed the Declaration of Helsinki’s ethical principles for medical research involving human participants. After a complete description of study procedures and an opportunity to ask questions, participants provided written informed consent and received financial compensation.
Procedures
Clinical assessments.
All participants underwent an initial clinical interview with a board-certified psychiatrist, which included the following assessments:
NE-YBOCS.
Severity of skin-picking disorder symptoms during the preceding seven days was assessed with the 10-item, clinician-administered NE-YBOCS14,15 adapted for DSM-5. The NE-YBOCS is a modification of the Yale-Brown Obsessive Compulsive Scale (Y-BOCS), a reliable and valid scale for obsessive-compulsive disorder (OCD).16 Responses to the 10 items were coded on a four-point scale and summed to produce a composite score ranging from 0 to 40, with higher scores reflecting greater illness severity.
Skin Picking Symptom Assessment Scale (SP-SAS).
The SP-SAS is a 12-item, self-report measure evaluating picking urges, thoughts, and behaviors during the previous seven days. Each item is rated 0–4, with a maximum total score of 48. Higher scores reflect greater severity of skin-picking symptoms. The SP-SAS has demonstrated good preliminary reliability and validity.17
Hamilton Anxiety Rating Scale (HAM-A).18
The HAM-A is a valid and reliable, 14-item, clinician-administered scale measuring global anxiety. Responses were coded on a 5-point scale of 0 (not present) to 4 (very severe), on which higher scores indicate higher levels of anxiety.
Hamilton Depression Rating Scale (HAM-D).19
The HAM-D is a valid and reliable, 17-item, clinician-administered scale assessing depressive symptoms; higher scores indicate greater depressiveness.
Quality of Life Inventory (QOLI).20
The QOLI is a valid and reliable, self-administered measure of life satisfaction across 16 domains associated with human happiness and contentment, including health, self-esteem, money, work, love, friendships, and community.
Cognitive assessments.
Neurocognitive testing was performed with a touch-screen computer using paradigms adopted from the Cambridge Neuropsychological Test Automated Battery (CANTABeclipse, version 3; Cambridge Cognition Ltd. [www.cambridgecognition.com]), which is well validated among diverse clinical populations. The cognitive domains of interest were cognitive flexibility (Intra-Extra Dimensional Set Shift task) and response inhibition (Stop Signal Task). We selected these domains because they have been previously implicated in skin-picking disorder.4,5
Intra-Extra Dimensional Set Shift task (IED).
The IED was derived from the Wisconsin Card Sorting Test and measures aspects of behavioral flexibility, specifically participants’ adaptability to rule changes. The primary outcome measure was total errors made across the whole task, adjusted for any stages not attempted owing to failing an earlier stage. Neuroimaging findings indicate that ED shifting depends on lateral prefrontal cortex activity.21
Stop Signal Task (SST).
The SST measures control over prepotent (i.e., habitual or dominant) motor behavior. Participants observed a series of left- or right-pointing arrows appearing on a computer screen and were instructed to rapidly press a button corresponding to the direction of each arrow. On a subset of trials (“stop” trials), an auditory tone followed presentation of the arrow, indicating that participants should attempt to suppress their motor response for the given trial and wait for the next arrow to be displayed. The primary outcome measure was the stop-signal reaction time (SSRT), defined as the average time taken to suppress a prepotent motor response on 50% of trials. Longer SSRTs indicate poorer response inhibition (i.e., greater motor impulsivity). According to neuroimaging data, successful response inhibition is accomplished by a right-lateralized neural network, including the inferior frontal gyrus and its associated white matter tracts.22
Brain imaging.
All participants underwent structural brain MRI at the University of Chicago or University of Minnesota using a 3-Tesla Philips Achieva Quasar Dual 16-channel system. A three-dimensional, T1-weighted magnetization-prepared 180° radio-frequency pulse and rapid gradient-echo scan was obtained with the following imaging parameters: slab orientation=sagittal, field of view=256×224×176, voxel size=1 mm3, inversion delay time TI=900 ms, TR/TE=8.9/3.7 ms, and flip angle=8°. All controls and 17 SPD participants underwent neuroimaging procedures at the University of Minnesota; three SPD participants did so at the University of Chicago.
Data analysis.
We compared demographic, clinical, and neurocognitive measures between groups using independent-sample t tests and likelihood chi-square tests, as appropriate. Effect sizes were reported for significant group differences (Cohen’s d). All data were analyzed using SPSS (version 22; IBM, Armonk, N.Y.). For group comparisons of baseline variables, statistical significance was defined as p<0.01 to account for multiple comparisons.
MRI scans were processed on the University of Chicago Midway computer cluster using previously validated methods implemented by FreeSurfer software (version 5.3 [http://surfer.nmr.mgh.harvard.edu]).23,24 In short, the FreeSurfer workflow consists of an initial cortical surface stream, followed by a volume-based (subcortical) stream. In the surface-based stream, FreeSurfer transforms data sets from individual participants to standard Talairach space,25 normalizes image intensity,26 extracts the skull and other outer nonbrain tissue from the pial surface,27 and segments the subcortical white matter and deep gray matter volumetric structures.28 Subsequently, FreeSurfer reconstructs the pial and white matter surfaces to produce representations of cortical thickness, calculated as the distance between each vertex and its corresponding vertex on the opposite surface.29 The volume-based stream assigns a neuroanatomical label to each subcortical voxel.30,31
We compared cortical thickness between the SPD and control groups using FreeSurfer’s Qdec interface, across the whole cortical surface, at voxel-wise p<0.001, uncorrected. Regions of significant correlation between cortical thickness and cognitive performance were identified across the whole surface, in each group, using the Qdec interface (voxel-wise p<0.001, uncorrected). The statistical threshold of p<0.001 was selected in light of the relatively small sample sizes in the study.
Additionally, volumes of a priori selected subcortical structures (putamen, caudate, nucleus accumbens, and hippocampus) were extracted using FreeSurfer’s automated parcellation algorithm. These regions of interest were selected because they have been implicated in previous neuroimaging work of SPD or trichotillomania, a closely related disorder. Potential group differences in the volumes of these subcortical structures were explored by exporting subject-level data from Freesurfer into SPSS (version 22.0) and using independent-sample t tests (p<0.05, uncorrected, two tailed). Correlational analysis (Spearman rs) was used to identify any relationships between structural abnormalities identified and cognitive performance, again in each group (p<0.05, uncorrected, two tailed).
Results
Participant Characteristics
The study sample included participants with SPD (N=20) and healthy controls (N=16), for whom cognitive data were available for 15 and eight, respectively. The groups were well matched in terms of demographic variables (Table 1). Participants with SPD had significantly greater HAM-D (p<0.001) and HAM-A (p<0.001) scores than did controls, though these scores were well beneath threshold for clinically significant mood or anxiety disorders (Table 1). The SPD group also reported significantly lower quality of life, as measured by QOLI scores (p=0.002). The mean NE-YBOCS score was 21.2 (SD=5.1), consistent with moderate overall disorder severity. None of the volunteers in the SPD sample had a current psychiatric disorder other than SPD, as was diagnosed by the SCID-I/P.
| Variable | SPD Case Subjects (N=20) | Control Subjects (N=16) | Statistical Comparison | Effect Size | ||||
|---|---|---|---|---|---|---|---|---|
| Mean | SD | Mean | SD | t | df | p | ||
| Age (years) | 29.5 | 9.4 | 34.0 | 15.6 | 1.02 | 21.9b | 0.32 | – |
| N | % | N | % | χ2 | df | p | ||
| Gender, female | 20 | 100.0 | 12 | 75.0 | 3.38 | 1 | 0.07 | – |
| Race/ethnicity, Caucasian | 19 | 95.0 | 13 | 81.3 | 2.71 | 2 | 0.26 | – |
| Marital status, single | 14 | 73.7 | 11 | 68.8 | 3.38 | 3 | 0.34 | – |
| Education, college graduate or higher | 10 | 55.6 | 12 | 75.0 | 2.26 | 3 | 0.52 | – |
| Mean | SD | Mean | SD | |||||
| NE-YBOCS total score | 21.2 | 5.1 | N/A | N/A | N/A | N/A | N/A | N/A |
| SP-SAS total score | 30.8 | 5.1 | N/A | N/A | N/A | N/A | N/A | N/A |
| Mean | SD | Mean | SD | t | df | p | ||
| HAM-A total score | 4.8 | 3.1 | 0.2 | 0.6 | 6.33 | 19.67b | <0.001* | 2.06 |
| HAM-D total score | 4.8 | 2.8 | 0.5 | 1.3 | 6.03 | 26.53b | <0.001* | 1.97 |
| Quality of Life Inventory T-score | 33.4 | 16.1 | 50.4 | 10.5 | 3.44 | 32 | 0.002* | 1.25 |
| IED total errors (adjusted) | 12.1c | 5.9 | 16.3d | 19.7 | 0.58 | 7.67b | 0.58 | – |
| SST stop-signal reaction time (ms) | 166.4c | 66.4 | 167.9d | 17.1 | 0.06 | 21 | 0.95 | – |
a
Abbreviations: HAM-A=Hamilton Anxiety Rating Scale; HAM-D=Hamilton Depression Rating Scale; IED=Intra-Extra Dimensional Set Shift task; NE-YBOCS=Yale-Brown Obsessive Compulsive Scale Modified for Neurotic Excoriation; SP-SAS=Skin Picking Symptom Assessment Scale; SST=Stop Signal Task. Bold indicates significant values.
b
Equal variance not assumed.
c
Cognitive data were missing for five case subjects.
d
Cognitive data were missing for eight control subjects.
*
p<0.01.
Two SPD participants were currently taking psychotropic medications, both of whom were taking stable doses for over a year. One participant was taking bupropion (450 mg/day), venlafaxine (225 mg/day), and trazodone (100 mg p.r.n.), and the other was taking lamotrigine (300 mg/day), venlafaxine (375 mg/day), and trazodone (100 mg p.r.n.). A third participant was taking a stable dose of N-acetylcysteine (1,200 mg b.i.d.) for more than one year.
In terms of neurocognitive outcomes, SPD participants (N=15) did not differ significantly from controls (N=8) in terms of performance on the IED (assessing cognitive flexibility) or the SST (assessing response impulsivity) (Table 1). The group differences on IED and SST remained statistically nonsignificant even when HAM-D, HAM-A, and QOLI were controlled for using analysis of covariance models (IED: F=0.395, df=1, 23, p=0.538; SST: F=0.842, df=1, 23, p=0.371).
Imaging Results
There were no significant differences in cortical thickness between SPD and control groups at p<0.001, uncorrected, or in striatal volumes of interest at p<0.01, uncorrected.
In the SPD participants, two significant clusters were identified in which cortical thickness correlated negatively with SSRTs: the first (F=–3.301, df=1, 14, p<0.001, uncorrected) was maximal at right insula (Talairach coordinates [96.0, 36.9, 6.1]; number of voxels, 224); and the second (F=–3.034, df=1, 14, p<0.001 uncorrected) was maximal at the right-inferior parietal lobe ([89.0, 40.0, −63.5]; number of voxels, 174). One significant cluster was identified in which cortical thickness correlated positively with SSRTs in SPD cases (F=3.176, df=1, 14, p<0.001 uncorrected), maximal at the left-lateral occipital cortex ([–14.2, –96.0, 17.9]; number of voxels, 135). These findings, however, did not withstand correction for multiple comparisons using a false discovery rate–adjusted p<0.05.
No significant cortical clusters were identified that correlated significantly with SSRTs in the control group, and no such significant clusters were found in either group relating to IED performance. There also were no significant correlations between cognitive task scores and structural volumes in other a priori regions of interest, namely, selected basal ganglia structures and the hippocampus.
Discussion
This is the first study to examine the relationship between neuroimaging abnormalities and cognitive dysfunction in SPD. We identified several significant relationships between response inhibition and cortical thickness in the SPD group but not in controls. The key findings were that longer SSRTs in the SPD group were associated with reduced thickness in the right insula and right-inferior parietal lobe and with greater thickness in the left-lateral occipital lobe. Contrary to our hypotheses, SPD participants did not differ significantly from controls in terms of cortical thickness or striatal volumes, nor did we find any significant correlations between basal ganglia volumes and SSRT in patients. These findings may have implications for understanding the neurobiological basis of SPD.
It may be useful to compare our neuroanatomical findings with those previously reported in OCD, given that SPD is currently conceptualized as an OCRD in DSM-5. In this study, we identified an association between excess cortical thickness in the left-lateral occipital lobe and inhibitory control impairment. These findings are consistent with a previous imaging study finding increased cortical thickness in the left-lateral occipital region in volunteers with OCD.32 Results from a large multisite study found reduced (not increased) cortical thickness in the same region among participants with more severe OCD symptoms (as measured by the Y-BOCS) but increased thickness in a secondary analysis of patients with OCD not taking psychotropic medications.33 These mixed findings may reflect clinical heterogeneity within OCRDs and suggest that nonfrontostriatal circuitry may also play a role in the pathophysiology of these disorders.
Our study also found a negative correlation between diminished inhibitory control (i.e., longer SSRTs) and cortical thickness in the right insula and right-inferior parietal lobe. Both structures are also associated with decreased cortical thickness in OCD, consistent with our results.33 Although our study design does not permit causal interpretations, it is plausible that abnormalities in these neural regions may contribute to the repetitive behaviors that characterize SPD. The right insula is consistently implicated in inhibitory control and is required to detect and process behaviorally salient stimuli (i.e., “stop” signals).34 In the setting of SPD, a decrease in right insula cortical thickness may disrupt the ability to reduce or stop the maladaptive behavior. Likewise, the inferior parietal cortex is responsible for linking tactile perception and fine finger movements.35 As such, cortical thinning in the inferior parietal cortex may alter the neural pathways that link tactile exploration and appropriate motor responses, resulting in skin picking at bumpy or uneven sites.
This study has several positive features, namely, that it is one of only a few neuroimaging studies in SPD and included participants who were free from psychiatric comorbidities, including OCD. However, several limitations should be considered. First, MRI results did not survive multiple testing correction using a false discovery rate–adjusted p value of 0.05. These results are not unexpected in light of our relatively small sample and availability of cognitive data for only a subset of participants in each group. The lack of significant group differences on imaging and cognitive measures may reflect limited statistical power. Additionally, given preliminary findings from genetic studies supporting a prominent role for individual-level plasticity in the development of the disorder,36 future imaging studies of SPD should consider multisite collaborations to increase sample size and generalizability of results. Second, although SPD participants were free from anxiety and depressive disorders, and scored well beneath threshold for clinically significant mood and anxiety symptoms, their scores on the HAM-A and HAM-D were statistically higher than were those of controls. Third, our neuroimaging findings are correlative and not causal. Longitudinal studies are needed to interpret relationships between neuroimaging-cognitive correlations and skin-picking behavior. Finally, some SPD participants were medicated, introducing sample heterogeneity that may have influenced the results. Our study, however, was not powered to detect the effect of medication treatment (if any) on brain structure in SPD.
Conclusions
Our findings suggest that greater motor impulsivity in SPD is associated with structural variation in the insular cortex and parietal and occipital regions. These findings are consistent with previous studies of OCD and suggest that cortical thickness in these regions may play a role in the pathophysiology of SPD. A better understanding of the neuropsychiatry of SPD may have utility for treatment interventions.
Footnote
H. Lundbeck A/S had no part in any of the studies reviewed in this article and did not contribute to this article in any form, including the decision to submit this article for publication.
References
1.
Hayes SL, Storch EA, Berlanga L: Skin picking behaviors: an examination of the prevalence and severity in a community sample. J Anxiety Disord 2009; 23:314–319
2.
Keuthen NJ, Koran LM, Aboujaoude E, et al: The prevalence of pathologic skin picking in US adults. Compr Psychiatry 2010; 51:183–186
3.
Grant JE, Stein DJ: Body-focused repetitive behavior disorders in ICD-11. Rev Bras Psiquiatr 2014; 36(Suppl 1):59–64
4.
Grant JE, Odlaug BL, Chamberlain SR: A cognitive comparison of pathological skin picking and trichotillomania. J Psychiatr Res 2011; 45:1634–1638
5.
Odlaug BL, Chamberlain SR, Grant JE: Motor inhibition and cognitive flexibility in pathologic skin picking. Prog Neuropsychopharmacol Biol Psychiatry 2010; 34:208–211
6.
Grant JE, Odlaug BL, Hampshire A, et al: White matter abnormalities in skin picking disorder: a diffusion tensor imaging study. Neuropsychopharmacology 2013; 38:763–769
7.
Sharp DJ, Bonnelle V, De Boissezon X, et al: Distinct frontal systems for response inhibition, attentional capture, and error processing. Proc Natl Acad Sci USA 2010; 107:6106–6111
8.
Odlaug BL, Hampshire A, Chamberlain SR, et al.: Abnormal brain activation in excoriation (skin-picking) disorder: evidence from an executive planning fMRI study. Br J Psychiatry 2016; 208:168–174
9.
Roos A, Grant JE, Fouche J-P, et al: A comparison of brain volume and cortical thickness in excoriation (skin picking) disorder and trichotillomania (hair pulling disorder) in women. Behav Brain Res 2015; 279:255–258
10.
Grant JE, Odlaug BL, Chamberlain SR, et al: Skin picking disorder. Am J Psychiatry 2012; 169:1143–1149
11.
First MB, Spitzer RL, Gibbon M, et al: Structured Clinical Interview for DSM-IV-Patient Edition (SCID-I/P; Version 2.0). New York, New York State Psychiatric Institute, Biometrics Research Department, 2000
12.
Odlaug BL, Grant JE: Impulse-control disorders in a college sample: results from the self-administered Minnesota Impulse Disorders Interview (MIDI). Prim Care Companion J Clin Psychiatry, 2010; 12(2)
13.
Grant JE: Impulse Control Disorders: A Clinician’s Guide to Understanding and Treating Behavioral Addictions. New York, W W Norton and Company, 2008
14.
Arnold LM, Mutasim DF, Dwight MM, et al: An open clinical trial of fluvoxamine treatment of psychogenic excoriation. J Clin Psychopharmacol 1999; 19:15–18
15.
Grant JE, Odlaug BL, Chamberlain SR, et al: A double-blind, placebo-controlled trial of lamotrigine for pathological skin picking: treatment efficacy and neurocognitive predictors of response. J Clin Psychopharmacol 2010; 30:396–403
16.
Goodman WK, Price LH, Rasmussen SA, et al: The Yale-Brown Obsessive Compulsive Scale. I. Development, use, and reliability. Arch Gen Psychiatry 1989; 46:1006–1011
17.
Grant JE, Odlaug BL, Kim SW: Lamotrigine treatment of pathologic skin picking: an open-label study. J Clin Psychiatry 2007; 68:1384–1391
18.
Hamilton M: The assessment of anxiety states by rating. Br J Med Psychol 1959; 32:50–55
19.
Hamilton M: A rating scale for depression. J Neurol Neurosurg Psychiatry 1960; 23:56–62
20.
Frisch MB, Cornell J, Villanueva M, et al: Clinical validation of the Quality of Life Inventory: a measure of life satisfaction for use in treatment planning and outcome assessment. Psychol Assess 1992; 4:92–101
21.
Hampshire A, Owen AM: Fractionating attentional control using event-related fMRI. Cereb Cortex 2006; 16:1679–1689
22.
Aron AR, Robbins TW, Poldrack RA: Inhibition and the right inferior frontal cortex: one decade on. Trends Cogn Sci 2014; 18:177–185
23.
Dale AM, Fischl B, Sereno MI: Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 1999; 9:179–194
24.
Reuter M, Schmansky NJ, Rosas HD, et al: Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 2012; 61:1402–1418
25.
Talairach J, Tournoux P: Co-Planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System: An Approach to Cerebral Imaging. New York, Thieme Medical, 1988
26.
Sled JG, Zijdenbos AP, Evans AC: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 1998; 17:87–97
27.
Ségonne F, Dale AM, Busa E, et al: A hybrid approach to the skull stripping problem in MRI. Neuroimage 2004; 22:1060–1075
28.
Fischl B, Salat DH, van der Kouwe AJW, et al: Sequence-independent segmentation of magnetic resonance images. Neuroimage 2004; 23(suppl 1):S69–S84
29.
Fischl B, Dale AM: Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci USA 2000; 97:11050–11055
30.
Fischl B, Salat DH, Busa E, et al: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 2002; 33:341–355
31.
Fischl B, van der Kouwe A, Destrieux C, et al: Automatically parcellating the human cerebral cortex. Cereb Cortex 2004; 14:11–22
32.
Fan Q, Palaniyappan L, Tan L, et al: Surface anatomical profile of the cerebral cortex in obsessive-compulsive disorder: a study of cortical thickness, folding and surface area. Psychol Med 2013; 43:1081–1091
33.
Fouche J-P, du Plessis S, Hattingh C, et al: Cortical thickness in obsessive-compulsive disorder: multisite mega-analysis of 780 brain scans from six centres. Br J Psychiatry 2017; 210:67–74
34.
Cai W, Ryali S, Chen T, et al: Dissociable roles of right inferior frontal cortex and anterior insula in inhibitory control: evidence from intrinsic and task-related functional parcellation, connectivity, and response profile analyses across multiple datasets. J Neurosci 2014; 34:14652–14667
35.
Jäncke L, Kleinschmidt A, Mirzazade S, et al: The role of the inferior parietal cortex in linking the tactile perception and manual construction of object shapes. Cereb Cortex 2001; 11:114–121
36.
Monzani B, Rijsdijk F, Cherkas L, et al: Prevalence and heritability of skin picking in an adult community sample: a twin study. Am J Med Genet B Neuropsychiatr Genet 2012; 159B:605–610
Information & Authors
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Published In
History
Received: 7 May 2017
Revision received: 20 September 2017
Accepted: 25 September 2017
Published online: 24 April 2018
Published in print: Summer 2018
Keywords
Authors
Funding Information
Wellcome Trust10.13039/100004440: 110049/Z/15/Z
Supported by a Wellcome Trust Clinical Fellowship (reference 110049/Z/15/Z) to Dr. Chamberlain and in part by the University of Chicago Research Computing Center.Dr. Grant has received research grant support from the American Foundation for Suicide Prevention, Takeda Pharmaceuticals, and the TLC Foundation for Body-Focused Repetitive Behaviors; he receives yearly compensation from Springer Publishing for acting as Editor-in-Chief of the Journal of Gambling Studies; and he has received royalties from American Psychiatric Publishing, McGraw Hill, Norton Press, Johns Hopkins University Press, and Oxford University Press. Dr. Chamberlain serves as a consultant for Cambridge Cognition and Shire. Dr. Odlaug has received research funding from the TLC Foundation for Body-Focused Repetitive Behaviors; he receives royalties from Oxford University Press; and he has consulted for and is currently employed by H. Lundbeck A/S. All other authors report no financial relationships with commercial interests.Metrics & Citations
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