Neural correlate of impulsivity in subjects at ultra-high risk for psychosis

Neural correlate of impulsivity in subjects at Tae Young Lee a, Sung Nyun Kim b,d,⁎, Joon Hwan Jang rea ubli Kor Received in revised form 9 April 2013 Accepted 9 April 2013 Available online 16 April 2013 Keywords: Anterior cingulate Impulsivity e is accumulating evi- ndividuals, as yet, no Progress in Neuro-Psychopharmacology & Biological Psychiatry 45 (2013) 165–169 Contents lists available at SciVerse ScienceDirect Progress in Neuro-Psychop Psychi j ourna l homepage: www.e study has specifically investigated impulsivity in these subjects. Higher impulsivity is seen in patients with attention-deficit hyper- activity disorder (ADHD), borderline personality disorder (BPD), and healthy controls; SCID-I, Structured Clinical Interview for DSM-IV Axis I; SIPS, Struc- tured Interview of Prodromal Symptoms; APS, attenuated positive symptoms state; BIPS, brief intermittent psychotic symptoms state; GRD, genetic risk with deterioration state; SCID-NP, Structured Clinical Interview for DSM-IV Axis I Non-Patient version; and aggression (Lee et al., 2008). Although ther dence regarding behavioral problems in UHR i deficit hyperactivity disorder; BPD, borderline personality disorder; ACC, anterior cin- gulate cortex; DLPFC, dorsolateral prefrontal cortex; OFC, orbitofrontal cortex; HC, 1. Introduction Impulsivity is one of themost commonly reported behavioral char- acteristics of patients with schizophrenia (Gut-Fayand et al., 2001; Hoptman et al., 2002; Ouzir, 2013). Because impulsivity is associated with serious behavioral problems including substance abuse, violence, and suicide attempts, early intervention before psychosis develops is critical for relieving these adverse manifestations and improving pa- tient prognosis (Kooyman et al., 2007). The term “ultra-high risk for psychosis” (UHR) refers to those who have sub-threshold psychotic symptoms that can be regarded as a risk factor for developing schizo- phrenia, and accordingly, this population has become the focus of early intervention strategies (Yung and McGorry, 1996). Previous re- search has found a high prevalence of substance use, risk factors for violence, and suicide risk in UHR subjects (Dragt et al., 2010; Hutton et al., 2011) aswell as impaired ability to control emotional expressionAbbreviations: UHR, subjects at ultra-high risk for psychosis; ADHD, attention- BIS-11, Barratt impulsiveness scale, version-11; K-W Wechsler Adult Intelligence Scale; VBM, voxel-based m interest. ⁎ Corresponding author at: Department of Psychiatry, S of Medicine, 101 Daehak-no, Chongno-gu, Seoul 110-744 2072 2972; fax: +82 2 747 9063. E-mail address: [email protected] (S.N. 0278-5846/$ – see front matter © 2013 Elsevier Inc. All http://dx.doi.org/10.1016/j.pnpbp.2013.04.008 Ultra-high risk for psychosis Voxel-based morphometry (DLPFC), and orbitofrontal cortex (OFC) were assessed. Then, a correlational analysis between the BIS-11 scores and significant clusters of gray matter volume was conducted in UHR subjects. Results: UHR subjects were more impulsive than HC subjects in terms of attention (t = 3.5187, p b 0.01), motor (t = 3.1751, p b 0.01), and non-planning (t = 4.4154, p b 0.01) scores. The gray matter volume of the ACC was negatively correlated with the motor (r = −0.472, p b 0.01) and non-planning (r = −0.354, p = 0.04) scores of the BIS-11 in UHR subjects. Conclusion: These results suggest that impulsivity in UHR subjects may reflect altered integrated conflict pro- cessing, which likely stems from abnormalities in the ACC, rather than altered reward/punishment process- ing or executive control. © 2013 Elsevier Inc. All rights reserved. schizophrenia. Although there is accumulating evidence regarding behavioral problems in individuals at ultra-high risk (UHR) for psychosis, as yet, no study has reported on impulsivity in this population. The aim of the present study was to assess impulsivity in UHR subjects and to investigate the associated gray matter correlates. Method: This study included 32 UHR subjects and 32 age- and gender-matched healthy controls (HCs). The Barratt Impulsiveness Scale version-11 (BIS-11) was employed to assess impulsivity. Differences between the groups in gray matter volume in the anterior cingulate cortex (ACC), dorsolateral prefrontal cortex Article history: Received 5 February 2013 Objective: Impulsivity is one of the most commonly reported behavioral characteristics of patients with a b s t r a c ta r t i c l e i n f o Na Young Shin e, Jun Soo Kwon a,b,d,e a Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Ko b Department of Neuropsychiatry, Seoul National University College of Medicine, Seoul, Rep c Institute of Forensic Psychiatry Ministry of Justice, Gongju, Republic of Korea d Institute of Human Behavioral Medicine, SNU-MRC, Seoul, Republic of Korea e Interdisciplinary Cognitive Science Program, Seoul National University, Seoul, Republic of AIS, Korean version of the orphometry; ROI, regions of eoul National University College , Republic of Korea. Tel.: +82 2 Kim). rights reserved. ultra-high risk for psychosis b, Geumsook Shim c, Wi Hoon Jung d, c of Korea ea harmacology & Biological atry l sev ie r .com/ locate /pnp substance dependence as well as those with schizophrenia. Previous studies have indicated that specific brain regions, including the anteri- or cingulate cortex (ACC) and orbitofrontal cortex (OFC), mediate im- pulsivity in schizophrenia (Narayan et al., 2007; Schiffer et al., 2010). The ACC, OFC, and dorsolateral prefrontal cortex (DLPFC) are also af- fected in individuals with ADHD, BPD, and substance dependence, 166 T.Y. Lee et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 45 (2013) 165–169 and these defects are correlated with impulsivity (Berlin et al., 2005; Matsuo et al., 2009; Passarotti et al., 2010). All of these brain regions are related with impulsivity, but the main functions of each region, such as executive control, regulation of internal conflict, and decision making, slightly differ. Moreover, the main regional abnormalities in each disorder are different and may be correlated with the behavioral characteristics (Hazlett et al., 2005, Schoenbaum et al., 2006, Seidman et al., 2005). Several studies have found that UHR subjects also have reduced gray matter volume in the ACC, DLPFC, and OFC (Fusar-Poli et al., 2011; Koutsouleris et al., 2012; Witthaus et al., 2009). Given that these regions may mediate impulsivity these findings suggest that UHR subjects may be more impulsive, similar to patients with schizophrenia. However, it is not clear whether the impulsivity in UHR subjects is related to defects in brain regions similar to those in patients with schizophrenia or other related disorders. In this study, we used the Barratt Impulsiveness Scale version-11 (BIS-11) to estimate impulsivity and a voxel-based morphometry (VBM) analysis to measure gray matter volume associated with impul- sivity. We hypothesized that UHR subjects would be more impulsive than healthy controls (HCs). Moreover, we expected that impulsivity would be correlated with a reduction in gray matter volume in UHR subjects. To our knowledge, no previous study has focused on impul- sivity in UHR subjects. Therefore, improving knowledge concerning the neural underpinnings of impulsivity in UHR subjects will be crucial for understanding the pathophysiology and the characteristics of the disorder. 2. Methods 2.1. Subjects and clinical assessments This study recruited 32 UHR subjects from the Seoul Youth Clinic in Seoul, South Korea. All UHR subjects participated in an intensive clin- ical interview administered by experienced psychiatrists, who used the Structured Clinical Interview for DSM-IV Axis I (SCID-I) disorders to identify past and current psychiatric illnesses. These subjects were also assessed using the Structured Interview of Prodromal Symptoms (SIPS; Miller et al., 2003). All subjects had to fulfill at least one of the three established criteria for prodromal psychosis state: present attenuated positive symptoms state (APS); have a brief intermit- tent psychotic symptoms state below the threshold required for a DSM-IV axis I psychotic disorder diagnosis (BIPS); and/or show a 30% decline in global functioning over the past year, as well as having a diagnosis of schizotypal personality disorder or a first-degree rela- tive with psychosis (genetic risk with deterioration state; GRD). The Korean version of the Wechsler Adult Intelligence Scale (K-WAIS) was administered to all subjects to provide an estimated IQ. The exclu- sion criteria for all subjects included the following: lifetime diagnosis of a psychotic disorder; substance use disorder; neurological disease or significant head injury; evidence of a medical illness that could manifest as psychiatric symptoms; and intellectual disability. Addi- tionally, 32 age- and gender-matched healthy controls (HCs) were recruited through internet advertisement. Exclusion criteria for HCs were: (1) past or current SCID-I Non-patient Edition (SCID-NP) axis I diagnoses and (2) any first- to third-degree biological relative with a psychiatric disorder. Informed consent forms were obtained from all subjects; the present study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of the Seoul National University Hospital. 2.2. Behavioral measures The BIS-11 (Patton et al., 1995) was employed to estimate impul- sivity. The BIS-11 is themost widely used self-report measure of impul- sive personality traits and has three higher-order factors: attention impulsiveness, which measures the tendency to not focus on the task at hand; motor impulsiveness, which measures the tendency to act on the spur of the moment; and non-planning impulsiveness, which mea- sures the tendency to not engage in careful thinking or planning. 2.3. Image acquisition All structural magnetic resonance imaging (MRI) scans were ac- quired in the sagittal plane using a 3-T scanner (MAGNETOM Trio Tim Syngo MR B17; Siemens, Erlangen, Germany) and T1-weighted 3-D magnetization-prepared rapid-acquisition gradient echo (MPRAGE) sequence. Parameters were as follows: TR/TE = 1670/1.89 ms, voxel size = 0.98 × 0.98 × 1 mm3, FOV = 250 mm, flip angle = 9°, 208 slices, and matrices = 256 × 256. 2.4. Image processing The VBM analysis was performed with the FSL software package (www.fmrib.ox.ac.uk/fsl/), v. 5.0. First, structural images were brain- extracted using the Brain Extraction Tool (BET), and then tissue-type segmentation was performed using FMRIB's automated segmentation tool (FAST). Next, the graymatter partial volume images were aligned with the Montreal Neurological Institute's 152 reference spaces using FMRIB's linear image registration tool (FLIRT). The resulting images of all participants were averaged to create a study-specific template to which the native graymatter images were then nonlinearly registered using the FSL nonlinear registration tool (FNIRT). The registered par- tial volume images were modulated by methods of division using the Jacobianwarp field to correct for local contraction or enlargement. All the modulated segmented images were smoothed with a Gaussian kernel with a sigma of 3 mm. 2.5. Regions of interest definition Regions of interest (ROIs) were defined by considering previous impulsivity studies (Lee et al., 2008; Schiffer et al., 2010). Gray matter ROIs were defined in the ACC, DLPFC, and OFC. The ROI regions were defined by the Harvard–Oxford cortical structural atlas. 2.6. Statistical analysis First, differences in demographics and clinical variables between the groups were analyzed using independent t-tests and Fisher's exact test. Second, differences in gray matter volume in the ACC, DLPFC, and OFC between the groups were assessed by a voxel-wise general linear model applied using permutation-based non-parametric testing with 5000 permutations. A voxel-wise statistical analysis was performed with a threshold-free cluster enhancement (TFCE) correction for multi- ple comparisons. The significance levelwith corrected family-wise error (FWE) was set at p b 0.05. Third, significant clusters were defined as ROIs. Then, a correlational analysis between the BIS-11 scores and the gray matter volume in the significant clusters was performed. Sta- tistical significance was defined as p b 0.05, and statistical analyses were performed using Stata, v. 12.0 (StataCorp; College Station, TX, US). 3. Results 3.1. Demographic and psychological data The demographic and clinical characteristics of UHR andHC subjects were collected (Table 1). Of theUHRsubjects, 32met the criteria for APS, five subjects met the criteria for GRD, and five subjects met the criteria for both APS and GRD. None of the UHR subjects was taking antipsy- chotics. Among those who were taking psychotropics, four were taking antidepressants at baseline assessment: 75 mg venlafaxine (n = 1), 10 mg lexapro (n = 1), 80 mg prozac (n = 1), and 25 mg clomipra- mine (n = 1). There were no significant differences in age, gender, exhibited gray matter volume loss in the ACC as compared with HC subjects but not in the DLPFC and OFC. There was a negative correla- tion of gray matter volume in significant clusters in the ACC with motor and non-planning impulsivity in UHR subjects. These results suggest that the ACC may participate in the neurocircuitry that regu- lates impulsivity in UHR subjects. To our knowledge, this is the first study to investigate the neural correlates of impulsivity in UHR subjects. Whereas other studies have used performance-based measures such as the Stroop, Go/No-Go, and Stop signal tasks, this study used the BIS-11, a self-report questionnaire, to estimate impulsivity. In previous studies, increased non-planning impulsivity according to the BIS-11 was negatively associated with gray matter volume in the ACC in subjects with a schizophrenia–addiction comorbidity (Schiffer et al., 2010). Likewise, impulsivity in the impulsiveness– venturesomeness empathy questionnaire was associated with re- Table 1 Demographic and clinical characteristics of the participants. UHR (n = 32) HC (n = 32) Statistical analysis Age: years 20.6 ± 2.5 21.9 ± 2.4 T62 = 1.98, p = 0.05 Gender (male/female) 22/10 20/12 χ2 1 = 2.77, p = 0.60 Handedness (right/left) 30/2 29/3 χ2 1 = 0.24, p = 0.89 Education: years 13.2 ± 1.4 13.7 ± 1.4 T62 = 1.59, p = 0.12 IQ 111.4 ± 11.8 110.5 ± 11.8 T62 = 0.28, p = 0.78 Impulsivity BIS-11 total score 70.3 ± 11.6 57.8 ± 11.3 T62 = 4.37, p b 0.01 BIS-11 attention 19.1 ± 3.5 16.4 ± 2.6 T62 = 3.52, p b 0.01 BIS-11 motor 22.8 ± 5.7 18.6 ± 4.9 T62 = 3.18, p b 0.01 BIS-11 non-planning 28.4 ± 4.6 22.8 ± 5.5 T62 = 4.42, p b 0.01 Age of onset: years 17.8 ± 2.9 Duration of illness: years 2.9 ± 2.1 SOPS Positive symptom scale 8.9 ± 2.9 167T.Y. Lee et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 45 (2013) 165–169 handedness, educational years, or IQ between the UHR and HC groups. UHR subjects had significantly higher BIS-11 impulsivity scores com- pared with the HC group; total impulsiveness score (t = 4.3745, df = 62, p b 0.01), attention impulsiveness score (t = 3.5187, df = 62, p b 0.01), motor impulsiveness score (t = 3.1751, df = 62, p b 0.01), and non-planning impulsiveness score (t = 4.4154, df = 62, p b 0.01). 3.2. MRI data UHR subjects exhibited lower graymatter volume in the ACC [clus- ter size = 400, t-value = 4.498 (50, 82, 45), FWE corrected p-value b 0.01] comparedwith values for HCs (Fig. 2). The graymatter volume in the DLPFC and OFC was not significantly reduced compared with values for HC. A significant cluster was defined as a ROI in the ACC. The ROI volume in the ACC was significantly correlated with total impulsiveness score (r = −0.390, p = 0.03), motor impulsive- ness score (r = −0.472, p b 0.01), and non-planning impulsiveness Negative symptom scale 15.9 ± 6.0 Disorganized symptom scale 4.1 ± 2.7 General symptom scale 7.4 ± 4.4 UHR, subjects at ultra-high risk for psychosis; HC, healthy control subjects; BIS-11, Barratt impulsiveness scale, version-11; and SOPS, scale of prodromal symptoms in the Structured Interview for Prodromal Syndromes. score on the BIS-11 (r = −0.354, p = 0.04) (Fig. 3). Attention impul- siveness score was not significantly correlated with the ROI volume in the ACC (p = 0.75). 4. Discussion In the present study, UHR subjects were more impulsive than HC subjects according to the BIS-11 scale. Additionally, UHR subjects Fig. 1. Images obtained by the voxel-wise group comparison analysis are overlaid on the can the regions of significantly reduced gray matter volume for subjects at ultra-high risk for ps (50, 82, 45), and FWE corrected p-value b 0.05]. duced OFC volumes in patients with schizophrenia (Kumari et al., 2009). However, other studies using performance-based measures for impulsivity found various correlations with regions such as the ACC, DLPFC, medial parietal regions, OFC, and ventro-lateral prefrontal cortex (Kaladjian et al., 2011; Ungar et al., 2010; Yucel et al., 2002). Be- cause exact comparison is difficult due to methodological differences amongMRI, fMRI, and PET, these discrepancies may arise from the dif- ferences in the characteristics of self-report and performance-based tests. In general, performance-basedmeasures providemore objective assessments of impulsivity in addition to being more amenable for re- peated measurements. However, given that performance-based mea- sures are administered by trained personnel and typically have time limitations and follow specific protocols, they are more likely to be as- sociated with performance anxiety and to require greater attention capacity (Reynolds et al., 2006). Therefore, performance-based mea- suresmay require various brain functions such as attention, executive, and motor control. The initial hypothesis was that UHR subjects would exhibit greater impulsivity compared with HCs. Consistent with this hypothesis, at- tention, motor, and non-planning impulsivity in UHR subjects were greater than in HCs. These findings support the concept that behav- ioral problems in UHR subjects are connected with a high level of impulsivity. Different aspects of impulsivity may play different roles in various psychiatric disorders. In substance abuse-related disorders, impulsiv- ity in patients may be related to their reduced ability to overcome ab- stinence during cravings or while in the withdrawal state. Therefore, the non-planning or motor aspect of impulsivity may be related to these disorders. Patients with bipolar disorder have greater motor ac- tivation (Cassidy et al., 1998) as well as manic symptoms including elevated mood, a decreased need for sleep, and increased verbal ex- pression, which may make the patients more impulsive. Their impul- sivity contributes to the highly recursive course of illness, comorbid onical T1 template of the Harvard–Oxford Cortical Structural Atlas. Red–yellow displays ychosis compared with healthy control subjects [cluster size = 964, t-value = −4.498 r cin sur 168 T.Y. Lee et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 45 (2013) 165–169 substance use, and a history of suicide attempts (Swann, 2010). Sev- eral studies indicate that patients with BPD have a high incidence of impulsive aggression and suicidal behavior, which may be related to the motor aspect of impulsivity. Impulsivity in BPD is considered a key factor that likely affects patients' misconduct or maladaptive be- havior. Impulsivity is also commonly considered one of the core fea- tures of ADHD and may be related to reduced inhibitory control, an executive function. In terms of behavior, patients with ADHD tend to be easily distracted, even by relatively unimportant stimuli. Mean- while, UHR subjects usually experience degraded or deteriorated general functioning. These unfamiliar and potentially embarrassing Fig. 2. Relationship between mean graymatter volume in significant clusters of the anterio p b 0.01), and non-planning impulsivity score (r = −0.354, p = 0.04). The volume mea transformed into the MNI space. experiences could also make UHR subjects more impulsive. However, there are few studies regarding the relationship between impulsivity in UHR and the associated negative consequences or prodromal symptoms. Therefore, it is necessary to investigate this relationship. In this study, UHR subjects exhibited reduced gray matter volume in the ACC compared with that in the HC group. This finding is in line with previous reports indicating that the ACC may be related to the pathophysiology of UHR (Fornito et al., 2008; Rothlisberger et al., 2012; Yucel et al., 2003) (Fig. 1). Two of the most investigated gray matter regions involved in mediating impulsivity are the DLPFC and OFC. However, there was no gray matter volume loss in the DLPFC or OFC in UHR subjects in this study. Conversely, in a longitudinal com- parison, individuals who later developed psychosis exhibited smaller gray matter volume in the OFC (Borgwardt et al., 2008; Pantelis et al., 2003). First-episode patients with schizophrenia showed reduced gray matter volume in the left OFC compared with UHR subjects, and reduced dorsolateral gray matter volume has commonly been ob- served in patients with schizophrenia, but not in UHR subjects (Gur et al., 2000; Schlaepfer et al., 1994). These findings suggest that impul- sivity in UHR subjects is mediatedmore by changes in the ACC than by changes in the DLPFC or OFC. The ACC and OFC are commonly associ- ated with decision-making and are functionally connected with each other (Cohen et al., 2005). However, the ACC neurons encode choice predictions and prediction errors using a common valuation currency, reflecting the integration of multiple decision parameters, whereas OFC neurons evaluate current choices relative to the value contexts that were recently experienced (Wallis and Kennerley, 2011). Thus, impulsivity in UHR subjects may be due to altered integrated conflict processing rather than altered reward/punishment processing or ex- ecutive control. The present study also found that motor and non-planning impul- sivity are inversely correlated with ACC volume in UHR subjects. However, attention impulsivity was not correlated with gray matter volume in the ACC. In previous studies, the total score on the BIS had a tendency to be inversely correlated with left ACC gray matter vol- ume, whereas motor and non-planning impulsivity scores were in- versely correlated with the OFC in healthy individuals (Matsuo et al., 2009). Increased non-planning impulsivity was negatively associated with ACC graymatter volume in schizophrenia–addiction comorbidity gulate cortex and the Barratt Impulsiveness Scale; motor impulsivity score (r = −0.472, es are expressed as arbitrary values because these measures were obtained from brains (Schiffer et al., 2010). In this regard, attention impulsivity in UHR sub- jectsmay be compensated for by other unaltered brain regions such as the DLPFC. Another possible explanation is that an association between theDLPFC and impulsivitymayhavebeenmissed because the correlation analysis was only performed with the ROI cluster in ACC that showed a significant group difference in GM volume and not with the DLPFC. The present study has some limitations. First, this study did not compare longitudinal brain changes in converters with those in non-converters. Reduced gray matter volume was more marked in UHR subjects who later developed psychosis than in those who did not (Borgwardt et al., 2008; Mechelli et al., 2011; Takahashi et al., 2009). Furthermore, UHR subjects exhibit progressive structural brain changes during the development of psychosis (Ziermans et al., 2012). Therefore, a longitudinal follow-up study is needed to deter- mine whether reduced gray matter volume in UHR subjects is associ- ated with impulsivity as in other psychiatric disorders. Additionally, a few of the UHR subjects were taking antidepressants, which raises the possibility of medication confounding the results. 5. Conclusion UHR subjects had significantly increased impulsivity comparedwith HC. This finding suggests that impulsivity in UHR subjects may stem from alterations in conflict processing with integrated parameters, which is modulated by the ACC, rather than from altered reward/ punishment processing or executive control. Future researchers investi- gating behavioral characteristics of UHR may benefit from clinical out- come prediction and intervention strategies in UHR. Acknowledgment This study was supported by a grant of the Korean Health Technol- ogy R&D Project, Ministry of Health & Welfare, Republic of Korea (A120476). None of the authors have any conflicts of interests to this study. References Berlin HA, Rolls ET, Iversen SD. Borderline personality disorder, impulsivity, and the orbitofrontal cortex. Am J Psychiatry 2005;162:2360–73. Borgwardt SJ, McGuire PK, Aston J, Gschwandtner U, Pfluger MO, Stieglitz RD, et al. Re- ductions in frontal, temporal and parietal volume associated with the onset of psy- chosis. Schizophr Res 2008;106:108–14. Cassidy F, Murry E, Forest K, Carroll BJ. Signs and symptoms of mania in pure and mixed episodes. J Affect Disord 1998;50:187–201. Cohen MX, Heller AS, Ranganath C. Functional connectivity with anterior cingulate and orbitofrontal cortices during decision-making. Brain Res Cogn Brain Res 2005;23: 61–70. Matsuo K, Nicoletti M, Nemoto K, Hatch JP, Peluso MA, Nery FG, et al. A voxel-based morphometry study of frontal grey matter correlates of impulsivity. Hum Brain Mapp 2009;30:1188–95. Mechelli A, Riecher-Rossler A, Meisenzahl EM, Tognin S, Wood SJ, Borgwardt SJ, et al. Neuroanatomical abnormalities that predate the onset of psychosis: a multicenter study. Arch Gen Psychiatry 2011;68:489–95. Miller TJ, McGlashan TH, Rosen JL, Cadenhead K, Cannon T, Ventura J, et al. Prodromal assessment with the structured interview for prodromal syndromes and the scale of prodromal symptoms: predictive validity, interrater reliability, and training to reliability. Schizophr Bull 2003;29:703–15. Narayan VM, Narr KL, Kumari V, Woods RP, Thompson PM, Toga AW, et al. Regional cortical thinning in subjects with violent antisocial personality disorder or schizo- phrenia. Am J Psychiatry 2007;164:1418–27. Ouzir M. Impulsivity in schizophrenia: a comprehensive update. Aggress Violent Behav 2013;18:247–54. Pantelis C, Velakoulis D, McGorry PD, Wood SJ, Suckling J, Phillips LJ, et al. Neuroana- tomical abnormalities before and after onset of psychosis: a cross-sectional and longitudinal MRI comparison. Lancet 2003;361:281–8. Passarotti AM, Sweeney JA, Pavuluri MN. Neural correlates of response inhibition in pe- diatric bipolar disorder and attention deficit hyperactivity disorder. Psychiatry Res 2010;181:36–43. Patton JH, Stanford MS, Barratt ES. Factor structure of the Barratt impulsiveness scale. J Clin Psychol 1995;51:768–74. Reynolds B, Ortengren A, Richards JB, de Wit H. Dimensions of impulsive behavior: 169T.Y. Lee et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 45 (2013) 165–169 psychosis. Can J Psychiatry 2010;55:165–71. Fornito A, Yung AR, Wood SJ, Phillips LJ, Nelson B, Cotton S, et al. Anatomic abnormal- ities of the anterior cingulate cortex before psychosis onset: an MRI study of ultra- high-risk individuals. Biol Psychiatry 2008;64:758–65. Fusar-Poli P, Borgwardt S, Crescini A, Deste G, Kempton MJ, Lawrie S, et al. Neuroanat- omy of vulnerability to psychosis: a voxel-based meta-analysis. Neurosci Biobehav Rev 2011;35:1175–85. Gur RE, Cowell PE, Latshaw A, Turetsky BI, Grossman RI, Arnold SE, et al. Reduced dor- sal and orbital prefrontal grey matter volumes in schizophrenia. Arch Gen Psychi- atry 2000;57:761–8. Gut-Fayand A, Dervaux A, Olie JP, Loo H, Poirier MF, Krebs MO. Substance abuse and suicidality in schizophrenia: a common risk factor linked to impulsivity. Psychiatry Res 2001;102:65–72. Hazlett EA, New AS, Newmark R, Haznedar MM, Lo JN, Speiser LJ, et al. Reduced ante- rior and posterior cingulate gray matter in borderline personality disorder. Biol Psychiatry 2005;58:614–23. Hoptman MJ, Volavka J, Johnson G, Weiss E, Bilder RM, Lim KO. Frontal white matter microstructure, aggression, and impulsivity in men with schizophrenia: a prelimi- nary study. Biol Psychiatry 2002;52:9-14. Hutton P, Bowe S, Parker S, Ford S. Prevalence of suicide risk factors in people at ultra-high risk of developing psychosis: a service audit. Early Interv Psychiatry 2011;5:375–80. Kaladjian A, Jeanningros R, Azorin JM, Anton JL, Mazzola-Pomietto P. Impulsivity and neural correlates of response inhibition in schizophrenia. Psychol Med 2011;41: 291–9. Kooyman I, Dean K, Harvey S, Walsh E. Outcomes of public concern in schizophrenia. Br J Psychiatry Suppl 2007;50:s29–36. Koutsouleris N, Borgwardt S, Meisenzahl EM, Bottlender R, Moller HJ, Riecher-Rossler A. Disease prediction in the at-risk mental state for psychosis using neuroanatom- ical biomarkers: results from the FePsy study. Schizophr Bull 2012;38:1234–46. Kumari V, Barkataki I, Goswami S, Flora S, Das M, Taylor P. Dysfunctional, but not func- tional, impulsivity is associated with a history of seriously violent behaviour and reduced orbitofrontal and hippocampal volumes in schizophrenia. Psychiatry Res 2009;173:39–44. Lee SJ, Yoo SY, Kang DH, Lee KJ, Ha TH, Wee W, et al. Potential vulnerability markers within the affective domain in subjects at genetic and clinical high risk for schizo- phrenia. Psychopathology 2008;41:236–44. personality and behavioral measures. Pers Indivd Diff 2006;40:305–15. RothlisbergerM, Riecher-Rossler A, Aston J, Fusar-Poli P, Radu EW, Borgwardt S. Cingulate volume abnormalities in emerging psychosis. Curr Pharm Des 2012;18:495–504. Schiffer B,Muller BW, ScherbaumN, ForstingM,Wiltfang J, Leygraf N, et al. Impulsivity-re- lated brain volume deficits in schizophrenia-addiction comorbidity. Brain 2010;133: 3093–103. Schlaepfer TE, Harris GJ, Tien AY, Peng LW, Lee S, Federman EB, et al. Decreased region- al cortical gray matter volume in schizophrenia. Am J Psychiatry 1994;151:842–8. Schoenbaum G, Roesch MR, Stalnaker TA. Orbitofrontal cortex, decision-making and drug addiction. Trends Neurosci 2006;29:116–24. Seidman LJ, Valera EM, Makris N. Structural brain imaging of attention-deficit/ hyperactivity disorder. Biol Psychiatry 2005;57:1263–72. Swann AC. Mechanisms of impulsivity in bipolar disorder and related illness. Epidemiol Psichiatr Soc 2010;19:120–30. Takahashi T, Wood SJ, Yung AR, Soulsby B, McGorry PD, Suzuki M, et al. Progressive grey matter reduction of the superior temporal gyrus during transition to psycho- sis. Arch Gen Psychiatry 2009;66:366–76. Ungar L, Nestor PG, Niznikiewicz MA, Wible CG, Kubicki M. Color Stroop and negative priming in schizophrenia: an fMRI study. Psychiatry Res 2010;181:24–9. Wallis JD, Kennerley SW. Contrasting reward signals in the orbitofrontal cortex and an- terior cingulate cortex. Ann N Y Acad Sci 2011;1239:33–42. Witthaus H, Kaufmann C, Bohner G, Ozgurdal S, Gudlowski Y, Gallinat J, et al. Grey mat- ter abnormalities in subjects at ultra-high risk for schizophrenia and first-episode schizophrenic patients compared to healthy controls. Psychiatry Res 2009;173: 163–9. Yucel M, Pantelis C, Stuart GW,Wood SJ, Maruff P, Velakoulis D, et al. Anterior cingulate activation during Stroop task performance: a PET to MRI coregistration study of in- dividual patients with schizophrenia. Am J Psychiatry 2002;159:251–4. Yucel M, Wood SJ, Phillips LJ, Stuart GW, Smith DJ, Yung A, et al. Morphology of the an- terior cingulate cortex in young men at ultra-high risk of developing a psychotic illness. Br J Psychiatry 2003;182:518–24. Yung AR, McGorry PD. The initial prodrome in psychosis: descriptive and qualitative aspects. Aust N Z J Psychiatry 1996;30:587–99. Ziermans TB, Schothorst PF, Schnack HG, Koolschijn PC, Kahn RS, van Engeland H, et al. Progressive structural brain changes during development of psychosis. Schizophr Bull 2012;38:519–30. Dragt S, Nieman DH, Becker HE, van de Fliert R, Dingemans PM, de Haan L, et al. Age of onset of cannabis use is associated with age of onset of high-risk symptoms for Neural correlate of impulsivity in subjects at ultra-high risk for psychosis 1. Introduction 2. Methods 2.1. Subjects and clinical assessments 2.2. Behavioral measures 2.3. Image acquisition 2.4. Image processing 2.5. Regions of interest definition 2.6. Statistical analysis 3. Results 3.1. Demographic and psychological data 3.2. MRI data 4. Discussion 5. Conclusion Acknowledgment References