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Decreased connectivity of the default mode network in pathological gambling: A resting state functional MRI study
Neuroscience Letters 583 (2014) 120–125
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Neuroscience Letters journal homepage: www.elsevier.com/locate/neulet
Decreased connectivity of the default mode network in pathological gambling: A resting state functional MRI study Myung Hun Jung a , Jae-Hun Kim b , Young-Chul Shin c , Wi Hoon Jung d , Joon Hwan Jang d , Jung-Seok Choi e , Do-Hyung Kang d , Jung-Seo Yi a , Chi-Hoon Choi f , Jun Soo Kwon d,g,∗ a
Department of Psychiatry, Hallym University College of Medicine, Chuncheon, Republic of Korea Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea c Department of Psychiatry, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea d Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea e Department of Psychiatry, SMG-SNU Boramae Hospital, Seoul National University, Seoul, Republic of Korea f Department of Diagnostic Radiology, National Medical Center, Seoul, Republic of Korea g Department of Brain & Cognitive Science, College of Natural Science, Seoul National University, Seoul, Republic of Korea b
h i g h l i g h t s • PG displayed decreased DMN in the left SFG, right MTG, and precuneus. • Decreased DMN (precuneus) were associated with severity of PG symptoms. • Altered DMN in PG was similar to neurobiological abnormalities in other addictions.
a r t i c l e
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Article history: Received 22 April 2014 Received in revised form 14 August 2014 Accepted 9 September 2014 Available online 18 September 2014 Keywords: Pathological gambling Functional connectivity Default mode network fMRI
a b s t r a c t The default mode network (DMN) represents neuronal activity that is intrinsically generated during a resting state. The present study used resting-state fMRI to investigate whether functional connectivity is altered in pathological gambling (PG). Fifteen drug-naive male patients with PG and 15 age-matched male control subjects participated in the present study. The pathological gambling modification of the Yale-Brown Obsessive Compulsive Scale (PG-YBOCS), the Beck Depression Inventory, and the Beck Anxiety Inventory were used to determine symptom severity in all participants. Participants were instructed to keep their eyes closed and not to focus on any particular thoughts during the 4.68-min resting-state functional scan. The patients with PG displayed decreased default mode connectivity in the left superior frontal gyrus, right middle temporal gyrus, and precuneus compared with healthy controls. The severity of PG symptoms in patients with PG was negatively associated with connectivity between the posterior cingulate cortex seed region and the precuneus (r = −0.599, p = 0.018). Decreased functional connectivity within DMN suggests that PG may share similar neurobiological abnormalities with other addictive disorders. Moreover, the severity of PG symptoms was correlated with decreased connectivity in the precuneus, which may be important in the response to treatment in patients with PG. © 2014 Elsevier Ireland Ltd. All rights reserved.
1. Introduction
Abbreviations: DMN, default-mode network; MTG, middle temporal gyrus; PCC, posterior cingulate cortex; PG, pathological gambling; SFG, superior frontal gyrus. ∗ Corresponding author at: Department of Psychiatry, Seoul National University College of Medicine, 101 Daehak-no, Chongno-gu, Seoul 110-744, Republic of Korea. Tel.: +82 2 2072 2972; fax: +82 2 747 9063. E-mail address: [email protected] (J.S. Kwon). http://dx.doi.org/10.1016/j.neulet.2014.09.025 0304-3940/© 2014 Elsevier Ireland Ltd. All rights reserved.
Pathological gambling (PG) is a progressive and chronic disorder, although PG is found to have a high remission rate in population based studies [11]. Recently, PG has been reclassified for “gambling disorder” under non-substance-related disorders, which belongs to the “Substance-Related and Addictive Disorders” in DSM-5 [1]. Previously PG belonged to the impulse-control disorders not elsewhere classified in Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV). Hollander [20] reported that PG belongs to the subtype of impulse-control disorders among the
M.H. Jung et al. / Neuroscience Letters 583 (2014) 120–125
subtypes of obsessive-compulsive spectrum disorders. It suggested that relatively psychopathological characteristics and neurobiological mechanisms of PG remain poorly understood. Recent neuroimaging studies have been conducted in patients with PG while they perform various cognitive tasks. These studies have reported the importance of reward and punishment processes in PG [14]. Furthermore, PG patients display greater attentional bias toward gambling cues, including a greater sensitivity and stronger desire to gamble compared with controls. Crockford et al. [8] reported increased dorsolateral prefrontal cortex activity in PG patients while they were viewing gambling compared with nature scenes. In other research, decreased activity in the anterior cingulate cortex, orbitofrontal cortex, and basal ganglia was reported in patients with PG compared with control subjects in response to a gambling video [31]. Similarly, decreased ventromedial prefrontal cortex activity related to response inhibition in response to the Stroop test was shown in PG patients, suggesting difficulties with impulse control [30]. Previous studies on resting-state functional connectivity have reported that specific brain areas are more activated during rest, and this network of connections has since been described as the ‘default-mode network’ (DMN) [32]. The DMN primarily comprises the anterolateral temporal cortex, parahippocampal gyrus, thalamus, pons, and cerebellum, as well as part of the medial prefrontal cortex and the posterior cingulate cortex (PCC) [13]. The PCC is associated with autobiographical memory [25] and is activated by self-reflection, self-confidence, and attitude [21]. In combination with the PCC, the medial prefrontal cortex is typically deactivated during a recognition challenge and activated during a resting state [32]. The DMN is a network of brain regions that are active when an individual is not focused on the outside world, and the brain is at wakeful rest [13,18]. Neuroimaging studies including DMN make it possible to distinguish particular brain areas that are involved in the development and maintenance of psychiatric disorders and have an advantage over psychiatric survey or behavioral approaches. In this study, we used resting-fMRI to investigate the DMN in patients with PG. The aims of this study were to investigate altered DMN resting-state functional connectivity in patients with PG and to determine whether there are any relationships between altered the degree of functional connectivity in the DMN and PG symptom severity.
2. Materials and methods 2.1. Subjects and clinical assessments Fifteen male patients (27.93 ± 3.59 years) who were recruited from the outpatient clinic at KangBuk Samsung Hospital in Seoul, Korea, participated in the present study. The patients diagnosed as a PG with the Structured Clinical Interview for DSM-IV (SCID) [12] and received a score ≥5 on the South Oaks Gambling Screen (SOGS) [23]. Fifteen age- and IQ-matched healthy controls (26.60 ± 4.29 years) also participated in the study and were assessed for psychiatric disorders with the Structured Clinical Interview for DSM-IV, Non-patient Version (SCID-NP). Some of the PG patients and healthy controls had participated in our previous studies [7]. PG symptom severity was measured with the 10-item YaleBrown Obsessive Compulsive Scale for pathological gambling (PG-YBOCS) [28]. The Beck Depression Inventory (BDI) [4] and the Beck Anxiety Inventory (BAI) [3] were also used to assess the severity of depression and anxiety symptoms, respectively. To measure intelligence, all participants took the Korean version of the Wechsler Adult Intelligence Scale (K-WAIS). Exclusion criteria for the present study included a history of psychotic disorder, seizure disorder, significant head injury, mental retardation, and
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substance abuse disorder (except for nicotine use). The Institutional Review Boards at the Kangbuk Samsung Hospital and the Seoul National University Hospital approved the study protocol, and written informed consent was obtained from all subjects prior to participation.
2.2. MR acquisition and preprocessing All images were acquired with a 1.5-T Avanto system (Siemens, Erlangen, Germany). Head restraint straps and foam blocks were used to minimize head movement, and earplugs and headphones were used to minimize scanner noise. Functional data were acquired using a T2*-weighted gradient echo-planar image sequence positioned parallel to the anterior–posterior commissure plane over the entire brain (repetition time (TR) = 2.34 s, echo time (TE) = 52 ms, field of view (FOV) = 220 mm, flip angle (FA) = 90◦ , voxel size = 3.44 mm × 3.44 mm × 5 mm, no interslice gap, interleaved, 25 axial slices). A 4.68-min scan (120 volumes) was performed on each participant. During the scan, subjects were explicitly instructed to close their eyes, relax, and refrain from focusing on any thoughts. T1 anatomical volume images were also acquired for each subject with the following parameters: TR = 11.6 s, TE = 4.76 ms, FOV = 230 mm, FA = 15◦ , voxel size = 0.45 mm × 0.45 mm × 0.9 mm. Data preprocessing was performed using the FMRIB Software Library (FSL; http://www.fmrib.ox.ac.uk/fsl/). The first four volumes of each functional time series were discarded to remove non-equilibrium effects from T1 saturation. Corrections for timing differences in slice acquisition were performed. Sync interpolation was used to realign all volumes to the middle image in an effort to remove signals correlated with head motion. Spatial normalization to a standard template provided by the Montreal Neurological Institute (MNI template) and temporal signal scaling to yield a whole-brain-mode value of 1000 were also performed. Spatial normalization was performed using affine transformation (12 DOF), and normalized image was resampled 2 mm × 2 mm × 2 mm voxel size. The following additional steps were performed with AFNI software (http://afni.nimh.nih.gov/afni/). Temporal band-pass filtering (0.009 Hz < f < 0.08 Hz) was performed to isolate the low-frequency fluctuations of interest; then, these were spatially smoothed with a 5-mm full-width at half-maximum Gaussian blur. Signals from the regions of no interest and the first temporal derivatives (headmotion parameter, global, white matter, and cerebrospinal fluid signals) were regressed out.
2.3. Posterior cingulate cortex functional connectivity analysis The DMN was defined as brain areas that were positively correlated with the PCC seed region. The PCC seed mask was created using the anatomically labeled template reported by TzourioMazoyer et al. [36]. For each subject, the representative time series for the PCC region was obtained by averaging the residual time series over all voxels in the PCC mask. PCC mask was defined bilaterally, and the averaging timeseries were extracted from both left and right PCC. Then, functional connectivity maps (PCC-FC maps) were produced by computing the Pearson’s correlation coefficients from each time course for the PCC from all other brain voxels. Correlation coefficients were normalized with the Fisher’s z transform. For within-group analyses, a one-sample t-test was performed using a false-discovery-rate (FDR) correction threshold of q < 0.05. For between-group analyses, two-sample t-tests were performed. Significant group differences were reported using the criteria of uncorrected p < 0.005 with a minimum cluster extent of 20
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Table 1 Demographic and clinical characteristics of the subjects.a Variables
Demographic data Age (years) Education (years) IQ score Clinical data Age of onset (years) Duration of illness (years) PG-YBOCS total score SOGS BDI BAI
Patients with PG (N = 15)
Control subjects (N = 15)
Mean ± SD
Mean ± SD
27.93 ± 3.59 14.80 ± 1.70 113.67 ± 9.96
26.60 ± 4.29 14.27 ± 1.39 114.47 ± 7.10
25.67 2.20 16.13 15.90 15.20 11.00
± ± ± ± ± ±
3.92 1.29 7.28 1.73 12.32 13.40
3.00 ± 4.81 3.67 ± 3.85
t
p
−0.92 −0.942 0.25
0.36 0.35 0.80
−3.51 −2.04
0.01b 0.05
Abbreviations: PG, pathological gambling; SD, standard deviation; IQ, intelligence quotient; PG-YBOCS, pathological gambling modification of Yale-Brown Obsessive Compulsive Scale; SOGS, South Oaks Gambling Screen; BDI, Beck Depression Inventory; BAI, Beck Anxiety Inventory. a Independent sample t-test was used. b p < 0.05.
voxels. The correlation between PCC functional connectivity within the DMN and symptom severity in PG patients was also examined. 3. Results No significant differences in age, education, and IQ scores were found between patients with PG and healthy controls. However, patients with PG obtained higher scores on the BDI (t = −3.51, p = 0.01) and BAI (t = −2.04, p = 0.05) compared with control subjects. Participant demographic and clinical characteristics are summarized in Table 1. Significant differences in connectivity to the PCC seed region within specific subregions of the DMN were noted in PG patients compared with controls (Fig. 1 and Table 2). Healthy controls displayed greater default mode connectivity in the left medial SFG, right middle temporal gyrus (MTG), and precuneus compared with PG patients (Fig. 2A–C). No areas were detected in which patients with PG showed significantly greater functional connectivity in the DMN compared with healthy controls. In the regions with significant differences in activity between PG patients and controls, PG symptom severity, as measured by the PGYBOCS, was correlated with decreased connectivity between the PCC seed region and the precuneus (r = −0.599, p = 0.018) (Fig. 2D). 4. Discussion The present study was the first to report decreased functional connectivity of the DMN in patients with PG during a resting state. Decreased functional connectivity of the left superior frontal gyrus (medial), right MTG, and precuneus was found in patients with
PG compared with healthy controls. Moreover, decreased functional connectivity between the PCC seed region precuneus was correlated with the severity of PG symptoms, as measured by the PG-YBOCS. There were some reports that have applied resting-state fMRI in patients with substance addictive disorder including alcohol [6], heroin [24], and nicotine [35] and other relevant addictive disorder such as internet gaming addicted adolescents [10] to further understand its mechanisms. Chanraud et al. [6] reported that the spontaneous slow fluctuations of fMRI signals in the PCC and cerebellar regions in alcoholics were less synchronized and suggested compensatory networking to achieve normal performance. Addictive subjects with heroin had increased functional connectivity in right hippocampus and decreased functional connectivity in right dorsal anterior cingulate cortex and left caudate in the DMN [24]. And nicotine was associated with a decreased activity in the DMN such as PCC, precuneus, paracentral lobule, and medial orbitofrontal cortex [35]. For substance addictive subjects, altered DMN suggested diminished cognitive control related to attention and self-monitoring, which might underlie weakened strength of cognitive control in the addictive state. Recently, Dalwani et al. [9] reported that male adolescents with conduct disorder and substance use disorder showed the reduced DMN activity in superior, medial and middle frontal gyrus, retrosplenial cortex, lingual gyrus, and bilateral middle temporal gyrus. These results might share similar neurobiological abnormalities with gambling disorder. Although decreased default mode connectivity findings in PG cannot be explained by direct effects of abused substances on the brain, the present study propose that PG is associated with abnormalities in the reward circuit and decision-making network
Fig. 1. Areas of positive correlation with posterior cingulate cortex seed region (default mode network) for (A) healthy controls and (B) patients with pathological gambling. (C) Areas in which patients with pathological gambling showed significantly lesser default mode network connectivity than healthy controls.
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Table 2 Regional differences in default mode network connectivity between patients with PG and control subjects.a Anatomical region
Side
BA
Peak coordination (MNI) x
Control subjects > patients with PG Superior frontal gyrus Middle temporal gyrus Precuneus
L R
10 21
−6 54 0
y 68 −24 −63
Cluster size
Z-value
z 27 −10 24
113 80 79
4.07 4.11 4.01
Abbreviations: BA, Brodmann area; MNI, Montreal Neurological Institute; PG, pathological gambling; L, left; R, right. a The threshold of between-group analyses set to p < 0.005 (uncorrected) and cluster size greater than 20 voxels (160 mm3 ).
structure of the brain during rest. The medial portion of the superior frontal gyrus is a part of the medial prefrontal cortex. The dorsomedial prefrontal cortex, which contains Brodmann areas 8–10, is involved in self-reference and self-reflection [5,17]. Specifically, the medial prefrontal cortex is responsible for the actualization of various self-related characteristics. During self-relevant processing, the ‘dorsal medial prefrontal cortex (dMPFC) subsystem’ is active which is made up of included the dMPFC, temporoparietal junction, lateral temporal cortex, and temporal pole [2]. The reduced resting-state functional connectivity in the superior frontal gyrus is likely associated with reduced self-related mental activities, including self-reflection, in patients with PG compared with healthy controls. The medial prefrontal cortex is a critical part of the mesolimbic circuit, which underlies the pathophysiology of addiction. In previous studies, ventral striatal and ventromedial prefrontal cortex activity was significantly decreased in PG patients compared with controls in response to financial
reward tasks, and a negative correlation between ventral striatal activity and PG symptom severity has been reported [33]. These results indicate decreased sensitivity to rewards in PG. Moreover, naltrexone, an opioid antagonist, is effective for PG symptoms related to gambling desire and also for inhibition of the associated reinforcing effects of the dopamine reward circuit [15]. The medial PFC is important for decision-making that is usually assessed by ‘gamble’ tasks, which are designed to simulate decisions in terms of uncertainty, reward and punishment [27]. Thus the decreased functional connectivity of the medial prefrontal cortex in patients with PG during the resting state reported in the present study support an association with an abnormal mesolimbic reward circuit and decision-making in PG. Previous positron emission tomography (PET) studies reported decreased regional cerebral blood flow (rCBF) in the temporal lobe, including the MTG, precentral gyrus, and cerebellum during reward-related decision-making process in control subjects [34].
Fig. 2. Plots for default mode connectivity in (A) SFG, (B) MTG, and (C) precuneus between control subjects (CTL) and patients with PG. (D) Correlation between PG-YBOCS score and default mode connectivity in the precuneus.
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As these areas are associated with decision-making, the decreased functional connectivity of the MTG during the resting state in patients with PG is likely associated with a decreased ability to cope in uncertain situations. In addition, the MTG was positively correlated with reward dependence measured using [18F] fluorodeoxyglucose PET in 31 healthy subjects whose temperament was assessed using the Temperament and Character Inventory [19]. The PCC and the precuneus are associated with the level of reward and the possibility of reward prediction and are consequently activated during reward processes in healthy subjects [26]. Symptoms of tolerance or craving in PG are similar to those found in substance abuse [29]. Previous studies have reported that PCC activity is negatively correlated with therapeutic effects in patients with cocaine abuse problems [22]. As less functional connectivity between the superior frontal gyrus and the PCC seed region was detected in patients with PG, the decreased functional connectivity between the precuneus and the PCC seed region suggest decreased sensitivity toward reward processes in these patients. We also found that the severity of gambling in patients with PG was negatively correlated with the level of functional connectivity between the PCC seed region and the precuneus. This region may be a useful marker of PG severity or a potential treatment target for the illness. There were several limitations to the present study. First, the study was conducted on a small number of subjects; therefore, the generalizability of the results to other groups (e.g., women) is limited. PG, however, occurs twice as often in men as in women. Despite the small number of participants, the PG group was relatively homogeneous, and none had undergone psychiatric drug or cognitive treatment prior to the study. Second, symptoms of depression, present in the subject sample, may have affected the results of the study. The BDI score, which measured the severity of depressive symptoms, was significantly higher in the PG group than in the control group. Moreover, increased functional connectivity of the subgenual cingulate cortex and the thalamus was found during the resting state in patients with depression, and a positive correlation between the duration of depressive episodes and resting connectivity in the subgenual cingulate cortex was noted [16], unlike in patients with PG.
5. Conclusions Decreased default mode connectivity was found in the left superior frontal gyrus, right MTG, and precuneus in patients with PG. Moreover, clinical PG symptom severity was negatively correlated with activity in the precuneus. These results provide evidence for altered reward processes and decision-making in patients with PG and demonstrate decreased functional connectivity in the brain regions that underlie these behavioral functions. And these findings suggest that PG may share similar neurobiological abnormalities with other addictive disorders.
Conflict of interest None.
Acknowledgment This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2013R1A2A1A03071089).
References [1] P.A. American, Diagnostic and Statistical Manual of Mental Disorders, American Psychiatric Press Inc., Washington, DC, 2013. [2] J.R. Andrews-Hanna, J.S. Reidler, J. Sepulcre, R. Poulin, R.L. Buckner, Functionalanatomic fractionation of the brain’s default network, Neuron 65 (2010) 550–562. [3] A.T. Beck, N. Epstein, G. Brown, R.A. Steer, An inventory for measuring clinical anxiety: psychometric properties, J. Consult. Clin. Psychol. 56 (1988) 893–897. [4] A.T. Beck, C.H. Ward, M. Mendelson, J. Mock, J. Erbaugh, An inventory for measuring depression, Arch. Gen. Psychiatry 4 (1961) 561–571. [5] F. Castelli, F. Happe, U. Frith, C. Frith, Movement and mind: a functional imaging study of perception and interpretation of complex intentional movement patterns, Neuroimage 12 (2000) 314–325. [6] S. Chanraud, A.L. Pitel, A. Pfefferbaum, E.V. Sullivan, Disruption of functional connectivity of the default-mode network in alcoholism, Cereb. Cortex 21 (2011) 2272–2281. [7] J.S. Choi, Y.C. Shin, W.H. Jung, J.H. Jang, D.H. Kang, C.H. Choi, S.W. Choi, J.Y. Lee, J.Y. Hwang, J.S. Kwon, Altered brain activity during reward anticipation in pathological gambling and obsessive-compulsive disorder, PLOS ONE 7 (2012) e45938. [8] D.N. Crockford, B. Goodyear, J. Edwards, J. Quickfall, N. el-Guebaly, Cueinduced brain activity in pathological gamblers, Biol. Psychiatry 58 (2005) 787–795. [9] M.S. Dalwani, J.R. Tregellas, J.R. Andrews-Hanna, S.K. Mikulich-Gilbertson, K.M. Raymond, M.T. Banich, T.J. Crowley, J.T. Sakai, Default mode network activity in male adolescents with conduct and substance use disorder, Drug Alcohol Depend. 134 (2014) 242–250. [10] W.N. Ding, J.H. Sun, Y.W. Sun, Y. Zhou, L. Li, J.R. Xu, Y.S. Du, Altered default network resting-state functional connectivity in adolescents with Internet gaming addiction, PLOS ONE 8 (2013) e59902. [11] S.A. Eisen, W.S. Slutske, M.J. Lyons, J. Lassman, H. Xian, R. Toomey, S. Chantarujikapong, M.T. Tsuang, The genetics of pathological gambling, Semin. Clin. Neuropsychiatry 6 (2001) 195–204. [12] M.B. First, R.L. Spitzer, M. Gibbon, J. Williams, Structured Clinical Interview for DSM-IV Axis I Disorder, New York State Psychiatric Institute, New York, 1996. [13] P. Fransson, Spontaneous low-frequency BOLD signal fluctuations: an fMRI investigation of the resting-state default mode of brain function hypothesis, Hum. Brain Mapp. 26 (2005) 15–29. [14] A.E. Goudriaan, J. Oosterlaan, E. de Beurs, W. Van den Brink, Pathological gambling: a comprehensive review of biobehavioral findings, Neurosci. Biobehav. Rev. 28 (2004) 123–141. [15] J.E. Grant, S.W. Kim, B.K. Hartman, A double-blind, placebo-controlled study of the opiate antagonist naltrexone in the treatment of pathological gambling urges, J. Clin. Psychiatry 69 (2008) 783–789. [16] M.D. Greicius, B.H. Flores, V. Menon, G.H. Glover, H.B. Solvason, H. Kenna, A.L. Reiss, A.F. Schatzberg, Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus, Biol. Psychiatry 62 (2007) 429–437. [17] D.A. Gusnard, E. Akbudak, G.L. Shulman, M.E. Raichle, Medial prefrontal cortex and self-referential mental activity: relation to a default mode of brain function, Proc. Natl. Acad. Sci. U. S. A. 98 (2001) 4259–4264. [18] D.A. Gusnard, M.E. Raichle, Searching for a baseline: functional imaging and the resting human brain, Nat. Rev. Neurosci. 2 (2001) 685–694. [19] Y. Hakamata, M. Iwase, H. Iwata, T. Kobayashi, T. Tamaki, M. Nishio, K. Kawahara, H. Matsuda, N. Ozaki, S. Honjo, T. Inada, Regional brain cerebral glucose metabolism and temperament: a positron emission tomography study, Neurosci. Lett. 396 (2006) 33–37. [20] E. Hollander, Obsessive-Compulsive Related Disorders, American Psychiatric Press Inc., Washington, DC, 1993. [21] S.C. Johnson, L.C. Baxter, L.S. Wilder, J.G. Pipe, J.E. Heiserman, G.P. Prigatano, Neural correlates of self-reflection, Brain 125 (2002) 1808–1814. [22] T.R. Kosten, B.E. Scanley, K.A. Tucker, A. Oliveto, C. Prince, R. Sinha, M.N. Potenza, P. Skudlarski, B.E. Wexler, Cue-induced brain activity changes and relapse in cocaine-dependent patients, Neuropsychopharmacology 31 (2006) 644–650. [23] H.R. Lesieur, S.B. Blume, The South Oaks Gambling Screen (SOGS): a new instrument for the identification of pathological gamblers, Am. J. Psychiatry 144 (1987) 1184–1188. [24] N. Ma, Y. Liu, X.M. Fu, N. Li, C.X. Wang, H. Zhang, R.B. Qian, H.S. Xu, X. Hu, D.R. Zhang, Abnormal brain default-mode network functional connectivity in drug addicts, PLoS ONE 6 (2011) e16560. [25] R.J. Maddock, A.S. Garrett, M.H. Buonocore, Remembering familiar people: the posterior cingulate cortex and autobiographical memory retrieval, Neuroscience 104 (2001) 667–676. [26] A.N. McCoy, M.L. Platt, Risk-sensitive neurons in macaque posterior cingulate cortex, Nat. Neurosci. 8 (2005) 1220–1227. [27] T.A. Paine, S.K. Asinof, G.W. Diehl, A. Frackman, J. Leffler, Medial prefrontal cortex lesions impair decision-making on a rodent gambling task: reversal by D1 receptor antagonist administration, Behav. Brain Res. 243 (2013) 247–254. [28] S. Pallanti, C.M. DeCaria, J.E. Grant, M. Urpe, E. Hollander, Reliability and validity of the pathological gambling adaptation of the Yale-Brown ObsessiveCompulsive Scale (PG-YBOCS), J. Gambl. Stud. 21 (2005) 431–443. [29] N.M. Petry, Gambling and substance use disorders: current status and future directions, Am. J. Addict. 16 (2007) 1–9. [30] M.N. Potenza, H.C. Leung, H.P. Blumberg, B.S. Peterson, R.K. Fulbright, C.M. Lacadie, P. Skudlarski, J.C. Gore, An FMRI Stroop task study of ventromedial
M.H. Jung et al. / Neuroscience Letters 583 (2014) 120–125 prefrontal cortical function in pathological gamblers, Am. J. Psychiatry 160 (2003) 1990–1994. [31] M.N. Potenza, M.A. Steinberg, P. Skudlarski, R.K. Fulbright, C.M. Lacadie, M.K. Wilber, B.J. Rounsaville, J.C. Gore, B.E. Wexler, Gambling urges in pathological gambling: a functional magnetic resonance imaging study, Arch. Gen. Psychiatry 60 (2003) 828–836. [32] M.E. Raichle, A.M. MacLeod, A.Z. Snyder, W.J. Powers, D.A. Gusnard, G.L. Shulman, A default mode of brain function, Proc. Natl. Acad. Sci. U. S. A. 98 (2001) 676–682. [33] J. Reuter, T. Raedler, M. Rose, I. Hand, J. Glascher, C. Buchel, Pathological gambling is linked to reduced activation of the mesolimbic reward system, Nat. Neurosci. 8 (2005) 147–148.
125
[34] R.D. Rogers, A.M. Owen, H.C. Middleton, E.J. Williams, J.D. Pickard, B.J. Sahakian, T.W. Robbins, Choosing between small, likely rewards and large, unlikely rewards activates inferior and orbital prefrontal cortex, J. Neurosci. 19 (1999) 9029–9038. [35] J. Tanabe, E. Nyberg, L.F. Martin, J. Martin, D. Cordes, E. Kronberg, J.R. Tregellas, Nicotine effects on default mode network during resting state, Psychopharmacology (Berl.) 216 (2011) 287–295. [36] N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou, F. Crivello, O. Etard, N. Delcroix, B. Mazoyer, M. Joliot, Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain, Neuroimage 15 (2002) 273–289.
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Neuroscience Letters journal homepage: www.elsevier.com/locate/neulet
Decreased connectivity of the default mode network in pathological gambling: A resting state functional MRI study Myung Hun Jung a , Jae-Hun Kim b , Young-Chul Shin c , Wi Hoon Jung d , Joon Hwan Jang d , Jung-Seok Choi e , Do-Hyung Kang d , Jung-Seo Yi a , Chi-Hoon Choi f , Jun Soo Kwon d,g,∗ a
Department of Psychiatry, Hallym University College of Medicine, Chuncheon, Republic of Korea Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea c Department of Psychiatry, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea d Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea e Department of Psychiatry, SMG-SNU Boramae Hospital, Seoul National University, Seoul, Republic of Korea f Department of Diagnostic Radiology, National Medical Center, Seoul, Republic of Korea g Department of Brain & Cognitive Science, College of Natural Science, Seoul National University, Seoul, Republic of Korea b
h i g h l i g h t s • PG displayed decreased DMN in the left SFG, right MTG, and precuneus. • Decreased DMN (precuneus) were associated with severity of PG symptoms. • Altered DMN in PG was similar to neurobiological abnormalities in other addictions.
a r t i c l e
i n f o
Article history: Received 22 April 2014 Received in revised form 14 August 2014 Accepted 9 September 2014 Available online 18 September 2014 Keywords: Pathological gambling Functional connectivity Default mode network fMRI
a b s t r a c t The default mode network (DMN) represents neuronal activity that is intrinsically generated during a resting state. The present study used resting-state fMRI to investigate whether functional connectivity is altered in pathological gambling (PG). Fifteen drug-naive male patients with PG and 15 age-matched male control subjects participated in the present study. The pathological gambling modification of the Yale-Brown Obsessive Compulsive Scale (PG-YBOCS), the Beck Depression Inventory, and the Beck Anxiety Inventory were used to determine symptom severity in all participants. Participants were instructed to keep their eyes closed and not to focus on any particular thoughts during the 4.68-min resting-state functional scan. The patients with PG displayed decreased default mode connectivity in the left superior frontal gyrus, right middle temporal gyrus, and precuneus compared with healthy controls. The severity of PG symptoms in patients with PG was negatively associated with connectivity between the posterior cingulate cortex seed region and the precuneus (r = −0.599, p = 0.018). Decreased functional connectivity within DMN suggests that PG may share similar neurobiological abnormalities with other addictive disorders. Moreover, the severity of PG symptoms was correlated with decreased connectivity in the precuneus, which may be important in the response to treatment in patients with PG. © 2014 Elsevier Ireland Ltd. All rights reserved.
1. Introduction
Abbreviations: DMN, default-mode network; MTG, middle temporal gyrus; PCC, posterior cingulate cortex; PG, pathological gambling; SFG, superior frontal gyrus. ∗ Corresponding author at: Department of Psychiatry, Seoul National University College of Medicine, 101 Daehak-no, Chongno-gu, Seoul 110-744, Republic of Korea. Tel.: +82 2 2072 2972; fax: +82 2 747 9063. E-mail address: [email protected] (J.S. Kwon). http://dx.doi.org/10.1016/j.neulet.2014.09.025 0304-3940/© 2014 Elsevier Ireland Ltd. All rights reserved.
Pathological gambling (PG) is a progressive and chronic disorder, although PG is found to have a high remission rate in population based studies [11]. Recently, PG has been reclassified for “gambling disorder” under non-substance-related disorders, which belongs to the “Substance-Related and Addictive Disorders” in DSM-5 [1]. Previously PG belonged to the impulse-control disorders not elsewhere classified in Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV). Hollander [20] reported that PG belongs to the subtype of impulse-control disorders among the
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subtypes of obsessive-compulsive spectrum disorders. It suggested that relatively psychopathological characteristics and neurobiological mechanisms of PG remain poorly understood. Recent neuroimaging studies have been conducted in patients with PG while they perform various cognitive tasks. These studies have reported the importance of reward and punishment processes in PG [14]. Furthermore, PG patients display greater attentional bias toward gambling cues, including a greater sensitivity and stronger desire to gamble compared with controls. Crockford et al. [8] reported increased dorsolateral prefrontal cortex activity in PG patients while they were viewing gambling compared with nature scenes. In other research, decreased activity in the anterior cingulate cortex, orbitofrontal cortex, and basal ganglia was reported in patients with PG compared with control subjects in response to a gambling video [31]. Similarly, decreased ventromedial prefrontal cortex activity related to response inhibition in response to the Stroop test was shown in PG patients, suggesting difficulties with impulse control [30]. Previous studies on resting-state functional connectivity have reported that specific brain areas are more activated during rest, and this network of connections has since been described as the ‘default-mode network’ (DMN) [32]. The DMN primarily comprises the anterolateral temporal cortex, parahippocampal gyrus, thalamus, pons, and cerebellum, as well as part of the medial prefrontal cortex and the posterior cingulate cortex (PCC) [13]. The PCC is associated with autobiographical memory [25] and is activated by self-reflection, self-confidence, and attitude [21]. In combination with the PCC, the medial prefrontal cortex is typically deactivated during a recognition challenge and activated during a resting state [32]. The DMN is a network of brain regions that are active when an individual is not focused on the outside world, and the brain is at wakeful rest [13,18]. Neuroimaging studies including DMN make it possible to distinguish particular brain areas that are involved in the development and maintenance of psychiatric disorders and have an advantage over psychiatric survey or behavioral approaches. In this study, we used resting-fMRI to investigate the DMN in patients with PG. The aims of this study were to investigate altered DMN resting-state functional connectivity in patients with PG and to determine whether there are any relationships between altered the degree of functional connectivity in the DMN and PG symptom severity.
2. Materials and methods 2.1. Subjects and clinical assessments Fifteen male patients (27.93 ± 3.59 years) who were recruited from the outpatient clinic at KangBuk Samsung Hospital in Seoul, Korea, participated in the present study. The patients diagnosed as a PG with the Structured Clinical Interview for DSM-IV (SCID) [12] and received a score ≥5 on the South Oaks Gambling Screen (SOGS) [23]. Fifteen age- and IQ-matched healthy controls (26.60 ± 4.29 years) also participated in the study and were assessed for psychiatric disorders with the Structured Clinical Interview for DSM-IV, Non-patient Version (SCID-NP). Some of the PG patients and healthy controls had participated in our previous studies [7]. PG symptom severity was measured with the 10-item YaleBrown Obsessive Compulsive Scale for pathological gambling (PG-YBOCS) [28]. The Beck Depression Inventory (BDI) [4] and the Beck Anxiety Inventory (BAI) [3] were also used to assess the severity of depression and anxiety symptoms, respectively. To measure intelligence, all participants took the Korean version of the Wechsler Adult Intelligence Scale (K-WAIS). Exclusion criteria for the present study included a history of psychotic disorder, seizure disorder, significant head injury, mental retardation, and
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substance abuse disorder (except for nicotine use). The Institutional Review Boards at the Kangbuk Samsung Hospital and the Seoul National University Hospital approved the study protocol, and written informed consent was obtained from all subjects prior to participation.
2.2. MR acquisition and preprocessing All images were acquired with a 1.5-T Avanto system (Siemens, Erlangen, Germany). Head restraint straps and foam blocks were used to minimize head movement, and earplugs and headphones were used to minimize scanner noise. Functional data were acquired using a T2*-weighted gradient echo-planar image sequence positioned parallel to the anterior–posterior commissure plane over the entire brain (repetition time (TR) = 2.34 s, echo time (TE) = 52 ms, field of view (FOV) = 220 mm, flip angle (FA) = 90◦ , voxel size = 3.44 mm × 3.44 mm × 5 mm, no interslice gap, interleaved, 25 axial slices). A 4.68-min scan (120 volumes) was performed on each participant. During the scan, subjects were explicitly instructed to close their eyes, relax, and refrain from focusing on any thoughts. T1 anatomical volume images were also acquired for each subject with the following parameters: TR = 11.6 s, TE = 4.76 ms, FOV = 230 mm, FA = 15◦ , voxel size = 0.45 mm × 0.45 mm × 0.9 mm. Data preprocessing was performed using the FMRIB Software Library (FSL; http://www.fmrib.ox.ac.uk/fsl/). The first four volumes of each functional time series were discarded to remove non-equilibrium effects from T1 saturation. Corrections for timing differences in slice acquisition were performed. Sync interpolation was used to realign all volumes to the middle image in an effort to remove signals correlated with head motion. Spatial normalization to a standard template provided by the Montreal Neurological Institute (MNI template) and temporal signal scaling to yield a whole-brain-mode value of 1000 were also performed. Spatial normalization was performed using affine transformation (12 DOF), and normalized image was resampled 2 mm × 2 mm × 2 mm voxel size. The following additional steps were performed with AFNI software (http://afni.nimh.nih.gov/afni/). Temporal band-pass filtering (0.009 Hz < f < 0.08 Hz) was performed to isolate the low-frequency fluctuations of interest; then, these were spatially smoothed with a 5-mm full-width at half-maximum Gaussian blur. Signals from the regions of no interest and the first temporal derivatives (headmotion parameter, global, white matter, and cerebrospinal fluid signals) were regressed out.
2.3. Posterior cingulate cortex functional connectivity analysis The DMN was defined as brain areas that were positively correlated with the PCC seed region. The PCC seed mask was created using the anatomically labeled template reported by TzourioMazoyer et al. [36]. For each subject, the representative time series for the PCC region was obtained by averaging the residual time series over all voxels in the PCC mask. PCC mask was defined bilaterally, and the averaging timeseries were extracted from both left and right PCC. Then, functional connectivity maps (PCC-FC maps) were produced by computing the Pearson’s correlation coefficients from each time course for the PCC from all other brain voxels. Correlation coefficients were normalized with the Fisher’s z transform. For within-group analyses, a one-sample t-test was performed using a false-discovery-rate (FDR) correction threshold of q < 0.05. For between-group analyses, two-sample t-tests were performed. Significant group differences were reported using the criteria of uncorrected p < 0.005 with a minimum cluster extent of 20
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Table 1 Demographic and clinical characteristics of the subjects.a Variables
Demographic data Age (years) Education (years) IQ score Clinical data Age of onset (years) Duration of illness (years) PG-YBOCS total score SOGS BDI BAI
Patients with PG (N = 15)
Control subjects (N = 15)
Mean ± SD
Mean ± SD
27.93 ± 3.59 14.80 ± 1.70 113.67 ± 9.96
26.60 ± 4.29 14.27 ± 1.39 114.47 ± 7.10
25.67 2.20 16.13 15.90 15.20 11.00
± ± ± ± ± ±
3.92 1.29 7.28 1.73 12.32 13.40
3.00 ± 4.81 3.67 ± 3.85
t
p
−0.92 −0.942 0.25
0.36 0.35 0.80
−3.51 −2.04
0.01b 0.05
Abbreviations: PG, pathological gambling; SD, standard deviation; IQ, intelligence quotient; PG-YBOCS, pathological gambling modification of Yale-Brown Obsessive Compulsive Scale; SOGS, South Oaks Gambling Screen; BDI, Beck Depression Inventory; BAI, Beck Anxiety Inventory. a Independent sample t-test was used. b p < 0.05.
voxels. The correlation between PCC functional connectivity within the DMN and symptom severity in PG patients was also examined. 3. Results No significant differences in age, education, and IQ scores were found between patients with PG and healthy controls. However, patients with PG obtained higher scores on the BDI (t = −3.51, p = 0.01) and BAI (t = −2.04, p = 0.05) compared with control subjects. Participant demographic and clinical characteristics are summarized in Table 1. Significant differences in connectivity to the PCC seed region within specific subregions of the DMN were noted in PG patients compared with controls (Fig. 1 and Table 2). Healthy controls displayed greater default mode connectivity in the left medial SFG, right middle temporal gyrus (MTG), and precuneus compared with PG patients (Fig. 2A–C). No areas were detected in which patients with PG showed significantly greater functional connectivity in the DMN compared with healthy controls. In the regions with significant differences in activity between PG patients and controls, PG symptom severity, as measured by the PGYBOCS, was correlated with decreased connectivity between the PCC seed region and the precuneus (r = −0.599, p = 0.018) (Fig. 2D). 4. Discussion The present study was the first to report decreased functional connectivity of the DMN in patients with PG during a resting state. Decreased functional connectivity of the left superior frontal gyrus (medial), right MTG, and precuneus was found in patients with
PG compared with healthy controls. Moreover, decreased functional connectivity between the PCC seed region precuneus was correlated with the severity of PG symptoms, as measured by the PG-YBOCS. There were some reports that have applied resting-state fMRI in patients with substance addictive disorder including alcohol [6], heroin [24], and nicotine [35] and other relevant addictive disorder such as internet gaming addicted adolescents [10] to further understand its mechanisms. Chanraud et al. [6] reported that the spontaneous slow fluctuations of fMRI signals in the PCC and cerebellar regions in alcoholics were less synchronized and suggested compensatory networking to achieve normal performance. Addictive subjects with heroin had increased functional connectivity in right hippocampus and decreased functional connectivity in right dorsal anterior cingulate cortex and left caudate in the DMN [24]. And nicotine was associated with a decreased activity in the DMN such as PCC, precuneus, paracentral lobule, and medial orbitofrontal cortex [35]. For substance addictive subjects, altered DMN suggested diminished cognitive control related to attention and self-monitoring, which might underlie weakened strength of cognitive control in the addictive state. Recently, Dalwani et al. [9] reported that male adolescents with conduct disorder and substance use disorder showed the reduced DMN activity in superior, medial and middle frontal gyrus, retrosplenial cortex, lingual gyrus, and bilateral middle temporal gyrus. These results might share similar neurobiological abnormalities with gambling disorder. Although decreased default mode connectivity findings in PG cannot be explained by direct effects of abused substances on the brain, the present study propose that PG is associated with abnormalities in the reward circuit and decision-making network
Fig. 1. Areas of positive correlation with posterior cingulate cortex seed region (default mode network) for (A) healthy controls and (B) patients with pathological gambling. (C) Areas in which patients with pathological gambling showed significantly lesser default mode network connectivity than healthy controls.
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Table 2 Regional differences in default mode network connectivity between patients with PG and control subjects.a Anatomical region
Side
BA
Peak coordination (MNI) x
Control subjects > patients with PG Superior frontal gyrus Middle temporal gyrus Precuneus
L R
10 21
−6 54 0
y 68 −24 −63
Cluster size
Z-value
z 27 −10 24
113 80 79
4.07 4.11 4.01
Abbreviations: BA, Brodmann area; MNI, Montreal Neurological Institute; PG, pathological gambling; L, left; R, right. a The threshold of between-group analyses set to p < 0.005 (uncorrected) and cluster size greater than 20 voxels (160 mm3 ).
structure of the brain during rest. The medial portion of the superior frontal gyrus is a part of the medial prefrontal cortex. The dorsomedial prefrontal cortex, which contains Brodmann areas 8–10, is involved in self-reference and self-reflection [5,17]. Specifically, the medial prefrontal cortex is responsible for the actualization of various self-related characteristics. During self-relevant processing, the ‘dorsal medial prefrontal cortex (dMPFC) subsystem’ is active which is made up of included the dMPFC, temporoparietal junction, lateral temporal cortex, and temporal pole [2]. The reduced resting-state functional connectivity in the superior frontal gyrus is likely associated with reduced self-related mental activities, including self-reflection, in patients with PG compared with healthy controls. The medial prefrontal cortex is a critical part of the mesolimbic circuit, which underlies the pathophysiology of addiction. In previous studies, ventral striatal and ventromedial prefrontal cortex activity was significantly decreased in PG patients compared with controls in response to financial
reward tasks, and a negative correlation between ventral striatal activity and PG symptom severity has been reported [33]. These results indicate decreased sensitivity to rewards in PG. Moreover, naltrexone, an opioid antagonist, is effective for PG symptoms related to gambling desire and also for inhibition of the associated reinforcing effects of the dopamine reward circuit [15]. The medial PFC is important for decision-making that is usually assessed by ‘gamble’ tasks, which are designed to simulate decisions in terms of uncertainty, reward and punishment [27]. Thus the decreased functional connectivity of the medial prefrontal cortex in patients with PG during the resting state reported in the present study support an association with an abnormal mesolimbic reward circuit and decision-making in PG. Previous positron emission tomography (PET) studies reported decreased regional cerebral blood flow (rCBF) in the temporal lobe, including the MTG, precentral gyrus, and cerebellum during reward-related decision-making process in control subjects [34].
Fig. 2. Plots for default mode connectivity in (A) SFG, (B) MTG, and (C) precuneus between control subjects (CTL) and patients with PG. (D) Correlation between PG-YBOCS score and default mode connectivity in the precuneus.
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As these areas are associated with decision-making, the decreased functional connectivity of the MTG during the resting state in patients with PG is likely associated with a decreased ability to cope in uncertain situations. In addition, the MTG was positively correlated with reward dependence measured using [18F] fluorodeoxyglucose PET in 31 healthy subjects whose temperament was assessed using the Temperament and Character Inventory [19]. The PCC and the precuneus are associated with the level of reward and the possibility of reward prediction and are consequently activated during reward processes in healthy subjects [26]. Symptoms of tolerance or craving in PG are similar to those found in substance abuse [29]. Previous studies have reported that PCC activity is negatively correlated with therapeutic effects in patients with cocaine abuse problems [22]. As less functional connectivity between the superior frontal gyrus and the PCC seed region was detected in patients with PG, the decreased functional connectivity between the precuneus and the PCC seed region suggest decreased sensitivity toward reward processes in these patients. We also found that the severity of gambling in patients with PG was negatively correlated with the level of functional connectivity between the PCC seed region and the precuneus. This region may be a useful marker of PG severity or a potential treatment target for the illness. There were several limitations to the present study. First, the study was conducted on a small number of subjects; therefore, the generalizability of the results to other groups (e.g., women) is limited. PG, however, occurs twice as often in men as in women. Despite the small number of participants, the PG group was relatively homogeneous, and none had undergone psychiatric drug or cognitive treatment prior to the study. Second, symptoms of depression, present in the subject sample, may have affected the results of the study. The BDI score, which measured the severity of depressive symptoms, was significantly higher in the PG group than in the control group. Moreover, increased functional connectivity of the subgenual cingulate cortex and the thalamus was found during the resting state in patients with depression, and a positive correlation between the duration of depressive episodes and resting connectivity in the subgenual cingulate cortex was noted [16], unlike in patients with PG.
5. Conclusions Decreased default mode connectivity was found in the left superior frontal gyrus, right MTG, and precuneus in patients with PG. Moreover, clinical PG symptom severity was negatively correlated with activity in the precuneus. These results provide evidence for altered reward processes and decision-making in patients with PG and demonstrate decreased functional connectivity in the brain regions that underlie these behavioral functions. And these findings suggest that PG may share similar neurobiological abnormalities with other addictive disorders.
Conflict of interest None.
Acknowledgment This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2013R1A2A1A03071089).
References [1] P.A. American, Diagnostic and Statistical Manual of Mental Disorders, American Psychiatric Press Inc., Washington, DC, 2013. [2] J.R. Andrews-Hanna, J.S. Reidler, J. Sepulcre, R. Poulin, R.L. Buckner, Functionalanatomic fractionation of the brain’s default network, Neuron 65 (2010) 550–562. [3] A.T. Beck, N. Epstein, G. Brown, R.A. Steer, An inventory for measuring clinical anxiety: psychometric properties, J. Consult. Clin. Psychol. 56 (1988) 893–897. [4] A.T. Beck, C.H. Ward, M. Mendelson, J. Mock, J. Erbaugh, An inventory for measuring depression, Arch. Gen. Psychiatry 4 (1961) 561–571. [5] F. Castelli, F. Happe, U. Frith, C. Frith, Movement and mind: a functional imaging study of perception and interpretation of complex intentional movement patterns, Neuroimage 12 (2000) 314–325. [6] S. Chanraud, A.L. Pitel, A. Pfefferbaum, E.V. Sullivan, Disruption of functional connectivity of the default-mode network in alcoholism, Cereb. Cortex 21 (2011) 2272–2281. [7] J.S. Choi, Y.C. Shin, W.H. Jung, J.H. Jang, D.H. Kang, C.H. Choi, S.W. Choi, J.Y. Lee, J.Y. Hwang, J.S. Kwon, Altered brain activity during reward anticipation in pathological gambling and obsessive-compulsive disorder, PLOS ONE 7 (2012) e45938. [8] D.N. Crockford, B. Goodyear, J. Edwards, J. Quickfall, N. el-Guebaly, Cueinduced brain activity in pathological gamblers, Biol. Psychiatry 58 (2005) 787–795. [9] M.S. Dalwani, J.R. Tregellas, J.R. Andrews-Hanna, S.K. Mikulich-Gilbertson, K.M. Raymond, M.T. Banich, T.J. Crowley, J.T. Sakai, Default mode network activity in male adolescents with conduct and substance use disorder, Drug Alcohol Depend. 134 (2014) 242–250. [10] W.N. Ding, J.H. Sun, Y.W. Sun, Y. Zhou, L. Li, J.R. Xu, Y.S. Du, Altered default network resting-state functional connectivity in adolescents with Internet gaming addiction, PLOS ONE 8 (2013) e59902. [11] S.A. Eisen, W.S. Slutske, M.J. Lyons, J. Lassman, H. Xian, R. Toomey, S. Chantarujikapong, M.T. Tsuang, The genetics of pathological gambling, Semin. Clin. Neuropsychiatry 6 (2001) 195–204. [12] M.B. First, R.L. Spitzer, M. Gibbon, J. Williams, Structured Clinical Interview for DSM-IV Axis I Disorder, New York State Psychiatric Institute, New York, 1996. [13] P. Fransson, Spontaneous low-frequency BOLD signal fluctuations: an fMRI investigation of the resting-state default mode of brain function hypothesis, Hum. Brain Mapp. 26 (2005) 15–29. [14] A.E. Goudriaan, J. Oosterlaan, E. de Beurs, W. Van den Brink, Pathological gambling: a comprehensive review of biobehavioral findings, Neurosci. Biobehav. Rev. 28 (2004) 123–141. [15] J.E. Grant, S.W. Kim, B.K. Hartman, A double-blind, placebo-controlled study of the opiate antagonist naltrexone in the treatment of pathological gambling urges, J. Clin. Psychiatry 69 (2008) 783–789. [16] M.D. Greicius, B.H. Flores, V. Menon, G.H. Glover, H.B. Solvason, H. Kenna, A.L. Reiss, A.F. Schatzberg, Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus, Biol. Psychiatry 62 (2007) 429–437. [17] D.A. Gusnard, E. Akbudak, G.L. Shulman, M.E. Raichle, Medial prefrontal cortex and self-referential mental activity: relation to a default mode of brain function, Proc. Natl. Acad. Sci. U. S. A. 98 (2001) 4259–4264. [18] D.A. Gusnard, M.E. Raichle, Searching for a baseline: functional imaging and the resting human brain, Nat. Rev. Neurosci. 2 (2001) 685–694. [19] Y. Hakamata, M. Iwase, H. Iwata, T. Kobayashi, T. Tamaki, M. Nishio, K. Kawahara, H. Matsuda, N. Ozaki, S. Honjo, T. Inada, Regional brain cerebral glucose metabolism and temperament: a positron emission tomography study, Neurosci. Lett. 396 (2006) 33–37. [20] E. Hollander, Obsessive-Compulsive Related Disorders, American Psychiatric Press Inc., Washington, DC, 1993. [21] S.C. Johnson, L.C. Baxter, L.S. Wilder, J.G. Pipe, J.E. Heiserman, G.P. Prigatano, Neural correlates of self-reflection, Brain 125 (2002) 1808–1814. [22] T.R. Kosten, B.E. Scanley, K.A. Tucker, A. Oliveto, C. Prince, R. Sinha, M.N. Potenza, P. Skudlarski, B.E. Wexler, Cue-induced brain activity changes and relapse in cocaine-dependent patients, Neuropsychopharmacology 31 (2006) 644–650. [23] H.R. Lesieur, S.B. Blume, The South Oaks Gambling Screen (SOGS): a new instrument for the identification of pathological gamblers, Am. J. Psychiatry 144 (1987) 1184–1188. [24] N. Ma, Y. Liu, X.M. Fu, N. Li, C.X. Wang, H. Zhang, R.B. Qian, H.S. Xu, X. Hu, D.R. Zhang, Abnormal brain default-mode network functional connectivity in drug addicts, PLoS ONE 6 (2011) e16560. [25] R.J. Maddock, A.S. Garrett, M.H. Buonocore, Remembering familiar people: the posterior cingulate cortex and autobiographical memory retrieval, Neuroscience 104 (2001) 667–676. [26] A.N. McCoy, M.L. Platt, Risk-sensitive neurons in macaque posterior cingulate cortex, Nat. Neurosci. 8 (2005) 1220–1227. [27] T.A. Paine, S.K. Asinof, G.W. Diehl, A. Frackman, J. Leffler, Medial prefrontal cortex lesions impair decision-making on a rodent gambling task: reversal by D1 receptor antagonist administration, Behav. Brain Res. 243 (2013) 247–254. [28] S. Pallanti, C.M. DeCaria, J.E. Grant, M. Urpe, E. Hollander, Reliability and validity of the pathological gambling adaptation of the Yale-Brown ObsessiveCompulsive Scale (PG-YBOCS), J. Gambl. Stud. 21 (2005) 431–443. [29] N.M. Petry, Gambling and substance use disorders: current status and future directions, Am. J. Addict. 16 (2007) 1–9. [30] M.N. Potenza, H.C. Leung, H.P. Blumberg, B.S. Peterson, R.K. Fulbright, C.M. Lacadie, P. Skudlarski, J.C. Gore, An FMRI Stroop task study of ventromedial
M.H. Jung et al. / Neuroscience Letters 583 (2014) 120–125 prefrontal cortical function in pathological gamblers, Am. J. Psychiatry 160 (2003) 1990–1994. [31] M.N. Potenza, M.A. Steinberg, P. Skudlarski, R.K. Fulbright, C.M. Lacadie, M.K. Wilber, B.J. Rounsaville, J.C. Gore, B.E. Wexler, Gambling urges in pathological gambling: a functional magnetic resonance imaging study, Arch. Gen. Psychiatry 60 (2003) 828–836. [32] M.E. Raichle, A.M. MacLeod, A.Z. Snyder, W.J. Powers, D.A. Gusnard, G.L. Shulman, A default mode of brain function, Proc. Natl. Acad. Sci. U. S. A. 98 (2001) 676–682. [33] J. Reuter, T. Raedler, M. Rose, I. Hand, J. Glascher, C. Buchel, Pathological gambling is linked to reduced activation of the mesolimbic reward system, Nat. Neurosci. 8 (2005) 147–148.
125
[34] R.D. Rogers, A.M. Owen, H.C. Middleton, E.J. Williams, J.D. Pickard, B.J. Sahakian, T.W. Robbins, Choosing between small, likely rewards and large, unlikely rewards activates inferior and orbital prefrontal cortex, J. Neurosci. 19 (1999) 9029–9038. [35] J. Tanabe, E. Nyberg, L.F. Martin, J. Martin, D. Cordes, E. Kronberg, J.R. Tregellas, Nicotine effects on default mode network during resting state, Psychopharmacology (Berl.) 216 (2011) 287–295. [36] N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou, F. Crivello, O. Etard, N. Delcroix, B. Mazoyer, M. Joliot, Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain, Neuroimage 15 (2002) 273–289.