Psychiatry Research: Neuroimaging 232 (2015) 208–213
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Psychiatry Research: Neuroimaging journal homepage: www.elsevier.com/locate/psychresns
Mapping brain volumetric abnormalities in never-treated pathological gamblers Daniel Fuentes a, Patricia Rzezak b,g, Fabricio R. Pereira b,g, Leandro F. Malloy-Diniz c, Luciana C. Santos b,g, Fábio L.S. Duran b,g, Maria A. Barreiros d, Cláudio C. Castro e, Geraldo F. Busatto b,g,n, Hermano Tavares b, Clarice Gorenstein f,g a
Psychology & Neuropsychology Unit, Institute of Psychiatry, Clinical Hospital, Medical School, University of São Paulo (IPq-HCFMUSP), Integrated Laboratories of Neuropsychology (LINEU), São Paulo, Brazil b Department of Psychiatry, HCFMUSP, Brazil c Department of Mental Health, School of Medicine, Federal University of Minas Gerais-UFMG, LINEU, Brazil d Institute of Radiology, HCFMUSP, Brazil e Section of Magnetic Resonance, Heart Institute, HCFMUSP, Brazil f Department of Pharmacology, Institute of Biomedical Sciences USP, Laboratory of Psychopharmacology-LIM-23, IPq-HCFMUSP, Brazil g Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, São Paulo, Brazil
art ic l e i nf o
a b s t r a c t
Article history: Received 16 February 2014 Received in revised form 17 March 2015 Accepted 2 April 2015 Available online 13 April 2015
Several magnetic resonance imaging (MRI) studies to date have investigated brain abnormalities in association with the diagnosis of pathological gambling (PG), but very few of these have specifically searched for brain volume differences between PG patients and healthy volunteers (HV). To investigate brain volume differences between PG patients and HV, 30 male never-treated PG patients (DSM-IV-TR criteria) and 30 closely matched HV without history of psychiatric disorders in the past 2 years underwent structural magnetic resonance imaging with a 1.5-T instrument. Using Freesurfer software, we performed an exploratory whole-brain voxelwise volume comparison between the PG group and the HV group, with false-discovery rate correction for multiple comparisons (po 0.05). Using a more flexible statistical threshold (po 0.01, uncorrected for multiple comparisons), we also measured absolute and regional volumes of several brain structures separately. The voxelwise analysis showed no clusters of significant regional differences between the PG and HV groups. The additional analyses of absolute and regional brain volumes showed increased absolute global gray matter volumes in PG patients relative to the HV group, as well as relatively decreased volumes specifically in the left putamen, right thalamus and right hippocampus (corrected for total gray matter). Our findings indicate that structural brain abnormalities may contribute to the functional changes associated with the symptoms of PG, and they highlight the relevance of the brain reward system to the pathophysiology of this disorder. & 2015 Elsevier Ireland Ltd. All rights reserved.
Keywords: Impulse control disorder Addiction Magnetic resonance imaging Putamen Thalamus Hippocampus
1. Introduction Pathological gambling (PG) is a disorder characterized by gambling behaviors that disturb interpersonal relationships and adversely affect both financial and socioeconomic status (Potenza, 2001). In the 1980s, PG became part of the medical nosography, being classified within the realm of impulse control disorders (American Psychiatric Association, 2000). Epidemiological studies have indicated an increasing prevalence of PG (Petry and Armentano, 1999; Shaffer et al., 1999), with the most recent estimates pointing to a prevalence of 1–4% of PG in the n Correspondence to: Centro de Medicina Nuclear, 31 andar, LIM-21, Rua Dr. Ovídio Pires de Campos, s/n, Postal code 05403-010, São Paulo, SP, Brazil. Tel.: þ 55 11 2661 8132; fax: þ55 11 26618193. E-mail address: [email protected] (G.F. Busatto).
http://dx.doi.org/10.1016/j.pscychresns.2015.04.001 0925-4927/& 2015 Elsevier Ireland Ltd. All rights reserved.
general population (Shaffer et al., 1999; Potenza, 2001; Shaffer and Korn, 2002). Maladaptative and persistent gambling behaviors such as excessive preoccupation with gambling, lying to conceal the extent of involvement with gambling, need to gamble with increasing amounts of money, unsuccessful efforts to stop gambling, and chasing one's losses, are all related to the difficulties in social adjustment faced by people with PG (Reuter et al., 2005). The neurobiological basis of PG remains unclear, although functional neuroimaging techniques have consistently indicated the presence of regional brain activity abnormalities underlying the symptoms of PG. Recent studies using functional magnetic resonance imaging (fMRI) have demonstrated lower activity of the prefrontal cortex, orbitofrontal cortex, insula and striatum in PG patients relative to healthy volunteers during the performance of tasks involving control inhibition, visual presentation of gambling
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cues, and delay discounting (Eber and Shaffer, 2000; Knutson et al., 2003; Volkow et al., 2003; Crockford et al., 2005; Tanabe et al., 2007; Beck et al., 2009; Frascella et al., 2010; Choi et al., 2012; Sescousse et al., 2013). Complementary studies using positron emission tomography (PET) suggested enhanced dopamine release in PG subjects in response to pharmacological challenge and gambling (Joutsa et al., 2012; Boileau et al., 2014). Even though these studies suggest the existence of a relationship between abnormal brain functioning and PG symptoms, all of the studies have methodological limitations, such as their relatively small sample sizes (ranging from 7 to 18 PG subjects) and the potentially confounding influence of pharmacological treatment (Balodis et al., 2012). Nevertheless, they provide evidence that the presence of functional abnormalities in the fronto-striatal reward system might be associated with the vulnerability to develop PG. Alternatively, the identified abnormalities in brain function might be a consequence of the repetitive behavior of pathological gaming. Much less is known about morphological brain changes possibly associated with PG, given the small number of investigations to date and their inconsistent findings. One recent morphometric MRI investigation showed reduced volumes of the amygdala and hippocampus in a PG sample including untreated patients with comorbid alcohol/drug abuse relative to healthy controls (Rahman et al., 2014), while a different MRI study reported increased volumes of the ventral striatum and right prefrontal cortex in a mixed sample of mixed treated and non-treated PG patients relative to healthy controls (Koehler et al., 2015). On the other hand, in two MRI studies investigating volumetric changes in samples of never-treated PG patients with no history of comorbid alcohol/drug abuse, no differences from healthy controls were detected in the measurements of either gray or white brain matter volumes (Joutsa et al., 2011; van Holst et al., 2012). Previous studies have associated PG with poorer performance on neuropsychological tests involving decision-making, cognitive control and flexibility (Goudriaan et al., 2005; Fuentes et al., 2006; Odlaug et al., 2011; Grant et al., 2012; Goudriaan et al., 2014), indicating an impairment of reward processing and a potential abnormality of related brain structures such as the prefrontal cortex and ventral striatum (Boog et al., 2014). Given the scarcity of previous morphometric MRI studies of PG, we carried out the present study with the purpose of comparing brain volumetric measurements between never-treated PG patients and healthy volunteers. We aimed to investigate if morphological abnormalities would distinguish PG individuals from controls in fronto-striatal circuits and medial temporal regions implicated in recent brain volumetric studies of PG (Rahman et al., 2014).
2. Methods 2.1. Participants Thirty-seven male subjects consecutively admitted to the Gambling Outpatient Unit of the Institute of Psychiatry of the University of São Paulo were invited to take part in the study. They had never received psychotherapeutic support or pharmacological treatment for psychiatric or neurological disorders. Eight (26.6%) subjects had previously attended Gamblers Anonymous meetings. All PG patients were screened to assess the magnitude of their gambling habits as determined by the South Oaks Gambling Screen (SOGS; score Z 5 as inclusion criterion) (Lesieur and Blume, 1987). Subsequently, they underwent a clinical interview to assess DSM-IV-TR criteria for PG (American Psychiatric Association, 2000). The Schedule for Clinical Assessment in Neuropsychiatry (SCAN) (Wing et al., 1990) was also administered to exclude other addictive disorders (except tobacco smoking, present in 19 patients) and major current psychiatric disorders. Six subjects were excluded due to comorbidities such as alcohol dependence (n¼ 3), depression with suicidal ideation (n¼ 1), bipolar disorder (n¼ 1), and obsessive–compulsive disorder (n¼ 1). One other PG subject was excluded due to claustrophobic anxiety during MRI scanning. The remaining PG patients (n¼ 30)
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were aged from 19 to 59 years (mean¼ 37.3 7 9.6 years), and all of them were righthanded. There were 28 Caucasian in this group and 22 patients completed, at least, high school, with a mean of 12.17 3.5 years of formal education. Intelligence Quotient in the patients based on the Block Design and Vocabulary subtests of the Wechsler Adult Intelligence Scale-Revised (Wechsler, 1981) was estimated as 1017 9.9. Thirty male, right-handed healthy volunteers (HV) also took part in the present study. They were recruited through advertisements in a recreational club located in the same neighborhood of the university hospital and were matched to participants in the PG group by age and educational level. They were screened for psychiatric disorders through the Self-Report Questionnaire (Harding et al., 1980) and interviewed with the SCAN (Wing et al., 1990) to exclude current psychiatric diagnoses, except for nicotine dependence. The HV group was aged from 20 to 59 years (mean age¼37.3 7 9.7 years). There were 24 Caucasians in this group, and 25 subjects completed at least high school education (average of 12.6 72.3 years of formal education). The mean estimated Intelligence Quotient for the HV group was 997 8.8. The local ethics committee approved data assessment, and all participants gave written consent after complete description of the study. 2.2. MRI scanning Structural MRI data were collected using a 1.5-T scanner (GE Sigma, Milwaukee, WI, USA). Whole-brain images were acquired using a three-dimensional T1-weighted fast field echo sequence (echo time¼ 5.2 ms, repetition time¼21.7 ms, flip angle¼ 201, field of view¼ 220 mm, 256 256 matrix) in contiguous axial slices with 1.5-mm thickness. An experienced radiologist visually inspected each dataset with the purpose of identifying artifacts during image acquisition and the presence of silent brain lesions. No gross structural brain abnormality was found in any participant. 2.3. Image processing and statistical analyses Initially, an exploratory whole-brain voxelwise analysis was carried out by means of FreeSurfer software (http://surfer.nmr.mgh.harvard.edu/) (Fischl and Dale, 2000) using a standard image-processing protocol described in detail elsewhere (Dale et al., 1999; Fischl et al., 1999). Briefly, we initially converted the original DICOM images to MGH format. Then, motion correction and conform, non-uniform intensity normalization and Talairach transformation algorithms were applied. Subsequently, intensity normalization, skull stripping, volumetric registration and volumetric labeling were computed to segment and smooth brain tissues. A kernel of 10 mm at the full width at half-maximum (FWHM) was used for spatial smoothing. After that, remaining images were inflated and parameterized into QSphere, which provides a spherical representation of brain tissue, gyri and sulci. Brain topology was then automatically fixed, mapped and registered on spherical parameters. Finally, an average curvature map was calculated to access cortical and sub-cortical parcellation scores. Between-group whole-brain voxelwise comparisons were carried out using an analysis of covariance (ANCOVA) model, considering age as a confounding factor. Also, we carried out a whole-brain voxelwise linear correlation analysis in the PG group between brain volumes and the number of DSM-IV comorbid diagnostic items that the patient presented, used as a measure of disorder severity. For these exploratory whole-brain analyses, we used a strict statistical threshold of po0.05 with false-discovery rate (FDR) correction for multiple comparisons. Using the same image-processing protocol above, we also measured the regional volumes of several brain structures separately, including the caudate nucleus, putamen, pallidum, thalamus, hippocampus, amygdala, nucleus accumbens, cingulate gyrus (caudal, posterior, rostral and isthmus portions), frontal lobe (caudal-middle, rostral-middle, lateral-orbital, medial-orbital, superior, frontalpole, paracentral, precentral, pars opercularis, pars orbitalis and pars triangularis), corpus callosum (posterior, mid-posterior, central, mid-anterior, and anterior portions), and ventricles (third, fourth, lateral and inferior horn). Following these analyses, the absolute volumes of global gray matter, white matter and lateral ventricles were automatically obtained (Fischl et al., 2004). Regional brain volumes for the above structures were calculated according to Eqs. (1) and (2), considering (1) the total intracranial volume and (2) the total gray matter volume: RIC ¼
V BR V IC
ð1Þ
RIC (regional volume relative to the intracranial volume), V BR (absolute volume of a specific brain region), V IC (absolute value of the intracranial volume) RGM ¼
V BR V GM
ð2Þ
RGM (regional volume relative to the total gray matter), V BR (absolute volume of a specific brain region), V GM (absolute volume the total gray matter). For region-of-interest (ROI) inter-group comparisons, we also used ANCOVA considering age as a confounding factor. Findings were reported as significant at a p o0.01 statistical threshold, uncorrected for multiple comparisons. The flexible statistical threshold was applied given the a priori prediction that brain
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abnormalities would be detected specifically in the fronto-striatal reward system and medial temporal structures in the PG group.
3. Results 3.1. Demographic and clinical variables No statistically significant differences were found between the PG and HV groups regarding age, years of formal education and estimated Intelligence Quotient (t o1, p 40.05). The mean SOGS ratings for the PG group were 14.1 73.1, indicating moderate/ severe pathological gambling habits (Lesieur and Blume, 1987). 3.2. Exploratory voxelwise analyses There were no voxel clusters of significant difference between the PG and HV groups at the po 0.05 level, corrected for multiple comparisons. At the same statistical threshold, there were also no significant correlations between brain indices and the number of comorbid DSM-IV-based diagnoses in PG patients. 3.3. Between-group brain volumetric comparisons There were no significant differences in absolute whole brain volumes between the PG (1357.107173.30) and HV (1289.367 125.01) groups (F1,57 ¼3.09, p¼0.084). The absolute total gray matter volumes were 684.2764.7 cm3 in the PG group and 621.6772.8 cm3 in the HV group (F1,57 ¼14.35, po0.001), indicating significantly increased global gray matter among the PG group with the HV group (Fig. 1). There were no significant differences in absolute total white matter volumes between the PG (539.1750.0) and HV (568.5795.0) groups (F1,57 ¼2.22, p¼ 0.141). There were no significant differences in the absolute volumes between the PG and
HV groups of any of the cortical or subcortical ROIs investigated (Table 1 and Fig. 1). Regarding brain volumes corrected for intracranial volumes, a significant relative decrease of total white matter volume was found in the PG group in comparison to the HV group both on the left (0.0167 0.002 vs. 0.017 70.002; F1,57 ¼ 8.947, p ¼0.004) and right (0.016 70.002 vs. 0.017 70.002; F1,57 ¼9.259, p ¼0.004) hemispheres. There were no significant between-group differences in relative volumes corrected for intracranial volume in any other ROI (Table 2 and Fig. 1). Inter-group comparison of regional brain volumes relative to total gray matter (Table 3) demonstrated significantly decreased volumes in PG patients relative to controls in the left putamen (F1,57 ¼ 7.31, p¼0.009), right hippocampus (F1,57 ¼ 8.34, p¼0.005) and right thalamus (F1,57 ¼7.32, p¼0.009) (see Fig. 1). There were no significant correlations between regional brain volumes and the number of comorbid DSM-IV-based diagnoses in PG patients (ro 0.381, p40.041, partial correlation indices corrected for age).
4. Discussion PG is a prevalent and potentially incapacitating behavioral addiction in humans (Potenza et al., 2001). Its pathophysiology has not yet been elucidated, and neuroimaging techniques can be useful tools to increase our understanding about the neural substrate underlying the symptoms of PG. Moreover, morphometric MRI studies of patients with PG provide an invaluable opportunity to delineate structural neural features that might be related to the vulnerability to addictions in general, without the confounding brain-damaging effects caused by the repetitive use of psychoactive substances (a problem that is present in MRI studies of the classic and more prevalent drug addictions such as alcohol and cocaine dependence) (Luciana et al., 2013; Ide et al., 2014).
Fig. 1. Brain volumetric comparisons between PG (pathological gambling) and HV (healthy volunteer) groups. Acc, nucleus accumbens; Amy: amygdala; Cau: caudate nucleus; Hip: hippocampus; Pal: globus pallidus; Put: putamen; Tha: thalamus; GM: gray matter; WM: white matter. n Significant statistical difference.
D. Fuentes et al. / Psychiatry Research: Neuroimaging 232 (2015) 208–213
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Table 1 Absolute regional brain volumes. HV
Left hemisphere
Right hemisphere
Accumbens Amygdala Caudate Hippocampus Pallidum Putamen Thalamus Cortex Cortical WM Accumbens Amygdala Caudate Hippocampus Pallidum Putamen Thalamus Cortex Cortical WM Total volume
PG
Mean
S.D.
Mean
S.D.
486.97 1510.50 3579.60 4320.97 1553.17 5239.13 9405.37 213,499.48 283,765.54 461.23 1506.23 3411.80 4457.20 1382.13 4902.00 9820.20 214,810.76 284,764.66 1,357,075.64
89.48 252.65 423.86 700.85 245.94 583.41 991.31 33,360.16 46,774.23 73.25 191.72 478.78 478.01 270.44 514.20 1155.89 35,097.11 48,525.38 173,299.14
524.00 1518.23 3720.67 4574.13 1602.33 5370.60 9688.67 239,372.45 269,715.89 474.27 1572.73 3512.83 4568.13 1416.53 5015.67 9986.63 241,498.41 269,413.28 1,289,358.42
89.39 173.78 434.68 418.54 182.96 693.71 1097.15 23,899.87 24,926.07 62.42 147.00 455.90 426.44 173.04 613.26 956.66 22,973.73 25,230.12 125,009.16
F
p
2.528 0.020 1.674 3.086 0.858 0.837 1.164 13.579 2.080 0.544 2.280 0.706 0.955 0.372 0.733 0.365 13.723 2.340 3.093
0.117 0.889 0.201 0.084 0.358 0.364 0.285 0.001 0.155 0.464 0.137 0.404 0.332 0.545 0.396 0.548 o 0.001 0.132 0.084
HV: healthy volunteers; PG: pathological gamblers; WM: white matter. Degrees of freedom for all comparisons ¼1, 57 (ANCOVA, with covariance for age). Table 2 Relative regional brain volumes corrected for intracranial volume. HV
Left hemisphere
Right hemisphere
Accumbens Amygdala Caudate Hippocampus Pallidum Putamen Thalamus Cortex Cortical WM Accumbens Amygdala Caudate Hippocampus Pallidum Putamen Thalamus Cortex Cortical WM
PG
Mean
S.D.
Mean
S.D.
1.16E 03 3.61E 03 8.57E 03 1.04E 02 3.74E 03 1.25E 02 2.25E 02 4.99E 01 6.99E 01 1.10E 03 3.59E 03 8.17E 03 1.06E 02 3.30E 03 1.18E 02 2.35E 02 5.01E 01 7.03E 01
2.69E 04 8.11E 04 1.77E 03 2.66E 03 9.67E 04 2.40E 03 4.51E 03 7.69E 03 2.72E 01 2.24E 04 6.58E 04 1.81E 03 1.96E 03 7.57E 04 2.49E 03 4.97E 03 7.69E 03 2.83E 01
1.11E 03 3.18E 03 7.78E 03 9.55E 03 3.35E 03 1.12E 02 2.03E 02 4.98E 01 5.64E 01 9.93E 04 3.28E 03 7.34E 03 9.53E 03 2.97E 03 1.05E 02 2.09E 02 5.02E 01 5.63E 01
9.14E 05 1.48E 04 3.89E 04 3.77E 04 1.90E 04 5.74E 04 1.10E 03 1.81E 02 2.34E 02 6.67E 05 1.45E 04 4.13E 04 3.65E 04 1.93E 04 5.76E 04 1.18E 03 1.70E 02 2.27E 02
F
p
0.424 1.085 0.068 0.163 0.074 0.494 0.177 2.853 8.947 0.101 0.000 0.152 0.622 0.116 0.446 0.466 2.896 9.259
0.518 0.302 0.796 0.688 0.787 0.485 0.675 0.097 0.004 0.752 1.000 0.698 0.434 0.735 0.507 0.497 0.094 0.004
HV: healthy volunteers; PG: pathological gamblers; WM: white matter. Degrees of freedom for all comparisons ¼1, 57 (ANCOVA, with covariance for age).
In the present morphometric MRI study, which evaluated a sample of never-treated PG patients, we found significantly increased absolute global gray matter volumes in the PG group relative to the HV group. This pattern suggests that there may be a tendency toward a greater overall concentration of gray matter in comparison to white matter in this population, and this may contribute to impaired general neuronal processing in PG. The fact that there were no significant between-group differences in white matter volumes in our study does not rule out, however, the possibility that there may be microstructural white matter abnormalities in association with PG, as suggested in preliminary MRI investigations using diffusion tensor imaging (Joutsa et al., 2011). Indeed, when a correction for the differences in intracranial volume was applied, a significant relative reduction of white matter volume in patients compared with controls became evident. Regarding regional brain findings, the exploratory voxelwise analysis showed no clusters of significant regional differences
between the PG and HV groups at a strict statistical threshold corrected for multiple comparisons, nor significant correlations between brain indices and the number of comorbid DSM-IV-based diagnoses in PG patients. Conversely, the ROI between-group comparisons with a more flexible statistical threshold suggested the presence of abnormalities in the relative volume of a number of brain regions (adjusted for total gray matter volumes) of PG patients relative to the HV group. The results of such regional brain volume comparisons provided some support to our hypothesis that structural brain abnormalities are present in the fronto-striatal reward system in association with PG, as we found relative volume reduction of the left basal ganglia in the PG group compared with the HV group. This brain region is predicted to show abnormalities in PG, as repeatedly reported in previous functional imaging studies of this disorder (Balodis et al., 2012; Sescousse et al., 2013). The thalamus, where we also found relatively decreased volumes in PG patients relative to controls, is additionally relevant to reward processing
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Table 3 Relative regional brain volumes corrected for total gray matter. HV
Left hemisphere
Right hemisphere
Accumbens Amygdala Caudate Hippocampus Pallidum Putamen Thalamus Cortex Cortical WM Accumbens Amygdala Caudate Hippocampus Pallidum Putamen Thalamus Cortex Cortical WM
PG
Mean
S.D.
Mean
S.D.
3.80E-04 1.18E-03 2.80E-03 3.36E-03 1.21E-03 4.10E-03 7.35E-03 1.67E-01 2.20E-01 3.61E-04 1.17E-03 2.66E-03 3.47E-03 1.08E-03 3.84E-03 7.68E-03 1.68E-01 2.21E-01
7.39E-05 1.99E-04 4.08E-04 5.02E-04 2.01E-04 6.04E-04 9.67E-04 2.96E-02 2.61E-02 6.52E-05 1.40E-04 3.98E-04 3.30E-04 2.32E-04 5.43E-04 1.13E-03 3.14E-02 2.86E-02
3.94E-04 1.13E-03 2.77E-03 3.40E-03 1.20E-03 4.00E-03 7.23E-03 1.78E-01 2.01E-01 3.55E-04 1.17E-03 2.62E-03 3.40E-03 1.06E-03 3.74E-03 7.47E-03 1.79E-01 2.00E-01
3.94E-04 1.13E-03 2.77E-03 3.40E-03 1.20E-03 4.00E-03 7.23E-03 1.78E-01 2.01E-01 3.55E-04 1.17E-03 2.62E-03 3.40E-03 1.06E-03 3.74E-03 7.47E-03 1.79E-01 2.00E-01
F
p
0.794 6.692 4.481 2.744 3.818 7.309 5.784 0.641 7.206 4.845 5.474 4.916 8.343 4.326 6.458 7.318 0.641 7.138
0.377 0.012 0.039 0.103 0.056 0.009 0.019 0.427 0.009 0.032 0.023 0.031 0.005 0.042 0.014 0.009 0.427 0.010
HV: healthy volunteers; PG: pathological gamblers; WM: white matter. Degrees of freedom for all comparisons ¼1, 57 (ANCOVA, with covariance for age).
(Sescousse et al., 2013), and it has been implicated in previous fMRI studies of PG (Potenza et al., 2003). Finally, our PG subjects also displayed diminished relative volume of the hippocampus, another brain region commonly implicated in the neurocircuitry of drug addiction (Fotros et al., 2013). The latter finding is consistent with a recent report of reduced hippocampal and amygdalar volumes in PG patients relative to healthy controls (Rahman et al., 2014). A number of methodological aspects distinguish our investigation from previous morphometric MRI studies of PG (Joutsa et al., 2011; van Holst et al., 2012). First, the PG condition of our patients was in the moderate/severe range (SOGS ¼14.1 73.1), whereas the PG patients in the study of van Holst et al. (van Holst et al., 2012) were in the mild/moderate (9.92 72.95) range of severity. Second, although Joutsa et al. (2011) studied subjects with similar SOGS ratings to ours (from 10 to 14), the size of their PG sample (n ¼ 12) may not have been large enough to statistically detect subtle brain volume differences between groups. Finally, Rahman et al. (2014) included PG subjects with a current or previous history of alcohol or drug abuse, while in our study PG subjects with such comorbidities were excluded; the relationship between substance abuse and gray and white matter abnormalities has been previously demonstrated, and this factor may add variability to brain volume measurements in neuroimaging studies of PG. The molecular mechanisms underlying the PG-related brain volumetric abnormalities reported herein remain to be elucidated. The localization of our morphometric findings is consistent with current neuroanatomical models of PG, involving brain regions relevant to reward processing. This topographic pattern is consistent with the proposed view that PG may be associated with abnormalities in the early development of fronto-striatal pathways and their two monoaminergic systems – dopaminergic and serotoninergic (Chambers et al., 2003). Although this reasoning implies that brain volume abnormalities reflect the vulnerability for the development of PG, an alternative explanation is that the present findings, for example, the increased global brain volume in our PG patients, are a consequence of repetitive gambling habits (probably reflecting increased neuropil due to repetitive behavior). The same doubt applies to the findings of recent morphometric MRI studies that have reported increased brain volumes in frequent video game players relative to moderate players (Kuhn and Gallinat, 2014). In
favor of the vulnerability hypothesis, Kuhn and Gallinat (2014) recently argued that brain volumetric differences between frequent and moderate video game players were preconditions that lead to a vulnerability for preoccupation with gaming. They suggested that these brain volume differences might make individuals prone to experiencing video gaming as more rewarding in the first place, and this could, in turn, facilitate skill acquisition and lead to further reward resulting from playing. Although video gaming and PG are different activities, their similarities are evident, and the same reasoning could easily be applied to PG. One future strategy to confirm the hypothesis that PG-related brain volume abnormalities are due to the vulnerability to this condition would be to carry out MRI studies investigating the relationship between brain volume abnormalities and molecular genetic features, especially dopaminergic polymorphisms thought to be related to PG (Steeves et al., 2009; Linnet et al., 2010). In regard to the limitations of the present study, the sample investigated was relatively modest in size, and this may have influenced on the negative results of the voxelwise whole-brain analysis using correction for multiple comparisons. It could also be argued that the sample investigated herein might not be representative of the general PG population, for instance, due to the inclusion of exclusively one gender, and only treatment-seeking PG subjects (who are usually at the most severe level of symptoms). Nonetheless, the rigorous selection of a pure diagnostic sample, i.e., without the influence of comorbidities (Lesieur and Blume, 1987), allows us to be more confident in relating brain volume abnormalities to the diagnosis of PG rather than other clinical features. Moreover, our PG subjects were entirely naïve to psychopharmacological agents and/or psychotherapy, thus avoiding a potential influence of earlier treatment on brain volume measurements. Another limitation of our study is the lack of information about smoking in general, and about nicotine dependence in the HV group. These restrictions preclude ruling out the possibility that differences in brain volumes between the PG and HV groups could be at least partially related to nicotine abuse, since nicotine dependence has been previously associated with increased striatal volumes (Das et al., 2012). Additionally, since the individual voxels are classified as part of a specific structure based on univariate statistics (on the parcellation step), suboptimal voxel size can lead to bias in the definition of regional structures and this might constitute one further limitation in the present study.
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In conclusion, this morphometric MRI study suggests the presence of regional gray matter volume abnormalities in nevertreated patients with PG, involving the basal ganglia, thalamus and hippocampus. These brain regions seem to be relevant to the pathophysiology of PG as well as to the neurobiology of addictions in general. Our findings suggest that there may be regional gray matter abnormalities in PG, but this remains to be verified by future studies. To extend and elucidate the findings reported herein, future case-control studies with larger samples are warranted in which in vivo brain morphometric and molecular imaging modalities are combined in the same subjects, as well as longitudinal studies involving repeated MRI measurements in PG and at-risk subjects.
Acknowledgments The authors thank all the staff from the Gambling Outpatient Unit at the Psychiatry Institute of University of São Paulo (IPq) and the Institute of the Heart of the University of São Paulo (INCOR). This work was supported by a grant from the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, Foundation for the Support of Research in the State of São Paulo; Grant no. 02/ 02604-6). Geraldo F. Busatto and Clarice Gorenstein are partially supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, National Council for Scientific and Technological Development, Grants no. 305237/2013-6 and 303341/2010-6, respectively). References American Psychiatric Association, 2000. Diagnostic and Statistical Manual of Mental Disorders, 4th ed. APA, Washington, DC, text rev. Balodis, I.M., Kober, H., Worhunsky, P.D., Stevens, M.C., Pearlson, G.D., Potenza, M.N., 2012. Diminished frontostriatal activity during processing of monetary rewards and losses in pathological gambling. Biol. Psychiatry 71, 749–757. Beck, A., Schlagenhauf, F., Wustenberg, T., Hein, J., Kienast, T., Kahnt, T., Schmack, K., Hägele, C., Knutson, B., Heinz, A., Wrase, J., 2009. Ventral striatal activation during reward anticipation correlates with impulsivity in alcoholics. Biol. Psychiatry 66, 734–742. Boileau, I., Payer, D., Chugani, B., Lobo, D.S., Houle, S., Wilson, A.A., Warsh, J., Kish, S.J., Zack, M., 2014. In vivo evidence for greater amphetamine-induced dopamine release in pathological gambling: a positron emission tomography study with [(11)C]-(þ )-PHNO. Mol. Psychiatry 19, 1305–1313. Boog, M., Höppener, P., v.d. Wetering, B.J., Goudriaan, A.E., Boog, M.C., Franken, I.H., 2014. Cognitive inflexibility in gamblers is primarily present in reward-related decision making. Front. Hum. Neurosci. 8, 569. Chambers, R.A., Taylor, J.R., Potenza, M.N., 2003. Developmental neurocircuitry of motivation in adolescence: a critical period of addiction vulnerability. Am. J. Psychiatry 160, 1041–1052. Choi, J.S., Shin, Y.C., Jung, W.H., Jang, J.H., Kang, D.H., Choi, C.H., Choi, S.W., Lee, J.Y., Hwang, J.Y., Kwon, J.S., 2012. Altered brain activity during reward anticipation in pathological gambling and obsessive–compulsive disorder. PloS One 7, e45938. Crockford, D.N., Goodyear, B., Edwards, J., Quickfall, J., el-Guebaly, N., 2005. Cueinduced brain activity in pathological gamblers. Biol. Psychiatry 58, 787–795. Dale, A.M., Fischl, B., Sereno, M.I., 1999. Cortical surface-based analysis. I. Segmentation and surface reconstruction. NeuroImage 9, 179–194. Das, D., Cherbuin, N., Anstey, K.J., Sachdev, P.S., Easteal, S., 2012. Lifetime cigarette smoking is associated with striatal volume measures. Addict. Biol. 17, 817–825. Eber, G.B., Shaffer, H.J., 2000. Trends in bio-behavioral gambling studies research: quantifying citations. J. Gambl. Stud. 16, 461–467. Fischl, B., Dale, A.M., 2000. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. U. S. A. 97, 11050–11055. Fischl, B., Sereno, M.I., Dale, A.M., 1999. Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system. NeuroImage 9, 195–207. Fischl, B., van der Kouwe, A., Destrieux, C., Halgren, E., Segonne, F., Salat, D.H., Busa, E., Seidman, L.J., Goldstein, J., Kennedy, D., Caviness, V., Makris, N., Rosen, B., Dale, A.M., 2004. Automatically parcellating the human cerebral cortex. Cereb. Cortex 14, 11–22. Fotros, A., Casey, K.F., Larcher, K., Verhaeghe, J.A., Cox, S.M., Gravel, P., Reader, A.J., Dagher, A., Benkelfat, C., Leyton, M., 2013. Cocaine cue-induced dopamine release in amygdala and hippocampus: a high-resolution PET [(1)(8)F]fallypride study in cocaine dependent participants. Neuropsychopharmacology 38, 1780–1788.
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Psychiatry Research: Neuroimaging journal homepage: www.elsevier.com/locate/psychresns
Mapping brain volumetric abnormalities in never-treated pathological gamblers Daniel Fuentes a, Patricia Rzezak b,g, Fabricio R. Pereira b,g, Leandro F. Malloy-Diniz c, Luciana C. Santos b,g, Fábio L.S. Duran b,g, Maria A. Barreiros d, Cláudio C. Castro e, Geraldo F. Busatto b,g,n, Hermano Tavares b, Clarice Gorenstein f,g a
Psychology & Neuropsychology Unit, Institute of Psychiatry, Clinical Hospital, Medical School, University of São Paulo (IPq-HCFMUSP), Integrated Laboratories of Neuropsychology (LINEU), São Paulo, Brazil b Department of Psychiatry, HCFMUSP, Brazil c Department of Mental Health, School of Medicine, Federal University of Minas Gerais-UFMG, LINEU, Brazil d Institute of Radiology, HCFMUSP, Brazil e Section of Magnetic Resonance, Heart Institute, HCFMUSP, Brazil f Department of Pharmacology, Institute of Biomedical Sciences USP, Laboratory of Psychopharmacology-LIM-23, IPq-HCFMUSP, Brazil g Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, São Paulo, Brazil
art ic l e i nf o
a b s t r a c t
Article history: Received 16 February 2014 Received in revised form 17 March 2015 Accepted 2 April 2015 Available online 13 April 2015
Several magnetic resonance imaging (MRI) studies to date have investigated brain abnormalities in association with the diagnosis of pathological gambling (PG), but very few of these have specifically searched for brain volume differences between PG patients and healthy volunteers (HV). To investigate brain volume differences between PG patients and HV, 30 male never-treated PG patients (DSM-IV-TR criteria) and 30 closely matched HV without history of psychiatric disorders in the past 2 years underwent structural magnetic resonance imaging with a 1.5-T instrument. Using Freesurfer software, we performed an exploratory whole-brain voxelwise volume comparison between the PG group and the HV group, with false-discovery rate correction for multiple comparisons (po 0.05). Using a more flexible statistical threshold (po 0.01, uncorrected for multiple comparisons), we also measured absolute and regional volumes of several brain structures separately. The voxelwise analysis showed no clusters of significant regional differences between the PG and HV groups. The additional analyses of absolute and regional brain volumes showed increased absolute global gray matter volumes in PG patients relative to the HV group, as well as relatively decreased volumes specifically in the left putamen, right thalamus and right hippocampus (corrected for total gray matter). Our findings indicate that structural brain abnormalities may contribute to the functional changes associated with the symptoms of PG, and they highlight the relevance of the brain reward system to the pathophysiology of this disorder. & 2015 Elsevier Ireland Ltd. All rights reserved.
Keywords: Impulse control disorder Addiction Magnetic resonance imaging Putamen Thalamus Hippocampus
1. Introduction Pathological gambling (PG) is a disorder characterized by gambling behaviors that disturb interpersonal relationships and adversely affect both financial and socioeconomic status (Potenza, 2001). In the 1980s, PG became part of the medical nosography, being classified within the realm of impulse control disorders (American Psychiatric Association, 2000). Epidemiological studies have indicated an increasing prevalence of PG (Petry and Armentano, 1999; Shaffer et al., 1999), with the most recent estimates pointing to a prevalence of 1–4% of PG in the n Correspondence to: Centro de Medicina Nuclear, 31 andar, LIM-21, Rua Dr. Ovídio Pires de Campos, s/n, Postal code 05403-010, São Paulo, SP, Brazil. Tel.: þ 55 11 2661 8132; fax: þ55 11 26618193. E-mail address: [email protected] (G.F. Busatto).
http://dx.doi.org/10.1016/j.pscychresns.2015.04.001 0925-4927/& 2015 Elsevier Ireland Ltd. All rights reserved.
general population (Shaffer et al., 1999; Potenza, 2001; Shaffer and Korn, 2002). Maladaptative and persistent gambling behaviors such as excessive preoccupation with gambling, lying to conceal the extent of involvement with gambling, need to gamble with increasing amounts of money, unsuccessful efforts to stop gambling, and chasing one's losses, are all related to the difficulties in social adjustment faced by people with PG (Reuter et al., 2005). The neurobiological basis of PG remains unclear, although functional neuroimaging techniques have consistently indicated the presence of regional brain activity abnormalities underlying the symptoms of PG. Recent studies using functional magnetic resonance imaging (fMRI) have demonstrated lower activity of the prefrontal cortex, orbitofrontal cortex, insula and striatum in PG patients relative to healthy volunteers during the performance of tasks involving control inhibition, visual presentation of gambling
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cues, and delay discounting (Eber and Shaffer, 2000; Knutson et al., 2003; Volkow et al., 2003; Crockford et al., 2005; Tanabe et al., 2007; Beck et al., 2009; Frascella et al., 2010; Choi et al., 2012; Sescousse et al., 2013). Complementary studies using positron emission tomography (PET) suggested enhanced dopamine release in PG subjects in response to pharmacological challenge and gambling (Joutsa et al., 2012; Boileau et al., 2014). Even though these studies suggest the existence of a relationship between abnormal brain functioning and PG symptoms, all of the studies have methodological limitations, such as their relatively small sample sizes (ranging from 7 to 18 PG subjects) and the potentially confounding influence of pharmacological treatment (Balodis et al., 2012). Nevertheless, they provide evidence that the presence of functional abnormalities in the fronto-striatal reward system might be associated with the vulnerability to develop PG. Alternatively, the identified abnormalities in brain function might be a consequence of the repetitive behavior of pathological gaming. Much less is known about morphological brain changes possibly associated with PG, given the small number of investigations to date and their inconsistent findings. One recent morphometric MRI investigation showed reduced volumes of the amygdala and hippocampus in a PG sample including untreated patients with comorbid alcohol/drug abuse relative to healthy controls (Rahman et al., 2014), while a different MRI study reported increased volumes of the ventral striatum and right prefrontal cortex in a mixed sample of mixed treated and non-treated PG patients relative to healthy controls (Koehler et al., 2015). On the other hand, in two MRI studies investigating volumetric changes in samples of never-treated PG patients with no history of comorbid alcohol/drug abuse, no differences from healthy controls were detected in the measurements of either gray or white brain matter volumes (Joutsa et al., 2011; van Holst et al., 2012). Previous studies have associated PG with poorer performance on neuropsychological tests involving decision-making, cognitive control and flexibility (Goudriaan et al., 2005; Fuentes et al., 2006; Odlaug et al., 2011; Grant et al., 2012; Goudriaan et al., 2014), indicating an impairment of reward processing and a potential abnormality of related brain structures such as the prefrontal cortex and ventral striatum (Boog et al., 2014). Given the scarcity of previous morphometric MRI studies of PG, we carried out the present study with the purpose of comparing brain volumetric measurements between never-treated PG patients and healthy volunteers. We aimed to investigate if morphological abnormalities would distinguish PG individuals from controls in fronto-striatal circuits and medial temporal regions implicated in recent brain volumetric studies of PG (Rahman et al., 2014).
2. Methods 2.1. Participants Thirty-seven male subjects consecutively admitted to the Gambling Outpatient Unit of the Institute of Psychiatry of the University of São Paulo were invited to take part in the study. They had never received psychotherapeutic support or pharmacological treatment for psychiatric or neurological disorders. Eight (26.6%) subjects had previously attended Gamblers Anonymous meetings. All PG patients were screened to assess the magnitude of their gambling habits as determined by the South Oaks Gambling Screen (SOGS; score Z 5 as inclusion criterion) (Lesieur and Blume, 1987). Subsequently, they underwent a clinical interview to assess DSM-IV-TR criteria for PG (American Psychiatric Association, 2000). The Schedule for Clinical Assessment in Neuropsychiatry (SCAN) (Wing et al., 1990) was also administered to exclude other addictive disorders (except tobacco smoking, present in 19 patients) and major current psychiatric disorders. Six subjects were excluded due to comorbidities such as alcohol dependence (n¼ 3), depression with suicidal ideation (n¼ 1), bipolar disorder (n¼ 1), and obsessive–compulsive disorder (n¼ 1). One other PG subject was excluded due to claustrophobic anxiety during MRI scanning. The remaining PG patients (n¼ 30)
209
were aged from 19 to 59 years (mean¼ 37.3 7 9.6 years), and all of them were righthanded. There were 28 Caucasian in this group and 22 patients completed, at least, high school, with a mean of 12.17 3.5 years of formal education. Intelligence Quotient in the patients based on the Block Design and Vocabulary subtests of the Wechsler Adult Intelligence Scale-Revised (Wechsler, 1981) was estimated as 1017 9.9. Thirty male, right-handed healthy volunteers (HV) also took part in the present study. They were recruited through advertisements in a recreational club located in the same neighborhood of the university hospital and were matched to participants in the PG group by age and educational level. They were screened for psychiatric disorders through the Self-Report Questionnaire (Harding et al., 1980) and interviewed with the SCAN (Wing et al., 1990) to exclude current psychiatric diagnoses, except for nicotine dependence. The HV group was aged from 20 to 59 years (mean age¼37.3 7 9.7 years). There were 24 Caucasians in this group, and 25 subjects completed at least high school education (average of 12.6 72.3 years of formal education). The mean estimated Intelligence Quotient for the HV group was 997 8.8. The local ethics committee approved data assessment, and all participants gave written consent after complete description of the study. 2.2. MRI scanning Structural MRI data were collected using a 1.5-T scanner (GE Sigma, Milwaukee, WI, USA). Whole-brain images were acquired using a three-dimensional T1-weighted fast field echo sequence (echo time¼ 5.2 ms, repetition time¼21.7 ms, flip angle¼ 201, field of view¼ 220 mm, 256 256 matrix) in contiguous axial slices with 1.5-mm thickness. An experienced radiologist visually inspected each dataset with the purpose of identifying artifacts during image acquisition and the presence of silent brain lesions. No gross structural brain abnormality was found in any participant. 2.3. Image processing and statistical analyses Initially, an exploratory whole-brain voxelwise analysis was carried out by means of FreeSurfer software (http://surfer.nmr.mgh.harvard.edu/) (Fischl and Dale, 2000) using a standard image-processing protocol described in detail elsewhere (Dale et al., 1999; Fischl et al., 1999). Briefly, we initially converted the original DICOM images to MGH format. Then, motion correction and conform, non-uniform intensity normalization and Talairach transformation algorithms were applied. Subsequently, intensity normalization, skull stripping, volumetric registration and volumetric labeling were computed to segment and smooth brain tissues. A kernel of 10 mm at the full width at half-maximum (FWHM) was used for spatial smoothing. After that, remaining images were inflated and parameterized into QSphere, which provides a spherical representation of brain tissue, gyri and sulci. Brain topology was then automatically fixed, mapped and registered on spherical parameters. Finally, an average curvature map was calculated to access cortical and sub-cortical parcellation scores. Between-group whole-brain voxelwise comparisons were carried out using an analysis of covariance (ANCOVA) model, considering age as a confounding factor. Also, we carried out a whole-brain voxelwise linear correlation analysis in the PG group between brain volumes and the number of DSM-IV comorbid diagnostic items that the patient presented, used as a measure of disorder severity. For these exploratory whole-brain analyses, we used a strict statistical threshold of po0.05 with false-discovery rate (FDR) correction for multiple comparisons. Using the same image-processing protocol above, we also measured the regional volumes of several brain structures separately, including the caudate nucleus, putamen, pallidum, thalamus, hippocampus, amygdala, nucleus accumbens, cingulate gyrus (caudal, posterior, rostral and isthmus portions), frontal lobe (caudal-middle, rostral-middle, lateral-orbital, medial-orbital, superior, frontalpole, paracentral, precentral, pars opercularis, pars orbitalis and pars triangularis), corpus callosum (posterior, mid-posterior, central, mid-anterior, and anterior portions), and ventricles (third, fourth, lateral and inferior horn). Following these analyses, the absolute volumes of global gray matter, white matter and lateral ventricles were automatically obtained (Fischl et al., 2004). Regional brain volumes for the above structures were calculated according to Eqs. (1) and (2), considering (1) the total intracranial volume and (2) the total gray matter volume: RIC ¼
V BR V IC
ð1Þ
RIC (regional volume relative to the intracranial volume), V BR (absolute volume of a specific brain region), V IC (absolute value of the intracranial volume) RGM ¼
V BR V GM
ð2Þ
RGM (regional volume relative to the total gray matter), V BR (absolute volume of a specific brain region), V GM (absolute volume the total gray matter). For region-of-interest (ROI) inter-group comparisons, we also used ANCOVA considering age as a confounding factor. Findings were reported as significant at a p o0.01 statistical threshold, uncorrected for multiple comparisons. The flexible statistical threshold was applied given the a priori prediction that brain
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abnormalities would be detected specifically in the fronto-striatal reward system and medial temporal structures in the PG group.
3. Results 3.1. Demographic and clinical variables No statistically significant differences were found between the PG and HV groups regarding age, years of formal education and estimated Intelligence Quotient (t o1, p 40.05). The mean SOGS ratings for the PG group were 14.1 73.1, indicating moderate/ severe pathological gambling habits (Lesieur and Blume, 1987). 3.2. Exploratory voxelwise analyses There were no voxel clusters of significant difference between the PG and HV groups at the po 0.05 level, corrected for multiple comparisons. At the same statistical threshold, there were also no significant correlations between brain indices and the number of comorbid DSM-IV-based diagnoses in PG patients. 3.3. Between-group brain volumetric comparisons There were no significant differences in absolute whole brain volumes between the PG (1357.107173.30) and HV (1289.367 125.01) groups (F1,57 ¼3.09, p¼0.084). The absolute total gray matter volumes were 684.2764.7 cm3 in the PG group and 621.6772.8 cm3 in the HV group (F1,57 ¼14.35, po0.001), indicating significantly increased global gray matter among the PG group with the HV group (Fig. 1). There were no significant differences in absolute total white matter volumes between the PG (539.1750.0) and HV (568.5795.0) groups (F1,57 ¼2.22, p¼ 0.141). There were no significant differences in the absolute volumes between the PG and
HV groups of any of the cortical or subcortical ROIs investigated (Table 1 and Fig. 1). Regarding brain volumes corrected for intracranial volumes, a significant relative decrease of total white matter volume was found in the PG group in comparison to the HV group both on the left (0.0167 0.002 vs. 0.017 70.002; F1,57 ¼ 8.947, p ¼0.004) and right (0.016 70.002 vs. 0.017 70.002; F1,57 ¼9.259, p ¼0.004) hemispheres. There were no significant between-group differences in relative volumes corrected for intracranial volume in any other ROI (Table 2 and Fig. 1). Inter-group comparison of regional brain volumes relative to total gray matter (Table 3) demonstrated significantly decreased volumes in PG patients relative to controls in the left putamen (F1,57 ¼ 7.31, p¼0.009), right hippocampus (F1,57 ¼ 8.34, p¼0.005) and right thalamus (F1,57 ¼7.32, p¼0.009) (see Fig. 1). There were no significant correlations between regional brain volumes and the number of comorbid DSM-IV-based diagnoses in PG patients (ro 0.381, p40.041, partial correlation indices corrected for age).
4. Discussion PG is a prevalent and potentially incapacitating behavioral addiction in humans (Potenza et al., 2001). Its pathophysiology has not yet been elucidated, and neuroimaging techniques can be useful tools to increase our understanding about the neural substrate underlying the symptoms of PG. Moreover, morphometric MRI studies of patients with PG provide an invaluable opportunity to delineate structural neural features that might be related to the vulnerability to addictions in general, without the confounding brain-damaging effects caused by the repetitive use of psychoactive substances (a problem that is present in MRI studies of the classic and more prevalent drug addictions such as alcohol and cocaine dependence) (Luciana et al., 2013; Ide et al., 2014).
Fig. 1. Brain volumetric comparisons between PG (pathological gambling) and HV (healthy volunteer) groups. Acc, nucleus accumbens; Amy: amygdala; Cau: caudate nucleus; Hip: hippocampus; Pal: globus pallidus; Put: putamen; Tha: thalamus; GM: gray matter; WM: white matter. n Significant statistical difference.
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Table 1 Absolute regional brain volumes. HV
Left hemisphere
Right hemisphere
Accumbens Amygdala Caudate Hippocampus Pallidum Putamen Thalamus Cortex Cortical WM Accumbens Amygdala Caudate Hippocampus Pallidum Putamen Thalamus Cortex Cortical WM Total volume
PG
Mean
S.D.
Mean
S.D.
486.97 1510.50 3579.60 4320.97 1553.17 5239.13 9405.37 213,499.48 283,765.54 461.23 1506.23 3411.80 4457.20 1382.13 4902.00 9820.20 214,810.76 284,764.66 1,357,075.64
89.48 252.65 423.86 700.85 245.94 583.41 991.31 33,360.16 46,774.23 73.25 191.72 478.78 478.01 270.44 514.20 1155.89 35,097.11 48,525.38 173,299.14
524.00 1518.23 3720.67 4574.13 1602.33 5370.60 9688.67 239,372.45 269,715.89 474.27 1572.73 3512.83 4568.13 1416.53 5015.67 9986.63 241,498.41 269,413.28 1,289,358.42
89.39 173.78 434.68 418.54 182.96 693.71 1097.15 23,899.87 24,926.07 62.42 147.00 455.90 426.44 173.04 613.26 956.66 22,973.73 25,230.12 125,009.16
F
p
2.528 0.020 1.674 3.086 0.858 0.837 1.164 13.579 2.080 0.544 2.280 0.706 0.955 0.372 0.733 0.365 13.723 2.340 3.093
0.117 0.889 0.201 0.084 0.358 0.364 0.285 0.001 0.155 0.464 0.137 0.404 0.332 0.545 0.396 0.548 o 0.001 0.132 0.084
HV: healthy volunteers; PG: pathological gamblers; WM: white matter. Degrees of freedom for all comparisons ¼1, 57 (ANCOVA, with covariance for age). Table 2 Relative regional brain volumes corrected for intracranial volume. HV
Left hemisphere
Right hemisphere
Accumbens Amygdala Caudate Hippocampus Pallidum Putamen Thalamus Cortex Cortical WM Accumbens Amygdala Caudate Hippocampus Pallidum Putamen Thalamus Cortex Cortical WM
PG
Mean
S.D.
Mean
S.D.
1.16E 03 3.61E 03 8.57E 03 1.04E 02 3.74E 03 1.25E 02 2.25E 02 4.99E 01 6.99E 01 1.10E 03 3.59E 03 8.17E 03 1.06E 02 3.30E 03 1.18E 02 2.35E 02 5.01E 01 7.03E 01
2.69E 04 8.11E 04 1.77E 03 2.66E 03 9.67E 04 2.40E 03 4.51E 03 7.69E 03 2.72E 01 2.24E 04 6.58E 04 1.81E 03 1.96E 03 7.57E 04 2.49E 03 4.97E 03 7.69E 03 2.83E 01
1.11E 03 3.18E 03 7.78E 03 9.55E 03 3.35E 03 1.12E 02 2.03E 02 4.98E 01 5.64E 01 9.93E 04 3.28E 03 7.34E 03 9.53E 03 2.97E 03 1.05E 02 2.09E 02 5.02E 01 5.63E 01
9.14E 05 1.48E 04 3.89E 04 3.77E 04 1.90E 04 5.74E 04 1.10E 03 1.81E 02 2.34E 02 6.67E 05 1.45E 04 4.13E 04 3.65E 04 1.93E 04 5.76E 04 1.18E 03 1.70E 02 2.27E 02
F
p
0.424 1.085 0.068 0.163 0.074 0.494 0.177 2.853 8.947 0.101 0.000 0.152 0.622 0.116 0.446 0.466 2.896 9.259
0.518 0.302 0.796 0.688 0.787 0.485 0.675 0.097 0.004 0.752 1.000 0.698 0.434 0.735 0.507 0.497 0.094 0.004
HV: healthy volunteers; PG: pathological gamblers; WM: white matter. Degrees of freedom for all comparisons ¼1, 57 (ANCOVA, with covariance for age).
In the present morphometric MRI study, which evaluated a sample of never-treated PG patients, we found significantly increased absolute global gray matter volumes in the PG group relative to the HV group. This pattern suggests that there may be a tendency toward a greater overall concentration of gray matter in comparison to white matter in this population, and this may contribute to impaired general neuronal processing in PG. The fact that there were no significant between-group differences in white matter volumes in our study does not rule out, however, the possibility that there may be microstructural white matter abnormalities in association with PG, as suggested in preliminary MRI investigations using diffusion tensor imaging (Joutsa et al., 2011). Indeed, when a correction for the differences in intracranial volume was applied, a significant relative reduction of white matter volume in patients compared with controls became evident. Regarding regional brain findings, the exploratory voxelwise analysis showed no clusters of significant regional differences
between the PG and HV groups at a strict statistical threshold corrected for multiple comparisons, nor significant correlations between brain indices and the number of comorbid DSM-IV-based diagnoses in PG patients. Conversely, the ROI between-group comparisons with a more flexible statistical threshold suggested the presence of abnormalities in the relative volume of a number of brain regions (adjusted for total gray matter volumes) of PG patients relative to the HV group. The results of such regional brain volume comparisons provided some support to our hypothesis that structural brain abnormalities are present in the fronto-striatal reward system in association with PG, as we found relative volume reduction of the left basal ganglia in the PG group compared with the HV group. This brain region is predicted to show abnormalities in PG, as repeatedly reported in previous functional imaging studies of this disorder (Balodis et al., 2012; Sescousse et al., 2013). The thalamus, where we also found relatively decreased volumes in PG patients relative to controls, is additionally relevant to reward processing
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Table 3 Relative regional brain volumes corrected for total gray matter. HV
Left hemisphere
Right hemisphere
Accumbens Amygdala Caudate Hippocampus Pallidum Putamen Thalamus Cortex Cortical WM Accumbens Amygdala Caudate Hippocampus Pallidum Putamen Thalamus Cortex Cortical WM
PG
Mean
S.D.
Mean
S.D.
3.80E-04 1.18E-03 2.80E-03 3.36E-03 1.21E-03 4.10E-03 7.35E-03 1.67E-01 2.20E-01 3.61E-04 1.17E-03 2.66E-03 3.47E-03 1.08E-03 3.84E-03 7.68E-03 1.68E-01 2.21E-01
7.39E-05 1.99E-04 4.08E-04 5.02E-04 2.01E-04 6.04E-04 9.67E-04 2.96E-02 2.61E-02 6.52E-05 1.40E-04 3.98E-04 3.30E-04 2.32E-04 5.43E-04 1.13E-03 3.14E-02 2.86E-02
3.94E-04 1.13E-03 2.77E-03 3.40E-03 1.20E-03 4.00E-03 7.23E-03 1.78E-01 2.01E-01 3.55E-04 1.17E-03 2.62E-03 3.40E-03 1.06E-03 3.74E-03 7.47E-03 1.79E-01 2.00E-01
3.94E-04 1.13E-03 2.77E-03 3.40E-03 1.20E-03 4.00E-03 7.23E-03 1.78E-01 2.01E-01 3.55E-04 1.17E-03 2.62E-03 3.40E-03 1.06E-03 3.74E-03 7.47E-03 1.79E-01 2.00E-01
F
p
0.794 6.692 4.481 2.744 3.818 7.309 5.784 0.641 7.206 4.845 5.474 4.916 8.343 4.326 6.458 7.318 0.641 7.138
0.377 0.012 0.039 0.103 0.056 0.009 0.019 0.427 0.009 0.032 0.023 0.031 0.005 0.042 0.014 0.009 0.427 0.010
HV: healthy volunteers; PG: pathological gamblers; WM: white matter. Degrees of freedom for all comparisons ¼1, 57 (ANCOVA, with covariance for age).
(Sescousse et al., 2013), and it has been implicated in previous fMRI studies of PG (Potenza et al., 2003). Finally, our PG subjects also displayed diminished relative volume of the hippocampus, another brain region commonly implicated in the neurocircuitry of drug addiction (Fotros et al., 2013). The latter finding is consistent with a recent report of reduced hippocampal and amygdalar volumes in PG patients relative to healthy controls (Rahman et al., 2014). A number of methodological aspects distinguish our investigation from previous morphometric MRI studies of PG (Joutsa et al., 2011; van Holst et al., 2012). First, the PG condition of our patients was in the moderate/severe range (SOGS ¼14.1 73.1), whereas the PG patients in the study of van Holst et al. (van Holst et al., 2012) were in the mild/moderate (9.92 72.95) range of severity. Second, although Joutsa et al. (2011) studied subjects with similar SOGS ratings to ours (from 10 to 14), the size of their PG sample (n ¼ 12) may not have been large enough to statistically detect subtle brain volume differences between groups. Finally, Rahman et al. (2014) included PG subjects with a current or previous history of alcohol or drug abuse, while in our study PG subjects with such comorbidities were excluded; the relationship between substance abuse and gray and white matter abnormalities has been previously demonstrated, and this factor may add variability to brain volume measurements in neuroimaging studies of PG. The molecular mechanisms underlying the PG-related brain volumetric abnormalities reported herein remain to be elucidated. The localization of our morphometric findings is consistent with current neuroanatomical models of PG, involving brain regions relevant to reward processing. This topographic pattern is consistent with the proposed view that PG may be associated with abnormalities in the early development of fronto-striatal pathways and their two monoaminergic systems – dopaminergic and serotoninergic (Chambers et al., 2003). Although this reasoning implies that brain volume abnormalities reflect the vulnerability for the development of PG, an alternative explanation is that the present findings, for example, the increased global brain volume in our PG patients, are a consequence of repetitive gambling habits (probably reflecting increased neuropil due to repetitive behavior). The same doubt applies to the findings of recent morphometric MRI studies that have reported increased brain volumes in frequent video game players relative to moderate players (Kuhn and Gallinat, 2014). In
favor of the vulnerability hypothesis, Kuhn and Gallinat (2014) recently argued that brain volumetric differences between frequent and moderate video game players were preconditions that lead to a vulnerability for preoccupation with gaming. They suggested that these brain volume differences might make individuals prone to experiencing video gaming as more rewarding in the first place, and this could, in turn, facilitate skill acquisition and lead to further reward resulting from playing. Although video gaming and PG are different activities, their similarities are evident, and the same reasoning could easily be applied to PG. One future strategy to confirm the hypothesis that PG-related brain volume abnormalities are due to the vulnerability to this condition would be to carry out MRI studies investigating the relationship between brain volume abnormalities and molecular genetic features, especially dopaminergic polymorphisms thought to be related to PG (Steeves et al., 2009; Linnet et al., 2010). In regard to the limitations of the present study, the sample investigated was relatively modest in size, and this may have influenced on the negative results of the voxelwise whole-brain analysis using correction for multiple comparisons. It could also be argued that the sample investigated herein might not be representative of the general PG population, for instance, due to the inclusion of exclusively one gender, and only treatment-seeking PG subjects (who are usually at the most severe level of symptoms). Nonetheless, the rigorous selection of a pure diagnostic sample, i.e., without the influence of comorbidities (Lesieur and Blume, 1987), allows us to be more confident in relating brain volume abnormalities to the diagnosis of PG rather than other clinical features. Moreover, our PG subjects were entirely naïve to psychopharmacological agents and/or psychotherapy, thus avoiding a potential influence of earlier treatment on brain volume measurements. Another limitation of our study is the lack of information about smoking in general, and about nicotine dependence in the HV group. These restrictions preclude ruling out the possibility that differences in brain volumes between the PG and HV groups could be at least partially related to nicotine abuse, since nicotine dependence has been previously associated with increased striatal volumes (Das et al., 2012). Additionally, since the individual voxels are classified as part of a specific structure based on univariate statistics (on the parcellation step), suboptimal voxel size can lead to bias in the definition of regional structures and this might constitute one further limitation in the present study.
D. Fuentes et al. / Psychiatry Research: Neuroimaging 232 (2015) 208–213
In conclusion, this morphometric MRI study suggests the presence of regional gray matter volume abnormalities in nevertreated patients with PG, involving the basal ganglia, thalamus and hippocampus. These brain regions seem to be relevant to the pathophysiology of PG as well as to the neurobiology of addictions in general. Our findings suggest that there may be regional gray matter abnormalities in PG, but this remains to be verified by future studies. To extend and elucidate the findings reported herein, future case-control studies with larger samples are warranted in which in vivo brain morphometric and molecular imaging modalities are combined in the same subjects, as well as longitudinal studies involving repeated MRI measurements in PG and at-risk subjects.
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