Sam-Wook Choi

Altered Brain Activity during Reward Anticipation in Pathological Gambling and Obsessive-Compulsive Disorder

Background Pathological gambling (PG) and obsessive-compulsive disorder (OCD) are conceptualized as a behavioral addiction, with a dependency on repetitive gambling behavior and rewarding effects following compulsive behavior, respectively. However, no neuroimaging studies to date have examined reward circuitry during the anticipation phase of reward in PG compared with in OCD while considering repetitive gambling and compulsion as addictive behaviors. Methods/Principal Findings To elucidate the neural activities specific to the anticipation phase of reward, we performed event-related functional magnetic resonance imaging (fMRI) in young adults with PG and compared them with those in patients with OCD and healthy controls. Fifteen male patients with PG, 13 patients with OCD, and 15 healthy controls, group-matched for age, gender, and IQ, participated in a monetary incentive delay task during fMRI scanning. Neural activation in the ventromedial caudate nucleus during anticipation of both gain and loss decreased in patients with PG compared with that in patients with OCD and healthy controls. Additionally, reduced activation in the anterior insula during anticipation of loss was observed in patients with PG compared with that in patients with OCD which was intermediate between that in OCD and healthy controls (healthy controls < PG < OCD), and a significant positive correlation between activity in the anterior insula and South Oaks Gambling Screen score was found in patients with PG. Conclusions Decreased neural activity in the ventromedial caudate nucleus during anticipation may be a specific neurobiological feature for the pathophysiology of PG, distinguishing it from OCD and healthy controls. Correlation of anterior insular activity during loss anticipation with PG symptoms suggests that patients with PG fit the features of OCD associated with harm avoidance as PG symptoms deteriorate. Our findings have identified functional disparities and similarities between patients with PG and OCD related to the neural responses associated with reward anticipation.

Latent class analysis on internet and smartphone addiction in college students

Purpose This study aimed to classify distinct subgroups of people who use both smartphone and the internet based on addiction severity levels. Additionally, how the classified groups differed in terms of sex and psychosocial traits was examined. Methods A total of 448 university students (178 males and 270 females) in Korea participated. The participants were given a set of questionnaires examining the severity of their internet and smartphone addictions, their mood, their anxiety, and their personality. Latent class analysis and ANOVA (analysis of variance) were the statistical methods used. Results Significant differences between males and females were found for most of the variables (all <0.05). Specifically, in terms of internet usage, males were more addicted than females (P<0.05); however, regarding smartphone, this pattern was reversed (P<0.001). Due to these observed differences, classifications of the subjects into subgroups based on internet and smartphone addiction were performed separately for each sex. Each sex showed clear patterns with the three-class model based on likelihood level of internet and smartphone addiction (P<0.001). A common trend for psychosocial trait factors was found for both sexes: anxiety levels and neurotic personality traits increased with addiction severity levels (all P<0.001). However, Lie dimension was inversely related to the addiction severity levels (all P<0.01). Conclusion Through the latent classification process, this study identified three distinct internet and smartphone user groups in each sex. Moreover, psychosocial traits that differed in terms of addiction severity levels were also examined. It is expected that these results should aid the understanding of traits of internet and smartphone addiction and facilitate further study in this field.

Similarities and differences among Internet gaming disorder, gambling disorder and alcohol use disorder: A focus on impulsivity and compulsivity

Background and aims: The aim of the present study was to test the impulsivities and compulsivities of behavioral addictions, including Internet gaming disorder (IGD) and gambling disorder (GD), by directly comparing them with alcohol use disorder (AUD) and a healthy control (HC) group. Methods: We enrolled male patients who were diagnosed with IGD, GD or AUD, with 15 patients per group, as well as 15 HCs. Trait impulsivity was measured using the Barratt Impulsiveness Scale version 11 (BIS-11). The stop-signal test (SST) from the Cambridge Neuro-psychological Test Automated Battery (CANTAB) was used to assess the patients’ abilities to inhibit prepotent responses. Compulsivity was measured using the intra–extra dimensional set shift (IED) test from the CANTAB. The Trail Making Test (TMT) was also used in this study. Results: The IGD and AUD groups scored significantly higher on the BIS-11 as a whole than did the HC group (p = 0.001 and p = 0.001, respectively). The IGD and AUD groups also scored significantly higher on the BIS-11 as a whole than did the GD group (p = 0.006 and p = 0.001, respectively). In addition, the GD group made significantly more errors (p = 0.017 and p = 0.022, respectively) and more individuals failed to achieve criterion on the IED test compared with the IGD and HC groups (p = 0.018 and p = 0.017, respectively). Discussion: These findings may aid in the understanding of not only the differences in categorical aspects between individuals with IGD and GD but also in impulsivity–compulsivity dimensional domains. Conclusion: Additional studies are needed to elucidate the neurocognitive characteristics of behavioral addictive disorders in terms of impulsivity and compulsivity.

Comparison of risk and protective factors associated with smartphone addiction and Internet addiction

Background and Aims Smartphone addiction is a recent concern that has resulted from the dramatic increase in worldwide smartphone use. This study assessed the risk and protective factors associated with smartphone addiction in college students and compared these factors to those linked to Internet addiction. Methods College students (N = 448) in South Korea completed the Smartphone Addiction Scale, the Young’s Internet Addiction Test, the Alcohol Use Disorders Identification Test, the Beck Depression Inventory I, the State–Trait Anxiety Inventory (Trait Version), the Character Strengths Test, and the Connor–Davidson Resilience Scale. The data were analyzed using multiple linear regression analyses. Results The risk factors for smartphone addiction were female gender, Internet use, alcohol use, and anxiety, while the protective factors were depression and temperance. In contrast, the risk factors for Internet addiction were male gender, smartphone use, anxiety, and wisdom/knowledge, while the protective factor was courage. Discussion These differences may result from unique features of smartphones, such as high availability and primary use as a tool for interpersonal relationships. Conclusions Our findings will aid clinicians in distinguishing between predictive factors for smartphone and Internet addiction and can consequently be utilized in the prevention and treatment of smartphone addiction.

Resting-state beta and gamma activity in Internet addiction.

Internet addiction is the inability to control ones use of the Internet and is related to impulsivity. Although a few studies have examined neurophysiological activity as individuals with Internet addiction engage in cognitive processing, no information on spontaneous EEG activity in the eyes-closed resting-state is available. We investigated resting-state EEG activities in beta and gamma bands and examined their relationships with impulsivity among individuals with Internet addiction and healthy controls. Twenty-one drug-naïve patients with Internet addiction (age: 23.33 ± 3.50 years) and 20 age-, sex-, and IQ-matched healthy controls (age: 22.40 ± 2.33 years) were enrolled in this study. Severity of Internet addiction was identified by the total score on Youngs Internet Addiction Test. Impulsivity was measured with the Barratt Impulsiveness Scale-11 and a stop-signal task. Resting-state EEG during eyes closed was recorded, and the absolute/relative power of beta and gamma bands was analyzed. The Internet addiction group showed high impulsivity and impaired inhibitory control. The generalized estimating equation showed that the Internet-addiction group showed lower absolute power on the beta band than did the control group (estimate = -3.370, p < 0.01). On the other hand, the Internet-addiction group showed higher absolute power on the gamma band than did the control group (estimate = 0.434, p < 0.01). These EEG activities were significantly associated with the severity of Internet addiction as well as with the extent of impulsivity. The present study suggests that resting-state fast-wave brain activity is related to the impulsivity characterizing Internet addiction. These differences may be neurobiological markers for the pathophysiology of Internet addiction.

Resting-state regional homogeneity as a biological marker for patients with Internet gaming disorder: A comparison with patients with alcohol use disorder and healthy controls

OBJECTIVE Internet gaming disorder (IGD) shares core clinical features with other addictive disorders, such as gambling disorder and substance use disorder. Designation of IGD as a formal disorder requires elucidation of its neurobiological features and comparison of these with those of other addictive disorders. The aims of the present study were to identify the neurobiological features of the resting-state brain of patients with IGD, alcohol use disorder (AUD), and healthy controls, and to examine brain regions related to the clinical characteristics of IGD. METHOD Functional magnetic resonance imaging was performed on 16 subjects with IGD, 14 subjects with AUD, and 15 healthy controls during the resting-state. We computed regional homogeneity (ReHo) measures to identify intrinsic local connectivity and to explore associations with clinical status and impulsivity. RESULTS We found significantly increased ReHo in the posterior cingulate cortex (PCC) of the IGD and AUD groups, and decreased ReHo in the right superior temporal gyrus (STG) of those with IGD, compared with the AUD and HC groups. We also found decreased ReHo in the anterior cingulate cortex of patients with AUD. Scores on Internet addiction severity were positively correlated with ReHo in the medial frontal cortex, precuneus/PCC, and left inferior temporal cortex (ITC) among those with IGD. Furthermore, impulsivity scores were negatively correlated with that in the left ITC in individuals with IGD. CONCLUSION Our results provide evidence of distinctive functional changes in the resting-state of patients with IGD and demonstrate that increased ReHo in the PCC may be a common neurobiological feature of IGD and AUD and that reduced ReHo in the STG may be a candidate neurobiological marker for IGD, differentiating individuals with this disorder from those with AUD and healthy controls.

Development of the Internet addiction scale based on the Internet Gaming Disorder criteria suggested in DSM-5

This study was conducted to develop and validate a standardized self-diagnostic Internet addiction (IA) scale based on the diagnosis criteria for Internet Gaming Disorder (IGD) in the Diagnostic and Statistical Manual of Mental Disorder, 5th edition (DSM-5). Items based on the IGD diagnosis criteria were developed using items of the previous Internet addiction scales. Data were collected from a community sample. The data were divided into two sets, and confirmatory factor analysis (CFA) was performed repeatedly. The model was modified after discussion with professionals based on the first CFA results, after which the second CFA was performed. The internal consistency reliability was generally good. The items that showed significantly low correlation values based on the item-total correlation of each factor were excluded. After the first CFA was performed, some factors and items were excluded. Seven factors and 26 items were prepared for the final model. The second CFA results showed good general factor loading, Squared Multiple Correlation (SMC) and model fit. The model fit of the final model was good, but some factors were very highly correlated. It is recommended that some of the factors be refined through further studies.

Diurnal variation and gender differences of plasma brain-derived neurotrophic factor in healthy human subjects

Diurnal changes of brain-derived neurotrophic factor (BDNF) mRNA and protein contents have been demonstrated in the rat central nervous system. In humans, however, insufficient data are available on diurnal variation of BDNF. We investigated plasma and serum BDNF levels at three different times of the day (9 a.m., 1 p.m. and 5 p.m.) in 18 healthy men and 18 healthy women. BDNF levels were assessed using an enzyme-linked immunosorbent assay (ELISA). We detected significant diurnal variation in plasma BDNF level in men, with a constant decrease throughout the day. However, we could not find diurnal variation in either the plasma BDNF of women or the serum BDNF in both men and women. When performing analysis of covariance with the age and body mass index as covariates, we found no significant differences of plasma and serum BDNF levels of the two sexes at the three time points. In psychiatric studies of blood BDNF level, diurnal variation in plasma BDNF level should be considered with respect to gender differences.

Shared psychological characteristics that are linked to aggression between patients with Internet addiction and those with alcohol dependence

BackgroundInternet addiction (IA) is considered as one of behavioral addictions. Although common neurobiological mechanisms have been suggested to underlie behavioral addiction and substance dependence, few studies have directly compared IA with substance dependence, such as alcohol dependence (AD).MethodsWe compared patients with IA, AD, and healthy controls (HC) in terms of the Five Factor Model of personality and with regard to impulsiveness, anger expression, and mood to explore psychological factors that are linked to aggression. All patients were treatment-seeking and had moderate-to-severe symptoms.ResultsThe IA and AD groups showed a lower level of agreeableness and higher levels of neuroticism, impulsivity, and anger expression compared with the HC group, which are characteristics related to aggression. The addiction groups showed lower levels of extraversion, openness to experience, and conscientiousness and were more depressive and anxious than the HCs, and the severity of IA and AD symptoms was positively correlated with these types of psychopathology.ConclusionsIA and AD are similar in terms of personality, temperament, and emotion, and they share common characteristics that may lead to aggression. Our findings suggest that strategies to reduce aggression in patients with IA are necessary and that IA and AD are closely related and should be dealt with as having a close nosological relationship.

Differential resting-state EEG patterns associated with comorbid depression in Internet addiction

OBJECTIVE Many researchers have reported a relationship between Internet addiction and depression. In the present study, we compared the resting-state quantitative electroencephalography (QEEG) activity of treatment-seeking patients with comorbid Internet addiction and depression with those of treatment-seeking patients with Internet addiction without depression, and healthy controls to investigate the neurobiological markers that differentiate pure Internet addiction from Internet addiction with comorbid depression. METHOD Thirty-five patients diagnosed with Internet addiction and 34 age-, sex-, and IQ-matched healthy controls were enrolled in this study. Patients with Internet addiction were divided into two groups according to the presence (N=18) or absence (N=17) of depression. Resting-state, eye-closed QEEG was recorded, and the absolute and relative power of the brain were analyzed. RESULTS The Internet addiction group without depression had decreased absolute delta and beta powers in all brain regions, whereas the Internet addiction group with depression had increased relative theta and decreased relative alpha power in all regions. These neurophysiological changes were not related to clinical variables. CONCLUSION The current findings reflect differential resting-state QEEG patterns between both groups of participants with Internet addiction and healthy controls and also suggest that decreased absolute delta and beta powers are neurobiological markers of Internet addiction.