New developments in brain research of internet and gaming disorder

Neuroscience and Biobehavioral Reviews 75 (2017) 314–330

Contents lists available at ScienceDirect

Neuroscience and Biobehavioral Reviews journal homepage: www.elsevier.com/locate/neubiorev

New developments in brain research of internet and gaming disorder Aviv Weinstein a,∗ , Abigail Livny b , Abraham Weizman c a

Dept. of Behavioral Science and the Integrative Cognition and Brain Center, University of Ariel, Science Park, Ariel 40700, Israel The Joseph Sagol Neuroscience Center, Sheba Medical Center, Tel-Hashomer, Israel c Research Unit, Geha Mental Health Center, Beilinson Campus, P.O. Box 102, Petah Tiqva 49 100. Israel b

a r t i c l e

i n f o

Article history: Received 11 May 2016 Received in revised form 22 January 2017 Accepted 29 January 2017 Available online 11 February 2017 Keywords: Internet gaming disorder Brain imaging fMRI Dopamine Reward

a b s t r a c t There is evidence that the neural mechanisms underlying Internet Gaming Disorder (IGD) resemble those of drug addiction. Functional Magnetic Resonance Imaging (fMRI) studies of the resting state and measures of gray matter volume have shown that Internet game playing was associated with changes to brain regions responsible for attention and control, impulse control, motor function, emotional regulation, sensory-motor coordination. Furthermore, Internet game playing was associated with lower white matter density in brain regions that are involved in decision-making, behavioral inhibition and emotional regulation. Videogame playing involved changes in reward inhibitory mechanisms and loss of control. Structural brain imaging studies showed alterations in the volume of the ventral striatum that is an important part of the brain’s reward mechanisms. Finally, videogame playing was associated with dopamine release similar in magnitude to those of drugs of abuse and lower dopamine transporter and dopamine receptor D2 occupancy indicating sub-sensitivity of dopamine reward mechanisms. © 2017 Elsevier Ltd. All rights reserved.

Contents 1.

2.

3.

4. 5.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314 1.1. Background- the neural mechanisms underlying reward and the effects of drugs of abuse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315 1.2. Brain imaging studies of the resting state in IGD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316 1.3. Studies on the brain’s gray matter volume and white matter density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316 1.4. Cortical thickness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 Functional connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320 2.1. Functional connectivity at a resting state . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320 2.2. Functional connectivity, reward and the striatum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320 Brain activation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320 3.1. Cue-exposure activation studies of videogame urges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320 3.2. Inhibitory control mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324 3.3. Reward . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324 Brain imaging studies on dopamine, 5-HT and other neurotransmitters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326 Declaration of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328 Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328

1. Introduction

∗ Corresponding author. E-mail addresses: [email protected], [email protected] (A. Weinstein). http://dx.doi.org/10.1016/j.neubiorev.2017.01.040 0149-7634/© 2017 Elsevier Ltd. All rights reserved.

Internet Gaming Disorder (IGD) involves excessive or poorly controlled preoccupations, urges or behaviors regarding computer and videogame play that lead to impairment or distress. There are three different models proposed for IGD: an impulse-control disor-

A. Weinstein et al. / Neuroscience and Biobehavioral Reviews 75 (2017) 314–330

der, an obsessive-compulsive disorder, and a behavioral addiction model (Grant et al., 2010). The behavioral addiction model argues that IGD shows the features of excessive use despite adverse consequences, withdrawal phenomena, and tolerance that characterize substance use disorders. In the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) (American Psychiatric Association, 2013), IGD is identified in Section III as a condition warranting more clinical research and experience before it might be considered for inclusion as a formal disorder (see (Weinstein et al., 2014; Weinstein and Aboujaoude, 2015) for review). The work group moved from a broad conceptualization (along the lines of problematic internet use) to a narrower one, focusing primarily on pathological online gaming and avoiding use of the term “addiction”. Noteworthy, the DSM-5 does not offer sufficient guidance on how to approach individuals with suspected Internet-related psychopathology or how to design or interpret research studies into this topic. Instead, clinicians and researchers have to rely on proposed definitions, along with several screening and assessment instruments developed for problematic internet use and problematic video game use (Weinstein and Aboujaoude, 2015). There is a debate whether IGD is the best clinical term for diagnosing Internet addiction. For example, Young (Young, 1998) considers online games a specific subtype of Internet activities, and she developed her Internet addiction criteria that were based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria for pathological gambling (American Psychiatric Association, 1994). Her theory states that online game addicts gradually lose control over their game play; that is, they are unable to decrease the amount of time spent playing while immersing themselves increasingly in this particular recreational activity and eventually develop problems in their real life (Young, 2009). Table 1 describes the proposed inclusion criteria for IGD Surveys in the US and Europe have indicated prevalence rates of between 1.5% and 8.2%, of the country’s population with varying diagnosis methods between countries (Durkee et al., 2012). Cross-sectional studies on samples of patients reported high comorbidity of IGD with psychiatric disorders, especially affective disorders, anxiety disorders and attention deficit hyperactivity disorder (ADHD) (Weinstein et al., 2014; Weinstein and Aboujaoude, 2015). Previous reviews have described brain-imaging studies in IGD until 2013 (Weinstein and Lejoyeux, 2015; Zhu et al., 2015; Kuss and Griffiths, 2012). In view of the rapid developments in brain research in IGD, this review will update these studies with new developments in brain imaging of IGD between 2013 and now. Secondly, it will analyze these findings in relation to the three models proposed for IGD namely behavioral addiction, impulse control disorder and obsessive-compulsive disorder in order to improve our clinical definition and diagnosis of this disorder. Finally, we will bring parallel evidence from brain imaging studies in pathological gambling which is now recognized as a behavioral addiction (American Psychiatric Association, 2013) and with compulsive sexual disorder. A PubMed search was conducted using the search terms ‘Internet addiction’ ‘Internet Gaming Disorder’ and ‘Pathological Internet use,’ each of which was combined with each of the terms ‘brain imaging,’ or ‘fMRI’ or ‘PET’ or ‘resting state’ using the conjunction ‘AND.’ Each term was required to be present in the ‘Title/Abstract’ of the paper. The search was further restricted by ‘English’ as the publication language and Publication Date from 2008 to May 2016. The only studies that were selected for the review were original research papers that were published in peer-reviewed journals. The search has yielded eligible 52 studies including 16 studies of the resting state, 13 studies of functional connectivity, 18 activation studies and 5 studies of pharmacology. As a general caution, throughout this review, in making group comparisons, there are

315

Table 1 Internet gaming disorder criteria in the Diagnostic and Statistical Manual of Mental Disorders. (DSM-5) Internet Gaming Disorder Proposed Criteria. Persistent and recurrent use of the Internet to engage in games, often with other players, leading to clinically significant Impairment or distress as indicated by five (or more) of the following in a month period: Preoccupation with Internet games. (The individual thinks about previous gaming activity or anticipates playing the next game; Internet gaming becomes a dominant activity in daily life). Note: This disorder is distinct from Internet gambling, which is included under gambling disorder. 2. Withdrawal symptoms when Internet gaming is taken away. (These symptoms are typically described as irritability, anxiety, or Sadness, but there are no physical signs of pharmacological withdrawal). 3. Tolerance—the need to spend increasing amounts of time engaged in Internet games. 4. Unsuccessful attempts to control the participation in Internet games. 5. Loss of interests in previous hobbies and entertainment as a result of, and with the exception of, Internet games. 6. Continued excessive use of Internet games despite knowledge of psychosocial problems. 7. Has deceived family members, therapists, or others regarding the amount of Internet gaming. 8. Use of Internet games to escape or relieve a negative mood (e.g., feelings of helplessness, guilt, anxiety). 9. Has jeopardized or lost a significant relationship, job, or educational or career opportunity because of participation in Internet games. Note: Only non-gambling Internet games are included in this disorder. Use of the Internet for required activities in a business or profession is not included; nor is the disorder intended to include other recreational or social Internet use. Similarly, sexual Internet sites are excluded. Specify current severity: Internet gaming disorder can be mild, moderate, or severe depending on the degree of disruption of normal activities. Individuals with less severe Internet gaming disorder may exhibit fewer symptoms and less disruption of their lives. Those with severe Internet gaming disorder will have more hours spent on the computer and more severe loss of relationships or career or school opportunities. In Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5). 2013, American Psychiatric Association. pp. 795–796.

reported differences between IGD group and control groups but these differences do not imply a causal role of IGD. Group differences may reflect predisposing factors rather than decreases due to IGD. 1.1. Background- the neural mechanisms underlying reward and the effects of drugs of abuse There is a vast body of pre-clinical evidence that the dopaminergic system mediates reward in general and the rewarding effects of drugs (Koob, 1992; Di Chiara and North, 1992; Wise, 1996; Di Chiara and Bassareo, 2007). The mesolimbic dopamine (DA) pathway that includes DA cells in ventral tegmental area projecting into nucleus accumbens seems to be crucial for drug reward (Wise, 2009). Other DA pathways such as the meso-striatal pathway includes DA cells in substantia nigra projecting into dorsal striatum and meso-cortical pathway includes DA cells in the ventral tegmental area projecting into frontal cortex are now also recognized as contributing to drug reward and addiction (Wise, 2009). The mode of DA cell firing also differently modulates the rewarding and conditioning effects, of drugs (predominantly phasic DA cell firing) compared with the changes in executive function that occur in addiction (predominantly tonic DA cell firing) (Wanat et al., 2009). Brain imaging studies using Positron Emission Tomography (PET) in humans showed that the stimulant drugs cocaine and methylphenidate released dopamine in the striatum (Volkow et al., 1996a) and there is further evidence that the dopaminergic striatalthalamic-orbitofrontal circuit mediates the rewarding effects of cocaine (Volkow et al., 1997a; Volkow et al., 1997b). Similarly,

316

A. Weinstein et al. / Neuroscience and Biobehavioral Reviews 75 (2017) 314–330

cocaine administration in fMRI activated the ventral tegmental area, pons, basal forebrain, caudate, cingulate, and the lateral prefrontal cortex (Breiter et al., 1997). These studies support the theory that the dopaminergic striatal-thalamic-orbitofrontal circuit underlies compulsive drug use. 1.2. Brain imaging studies of the resting state in IGD Resting state is a method of functional brain imaging that evaluates regional interactions that occur when a subject is not performing an explicit task. The resting state approach is useful to explore the brain’s functional organization and to examine if it alters in neurological or psychiatric diseases (De Luca et al., 2006). Excessive Internet game use was associated with abnormal resting state activity in the orbito-frontal cortex, striatum, and sensory regions, which are responsible for impulse control, reward processing, and somatic representation of previous experiences (Park et al., 2010). The study measured regional cerebral metabolic rates of glucose (rCMRglu) in PET in IGD and control participants. The orbito-frontal cortex, striatum, and sensory regions are also associated with other types of impulse control disorders and substance/non-substance use disorder. The resting brain activity can be observed through changes in blood flow in the brain that creates a blood-oxygen-level dependent (BOLD) signal that can be measured using fMRI (De Luca et al., 2006). Using the arterial spinlabeling perfusion technique(footnote 1) in fMRI, adolescents with IGD showed higher global Cerebral Blood Flow (CBF) in areas that are important for learning and memory (amygdala/hippocampus),1 conscious urges to use drugs (insula) executive function and inhibition (pre-frontal cortex, anterior cingulate cortex and parietal lobe) (Feng et al., 2013). There were lower CBF measures in the middle temporal gyrus, middle occipital gyrus, and cingulate gyrus. The results so far indicate that IGD seems to share psychological and neural mechanisms with other types of impulse control disorders and substance use disorder (Volkow et al., 2010). Since there are only few publications that present structural changes in IGD, these findings need to be replicated in the future. See Table 2 for structural studies on the resting state in IGD. Another measure of brain connectivity in the resting state is regional homogeneity (ReHo) (footnote 2) in fMRI.2 ReHo represents the temporal homogeneity of the regional BOLD signal of fMRI, which may reflect neural activity. Therefore, abnormal ReHo in certain regions of the brain may be associated with the neurobiological impairments underpinning various neuropsychiatric disorders that involve temporal changes in disruption to local function. IGD individuals showed enhanced regional homogeneity (ReHo) in the brainstem, inferior parietal lobule, cerebellum, and middle frontal gyrus that relate with sensory-motor coordination (Dong et al., 2012a). IGD individuals also had decreased ReHo in temporal, occipital and parietal brain regions that are responsible for visual and auditory functions. Long-time online game playing

1 Arterial Spin labeling (ASL) The goal of Arterial Spin Labeling (ASL) methods is to image tissue perfusion. Perfusion is defined as the amount of blood delivered to capillary beds in a given tissue per unit time. Because the microvasculature is too small to be spatially resolved using current MRI techniques, perfusion cannot be measured by directly imaging blood flow using angiographic methods, and it is necessary to measure the net delivery of blood to each imaging voxel instead. The general approach taken by ASL is to modify the magnetization of arterial blood, use this magnetically tagged blood as an endogenous tracer, and measure the delivery of the tracer to target tissues (Wolf and Detre, 2007). 2 Regional Homogeneity (ReHo) is a voxel-based measure of brain activity that evaluates the similarity or synchronization between the time series of a given voxel and its nearest neighbors (Zang et al., 2004). This measure is based on the hypothesis that intrinsic brain activity is manifested by clusters of voxels rather than single voxels. ReHo requires no a priori definition of ROIs and can provide information about the local/regional activity of regions throughout the brain.

presumably enhanced the brain synchronization in sensory-motor coordination and decreased the excitability in visual and auditory related brain regions. This evidence is further supported by findings of enhanced ReHo measures in brainstem, cerebellum, limbic lobe and frontal lobe in IGD college students (Liu et al., 2010). Similarly to drug use disorders, the connections or synchronization among these regions shown by ReHo enhancement and the frontal lobe corroborates the evidence for enhancement of reward pathways. Finally, both IGD and alcohol use disorder participants had increased ReHo in the posterior cingulate cortex whereas IGD patients had decreased ReHo in the superior temporal gyrus compared with alcohol use disorder and healthy control participants (Kim et al., 2015). The increase in posterior cingulate cortex has been associated with reward at uncertainty or reflecting risk preferences in addiction. Reduced ReHo in the superior temporal gyrus relates presumably to impairments in higher audio-visual information processing as well as response inhibition, although both findings need to be replicated. In conclusion, there are brain regions that are only indirectly involved in drug addiction such as the parietal and occipital cortex. Furthermore, brain regions that are involved in memory (amygdala and hippocampus) and conscious urges to use drugs (insula) are involved in many other processes. However, these regions serve also for the function and maintenance of drug addiction. 1.3. Studies on the brain’s gray matter volume and white matter density The brain’s gray matter is a major component of the central nervous system made up of neuronal cell bodies and it is involved in motor control, perception, memory, emotions, and speech. A study that measured gray matter volume in fMRI together with performance on the Monetary Incentive Delay task and the Cambridge Gambling Task showed higher left striatal grey matter volume in frequent video game players that also negatively correlated with deliberation time on the Cambridge Gambling Task (Kuhn et al., 2011). Furthermore, there was enhanced activity in the left striatum during feedback of loss compared with no loss on the Monetary Incentive Delay task in frequent video game players that negatively correlated with deliberation time on the task. An association of video game playing with higher left ventral striatum volume could reflect altered reward processing and represent adaptive neural plasticity in frequent videogame players. Other studies showed that IGD participants had lower gray matter density in areas responsible for behavioral and emotional problems (cingulate gyrus) urges (insula), and regulation of emotional behavior (lingual gyrus) which are major concern in IGD (Zhou et al., 2011). Patients with IGD showed increased gray matter volumes of the thalamus whereas pro-gamers showed such increase in the cingulate gyrus and these changes may be associated with the effects of gaming on attention and sensory-motor coordination (Han et al., 2012). Furthermore, lower diffusional kurtosis imaging (DKI footnote 3) was reported in adolescents with IGD (Sun et al., 2014).3 These areas are associated with attention and control (anterior cingulate cortex), impulse control (orbito-frontal cortex), motor function (supplementary motor area, primary motor cortex), emotional regulation (lingual gyrus), and are compatible with models of drug addiction (Volkow et al., 2010). Finally, IGD adolescents showed reduced gray matter volume of the anterior cingulate cortex, pre-cuneus, supplementary motor area, superior parietal lobule, dorsal lateral prefrontal cortex,

3 Diffusional kurtosis imaging (DKI) extends conventional diffusion tensor imaging (DTI) by estimating the kurtosis of the water diffusion probability distribution function. Qualitatively, a large diffusional kurtosis suggests a high degree of diffusional heterogeneity and microstructural complexity (Jensen et al., 2005).

Table 2 Resting state and structural studies of internet and gaming disordera . Methods

Participants

Main Findings

Park et al. (2010)

Regional cerebral metabolic rates of glucose (rCMRglu) in Positron Emission Tomography (PET) Regional homogeneity (ReHo) measure in MRI

Eleven Internet and gaming overusers and nine control participants

Increased activity in the OFC, striatum, and sensory regions.

Nineteen IGD college students (11 males 8 females) and nineteen control partcipants.

Kuhn et al. (2011)

Gray matter volume measure in MRI

Seventy six frequent compared with seventy eight infrequent adolescent video game players (14 years old).

Zhou et al. (2011)

Gray matter volume measure in MRI

Yuan et al. (2011)

White matter fractional anisotropy (FA) changes using the diffusion tensor imaging (DTI) in MRI

Eighteen Internet addicted adolescents (16 males 2 females) and fifteen control participants (13 males) Eighteen adolescents with IGD (12 males) and eighteen control participants.

Enhanced ReHo in the cerebellum, brainstem, right cingulate gyrus, bilateral para-hippocampus, right frontal lobe (rectal gyrus, inferior frontal gyrus and middle frontal gyrus), left superior frontal gyrus, left pre-cuneus, right post-central gyrus, right middle occipital gyrus, right inferior temporal gyrus, left superior temporal gyrus and middle temporal gyrus Higher left striatal gray matter volume Left striatal grey matter volume negatively correlated with deliberation time on Cambridge Gambling Task Activity on the Monetary Incentive Delay task was enhanced during feedback of loss compared with no loss. Lower gray matter density in the left ACC, left PCC, left insula, and left lingual gyrus

Dong et al. (2012a,b,c,d)

Regional homogeneity (ReHo) measure Fifteen Internet and gaming disorder in MRI and fourteen control paritcipants

Han et al. (2012)

Gray matter volume measure in MRI

Lin et al. (2012)

Brain white matter integrity measured by Diffusion Tensor Imaging (DTI) in MRI. Whole brain voxel-wise analysis of fractional anisotropy (FA) was performed by tract-based spatial statistics (TBSS) White matter integrity using diffusion tensor imaging (DTI) in MRI

Liu et al. (2010)

Dong et al. (2012a,b,c,d)

Weng et al. (2013)

Gray matter density and white matter density changes using Voxel-based morphometry (VBM) analysis and tract-based spatial statistics (TBSS) was reported by

Twenty IGD participants, eighteen male control participants and seventeen pro-gamers. Seventeen Internet Addiction Disorder (14 males) and sixteen control adolescents.

Sixteen Internet Gaming Addicted participants and fifteen control participants. Seventeen IGD participants (13 females and 4 males) and seventeen control participants (15 females 2 males).

Decreased gray matter volume in the bilateral DLPFC, the SMA, the OFC, the cerebellum and the left rostral ACC Enhanced FA value of the left PLIC and reduced FA value in the white matter within the right PHG. Gray matter volumes of the DLPFC, rACC, SMA, and white matter FA changes of the PLIC correlated with the duration of internet addiction. Enhanced regional homogeneity (ReHo) in the brainstem, inferior parietal lobule, left posterior cerebellum, and left middle frontal gyrus, decreased ReHo in temporal, occipital and parietal cortex. Increased impulsiveness and perseverative errors, and volume in left thalamus gray matter, but decreased gray matter volume in inferior temporal gyri, right middle occipital gyrus, and left inferior occipital gyrus. Lower FA in the OFC, corpus callosum, cingulate, inferior frontal-occipital fasciculus, and corona radiation, internal and external capsules, FA values in the left genu of the corpus callosum negatively correlated with scores on the Screen for Child Anxiety Related Emotional Disorders, and between FA values in the left external capsule and Young’s Internet addiction scale.

A. Weinstein et al. / Neuroscience and Biobehavioral Reviews 75 (2017) 314–330

Citation

Higher fractional anisotropy (FA), in the thalamus and left PCC. Higher FA in the thalamus was associated with greater severity of internet addiction. Gray matter atrophy in the right OFC, bilateral insula, and right supplementary motor area. Reduced FA in the right genu of corpus callosum, bilateral frontal lobe white matter, and right external capsule. Gray matter volumes of the right OFC, bilateral insula and FA values of the right external capsule positively correlated with Young Internet Addiction scores.

317

318

Table 2 (Continued) Methods

Participants

Main Findings

Hong et al. (2013a,b)

Cortical thickness in MRI

Decreased cortical thickness in the right lateral OFC.

Yuan et al. (2013)

Cortical thickness in MRI.

Fifteen male adolescents diagnosed with internet addiction and fifteen male control participants. Eighteen adolescents with internet gaming disorder and eighteen control participants.

Sun et al. (2014)

Diffusional kurtosis imaging (DKI) in the detection of gray matter diffusion

Eighteen participants with Internet Gaming Disorder and twenty one control participants.

Wang et al. (2015a,b)

Gray matter volume measure in MRI

Twenty-eight internet participants with Internet Gaming Disorder and twenty-eight control participants

Kim et al. (2015)

Regional homogeneity (ReHo) measure in MRI

Lin et al. (2015a,b)

Gray matter density and white matter density changes using voxel-based morphometric analysis in MRI. Diffusion tensor imaging mean diffusivity (MD)

Sixteen patients with Internet gaming addiction (IGD), fourteen alcohol use disorder (AUD) and fifteen control participants. Thirty five participants with Internet Gaming Disorder and thirty six control participants. A hundred and fourteen boys and a hundred and twenty six girls

Takeuchi et al. (2016)

Increased cortical thickness in the left precentral cortex, precuneus, inferior middle frontal cortex temporal and middle temporal cortices. Decreased cortical thicknesses of the left lateral OFC, insula, lingual gyrus, the right postcentral gyrus,entorhinal cortex and inferior parietal cortex. cortical thicknesses of the left precentral cortex, pre-cuneus and lingual gyrus correlated with duration of online gaming addiction and the cortical thickness of the orbito-frontal cortex correlated with the impaired task performance during the color-word Stroop task. Lower gray matter diffusion in the right anterolateral cerebellum, right inferior and superior temporal gyri, right SMA, middle occipital gyrus, right pre-cuneus, postcentral gyrus, right inferior frontal gyrus, left lateral lingual gyrus, left paracentral lobule, left ACC, and median cingulate cortex, bilateral fusiform gyrus, insula, PCC, and thalamus. Higher GM volume in the right inferior and middle temporal gyri, and right PHG, and lower volume in the left precentral gyrus. Gray matter volume of the bilateral ACC, pre-cuneus, SMA, SPL, left DLPFC, left insula, and bilateral cerebellum decreased in IGD participants compared with healthy control participants. Gray matter volume of the ACC negatively correlated with the incongruent response errors on the Stroop IGD and AUD participants had increased ReHo in the PCC. IGD participants showed decreased ReHo in the right superior temporal gyrus compared with AUD and control participants. Patients with AUD showed decreased ReHo in the ACC. Higher FA in the thalamus and left PCC Higher FA in the thalamus was associated with greater severity of internet addiction The amount of videogame play was associated with increased MD in the left middle, inferior, and orbital frontal cortex; left pallidum; left putamen; left hippocampus; left caudate; right putamen; right insula; and thalamus in both cross-sectional and longitudinal analyses. Higher MD in the areas of the left thalamus, left hippocampus, left putamen, left insula, and left Heschl gyrus was associated with lower intelligence.

Abbreviations;: dorsolateral prefrontal cortex (DLPFC) Supplementary Motor Area (SMA) Orbito-Frontal Cortex (OFC), Anterior Cingulate Cortex (ACC) Posterior Limb of the Internal Capsule (PLIC), Para-Hippocampal Gyrus (PHG), Posterior Cingulate Cortex (PCC), Superior Temporal gyrus (STG), Medial Prefrontal Cortex (MPFC), Angular Gyrus (AG), Superior Parietal Lobule (SPL). a Studies arranged chronologically.

A. Weinstein et al. / Neuroscience and Biobehavioral Reviews 75 (2017) 314–330

Citation

A. Weinstein et al. / Neuroscience and Biobehavioral Reviews 75 (2017) 314–330

319

Fig. 1. Regions that showed reduced gray matter volume in IGD participants in more than two studies- anterior cingulate (Zhou et al., 2011; Yuan et al., 2011; Wang et al., 2015a,b) supplementary motor area (Wang et al., 2015a,b; Weng et al., 2013; Sun et al., 2014) Cerebellum (Yuan et al., 2011; Wang et al., 2015a,b) Insula (Zhou et al., 2011; Weng et al., 2013; Sun et al., 2014; Wang et al., 2015a,b) Inferior Temporal gyrus (Han et al., 2012; Sun et al., 2014).

insula, and bilateral cerebellum (Wang et al., 2015a). These findings are compatible with previous studies on gray matter volume in IGD (Zhou et al., 2011; Yuan et al., 2011; Ko et al., 2013a; Weng et al., 2013). Moreover, gray matter volume of the anterior cingulate cortex negatively correlated with the incongruent response errors of Stroop task that is a measure of cognitive control mechanism (Wang et al., 2015a). Since few studies present structural changes in gray matter in IGD, these findings need further replication. Fig. 1 shows brain regions with reduced gray matter volume in frequent IGD players. The brain’s white matter is another component of the central nervous system that consists mostly of glial cells and myelinated axons that transmit signals from cerebellum to other brain centers. Diffusion Tensor Imaging (DTI) (footnote 4) evaluated brain white matter integrity by measuring Fractional anisotropy (FA) (footnote 5) in adolescents with IGD and control participants (Lin et al., 2012).4 High FA indicates greater white matter integrity.5 The analysis of FA by tract-based spatial statistics (TBSS) (footnote 6) demonstrated lower FA in the orbito-frontal cortex, corpus callosum, cingulate, inferior frontal-occipital fasciculus,6 and corona radiation, internal and external capsules in IGD participants and FA values in the left external capsule correlated with Young’s Internet addiction scale. Both findings possibly reflect negative changes in white matter density as result of game play. IGD participants also showed higher FA in the thalamus and left posterior cingulate cortex relative to healthy control participants presumably indicating greater white matter integrity (Dong et al., 2012d). Secondly, higher FA in the thalamus was associated with greater severity of IGD but it unclear whether this is a pre-existing vulnerability factor, or may arise secondary to IGD, perhaps as a direct result of excessive Internet game playing. Further studies combined measurement of white matter FA changes and gray matter volume using DTI analysis and an

4 Diffusion tensor imaging (DTI) is be used to map and characterize the threedimensional diffusion of water as a function of spatial location (Alexander et al., 2007). The diffusion tensor describes the magnitude, the degree of anisotropy, and the orientation of diffusion anisotropy. Estimates of white matter connectivity patterns in the brain can be obtained using diffusion anisotropy. 5 Fractional anisotropy (FA) reflects the directionality of water diffusion and coherence of white matter fiber tracts. FA is a measure often used in diffusion imaging where it is thought to reflect fiber density, axonal diameter, and myelination in white matter (Smith et al., 2006). 6 Track Based Spatial Statistics (TBSS) analyses are fully automated, simple to apply and investigate the whole brain. It does not require pre-specifying and prelocalizing regions or features of interest. Limitations include problems caused by alignment inaccuracies, and the lack of a principled way for choosing smoothing extent (Smith et al., 2006).

optimized voxel-based morphometry (VBM) technique (footnote 7) in adolescents with IGD.7 Decreased gray matter volume in areas responsible for attention, motor and cognitive control (dorso-lateral prefrontal cortex, anterior cingulate cortex and supplementary motor area and reduced white matter in areas responsible for memory encoding and retrieval (para-hippocampal gyrus) and relaying sensory and motor information (limb of the internal capsule) were reported by Yuan et al. (2011). There was further evidence for gray matter atrophy in the right orbito-frontal cortex, bilateral insula, and right supplementary motor area in IGD (Weng et al., 2013). They also had reduced FA in the right genu of the corpus callosum, bilateral frontal lobe white matter, and right external capsule. Lower gray matter density in areas responsible for cognitive and motor control (orbito-frontal cortex and supplementary motor area) and reduced white matter in areas responsible for cognitive planning and control (frontal lobe and external capsule) were reported (Lin et al., 2015a). IGD participants also showed lower white matter density in the inferior frontal gyrus, insula, amygdala, and anterior cingulate cortex brain regions that are involved in decision-making, behavioral inhibition and emotional regulation. IGD participants showed reduced FA the anterior cingulate cortex and right dorsolateral-prefrontal cortex pathways and they were associated with executive function measured on the Stroop task (Yuan et al., 2016). Finally, a study on a large sample of school children in Japan used diffusion tensor imaging mean diffusivity (MD) measurement (Takeuchi et al., 2016). Increased videogame play was associated with delayed development of the microstructure in extensive brain regions such as the orbito-frontal cortex, pallidum, putamen, hippocampus, caudate/putamen insula and the thalamus. Furthermore, higher MD in the areas of the thalamus, hippocampus, putamen and the insula was associated with lower intelligence. Since there are few studies that present structural changes in IGD, replication of these findings is required. Furthermore, these are cross-sectional studies precluding any inference on causality.

7 Volume Based Morphometry (VBM) aims to identify differences in the local composition of brain tissue, while discounting large scale differences in gross anatomy and position. This is achieved by spatially normalizing all the structural images to the same stereotactic space, segmenting the normalized images into gray and white matter, smoothing the gray and white matter images and finally performing a statistical analysis to localize significant differences between two or more experimental groups (Mechelli et al., 2005).

320

A. Weinstein et al. / Neuroscience and Biobehavioral Reviews 75 (2017) 314–330

1.4. Cortical thickness A study that measured cortical thickness in fMRI together with the color-word Stroop task revealed increased cortical thickness in the left precentral cortex, pre-cuneus, and middle frontal cortex, inferior temporal and middle temporal cortices in adolescents with IGD (Yuan et al., 2013). The cortical thicknesses of the left lateral orbito-frontal cortex, insula, lingual gyrus, the right postcentral gyrus, entorhinal cortex and inferior parietal cortex decreased. The cortical thickness of the orbito-frontal cortex correlated with impaired performance on the color-word Stroop task. Male adolescents with IGD had also shown decreased cortical thickness in the right lateral orbito-frontal cortex compared with control participants (Hong et al., 2013a). The apparent contradiction between the two studies showing increased and decreased cortical thickness seems to suggest that the changes are not robust and merit further investigation. 2. Functional connectivity Functional connectivity is the temporal dependency of neuronal activation patterns of anatomically separated brain regions. In the past years, an increasing body of neuroimaging studies has started to explore functional connectivity by measuring the level of coactivation of resting-state fMRI time-series between brain regions (van den Heuvel and Hulshoff Pol, 2010). See Table 3 for studies of Functional connectivity in IGD. 2.1. Functional connectivity at a resting state Participants with IGD exhibited increased functional connectivity between the cerebellum, posterior lobe and middle temporal gyrus and decreased connectivity between the inferior parietal lobule and right inferior temporal gyrus compared with participants without IGD (Ding et al., 2013). The increased functional connectivity in the bilateral posterior cerebellum may be associated with cognitive regulation, signal processing, and storage of relevant auditory-verbal memory processes. The decreased connectivity in the inferior temporal gyrus may be the result of a long duration of game playing (Dong et al., 2012c). These alterations are consistent with those in patients with substance use disorder, thus suggesting that IGD may share similar neurobiological abnormalities with substance use disorder (Ma et al., 2010). Chronic heroin users showed increased functional connectivity between nucleus accumbens and ventral/rostral anterior cingulate cortex, between nucleus accumbens and orbital frontal cortex, and between amygdala and orbito-frontal cortex and reduced functional connectivity between prefrontal cortex and orbito-frontal cortex and between prefrontal cortex and anterior cingulate cortex (Ma et al., 2010). Consistent with current models emphasizing the role of cortico-subcortical pathology in addiction (Sutherland et al., 2012), adolescents with IGD also showed reduced functional connectivity in cortical-subcortical circuits (pre-frontal and parietal cortex), the putamen and the insula (Hong et al., 2013b). IGD adolescents also had disruption in the functional connectivity in areas responsible for learning memory and executive function (anterior cingulate cortex and middle cingulate), processing of auditory, visual, and somato-sensory stimuli (inferior parietal lobe) and relay of sensory and motor signals (thalamus) (Wee et al., 2014). Other disorders such as impulse control disorders and attention disorders also show impaired functional connectivity of brain networks responsible for executive function and sensory processing. However, the enhanced resting-state functional connectivity within the regions associated with reward, memory, craving and motivation, and reduced connections within the regions associated with cog-

nitive control and craving in the addictive brain is unique to drug addiction. The activity in the anterior and middle cingulate and thalamus correlated with the IGD severity and impulsivity. Disruption in functional connectivity may also affect motivation and reward. Smokers with IGD exhibited decreased functional connectivity with posterior cingulate cortex in the right rectus gyrus compared with non-smokers IGD (Chen et al., 2014). The rectus gyrus is part of the orbito-frontal cortex, which is involved in the evaluation of reward of stimuli and the explicit representation of reward expectancy for substances. 2.2. Functional connectivity, reward and the striatum Lower functional connectivity in IGD affected executive control networks and it negatively correlated with the Stroop effect (Dong et al., 2015). Impulsivity also correlated negatively with functional connectivity in the amygdala, dorso-lateral prefrontal cortex and the orbito-frontal cortex (Ko et al., 2015) and it was associated with alterations over the frontal-limbic connections (Park et al., 2015). Consistent with the evidence of the role of motivation and reward in drug addiction, there is evidence that the striatum plays an important role in the underlying pathophysiology of IGD. Adolescents with IGD showed significantly reduced dorsal putamen functional connectivity with the posterior insula-parietal operculum (Hong et al., 2015). IGD participants also showed increased volumes of dorsal striatum (caudate) and ventral striatum (nucleus accumbens) (Cai et al., 2015). In addition, caudate volume correlated with Stroop task performance and NA volume was associated with the Internet addiction test (IAT) score in the IGD group. The insula that plays a key role in the manifestation of urges for drugs is also associated with the core symptoms of IGD. IGD participants exhibited enhanced resting state functional connectivity between the anterior insula and the anterior cingulate cortex, putamen, angular gyrus, and pre-cuneous, areas that are involved in salience, craving, self-monitoring, and attention (Zhang et al., 2015). Finally, IGD participants had decreased connectivity measures between the left and right superior, inferior and middle frontal gyrus (Wang et al., 2015b). In conclusion, participants with IGD showed reduced connectivity in several areas including the cerebellum, amygdala, orbito-frontal cortex and dorso-lateral prefrontal cortex, striatum, anterior cingulate cortex, thalamus and the insula. These functional brain networks are responsible for executive function, cognitive control, sensory processing motivation and reward and they are common to IGD and substance use disorders. As mentioned earlier, in making group comparisons, group differences between IGD users and control participants may reflect predisposing factors rather than decreases due to IGD. Furthermore, there are only few studies with several regions that have been replicated so the evidence needs to be interpreted with caution. 3. Brain activation Many studies of brain function with PET or fMRI involve the interpretation of a subtracted PET/fMRI image, usually the difference between two images under baseline and stimulation conditions. The purpose of these studies is to see which areas of the brain activated the stimulation condition (Worsley et al., 1996). See Table 4 for activation studies of IGD in fMRI 3.1. Cue-exposure activation studies of videogame urges The first brain activation study in IGD participants has used an fMRI procedure to measure contrasts between a spaceinfringement game and a control task (Hoeft et al., 2008). Males

Table 3 Studies of Functional connectivity in fMRIa . Method

Participants

Main findings

Ding et al. (2013)

Functional connectivity in fMRI

Seventeen adolescents with Internet Gaming Disorder and twenty four control adolescents.

Hong et al. (2013a,b)

Functional connectivity in fMRI

Feng et al. (2013)

Arterial spin-labeling (ASL) perfusion in fMRI

Twelve adolescents with internet addiction and eleven control participants. Fifteen adolescents with IGA and eighteen control adolescents.

Increased functional connectivity in the bilateral cerebellum posterior lobe and middle temporal gyrus. Decreased connectivity in the bilateral inferior parietal lobule and right inferior temporal gyrus Connectivity with the PCC positively correlated with Internet Addiction Scores in the right pre-cuneus, PCC, thalamus, caudate, nucleus accumbens, SMA, and lingual gyrus. It negatively correlated with the right cerebellum, anterior lobe and left SPL. Reduced functional connectivity in cortico-subcortical circuits (∼24% with Pre-frontal and ∼27% with parietal cortex). Bilateral putamen was the most extensively involved subcortical brain region.

Wee et al. (2014)

Functional connectivity in fMRI

Chen et al. (2014)

Functional connectivity in fMRI

Dong et al. (2015)

Functional connectivity in fMRI

Ko et al. (2015)

Gray matter density (GMD) and Functional connectivity in fMRI

Thirty males with IGD and thirty control participants.

Hong et al. (2015)

Functional connectivity in fMRI in subdivisions of striataum

Twelve male adolescents with internet gaming disorder and eleven male control participants.

Cai et al. (2015)

Functional connectivity in fMRI in striatal nuclei (caudate, putamen, and nucleus accumbens) volumes Functional connectivity and voxel-mirrored homotopic connectivity (VMHC) method Functional connectivity of the insula in fMRI

Twenty-seven adolescents with IGD and thirty control participants.

Wang et al. (2015a,b)

Zhang et al. (2015)

Park et al. (2015)

Functional connectivity in fMRI

Yuan et al. (2016)

Functional connectivity in fMRI

Seventeen adolescents with IGD and sixteen control participants. Twenty-nine smokers with IGD, twenty two nonsmokers with IGD, and thirty control participants. Thirty-five IGD and thirty six control participants.

Seventeen participants with IGD and twenty four healthy control participants. Seventy four young adults with Internet gaming disorder (IGD) and forty one control participants.

Nineteen Internet gaming disorder adolescents and twenty age-matched control participants. Twenty-eight IGD adolescents and twenty-five control participants

Higher global Cerebral Blood Flow (CBF) in the left inferior temporal lobe/Fusiform gyrus, left PHG/amygdala, right medial frontal lobe/ACC, left and right insula, right middle temporal gyrus, right pre-central gyrus, left SMA, left cingulate gyrus, and right inferior parietal lobe. Lower CBF in the left middle temporal gyrus, left middle occipital gyrus, and right cingulate gyrus. Disruption in the functional connectivity with the frontal, occipital, and parietal lobes. Functional connectivity with the frontal, occipital, and parietal lobes correlated with the IAD severity. Decreased resting state functional connectivity with posterior cingulate cortex in the right rectus gyrus. Increased resting state functional connectivity with the left middle frontal gyrus in smokers with IGA compared with nonsmokers with IGA. Lower functional connectivity in executive control networks functional-connectivity measures in executive control networks were negatively correlated with Stroop effect and positively correlated with brain activations in executive-control regions across groups. Lower GMD in the bilateral amygdala and higher impulsivity. Lower functional connectivity with the left amygdala over the left DLPFC and with the right amygdala over the left DLPFC and OFC. Higher functional connectivity with the bilateral amygdala over the contralateral insula. The functional connectivity between the left amygdala and DLPFC negatively correlated with impulsivity. The functional connectivity of the right amygdala to the left DLPFC and OFC also negatively correlated with impulsivity. Reduced dorsal putamen functional connectivity with the posterior insula-parietal operculum. Time spent playing online games predicted significantly greater functional connectivity between the dorsal putamen and bilateral primary somatosensory cortices Lower functional connectivity between the dorsal putamen and bilateral sensorimotor cortices in healthy control participants. Increased volumes of dorsal striatum (caudate) and ventral striatum (nucleus accumbens) and more errors on the Stroop task. Caudate volume correlated with Stroop task performance and nucleus accumbens (NAc) volume was associated with the internet addiction test (IAT) score in the IGD group. Decreased VMHC between the left and right superior frontal gyrus (orbital part), inferior frontal gyrus (orbital part), middle frontal gyrus and superior frontal gyrus. Enhanced functional connectivity between the anterior insula and a network of regions including ACC, putamen, angular gyrus, and pre-cuneous. Stronger functional connectivity between the posterior insula and postcentral gyrus, pre-central gyrus, SMA, STG. IGD severity was positively associated with connectivity between the anterior insula and AG, and STG, and with connectivity between the posterior insula and STG. Duration of Internet gaming was positively associated with connectivity between the anterior insula and ACC. Higher impulsiveness and higher global efficiency and lower local efficiency pathological states Topological alterations were specifically attributable to inter-regional connections incident on the frontal region, and the degree of impulsiveness was associated with the topological alterations over the frontal-limbic connections. Reduced FA in salience network, right central executive network tracts, and between-network (the ACC-right DLPFC tracts). Correlation between the effective and structural connection from salience network to central executive network and the number of errors during incongruent condition in Stroop task in both IGD and control participants.

A. Weinstein et al. / Neuroscience and Biobehavioral Reviews 75 (2017) 314–330

Citation

Abbreviations: dorsolateral prefrontal cortex (DLPFC) Supplementary Motor Area (SMA) Orbito-Frontal Cortex (OFC), Anterior Cingulate Cortex (ACC) Posterior Limb of the Internal Capsule (PLIC), Para-Hippocampal Gyrus (PHG), Posterior Cingulate Cortex (PCC), Superior Temporal gyrus (STG), Medial Prefrontal Cortex (MPFC), Angular Gyrus (AG), Superior Parietal Lobule (SPL). a Studies arranged chronologically. 321

322

Table 4 Activation studiesa . Methods

Participants

Main findings

Hoeft et al. (2008)

Brain activation contrasting a space-infringement game with a control task in fMRI Brain activation using gaming pictures and neutral pictures together with fMRI scanning.

Eleven males and eleven female healthy students.

Males showed greater activation and functional connectivity compared to females in the meso-cortico-limbic system.

Ten participants with IGD and ten control participants.

The right orbitofrontal cortex, right nucleus accumbens, bilateral ACC and medial frontal cortex, right DLPFC, and right caudate nucleus were activated in the addicted group. The activation of the region-of-interest (ROI) defined by the above brain areas was positively correlated with self-reported gaming urges and recalling of gaming experience. Activation of ACC and OFC in response to video-game cues. The change of craving for Internet video games positively correlated with the change in activity of the ACC.

Ko et al. (2009)

Han et al. (2010a,b)

Dong and Huang (2011) Sun et al. (2012)

Responses to Internet video-game cues in the Orbito-frontal and cingulate cortices in fMRI A guessing task measuring monetary gain and loss in fMRI The World of Warcraft (WoW) game figures with fMRI

Eleven male participants with online gaming addiction and eight control participants. Fourteen male Internet addicts and thirteen male control participants. Ten participants with internet and gaming disorder and ten control participants.

Kim et al. (2012)

A ball-throwing animation task in fMRI

Seventeen adolescent internet and gaming disorder participants and seventeen control adolescents.

Dong et al. (2012a,b,c,d)

The Stroop task and an event-related fMRI

Ko et al. (2013a)

Gaming pictures and neutral pictures in fMRI scanning.

Ko et al. (2013b)

Images associated with online games, smoking, and neutral images in fMRI. A dot probe paradigm with short-presentation (attentional bias) and long-presentation (cue reactivity) trials in fMRI

Twelve male Internet gaming disorder participants and twelve control participants. Fifteen participants with internet and gaming disorder and fifteen remitted IGD participants and fifteen control participants. Sixteen participants with both IGD and nicotine dependence and sixteen control participants. Eight male Internet gaming disorder participants and nine control participants.

Lorenz et al. (2013)

Increased activation in OFC cortex in gain trials and decreased anterior cingulate activation in loss trials. DLPFC, bilateral temporal cortex, cerebellum, right inferior parietal lobule, right cuneus, right hippocampus, para-hippocampal gyrus and the left caudate nucleus were activated. Craving measures were positively associated with activity in the bilateral pre-frontal cortex, ACC and right inferior parietal lobe. Activation in the thalamus, bilateral precentral area, bilateral middle frontal area, and the right temporal-parietal junction. Higher activation in the left temporal-parietal-occipital junction, right para-hippocampal area. The duration of internet use correlated with the activity of posterior area of left middle temporal gyrus. Higher activations in the left AG during avatar-perception. A positive correlation between gender identity and brain activation in the left AG during self-perception. Brain activity in bilateral DLPFC, pre-cuneus, left PHG, PCC and right ACC were activated in response to gaming cues. Higher activation over right DLPFC and left PHG than the remission group. The ACC and the PHG were activated in both cue-induced gaming urge and smoking craving among the comorbid group in comparison to the control group. Greater brain responses in short-presentation trials in MPFC and ACC and in long-presentation trials in lingual gyrus.

’Stroop effect’-related activity in the ACC PCC Diminished efficiency of response-inhibition processes

A. Weinstein et al. / Neuroscience and Biobehavioral Reviews 75 (2017) 314–330

Citation

Table 4 (Continued) Methods

Participants

Main findings

Dong et al. (2013)

A continuous wins-and-losses task to monitor decision-making in fMRI

Sixteen male IGD participants and fifteen control participants.

Leménager et al. (2014)

Images of Massively Multiplayer Online Role-Playing Games (MMORPGs) during fMRI. Go/NoGo tasks with/without gaming distraction in the fMRI.

Sixteen Internet gaming disorder participants and seventeen control participants.

Longer response time, lower repeat rate and greater Stroop effect. Increased brain activities in the inferior frontal cortex, insula, ACC. Decreased activation in the caudate and PCC after continuous wins. Increased brain activities in the inferior frontal gyrus and decreased brain activation in the PCC after continuous losses. Decreased bilateral brain activations in the AG and the Middle Occipital gyrus during self-perception.

Ko et al. (2014)

Response inhibition using fMRI

Ding et al. (2014)

A response-inhibition using Go/No-Go task in fMRI.

Twenty six subjects with Internet gaming disorder and twenty three control participants. Seventeen adolescents with IGD and seventeen control participants

Lin et al. (2014)

A probability-discounting task with fMRI.

Chen et al. (2015)

Response inhibition using Go/No go task in fMRI.

Liu et al. (2016)

Task-state in fMRI.

Liu et al. (2014)

Eleven Internet gaming disorder and eleven control participants.

Nineteen IGD participants and 21 control participants. Fifteen men with IGD for at least 1 year, and 15 control participants. 19 Internet gaming disorder individuals and 19 control participants.

The control group increased brain activations in the right DLPFC and SPL under gaming cue distraction in comparison with the IGD group. Brain activation of the right DLPFC and SPL were negatively associated with performance of response inhibition among the IGD group. Higher score for impulsivity and higher brain activation in the left OFC and bilateral caudate nucleus. Both groups exhibited activation of the insula and ACC during error processing. Increased activity during No-Go trials in the left superior medial frontal gyrus, right ACC, right superior/middle frontal gyrus, left inferior parietal lobule, left precentral gyrus, and left pre-cuneus and cuneus. The bilateral middle temporal gyrus, bilateral inferior temporal gyrus, and right SPL showed decreased activity. Activation of the left superior medial frontal gyrus was positively associated with Barratt Impulsivity Scale and Chen Internet Addiction Scale (CIAS) Decreased activation in the inferior frontal gyrus and the precentral gyrus when choosing the probabilistic options. Higher impulsivity and lower activity of the right SMA/pre-SMA activation of the right SMA, DLPFC, and caudate for response inhibition in control group. . Increased activation in the right SPL, right insular lobe, right pre-cuneus, right cingulate gyrus, right STG, and left brainstem.

A. Weinstein et al. / Neuroscience and Biobehavioral Reviews 75 (2017) 314–330

Citation

Abbreviations;: dorsolateral prefrontal cortex (DLPFC) Supplementary Motor Area (SMA) Orbito-Frontal Cortex (OFC), Anterior Cingulate Cortex (ACC) Posterior Limb of the Internal Capsule (PLIC), Para-Hippocampal Gyrus (PHG), Posterior Cingulate Cortex (PCC), Superior Temporal gyrus (STG), Medial Prefrontal Cortex (MPFC), Angular Gyrus (AG), Superior Parietal Lobule (SPL). a Studies arranged chronologically.

323

324

A. Weinstein et al. / Neuroscience and Biobehavioral Reviews 75 (2017) 314–330

showed greater activation and functional connectivity compared to females in the meso-cortico-limbic system. These findings may be attributable to higher motivational states in males, as well as gender differences in reward prediction, learning reward values, and cognitive state during computer video game playing. These gender differences may help explain why males are more attracted to, and more likely to become “hooked” on video games. The following study has shown gaming pictures and neutral pictures in fMRI to IGD and control participants (Ko et al., 2009). The IGD group showed activation of the right hemispheric orbito-frontal cortex, nucleus accumbens, bilateral anterior cingulate cortex and medial frontal cortex, right dorso-lateral prefrontal cortex, and right caudate nucleus. The activation of these regions positively correlated with self-reported gaming urges and recalling of gaming experience provoked by the gaming pictures. The neural substrate of cue-induced gaming urge/craving in online gaming disorder is therefore similar to that of the cue-induced craving in substance use disorder. Since then, several studies have followed changes in gaming cue-induced brain activity in IGD. They have followed changes in brain activity between baseline and following 6 weeks of Internet video-game play using fMRI (Han et al., 2010a). During a standardized 6-week video-game play period, brain activity in the anterior cingulate cortex and orbito-frontal cortex of the IGD group but not in the control group, increased in response to Internet video-game cues. In addition, the change of craving for Internet video games positively correlated with the change in activity of the anterior cingulate cortex in all participants. These changes in Frontal-lobe activity with extended video-game play may be similar to those observed during the early stages of addiction. A following study showed that gaming cues activated bilateral dorso-lateral prefrontal cortex, pre-cuneus, left para-hippocampus gyrus, posterior cingulate cortex and right anterior cingulate cortex in the IGD group (Ko et al., 2013b). These regions also positively correlated with subjective gaming urge during cue exposure. The IGD group had also stronger activation over right dorso-lateral prefrontal cortex and left para-hippocampal gyrus than a group of remitted former IGD players. The two areas could be candidate markers for current IGD. Furthermore, the brain correlates of cue-induced gaming urge and smoking craving in participants with both Internet gaming disorder and nicotine dependence were compared (Ko et al., 2013a). Cue-induced gaming urge and smoking craving activated the anterior cingulate cortex and the para-hippocampal gyrus in the comorbid group in comparison with the control group. Both IGD and nicotine dependence share therefore similar mechanisms of cue-induced reactivity of the frontal-limbic network in particular the para-hippocampal gyrus and the anterior cingulate cortex. World of Warcraft (WoW) game figures activated the dorsolateral prefrontal cortex, bilateral temporal cortex, cerebellum, right inferior parietal lobule, right cuneus, right hippocampus, para-hippocampal gyrus and the left caudate nucleus in fMRI in IGD participants (Sun et al., 2012). Craving measures were positively associated with activity in the bilateral pre-frontal cortex, anterior cingulate cortex and right inferior parietal lobe. These regions were associated with cognitive, emotion and motivation-related function in drug addiction. Furthermore, a prospective study of IGD and control participants in which they all received Internet videogame stimuli in fMRI showed that IGD participants had increased activation in regions that are associated with visuo-spatial orientation, space, attention, mental imagery and executive function (right superior parietal lobule, insular lobe, pre-cuneus, cingulate gyrus, superior temporal gyrus, and left brainstem) (Liu et al., 2016). IGD participants also showed attention bias to short presentations of game pictures and enhanced brain responses in medial pre-frontal cortex and anterior cingulate cortex. There was high connectivity between the right inferior frontal gyrus, which is associated with

inhibition processing, and left orbito-frontal cortex and ventral striatum that are associated with cue reactivity in IGD participants (Lorenz et al., 2013). Finally, IGD adolescents showed activation of thalamus, bilateral precentral area, bilateral middle frontal area, the right temporalparietal junction and the right para-hippocampal gyrus during ball-throwing animations simulating the experience of ‘disembodied state’ in cyberspace (Kim et al., 2012). Massively multi player online role players showed decreased activation of the angular gyrus and the middle occipital gyrus during evaluating of their own body image, areas that have been associated with visual-spatial attention and body self-awareness (Leménager et al., 2014). In conclusion, several studies have shown a consistent pattern of brain regions that were activated in response to video playing stimuli. Fig. 2 shows brain regions that were activated in response to videogame cues in frequent IGD players. 3.2. Inhibitory control mechanisms Similarly to substance and alcohol use disorder, individuals with IGD may display faulty inhibitory control mechanism. IGD participants showed diminished efficiency of response-inhibition processes indicated by greater ‘Stroop effect’-related activity in the anterior and posterior cingulate cortex (Dong et al., 2012b). IGD participants also committed more commission errors on Go/No Go tasks when a gaming picture presented at the background (Liu et al., 2014). The results indicate an impaired function of response inhibition in IGD under gaming distraction and a lack of activation of the dorso-lateral prefrontal cortex and superior parietal lobule that help keeping cognitive control and attention allocation for response inhibition under gaming cue distraction. There is further evidence for impulsivity and response inhibition and impaired function in the insula during error processing and greater activation of the frontal-striatal network in order to maintain response inhibition performance in IGD (Ko et al., 2014). IGD participants performing the Go/No Go task in fMRI also showed greater impulsivity and lower activity of the right supplementary motor area/presupplementary motor area (Chen et al., 2015). In adolescents with IGD, during No-Go trials there was increased activity in the left superior medial frontal gyrus, right anterior cingulate cortex, right superior/middle frontal gyrus, left inferior parietal lobe, left precentral gyrus, and left pre-cuneus and cuneus (Ding et al., 2014). IGD participants failed to recruit frontal-basal ganglia pathway and inhibit unwanted actions on the Go-Stop paradigm (Li et al., 2014). Impaired function of the pre-frontal cortex may relate to high impulsivity that in turn may contribute to impaired cognitive control and the development of IGD. The impairment in performance of response inhibition tasks is a consistent finding that is followed by failure to recruit frontal-basal ganglia pathways and use of other brain areas during inhibition in both adolescents and adults with IGD. 3.3. Reward Behavioral addictions such as IGD similarly to drug abusers are associated with faulty decision-making and preference for immediate reward to long-term gains. IGD individuals subjectively experienced monetary gain and loss during the performance of a guessing task while reward and punishment processing were measured (Dong et al., 2011). IGD participants also showed increased activation in orbito-frontal cortex in gain trials and decreased anterior cingulate cortex activation in loss trials implicating enhanced reward sensitivity and decreased loss sensitivity. Consistent with this finding, IGD participants who performed on a continuous winsand-losses task which measures decision making in fMRI showed increased brain activity in the inferior frontal cortex, insula, ante-

A. Weinstein et al. / Neuroscience and Biobehavioral Reviews 75 (2017) 314–330

325

Fig. 2. Regions that showed activation in response to internet and videogame cues in IGD participants – Caudate Nucleus (Ko et al., 2009; Sun et al., 2012; Dong et al., 2013; Ko et al., 2014) orbito-frontal cortex (Ko et al., 2009; Han et al., 2010a,b; Dong and Huang, 2011; Ko et al., 2014) anterior cingulate (Ko et al., 2009; Han et al., 2010a,b; Dong and Huang, 2011; Ko et al., 2013a,b; Lorenz et al., 2013; Dong et al., 2013; Ding et al., 2014), Dorsolateral Prefrontal Cortex (Ko et al., 2009; Sun et al., 2012; Ko et al., 2013a; Liu et al., 2014) Para-hippocampus (Sun et al., 2012; Ko et al., 2013a,b) Precuneus (Ko et al., 2013a; Ding et al., 2014; Liu et al., 2016) Posterior cingulate cortex (Ko et al., 2013a,b; Lorenz et al., 2013; Dong et al., 2013) Inferior Frontal Cortex (Dong et al., 2013; Lin et al., 2015b).

rior cingulate cortex and decreased activation in the caudate and posterior cingulate cortex after continuous wins (Dong et al., 2013). In addition, IGD participants showed increased brain activities in the inferior frontal gyrus and decreased brain activation in the posterior cingulate cortex after continuous losses. IGD participants engaged more brain resources in order to perform on the decisionmaking task and they showed impaired executive function. They also did not pay adequate attention to considering previous selections and relevant outcomes during decision-making. This finding implies impaired decision making together with enhanced compensatory brain-mechanisms. Consistent with the mechanism of impulsive decision-making, IGD participants who performed on a probability-discounting task in fMRI preferred the probabilistic options to fixed ones and were faster to respond compared with control participants (Lin et al., 2015b). They also showed decreased activation in the inferior frontal gyrus and the precentral gyrus when choosing the probabilistic options than control participants. Both of the behavioral performance and fMRI results so far indicated impaired risk evaluation, which might be the reason why IGD participants continue playing online games despite the risks of widely known negative consequence a mechanism that is also typical of individuals with substance use and pathological gambling disorder. However, with IGD the negative consequences of ongoing activity are much more subtle compared with substance use and pathological gambling hence despite of the commonalities on a neurobiological level there is not enough evidence for pathophysiology in IGD. Finally, IGD adolescents had decreased reward sensitivity irrespective of the type of reward and feedback (Kim et al., 2014). Adolescent IGD may be only sensitive to error monitoring regardless of positive feelings, such as sense of satisfaction or achievement. Further studies are required to investigate decision making in adolescents and concomitant brain regions. 4. Brain imaging studies on dopamine, 5-HT and other neurotransmitters Neurotransmitters such as DA, serotonin (5-HT) play an important role in drug and alcohol dependence, mainly by mediating dopamine reward and withdrawal mechanisms (Goldstein and Volkow, 2002; Fowler et al., 2007). It is therefore useful to investigate the function of these neurotransmitters in IGD. See Table 5 for Studies measuring dopamine receptor and transporter deficiency in IGD. Consistent with previous evidence that drug and alcohol use disorders (Volkow et al., 1993; Volkow et al., 1996b; Volkow et al., 2001; Wang et al., 1997) are associated with deficient dopamine reward activity, it is important to investigate whether IGD would also be associated with reduced levels of dopaminergic recep-

tor availability in the striatum. Dopamine D2 receptor binding potential was measured using the radiolabeled ligand [11 C] raclopride and positron emission tomography in men with and without IGD (Kim et al., 2011). IGD participants showed reduced levels of dopamine D2 receptor availability in subdivisions of the striatum including the bilateral dorsal caudate and right Putamen thus contributing to the hypothesis of deficient dopamine reward mechanisms in IGD. To test whether there is dopamine deficiency at the pre-synaptic level striatal dopamine transporter (DAT) levels were measured using (99 m) Tc-TRODAT-1 in single photon emission computed tomography (SPECT) in individuals with IGD (Hou et al., 2012). Analysis of DAT availability showed that the volume of the striatum and the striatum/whole brain ratio were reduced in individuals with IGD compared with control participants. Taken together, these results suggest that IGD is associated with dysfunction in the dopaminergic brain systems. These findings also support the claim that IGD may share similar neurobiological abnormalities with other addictive disorders. Finally, PET radio ligands [11 ] C-Nmethylspiperone ([11 ] C-NMSP) was used to assess the availability of D2 and 5-HT2A receptors and [18 ] F-fluoro-d-glucose ([18 ] F-FDG) was used to assess regional brain glucose metabolism in male IGD and control participants (Tian et al., 2014). The same individuals underwent PET imaging under both resting and Internet gaming task conditions. IGD participants showed a significant decrease in rCMRglu metabolism in the Prefrontal, Temporal, and Limbic systems. Lower levels of D2 receptor availability were observed in the striatum, and they were correlated with years of use. A low level of D2 receptors in the Striatum was associated with decreased rCMRglu metabolism in the orbito-frontal cortex. The results seem to indicate that D2 and 5-HT2A receptor-mediated dysregulation of the orbito-frontal cortex could underlie a mechanism for loss of control and compulsive behavior in IGD. Previous studies showed dopamine-release in the ventral striatum while playing computer games in a similar magnitude to amphetamine-induced dopamine release in healthy participants (Koepp et al., 1998; Weinstein, 2010) and in Parkinson’s patients with addiction, obsession and gambling compared with Parkinson’s patients without these symptoms (Steeves et al., 2009). These findings identify striatal function driven by dopamine as a core candidate promoting addictive behavior. Consistent with the evidence for the role of dopamine in addiction, pathological gamblers have an increased striatal dopamine release while losing money, a biological signal that may hinder the termination of gambling and excessive gambling in Parkinson’s patients due to dopaminergic medication (Linnet et al., 2010). Finally, Magnetic Resonance Spectroscopy (MRS) was utilized to measure pre-frontal and temporal cortical function in patients with IGD (Han et al., 2014). MRS studies of human brain disorders have detected decreases in brain N-acetyl aspartate (NAA) concen-

326

A. Weinstein et al. / Neuroscience and Biobehavioral Reviews 75 (2017) 314–330

Table 5 Studies measuring dopamine receptor and transporter deficiency in videogame playersa . Citation

Methods

Participants

Main findings

Weinstein (2010)

The radiolabeled ligand [123 IBZM] and Single Photon Computerized Tomography (SPECT) to assess dopamine D2 receptor binding potential in the ventral striatum before and after playing a motorbike riding videogame The radiolabeled ligand [11 C]raclopride and positron emission tomography to assess dopamine D2 receptor binding potential Striatal dopamine transporter (DAT) levels measured by (99m)Tc-TRODAT-1 single photon emission computed tomography (SPECT) Methylspiperone [11 ]C-NMSP) to assess the availability of D2 /5-HT2A receptors and [18]F-fluoro-d-glucose ([18 ]F-FDG) to assess regional brain glucose metabolism in positron emission tomography (PET)

Eight healthy control participants.

Motorbike riding videogame released 10.5% dopamine in the striatum in control participants

Five male internet addicts and seven male control participants.

Reduced levels of dopamine D2 receptor availability in subdivisions of the striatum including the bilateral dorsal caudate and right putamen. DAT expression level of the striatum was decreased. The volume, weight and whole brain were greatly reduced.

Kim et al. (2011)

Hou et al. (2012)

Tian et al. (2014)

Han et al. (2014)

a

Magnetic Resonance Spectroscopy (MRS)

Five male internet addicts and nine male control participants.

Twelve adult male IGD and fourteen control participants.

Seventy three young IGD participants and thirty eight control participants.

Decrease in glucose metabolism in the prefrontal, temporal, limbic regions Dysregulation of D2 receptors in the striatum correlated with years of use. Low level of D2 receptors in the striatum was associated with decreased glucose metabolism in the OFC. Lower levels of NAA in the right frontal cortex. Lower levels of choline in the medial temporal cortex. The Young Internet Addiction Scale (YIAS) scores and perseverative responses negatively correlated with the level of NAA in the right frontal cortex. The Beck Depressive Inventory (BDI) scores negatively correlated with choline levels in the right temporal Lobe.

Studies arranged chronologically Abbreviations: Orbito-Frontal Cortex (OFC).

trations when neuronal loss or dysfunction is involved. Increased choline which is a precursor of acetylcholine indicates increase in cell production or membrane breakdown, which can suggest demyelination. Levels of NAA in the right frontal cortex and the levels of choline in the medial temporal cortex were lower in IGD participants compared with control participants. Furthermore, Young Internet Addiction Scale (YIAS) scores and perseverative responses in IGD participants negatively correlated with the level of NAA in the right frontal cortex. Beck Depression Inventory scores in IGD participants negatively correlated with Choline levels in the right temporal lobe. Although, participants with IGD in this study were free from psychiatric co-morbidity, they appear to share characteristics with Attention Deficit Hyperactivity Disorder (ADHD) and major depressive disorder in terms of neurochemical changes in frontal and temporal cortices. 5. Discussion The studies reviewed so far show consistent findings demonstrating the resemblance between the neural mechanisms underlying substance use disorder and IGD. The evidence reviewed so far supports the behavioral addiction model of IGD since it shows structural changes and altered functional mechanisms of reward and craving in IGD that are similar to substance use disorders. The behavioral addiction model argues that IGD shows the features of excessive use despite adverse consequences, withdrawal phenomena, and tolerance that characterize substance use disorders. Although there is clinical evidence for withdrawal and tolerance as result of IGD (Young, 2009) there is no brain imaging evidence to support it. Furthermore, since there are no longitudinal studies

on structural and functional changes as result of IGD little can be inferred about the effects of these changes on the development and maintenance of IGD. The neuroimaging findings are also compatible with recent theoretical models which try to explain the development and maintenance of IGD (Dong and Potenza (Dong and Potenza, 2014) and Brand et al. (Brand et al., 2016a). Dong and Potenza (Dong and Potenza, 2014) proposed a cognitive-behavioral model for IGD which focuses on three domains- motivational drives related to reward-seeking and stress-reduction, behavioral control relating to executive inhibition, and decision-making that involves weighing the pros and cons of engaging in motivated behaviors. According to this model, neural processes underlying attention, response inhibition and behavioral flexibility in individuals with IGD relate importantly to IGD severity, although the extent to which these findings reflect predisposing factors or neural functions that arise during phases of IGD development is not yet understood. Brand et al. (Brand et al., 2016a) proposed a model describing the processes underlying the development and maintenance of Internet-use disorders. Internet-use disorders are considered to be the consequence of interactions between neurobiological and psychological predisposing factors, moderating factors, such as coping styles and cognitive biases, affective and cognitive responses to situational triggers in combination with reduced executive function. The neural correlates of Internet-gaming disorder and other Internet-use disorders may reflect a maladaptive interaction of cue-reactivity/craving and reduced prefrontal/executive function, as suggested for substance addiction (Brand et al., 2016a). Both models argue that an interaction of limbic and prefrontal brain areas reflect the behavioral aspects of cue-reactivity and craving

A. Weinstein et al. / Neuroscience and Biobehavioral Reviews 75 (2017) 314–330

together with diminished executive and inhibitory control resulting in the excessive/addictive use of games or other applications. There is supporting evidence that IGD is associated with dysfunctional frontal-lobe mechanisms responsible for reward and self-regulation. A recent meta-analysis of fMRI in individuals with IGD found a significant activation in the bilateral medial frontal gyrus and the left cingulate gyrus, as well as the left medial temporal gyrus and fusiform gyrus (Meng et al., 2015). Furthermore, the on-line time of IGD participants positively correlated with activations in the left medial frontal gyrus and the right cingulate gyrus. These findings implicate the important role of dysfunctional prefrontal lobe in the neuropathological mechanism of IGD. Considering the overlapped role of prefrontal lobe in the reward and self-regulatory system, this is supportive evidence for the classification of IGD as a behavioral addiction. The association between IGD and impaired self-regulation is compatible with the model of IGD as an impulse-control disorder, although the main evidence is for reward deficiency. Furthermore, due to its association with reward deficiency, IGD, similarly to ADHD, may be classified as a reward deficiency syndrome (Blum et al., 2008; Weinstein and Weizman, 2012). Studies of the resting state, functional and structural studies all support the notion that the brain’s reward system and other related systems mediating loss of control and inhibition are undergoing changes as result of IGD. Furthermore, studies on gaming cue-induced reactivity have shown an activation pattern that is similar to drug-dependent patients who were exposed to drug cues. There is also some evidence that pharmacological treatment with medication such as bupropion can alter this activity and attenuate cue-induced brain activity in excessive video-game users (Han et al., 2007), similar to the attenuation that occurs in nicotinedependent users (Weinstein et al., 2010). Furthermore, regular or chronic IGD resulted in reduced brain’s dopamine indicated by lower dopamine transporter density and lower dopamine D2 receptor occupancy in the brains of videogame players. This evidence is compatible with previous evidence for reduced dopamine D2 receptor occupancy in cocaine, methamphetamine, heroin and alcohol abusers (Volkow et al., 1993; Volkow et al., 1996b; Volkow et al., 2001; Wang et al., 1997). It seems that excessive use of the brain’s dopamine reward system as result of videogame playing resembles the down-regulation seen in case of drug and alcohol abuse. Finally, videogame playing induced striatal dopamine release indicating that this is a major force promoting addictive behavior. Although videogame playing released dopamine to a similar extent as drugs such as cocaine and amphetamines the two processes are entirely different pharmacologically, one is endogenous dopamine whereas the other is exogenous dopamine. A single pharmaco-genetic study supports the hypothesis that IGD is similar to substance use disorder (Han et al., 2010b). It found that IGD participants in Korea may have high reward dependence on Cloninger’s Personality Questionnaire TPQ (Cloninger et al., 1991), and higher prevalence of associated dopamine genes (Taq1A1 variation of dopamine D2 receptor and low activity Val158Met in the Catecholamine-O-Methyltransferase COMT alleles) than control participants (Han et al., 2010b). A second Korean study has shown that IGD users IGD participants may have genetic and personality traits similar to depressed patients. They had higher frequencies of the long-arm allele (SS-5HTTLPR), greater harm avoidance on Cloninger’s Personality Questionnaire TPQ (Cloninger et al., 1991), and higher Beck Depression Inventory scores than control participants. SS-5HTTLPR frequency was associated with harm avoidance in IGD users (Lee et al., 2009). The pharmaco-genetic evidence of serotonergic vulnerability and harm-avoidance together with MRS imaging evidence for neurochemical changes support the clinical evidence for comorbidity of IGD with mood disorders and ADHD. Despite the evidence for con-

327

nection of IGD with serotonergic vulnerability and harm-avoidance there is little brain imaging evidence to support the model of internet addiction as an obsessive-compulsive disorder. The evidence reviewed so far on changes in the reward circuitry in IGD is parallel to other behavioral addictions like pathological gambling (PG) and compulsive sexual disorder (CSD). Pathological gambling is associated with changes in the reward circuitry in the brain, with craving and with reduced activity of the meso-limbic reward system that may be related to impaired impulse control (Reuter et al., 2005). Brain imaging studies have shown that gambling cues have evoked gambling urges in pathological gamblers which were associated with deactivation in areas that were normally associated with craving (frontal para-limbic and limbic brain structures) in fMRI (Potenza et al., 2003a). Particularly when viewing gambling cues, pathological gamblers demonstrated relatively decreased activity in brain regions that are associated with impulse regulation. The deactivation of regions associated with impulse regulation is supported by evidence for decreased activity in the ventral-medial pre-frontal cortex during performance of the Stroop task in pathological gamblers. This region is implicated in disorders characterized by poor impulse control (Potenza et al., 2003b). The latter finding is consistent with a recent brain imaging study showing that pathological gamblers had lower cerebral glucose metabolic rates (rCMRglu) in the ventral parts of the striatum and thalamus, and higher rates of rCMRglu in the dorsal parts as compared with the control participants (Pallanti et al., 2010). Because of their extensive connectivity to the frontal cortex, striatal and thalamic functional alteration may contribute to faulty decision-making processes in PG patients. Compulsive sexual behavior has also been conceptualized as a “behavioral” addiction with common neural circuits that are responsible for processing of natural and drug reward. A study investigating the responses to sexually explicit materials in individuals with and without compulsive sexual behavior in fMRI (Voon et al., 2014) showed that exposure to sexually explicit cues in compulsive sexual disorder was associated with activation of the dorsal anterior cingulate, ventral striatum and amygdala, areas that were previously activated in response to drug cues. The greater engagement of cortico-striatal limbic circuitry in compulsive sexual behavior following exposure to sexual cues suggests that the neural mechanisms underlying compulsive sexual disorder are similar to IGD and pathological gambling. Finally, Brand (Brand et al., 2016b) showed enhanced response of the ventral striatum in heterosexual male participants to pornographic materials and that Ventral striatum activity was correlated with the self-reported symptoms of Internet pornography addiction. Mechanisms for reward anticipation in ventral striatum may contribute to a neural explanation of why individuals with certain preferences and sexual fantasies are at-risk for losing their control over Internet pornography consumption. This evidence is compatible to previous evidence of the involvement of the ventral striatum in reward in pathological gambling and IGD. One of the major limitations in brain imaging studies of IGD is they are mainly cross-sectional studies without baseline measures that rely on associations between structural and functional brain changes in the brain and Internet and videogame characteristics. These associations do not provide any proof that IGD activity plays a causal role in the development of the adolescent or adult brain. There are factors that may mediate such associations such as educational, cognitive, emotional and social factors. The use of tasks that measure specific executive functions such as cognitive control, decision-making and reward together with structural and functional brain imaging strengthen the conclusions drawn from these studies in IGD. There are also methodological considerations of age (use of adolescents and students), culture (most studies were done in the Far East) and lack of comparison groups with sub-

328

A. Weinstein et al. / Neuroscience and Biobehavioral Reviews 75 (2017) 314–330

stance use disorders. It is established that problematic internet use co-occurs with substance use disorder (Ko et al., 2012). However, there is no consistent evidence for it co-occurring with problematic online gaming. Since the studies so far have not reported frequency of substance use disorder among their subjects it is difficult to estimate to what extent it has influenced the studies under review. Another major limitation in the reported studies is that it is difficult to make any conclusions regarding the specific activities the individuals engage in (see discussion by Kuss and Griffiths (Kuss and Griffiths, 2012). Some authors specifically address online gaming addiction whereas others used the term Internet addiction and Internet gaming addiction almost interchangeably and that does not allow for any conclusions with regards to differences and similarities between the two. The findings so far of the effects of regular use of internet and gaming on the brain’s structure and function have profound psychological and clinical implications. Use of internet and videogames has a structural and functional effect on areas mediating dopamine reward such as the striatum. Furthermore, regular use of IGD is associated with impaired function of the prefrontal cortex and its connections with limbic regions that relates to high impulsivity that in turn may contribute to impaired cognitive control in IGD. It should be clarified that computer videogame playing is different from online gaming. Online gamers spend more time gaming than those who play offline games, mostly because of the social nature of these games. They find online games more pleasant and satisfying than offline games and sometimes prefer playing games to real-life activities; consequently, online games show problematic use more often than offline games do (See (Király et al., 2014)). Furthermore, there is evidence showing that videogame playing can be actually beneficial to the brain. For example Clemenson and Stark (Clemenson and Stark, 2015) showed that video gamers who played complex 3D video games performed better on a demanding recognition memory task. In addition, training naive video gamers in a rich 3D video game resulted in a significant improvement in hippocampus-associated cognition using several behavioral measures. These results suggest that modern day video games may provide meaningful stimulation to the human hippocampus. Bearing in mind that there are videogame play affects the brain both positively and negatively, in case of individuals who show lack of control and negative consequences of videogame play there should be increased awareness among parents and educators, increased control practices, harm-reduction and treatment strategies for IGD. In conclusion, there is an emerging pattern that IGD is associated with similar brain mechanisms responsible for substance use disorders. The brain imaging studies in IGD show similarity in brain mechanisms between IGD and substance use disorder and therefore supports the classification of IGD as a behavioral addiction. This model seems in our view to fit better than other models classifying IGD as an impulse control disorder or as an obsessive-compulsive disorder.

Declaration of interest The author reports no conflicts of interest. The author alone is responsible for the content and writing of the paper.

Acknowledgement Prof. Weinstein is supported by grant from the National Institute for Psychobiology in Israel and the Israeli Anti-Drug Authority.

References Grant, J.E., Potenza, M.N., Weinstein, A., Gorelick, D.A., 2010. Introduction to behavioral addictions. Am. J. Drug Alcohol Abuse 36 (5), 233–241. American Psychiatric Association, 2013. Diagnostic and Statistical Manual of Mental Disorders: DSM-5. American Psychiatric Association, Washington, D.C. Weinstein, A.M., Feder, K., Rosenberg Dannon, K.P., 2014. Internet addictioncriteria evidence and treatment. In: Rosenberg, K.P., Feder, L.C. (Eds.), Behavioral Addictions: Criteria, Evidence and Treatment. Elsevier Science, Burlington, USA, pp. 99–117. Weinstein, A., Aboujaoude, E., 2015. Problematic internet use: an overview. In: Aboujaoude, E., Starcevic, V. (Eds.), Mental Health in the Digital Age: Grave Dangers, Great Promise. Oxford University Press, USA. Young, K.S., 1998. Caught in the Net. Wiley, New York, NY, USA. American Psychiatric Association, 1994. Diagnostic and Statistical Manual of Mental Disorders: DSM-IV, 4th ed. American Psychiatric Association, Washington (DC). Young, K., 2009. Internet addiction: diagnosis and treatment considerations. J. Contemp. Psychother. 39 (4), 241–246. Durkee, T., Kaess, M., Carli, V., Parzer, P., Wasserman, C., Floderus, B., et al., 2012. Prevalence of pathological Internet use among adolescents in Europe: demographic and social factors. Addiction 107 (12), 2210–2222. Weinstein, A., Lejoyeux, M., 2015. New developments on the neurobiological and pharmaco-genetic mechanisms underlying Internet and videogame addiction. Am. J. Addict. 24 (2), 117–125. Zhu, Y., Zhang, H., Tian, M., 2015. Molecular and functional imaging of Internet addiction. BioMed Res. Int. 2015, 378675. Kuss, D.J., Griffiths, M.D., 2012. Internet and gaming addiction: a systematic literature review of neuroimaging studies. Brain Sci. 2 (3), 347–374. Koob, G.F., 1992. Drugs of abuse: anatomy, pharmacology and function of reward pathways. Trends Pharmacol. Sci. 13, 177–184. Di Chiara, G., North, R.A., 1992. Neurobiology of opiate abuse. Trends Pharmacol. Sci. 13, 185–193. Wise, R.A., 1996. Neurobiology of addiction. Curr. Opin. Neurobiol. 6, 243–251. Di Chiara, G., Bassareo, V., 2007. Reward system and addiction: what dopamine does and doesn’t do. Curr. Opin. Pharmacol. 7, 69–76. Wise, R.A., 2009. Roles for nigrostriatal—not just mesocorticolimbic—dopamine in reward and addiction. Trends Neurosci. 32, 517–524. Wanat, M.J., Willuhn, I., Clark, J.J., Phillips, P.E., 2009. Phasic dopamine release in appetitive behaviors and drug addiction. Curr. Drug Abuse Rev. 2, 195–213. Volkow, N.D., Ding, Y.-S., Fowler, J.S., Wang, G.-J., 1996a. Cocaine addiction: hypothesis derived from imaging studies with PET. J. Addict. Dis. 15, 55–71. Volkow, N.D., Wang, G.J., Fischman, M.W., Foltin, R.W., Fowler, J.S., Abumrad, N.N., Vitkun, S., Logan, J., Gatley, S.J., Pappas, N., Hitzemann, R., Shea, C.E., 1997a. Relationship between subjective effects of cocaine and dopamine transporter occupancy. Nature 386, 827–830. Volkow, N.D., Wang, G.J., Fowler, J.S., Logan, J., Gatley, S.J., Hitzemann, R., Chen, A.D., Pappas, N., 1997b. Decreased striatal dopaminergic responsivity in detoxified cocaine abusers. Nature 386, 830–833. Breiter, H.C., Gollub, R.L., Weisskoff, R.M., Kennedy, D.N., Makris, N., Berke, J.D., Goodman, J.M., Kantor, H.L., Gastfriend, D.R., Riorden, J.P., Mathew, R.T., Rosen, B.R., Hyman, S.E., 1997. Acute effects of cocaine on human brain activity and emotion. Neuron 19 (3), 591–611. De Luca, M., Beckmann, C.F., De Stefano, N., Matthews, P.M., Smith, S.M., 2006. fMRI resting state networks define distinct modes of long-distance interactions in the human brain. Neuroimage 29, 1359–21367. Park, H.S., Kim, S.H., Bang, S.A., Yoon, E.J., Cho, S.S., Kim, S.E., 2010. Altered regional cerebral glucose metabolism in Internet game overusers: a 18F-fluorodeoxyglucose positron emission tomography study. CNS Spectr. 15 (3), 159–166. Feng, Q., Chen, X., Sun, J., Zhou, Y., Sun, Y., Ding, W., et al., 2013. Voxel-level comparison of arterial spin-labeled perfusion magnetic resonance imaging in adolescents with Internet gaming addiction. Behav. Brain Funct. 12;9 (1), 33. Volkow, N.D., Wang, G.J., Fowler, J.S., Tomasi, D., Telang, F., Baler, R., 2010. Addiction: decreased reward sensitivity and increased expectation sensitivity conspire to overwhelm the brain’s control circuit. Bioessays 32, 748–755. Dong, G., Huang, J., Du, X., 2012a. Alterations in regional homogeneity of resting-state brain activity in Internet gaming addicts. Behav. Brain Funct. 8, 41. Liu, J., Gao, X.P., Osunde, I., Li, X., Zhou, S.K., Zheng, H.R., Li, L.J., 2010. Increased regional homogeneity in Internet addiction disorder a resting state functional magnetic resonance imaging study. Chin. Med. J. (Engl.) 123 (14), 1904–1908. Kim, H., Kim, Y.K., Gwak, A.R., Lim, J.A., Lee, J.Y., Jung, H.Y., et al., 2015. 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. Prog. Neuropsychopharmacol. Biol. Psychiatry 3 (60), 104–111. Kuhn, S., Romanowski, A., Schilling, C., Lorenz, R., Morsen, C., Seiferth, N., et al., 2011. The neural basis of video gaming. Transl. Psychiatr., e53. Zhou, Y., Lin, F.C., Du, Y.S., Qin, L.D., Zhao, Z.M., Xu, J.R., et al., 2011. Gray matter abnormalities In internet addiction: a voxel-Based morphometry study. Eur. J. Radiol. 79 (1), 92–95. Han, D.H., Lyoo, I.K., Renshaw, P.F., 2012. Differential regional gray matter volumes in patients with on-line game addiction and professional gamers. J. Psychiatr. Res. 46, 507–515. Sun, Y., Sun, J., Zhou, Y., Ding, W., Chen, X., Zhuang, Z., et al., 2014. Assessment of in vivo microstructure alterations in gray matter using DKI in Internet gaming addiction. Behav. Brain Funct. 24 (10), 37.

A. Weinstein et al. / Neuroscience and Biobehavioral Reviews 75 (2017) 314–330 Wang, H., Jin, C., Yuan, K., Shakir, T.M., Mao, C., Niu, X., et al., 2015a. The alteration of gray matter volume and cognitive control in adolescents with Internet gaming disorder. Front. Behav. Neurosci. 9, 64. Yuan, K., Qin, W., Wang, G., Zeng, F., Zhao, L., Yang, X., et al., 2011. Microstructure abnormalities in adolescents with internet addiction disorder. PLoS One 6, e20708. Ko, C.H., Liu, G.C., Yen, J.Y., Yen, C.F., Chen, C.S., Lin, W.C., 2013a. The brain activations for both cue-induced gaming urge and smoking craving among subjects comorbid with Internet gaming addiction and nicotine dependence. J. Psychiatr. Res. 47 (4), 486–493. Weng, C.B., Qian, R.B., Fu, X.M., Lin, B., Han, X.P., Niu, C.S., et al., 2013. Gray matter and white matter abnormalities in online game addiction. Eur. J. Radiol. 82 (8), 1308–1312. Lin, F., Zhou, Y., Du, Y., Qin, L., Zhao, Z., Xu, J., et al., 2012. Abnormal white matter integrity in adolescents with internet addiction disorder: a tract-Based spatial statistics study. PLoS One 7 (1), e30253. Dong, G., DeVito, E., Huang, J., Du, X., 2012d. Diffusion tensor imaging reveals thalamus and posterior cingulate cortex abnormalities in Internet gaming addicts. J. Psychiatr. Res. 46 (9), 1212–1216. Lin, X., Dong, G., Wang, Q., Du, X., 2015a. Abnormal gray matter and white matter volume in Internet gaming addicts. Addict. Behav. 40, 137–143. Yuan, K., Qin, W., Yu, D., Bi, Y., Xing, L., Jin, C., 2016. Core brain networks interactions and cognitive control in Internet gaming disorder individuals in late adolescence/early adulthood. Brain Struct. Funct. 221 (3), 1427–1442. Takeuchi, H., Taki, Y., Hashizume, H., Asano, K., Asano, M., Sassa, Y., Yokota, S., Kotozaki, Y., Nouchi, R., Kawashima, R., 2016. Impact of videogame play on the brain’s microstructural properties: cross-sectional and longitudinal analyses. Mol. Psychiatry 21 (12), 1781–1789. Yuan, K., Cheng, P., Dong, T., Bi, Y., Xing, L., Yu, D., et al., 2013. Cortical thickness abnormalities in late adolescence with online gaming addiction. PLoS One 8 (1), e53055. Hong, S.B., Kim, J.W., Choi, E.J., Kim, H.H., Suh, J.E., Kim, C.D., et al., 2013a. Reduced orbitofrontal cortical thickness in male adolescents with Internet addiction. Behav. Brain Funct. 12 (9), 11. van den Heuvel, M.P., Hulshoff Pol, H.E., 2010. Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur. Neuropsychopharmacol. 20 (8), 519–534. Ding, W.N., Sun, J.H., Sun, Y.W., Zhou, Y., Li, L., Xu, J.R., et al., 2013. Altered default network resting-state functional connectivity in adolescents with Internet gaming addiction. PLoS One 8 (3), e59902. Dong, G., Devito, E.E., Du, X., Cui, Z., 2012c. Impaired inhibitory control in ‘Internet addiction disorder’: A functional magnetic resonance imaging study. Psychiatry Res. 203 (2–3), 153–158. Ma, N., Liu, Y., Li, N., Wang, C.X., Zhang, H., Jiang, X.F., Xu, H.S., Fu, X.M., Hu, X., Zhang, D.R., 2010. Addiction related alteration in resting-state brain connectivity. Neuroimage 49 (1), 738–744. Sutherland, M.T., McHugh, M.J., Pariyadath, V., Stein, E.A., 2012. Resting state functional connectivity in addiction: lessons learned and a road ahead. Neuroimage 62, 2281–2295. Hong, S.B., Zalesky, A., Cocchi, L., Fornito, A., Choi, E.J., Kim, H.H., et al., 2013b. Decreased functional brain connectivity in adolescents with Internet addiction. PLoS One 8 (2), e57831. Wee, C.Y., Zhao, Z., Yap, P.T., Wu, G., Shi, F., Price, T., et al., 2014. Disrupted brain functional network in Internet addiction disorder: a resting-state functional magnetic resonance imaging study. PLoS One 9 (9), e107306. Chen, X., Wang, Y., Zhou, Y., Sun, Y., Ding, W., Zhuang, Z., et al., 2014. Different resting-state functional connectivity alterations in smokers and nonsmokers with Internet gaming addiction. BioMed Res. Int. 2014, 825787. Dong, G., Lin, X., Potenza, M.N., 2015. Decreased functional connectivity in an executive control network is related to impaired executive function in Internet gaming disorder. Prog. Neuropsychopharmacol. Biol. Psychiatry 3 (57), 76–85. Ko, C.H., Hsieh, T.J., Wang, P.W., Lin, W.C., Yen, C.F., Chen, C.S., et al., 2015. Altered gray matter density and disrupted functional connectivity of the amygdala in adults with Internet gaming disorder. Prog. Neuropsychopharmacol. Biol. Psychiatry 57, 185–192. Park, C.H., Chun, J.W., Cho, H., Jung, Y.C., Choi, J., Kim, D.J., 2015. Is the Internet gaming-addicted brain close to be in a pathological state? Addict. Biol., http:// dx.doi.org/10.1111/adb.12282 (Epub ahead of print). Hong, S.B., Harrison, B.J., Dandash, O., Choi, E.J., Kim, S.C., Kim, H.H., et al., 2015. A selective involvement of putamen functional connectivity in youth with Internet gaming disorder. Brain Res. 1602, 85–95. Cai, C., Yuan, K., Yin, J., Feng, D., Bi, Y., Li, Y., et al., 2015. Striatum morphometry is associated with cognitive control deficits and symptom severity in Internet gaming disorder. Brain Imaging Behav. (Epub ahead of print). Zhang, J.T., Yao, Y.W., Li, C.S., Zang, Y.F., Shen, Z.J., Liu, L., et al., 2015. Altered resting-state functional connectivity of the insula in young adults with Internet gaming disorder. Addict. Biol. (Epub ahead of print). Wang, Y., Yin, Y., Sun, Y.W., Zhou, Y., Chen, X., Ding, W.N., et al., 2015b. Decreased prefrontal lobe interhemispheric functional connectivity in adolescents with Internet gaming disorder: a primary study using resting-state FMRI. PLoS One 10 (3), e0118733. Worsley, K.J., Marrett, S., Neelin, P., Vandal, A.C., Friston, K.J., Evans, A.C., 1996. A unified statistical approach for determining significant signals in images of cerebral activation. Hum. Brain Map. 4, 458–473.

329

Hoeft, F., Watson, C.L., Kesler, S.R., Bettinger, K.E., Reiss, A.L., 2008. Gender differences in the mesocorticolimbic system during computer game-play. J. Psychiatr. Res. 42 (4), 253–258. Ko, C.H., Liu, G.C., Hsiao, S., Yen, J.Y., Yang, M.J., Lin, W.C., et al., 2009. Brain activities associated with gaming urge of online gaming addiction. J. Psychiatr. Res. 43 (7), 739–747. Han, D.H., Kim, Y.S., Lee, Y.S., Min, K.J., Renshaw, P.F., 2010a. Changes in cue-induced, prefrontal cortex activity with video-game play. Cyberpsychol. Behav. Soc. Netw. 13 (6), 655–661. Ko, C.H., Liu, G.C., Yen, J.Y., Chen, C.Y., Yen, C.F., Chen, C.S., 2013b. Brain correlates of craving for online gaming under cue exposure in subjects with Internet gaming addiction and in remitted subjects. Addict. Biol. 18 (3), 559–569. Sun, Y., Ying, H., Seetohul, R.M., Xuemei, W., Ya, Z., Qian, L., et al., 2012. Brain fMRI study of crave induced by cue pictures in online game addicts (male adolescents). Behav. Brain Res. 233 (2), 563–576. Liu, J., Li, W., Zhou, S., Zhang, L., Wang, Z., Zhang, Y., et al., 2016. Functional characteristics of the brain in college students with Internet gaming disorder. Brain Imaging Behav. 10 (1), 60–67. Lorenz, R.C., Krüger, J.K., Neumann, B., Schott, B.H., Kaufmann, C., Heinz, A., Wüstenberg, T., 2013. Cue reactivity and its inhibition in pathological computer game players. Addict. Biol. 18 (1), 134–146. Kim, Y.R., Son, J.W., Lee, S.I., Shin, C.J., Kim, S.K., Ju, G., et al., 2012. Abnormal brain activation of adolescent internet addict in a ball-throwing animation task: possible neural correlates of disembodiment revealed by fMRI. Prog. Neuropsychopharmacol. Biol. Psychiatry 39 (1), 88–95. Leménager, T., Dieter, J., Hill, H., Koopmann, A., Reinhard, I., Sell, M., et al., 2014. Neurobiological correlates of physical self-concept and self-identification with avatars in addicted players of Massively Multiplayer Online Role-Playing Games (MMORPGs). Addict. Behav. 39 (12), 1789–1797. Dong, G., Devito, E.E., Du, X., Cui, Z., 2012b. Impaired inhibitory control in ‘internet addiction disorder’: a functional magnetic resonance imaging study. Psychiatr. Res. 203 (2–3), 153–158. Liu, G.C., Yen, J.Y., Chen, C.Y., Yen, C.F., Chen, C.S., Lin, W.C., et al., 2014. Brain activation for response inhibition under gaming cue distraction in internet gaming disorder. Kaohsiung J. Med. Sci. 30 (1), 43–51. Ko, C.H., Hsieh, T.J., Chen, C.Y., Yen, C.F., Chen, C.S., Yen, J.Y., et al., 2014. Altered brain activation during response inhibition and error processing in subjects with Internet gaming disorder: a functional magnetic imaging study. Eur. Arch. Psychiatry Clin. Neurosci. 264 (8), 661–672. Chen, C.Y., Huang, M.F., Yen, J.Y., Chen, C.S., Liu, G.C., Yen, C.F., et al., 2015. Brain correlates of response inhibition in Internet gaming disorder. Psychiatry Clin. Neurosci. 69 (4), 201–209. Ding, W.N., Sun, J.H., Sun, Y.W., Chen, X., Zhou, Y., Zhuang, Z.G., et al., 2014. Trait impulsivity and impaired prefrontal impulse inhibition function in adolescents with internet gaming addiction revealed by a Go/No-Go fMRI study. Behav. Brain Funct. 30 (10), 20. Li, B., Friston, K.J., Liu, J., Liu, Y., Zhang, G., Cao, F., et al., 2014. Impaired frontal-basal ganglia connectivity in adolescents with internet addiction. Sci. Rep. 4, 5027. Dong, G., Huang, J., Du, X., 2011. Enhanced reward sensitivity and decreased loss sensitivity in internet addicts: an fMRI study during a guessing task. J. Psychiatr. Res. 45 (11), 1525–1529. Dong, G., Hu, Y., Lin, X., Lu, Q., 2013. What makes Internet addicts continue playing online even when faced by severe negative consequences? Possible explanations from an fMRI study. Biol. Psychol. 94 (2), 282–289. Lin, X., Zhou, H., Dong, G., Du, X., 2015b. Impaired risk evaluation in people with Internet gaming disorder: fMRI evidence from a probability discounting task. Prog. Neuropsychopharmacol. Biol. Psychiatry 2 (56), 142–148. Kim, J.E., Son, J.W., Choi, W.H., Kim, Y.R., Oh, J.H., Lee, S., et al., 2014. Neural responses to various rewards and feedback in the brains of adolescent Internet addicts detected by functional magnetic resonance imaging. Psychiatr. Clin. Neurosci. 68 (6), 463–470. Goldstein, R.Z., Volkow, N.D., 2002. Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex. Am. J. Psychiatry 159 (10), 1642–1652. Fowler, J.S., Volkow, N.D., Kassed, C.A., Chang, L., 2007. Imaging the addicted human brain. Sci. Pract. Perspect. 3 (2), 4–16. Volkow, N.D., Fowler, J.S., Wang, G.-J., Hitzemann, R., Logan, J., Schlyer, D., et al., 1993. Decreased dopamine D2 receptor availability is associated with reduced frontal metabolism in cocaine abusers. Synapse 14, 169–177. Volkow, N.D., Wang, G.J., Fowler, J.S., Logan, J., Hitzemann, R.J., Ding, Y.S., et al., 1996b. Decreases in dopamine receptors but not in dopamine transporters in alcoholics. Alcohol. Clin. Exp. Res. 20, 1594–1598. Volkow, N.D., Chang, L., Wang, G.J., Fowler, J.S., Ding, Y.S., Sedler, M., et al., 2001. Low level of brain dopamine D2 receptors in methamphetamine abusers: association with metabolism in the orbitofrontal cortex. Am. J. Psychiatry 158 (12), 2015–2021. Wang, G.-J., Volkow, N.D., Fowler, J.S., Logan, J., Hitzemann, R.J., Pappas, N.S., et al., 1997. Dopamine D2 receptor availability in opiate-dependent subjects before and after naloxone precipitated withdrawal. Neuropsychopharmacology 16, 174–182. Kim, S.H., Baik, S.H., Park, C.S., Kim, S.J., Choi, S.W., Kim, S.E., 2011. Reduced striatal dopamine D2 receptors in people with Internet addiction. Neuroreport 22 (8), 407–411. Hou, H., Jia, S., Hu, S., Fan, R., Sun, W., Sun, T., et al., 2012. Reduced striatal dopamine transporters in people with internet addiction disorder. J. Biomed. Biotechnol., 854524.

330

A. Weinstein et al. / Neuroscience and Biobehavioral Reviews 75 (2017) 314–330

Tian, M., Chen, Q., Zhang, Y., Du, F., Hou, H., Chao, F., et al., 2014. PET imaging reveals brain functional changes in internet gaming disorder. Eur. J. Nucl. Med. Mol. Imaging 41 (7), 1388–1397. Koepp, M.J., Gunn, R.N., Lawrence, A.D., Cunningham, V.J., Dagher, A., Jones, T., et al., 1998. Evidence for striatal dopamine release during a video game. Nature 393, 266–268. Weinstein, A., 2010. Computer and video game addiction. Am. J. Drug Alcohol Abuse 36 (5), 268–276. Steeves, T.D.L., Miyasaki, J., Zurowski, M., Lang, A.E., Pellecchia, G., Van Eimeren, T., et al., 2009. Increased Striatal dopamine release in Parkinsonian patients with pathological gambling: a [11C] raclopride PET study. Brain 132, 1376–1385. Linnet, J., Peterson, E., Doudet, D.J., Gjedde, A., Moller, A., 2010. Dopamine release in ventral striatum of pathological gamblers losing money. Acta Psychiatr. Scand. 112, 326–333. Han, D.H., Lee, Y.S., Shi, X., Renshaw, P., 2014. Proton magnetic resonance spectroscopy (MRS) in on-line game addiction. J. Psychiatr. Res. 58, 63–68. Dong, G., Potenza, M.N., 2014. Cognitive-behavioral model of Internet gaming disorder: theoretical underpinnings and clinical implications. J. Psychiatr. Res. 58, 7–11. Brand, M., Young, K.S., Laier, C., Wölfling, K., Potenza, M.N., 2016a. Integrating psychological and neurobiological considerations regarding the development and maintenance of specific Internet-use disorders: an Interaction of Person-Affect-Cognition-Execution (I-PACE) model. Neurosci. Biobehav. Rev. 71, 252–266. Meng, Y., Deng, W., Wang, H., Guo, W., Li, T., 2015. The prefrontal dysfunction in individuals with Internet gaming disorder: a meta-analysis of functional magnetic resonance imaging studies. Addict. Biol. 20 (4), 799–808. Blum, K., AL-C, Chen, Braverman, E.B., Comings, D.E., Chen, T.J.H., Arcuri, V., et al., 2008. Attention-deficit-hyperactivity disorder and reward deficiency syndrome. Neuropsychiatr. Dis. Treat. 4 (5), 893–918. Weinstein, A.M., Weizman, A., 2012. Emerging association between addictive gaming and attention-Deficit/Hyperactivity disorder. Curr. Psychiatry Rep. 14 (5), 590–597. Han, D.H., Lee, Y.S., Yang, K.C., Kim, E.Y., Lyoo, I.K., Renshaw, P.F., 2007. Dopamine genes and reward dependence in adolescents with excessive internet video game play. J. Addict. Med. 1 (3), 133–138. Weinstein, A., Greif, J., Yemini, Z., Lerman, H., Weizman, A., Even-Sapir, E., 2010. Attenuation of cue-induced smoking urges and brain reward activity in successfully-treated smokers with bupropion. J. Psychopharmacol. 24, 829–838. Han, D.H., Hwang, J.W., Renshaw, P.F., 2010b. Bupropion sustained release treatment decreases craving for video games and cue-induced brain activity in patients with Internet video game addiction. Exp. Clin. Psychopharmacol. 18 (4), 297–304. Cloninger, C.R., Przybeck, T.R., Svrakic, D.M., 1991. The tridimensional personality questionnaire: U.S normative data. Psychol. Rep. 69 (3 Pt. 1), 1047–1057. Lee, Y., Han, D., Yang, K., Daniels, M., Na, C., Kee, B., Renshaw, P., 2009. Depression like characteristics of 5HTTLPR polymorphism and temperament in excessive

internet users. J. Affect. Dis. 109 (1), 165–169. Reuter, J., Raedler, T., Rose, M., Hand, I., Glascher, J., Buchel, C., 2005. Pathological gambling is linked to reduced activation of the mesolimbic reward system. Nat. Neurosci. 8 (2), 147–148. Potenza, M., Steinberg, M.A., Skudlarski, P., Fulbright, R., Lacadie, C., Wilber, M., Rounsaville, B., Gore, J., Wexler, B., 2003a. Gambling urges in pathological gambling: a functional magnetic resonance imaging study. Arch. Gen. Psychiatry 60 (8), 828–836. Potenza, M., Hoi-Chung, L., Blumberg, H.P., Peterson, B.S., Fulbright, R., Lacadie, C.M., Skudlarski, P., Gore, J.C., 2003b. An fMRI Stroop study of ventromedial prefrontal cortical function in pathological gamblers. Am. J. Psychiatry 160, 1990–1994. Pallanti, S., Haznedar, M.M., Hollander, E., Licalzi, E.M., Bernardi, S., Newmark, R., Basal, Buchsbaum M.S., 2010. Ganglia activity in pathological gambling: a fluorodeoxyglucose-positron emission tomography study. Neuropsychobiology 62 (2), 132–138. Voon, V., Mole, T.B., Banca, P., Porter, L., Morris, L., Mitchell, S., et al., 2014. Neural correlates of sexual cue reactivity in individuals with and without compulsive sexual behaviours. PLoS One 9 (7), e102419, http://dx.doi.org/10.1371/journal. pone.0102419. Brand, M., Snagowski, J., Laier, C., Maderwald, S., 2016b. Ventral striatum activity when watching preferred pornographic pictures is correlated with symptoms of Internet pornography addiction. Neuroimage 129, 224–232. Ko, C.-H., Yen, J.-Y., Yen, C.-F., Chen, C.-S., Chen, C.-C., 2012. The association between Internet addiction and psychiatric disorder: a review of the literature. Eur. Psychiatry 27 (1), 1–8, http://dx.doi.org/10.1016/j.eurpsy.2010.04.011. Király, O., Nagygyörgy, K., Griffiths, M.D., Demetrovics, Z., 2014. Problematic online gaming. In: Rosenberg, K.P., Feder, L.C. (Eds.), Behavioral Addictions: Criteria, Evidence and Treatment. Elsevier Science, Burlington, USA, pp. 61–97. Clemenson, G.D., Stark, C.E.L., 2015. Virtual environmental enrichment through video games improves hippocampal-Associated memory. J. Neurosci. 35 (49), 16116–16125, http://dx.doi.org/10.1523/jneurosci.2580-15.2015. Wolf, R.L., Detre, J.A., 2007. Clinical neuroimaging using arterial spin-labeled perfusion MRI. Neurotherapeutics 4 (3), 346–359. Zang, Y., Jiang, T., Lu, Y., He, Y., 2004. Tian L.Regional homogeneity approach to fMRI data analysis. Neuroimage 22, 394–400. Jensen, J.H., Helpern, J.A., Ramani, A., Lu, H., Kaczynski, K., 2005. Diffusional kurtosis imaging: the quantification of non-Gaussian water diffusion by means of MRI. Magn. Reson. Med. 53, 1432–1440. Alexander, A.L., Lee, J.E., Lazar, M., Field, A.S., 2007. Diffusion tensor imaging of the brain. Neurotherapeutics 4 (3), 316–329. Smith, S.M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T.E., et al., 2006. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31, 1487–1505. Mechelli, A., Price, C.J., Friston, K.J., Ashburner, J., 2005. Voxel-Based morphometry of the human brain: methods and applications. Curr. Med. Imaging Rev. 1 (00-00 1 1573–4056/05).