REVIEW ARTICLE

HUMAN NEUROSCIENCE
published: 27 May 2014
doi: 10.3389/fnhum.2014.00375

Prefrontal control and Internet addiction: a theoretical

model and review of neuropsychological and
neuroimaging findings
Matthias Brand 1,2 *, Kimberly S. Young 3 and Christian Laier 1
1
Department of General Psychology: Cognition, University of Duisburg-Essen, Duisburg, Germany
2
Erwin L. Hahn Institute for Magnetic Resonance Imaging, Essen, Germany
3
Center for Internet Addiction, Russell J. Jandoli School of Journalism and Mass Communication, St. Bonaventure University, Olean, NY, USA

Edited by: Most people use the Internet as a functional tool to perform their personal goals in everyday-
Ali Mazaheri, University of
life such as making airline or hotel reservations. However, some individuals suffer from a
Amsterdam, Netherlands
loss of control over their Internet use resulting in personal distress, symptoms of psy-
Reviewed by:
Arun Bokde, Trinity College Dublin, chological dependence, and diverse negative consequences. This phenomenon is often
Ireland referred to as Internet addiction. Only Internet Gaming Disorder has been included in the
Thilo Van Eimeren, Christian-Albrechts appendix of the DSM-5, but it has already been argued that Internet addiction could also
University, Germany
comprise problematic use of other applications with cybersex, online relations, shopping,
*Correspondence:
and information search being Internet facets at risk for developing an addictive behavior.
Matthias Brand , Department of
General Psychology: Cognition, Neuropsychological investigations have pointed out that certain prefrontal functions in par-
University of Duisburg-Essen, ticular executive control functions are related to symptoms of Internet addiction, which is
Forsthausweg 2, Duisburg 47057, in line with recent theoretical models on the development and maintenance of the addic-
Germany
tive use of the Internet. Control processes are particularly reduced when individuals with
e-mail: matthias.brand@uni-due.de
Internet addiction are confronted with Internet-related cues representing their first choice
use. For example, processing Internet-related cues interferes with working memory per-
formance and decision making. Consistent with this, results from functional neuroimaging
and other neuropsychological studies demonstrate that cue-reactivity, craving, and deci-
sion making are important concepts for understanding Internet addiction. The findings on
reductions in executive control are consistent with other behavioral addictions, such as
pathological gambling. They also emphasize the classification of the phenomenon as an
addiction, because there are also several similarities with findings in substance depen-
dency. The neuropsychological and neuroimaging results have important clinical impact,
as one therapy goal should enhance control over the Internet use by modifying specific
cognitions and Internet use expectancies.
Keywords: Internet addiction, executive functions, cue-reactivity, craving, neuroimaging

INTRODUCTION we used to select the articles referred to in this review. We used two
GENERAL INTRODUCTION AND SEARCH METHODS databases searching for suitable articles: PubMed and PsycInfo.
Most people use the Internet as a functional tool in everyday-life The search was conducted using the terms: “Internet addiction,”
and many individuals cannot imagine living without the Internet “Compulsive Internet use,” and “Internet use disorder.” After a
in business or private life. The Internet provides multifariousness general overview over the found articles, each of the terms was
of possibilities for communication, entertainment, and dealing combined with each of the terms “prefrontal cortex” or “executive
with everyday-life requirements (e.g., making restaurant reserva- functions” or “neuropsychology” or “control processes” or “deci-
tions, searching for information, keeping updated with respect to sion making” or “neuroimaging” or “functional brain imaging”
political and society issues, etc.). With the growth of the Internet using the conjunction“AND.”Each term was required to be present
over the last two decades, the number of subjects experiencing in the“Title/Abstract”of the paper. Both searches were further lim-
massive negative consequences in their lives has also grown exten- ited by “English” as the publication language. We selected original
sively. These persons experience a loss of control over their Internet research papers as well as review articles. We also used the func-
use and report social problems as well as school and/or work tion “related articles.” Given the limited space, we had to exclude
difficulties (Young, 1998a; Beard and Wolf, 2001). several articles. We aimed at including both classical articles and
This contribution is a narrative review on Internet addiction very current studies. On the other hand, we also included some
and prefrontal control processes. It reflects the ideas and opinions articles of other research areas (e.g., pathological gambling, sub-
of the authors based on their literature search and experiences. stance dependency), whenever it seemed appropriate. In summary,
Nevertheless, we would like to briefly comment on the procedure following a systematic search for relevant articles, we selected the

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Brand et al. Internet addiction

studies and reviews cited on the basis of a subjective impression. criteria proposed have significant similarities with the criteria used
We thereby aimed at summarizing the most important views and for diagnosing other forms of addiction and include:
findings on Internet addiction with a focus on the link between
control processes and symptoms of Internet addiction. We also • preoccupation with Internet games
aimed at summarizing some very recent findings and ideas, which • withdrawal symptoms of irritability, anxiety, or sadness
may be helpful for inspiring both future scientific studies and new • development of tolerance
therapeutic approaches. • unsuccessful attempts to control the behavior
• loss of interest in other activities
HISTORY OF INTERNET ADDICTION RESEARCH, TERMINOLOGY, AND • continued excessive use despite knowledge of psychosocial
SYMPTOMS problems
The first scientific description of a young man who developed • deceiving others regarding the amount of time spent gaming
severe psychosocial problems due to his excessive Internet use • use of this behavior to escape or relieve a negative mood
was done by Young (1996). It was followed by a growing number • jeopardizing/losing a significant relationship/job/educational
of other single- and multiple-case studies (e.g., Griffiths, 2000). opportunity
Today, a relatively large literature exists on the phenomenology,
the epidemiology for different countries, and co-morbidity of a The APA has now focused on Internet gaming. We argue, how-
problematic or pathological Internet use (see recent review by ever, that also other applications can be used addictively (Young
Spada, 2014). The prevalence rates reported in the last years have et al., 1999; Meerkerk et al., 2006). Therefore, we summarize
a wide variety from 0.8 in Italy to 26.7% in Hong Kong (see the results of previous studies on Internet addiction in a broader
excellent review by Kuss et al., 2013). Reasons for this extreme way, although a great proportion of studies published so far con-
variance are most likely some cultural effects, but also the fact that centrated on Internet gaming. Although not all criteria must be
until now, no standard assessment tool, no clearly defined cut-off fulfilled, we would like to highlight one specific criterion, which
scores, and even no fully accepted diagnostic criteria have been seems very important and is most frequently fulfilled in patients
established (see exception for Internet Gaming disorder described suffering from Internet addiction. This criterion is: “Unsuccess-
below). ful attempts to control the behavior” or to say it shorter: “Loss
Although the clinical relevance is obvious and many clini- of control.” This criterion is also a factor frequently found when
cians see patients suffering from severe negative consequences analyzing the factorial structure of questionnaires used to assess
due to an overuse of the Internet in general or certain Internet Internet addiction (Chang and Law, 2008; Korkeila et al., 2010;
applications, the terminology used for this phenomenon and its Widyanto et al., 2011; Lortie and Guitton, 2013; Pawlikowski et al.,
classification are still under debate (Young, 1998b, 1999; Charlton 2013). Consequently, the ability to control one’s own Internet
and Danforth, 2007; Starcevic, 2013). Young (2004) argues that the use is an important factor preventing people from developing an
criteria, which have been defined for pathological gambling and Internet addiction. In turn, if an individual suffers from Inter-
substance dependency should also be applied to Internet addic- net addiction, one therapy goal must be to give the patient back
tion. This is also in accordance with some other researchers, for the control over his/her Internet use. But why is it so difficult for
example with the component model on addictive behaviors by some individuals to control the Internet use? One reason may be
Griffiths (2005). Nevertheless, there is a sum of different terms that Internet-related cues interfere with control processes medi-
used in the scientific literature when referring to an overuse of ated by the prefrontal cortex. We will summarize some recent
the Internet, such as Internet addiction (Young, 1998b, 2004; findings from neuropsychological research emphasizing that in
Hansen, 2002; Chou et al., 2005; Widyanto and Griffiths, 2006; fact Internet-related stimuli interfere with decision making and
Young et al., 2011), compulsive Internet use (Meerkerk et al., other prefrontal functions, such as working memory and further
2006, 2009, 2010), Internet-related addictive behavior (Brenner, executive functions. We will argue that reductions of prefrontal
1997), Internet-related problems (Widyanto et al., 2008), prob- control processes play a major role in developing and maintaining
lematic Internet use (Caplan, 2002), and pathological Internet use an addictive use of the Internet.
(Davis, 2001). We prefer the term Internet addiction, since we see Before we describe the role of control processes, we summa-
some important parallels between Internet addiction and other rize recent models on Internet addiction, in order to make clear
so-called behavioral addictions (e.g., Grant et al., 2013) and sub- why specific cognitive processes may interact with other people’s
stance dependency (see also Griffiths, 2005; Meerkerk et al., 2009), characteristics, such as personality and psychopathological symp-
which we will summarize in Sections “Neuropsychological Cor- toms in the development and maintenance of Internet addiction
relates of Internet Addiction” and “Neuroimaging Correlates of in general or specific types of Internet addiction.
Internet Addiction.”
While there is great consensus about the multiple applications GENERALIZED AND SPECIFIC INTERNET ADDICTION
the Internet provides and which can be addictively used, such as Davis (2001) introduced a theoretical cognitive–behavioral model
gaming and gambling, pornography, social networking sites, shop- on pathological or problematic Internet use and differentiates
ping sites, and so on, only Internet Gaming Disorder has recently between a generalized pathological Internet use, which we call
been included in the appendix of the DSM-5 (APA, 2013), making generalized Internet addiction (GIA), and a specific pathological
clear that more research is needed on this phenomenon to collect Internet use, for which we use the term specific Internet addiction
evidence for its clinical relevance and underlying mechanisms. The (SIA). Davis argues that GIA is frequently linked to communicative

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Brand et al. Internet addiction

applications of the Internet and that a lack of social support in real we argue that Internet use expectancies play an important role.
life and feelings of social isolation or loneliness are main factors These expectancies may involve anticipations of how the Internet
contributing to the development of GIA. Maladaptive cognitions can be helpful for distracting from problems or escaping from
about the world in general and the own Internet use in particu- reality, or – more generally spoken – for reducing negative emo-
lar may then intensify the overuse of the Internet to distract from tions. Those expectancies may also interact with the user’s general
problems and negative mood (see also Caplan, 2002, 2005). In con- coping style (e.g., to tend toward substance abuse to distract from
trast, for the overuse of certain Internet applications, for example, problems) and self-regulation capacities (Billieux and Van der Lin-
gambling sites or pornography, a specific individual predisposi- den, 2012). When going online, the user receives reinforcement in
tion is the main factor, Davis argues. Consequently, it is assumed terms of (dysfunctional) coping with negative feelings or problems
that GIA is directly linked to the options the Internet itself pro- in everyday-life. At the same time, the Internet use expectancies
vides, while SIA can also be developed outside the Internet, but are positively reinforced, because the Internet acted as anticipated
is aggravated by the enormous functions offered by the Internet (e.g., reducing feelings of emotional or social loneliness). Given
applications. the strong reinforcing character of certain Internet applications,
The model by Davis (2001) significantly inspired research the cognitive control about the Internet use becomes more effort-
on Internet addiction. However, neuropsychological mechanisms ful. This should be particularly the case if Internet-related cues
and – particularly – control processes mediated by executive func- interfere with executive processes. We will go back to this topic in
tions and prefrontal brain areas have not been addressed directly. Sections “Neuropsychological Functions in Subjects with Internet
Additionally, we argue that reinforcing mechanisms conflict with Addiction” and “Functional Neuroimaging in Internet Addiction.”
control processes. Conditioning also plays an important role Regarding the development and maintenance of an addictive
resulting in a strong relationship between Internet-related stim- use of specific Internet applications (SIA), we argue – consis-
uli (or even computer-related stimuli) and positive or negative tent with previous research and in accordance with the model by
reinforcement. This conditioned relationship makes it increas- Davis (2001) – that psychopathological symptoms are particularly
ingly harder for an individual to cognitively control the Internet involved (Brand et al., 2011; Kuss and Griffith, 2011; Pawlikowski
use, even though negative consequences related to the Internet and Brand, 2011; Laier et al., 2013a; Pawlikowski et al., 2014). We
overuse are experienced in the long run. These kinds of condi- also hypothesize that specific person’s predispositions increase the
tioning processes are well-known for other forms of addiction probability that an individual receives gratification from the use
and substance dependency (e.g., Robinson and Berridge, 2000, of certain applications and overuses these applications again. One
2001; Everitt and Robbins, 2006; Robinson and Berridge, 2008; example for such a specific predisposition is a high sexual exci-
Loeber and Duka, 2009). We also argue that positive and negative tation (Cooper et al., 2000a,b; Bancroft and Vukadinovic, 2004;
reinforcement are differentially involved in the development and Salisbury, 2008; Kafka, 2010), which makes it more likely that
maintenance of GIA and SIA. Finally, we hypothesize that cer- an individual uses Internet pornography, because he/she antici-
tain cognitions interact with control processes in developing and pates sexual arousal and gratification (Meerkerk et al., 2006; Young,
maintaining an addictive use of the Internet. Here, expectancies 2008). We believe that the expectancy that such Internet applica-
about what the Internet can provide and what a person may expect tions can satisfy certain desires increases the likelihood that these
from using the Internet may be in a conflict with the individual’s Internet applications are used frequently, as assumed in addictive
expectancies about potential negative consequences in the short behavior in general (Robinson and Berridge, 2000, 2003; Everitt
or the long run, which are associated with an Internet overuse. and Robbins, 2006) and that the individual can develop a loss of
Based on previous research and considering the theoretical control over his/her use of such applications. As a result, gratifica-
arguments by Davis, we have recently developed a new model sum- tion is experienced and consequently the use of such applications
marizing potential mechanisms, which contribute to the develop- and also the specific Internet use expectancies and the coping style
ment to either GIA or SIA (see Figure 1). For the development are reinforced positively. This has already been shown, for example
and maintenance of GIA, we argue that the user has some needs for cybersex addiction (Brand et al., 2011; Laier et al., 2013a) and
and goals and that these can be satisfied by using certain Internet is most likely also a mechanism for online gaming (e.g., Tychsen
applications. We also assume that psychopathological symptoms, et al., 2006; Yee, 2006). The more general psychopathological ten-
in particular depression and social anxiety (e.g., Whang et al., 2003; dencies (e.g., depression and social anxiety) are supposed to be
Yang et al., 2005) and dysfunctional personality facets, such as low negatively reinforced. This may be due to the fact that also specific
self-efficacy, shyness, stress vulnerability, and procrastination ten- Internet applications (e.g., Internet pornography) can be used to
dencies (Whang et al., 2003; Chak and Leung, 2004; Caplan, 2007; distract from problems in the real life or to avoid negative feelings,
Ebeling-Witte et al., 2007; Hardie and Tee, 2007; Thatcher et al., such as loneliness or social isolation. The main arguments of our
2008; Kim and Davis, 2009) are predisposing factors for develop- model are summarized in Figure 1.
ing a GIA. In addition, social cognitions, such as perceived social In both conditions (GIA and SIA), the loss of control over
isolation and a lack of social support offline are supposed to be the use of the Internet in general or of specific applications is sup-
related to GIA (Morahan-Martin and Schumacher, 2003; Caplan, posed to be the main consequence of the conditioning processes of
2005). These associations have already been well-documented in Internet-related cues and positive and negative reinforcement. The
the literature. However, we believe that these predisposing charac- question remains how these processes interact with higher-order
teristics act in concert with users’ specific cognitions. In particular, cognitive functions. For example, what are the mechanisms behind

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Brand et al. Internet addiction

FIGURE 1 | The proposed model on the development and maintenance processes are related to higher-order cognitive functions. We believe that if
of generalized and specific Internet addiction. (A) Demonstrates the an individual with GIA faces the situation that he/she is confronted with the
proposed way of using the Internet as a tool for dealing with personal needs possibility to go online (or to use a certain Internet application in an
and goals in everyday-life. In (B), the proposed mechanisms underlying individual with SIA), these cues are so strong that the individual reacts
generalized Internet addiction (GIA) are summarized. (C) Illustrates the relatively automatically with a wanting reaction. Cognitive control over this
proposed processes involved in specific Internet addiction (SIA), for reaction is difficult if the expectancies that using the Internet would reduce
example the addictive use of certain Internet applications, such as gaming, craving and result in positive and/or negative reinforcement. We will
cybersex, communication, and so on. We argue that in both conditions, GIA summarize neuropsychological and neuroimaging findings on the link
and SIA, reductions in prefrontal control processes are related to the between executive control functions, cue-reactivity, and an addicted use of
individuals’ loss of control over their Internet use. As outlined in Section the Internet in Sections “Neuropsychological Correlates of Internet
“General Comments on Neuropsychological Research in Addiction,” control Addiction” and “Neuroimaging Correlates of Internet Addiction.”

the behavior to use the Internet again and again, although a person mechanisms potentially contributing to the loss of control in the
explicitly knows that he/she will experience negative consequences next sections.
in the long run? Do they have a myopia for the future or is the
reaction to the Internet-related stimuli so strong that they experi- NEUROPSYCHOLOGICAL CORRELATES OF INTERNET
ence cue-reactivity and craving, as it is well-known from substance ADDICTION
dependency (e.g., Grant et al., 1996; Anton, 1999; Childress et al., GENERAL COMMENTS ON NEUROPSYCHOLOGICAL RESEARCH IN
1999; Tiffany and Conklin, 2000; Bonson et al., 2002; Brody et al., ADDICTION
2002, 2007; Franken, 2003; Dom et al., 2005; Heinz et al., 2008; Cognitive control refers to the ability to control one’s own actions,
Field et al., 2009)? We will focus on these neuropsychological behavior, and even thoughts and is a multifarious construct (Cools

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Brand et al. Internet addiction

and D’Esposito, 2011). Although reductions in cognitive control

are sometimes regarded as the main component of impulsivity,
in neuropsychological research control mechanisms are ascribed
to executive functions. Executive functions are control systems
allowing us to regulate our behavior that is planned, goal ori-
ented, flexible, and effective (Shallice and Burgess, 1996; Jurado
and Rosselli, 2007; Anderson et al., 2008). These functions are
strongly linked to parts of the prefrontal cortex, in particular the
dorsolateral prefrontal cortex (e.g., Alvarez and Emory, 2006; Bari
and Robbins, 2013; Yuan and Raz, 2014). The prefrontal cortex
is connected to parts of the basal ganglia (e.g., Hoshi, 2013). For
these connections, the term fronto-striatal loops is frequently used.
Fronto-striatal loops include a more cognitive loop, which mainly
connects the nucleus caudatus and putamen with the dorsolateral
section of the prefrontal cortex (via the thalamus) and the lim-
bic loop connecting limbic structures, such as the amygdala, and
structures that are linked to motivational aspects of behavior, such
as the nucleus accumbens, with the orbitofrontal and ventrome-
dial part of the prefrontal brain area (Alexander and Crutcher,
1990). These parts of the brain are crucially involved in execu-
tive functions and other higher-order cognitions, but they are also
main neural correlates of addictive behavior. Figure 2 summarizes
these brain structures.
Before we focus on this issue in Section “Neuroimaging Cor-
relates of Internet Addiction,” neuropsychological correlates of an
addictive use of the Internet are summarized. In addiction research
with a neuropsychological focus, executive functions, decision
making, and attentional processes have been investigated exten-
sively using traditional neuropsychological tasks, such as gambling
tasks. These approaches have already been transferred to behav-
ioral addictions, such as pathological gambling (e.g., Goudriaan
et al., 2004; Brand et al., 2005b; Goudriaan et al., 2005, 2006; van
Holst et al., 2010; Conversano et al., 2012) and compulsive buying
(e.g., Black et al., 2012).
FIGURE 2 | The prefrontal cortex regions and associated brain
structures most likely involved in development and maintenance of an
NEUROPSYCHOLOGICAL FUNCTIONS IN SUBJECTS WITH INTERNET
addictive use of the Internet. (A) Shows the lateral view of the brain
ADDICTION including medial parts such as anterior cingulate gyrus and amygdala, and
Over the last years, a sum of studies has also been published, which (B) illustrates the medio-sagittal view of the prefrontal cortex. These cortical
assessed general neuropsychological functions in individuals with and subcortical brain regions are main correlates of substance addiction
either GIA or a certain SIA. Most of the studies, however, were and other behavioral additions. The dorsolateral prefrontal cortex (dlPFC)
plays a crucial role in executive functions, cognitive control, and decision
done with excessive Internet gamers. One example is the study by
making under explicit conditions. It is connected to several basal ganglia, in
Sun et al. (2009). They used the Iowa Gambling Task (Bechara particular to the nucleus caudatus and putamen via the so-called
et al., 2000), which had been used in many studies with differ- fronto-striatal loops. The orbitofrontal cortex (OFC) and the ventromedial
ent patient populations with neurological and psychiatric diseases prefrontal cortex (vmPFC) are critically linked to reward anticipation,
including substance dependency and behavioral addictions before emotion processing, and decision making under ambiguity. These
structures are connected with limbic structures (amygdala) and the ventral
(cf. Dunn et al., 2006). This task assesses decision making under
striatum (nucleus accumbens, Nc. acc.) via the limbic part of frontal–striatal
ambiguous conditions. Performing well on the task requires par- loops. The nucleus accumbens receives direct dopaminergic and indirect
ticularly learning from feedback. The excessive Internet users in (via glutamate and GABA) projections from the ventral tegmental area (VTA)
the study by Sun et al. (2009) had problems in performing the of the midbrain. The dorsomedial prefrontal cortex (dmPFC) and the anterior
Iowa Gambling Task, indicating decision-making deficits, which cingulate cortex (ACC) receive dopaminergic projections from the nucleus
accumbens, and they are most likely involved in the so-called wanting
had frequently been linked to addictive behaviors (Bechara, 2005). component of craving. The anterior cingulate gyrus has also been discussed
In another study by Pawlikowski and Brand (2011), it was shown as being critical for conflict processing.
that excessive Internet gamers make more risky and disadvan-
tageous choices, even when the rules for positive and negative
consequences are explicitly explained, measured by the Game of (Brand et al., 2008b), and pathological gambling (Brand et al.,
Dice Task (Brand et al., 2005a). This result is consistent with find- 2005b). Furthermore, performing the Dice Task well is linked to
ings in other samples with addiction, such as opiate dependency prefrontal integrity (Labudda et al., 2008) and executive functions

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Brand et al. Internet addiction

(e.g., Brand et al., 2006; Brand et al., 2008a, 2009). Consequently, NEUROIMAGING CORRELATES OF INTERNET ADDICTION
the results suggest that patients with Internet addiction may have GENERAL COMMENTS ON NEUROIMAGING RESEARCH IN THE
reductions in prefrontal control and other executive functioning. CONTEXT OF ADDICTION
With respect to the ability to inhibit responses to certain stimuli, Most studies investigating neural correlates of Internet addiction
the individuals investigated by Sun et al. (2009) performed nor- with functional imaging techniques have been conducted with
mally on a Go/No-Go Task, which measures response inhibition Internet gamers. These studies have revealed great similarities with
functions. This result on intact response inhibition is consistent brain circuits involved in the problematic behavior in substance-
with the findings by Dong et al. (2010) and also consistent with related addictions and pathological gambling, which will be dis-
normal performance on the classical Stroop paradigm (see behav- cussed in the following sections. Two different approaches can be
ioral data in Dong et al., 2013b). However, in another study, Dong distinguished: functional activation studies as well as structural
et al. (2011b) reported higher response errors in the incongruent investigations and resting-state imaging including diffusion ten-
condition of the Stroop paradigm in male Internet addicted indi- sor imaging. The goal of both approaches is the same: a better
viduals. In all these studies on inhibitory control, however, neutral understanding of the brain mechanisms involved in the excessive
versions of the Go/No-Go task or the Stroop paradigm have been and addictive use of the Internet or certain Internet applications.
used, meaning that all stimuli were unrelated to the Internet. One The overall research questions are: does the brain change over
may hypothesize that individuals with Internet addiction react dif- time insofar that it learns to react on Internet cues specifically,
ferently on stimuli, which explicitly show Internet-related content and do these brain reactions determine the loss of control over
and have difficulty in inhibiting responses only to those stimuli, the Internet use? From substance-dependency research, it is well-
as it has been shown in substance-dependent individuals (e.g., known that different brain areas are involved in the controlled
Pike et al., 2013). This was reported by Zhou et al. (2012) using a and deliberative substance intake (e.g., with respect to alcohol)
shifting-task with Internet game-related cues. The authors argue as compared to an uncontrolled and habitual use. In the first
that reductions in response inhibition and lower mental flexibil- stages of drug-dependency development, frontal brain areas are
ity may be responsible for the maintenance of Internet gaming particularly involved in the decision to consume a certain drug,
addiction. motivated by its reinforcing effects (Goldstein and Volkow, 2002).
Concentrating on other forms of Internet addiction, namely As a result of classical and instrumental conditioning processes
the excessive use of Internet pornography, which is also one of (Everitt and Robbins, 2006), the nucleus accumbens and parts of
the main types of SIA (Meerkerk et al., 2006), beyond Internet the dorsal striatum together with limbic and para-limbic regions
gaming, first studies have used classical paradigms assessing cog- (e.g., the orbitofrontal cortex) learn to habitually react on drug
nitive functions and modified them in terms of including Internet cues with craving and the dorsolateral prefrontal cortex, which
pornographic pictures as stimuli. For example, Laier et al. (2014) is linked to higher-order cognitive functions, loses its regulatory
used the Iowa Gambling Task, but included pornographic and influences (Bechara, 2005; Goldstein et al., 2009). This is most
neutral pictures on the card decks. One group of participants likely the consequence of changes in the dopaminergic reward
performed the task with pornographic pictures on the disadvan- system by frontal-guided changes of glutaminergic innervation
tageous decks (A and B) and neutral pictures on the advantageous of the nucleus accumbens and related brain areas (Kalivas and
decks (C and D) and the other group performed the task with Volkow, 2005). In individuals with substance-dependency envi-
reversed picture-deck association (pornographic pictures on the ronmental factors, such as the presence of drug-related cues, lead
advantageous decks C and D). The results demonstrated that the to activations of the ventral striatum, the anterior cingulate cor-
group performing the task with pornographic pictures on the dis- tex, and also mediofrontal cortex areas (Kühn and Gallinat, 2011;
advantageous decks had lower scores than the other group. This Schacht et al., 2013). These areas, but also the amygdala and the
means that they continued selecting the cards from the decks with orbitofrontal cortex, are related to craving (Chase et al., 2011). In
pornographic pictures, even though they received high losses. This the next section, we will summarize previous neuroimaging find-
effect was particularly observed in subjects who responded with ings on neural correlates of Internet addiction and will argue that
a subjective craving reaction on the presentation of pornographic the processes underlying substance dependency are also valid for
stimuli (in another paradigm, also included in the study). This Internet addiction.
finding is consistent with the results of another study by the same
group of authors (Laier et al., 2013b), in which they reported lower FUNCTIONAL NEUROIMAGING IN INTERNET ADDICTION
working memory performance for pornographic stimuli than for Current studies on Internet addiction and in particular on Internet
positive, negative, and neutral pictures. The authors conclude gaming addiction have applied neuroimaging methods to identify
that sexual arousal as reaction to Internet pornographic pictures brain circuits involved in cue-reactivity and craving in those indi-
interferes with cognitive functions. viduals who experience a loss of control over their Internet (games)
We now argue that particularly cognitive control processes are use. A systematic review of those studies published in 2012 and
affected when Internet addicted individuals are confronted with earlier has been provided by Kuss and Griffiths (2012). They iden-
the addiction-related stimuli. However, this hypothesized mech- tified 18 studies, which used either functional magnetic resonance
anism needs further investigations for certain types SIA. Most imaging (fMRI), positron emission tomography (PET), structural
importantly, this mechanism can be investigated best by using MRI or electroencephalography (EEG). When excluding the EEG
cognitive tasks, which include addiction-related stimuli and not studies (six studies summarized by Kuss and Griffith) and the two
with simple standard cognitive tasks. structural MRI studies, the systematic review concentrated on 10

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Brand et al. Internet addiction

studies with classical functional brain methods. We now applied in the dorsolateral prefrontal cortex while watching StarCraft
the same search and inclusion criteria as documented in the review pictures were also decreased compared to the first fMRI investiga-
by Kuss and Griffiths (2012) and identified 13 studies (excluding tion. Summarized, subjects with Internet addiction show craving
EEG studies) published in peer-reviewed journals from January reactions toward certain Internet-related cues on both subjec-
2013 to end of January 2014. We here concentrate exemplarily on tive and neural level. Craving reactions are correlated with pre-
those earlier and current studies, which notably contribute to a bet- frontal brain changes, which are comparable to those reported for
ter understanding of the link between prefrontal control processes substance-dependent patients.
and loss of control of the Internet use in individuals with Internet Also using fMRI, Dong et al. (2013b) investigated decision-
addiction. making competence in individuals with Internet addiction (with-
One of the earliest studies on potential brain correlates of crav- out specifying the type of Internet addiction). They used a card
ing in subjects with Internet (gaming) addiction was reported by game with two options and manipulated the sequence of wins
Ko et al. (2009). They studied excessive World-of-Warcraft (WoW) and losses, resulting in three conditions: continuous wins, contin-
players (all participants played at least 30 h a week) with fMRI uous losses, and discontinuous wins and losses as control condi-
using a picture paradigm, which is comparable with those previ- tion. Behaviorally, the individuals with Internet addiction needed
ously used in alcohol addiction research (e.g., Braus et al., 2001; longer for their decisions, in particular in the loss condition. Com-
Grüsser et al., 2004). The results were very similar to those reported pared to the control subjects, the patients with Internet addiction
in substance-dependent individuals (Schacht et al., 2013). The had stronger brain activity in the inferior frontal gyrus, the anterior
WoW players had, compared to the control group, stronger activa- cingulate gyrus, and the insula in the win condition and stronger
tions within the nucleus accumbens, the orbitofrontal cortex, and activity in the inferior frontal gyrus also in the loss condition.
the caudate while watching WoW pictures. These activities were The posterior cingulate region and the caudate were less acti-
also correlated positively with subjective gaming urge. A compara- vated in patients with Internet addiction compared to the control
ble finding was reported by Sun et al. (2012), who also investigated group. The authors conclude that patients with Internet addiction
excessive WoW players with a picture paradigm to induce crav- have reductions in decision-making performance, because they
ing. Here, activities in bilateral sections of the prefrontal cortex, need more endeavor to executive functions. In another publica-
in particular the dorsolateral prefrontal cortex, and the anterior tion with the same groups and tasks, the authors also reported
cingulate cortex were positively correlated with subjective crav- a higher sensitivity for wins in comparison to losses in Internet
ing when watching WoW pictures. The results emphasize the view addicted subjects (Dong et al., 2013a), which was accompanied
that the brain of Internet addicted individuals reacts with crav- by stronger activations in the inferior frontal gyrus and decreased
ing to the confrontation with Internet-related cues in the same activity in the posterior cingulate cortex in subjects with Internet
way as the brain of substance-dependent individuals reacts on addiction compared to the control group. These results fit with
substance-related stimuli. Consistent with this, Han et al. (2011) earlier investigations with the same guessing task (Dong et al.,
found that the desire to play was positively related to activity 2011a). Problems in making good decisions, meaning that individ-
in the right mediofrontal lobe and right parahippocampal gyrus uals with Internet addiction continue playing games even though
even in healthy subjects, who were trained to play a certain video they are confronted with negative consequences, might be related
game for 10 days. Changes in prefrontal brain areas related to to their problems in everyday-life (see also discussion in Paw-
cue-reactivity and gaming urges in excessive players have also likowski and Brand, 2011). The argument of more endeavor in
been reported in other previous studies (e.g., Han et al., 2010b; executive functions when being confronted with complex situa-
Ko et al., 2013a; Lorenz et al., 2013) and comparisons between tions of decision making or when cognitive flexibility is required is
cue-reactivity on gaming stimuli and substance dependency (e.g., confirmed by another fMRI study on cognitive flexibility of Inter-
tobacco) have been discussed (Ko et al., 2013b). Results illustrate net addicted subjects (Dong et al., 2014). There is also first evidence
similarities between Internet addiction and other addiction con- for decreased error monitoring in subjects with Internet addiction,
ditions with respect to underlying mechanisms of development, in which is related to stronger activity in the anterior cingulate gyrus
particular conditioning processes (Robinson and Berridge, 2001, (Dong et al., 2013c), a region also known to be involved in cognitive
2003; Thalemann et al., 2007). There is also some evidence for control and conflict management (e.g., Botvinick et al., 2004). The
early functional brain adaptations in adolescent Internet users in results are consistent with another study on Internet addiction by
frontal, temporal, and temporo-parietal–occipital junction area, Dong et al. (2012b), in which greater activity in the anterior (and
as revealed by a ball-throwing paradigm (Kim et al., 2012). One also posterior) cingulate cortex was revealed for the interference
first study linked cue-reactivity and craving with therapy success condition of the Stroop paradigm.
in subjects addicted to Internet games (Han et al., 2010a): at the Again, most studies used neutral stimuli when examining
first investigation with a picture paradigm and fMRI, the group the neural correlates of cognitive functions in Internet addic-
of excessive StarCraft players (StarCraft is a real-time strategy tion. Although these studies converge to the view that cognitive
video game), compared to volunteers with low StarCraft expe- control processes are reduced in Internet addicted subjects, it
riences, showed stronger activations in the dorsolateral prefrontal would be important to investigate what happens in the brain
cortex, occipital areas, and left parahippocampal gyrus. Follow- of Internet addicts when being confronted with Internet-related
ing a 6-week therapy with bupropion, which is frequently used stimuli. Given that individuals react with craving toward Internet-
in substance-dependence therapy, the craving reactions and play- related cues (see literature review above), and that they obviously
ing time were reduced in the Internet gamers and the activity have some certain problems in executive control even in neutral

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Brand et al. Internet addiction

situations, these executive and decision-making functions should cingulate gyrus and right precuneus, parts of the thalamus, cau-
be even worse when being in a situation, which offers Internet- date, ventral striatum, supplementary motor area, and lingual
related stimuli. This should be investigated in the future, because gyrus was correlated with severity of the problematic behavior
in daily life, the individuals are frequently confronted with the in Internet gamers (Ding et al., 2013). However, in another study
Internet and it would be clinically relevant to understand how by Dong et al. (2012a), using diffusion tensor imaging, increased
the brain reacts toward those stimuli in interaction with reduced connectivity between several brain areas in patients with Inter-
executive control functions. net addiction for games were reported, including thalamus and
posterior cingulate cortex. The fractional anisotropy in the inter-
STRUCTURAL AND RESTING-STATE NEUROIMAGING IN INTERNET nal capsule was also correlated with the duration of the addictive
ADDICTION behavior (Yuan et al., 2011). Reduced connectivity was also found
A study on both structural and functional neural correlates of between prefrontal and subcortical as well as parietal and subcorti-
Internet/computer gaming with a large sample (N = 154) ado- cal structures, in particular with the putamen (Hong et al., 2013b).
lescents reported higher gray matter volume in left ventral striatal There are some references for changes in regional homogeneity
region in frequent/excessive compared to infrequent players (Kühn with both increased homogeneity in middle frontal and pari-
et al., 2011). In the functional part of the study, activity in the etal gyri (and further regions of brainstem and cerebellum) and
region of the ventral striatum was higher in frequent compared decreased homogeneity in certain temporal, parietal, and occipital
to infrequent players in the loss condition of a monetary incen- areas in individuals with Internet gaming addiction (Dong et al.,
tive delay task. The authors conclude that the volume changes in 2012c).
the left ventral striatal region may reflect changes in reward sensi- Another line of arguments for the involvement of cue-reactivity
tivity linked to frequent playing of computer games. Gray matter and craving, which might interfere with cognitive control over the
density was also examined by Yuan et al. (2011). In a smaller sam- Internet use, comes from studies investigating the dopamine sys-
ple (N = 18) of adolescents with Internet addiction, decreased tem in patients with Internet addiction. Although these studies are
gray matter volume was found in several prefrontal regions: the preliminary given, for example, very small samples sizes and their
dorsolateral prefrontal cortex (bilaterally), the orbitofrontal cor- results have to be treated with caution: there are some first hints
tex, and the supplementary motor area, as well as in posterior that the dopamine system is altered in Internet addicted individ-
parts of the brain (cerebellum and the left rostral anterior cingu- uals. One example is a SPECT study (Hou et al., 2012) showing
late cortex). The changes in the prefrontal areas were correlated that the level of dopamine transporter expression in the striatum
with reported duration of the disorder. The authors conclude that is decreased in individuals with Internet addiction. This finding
these brain changes may be responsible for an impairment of cog- is consistent with the results of a study with raclopride PET (Kim
nitive control in subjects with Internet addiction and that these et al., 2011), in which a reduced availability of dopamine 2 recep-
changes have some important similarities with those observed in tors in the striatum was found in Internet addicts (see also the
substance dependency. Reductions in gray matter density were review by Jovic and Ðin− dić, 2011).
also found in the left anterior and posterior cingulate cortex, as Although this is speculative so far, changes in dopaminergic
well as in the insula (Zhou et al., 2011) and in the orbitofrontal functioning may – at least partly – explain the loss of control
cortex (Hong et al., 2013a; Yuan et al., 2013). The changes in over the Internet use in individuals with Internet addiction. This
the orbitofrontal region were correlated with performance in the assumption fits well with recent models on the development
Stroop paradigm (Yuan et al., 2013), indicating functional reduc- of addictive behavior in general, as suggested by Robinson and
tions in prefrontal control processes. Gray matter reductions in the Berridge (2008), as already mentioned. Given that the parts of the
(right) orbitofrontal cortex in individuals with SIA for games, in prefrontal cortex involved in cognitive control, in particular the
addition also in the insula (bilaterally), and the right supplemen- dorsolateral prefrontal cortex (see Figure 2) receives dopaminer-
tary motor area were reported by Weng et al. (2013). Interestingly, gic projections from the basal ganglia and the nucleus accumbens,
the volume of the orbitofrontal cortex was correlated with the functional changes in these structures can also reduce the integrity
scores in the Internet Addiction Test (Young, 1998a), measuring of executive control (Cools and D’Esposito, 2011). Given that the
symptom severity. basal ganglia are inter-connected with each other and the thala-
In addition to gray matter, abnormalities in patients with Inter- mus by projections that include other neurotransmitter systems, in
net addiction, functional connectivity shows some changes. These particular glutamate and GABA, changes in the dopaminergic sys-
connectivity alterations fit well, at least partially, with the struc- tem may also cause more global dysfunctions of the fronto-striatal
tural changes. For example, Lin et al. (2012) found lower fractional loops, including both the cognitive and the limbic loop (Alexan-
anisotropy in large parts of the brain of individuals with Inter- der and Crutcher, 1990). We have commented on the link between
net addiction including the orbitofrontal cortex. Further changes fronto-striatal loops and executive control functions in Section
in fractional anisotropy were found in the white matter of the “Neuropsychological Correlates of Internet Addiction.” Consider-
parahippocampal gyrus (Yuan et al., 2011), bilateral frontal lobe ing the preliminary results on dopaminergic alterations in Internet
white matter (Weng et al., 2013), and both internal (Yuan et al., addicted individuals, we argue that changes in this and other basal
2011) and external capsule (Weng et al., 2013). Also, reductions ganglia neurotransmitter systems are related to the loss of control
in functional connectivity (using resting-state fMRI) were found over the Internet use by functional changes of prefrontal integrity.
in the right inferior temporal gyrus, bilateral parietal cortex and Beyond the investigations of the dopamine system, further
posterior cingulate cortex, and connectivity between the posterior studies have addressed resting-state brain functionality in patients

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Brand et al. Internet addiction

with Internet addiction. Using 18-FDG-PET, measuring glucose on neuropsychological and neuroimaging correlates of an addic-
metabolism in the brain, Park et al. (2010) demonstrated that tive Internet use in different countries using certain populations,
excessive Internet gamers had increased glucose metabolism in including male and female participants of different age groups and
the region of the (right) orbitofrontal cortex, and also in parts with certain types of Internet addiction to systematically address
of the basal ganglia (left caudate, insula), while posterior regions and better understand this clinical phenomenon.
(e.g., parietal and occipital areas) showed decreased metabolism. Assuming that the current results of reduced prefrontal control
In summary, there are some first evidences for structural and in Internet addicted individuals will be confirmed by further sam-
resting-state brain changes in individuals with Internet addiction. ples, we here discuss the potential impact on treatment procedures.
These include both gray and white matter changes in the prefrontal The first treatment model for Internet addiction was introduced
brain areas and additional brain regions. There are also first evi- by Young (2011), which has been named cognitive–behavioral
dences for changes in the dopaminergic system, which might be therapy for Internet addiction (CBT-IA). Cognitive–behavioral
related to reinforcement processing and craving. Given that most therapy is the method of choice (Cash et al., 2012; Winkler et al.,
studies were done with rather small samples, with one exception 2013), although the number of empirical studies on treatment
only (Kühn et al., 2011), and no consistent or systematic differen- outcome is still limited (Young, 2013), as it is the case for other
tiation between different types of Internet addiction and between behavioral addictions (Grant et al., 2013). Within CBT-IA model
adolescent versus adult patients, the results must be treated with proposed by Young (2011), individual characteristics as well as
caution. specific cognitions have been hypothesized to be key elements,
which should be addressed in the therapy. CBT-IA consists of
SUMMARY AND CLINICAL IMPLICATIONS three phases, in which instantly Internet behavior is monitored
In summary, neuropsychological and neuroimaging research on in accordance to its incidental situational, emotional, and cogni-
excessive and addictive use of the Internet is a rapidly growing sci- tive conditions as well as with its subsequent positive and negative
entific field, which has revealed a sum of very interesting results. reinforcing effects to identify cognitive assumptions and distor-
These results have both scientific and clinical impact and help tions about one’s own self, Internet use, situational triggers, and
to better understand the neurobiological basis of Internet addic- high-risk situations. In the second phase, cognitive biases about
tion. The results converge to the view that an addictive use of the one’s own self and the Internet as well as denial about treatment
Internet is linked to functional brain changes involving parts of is proposed to be analyzed and treated by methods of cognitive
the prefrontal cortex, accompanied by changes in other cortical restructuring and reframing. In the third phase of treatment, per-
(e.g., temporal) and subcortical (e.g., ventral striatum) regions. sonal, social, psychiatric, and occupational issues related to the
Additionally, there are some hints for structural brain changes, development and maintenance of Internet addiction need to be
which also involve parts of the prefrontal cortex. The functional understood and changed. The efficacy of all three treatment phases
changes in prefrontal and striatal areas are primarily observable depends on prefrontal processes, in particular executive functions,
when individuals with Internet addiction perform certain tasks, in such as planning, monitoring, self-reflection, cognitive flexibility,
particular those measuring executive functions and cue-reactivity. and working memory.
These results, together with those emerging from neuropsycholog- With respect to the proposed model on development and main-
ical studies, suggest that prefrontal control processes are reduced in tenance of GIA and SIA (Figure 1), control processes and executive
individuals who are addicted to the Internet and may be related to functions may significantly influence the person’s cognitions, in
the patients’ loss of control over their Internet use. However, there particular coping style and Internet use expectancies. If a client
are some limitations of the research findings existing so far. First, has reduced prefrontal control processes, in particular in situa-
as already mentioned, the combination of assessing higher-order tions in which he/she is confronted with Internet-related cues,
cognitive functions and the confrontation with Internet-related he/she may have difficulties in developing other coping strate-
stimuli should be investigated more extensively. Second, more gies to deal with daily hassles than turning to the Internet. The
studies on different types of Internet addiction (i.e., different reinforcement that is experienced when using the Internet may
specific forms, such as gaming, communication, pornography) then strengthen the Internet use expectancies, which in turn may
are needed to better understand common and specific neuropsy- result in ignoring other ways to cope with negative mood. The
chological and neural correlates of Internet addiction (GIA and client may focus his/her view on the world and the own cognitions
certain types of SIA). Third, the age of participants has not been on Internet-related issues and these cognitions are permanently
addressed systematically. While some studies were conducted on reinforced (both positively and negatively) by using the Internet.
adolescents, other results were obtained from adult participants, Reduced prefrontal control processes may result in a restricted and
and it is hard to compare the neural correlates of Internet addiction cramped perception of situational features and ways to deal with
across different age groups. Fourth, little is known about gender everyday-life requirements. It is then even harder for the ther-
as a further variable potentially influencing the underlying mech- apist to convey control mechanisms to the client, if prefrontal
anisms of GIA and different types of SIA. However, most of the control processes are reduced. Monitoring and controlling sit-
previous studies were done with male participants. Fifth, most of uational triggers, which are fundamental ingredients in getting
the neuroimaging studies were conducted in Asia. Although these back the control over the Internet use, also rely on prefrontal con-
studies have been excellently performed and are very influential trol processes. We therefore argue that in the context of clinical
in the field, some cultural effects on the phenomenon of Internet treatment it is important to assess the client’s cognitive functions,
addiction cannot be excluded. Consequently, we need more studies in particular executive functions, before working with the client

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Brand et al. Internet addiction

on her/his specific Internet-related cognitions. This is speculative, Billieux, J., and Van der Linden, M. (2012). Problematic use of the Internet
because no empirical study on neurocognitive functions as pre- and self-regulation: a review of the initial studies. Open Addict. J. 5, 24–29.
doi:10.2174/1874941991205010024
dictors of therapy outcome exists, so far. However, we argue that
Black, D., Shaw, M., Mccormick, B., Bayless, J. D., and Allen, J. (2012). Neuropsycho-
including neuropsychological training with a focus on general and logical performance, impulsivity, ADHD symptoms, and novelty seeking in com-
Internet specific control processes should result in an even better pulsive buying disorder. Psychiatry Res. 200, 581–587. doi:10.1016/j.psychres.
outcome. 2012.06.003
All the findings and clinical implications discussed here have Bonson, K. R., Grant, S. J., Contoreggi, C. S., Links, J. M., Metcalfe, J., Weyl, H. L.,
et al. (2002). Neural systems and cue-induced cocaine craving. Neuropsychophar-
several similarities with other forms of addictive behaviors. They
macology 26, 376–386. doi:10.1016/S0893-133X(01)00371-2
are consistent with neurobiological and psychological models of Botvinick, M. M., Cohen, J. D., and Carter, C. S. (2004). Conflict monitor-
addition (Robinson and Berridge, 2003; Everitt and Robbins, 2006) ing and anterior cingulate cortex: an update. Trends Cogn. Sci. 8, 539–546.
and with neuropsychological and neuroimaging findings in sub- doi:10.1016/j.tics.2004.10.003
stance dependency and other forms of behavioral additions (Grant Brand, M., Fujiwara, E., Borsutzky, S., Kalbe, E., Kessler, J., and Markowitsch, H. J.
(2005a). Decision-making deficits of Korsakoff patients in a new gambling task
et al., 2006; van Holst et al., 2010). They should inspire incorpo- with explicit rules: associations with executive functions. Neuropsychology 19,
rating neurobiological findings into treatment designs for Internet 267–277. doi:10.1037/0894-4105.19.3.267
addiction, as it has been proposed for other forms of behavioral Brand, M., Kalbe, E., Labudda, K., Fujiwara, E., Kessler, J., and Markowitsch, H. J.
addictions (Potenza et al., 2013). Most of the current articles (2005b). Decision-making impairments in patients with pathological gambling.
on neuropsychological and neuroimaging correlates of Internet Psychiatry Res. 133, 91–99. doi:10.1016/j.psychres.2004.10.003
Brand, M., Heinze, K., Labudda, K., and Markowitsch, H. J. (2008a). The role of
addiction conclude that this clinically relevant disorder should be strategies in deciding advantageously in ambiguous and risky situations. Cogn.
classified as a behavioral addiction. We agree with this conclusion Process. 9, 159–173. doi:10.1007/s10339-008-0204-4
and hope that this review will inspire future research on neuropsy- Brand, M., Roth-Bauer, M., Driessen, M., and Markowitsch, H. J. (2008b). Executive
chological and neurobiological mechanisms of the development functions and risky decision-making in patients with opiate dependence. Drug
Alcohol Depend. 97, 64–72. doi:10.1016/j.drugalcdep.2008.03.017
and maintenance of an addictive use of the Internet in general and
Brand, M., Labudda, K., and Markowitsch, H. J. (2006). Neuropsychological cor-
certain Internet applications in specific, as well as on predictors relates of decision-making in ambiguous and risky situations. Neural Netw. 19,
for treatment efficacy. 1266–1276. doi:10.1016/j.neunet.2006.03.001
Brand, M., Laier, C., Pawlikowski, M., and Markowitsch, H. J. (2009). Decision
making with and without feedback: the role of intelligence, strategies, exec-
AUTHOR CONTRIBUTIONS utive functions, and cognitive styles. J. Clin. Exp. Neuropsychol. 31, 984–998.
Matthias Brand wrote the first draft of the paper, supervised the doi:10.1080/13803390902776860
preparation of the manuscript, contributed intellectual and practi- Brand, M., Laier, C., Pawlikowski, M., Schächtle, U., Schöler, T., and Altstötter-
cal work to the manuscript, and revised the text. Kimberly S. Young Gleich, C. (2011). Watching pornographic pictures on the Internet: role of sexual
arousal ratings and psychological-psychiatric symptoms for using Internet sex
edited the draft, revised it critically, and contributed intellectu-
sites excessively. Cyberpsychol. Behav. Soc. Netw. 14, 371–377. doi:10.1089/cyber.
ally and practically to the manuscript. Christian Laier contributed 2010.0222
particularly to the theoretical part of the manuscript and revised Braus, D. F., Wrase, J., Grüsser, S., Hermann, D., Ruf, M., Flor, H., et al. (2001).
the manuscript. All authors finally approved the manuscript. All Alcohol-associated stimuli activate the ventral striatum in abstinent alcoholics.
authors are accountable for all aspects of the work. J. Neural Transm. 108, 887–894. doi:10.1007/s007020170038
Brenner, V. (1997). Psychology of computer use: XLVII. Parameters of Internet use,
abuse, and addiction: the first 90 days of the Internet usage survey. Psychol. Rep.
REFERENCES 80, 879–882. doi:10.2466/pr0.1997.80.3.879
Alexander, G. E., and Crutcher, M. D. (1990). Functional architecture of basal ganglia Brody, A. L., Mandelkern, M. A., London, E. D., Childress, A. R., Lee, G. S., Bota,
circuits: neural substrates of parallel processing. Trends Neurosci. 13, 266–271. R. G., et al. (2002). Brain metabolic changes during cigarette craving. Arch. Gen.
Alvarez, J. A., and Emory, E. (2006). Executive function and the frontal lobes: a meta- Psychiatry 59, 1162–1172. doi:10.1001/archpsyc.59.12.1162
analytic review. Neuropsychol. Rev. 16, 17–42. doi:10.1007/s11065-006-9002-x Brody, A. L., Mandelkern, M. A., Olmstead, R. E., Jou, J., Tiongson, E., Allen, V., et al.
Anderson, V., Anderson, P., and Jacobs, R. (eds) (2008). Executive Function and the (2007). Neural substrates of resisting craving during cigarette cue exposure. Biol.
Frontal Lobes: A Life Span Perspective. New York: Taylor & Francis. Psychiatry 62, 642–651. doi:10.1016/j.biopsych.2006.10.026
Anton, R. F. (1999). What is craving? Models and implications for treatment. Alcohol Caplan, S. E. (2002). Problematic Internet use and psychosocial well-being: develop-
Res. Health 23, 165–173. ment of a theory-based cognitive-behavioral measurement instrument. Comput.
APA. (2013). Diagnostic and Statistical Manual of Mental Disorders, 5th Edn. Wash- Human Behav. 18, 553–575. doi:10.1016/S0747-5632(02)00004-3
ington, DC: APA. Caplan, S. E. (2005). A social skill account of problematic Internet use. J. Commun.
Bancroft, J., and Vukadinovic, Z. (2004). Sexual addiction, sexual compulsivity, sex- 55, 721–736. doi:10.1111/j.1460-2466.2005.tb03019.x
ual impulsivity or what? Toward a theoretical model. J. Sex. Res. 41, 225–234. Caplan, S. E. (2007). Relations among loneliness, social anxiety, and problematic
doi:10.1080/00224490409552230 Internet use. Cyberpsychol. Behav. 10, 234–242. doi:10.1089/cpb.2006.9963
Bari, A., and Robbins, T. W. (2013). Inhibition and impulsivity: behavioral and Cash, H., Rae, C. D., Steel, A. H., and Winkler, A. (2012). Internet addiction:
neural basis of response control. Prog. Neurobiol. 108, 44–79. doi:10.1016/j. a brief summary of research and practice. Curr. Psychiatry Rev. 8, 292–298.
pneurobio.2013.06.005 doi:10.2174/157340012803520513
Beard, K. W., and Wolf, E. M. (2001). Modification in the proposed diagnostic Chak, K., and Leung, L. (2004). Shyness and locus of control as predictors of Inter-
criteria for Internet addiction. Cyberpsychol. Behav. 4, 377–383. doi:10.1089/ net addiction and Internet use. Cyberpsychol. Behav. 7, 559–570. doi:10.1089/
109493101300210286 cpb.2004.7.559
Bechara, A. (2005). Decision making, impulse control and loss of willpower Chang, M. K., and Law, S. P. M. (2008). Factor structure for Young’s Internet
to resist drugs: a neurocognitive perspective. Nat. Neurosci. 8, 1458–1463. addiction test: a confirmatory study. Comput. Human Behav. 24, 2597–2619.
doi:10.1038/nn1584 doi:10.1016/j.chb.2008.03.001
Bechara, A., Tranel, D., and Damasio, H. (2000). Characterization of the decision- Charlton, J. P., and Danforth, I. D. W. (2007). Distinguishing addiction and high
making deficit of patients with ventromedial prefrontal cortex lesions. Brain 123, engagement in the context of online game playing. Comput. Human Behav. 23,
2189–2202. doi:10.1093/brain/123.11.2189 1531–1548. doi:10.1016/j.chb.2005.07.002

Frontiers in Human Neuroscience www.frontiersin.org May 2014 | Volume 8 | Article 375 | 10

Brand et al. Internet addiction

Chase, H. W., Eickhoff, S. B., Laird, A. R., and Hogarth, L. (2011). The neural basis Everitt, B. J., and Robbins, T. W. (2006). Neural systems of reinforcement for drug
of drug stimulus processing and craving: an activation likelihood estimation addiction: from actions to habits to compulsion. Nat. Neurosci. 8, 1481–1489.
meta-analysis. Biol. Psychiatry 70, 785–793. doi:10.1016/j.biopsych.2011.05.025 doi:10.1038/nn1579
Childress, A. R., Mozley, P. D., Mcelgin, W., Fitzgerald, J., Reivich, M., and O’Brian, Field, M., Munafò, M. R., and Franken, I. H. A. (2009). A meta-analytic investigation
C. P. (1999). Limbic activation during cue-induced cocaine craving. Am. J. Psy- of the relationship between attentional bias and subjective craving in substance
chiatry 156, 11–18. abuse. Psychol. Bull. 135, 589–607. doi:10.1037/a0015843
Chou, C., Condron, L., and Belland, J. C. (2005). A review of the research on Internet Franken, I. H. A. (2003). Drug craving and addiction: integrating psychologi-
addiction. Educ. Psychol. Rev. 17, 363–387. doi:10.1007/s10648-005-8138-1 cal and neuropsychopharmacological approaches. Prog. Neuropsychopharmacol.
Conversano, C., Marazziti, D., Carmassi, C., Baldini, S., Barnabei, G., and Dell’Osso, Biol. Psychiatry 27, 563–579. doi:10.1016/S0278-5846(03)00081-2
L. (2012). Pathological gambling: a systematic review of biochemical, neu- Goldstein, R. Z., Craig, A. D., Bechara, A., Garavan, H., Childress, A. R., Paulus, M.
roimaging, and neuropsychological findings. Harv. Rev. Psychiatry 20, 130–148. P., et al. (2009). The neurocircuitry of impaired insight in drug addiction. Trends
doi:10.3109/10673229.2012.694318 Cogn. Sci. 13, 372–380. doi:10.1016/j.tics.2009.06.004
Cools, R., and D’Esposito, M. (2011). Inverted-U-shaped dopamine actions on Goldstein, R. Z., and Volkow, N. D. (2002). Drug addiction and its underlying neu-
human working memory and cognitive control. Biol. Psychiatry 69, e113–e125. robiological basis: neuroimaging evidence for the involvement of the frontal
doi:10.1016/j.biopsych.2011.03.028 cortex. Am. J. Psychiatry 159, 1642–1652. doi:10.1176/appi.ajp.159.10.1642
Cooper, A., Delmonico, D. L., and Burg, R. (2000a). Cybersex users, abusers, and Goudriaan, A. E., Oosterlaan, J., Beurs, E., and Van Den Brink, W. (2004). Patho-
compulsives: new findings and implications. Sex. Addict. Compulsivity 7, 5–29. logical gambling: a comprehensive review of biobehavioral findings. Neurosci.
doi:10.1080/10720160008400205 Biobehav. Rev. 28, 123–141. doi:10.1016/j.neubiorev.2004.03.001
Cooper, A., Mcloughlin, I. P., and Campell, K. M. (2000b). Sexuality in cyber- Goudriaan, A. E., Oosterlaan, J., Beurs, E., and Van Den Brink, W. (2005). Decision
space: update for the 21st century. Cyberpsychol. Behav. 3, 521–536. doi:10.1089/ making in pathological gambling: a comparison between pathological gamblers,
109493100420142 alcohol dependents, persons with Tourette syndrome, and normal controls. Brain
Davis, R. A. (2001). A cognitive-behavioral model of pathological Internet use. Res. Cogn. Brain Res. 23, 137–151. doi:10.1016/j.cogbrainres.2005.01.017
Comput. Human Behav. 17, 187–195. doi:10.1016/S0747-5632(00)00041-8 Goudriaan, A. E., Oosterlaan, J., Beurs, E., and Van Den Brink, W. (2006). Neu-
Ding, W.-N., Sun, J.-H., Sun, Y.-W., Zhou, Y., Li, L., Xu, J.-R., et al. (2013). Altered rocognitive functions in pathological gambling: a comparison with alcohol
default network resting-state functional connectivity in adolescents with Internet dependence, Tourette syndrome and normal controls. Addiction 101, 534–547.
gaming addiction. PLoS ONE 8:e59902. doi:10.1371/journal.pone.0059902 doi:10.1111/j.1360-0443.2006.01380.x
Dom, G., Sabbe, B., Hulstijn, W., and Van Den Brink, W. (2005). Substance Grant, J. E., Brewer, J. A., and Potenza, M. N. (2006). The neurobiology of substance
use disorders and the orbitofrontal cortex: systematic review of behavioural and behavioral addictions. CNS Spectr. 11, 924–930.
decision-making and neuroimaging studies. Br. J. Psychiatry 187, 209–220. Grant, J. E., Schreiber, L. R., and Odlaug, B. L. (2013). Phenomenology and treatment
doi:10.1192/bjp.187.3.209 of behavioural addictions. Can. J. Psychiatry 58, 252–259.
Dong, G., Devito, E., Huang, J., and Du, X. (2012a). Diffusion tensor imag- Grant, S., London, E. D., Newlin, D. B., Villemagne, V. L., Liu, X., Contoreggi,
ing reveals thalamus and posterior cingulate cortex abnormalities in Internet C., et al. (1996). Activation of memory circuits during cue-elicited cocaine
gaming addicts. J. Psychiatr. Res. 46, 1212–1216. doi:10.1016/j.jpsychires.2012. craving. Proc. Natl. Acad. Sci. U.S.A. 93, 12040–12045. doi:10.1073/pnas.93.21.
05.015 12040
Dong, G., Devito, E. E., Du, X., and Cui, Z. (2012b). Impaired inhibitory control in Griffiths, M. D. (2000). Does Internet and computer “addiction” exist? Some case
“Internet addiction disorder”: a functional magnetic resonance imaging study. study evidence. Cyberpsychol. Behav. 3, 211–218. doi:10.1089/109493100316067
Psychiatry Res. 203, 153–158. doi:10.1016/j.pscychresns.2012.02.001 Griffiths, M. D. (2005). A “components” model of addiction within a biopsychoso-
Dong, G., Huang, J., and Du, X. (2012c). Alterations in regional homogeneity of cial framework. J. Subst. Use 10, 191–197. doi:10.1080/14659890500114359
resting-state brain activity in Internet gaming addicts. Behav. Brain Funct. 8, 41. Grüsser, S., Wrase, J., Klein, S., Hermann, D., Smolka, M. N., Ruf, M., et al. (2004).
doi:10.1186/1744-9081-8-41 Cue-induced activation of the striatum and medial prefrontal cortex is associ-
Dong, G., Hu, Y., and Lin, X. (2013a). Reward/punishment sensitivities among Inter- ated with subsequent relapse in abstinent alcoholics. Psychopharmacology 175,
net addicts: implications for their addictive behaviors. Prog. Neuropsychophar- 296–302. doi:10.1007/s00213-004-1828-4
macol. Biol. Psychiatry 46, 139–145. doi:10.1016/j.pnpbp.2013.07.007 Han, D., Hwang, J. Y., and Renshaw, P. F. (2010a). Bupropion sustained release
Dong, G., Hu, Y., Lin, X., and Lu, Q. (2013b). What makes Internet addicts continue treatment decreases craving for video games and cue-induced brain activity in
playing online even when faced by severe negative consequences? Possible expla- patients with Internet video game addiction. Exp. Clin. Psychopharmacol. 18,
nations from an fMRI study. Biol. Psychol. 94, 282–289. doi:10.1016/j.biopsycho. 297–304. doi:10.1037/a0020023
2013.07.009 Han, D., Kim, Y., and Lee, Y. (2010b). Changes in cue-induced, prefrontal cor-
Dong, G., Shen, Y., Huang, J., and Du, X. (2013c). Impaired error-monitoring func- tex activity with video-game play. Cyberpsychol. Behav. Soc. Netw. 13, 655–661.
tion in people with Internet addiction disorder: an event-related FMRI study. doi:10.1089/cyber.2009.0327
Eur. Addict. Res. 19, 269–275. doi:10.1159/000346783 Han, D. H., Bolo, N., Daniels, M. A., Arenella, L., Lyoo, I. K., and Renshaw, P. F.
Dong, G., Huang, J., and Du, X. (2011a). Enhanced reward sensitivity and decreased (2011). Brain activity and desire for Internet video game play. Compr. Psychiatry
loss sensitivity in Internet addicts: an fMRI study during a guessing task. J. Psy- 52, 88–95. doi:10.1016/j.comppsych.2010.04.004
chiatr. Res. 45, 1525–1529. doi:10.1016/j.jpsychires.2011.06.017 Hansen, S. (2002). Excessive Internet usage or “Internet addiction”? The implica-
Dong, G., Zhou, H., and Zhao, X. (2011b). Male Internet addicts show impaired tions of diagnostic categories for student users. J. Comput. Assist. Learn. 18,
executive control ability: evidence from a color-word Stroop task. Neurosci. Lett. 235–236. doi:10.1046/j.1365-2729.2002.t01-2-00230.x
499, 114–118. doi:10.1016/j.neulet.2011.05.047 Hardie, E., and Tee, M. Y. (2007). Excessive Internet use: the role of personality, lone-
Dong, G., Lin, X., Zhou, H., and Lu, Q. (2014). Cognitive flexibility in Internet liness, and social support networks in Internet addiction. Aust. J. Emerg. Tech.
addicts: fMRI evidence from difficult-to-easy and easy-to-difficult switching sit- Soc. 5, 34–47.
uations. Addict. Behav. 39, 677–683. doi:10.1016/j.addbeh.2013.11.028 Heinz, A., Beck, A., Grüsser, S. M., Grace, A. A., and Wrase, J. (2008). Identifying
Dong, G., Lu, Q., Zhou, H., and Zhao, X. (2010). Impulse inhibition in people the neural circuitry of alcohol craving and relapse vulnerability. Addict. Biol. 14,
with Internet addiction disorder: electrophysiological evidence from a Go/NoGo 108–118. doi:10.1111/j.1369-1600.2008.00136.x
study. Neurosci. Lett. 485, 138–142. doi:10.1016/j.neulet.2010.09.002 Hong, S.-B., Kim, J.-W., Choi, E.-J., Kim, H.-H., Suh, J.-E., Kim, C.-D., et al. (2013a).
Dunn, B. D., Dalgleish, T., and Lawrence, A. D. (2006). The somatic marker hypoth- Reduced orbitofrontal cortical thickness in male adolescents with Internet addic-
esis: a critical evaluation. Neurosci. Biobehav. Rev. 30, 239–271. doi:10.1016/j. tion. Behav. Brain Funct. 9, 11. doi:10.1186/1744-9081-9-11
neubiorev.2005.07.001 Hong, S.-B., Zalesky, A., Cocchi, L., Fornito, A., Choi, E.-J., Kim, H.-H., et al. (2013b).
Ebeling-Witte, S., Frank, M. L., and Lester, D. (2007). Shyness, Internet use, and Decreased functional brain connectivity in adolescents with Internet addiction.
personality. Cyberpsychol. Behav. 10, 713–716. doi:10.1089/cpb.2007.9964 PLoS ONE 8:e57831. doi:10.1371/journal.pone.0057831

Frontiers in Human Neuroscience www.frontiersin.org May 2014 | Volume 8 | Article 375 | 11

Brand et al. Internet addiction

Hoshi, E. (2013). Cortico-basal ganglia networks subserving goal-directed behav- Laier, C., Schulte, F. P., and Brand, M. (2013b). Pornographic picture process-
ior mediated by conditional visuo-goal association. Front. Neural Circuits 7:158. ing interferes with working memory performance. J. Sex Res. 50, 642–652.
doi:10.3389/fncir.2013.00158 doi:10.1080/00224499.2012.716873
Hou, H., Jia, S., Hu, S., Fan, R., Sun, W., Sun, T., et al. (2012). Reduced striatal Lin, F., Zhou, Y., Du, Y., Qin, L., Zhao, Z., Xu, J., et al. (2012). Abnormal
dopamine transporters in people with Internet addiction disorder. J. Biomed. white matter integrity in adolescents with Internet addiction disorder: a tract-
Biotechnol. 2012, 854524. doi:10.1155/2012/854524 based spatial statistics study. PLoS ONE 7:e30253. doi:10.1371/journal.pone.
− N. (2011). Influence of dopaminergic system on Internet addic-
Jovic, J., and Ðindić, 0030253
tion. Acta Med. Medianae 50, 60–66. doi:10.5633/amm.2011.0112 Loeber, S., and Duka, T. (2009). Acute alcohol impairs conditioning of a behavioural
Jurado, M., and Rosselli, M. (2007). The elusive nature of executive functions: a reward-seeking response and inhibitory control processes – implications for
review of our current understanding. Neuropsychol. Rev. 17, 213–233. doi:10. addictive disorders. Addiction 104, 2013–2022. doi:10.1111/j.1360-0443.2009.
1007/s11065-007-9040-z 02718.x
Kafka, M. P. (2010). Hypersexual disorder: a proposed diagnosis for DSM-V. Arch. Lorenz, R. C., Krüger, J.-K., Neumann, B., Schott, B. H., Kaufmann, C., Heinz, A.,
Sex. Behav. 39, 377–400. doi:10.1007/s10508-009-9574-7 et al. (2013). Cue reactivity and its inhibition in pathological computer game
Kalivas, P. W., and Volkow, N. D. (2005). The neural basis of addiction: a pathology players. Addict. Biol. 18, 134–146. doi:10.1111/j.1369-1600.2012.00491.x
of motivation and choice. Am. J. Psychiatry 162, 1403–1413. doi:10.1176/appi. Lortie, C. L., and Guitton, M. J. (2013). Internet addiction assessment tools:
ajp.162.8.1403 dimensional structure and methodological status. Addiction 108, 1207–1216.
Kim, H. K., and Davis, K. E. (2009). Toward a comprehensive theory of problem- doi:10.1111/add.12202
atic Internet use: evaluating the role of self-esteem, anxiety, flow, and the self- Meerkerk, G. J., Van Den Eijnden, R. J. J. M., Franken, I. H. A., and Gar-
rated importance of Internet activities. Comput. Human Behav. 25, 490–500. retsen, H. F. L. (2010). Is compulsive Internet use related to sensitivity to
doi:10.1016/j.chb.2008.11.001 reward and punishment, and impulsivity? Comput. Human Behav. 26, 729–735.
Kim, S. H., Baik, S.-H., Park, C. S., Kim, S. J., Choi, S. W., and Kim, S. E. (2011). doi:10.1016/j.chb.2010.01.009
Reduced striatal dopamine D2 receptors in people with Internet addiction. Neu- Meerkerk, G. J., Van Den Eijnden, R. J. J. M., and Garretsen, H. F. L. (2006). Predict-
roreport 22, 407–411. doi:10.1097/WNR.0b013e328346e16e ing compulsive Internet use: it’s all about sex! Cyberpsychol. Behav. 9, 95–103.
Kim,Y.-R., Son, J.-W., Lee, S.-I., Shin, C.-J., Kim, S.-K., Ju, G., et al. (2012). Abnormal doi:10.1089/cpb.2006.9.95
brain activation of adolescent Internet addict in a ball-throwing animation task: Meerkerk, G. J., Van Den Eijnden, R. J. J. M., Vermulst, A. A., and Garretsen, H.
possible neural correlates of disembodiment revealed by fMRI. Prog. Neuropsy- F. L. (2009). The Compulsive Internet Use Scale (CIUS): some psychometric
chopharmacol. Biol. Psychiatry 39, 88–95. doi:10.1016/j.pnpbp.2012.05.013 properties. Cyberpsychol. Behav. 12, 1–6. doi:10.1089/cpb.2008.0181
Ko, C. H., Liu, G. C., Hsiao, S., Yen, J. Y., Yang, M. J., Lin, W. C., et al. (2009). Brain Morahan-Martin, J., and Schumacher, P. (2003). Loneliness and social uses of
activities associated with gaming urge of online gaming addiction. J. Psychiatr. the Internet. Comput. Human Behav. 19, 659–671. doi:10.1016/S0747-5632(03)
Res. 43, 739–747. doi:10.1016/j.jpsychires.2008.09.012 00040-2
Ko, C.-H., Liu, G.-C., Yen, J.-Y., Chen, C.-Y., Yen, C.-F., and Chen, C.-S. (2013a). Park, H. S., Kim, S. H., Bang, S. A., Yoon, E. J., Cho, S. S., and Kim, S. E. (2010).
Brain correlates of craving for online gaming under cue exposure in subjects with Altered regional cerebral glucose metabolism in Internet game overusers: a
Internet gaming addiction and in remitted subjects. Addict. Biol. 18, 559–569. 18F-fluorodeoxyglucose positron emission tomography study. CNS Spectr. 15,
doi:10.1111/j.1369-1600.2011.00405.x 159–166.
Ko, C.-H., Liu, G.-C., Yen, J.-Y., Yen, C.-F., Chen, C.-S., and Lin, W.-C. (2013b). The Pawlikowski, M., Altstötter-Gleich, C., and Brand, M. (2013). Validation and psycho-
brain activations for both cue-induced gaming urge and smoking craving among metric properties of a short version of Young’s Internet addiction test. Comput.
subjects comorbid with Internet gaming addiction and nicotine dependence. Human Behav. 29, 1212–1223. doi:10.1016/j.chb.2012.10.014
J. Psychiatr. Res. 47, 486–493. doi:10.1016/j.jpsychires.2012.11.008 Pawlikowski, M., and Brand, M. (2011). Excessive Internet gaming and decision
Korkeila, J., Kaarlas, S., Jääskeläinen, M., Vahlberg, T., and Taiminen, T. (2010). making: do excessive World of Warcraft-players have problems in decision mak-
Attached to the web – harmful use of the Internet and its correlates. Eur. Psychi- ing under risky conditions? Psychiatry Res. 188, 428–433. doi:10.1016/j.psychres.
atry 25, 236–241. doi:10.1016/j.eurpsy.2009.02.008 2011.05.017
Kühn, S., and Gallinat, J. (2011). Common biology of craving across legal and ille- Pawlikowski, M., Nader, I. W., Burger, C., Biermann, I., Stieger, S., and Brand, M.
gal drugs – a quantitative meta-analysis of cue-reactivity brain response. Eur. (2014). Pathological Internet use – it is a multidimensional and not a unidimen-
J. Neurosci. 33, 1318–1326. doi:10.1111/j.1460-9568.2010.07590.x sional construct. Addict. Res. Theory 22, 166–175. doi:10.3109/16066359.2013.
Kühn, S., Romanowski, A., Schilling, C., Lorenz, R., Mörsen, C., Seiferth, N., 793313
et al. (2011). The neural basis of video gaming. Transl. Psychiatry 15, e53. Pike, E., Stoops, W. W., Fillmore, M. T., and Rush, C. R. (2013). Drug-related stimuli
doi:10.1038/tp.2011.53 impair inhibitory control in cocaine abusers. Drug Alcohol Depend. 133, 768–771.
Kuss, D. J., and Griffith, M. D. (2011). Internet gaming addiction: a system- doi:10.1016/j.drugalcdep.2013.08.004
atic review of empirical research. Int. J. Ment. Health Addict. 10, 278–296. Potenza, M. N., Balodis, I. M., Franco, C. A., Bullock, S., Xu, J., Chung, T., et al.
doi:10.1007/s11469-011-9318-5 (2013). Neurobiological considerations in understanding behavioral treatments
Kuss, D. J., and Griffiths, M. D. (2012). Internet and gaming addiction: a sys- for pathological gambling. Psychol. Addict. Behav. 27, 380–392. doi:10.1037/
tematic literature review of neuroimaging studies. Brain Sci. 2, 347–374. a0032389
doi:10.3390/brainsci2030347 Robinson, T. E., and Berridge, K. C. (2000). The psychology and neurobiology of
Kuss, D. J., Griffiths, M. D., Karila, M., and Billieux, J. (2013). Internet addiction: addiction: an incentive-sensitization view. Addiction 95, 91–117. doi:10.1046/j.
a systematic review of epidemiological research for the last decade. Curr. 1360-0443.95.8s2.19.x
Pharm. Des. [Epub ahead of print]. Robinson, T. E., and Berridge, K. C. (2001). Incentive-sensitization and addiction.
Labudda, K., Woermann, F. G., Mertens, M., Pohlmann-Eden, B., Markowitsch, H. J., Addiction 96, 103–114. doi:10.1046/j.1360-0443.2001.9611038.x
and Brand, M. (2008). Neural correlates of decision making with explicit infor- Robinson, T. E., and Berridge, K. C. (2003). Addiction. Annu. Rev. Psychol. 54, 25–53.
mation about probabilities and incentives in elderly healthy subjects. Exp. Brain doi:10.1146/annurev.psych.54.101601.145237
Res. 187, 641–650. doi:10.1007/s00221-008-1332-x Robinson, T. E., and Berridge, K. C. (2008). The incentive sensitization theory
Laier, C., Pawlikowski, M., and Brand, M. (2014). Sexual picture processing inter- of addiction: some current issues. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363,
feres with decision-making under ambiguity. Arch. Sex. Behav. 43, 473–482. 3137–3146. doi:10.1098/rstb.2008.0093
doi:10.1007/s10508-013-0119-8 Salisbury, R. M. (2008). Out of control sexual behaviors: a developing practice
Laier, C., Pawlikowski, M., Pekal, J., Schulte, F. P., and Brand, M. (2013a). Cyber- model. Sex. Relatsh. Ther. 23, 131–139. doi:10.1080/14681990801910851
sex addiction: experienced sexual arousal when watching pornography and Schacht, J. P., Anton, R. F., and Myrick, H. (2013). Functional neuroimaging stud-
not real-life sexual contacts makes the difference. J. Behav. Addict. 2, 100–107. ies of alcohol cue reactivity: a quantitative meta-analysis and systematic review.
doi:10.1556/JBA.2.2013.002 Addict. Biol. 18, 121–133. doi:10.1111/j.1369-1600.2012.00464.x

Frontiers in Human Neuroscience www.frontiersin.org May 2014 | Volume 8 | Article 375 | 12

Brand et al. Internet addiction

Shallice, T., and Burgess, P. (1996). The domain of supervisory processes and tem- Young, K. S. (1998a). Caught in the Net: How to Recognize the Signs of Internet
poral organization of behaviour. Philos. Trans. R. Soc. Lond. B Biol. Sci. 351, Addiction – And a Winning Strategy for Recovery. New York, NY: John Wiley &
1405–1412. doi:10.1098/rstb.1996.0124 Sons, Inc.
Spada, M. M. (2014). An overview of problematic Internet use. Addict. Behav. 39, Young, K. S. (1998b). Internet addiction: the emergence of a new clinical disorder.
3–6. doi:10.1016/j.addbeh.2013.09.007 Cyberpsychol. Behav. 3, 237–244. doi:10.1089/cpb.1998.1.237
Starcevic, V. (2013). Is Internet addiction a useful concept? Aust. N. Z. J. Psychiatry Young, K. S. (1999). Internet addiction: symptoms, evaluation, and treatment. Innov.
47, 16–19. doi:10.1177/0004867412461693 Clin. Pract. 17, 19–31.
Sun, D.-L., Chen, Z. J., Ma, N., Zhang, X.-C., Fu, X.-M., and Zhang, D. R. (2009). Young, K. S. (2004). Internet addiction: a new clinical phenomenon and its conse-
Decision-making and prepotent response inhibition functions in excessive Inter- quences. Am. Behav. Sci. 48, 402–415. doi:10.1177/0002764204270278
net users. CNS Spectr. 14, 75–81. Young, K. S. (2008). Internet sex addiction: risk factors, stages of development, and
Sun, Y., Ying, H., Seetohul, R. M., Xuemei, W., Ya, Z., Qian, L., et al. (2012). Brain treatment. Am. Behav. Sci. 52, 21–37. doi:10.1177/0002764208321339
fMRI study of crave induced by cue pictures in online game addicts (male ado- Young, K. S. (2011). CBT-IA: the first treatment model to address Internet addiction.
lescents). Behav. Brain Res. 233, 563–576. doi:10.1016/j.bbr.2012.05.005 J. Cogn. Ther. 25, 304–312. doi:10.1891/0889-8391.25.4.304
Thalemann, R., Wölfling, K., and Grüsser, S. M. (2007). Specific cue reactivity on Young, K. S. (2013). Treatment outcomes using CBT-IA with Internet-addicted
computer game-related cues in excessive gamers. Behav. Neurosci. 121, 614–618. patients. J. Behav. Addict. 2, 209–215. doi:10.1556/JBA.2.2013.4.3
doi:10.1037/0735-7044.121.3.614 Young, K. S., Pistner, M., O’Mara, J., and Buchanan, J. (1999). Cyber disorders: the
Thatcher, A., Wretschko, G., and Fridjhon, P. (2008). Online flow experiences, prob- mental health concern for the new millennium. Cyberpsychol. Behav. 2, 475–479.
lematic Internet use and Internet procrastination. Comput. Human Behav. 24, doi:10.1089/cpb.1999.2.475
2236–2254. doi:10.1016/j.chb.2007.10.008 Young, K. S., Yue, X. D., and Ying, L. (2011). “Prevalence estimates and etiologic
Tiffany, S. T., and Conklin, C. A. (2000). A cognitive processing model of alcohol models of Internet addiction,” in Internet Addiction, eds K. S. Young and C. N.
craving and compulsive alcohol use. Addiction 95, 145–153. doi:10.1046/j.1360- Abreu (Hoboken, NJ: John Wiley & Sons), 3–18.
0443.95.8s2.3.x Yuan, K., Cheng, P., Dong, T., Bi, Y., Xing, L., Yu, D., et al. (2013). Cortical thick-
Tychsen, A., Hitchens, M., Brolund, T., and Kavakli, M. (2006). Live action role- ness abnormalities in late adolescence with online gaming addiction. PLoS ONE
playing games: control, communication, storytelling, and MMORPG similari- 8:e53055. doi:10.1371/journal.pone.0053055
ties. Game. Cult. 1, 252–275. doi:10.1177/1555412006290445 Yuan, K., Qin, W., Wang, G., Zeng, F., Zhao, L., Yang, X., et al. (2011). Microstruc-
van Holst, R. J., Van Den Brink, W., Veltman, D. J., and Goudriaan, A. E. (2010). Why ture abnormalities in adolescents with Internet addiction disorder. PLoS ONE
gamblers fail to win: a review of cognitive and neuroimaging findings in patho- 6:e20708. doi:10.1371/journal.pone.0020708
logical gambling. Neurosci. Biobehav. Rev. 34, 87–107. doi:10.1016/j.neubiorev. Yuan, P., and Raz, N. (2014). Prefrontal cortex and executive functions in healthy
2009.07.007 adults: a meta-analysis of structural neuroimaging studies. Neurosci. Biobehav.
Weng, C.-B., Qian, R.-B., Fu, X.-M., Lin, B., Han, X.-P., Niu, C.-S., et al. (2013). Gray Rev. 42C, 180–192. doi:10.1016/j.neubiorev.2014.02.005
matter and white matter abnormalities in online game addiction. Eur. J. Radiol. Zhou, Y., Lin, F.-C., Du, Y.-S., Qin, L.-D., Zhao, Z.-M., Xu, J.-R., et al. (2011). Gray
82, 1308–1312. doi:10.1016/j.ejrad.2013.01.031 matter abnormalities in Internet addiction: a voxel-based morphometry study.
Whang, L. S. M., Lee, S., and Chang, G. (2003). Internet over-users’ psychological Eur. J. Radiol. 79, 92–95. doi:10.1016/j.ejrad.2009.10.025
profiles: a behavior sampling analysis on Internet addiction. Cyberpsychol. Behav. Zhou, Z., Yuan, G., and Yao, J. (2012). Cognitive biases toward Internet game-related
6, 143–150. doi:10.1089/109493103321640338 pictures and executive deficits in individuals with an Internet game addiction.
Widyanto, L., and Griffiths, M. D. (2006). “Internet addiction”: a critical review. Int. PLoS ONE 7:e48961. doi:10.1371/journal.pone.0048961
J. Ment. Health Addict. 4, 31–51. doi:10.1007/s11469-006-9009-9
Widyanto, L., Griffiths, M. D., and Brunsden, V. (2011). A psychometric compari-
son of the Internet addiction test, the Internet-Related Problem Scale, and self- Conflict of Interest Statement: The authors declare that the research was conducted
diagnosis. Cyberpsychol. Behav. Soc. Netw. 14, 141–149. doi:10.1089/cyber.2010. in the absence of any commercial or financial relationships that could be construed
0151 as a potential conflict of interest.
Widyanto, L., Griffiths, M. D., Brunsden, V., and Mcmurran, M. (2008). The psycho-
metric properties of the Internet related problem scale: a pilot study. Int. J. Ment.
Health Addict. 6, 205–213. doi:10.1007/s11469-007-9120-6 Received: 27 March 2014; accepted: 14 May 2014; published online: 27 May 2014.
Winkler, A., Dörsing, B., Rief, W., Shen, Y., and Glombiewski, J. A. (2013). Treat- Citation: Brand M, Young KS and Laier C (2014) Prefrontal control and Internet
ment of Internet addiction: a meta-analysis. Clin. Psychol. Rev. 33, 317–329. addiction: a theoretical model and review of neuropsychological and neuroimaging
doi:10.1016/j.cpr.2012.12.005 findings. Front. Hum. Neurosci. 8:375. doi: 10.3389/fnhum.2014.00375
Yang, C., Choe, B., Baity, M., Lee, J., and Cho, J. (2005). SCL-90-R and 16PF profiles This article was submitted to the journal Frontiers in Human Neuroscience.
of senior high school students with excessive Internet use. Can. J. Psychiatry 50, Copyright © 2014 Brand, Young and Laier. This is an open-access article distributed
407–414. under the terms of the Creative Commons Attribution License (CC BY). The use, dis-
Yee, N. (2006). Motivations for play in online games. Cyberpsychol. Behav. 9, 772–775. tribution or reproduction in other forums is permitted, provided the original author(s)
doi:10.1089/cpb.2006.9.772 or licensor are credited and that the original publication in this journal is cited, in
Young, K. S. (1996). Addictive use of the Internet: a case that breaks the stereotype. accordance with accepted academic practice. No use, distribution or reproduction is
Psychol. Rep. 79, 899–902. doi:10.2466/pr0.1996.79.3.899 permitted which does not comply with these terms.

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