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Internet addiction and functional brain networks: task-related fMRI study


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A common brain-related feature of addictions is the altered function of higher-order brain networks. Growing evidence suggests that Internet-related addictions are also associated with breakdown of functional brain networks. Taking into consideration the limited number of studies used in previous studies in Internet addiction (IA), our aim was to investigate the functional correlates of IA in the default mode network (DMN) and in the inhibitory control network (ICN). To observe these relationships, task-related fMRI responses to verbal Stroop and non-verbal Stroop-like tasks were measured in 60 healthy university students. The Problematic Internet Use Questionnaire (PIUQ) was used to assess IA. We found significant deactivations in areas related to the DMN (precuneus, posterior cingulate gyrus) and these areas were negatively correlated with PIUQ during incongruent stimuli. In Stroop task the incongruent_minus_congruent contrast showed positive correlation with PIUQ in areas related to the ICN (left inferior frontal gyrus, left frontal pole, left central opercular, left frontal opercular, left frontal orbital and left insular cortex). Altered DMN might explain some comorbid symptoms and might predict treatment outcomes, while altered ICN may be the reason for having difficulties in stopping and controlling overuse.
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Internet addiction and functional
brain networks: task-related fMRI
Gergely Darnai1,2,3*, Gábor Perlaki3,4,5, András N. Zsidó1, Orsolya Inhóf1, Gergely Orsi3,4,5,
Réka Horváth2, Szilvia Anett Nagy3,4,5,6, Beatrix Lábadi1, Dalma Tényi2, Norbert Kovács2,3,
Tamás Dóczi3,5, Zsolt Demetrovics
7 & József Janszky2,3
A common brain-related feature of addictions is the altered function of higher-order brain networks.
Growing evidence suggests that Internet-related addictions are also associated with breakdown of
functional brain networks. Taking into consideration the limited number of studies used in previous
studies in Internet addiction (IA), our aim was to investigate the functional correlates of IA in the default
mode network (DMN) and in the inhibitory control network (ICN). To observe these relationships,
task-related fMRI responses to verbal Stroop and non-verbal Stroop-like tasks were measured in 60
healthy university students. The Problematic Internet Use Questionnaire (PIUQ) was used to assess IA.
We found signicant deactivations in areas related to the DMN (precuneus, posterior cingulate gyrus)
and these areas were negatively correlated with PIUQ during incongruent stimuli. In Stroop task the
incongruent_minus_congruent contrast showed positive correlation with PIUQ in areas related to
the ICN (left inferior frontal gyrus, left frontal pole, left central opercular, left frontal opercular, left
frontal orbital and left insular cortex). Altered DMN might explain some comorbid symptoms and might
predict treatment outcomes, while altered ICN may be the reason for having diculties in stopping and
controlling overuse.
Internet – along with new technologies – has improved many aspects of our lives and it is now essential part of
the everyday routine, including professional and social functioning. e benets that Internet brought into our
life are multiple, however, excessive use can contribute to various psychological and medical problems, such as
depression1, anxiety2, body image disturbance3, sleeplessness4, and poor dietary behavior3. Problematic Internet
use is considered as a relatively new, fast growing behavioral addiction5 that has a potential threat to public
ough Internet addiction (IA) is not considered as a distinct mental disorder a more specic form, problem-
atic video gaming, operationalized as ‘Internet gaming disorder’ (IGD), was included in Section 3 (‘Emerging
Measures and Models’) of DSM-5, as a condition warranting further study7. It is important to note that the
11th revision of the International Classication of Diseases (ICD-11) also includes gaming disorder in section
“Disorders Due to Substance Use or Addictive Behaviours”8. Several authors claim that it is important to dis-
tinguish between IA and IGD. E.g. Montag and colleagues investigated general Internet addiction and specic
forms of Internet addiction (incl. video gaming, shopping, social network and pornography) in three European
and one Asian country. ey found that only online social network showed constant correlation patterns with
generalized IA, other specic forms must be distinguished from IA during scientic investigations9. Over the
last few years, researchers have increased eorts to investigate brain-related alterations to understand the phe-
nomena deeper. ese neuroimaging studies – utilizing mainly structural and functional magnetic resonance
imaging (MRI) – identied abnormalities in frontal brain regions (orbitofrontal and prefrontal cortex) and the
brain’s reward system (putamen, nucleus accumbens) that play crucial role in associative learning10 and cognitive
control11,12. Moreover, IA shares several aspects of substance addiction, such as obsessive thinking about the
1Institute of Psychology, University of Pécs, Pécs, Hungary. 2Department of Neurology, University of Pécs, Medical
School, Pécs, Hungary. 3MTA-PTE Clinical Neuroscience MR Research Group, Pécs, Hungary. 4Pécs Diagnostic
Centre, Pécs, Hungary. 5Department of Neurosurgery, University of Pécs, Medical School, Pécs, Hungary. 6MTA-PTE
Stress Neurobiology Research Group, Pécs, Hungary. 7Institute of Psychology, Eötvös Loránd University, Budapest,
Hungary. *email:
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substance/Internet (daydreaming, rumination, and fantasizing), neglecting everyday activities, social life and
essential needs and diculties in controlling the use5,13.
Another common brain-related feature of substance and behavioral addictions is the altered function of
higher-order brain networks. Growing evidence suggests that addictions are not only associated with structural
and functional breakdown of isolated regions but rather with system-level alterations between brain regions14.
Functional brain imaging data have revealed that the human brain is topologically organized into a set of coherent
spatio-temporal networks, such as default-mode network (DMN)15. e DMN was rst mentioned by Shulman
in 199716 who noted several brain areas in the cerebral cortex that constantly decreased their activity while per-
forming highly demanding tasks. It can be divided into two main subdivisions: the medial prefrontal cortex, and
the posterior cingulate cortex with the nearby precuneus and lateral parietal cortex17. e DMN-related func-
tional MRI (fMRI) studies can be divided into two types of design. In the resting-state experiments, the subjects
lie passively (with closed eyes or with eyes focusing on a xation cross) without any tasks during scanning. In
task-related experiments, fMRI data are acquired during a certain cognitive task and researchers usually focus on
deactivations in the brain18. However, there are some studies that revealed task-induced activations in the DMN,
e.g. when internally directed/self-related cognition is required19.
Several studies revealed altered DMN in addictions. ese researches focused primarily on gambling dis-
order20 and Internet gaming disorder15,20 or substance addictions, such as heroin21, alcohol22, nicotine23, and
cannabis24. It was also demonstrated that functional connectivity within DMN may predict successful quitting25,
the intensity of withdrawal-induced craving26 and the degree of cognitive decline27 in addictions. ese observa-
tions suggest that dysfunctions in the DMN play important role in the pathogenesis and persistence of addictive
disorders. To our knowledge the only study focusing on DMN in adult Internet addicts was conducted by Li and
colleagues28. ey assessed grey matter volumetry and functional connectivity to investigate brain alterations in
healthy young adults with an IA tendency. ey found altered relationship between the dorsolateral prefrontal
cortex (as key node of the cognitive control network) and the anterior cingulate/prefrontal cortices (as key nodes
of the DMN).
Impaired inhibitory control is another important feature of addictions (including IA)29,30. Pre-existing inhib-
itory control problems may increase vulnerability to develop addictive disorders, and may serve as a risk factor
for their maintenance31. Dong et al. tried to identify neural correlates of response inhibition in IA. ey used
Stroop-related fMRI30 and event-related brain potential29 techniques and showed that cingulate and medial fron-
tal cortices are impaired in IA. ere are some practical and theoretical dierences in our study, compared with
that one conducted by Dong and colleagues. Firstly, they investigated only males with relatively low sample size
(24 participants in total). Secondly, we claim, that in the lack of well-established diagnostic criteria and clear
cuto points, continuous measure of IA is recommended. irdly, since Stroop task is considered as a highly
demanding task, in our study we focused on DMN activation as well (using dierent contrast in the higher level
analysis). Congdon et al.32 suggested that brain areas related to response inhibition ability should be considered as
elements of the “inhibitory control network” (ICN). According to their study, the inferior frontal and medial fron-
tal gyri, the opercular, insular, orbital posterior cingulate and posterior parietal cortices are involved in the ICN.
Taking into consideration the limited number of studies and methods used in previous functional studies in
IA, our aim was to investigate the functional correlates of IA in the DMN and in the ICN. To observe these rela-
tionships, task-related fMRI responses to verbal Stroop and non-verbal Stroop-like tasks were investigated. We
hypothesized that blood-oxygen-level dependent (BOLD) signal changes in the regions of the DMN and ICN are
correlated with IA scores. In the lack of well-established diagnostic criteria, we decided to use a multidimensional
continuous measure of IA.
Materials and Methods
Participants. Our study was conducted through online recruiting. A total of 602 adults participated in the
online survey on problematic internet use. According to gender, age and MRI safety parameters sixty healthy
Caucasian university students (30 males) aged between 18 and 30 (mean ± SD: 22.0 ± 2.08 years) were included.
All participants underwent a brief interview by a clinical psychologist and neurological expert to screen out
participants with a current psychiatric and neurologic diagnosis. Subjects with chronic illnesses, neurological or
psychiatric disorders were not included (for more details about the selection procedure, see the Fig.1). All sub-
jects had right-hand dominance according to the Edinburgh Handedness Inventory (Oldeld, 1971). Participants
spend on average 2.75 (SD = 2.74) hours online per day and consider themselves 45.27% (SD = 29.05) addicted
to the Internet. ey were either paid or received course credits for their participation and were naive with regard
to the purpose of the experiment.
e study was approved by the Regional Research Ethics Committee of the Clinical Center, University of Pécs.
All procedures performed in studies involving human participants were in accordance with the ethical stand-
ards of the institutional and national research committee and with the 1964 Helsinki declaration and its later
amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
Assessment. Without clear diagnostic criteria, it is highly recommended to measure excessive Internet use
with a continuous questionnaire without using unclear cut-o scores11. erefore, we used the Problematic Internet
Use Questionnaire (PIUQ), a validated self-report scale with good reliability and validity characteristics13,33.
e questionnaire contains 18 items, each scored on a 5-point Likert-type scale ranging from 1 (never) to 5
(always). A conrmatory factor analysis veried the three factor model of questionnaire, each subscale contains
six items. Obsession subscale refers to obsessive thinking about the Internet (daydreaming, rumination and fan-
tasizing) and with- drawal symptoms caused by the lack of Internet use (anxiety, depression). (“How oen do
you feel tense, irritated, or stressed if you cannot use the Internet for as long as you want to?”). Neglect subscale
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contains items about neglecting everyday activities, social life and essential needs (“How oen do you spend time
online when you’d rather sleep?”). Control disorder subscale reects diculties in controlling time spent on the
Internet (“How oen do you realize saying when you are online, ‘just a couple of more minutes and I will stop’?”).
Since in this study we focused on global psychological consequences of Internet addiction, we used PIUQ total
score in statistical analyses, that was computed by summing the scores on all the items of the scale.
Stimuli. Using Presentation soware (Neurobehavioral Systems, Inc., Berkeley, CA, USA), the visual stimuli were
presented via MRI-compatible goggles (VisualSystem, NordicNeuroLab AS, Bergen, Norway) and subjects’ responses
were collected via MRI-compatible response buttons (ResponseGrip, NordicNeuroLab AS, Bergen, Norway).
Since indirect behavioral evidences suggest that language network and verbal processes might be impaired
in IA34, we decided to use two dierent tasks in this study for controlling this possible interfering eect: verbal
Stroop task and non-verbal Stroop-like task.
Verbal stroop task. A series of colored words were displayed against a black background. Half of the words
were written in the same ink color as the meaning of the word (congruent stimuli, e.g., the word “green” displayed
in green color), while the other half were written in colors other than the word’s meaning (incongruent stimuli,
e.g., the word “green” displayed in blue color). Four colored words (blue, green, red, and yellow) and their corre-
sponding colors were used. Subjects had to choose the ink color (and neglect the meaning) of the words by using
the four response buttons (le thumb = red; le index = blue; right thumb = green; right index = yellow). Colored
circle thumbnails were presented on the bottom of screen to aid the subjects in which button to use for dierent
colors (Fig.1).
Non-verbal stroop-like task. A series of white arrows pointing either le or right was displayed against
a black background either on the le or right side of a centered xation cross. Half of the stimuli were pointing
in the same direction as their position on the screen (congruent stimuli, e.g., a leward pointing arrow on the
Figure 1. e selection of the nal sample. 602 subjects completed the online survey. 139 of them reported
smoking habits or excessive alcohol drinking and 28 individuals did not fall within the expected age range
(18–30). Since there was overlap between the groups (5 subjects), totally 162 subjects were excluded at this stage
of the process. Finally, 30 male and 30 female participants with no neurological and psychiatric symptoms and
no risk factors related to MRI measurements were chosen randomly from the remaining 440 subjects.
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le side of the xation cross), while the other half were pointing in an opposite direction as their position on the
screen (incongruent stimuli, e.g., leward pointing arrow on the right side of the xation cross). Subjects had to
choose the direction (and neglect the position) of the arrows by pressing one of the two buttons with their le or
right index ngers.
Experimental design. Stimulus presentations were analogous in both tasks. Stimuli were presented for
1200 ms each, with an interstimulus duration of 800 ms. Sixteen stimuli were presented in each session (32 s),
with 8 congruent and 8 incongruent stimuli, presented pseudo randomly. Half of the stimuli required le-handed,
while the other half required right-handed responses within each session.
e experiment started with a 20 s baseline session with a xation cross in the center of the screen. e base-
line section was followed by the rst nonverbal Stroop-like task session that started with a 6 s instruction period
followed by the congruent and incongruent stimuli. en, a 20 s baseline session with xation cross was con-
ducted following by the verbal Stroop task (again, starting with 6 s instruction period). e verbal Stroop and
non-verbal Stroop-like task sessions were presented successively, always interleaved with 20 s baseline. e whole
experiment consisted of 5–5 repetitions with 10 baseline sessions, resulting in a total measurement time of 580 s
(Fig.2). Reaction times (RTs) and error rates (ERs) were recorded for each condition (verbal_congruent, ver-
bal_incongruent, non-verbal_congruent, non-verbal_incongruent).
Imaging data acquisition and visual analysis. All measurements were performed on a 3 T
Magnetom TIM Trio human whole-body MRI scanner (Siemens AG, Erlangen, Germany) with a 12-channel
head coil. Functional images were acquired using a 2D single-shot echo-planar imaging (EPI) sequence (TR/
TE = 2000/30 ms; Flip angle = 76°; 36 axial slices with a thickness of 3 mm; FOV = 192 × 192 mm2; matrix
size = 64 × 64; receiver bandwidth = 2170 Hz/pixel; no gap; interleaved slice order to avoid crosstalk between
contiguous slices). For distortion correction purposes, eld mapping sequence (TR/TE1/TE2 = 400/4.92/7.38 ms;
Flip angle = 60°; 36 axial slices; FOV = 228 × 228 mm2; matrix size = 76 × 76; receiver bandwidth = 259 Hz/pixel)
with the same voxel size, orientation and adjustment parameters as the fMRI scan was acquired right aer the
fMRI measurement. Anatomical images were obtained using an isotropic T1-weighted 3D-MPRAGE sequence
(TR/TI/TE: 2530/1100/3.37 ms; Flip angle = 7°; 176 sagittal slices with a thickness of 1 mm; FOV = 256 × 256
mm2; matrix size = 256 × 256; receiver bandwidth = 200 Hz/pixel). e MPRAGE anatomical images were vis-
ually checked by MRI experts. ere were no brain abnormalities according to the visual analysis of the MR
Functional MRI data processing and analysis. Pre-processing and statistical analyses were performed
using FEAT (FMRI Expert Analysis Tool) Version 6.00, part of FSL (FMRIB’s Soware Library, http://www.fmrib. Pre-processing included MCFLIRT motion correction, slice timing correction, brain extraction,
spatial smoothing with 5 mm full width at half maximum, and a high-pass temporal lter of 104 s (estimated
using FSLs cutocalc tool). e temporal ltering applied to the data was used for the model as well. Whole brain
general linear model (GLM) time-series statistical analyses of individual data sets were carried out using FILM
(FMRIB’s Improved Linear Model) with local autocorrelation correction.
Figure 2. Verbal Stroop and Non-verbal Stroop-like task: design and stimuli examples. e experiment started
with a baseline session. It was followed by task sessions that were presented successively. Subjects had to choose
the direction of the arrows (Stroop-like task) or ink color of the words (Stroop task).
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First level analysis included ve regressors: verbal (Stroop task) congruent, verbal incongruent, non-verbal
(Stroop-like) congruent and non-verbal incongruent conditions, and an additional regressor to model BOLD
changes induced by the task instructions.
The single-session data sets were registered into the MNI152 standard space using a two-step process.
First, functional (EPI) image of each subject was registered to that subject’s T1 structural scan using BBR (6
degrees-of-freedom) incorporating simultaneous B0 eld unwarping (i.e. distortion correction) with a combina-
tion of FUGUE and BBR tools35,36. en, each subject’s T1 image was registered to the 2 mm MNI152 standard
space T1 image using a 12 degrees-of-freedom linear t followed by nonlinear registration (FNIRT, warp resolu-
tion = 8 mm). Next, for each subject, these two registrations were combined and applied to the rst-level statisti-
cal maps to take them into standard space.
Second-level mixed-eects analyses were carried out using FLAME (FMRIB’s Local Analysis of Mixed Eects,
stage 1 and 2) with outlier de-weighting to investigate the following questions:
i. Deactivation pattern during high-demand incongruent condition (verbal and non-verbal separately).
ii. Correlation between PIUQ and BOLD signal change during high-demand incongruent condition (verbal
and non-verbal tasks separately).
iii. Activation pattern during the incongruent condition (verbal and non-verbal separately).
iv. Correlation of PIUQ with BOLD signal change between incongruent and congruent conditions (incongru-
ent_minus_congruent; verbal and non-verbal separately).
Statistical maps were considered to be signicant at Z > 2.3 and a family-wise error corrected cluster signi-
cance threshold of p = 0.05.
Brain areas were considered to be part of the DMN if the following assumptions were satised for the local
maximas (LMs): a) deactivation measured during incongruent condition and b) according to the Harvard-Oxford
Cortical Structural Atlas ( the LMs are in the areas related to the
DMN (incl. medial prefrontal, posterior cingulate cortex, precuneus or lateral parietal cortex)17.
Brain areas were considered to be part of the ICN if the following assumptions were satised for the LMs: a)
activation measured during incongruent_minus_congruent contrast indicating congruency eect or interference
eect; b) according to the Harvard-Oxford Cortical Structural Atlas the LMs are in the areas related to the ICN
(incl. inferior frontal and medial frontal gyri, the opercular, insular, orbitofrontal, posterior cingulate and poste-
rior parietal cortices)32.
Statistical analyses for behavioral data. Statistical analyses were performed using IBM SPSS Statistics
for Windows, Version 22.0 (IBM Corp. Released 2013. Armonk, NY, USA). Mean reaction time (RT) and error
rate (ER) dierences between congruent and incongruent stimuli were assessed by paired samples t-test or
Wilcoxon signed-rank test depending on the distribution of the data. Since PIUQ scores did not show normal
distribution, Spearman’s rank correlation was used to study the associations between RTs/ERs and PIUQ.
Behavioral results. e mean of the total score on the PIUQ for our sample is 32.85 (SD = 12.4, 95% CI:
Signicant dierences were found between congruent and incongruent stimuli in RTs and ERs regarding both
the verbal Stroop and the non-verbal Stroop-like task (Table1). No signicant correlations were found between
task performance scores (incl. reaction times – RT, error rates – ER and congruency eect [incongruent_minus_
congruent for RTs and ERs) and PIUQ total.
fMRI results. e correlations between PIUQ and BOLD fMRI results for incongruent and incongruent_
minus_congruent contrasts are presented in Table2 (see also Fig.3). During the incongruent stimuli in the verbal
Stroop task, signicant negative correlations were found in the bilateral posterior cingulate gyri and bilateral
Tas k Condition Mean (SD) or
Median (min-max) t/Z rho
Correlation with PIUQ
Verbal Stroop RTaCong. 0.87 (0.09) 4.96*** 0.077
Incong. 0.96 (0.17) 0.077
Verbal Stroop ERbCong. 1 (0–18) 5.80*** 0.01
Incong. 5 (0–18) 0.02
Non-verbal Stroop RTaCong. 0.65 (0.10) 8.43*** 0.11
Incong. 0.68 (0.10) 0.12
Non-verbal Stroop ERbCong. 0 (0–4) 4.16*** 0.15
Incong. 1 (0–5) 0.04
Table 1. Dierences in task performances between conditions and correlations with PIUQ. aPaired samples
t-test, mean (SD) and t values are presented; bWilcoxon signed-rank test, median (min-max) and Z scores are
presented; ***p < 0.001, RT – reaction time, ER – error rate, PIUQ – Problematic Internet Use Questionnaire,
rho – Spearman’s correlation coecient.
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precuneus. During the incongruent stimuli in non-verbal Stroop-like task, signicant negative correlations were
detected in the bilateral precuneus, the right middle frontal gyrus and right precentral gyrus. BOLD signal was
decreased with the severity of Internet addiction in both tasks.
Incongruent_minus_congruent contrast for Stroop task positively correlated with PIUQ in several le sided
cortical areas including the inferior frontal gyrus (pars opercularis), frontal pole, central and frontal opercular
cortex, frontal orbital cortex and insular cortex. e same contrast for non-verbal Stroop-like task showed no
signicant relationship with PIUQ.
Compared to the baseline, signicant deactivations were found in the areas related to the DMN during incon-
gruent stimuli in Stroop and Stroop-like tasks (see Fig.S1). Areas related to the ICN showed signicant activation
during incongruent stimuli in Stroop task (Fig.S2).
In this study, functional correlates of Internet addiction were demonstrated during verbal Stroop and non-verbal
Stroop-like task in young adult Internet users. Since the dierent task-related fMRI contrasts represent dierent
psychological and neural domains they will be discussed separately.
Incongruent stimuli induced BOLD signal changes were found to be negatively correlated with PIUQ in bilat-
eral precuneus and posterior cingulate gyri (PCG) during Stroop task and bilateral precuneus, during Stroop-like
task. Moreover, we found signicant deactivations in these areas during incongruent stimuli for both verbal
Stroop and non-verbal Stroop-like tasks. Brain areas in the cerebral cortex with constantly decreasing activity
during demanding tasks are considered being part of the DMN16. Previous studies investigating Internet addic-
tion and Internet gaming disorder (IGD) also found alterations in the DMN. However, these studies revealed
the involvement of the anterior part of the network and used merely resting-state fMRI. Li et al.28 demonstrated
decreased anticorrelation between the right dorsolateral prefrontal cortex and the anterior part of the DMN
(medial prefrontal cortex and anterior cingulate gyrus) that might lead to dysfunctions of the cognitive control
network and DMN, including diminished cognitive eciency. Wang et al. also revealed reduced functional con-
nectivity in the anterior part of the DMN, as well as decreased interactions between the salient network and DMN
in adolescents37. Authors claim that this may serve as neural background of the uncontrolled Internet use and
they also suggest that IA may share similar neurobiological abnormalities than other addictions. Similar impaired
connectivity patterns were found in IGD15. In addition, functional connectivity among DMN regions were in
negative correlation with anger and depression38, as well as with executive control dysfunction39. e results
of our study have signicant contribution to advance our knowledge on the eld. We are the rst to show that
task-related changes in activation of DMN-related structures are also signicantly related to IA. e highlighted
areas (precuneus and PCG) are parts of the posterior part of the DMN and previous studies revealed that the
middle frontal gyrus observed in the current study is also strongly related to the DMN40.
Impaired DMN may explain some comorbid symptoms that oen occur in IA, thereby it may have important
clinical implications. Firstly, many evidences indicate that DMN plays an important role in cognition, particu-
larly in attention and memory processes. Wide range of studies showed that vigilance41, semantic processing42,
Cluster Area #voxels Max.
MNI coordinates
x y z
Verbal Stroop task
Le posterior cingulate gyrusa506 4.09 0 42 34
Right precuneusa14 50 46
Right posterior cingulate gyrusa434 3.78 8 40 30
2. Le precuneusa458 10
Non-verbal Stroop-like task
1. Le precuneusa386 3.95 260 52
Right precuneusa868 50
2. Right middle frontal gyrusa378 4 40 8 38
Right precentral gyrusa42 2 42
Verbal Stroop task
Le inferior frontal gyrus pars opercularisb275 3.45 50 16 0
Le frontal poleb36 40 8
Le central opercular cortexb38 8 10
Le frontal opercular cortexb42 24 2
Le frontal orbital cortexb44 20 6
Le insular cortexb42 10 2
Non-verbal Stroop-like task n.s.
Table 2. Correlations between PIUQ and fMRI BOLD signal changes. aNegative correlation bpositive
correlation; n.s. not signicant; MNI coordinates are listed for the local maximas.
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episodic, autobiographical memory43 and creative problem solving44 are strongly associated with DMN activa-
tion, moreover, diminished vascular functionality for the DMN in mid-life may serve as a preclinical marker for
brain dysfunction in later life45. ese observations may implicate long term negative eect of IA on cognition
- similarly to those presented in nicotine addiction27, and suggest that DMN alterations serve as the common
neuro-functional background behind IA and attention-related decits46. Although, it must be note that IA is still
a relatively young phenomenon and more evidences are needed to prove this assumption. Secondly, according
to previous studies connectivity within DMN can be changed with pharmacotherapies via bottom-up and with
cognitive behavioral therapy (CBT) via top-down mechanisms in some psychiatric disorders (for review see47),
however, the exact mechanisms are unclear. Furthermore, individual variations in pre-treatment functional con-
nectivity may be a reliable predictor of treatment ecacy in schizophrenia48, major depression49 and smoking27.
It is important to note that these studies did not investigate the DMN as a holistic functional brain system, they
focused rather on its connection with other networks (executive control network)27, on a highlighted component
of the DMN (dorsolateral prefrontal cortex)48 and subnetworks of the DMN (anterior and posterior subnetworks;
abnormal functional connectivity within the posterior part was normalized aer antidepressant treatment, while
anterior subnetwork remained persistent against treatment)49. Since CBT seems to be successful methods for
improving IA status50, individual dierences in DMN might also predict treatment outcome and be reliable and
objective tool for assessing therapeutic ecacy. Further studies are needed to explore this.
In Stroop task the incongruent minus congruent contrast showed signicant positive correlation with PIUQ
in the le inferior frontal gyrus, le frontal pole, le central opercular, le frontal opercular, le frontal orbital
and le insular cortex. Activation in these areas were reported to be correlated with individual dierences in task
performances requiring good inhibition ability32,51. Congdon et al. used Stop-signal task-related probabilistic
ICA (independent component analysis) and referred two components that included similar regions found in
our study32. ey demonstrated that engagement of networks that include these regions is positively related to
response inhibition that is the ability to suppress a habitual response or routine behavior, including motor actions
and higher-order responses (thoughts, emotions etc.). erefore, these networks are essential to stop problem-
atic behaviors – such as excessive Internet use. Ergo, it is not surprising that Internet addiction was found to be
associated with impaired inhibition in previous studies30,52. Here we suggest that a special “inhibitory control
network (ICN)” that was found by Congdon and colleagues32 should be considered as a possible pathogenic
factor in the development of IA. However, signicant correlations were only found during the Stroop task as an
important unexpected result. e most plausible explanation is that the Stroop-like non-verbal task was not sen-
sitive enough to detect individual dierences due to the relatively simple nature of the task even in incongruent
condition. Despite there were signicant dierences between incongruent and congruent conditions in RT and
ER, incongruency eect did not occur in an expected way. Another possible explanation might be that the verbal
domain is altered in IA. is is supported by the presence of le hemisphere specialization in our study that is
known to be typical for language processing53. At this stage of knowledge, this explanation seems highly specula-
tive, and further studies are needed.
Some limitations must be considered. First, the cross-sectional nature of the study limits our ability to dis-
criminate between cause and eect. Does excessive Internet use lead to brain-related changes or vica versa?
Secondly, IA is known to have various comorbid symptoms such as depression, attention decit hyperactivity
disorder, anxiety disorders etc. Although, we excluded patients with diagnosed comorbid diseases, subjects with
subclinical symptoms may have inuenced our results. Another limitation is related to recruitment through
online platforms. e major problems with this method are that the representativeness of the sample cannot be
fully determined and problematic Internet users could be overrepresented in the sample.
Figure 3. Group level negative associations between PIUQ and BOLD signal changes during incongruent
condition in verbal Stroop task (A) and non-verbal Stroop-like task (B). Group level positive associations
between PIUQ and BOLD signal changes during incongruent_minus_congruent contrast in Stroop task (C).
Images were thresholded using clusters determined by Z > 2.3 and a corrected cluster signicance threshold of
p = 0.05. Axial slices are shown in radiological convention for MNI slice coordinates from Z = 8 to 48 mm in
(A), Z = 22 to 62 mm in (B) and Z = 16 to 24 mm in (C).
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SCIENTIFIC REPORTS | (2019) 9:15777 |
Taken together, our results suggest that problematic Internet use in healthy young adults has functional brain
correlates in regions that are related to the DMN. Since similar associations were found in IGD54, smoking18 and
other substance related addictions21 our results suggest long-term negative eects of IA on brain functions. As
described before, DMN is strongly related to human cognitive performance and some features of the DMN were
able to predict therapy outcomes in psychiatric disorders. erefore, we suggest, that altered DMN might explain
some comorbid symptoms (including attention decit) and might predict treatment outcomes (for further infor-
mation about Internet-related changes in human cognition see the review article of Loh & Kanai55). Although
we must highlight, that in the lack of well-designed, reliable researches, these assumptions are speculative at the
moment. Activation in the ICN was also found to be correlated with problematic Internet use suggesting that
diculties in stopping and controlling overuse are not results of “weak character of the person” but the impaired
brain mechanisms responsible for behavioral inhibition. However, it must be highlighted that, since we investi-
gated only healthy young people, our conclusions should be applied only to this group. Longitudinal studies and
studies with other age groups (e.g. adolescents) are highly needed to test if these ndings can be generalized to
other groups and whether these functional correlates are the result of overall additive tendencies or specic to
maladaptive Internet use. It would be also important to know that controlled Internet use or complete abstinence
could reduce these brain-related changes.
Received: 16 May 2019; Accepted: 11 October 2019;
Published: xx xx xxxx
1. im, . et al. Internet addiction in orean adolescents and its relation to depression and suicidal ideation: A questionnaire survey.
International Journal of Nursing Studies 43, 185–192 (2006).
2. Norett, L. Quantitative research. Nurs. Stand. 27, 59–59 (2013).
3. odgers, . F., Melioli, T., Laconi, S., Bui, E. & Chabrol, H. Internet Addiction Symptoms, Disordered Eating, and Body Image
Avoidance. Cyberpsychology, Behav. Soc. Netw. 16, 56–60 (2013).
4. Cheung, L. M. & Wong, W. S. e eects of insomnia and internet addiction on depression in Hong ong Chinese adolescents: An
exploratory cross-sectional analysis. J. Sleep Res. 20, 311–317 (2011).
5. Young, . S. Internet Addiction: e Emergence of a New Clinical Disorder. CyberPsychology Behav. 1, 237–244 (1998).
6. Mihajlov, M. & Vejmela, L. Internet addiction: A review of the rst twenty years. Psychiatr. Danub. 29, 260–272 (2017).
7.irály, O., Griffiths, M. D. & Demetrovics, Z. Internet Gaming Disorder and the DSM-5: Conceptualization, Debates, and
Controversies. Curr. Addict. Reports 2, 254–262 (2015).
8. eed, G. M. et al. Innovations and changes in the ICD-11 classication of mental, behavioural and neurodevelopmental disorders.
World Psychiatry 18, 3–19 (2019).
9. Montag, C. et al. Is it meaningful to distinguish between generalized and specic Internet addiction? Evidence from a cross-cultural
study from Germany, Sweden, Taiwan and China. Asia-Pacic. Psychiatry 7, 20–26 (2015).
10. Day, J. J., oitman, M. F., Wightman, . M. & Carelli, . M. Associative learning mediates dynamic shis in dopamine signaling in
the nucleus accumbens. Nat. Neurosci. 10, 1020–1028 (2007).
11. Altbäcer, A. et al. Problematic internet use is associated with structural alterations in the brain reward system in females. Brain
Imaging Behav. 10, 953–959 (2016).
12. ühn, S. & Gallinat, J. Brains online: Structural and functional correlates of habitual Internet use. Addict. Biol. 20, 415–422 (2015).
13. Demetrovics, Z., Szeredi, B. & ózsa, S. e three-factor model of Internet addiction: e development of the Problematic Internet
Use Questionnaire. Behav. Res. Methods 40, 563–574 (2008).
14. Steele, V. ., Ding, X. & oss, T. J. Addiction: Informing drug abuse interventions with brain networs. Connectomics 101–122, (2019).
15. Zhang, J. T. et al. Altered coupling of default-mode, executive-control and salience networs in Internet gaming disorder. Eur.
Psychiatry 45, 114–120 (2017).
16. Shulman, G. L. et al. Common Blood Flow Changes across Visual Tass: II. Decreases in Cerebral Cortex. J. Cogn. Neurosci. 9,
648–663 (1997).
17. aichle, M. E. e Brain’s Default Mode Networ. Annu. Rev. Neurosci. 38, 433–447 (2015).
18. Ding, X. & Lee, S.-W. Changes of Functional and Eective Connectivity in Smoing eplenishment on Deprived Heavy Smoers: A
esting-State fMI Study. PLoS One 8, e59331 (2013).
19. Zhang, . & Volow, N. D. Brain default-mode networ dysfunction in addiction. Neuroimage 200, 313–331 (2019).
20. Yuan, . et al. Core brain networs interactions and cognitive control in internet gaming disorder individuals in late adolescence/
early adulthood. Brain Struct. Funct. 221, 1427–1442 (2016).
21. Ma, N. et al. Abnormal brain default-mode networ functional connectivity in drug addicts. PLoS One 6, e16560 (2011).
22. Arcurio, L. ., Finn, P. . & James, T. W. Neural mechanisms of high-ris decisions-to-drin in alcohol-dependent women. Addict.
Biol. 20, 390–406 (2015).
23. Lin, F., Wu, G., Zhu, L. & Lei, H. Altered brain functional networs in heavy smoers. Addict. Biol. 20, 809–819 (2015).
24. Wetherill, . . et al. Cannabis, cigarettes, and their co-occurring use: Disentangling dierences in default mode networ functional
connectivity. Drug Alcohol Depend. 153, 116–123 (2015).
25. Weiland, B. J., Sabbineni, A., Calhoun, V. D., Welsh, . C. & Hutchison, . E. educed executive and default networ functional
connectivity in cigarette smoers. Hum. Brain Mapp. 36, 872–882 (2015).
26. Huan g, W. et al. The development and expression of physical nicotine dependence corresponds to structural and functional
alterations in the anterior cingulate-precuneus pathway. Brain Behav. 4, 408–417 (2014).
27. Cole, D. M. et al. Nicotine replacement in abstinent smoers improves cognitive withdrawal symptoms with modulation of resting
brain networ dynamics. Neuroimage 52, 590–599 (2010).
28. Li, W. et al. Brain structures and functional connectivity associated with individual dierences in Internet tendency in healthy young
adults. Neuropsychologia 70, 134–144 (2015).
29. Dong, G., Zhou, H. & Zhao, X. Male Internet addicts show impaired executive control ability: Evidence from a color-word Stroop
tas. Neurosci. Lett. 499, 114–118 (2011).
30. Dong, G., DeVito, E. E., Du, X. & Cui, Z. Impaired inhibitory control in ‘internet addiction disorder’: A functional magnetic
resonance imaging study. Psychiatry Res. Neuroimaging 203, 153–158 (2012).
31. de Wit, H. Impulsivity as a determinant and consequence of drug use: a review of underlying processes. Addict. Biol. 14, 22–31
32. Congdon, E. et al. Engagement of large-scale networs is related to individual dierences in inhibitory control. Neuroimage 53,
653–663 (2010).
Content courtesy of Springer Nature, terms of use apply. Rights reserved
SCIENTIFIC REPORTS | (2019) 9:15777 |
33. oronczai, B. et al. Conrmation of the ree-Factor Model of Problematic Internet Use on O-Line Adolescent and Adult Samples.
Cyberpsychology, Behav. Soc. Netw. 14, 657–664 (2011).
34. Nie, J., Zhang, W. & Liu, Y. Exploring depression, self-esteem and verbal uency with dierent degrees of internet addiction among
Chinese college students. Compr. Psychiatry 72, 114–120 (2017).
35. Greve, D. N. & Fischl, B. Accurate and robust brain image alignment using boundary-based registration. Neuroimage 48, 63–72
36. Jeninson, M., Bannister, P., Brady, M. & Smith, S. Improved Optimization for the obust and Accurate Linear egistration and
Motion Correction of Brain Images Improved Optimization for the obust and Accurate Linear egistration and Motion Correction
of Brain Images. Neuroimage 17, 825–841 (2002).
37. Wang, L. et al. Altered default mode, fronto-parietal and salience networs in adolescents with Internet addiction. Addict. Behav. 70,
1–6 (2017).
38. Dong, G., Li, H., Wang, L. & Potenza, M. N. e correlation between mood states and functional connectivity within the default
mode networ can dierentiate Internet gaming disorder from healthy controls. Prog. Neuro-Psychopharmacology Biol. Psychiatry
77, 185–193 (2017).
39. Wang, L. et al. Dysfunctional default mode networ and executive control networ in people with Internet gaming disorder:
Independent component analysis under a probability discounting tas. Eur. Psychiatry 34, 36–42 (2016).
40. Uddin, L. Q., elly, A. M., Biswal, B. B., Castellanos, F. X. & Milham, M. P. Functional connectivity of default mode networ
components: correlation, anticorrelation, and causality. Hum. Brain Mapp. 30, 625–37 (2009).
41. Hinds, O. et al. Computing moment-to-moment B OLD activation for real-time neurofeedbac. Neuroimage 54, 361–368 (2011).
42. Binder, J. . & Desai, . H. e neurobiology of semantic memory. Trends in Cognitive Sciences 15, 527–536 (2011).
43. Philippi, C. L., Tranel, D., Du, M. & udrauf, D. Damage to the default mode networ disrupts autobiographical memory retrieval.
Soc. Cogn. Aect. Neurosci. 10, 318–326 (2015).
44. ühn, S. et al. e Importance of the Default Mode Networ in Creativity-A Structural MI Study. J. Creat. Behav. 48, 152–163
45. Haight, T. J. et al. Vascular ris factors, cerebrovascular reactivity, and the default-mode brain networ. Neuroimage 115, 7–16
46. Yen, J.-Y., o, C.-H., Yen, C.-F., Wu, H.-Y. & Yang, M.-J. e Comorbid Psychiatric Symptoms of Internet Addiction: Attention
Decit and Hyperactivity Disorder (ADHD), Depression, So cial Phobia, and Hostility. J. Adolesc. Heal. 41, 93–98 (2007).
47. Weingarten, C. P. & Strauman, T. J. Neuroimaging for psychotherapy research: current trends. Psychother. Res. 25, 185–213
48. umari, V. et al. Dorsolateral Prefrontal Cortex Activity Predicts esponsiveness to Cognitive-Behavioral erapy in Schizophrenia.
Biol. Psychiatry 66, 594–602 (2009).
49. Li, B. et al. A treatment-resistant default mode subnetwor in major depression. Biol. Psychiatry 74, 48–54 (2013).
50. Winler, A., Dörsing, B., ief, W., Shen, Y. & Glombiewsi, J. A. Treatment of internet addiction: A meta-analysis. Clin. Psychol. Rev.
33, 317–329 (2013).
51. Goghari, V. M. & MacDonald, A. W. e neural basis of cognitive control: esponse selection and inhibition. Brain Cogn. 71, 72–83
52. Dong, G., Lu, Q., Zhou, H. & Zhao, X. Impulse inhibition in people with Internet addiction disorder: Electrophysiological evidence
from a Go/NoGo study. Neurosci. Lett. 485, 138–142 (2010).
53. Vigneau, M. et al. Meta-analyzing le hemisphere language areas: Phonology, semantics, and sentence processing. NeuroImage 30,
1414–1432 (2006).
54. Ding, W. N. et al. Altered Default Networ esting-State Functional Connectivity in Adolescents with Internet Gaming Addiction.
PLoS One 8, e59902 (2013).
55. Loh, . . & anai, . How Has the Internet eshaped Human Cognition? Neuroscientist 22, 506–520 (2016).
is paper was supported by the PTE ÁOK-KA-2017-05 and PTE ÁOK-KA-2017-06, Hungarian Brain Research
Program 20017-1.2.1-NKP 2017-00002 and KTIA_NAP_13-2-2014-0019 government-based funds, EFOP-
3.6.2-16-2017-00008. e role of neuro-inammation in neurodegeneration: from molecules to clinics, ÚNKP-
17-3 -I.-PTE-173, ÚNKP-17- 4 -I.- PTE-311, ÚNKP-17-4-I-PTE-76, ÚNKP-17-4-III-PTE-93, and ÚNKP-17-3-
III-PTE-315 New National Excellence Program of the Ministry of Human Capacities, N.K. and J.J. was supported
by the SNN125143 research grant, G.P. and G.O. was supported by the János Bolyai Research Scholarship of the
Hungarian Academy of Sciences. Z.D. was supported by the Hungarian National Research, Development and
Innovation Oce (Grant numbers: K111938, KKP126835).
Author contributions
G.D. was responsible for writing the manuscript and supervising the whole process. G.P., A.N.Z., I.O. and
G.O. were responsible for M.R.I. measurements and the statistical analysis. R.H., S.A.N., B.L. and D.T. were
responsible for the manuscript review and critique. N.K., T.D., Z.D. and J.J. contributed equally to the conception,
organization, and execution of the research project.
Competing interests
e authors declare no competing interests.
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Supplementary information is available for this paper at
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... Attention deficit, hyperactivity and impulsivity may also act as correlates of PIU-associated mental dysfunction. Recent evidence indicates that reduced cognitive efficiency and cognitive control is associated with attention problems in those addicted to the internet (Darnai et al., 2019). Self-control, self-regulation and stress reactivity mechanisms are assumed to be essential elements in initiating and maintaining addictive behaviors like PIU (Tang et al., 2015). ...
... This may affect the so-called default mode network in the brain that is responsible for the resting state. Research indicates its importance in attention-related deficits (Darnai et al., 2019). In addition, mindfulness meditation as a brain resting-state intervention seems to enhance the abilities of self-control and attention (Tang et al., 2015). ...
... The third component is the interoceptive component and would involve the insula acting as a mediator between the other two dynamic components by integrating an interoceptive signal and enhancing the impulsive system, disrupting the controlled response, thus emphasizing motivation towards the substance [8]. Thus, consistent with previous findings and emerging models of addiction, the brain systems identified in the present study may add evidence for SUD-related intrinsic connectivity changes in 1/ executive functioning sustained by the orbitofrontal cortex [54], the superior frontal cortex [55], and the central opercular cortex [56]; in 2/ automatic processes involving limbic areas such as the anterior cingulate cortex [57], the thalamus [58] and 3/ interoception mechanisms mediated by the insula [8,59], and the precuneus [60]. MSSD has been extensively used in emotion research as an index of instability (of mood or affect, for example) arising from a deficit of regulation, and hence less control over large shifts or fluctuations [61]. ...
... Nevertheless, problematic usage of the internet was independently associated with burnout in a multivariate analysis drawing our attention to the complexity of its background. Moreover, altered function of higher-order brain networks may explain this association as both volumetric changes and alteration of task-related activities can be detected in similar brain areas, but the association merits further investigation (2,3,(62)(63)(64)(65). ...
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Introduction The extensive availability of the internet has led to the recognition of problematic usage of the internet (PUI) or so called internet addiction (IA), probably mostly involving adolescents. Aim Here we present a study focusing on the incidence and consequences (including burnout, which is relatively rarely studied) of internet addiction among high school students using a questionnaire-based non-random sampling cross-sectional survey. Included questionnaires were the Problematic Internet Use Questionnaire, the Maslach Burnout Inventory General Survey for Students MBI-GS (S), the 9-item short version of Beck Depression Inventory (BDI-SF), the Athens Insomnia Questionnaire and the EQ-5D (quality of life) questionnaire. Data were evaluated the exertion of Student’s t -test, chi square test and Pearson’s rank-order correlation. Logistic regression analysis was used to determine the significance of the different parameters as independently associated with PUI. Results Overall 3,000 paper-based questionnaires were successfully delivered and 2,540 responses received (response rate of 84.6%). 1,309 males (mean age 17.6 ± 1.43 years) (51.5%) and 1,231 females (mean age 17.5 ± 1.4 years) (48.5%) took part in our study. Problematic usage of the internet was detected in 486 (19.1%) students (232 males, mean age 17.6 ± 1.35 years and 254 females, mean age 17.34 ± 1.37 years). In a logistic regression analysis sleep disturbance (OR: 1.84, 95% CI: 1.83–2.03), depression (OR: 1.97, 95% CI: 1.77–2.02) and burnout (OR: 1.8, 95% CI: 1.16–1.94) were significantly associated with PUI. Conclusion Nearly one fifth of our study population suffered from PUI, which was strongly associated with school burnout, insomnia and depression, which underlines the importance of this phenomenon.
... For instance, technologies that in the short-term hijack the dopamine system to bias information processing (e.g., attention), may in the long-term facilitate the establishment of technology addiction and degrade information processing capacity. Various addictions, including internet and smartphone addictions, are associated with structural and functional abnormalities in the brain [76][77][78]. While these abnormalities are commonly attributed to addiction susceptibility, relatively recent research suggests that the duration of internet addiction can cause atrophy and underdevelopment in neural structures relevant for cognitive processing [79,80]. ...
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... The functional connectivity between the dorsolateral prefrontal cortex and the posterior cingulate cortex of IGAs was significantly lower than that of healthy subjects, and the degree of reduction was worse than that of the Stroop task performance (48,49). Similar to heroin addiction, the decline in the functional connectivity of brain regions in the cognitive control circuit of IGAs will lead to their uncontrollable addictive behavior (50,51). ii. ...
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... In 2010, Barrett described internet pornography as an example of supranormal stimulus, due to the numerous artificial scenarios available to the consumer to choose from, seeking for new, more perfect content, thus greater reward, entering the "addictive mode" [60]. Also, similarities have been found in brain dysfunction between substance and pornography addiction [61]. ...
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A significant increase in pornography use has been reported in the adolescent population worldwide over the past few years, with intensification of the phenomenon during the COVID-19 pandemic. The aim of the present review is to provide data on the frequency of pornography consumption among adolescents during the pandemic and raise awareness about its potential impact on personal beliefs and sexual attitudes in the long term. A comprehensive literature review was performed in two scientific databases using the crossmatch of the terms “pornography”, “adolescents” and “COVID-19”. A significant increase in pornography consumption in adolescents was documented during the COVID-19 pandemic as a result of social detachment. Fulfilment of sexual desires in the context of social distancing, alleviation of COVID-19-related boredom and psychological strain, and coping with negative emotions are some of the reported reasons for increased pornography use during the pandemic. However, concerns have been raised in the literature regarding potentially negative effects of excessive pornography use from an early age, including the development of pornography addiction, sexual dissatisfaction and aggressive sexual attitudes reinforced by gender preoccupations and sexual inequality beliefs. Conclusion: The extent to which increased pornography consumption from an early age during the COVID-19 pandemic may have affected adolescents’ mental well-being, personality construction and sexual behaviour is yet to be seen. Vigilance from the society as a whole is required so that potential negative adverse effects of adolescent pornography use and potential social implications are recognized early and managed. Further research is needed so that the full impact of the COVID-19-related pornography use in the adolescent population is revealed.What is Known: •A significant increase in pornography consumption has been documented in the adolescent population worldwide over the past decades due to its quick, affordable and easy access from electronic devices and the possibility of anonymous and private participation. •During the COVID-19 pandemic, this phenomenon was intensified as a coping mechanism to social isolation and increased psychosocial strain. What is New: •Concerns have been raised regarding the risk of pornography addiction in adolescents during the COVID-19 pandemic, making the post-pandemic adaptation challenging. •Awareness is raised in parents, health care providers and policy makers about the potential negative impacts of pornography consumption from an early, vulnerable age, such as sexual dissatisfaction and development of aggressive sexual attitudes and sex inequality beliefs.
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The pandemic has had a huge impact on the mental health of the population, manifesting symptoms of anxiety, depression, panic, anguish, fear and reaction to stress. The educational field has been mostly affected, both by the changes produced in online classes and by the return to face-to-face classes, which has generated adaptation difficulties for teachers, students and parents. It is argued that the pandemic has affected the development of socio-emotional skills, generating a gap, which has affected aggressive behavior, lack of empathy, anxiety, depressive symptoms, conflict resolution difficulties, impulse regulation, and episodes of violence. school in the child and adolescent population. It is argued that the development of the brain in the child and adolescent population has been affected, both by confinement, psychosocial vulnerability and by increased exposure to the Internet and social networks. Finally, the challenges that the educational system must face to overcome this situation are addressed, which must include all the actors, projects or programs linked to culture, safety and promotion of school mental health.
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Aberrant patterns of brain functional connectivity in the default mode network (DMN) have been observed across different classes of substance use disorder (SUD) and are associated with craving and relapse. In addicted individuals resting functional connectivity (RSFC) of the anterior DMN, which participates in attribution of personal value and emotional regulation, tends to be decreased, whereas RSFC of the posterior DMN, which directs attention to the internal world, tends to be increased. Aberrant RSFC within the DMN is believed to contribute to impaired self-awareness, negative emotions and to ruminations in addiction. Additionally, the disrupted connectivity between DMN and cortical regions involved with executive function, memory and emotion could be critical to drug-taking regardless of negative consequences and to stress-triggered relapse. At the system level, the dynamics of DMN interactions with the executive control and the salience networks are also disturbed in addiction. The DMN is prominently engaged during the withdrawal and preoccupation phases of the addiction cycle at the expense of the executive control network and with an enhanced participation of the salience network. In contrast, DMN prominence appears to be transitorily decreased during the intoxication phases. There is also growing evidence that disruption of the DMN in addiction reflects in part changes in dopaminergic, glutamatergic, and GABAergic signaling associated with acute and chronic drug use. Findings are starting to reveal DMN RSFC as a potential biomarker for predicting clinical outcomes in SUD and identify the DMN as a promising target for the treatment of addiction.
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Following approval of the ICD‐11 by the World Health Assembly in May 2019, World Health Organization (WHO) member states will transition from the ICD‐10 to the ICD‐11, with reporting of health statistics based on the new system to begin on January 1, 2022. The WHO Department of Mental Health and Substance Abuse will publish Clinical Descriptions and Diagnostic Guidelines (CDDG) for ICD‐11 Mental, Behavioural and Neurodevelopmental Disorders following ICD‐11’s approval. The development of the ICD‐11 CDDG over the past decade, based on the principles of clinical utility and global applicability, has been the most broadly international, multilingual, multidisciplinary and participative revision process ever implemented for a classification of mental disorders. Innovations in the ICD‐11 include the provision of consistent and systematically characterized information, the adoption of a lifespan approach, and culture‐related guidance for each disorder. Dimensional approaches have been incorporated into the classification, particularly for personality disorders and primary psychotic disorders, in ways that are consistent with current evidence, are more compatible with recovery‐based approaches, eliminate artificial comorbidity, and more effectively capture changes over time. Here we describe major changes to the structure of the ICD‐11 classification of mental disorders as compared to the ICD‐10, and the development of two new ICD‐11 chapters relevant to mental health practice. We illustrate a set of new categories that have been added to the ICD‐11 and present the rationale for their inclusion. Finally, we provide a description of the important changes that have been made in each ICD‐11 disorder grouping. This information is intended to be useful for both clinicians and researchers in orienting themselves to the ICD‐11 and in preparing for implementation in their own professional contexts.
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Easy access to communication and information technologies has increased our dependence on technology for various aspects of our lives. Nevertheless, this remarkable growth of Internet Usage has been inextricably paired with a rise of excessive and dysfunctional Internet use. Conceptualized around 1996, a few years after the inception of the World Wide Web, Internet addiction has developed into a global issue influencing varying segments of the population at different levels. Despite heated debates on its addictive nature, consensus is emerging regarding the existence of this problematic behavior. In this paper we provide a comprehensive overview of the literature on Internet addiction in last 20 years. Purpose of this paper is to present crucial findings on Internet addiction to health profession. Besides numerous benefits of Internet use, the virtual environment brings various risks in every age group. The Internet is very significant in the everyday activities of children and youth and professional interventions with this age group should be specific considering their developmental characteristics. Exposure to online risks can have long-lasting and intense negative effects. Effective programs in prevention and treatment should include a multi-sectoral and interdisciplinary approach. Detail review of the symptomatology, diagnosis model an possibilities of treatment can be multiple beneficial to the health professionals and other helping professions due to actual needs for interventions in the field of the internet addiction treatment. Internet addiction is slowly becoming a societal concern as it particularly affects adolescents and children, who are more exposed and consequently more vulnerable. Findings presented in the paper can benefit in practice of treatment internet addiction and also as framework for further researches in the field.
Brain networks related to the chronic relapsing brain disease of addiction have been identified using preclinical and clinical models. The field has primarily identified reward processing, salience, executive control, and the default-mode network to be dysregulated in substance use disorders (SUDs). An imbalance between reward (stronger) and executive control (weaker) networks often characterizes addiction. Seed-based, independent component analysis, and graph theory methods are implemented to identify and analyze these networks. In this chapter, we describe what is known about the disease with regard to these networks with an eye toward informing interventions. Specifically, multimodal datasets could yield a new understanding of risk factors and potentially new targets for intervention. Noninvasive brain stimulation, with focal manipulation, could specifically target dysregulated networks associated with the disease. Overall, we believe a network-level understanding of the disease could facilitate the development of effective interventions necessary for a population suffering from a disease without effective treatments.
Background Recently, a triple-network model suggested the abnormal interactions between the executive-control network (ECN), default-mode network (DMN) and salience network (SN) are important characteristics of addiction, in which the SN plays a critical role in allocating attentional resources toward the ECN and DMN. Although increasing studies have reported dysfunctions in these brain networks in Internet gaming disorder (IGD), interactions between these networks, particularly in the context of the triple-network model, have not been investigated in IGD. Thus, we aimed to assess alterations in the inter-network interactions of these large-scale networks in IGD, and to associate the alterations with IGD-related behaviors. Methods DMN, ECN and SN were identified using group-level independent component analysis (gICA) in 39 individuals with IGD and 34 age and gender matched healthy controls (HCs). Then alterations in the SN-ECN and SN-DMN connectivity, as well as in the modulation of ECN versus DMN by SN, using a resource allocation index (RAI) developed and validated previously in nicotine addiction, were assessed. Further, associations between these altered network coupling and clinical assessments were also examined. Results Compared with HCs, IGD had significantly increased SN-DMN connectivity and decreased RAI in right hemisphere (rRAI), and the rRAI in IGD was negatively associated with their scores of craving. Conclusions These findings suggest that the deficient modulation of ECN versus DMN by SN might provide a mechanistic framework to better understand the neural basis of IGD and might provide novel evidence for the triple-network model in IGD.
The default-mode network (DMN) has been suggested to support a baseline state of brain activity. However, whether connectivity within the DMN is associated with mood states remains incompletely understood. The current study examined the correlation between mood state and the functional connectivity (FC) among DMN regions, and examined if the FC can differentiate Internet gaming disorder (IGD) from healthy controls (HC). Resting state data were collected within 108 college students (IGD,41; HC,67). Negative correlations were observed between measures of: (1) Depression and FCs among ventral DMN regions; (2) Anger and FCs among dorsal DMN regions; and, (3) Anger and Depression and FCs of both the ventral and dorsal DMN. The results suggest that negative mood states of Depression and Anger might reflect poorer, or might impair, FCs among DMN regions. In addition, the FC among DMNs could be useful indexes in differencing IGD from HC. Future studies should examine the extent to which the findings may extend to clinical populations and whether increased connectivity of DMN regions may represent a mechanism for reducing negative mood states.
Internet addiction (IA) is a condition characterized by loss of control over Internet use, leading to a variety of negative psychosocial consequences. Recent neuroimaging studies have begun to identify IA-related changes in specific brain regions and connections. However, whether and how the interactions within and between the large-scale brain networks are disrupted in individuals with IA remain largely unexplored. Using group independent component analysis, we extracted five intrinsic connectivity networks (ICNs) from the resting-state fMRI data of 26 adolescents with IA and 43 controls, including the anterior and posterior default mode network (DMN), left and right fronto-parietal network (FPN), and salience network (SN). We then examined the possible group differences in the functional connectivity within each ICN and between the ICNs. We found that, compared with controls, IA subjects showed: (1) reduced inter-hemispheric functional connectivity of the right FPN, whereas increased intra-hemispheric functional connectivity of the left FPN; (2) reduced functional connectivity in the dorsal medial prefrontal cortex (mPFC) of the anterior DMN; (3) reduced functional connectivity between the SN and anterior DMN. Our findings suggest that IA is associated with imbalanced interactions among the DMN, FPN and SN, which may serve as system-level neural underpinnings for the uncontrollable Internet-using behaviors.
Background: The aims of this study were to explore depression, self-esteem and verbal fluency functions among normal internet users, mild internet addictions and severe internet addictions. Methods: The survey sample consisted of 316 college students, and their internet addiction symptoms, depression and self-esteem symptoms were assessed using the Revised Chen Internet Addiction Scale (CIAS-R), Zung Self-Rating Depression Scale (ZSDS), Rosenberg Self-Esteem Scale (RSES), respectively. From this sample, 16 students with non-addictions, 19 students with mild internet addiction (sub-MIA) and 15 students with severe internet addiction (sub-SIA) were recruited and subjected to the classical verbal fluency tests, including the semantic and phonemic fluency task. Results: The results indicated that severe internet addiction in the survey sample showed the highest tendency towards depressive symptoms and lowest self-esteem scores, and sub-SIA showed poor performance on the semantic fluency task. Conclusion: In conclusion, severe internet addiction was significantly associated with depression, low self-esteem and semantic verbal fluency problems.
Background: The present study identified the neural mechanism of risky decision-making in Internet gaming disorder (IGD) under a probability discounting task. Methods: Independent component analysis was used on the functional magnetic resonance imaging data from 19 IGD subjects (22.2±3.08years) and 21 healthy controls (HC, 22.8±3.5years). Results: For the behavioral results, IGD subjects prefer the risky to the fixed options and showed shorter reaction time compared to HC. For the imaging results, the IGD subjects showed higher task-related activity in default mode network (DMN) and less engagement in the executive control network (ECN) than HC when making the risky decisions. Also, we found the activities of DMN correlate negatively with the reaction time and the ECN correlate positively with the probability discounting rates. Conclusions: The results suggest that people with IGD show altered modulation in DMN and deficit in executive control function, which might be the reason for why the IGD subjects continue to play online games despite the potential negative consequences.