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Minkyung Park Department of Psychiatry, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

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Joon Hwan Jang Department of Psychiatry, Seoul National University Health Service Center, Seoul, Republic of Korea
Department of Human Systems Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea

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So Young Yoo Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea

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Areum Choi Department of Psychiatry, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

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Heejung Kim Department of Nuclear Medicine, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea
Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, Republic of Korea

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Jung-Seok Choi Department of Psychiatry, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

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Abstract

Background and aims

Resting-state brain activity may be associated with the ability to perform tasks; however, a multimodal approach involving resting-state functional magnetic resonance imaging (fMRI) and event-related potentials (ERPs) has not been widely used to investigate addictive disorders.

Methods

We explored resting-state fMRI and auditory oddball ERP values from 26 with internet gaming disorder (IGD) patients and 27 age- and intelligence quotient-matched healthy controls (HCs). To assess the characteristics of resting-state fMRI, we calculated regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), and fractional amplitude of low-frequency fluctuation (fALFF); we also calculated the P3 component of the ERPs.

Results

Compared with HCs, the individuals with IGD exhibited significant decreases in ReHo and fALFF values in the left inferior occipital gyrus, increased ReHo and ALFF values in the right precuneus, increased ALFF in the left superior frontal gyrus, and lower P3 amplitudes in the midline centro-parietal area during the auditory ERP task. Furthermore, the regional activity of resting-state fMRI in the right inferior temporal gyrus and the occipital regions were positively correlated with the P3 amplitudes in IGD patients, whereas ReHo values of the left hippocampus and the right amygdala were negatively correlated with P3.

Discussion and conclusions

Our results suggest that IGD patients have difficulty interacting effectively with cognitive function and sensory processing, although its interpretations need some cautions. The findings in this study will broaden the overall understanding of the neurobiological mechanisms that underlie IGD pathophysiology.

Abstract

Background and aims

Resting-state brain activity may be associated with the ability to perform tasks; however, a multimodal approach involving resting-state functional magnetic resonance imaging (fMRI) and event-related potentials (ERPs) has not been widely used to investigate addictive disorders.

Methods

We explored resting-state fMRI and auditory oddball ERP values from 26 with internet gaming disorder (IGD) patients and 27 age- and intelligence quotient-matched healthy controls (HCs). To assess the characteristics of resting-state fMRI, we calculated regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), and fractional amplitude of low-frequency fluctuation (fALFF); we also calculated the P3 component of the ERPs.

Results

Compared with HCs, the individuals with IGD exhibited significant decreases in ReHo and fALFF values in the left inferior occipital gyrus, increased ReHo and ALFF values in the right precuneus, increased ALFF in the left superior frontal gyrus, and lower P3 amplitudes in the midline centro-parietal area during the auditory ERP task. Furthermore, the regional activity of resting-state fMRI in the right inferior temporal gyrus and the occipital regions were positively correlated with the P3 amplitudes in IGD patients, whereas ReHo values of the left hippocampus and the right amygdala were negatively correlated with P3.

Discussion and conclusions

Our results suggest that IGD patients have difficulty interacting effectively with cognitive function and sensory processing, although its interpretations need some cautions. The findings in this study will broaden the overall understanding of the neurobiological mechanisms that underlie IGD pathophysiology.

Introduction

A balance between top-down and bottom-up strategies is needed to maintain mental health and well-being. Internet gaming disorder (IGD) is a mental health condition defined by the loss of control over internet gaming and repeated use of internet games; IGD has been included as a gaming disorder in the 11th revision of the International Classification of Diseases (ICD-11). Although proper internet gaming activities can help to alleviate loneliness and stress, excessive gaming can have adverse consequences on mental health (Mestre-Bach, Granero, Fernández-Aranda, Jiménez-Murcia, & Potenza, 2023; Park et al., 2021).

Many neuroimaging and neurophysiology studies have investigated the features of human brain function. Advanced functional magnetic resonance imaging (fMRI) has high spatial resolution but low time resolution (i.e., hundreds of milliseconds). EEG has excellent time resolution (i.e., milliseconds) but its spatial resolution is limited because of head volume conduction (Nunez et al., 1997). Therefore, fMRI and EEG are complementary neuroimaging techniques. The integration of fMRI and EEG may be useful for exploring neural correlates between fMRI and EEG features; it may also help to integrate neural brain activities from different perspectives.

Resting-state fMRI studies have revealed alterations in various aspects of brain function among patients with IGD. Regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), and fractional amplitude of low-frequency fluctuation (fALFF) have been used to investigate the regional characteristics of resting-state fMRI signals (Zou et al., 2008). ReHo assumes that the regional blood oxygen level-dependent (BOLD) signal within a particular voxel is temporally similar to the signals of its neighbors and could be modulated in related cognitive tasks (Zang, Jiang, Lu, He, & Tian, 2004). ALFF and fALFF are used to characterize the regional intensity of brain activity by indexing low-frequency fluctuations in spontaneous neuronal activity (Yu-Feng et al., 2007). ALFF is sensitive to physiological noise but may also be sensitive to pathological changes. fALFF is a mathematical method for suppressing physiological noise (Zou et al., 2008). Therefore, analyses using ReHo, ALFF, and fALFF provide a more comprehensive understanding of the neural bases of the brain during the exploration of regional activity in resting-state fMRI. Abnormalities in ReHo, ALFF, and fALFF may be associated with dysfunction in regional brain activity and have been identified in patients with various neurological and psychiatric disorders (Han et al., 2011; Hoptman et al., 2010).

Previous studies have shown that IGD patients exhibit enhanced ReHo in brain regions involved in sensory-motor coordination and decreased ReHo in brain regions responsible for visual and auditory functions (Dong, Huang, & Du, 2012). Compared with healthy controls (HCs), IGD patients have increased ReHo values in the posterior cingulate cortex; moreover, changes in ReHo in the precuneus/posterior cingulate cortex are associated with the severity of IGD symptoms in the individuals with IGD (Kim et al., 2015). Changes in ALFF have been reported in the left orbitofrontal cortex, the left precuneus, and the left supplementary motor cortex in IGD patients (Yuan, Jin, et al., 2013). fALFF values in the posterior lobe of the cerebellum and superior temporal gyrus change in patients with IGD, which may have effects on executive functioning and movement flexibility (Lin, Jia, Zang, & Dong, 2015).

Multiple EEG studies have been published regarding IGD (Park et al., 2021). An event-related potential (ERP) study showed that the individuals with IGD had reduced P3 amplitude of the ERP at midline centro-parietal sites compared with HCs, which reflected cognitive deficiencies in patients with IGD (M. Park et al., 2016). The P3 component of ERPs derived from the EEG signal has been widely used to investigate brain functions and is regarded as an index of various cognitive processes (e.g., executive functioning, attention, and working memory) (Polich & Herbst, 2000). Some human lesion studies reported that the frontal, parietal, and temporal regions are associated with the P3 components (Bledowski et al., 2004; Knight, Scabini, Woods, & Clayworth, 1989; Kok, 2001). Many studies have revealed that P3 generators are involved in multiple neural circuits in the temporal, parietal, and precuneus brain regions (Mulert et al., 2004; Soltani & Knight, 2000; Volpe et al., 2007), although the exact neural origins of the P3 are not clearly known (O'Connell et al., 2012; Polich, 2007).

In healthy people at rest, the fMRI BOLD signal is associated with fluctuations of EEG power in various frequency bands (Mantini, Perrucci, Del Gratta, Romani, & Corbetta, 2007; Scheeringa et al., 2008). Simultaneous EEG-fMRI studies have shown that healthy people at rest have positive or negative associations between the fMRI BOLD signals and alpha power activity in the DMN region and the occipital cortex (Goldman, Stern, Engel, & Cohen, 2002; Jann et al., 2009).

Many studies have used either fMRI or EEG techniques to investigate IGD, but few multimodal studies have been conducted with a focus on regional activity in resting-state fMRI and ERPs. Thus, there is a need to examine whether the regional activity in resting-state fMRI is associated with ERP components; this information will help to characterize the neural relationships that underlie IGD. In this study, we used the ReHo, ALFF, and fALFF values of resting-state fMRI, as well as auditory oddball ERPs, to investigate neural and neurophysiological features of IGD, along with the neural correlates of those features. We hypothesized that patients with IGD would have abnormalities in resting-state fMRI and ERP features in brain regions associated with cognitive functioning and sensory processing; we presumed that these dysfunctional features would have neural correlates.

Materials and methods

Participants and clinical assessments

Twenty-six male individuals with IGD and 27 age- and intelligence quotient-matched male HCs were enrolled in this study. All individuals (aged 18 and 39 years) with IGD had presented to an outpatient clinic of SMG-SNU Boramae Medical Center, Seoul, South Korea, for the treatment of excessive and problematic internet gaming activities. The participants were interviewed by an experienced psychiatrist for the diagnosis of IGD and comorbid psychiatric disorders based on criteria in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). IGD severity was assessed using Young's Internet Addiction Test (Young, 1996). All participants were medication-naive at the time of the assessment. None of the individuals with IGD had comorbid psychiatric diagnoses. Individuals with IGD engaged in internet gaming for >4 h per day and >30 h per week; HCs engaged in internet gaming for <2 h per day. For all participants, the exclusion criteria were a history of head injury, neurological disease, psychiatric or psychotic disorder, and an estimated intelligence quotient score <80 according to the Korean version of the Wechsler Adult Intelligence Scale.

Resting-state fMRI acquisition

fMRI data were acquired using a Philips Achieva 3T MRI scanner (Philips Medical Systems, Best, Netherlands) with a standard whole-head coil. Functional data were obtained using a T2*-weighted echo-planar image sequence (slice thickness = 4 mm, in-plane dimension = 144 × 144, 35 slices, field of view = 220 × 220 mm, voxel size = 1.53 × 1.53 × 4 mm, repetition time = 2,700 ms, echo time = 35 ms, and flip angle = 90°; 180 volumes). Participants were asked to rest with their eyes open, to remain awake, and to not think about anything specific during the fMRI scan. The anatomical images were acquired using a T1-weighted spoiled gradient-recalled three-dimensional MRI sequence (repetition time = 9.9 ms, echo time = 4.6 ms, slice thickness = 1.0 mm, flip angle = 8°, field of view = 220 × 220 mm, and voxel size = 1 × 1 × 1 mm; 224 slices).

Resting-state fMRI preprocessing

Functional resting images were preprocessed using the Data Processing Assistant for Resting-State fMRI (http://www.restfmri.net, version 5.0) (Chao-Gan & Yu-Feng, 2010), implemented in MATLAB (MathWorks, Natick, MA, USA). The first 10 functional images were discarded for signal equilibration. The remaining 170 volumes were subjected to slice timing correction and realignment via six-parameter rigid-body spatial transformation. Data from individuals with an estimated head motion >2.0 mm of maximal rotation and 2.0° of any angular motion during the scan were excluded from further analysis. All data were spatially normalized to the Montreal Neurological Institute space and resampled in an isotropic voxel size of 3 mm. ReHo, ALFF, and fALFF values were calculated from resting-state fMRI data. Data from 3 IGD group and 2 HCs were excluded from the resting-state fMRI and EEG analysis in accordance with the above criteria.

Calculation of ReHo

The Friston 24 motion parameters, white matter, and cerebrospinal fluid were regressed as nuisance variables from the normalized BOLD signal before calculation of ReHo. To correct for excessive head motion, scrubbing procedures were used for volumes with a framewise displacement value >0.5. This involves scrubbing the time points one frame before and two frames after the bad point. We used the method of using each bad point as a separate regressor. Images were detrended to remove linear trends and temporally band-pass filtered (0.01–0.1 Hz) to reduce high-frequency physiological noise. ReHo maps were calculated with Kendall's coefficient of concordance. Each voxel was calculated with its 26-voxel neighborhood (Zang et al., 2004). For standardization, individual ReHo maps were divided by the global mean ReHo value within the whole brain mask.

Calculation of ALFF and fALFF

Spatial smoothing of normalized data was performed with a 4-mm full-width-half-maximum Gaussian kernel before calculation of ALFF and fALFF. The Friston 24 motion parameters, white matter, and cerebrospinal fluid were regressed out as nuisance variables from the smoothed images. Scrubbing procedures were performed. Finally, linear trends were removed during preprocessing for ALFF and fALFF, and then band-pass filtering (0.01–0.1 Hz) was performed during preprocessing. The time series for each voxel was transformed into the frequency domain using a fast Fourier transform method, and the mean square root of the power spectrum was obtained for the ALFF calculation. The fALFF value was calculated as the ratio of the amplitude within the low frequency (0.01–0.1 Hz) to the amplitude of the entire frequency range.

Auditory oddball task and EEG

EEG was conducted in a dimly lit, electrically shielded room. In accordance with the modified International 10–20 system, EEG signals were recorded using 64 Ag/AgCl electrodes (NeuroScan SynAmps2; Compumedics USA, El Paso, TX, USA) with a sampling rate of 1,000 Hz. Reference electrodes were linked to the mastoids, and the ground electrode was located between the FPz and Fz electrode sites. Horizontal electrooculogram signals were recorded with electrodes on the left and right eyes, whereas vertical electrooculogram electrodes were attached above and below the left eye. Impedances were set at < 5 kΩ. Participants were seated in a comfortable chair, then instructed to relax and avoid body movements throughout the experiment. The auditory oddball task required participants to press a response button for infrequently presented deviant stimuli (15%) and demonstrate a withdrawal response for standard stimuli (85%). The task consisted of three blocks of 100 trials each. In total, 300 stimuli comprised deviant stimuli (high-frequency tone of 2,000 Hz) and standard stimuli (low-frequency tone of 1,000 Hz) at 85 dB SPL, with 10 ms rise and fall times; the stimuli were presented binaurally through earphones in a pseudorandomized order, using a STIM 2 sound generator (Compumedics). The duration of each stimulus was 100 ms, and each intertrial interval was 1,250 ms. Response speed and accuracy were equally emphasized.

ERP analysis

Electrophysiological signals were preprocessed using Curry 7 software. The electrophysiological data were re-referenced to a common mean reference and band-pass filtered between 0.1 and 30 Hz. The data were visually inspected by an expert who detected and excluded gloss artifacts, such as movement. Eye blinks and eye movements were reduced based on a regression method implemented in Curry 7 (Semlitsch, Anderer, Schuster, & Presslich, 1986). Continuous EEG data were epoched from 100 ms before the stimulus to 900 ms after the stimulus. Baseline correction was conducted using the mean amplitude before stimulus onset. Single-trial epochs from any channel with voltages > ± 70 μV were automatically excluded from further analysis. Only artifact-free epochs and correct responses to infrequent stimuli at midline centro-parietal sites (CPz and Pz) were averaged and analyzed. The P3 component was defined as the largest positive deflection in the time range from 248 to 500 ms after stimulus onset.

Statistical analysis

Demographic and clinical characteristics, as well as behavioral data, were compared between groups using an independent two-sample t-test.

Statistical analyses for the resting-stage fMRI ReHo, ALFF, and fALFF maps were performed in SPM12 (http://www.fil.ion.ucl.ac.kr/spm). To evaluate between-group differences on the ReHo, ALFF, and fALFF maps, two-sample t-tests were performed in a voxel-by-voxel manner. Age was included as a nuisance variable to remove the residual effect. Regression analysis was conducted after controlling for age to determine associations of the ReHo, ALFF, and fALFF maps with P3 amplitudes from the oddball task. We had further performed regression analyzes to determine the association of ReHo, ALFF, and fALFF values with Beck Depression Inventory (BDI) or Beck Anxiety Inventory (BAI) scores in IGD individuals. In fMRI analysis, clinical features such as BDI or BAI are not considered nuisance variables in our study. Since clinical features such as BDI and BAI can be characteristics in the individuals with IGD, examining the brain functions related to the clinical features rather than controlling the characteristics themselves can determine the difference between the individuals with IGD and the normal group. An uncorrected p-value <0.001 with a minimum cluster size of 10 contiguous voxels was considered statistically significant.

We performed correlation analysis to determine the associations of IGD symptom severity with the region of interest in the ReHo, ALFF, and fALFF maps showing significant group differences of fMRI, using Pearson's correlation analysis with SPSS software, ver. 25.0 (SPSS Inc., Chicago, IL, USA).

In the auditory oddball ERP task, repeated-measures analysis of variance was performed for the amplitudes and latencies of the P3 component, with the electrode site as the within-subject factor and group as the between-subject factor. Additionally, Pearson's correlation analysis was conducted to identify relationships between P3 components with group differences and IGD severity scores in individuals with IGD. All statistical analyses were two-tailed, and P-values <0.05 were considered statistically significant.

Ethics

The institutional review board of SMG- SNU Boramae Medical Center approved the study protocol, and the study followed the guidelines of the Declaration of Helsinki. All participants understood the study procedure and provided written informed consent.

Results

Demographic data

The demographic and clinical characteristics of the participants are presented in Table 1. The two groups did not differ in terms of age or intelligence quotient. However, they significantly differed in terms of the duration of education, as well as the Young's Internet Addiction Test, BDI, and BAI scores.

Table 1.

Demographic characteristics, clinical characteristics, and behavioral data of study participants

Healthy control (n = 25)Internet gaming disorder (n = 23)tp
Mean (SD)Mean (SD)
Age (year)25.04 (3.86)23.13 (5.21)1.450.15
IQ118.28 (9.56)118.52 (11.87)−0.080.94
Young's Internet Addiction Test (IAT)27.83 (8.29)72.22 (12.76)−14.20<0.001***
BDI3.17 (3.29)19.74 (9.96)−7.59<0.001***
BAI4.83 (5.27)16.78 (14.16)−3.800.001**
Accuracy (%)98.49 (2.70)97.98 (4.11)0.510.98
Reaction Time (ms)351.80 (43.43)351.53 (39.16)0.020.98

Abbreviations: ** indicates p < 0.01; *** indicates p < 0.001; SD, standard deviation; IQ, intelligence quotient; BDI, Beck Depression Inventory; BAI, Beck Anxiety Inventory.

ERP and behavioral results

Analysis of P3 amplitudes revealed a significant main effect of group [F (1, 43) = 5.242, p = 0.027, ηp2 = 0.109]. No main effect of electrode site was observed, and no interaction with P3 amplitudes was detected. Compared with HCs, individuals with IGD had significantly lower P3 amplitudes at CPz [F (1, 43) = 5.823, p = 0.020, ηp2 = 0.119], but not at Pz (Fig. 1). No main effect of electrode site and no interaction with P3 latency was observed. The average number of trials for the P3 component is 35.74 trials, and no group difference was found in the number of trials for the P3 component (t = 0.656, p = 0.515, HC; mean = 36.14, sd = 4.04 vs. IGD: mean = 35.33 sd = 3.98). No differences in accuracy or reaction times on the auditory oddball task were observed between the two groups.

Fig. 1.
Fig. 1.

Group differences in regional activity of resting-state fMRI (ReHo, ALFF, and fALFF) and P3 amplitudes of auditory oddball ERP between patients with Internet gaming disorder (IGD) and healthy controls (HCs). Brain regions with significant changes in regional fMRI between patients with IGD and HCs. IGD patients showed increased resting-state fMRI activities in warm-colored brain regions and decreased activities in cool-colored brain regions. Compared with HCs, the IGD group had significantly increased ReHo (A) and ALFF (B) values in the right precuneus, and increased fALFF (C) in the left superior frontal gyrus. The IGD group exhibited significant increases of ReHo (D) and fALFF (E) values in the left inferior occipital gyrus. (F) Grand-average ERP waveforms at the midline centro-parietal area and topographic maps indicating scalp distribution of P3 amplitudes in two groups

*Abbreviations: ReHo, regional homogeneity; ALFF, amplitude of low-frequency fluctuation; fALFF, fractional amplitude of low-frequency fluctuation; ERPs, event related potentials; CPz, midline centro-parietal.

Citation: Journal of Behavioral Addictions 12, 4; 10.1556/2006.2023.00063

Group differences in regional activity of fMRI (ReHo, ALFF, and fALFF) maps

Group differences in the ReHo, ALFF, and fALFF values are shown in Fig. 1 and Table 2. Compared with HCs, a significant decrease in ReHo was detected in the left inferior occipital gyrus (IOG)/occipital fusiform gyrus in individuals with IGD, whereas an increase in the right precuneus was observed. Individuals with IGD exhibited significant increases in ALFF in the left superior frontal gyrus (SFG) and right precuneus, compared with HCs. Additionally, individuals with IGD had significantly lower fALFF in the left IOG, compared with HCs.

Table 2.

Brain regions with significant differences in regional fMRI (ReHo, ALFF, and fALFF) between patients with internet gaming disorder (IGD) and healthy controls (HC)

ConditionRegionPeak MNI coordinatesT-valuesvoxels
xyz
IGD > HC in ReHoR. precuneus15−63364.0915
IGD > HC in ALFFR. precuneus15−63394.0210
IGD > HC in ALFFL. superior frontal gyrus−6−6724.0712
HC > IGD in ReHoL. inferior occipital gyrus−27−93−94.1221
HC > IGD in fALFFL. inferior occipital gyrus−24−96−34.4111

Abbreviations: ReHo, regional homogeneity; ALFF, amplitude of low-frequency fluctuation; fALFF, fractional amplitude of low-frequency fluctuation; MNI, Montreal Neurological Institute (p < 0.001, uncorrected, cluster threshold ≥10).

Correlations between P3 amplitudes and regional activity of fMRI (ReHo, ALFF, and fALFF) maps

The relationships of P3 amplitudes with regional activity in the ReHo, ALFF, and fALFF maps are shown in Fig. 2 and Table 3. In the IGD group, significant positive correlations were found between ReHo and P3 amplitudes at CPz in the right inferior temporal gyrus (ITG), right medial orbital gyrus, right lingual gyrus, right calcarine cortex, and right superior occipital gyrus; negative correlations were found in the left hippocampus and right amygdala. A significant positive correlation was observed between ALFF and P3 amplitudes in the right calcarine cortex/lingual gyrus in the IGD group, whereas a positive correlation was detected in the left postcentral gyrus/precentral gyrus in HCs. Additionally, a positive correlation was observed between fALFF and P3 amplitudes in the right lingual gyrus/calcarine cortex in the IGD group.

Fig. 2.
Fig. 2.

Brain regions showing correlations between regional activity of resting-state fMRI (ReHo, ALFF, and fALFF) and P3 amplitudes of auditory oddball ERP in patients with Internet gaming disorder (IGD) and those scatter plots. ReHo, ALFF, and fALFF maps of resting-state fMRI were correlated with P3 amplitudes of ERPs in the IGD group. A Warm-colored brain regions and scatter plots display mean positive correlations between ReHo and P3 amplitudes at the midline centro-parietal (CPz) area of auditory oddball ERP in IGD. B Cool-colored brain regions and scatter plots display mean negative correlations between ReHo and P3 amplitudes at CPz in IGD. C Warm-colored brain regions and scatter plots display mean positive correlations between ALFF and P3 amplitudes at CPz in IGD. D Warm-colored brain regions and scatter plots display mean positive correlations between fALFF and P3 amplitudes at CPz in IGD

*Abbreviations: ReHo, regional homogeneity; ALFF, amplitude of low-frequency fluctuation; fALFF, fractional amplitude of low-frequency fluctuation; ERPs, event related potentials; ITG, inferior temporal gyrus; mOFC, medial orbitofrontal cortex; LING, lingual gyrus; CAL, calcarine cortex; SOG, superior occipital gyrus.

Citation: Journal of Behavioral Addictions 12, 4; 10.1556/2006.2023.00063

Table 3.

Associations of resting-state fMRI (ReHo, ALFF, and fALFF) with P3 amplitudes

ConditionRegionPeak MNI coordinatesT-valuesvoxels
xyz
Positive correlation in IGD
ReHoR. inferior temporal gyrus48−51−186.3019
ReHoR. medial orbital gyrus1245−275.1024
ReHoR. lingual gyrus18−72−34.9422
ReHoR. calcarine cortex6−9304.2711
ReHoR. superior occipital gyrus27−84154.2610
ALFFR. calcarine cortex6−7265.9125
fALFFR. lingual gyrus6−6334.8827
Negative correlation in IGD
ReHoL. Hippocampus−27−4236.3014
ReHoR. Amygdala18−3−125.2435

Abbreviations: ReHo, regional homogeneity; ALFF, amplitude of low-frequency fluctuation; fALFF, fractional amplitude of low-frequency fluctuation; MNI, Montreal Neurological Institute; IGD, internet gaming disorder; R, right; L, left (p < 0.001, uncorrected, cluster threshold ≥10).

Correlations between IGD symptom severity and ROI values of regional activity of fMRI (ReHo, ALFF, and fALFF) or P3 amplitudes in individuals with IGD

We found no significant correlations between IGD symptom severity and regional activity of fMRI (ReHo, ALFF, and fALFF) or P3 amplitudes in individuals with IGD. ROI values in the precuneus of ReHo (r = 0.140, p = 0.524) and ALFF (r = 0.087, p = 0.693) were not associated with IGD symptom severity in individuals with IGD. ROI values in the left SFG of ALFF (r = - 0.087, p = 0.694) or left IOG of ReHo (r = - 0.349, p = 0.103) and fALFF (r = - 0.080, p = 0.716) were not associated with IGD symptom severity in individuals with IGD.

Correlations between BDI or Bai scores and regional activity of fMRI (ReHo, ALFF, and fALFF) maps

We found that BDI scores were negatively associated with ReHo and fALFF values of the occipital area, and BAI scores were also negatively associated with fALFF of the occipital area (shown in Table 4).

Table 4.

Correlations between regional activity of fMRI and depression or anxiety scores in individuals with IGD

ConditionRegionPeak MNI coordinatesT-valuesvoxels
xyz
Negative correlation between fMRI and BDI
ReHoL. calcarine cortex−15−78154.74113
ReHoR. inferior occipital gyrus51−75−64.3713
ALFFL. cuneus−3−84245.7535
fALFFL. superior occipital gyrus−18−81155.7531
fALFFL. calcarine cortex0−10204.8016
fALFFR. calcarine cortex15−6694.7912
Negative correlation between fMRI and BAI
ReHoR. middle cingulate gyrus6−16454.9910
fALFFR. inferior occipital gyrus27−9335.2610
fALFFR. lingual gyrus18−9335.0213

Abbreviations: ReHo, regional homogeneity; ALFF, amplitude of low-frequency fluctuation; fALFF, fractional amplitude of low-frequency fluctuation; MNI, Montreal Neurological Institute; IGD, internet gaming disorder; R, right; L, left (p < 0.001, uncorrected, cluster threshold ≥10).

Discussion

This study was performed to investigate neural features and neural correlates between regional activity of resting-state fMRI (ReHo, ALFF, and fALFF) and auditory oddball ERPs in individuals with IGD. To our knowledge, this is the first study to utilize multimodal technologies that combined regional neural activity in resting-state fMRI and ERPs to provide insights into the neural correlates that underlie IGD. We demonstrated that individuals with IGD had altered regional activity of resting-state fMRI and reduced P3 amplitudes of ERPs. Additionally, we detected a correlation between the spontaneous regional activity and P3 amplitudes of ERPs at CPz in the IGD group.

Analyses of regional activity in resting-state fMRI revealed that individuals with IGD had higher ReHo and ALFF values in the right precuneus and increased ALFF in the left SFG, compared with HCs. In contrast, the individuals with exhibited lower ReHo and fALFF values in the left IOG, compared with HCs. ALFF and fALFF both analyze low-frequency fluctuations in the BOLD signal, but ALFF is sensitive to local neural activity, while fALFF, a normalized version of ALFF, is considered to provide a physiologically-adjusted measure of regional brain activity, reducing potential confounds caused by global signal fluctuations. Although some group differences were found in ALFF and fALFF values, the brain regions where group differences were found in ReHo values were also found in the same area ALFF and fALFF values, respectively. These results are consistent with the findings in previous studies (Dong et al., 2012; Kim et al., 2015). The precuneus plays a key role in the DMN and is associated with various cognitive functions (Cavanna & Trimble, 2006; Fransson & Marrelec, 2008; Utevsky, Smith, & Huettel, 2014). An ERP study demonstrated activation of the precuneus in deviant P3 compared with standard P3, suggesting an increase in attentional demands from top-down control (Justen & Herbert, 2018). Adolescents with IGD have higher ALFF values in the precuneus and orbitofrontal cortex (OFC) (Yuan, Jin, et al., 2013), and a positive correlation has been observed between ReHo in the precuneus and IGD symptom severity in patients with IGD (H. Kim et al., 2015). A structural study showed that adolescents with IGD develop increased cortical thickness in the precuneus and temporal cortex, as well as decreased cortical thickness in the OFC and lingual gyrus (Yuan, Cheng, et al., 2013). A recent study of patients with IGD suggested that the precuneus serves as a hub for integrating information between executive control and subcortical cravings (Dong et al., 2020).

The SFG is involved in top-down cognitive control of behavior, including decision-making, selective attention, inhibitory control, cravings, and motor control (Corbetta & Shulman, 2002; Hopfinger, Buonocore, & Mangun, 2000). Patients with IGD have aberrant functional activity and connectivity in the SFG (Wang et al., 2015). Adolescents with IGD reportedly exhibit greater response inhibition activity in the SFG, while SFG activation is correlated with symptom severity and impulsivity scores; thus, adolescents with IGD may exhibit decreased response inhibition and prefrontal cortex dysfunction (Ding et al., 2014).

The lower ReHo and fALFF values in the left IOG suggest that individuals with IGD have decreased synchronization and intensity of regional activity in occipital areas, compared with HCs. The occipital lobe is a key visual processing area. There are also reports that the visual cortex is activated by auditory attention (Cate et al., 2009; McDonald, Stormer, Martinez, Feng, & Hillyard, 2013). Individuals with IGD had lower regional activity in the occipital region; this finding may have been affected by excessive exposure to auditory and visual stimuli over a long period. These findings are consistent with the results of a previous ReHo study, in which IGD patients exhibited lower ReHo values in temporal, occipital, and parietal regions (Dong et al., 2012).

The higher ReHo and ALFF values in the right precuneus and left SFG, and the lower ReHo and fALFF values in the left IOG, represent changes in regional synchronization and intensity in these regions in individuals with IGD; such changes suggest impairments in the DMN and cognitive function, as well as dysfunctional sensory processing. These results also suggest that individuals with IGD are likely to have an imbalance between top-down cognitive control and bottom-up sensory processing.

Compared with HCs, individuals with IGD had lower P3 amplitudes at CPz area on the auditory oddball task, which is regarded as an index of cognitive resources (Park et al., 2016); this finding indicated that patients with IGD had cognitive and sensory dysfunction.

The present study focused on the relationship between regional neural activity on resting-state fMRI and ERPs during a cognitive task in individuals with IGD. The regional activity of resting-state fMRI in the right ITG, OFC, and occipital cortex (e.g., right lingual gyrus, calcarine cortex, and superior occipital gyrus) was positively correlated with the P3 amplitudes of ERPs in individuals with IGD. Additionally, ReHo activities in the left hippocampus and right amygdala were negatively correlated with P3 amplitudes in the IGD group. These relationships between the low-frequency resting-state BOLD signal and activity-related ERP confirmed that fMRI brain activity under task-independent conditions was associated with activity levels in task-relevant ERPs, even when different neuroimaging modalities were used. These findings may be related to the lack of active interaction between brain regions.

The ITG is presumed to play a central role in cognitive processing, including semantic memory, language, certain aspects of visual perception, and multimodal and higher sensory processing (Mesulam, 1998; Onitsuka et al., 2004). Previous neuroimaging studies have identified changes in the superior and ITG, as well as the occipital cortex, in patients with IGD (Yuan, Cheng, et al., 2013). The OFC, with connections to the prefrontal, limbic, and sensory areas, is involved in various human behaviors (Bechara, Damasio, & Damasio, 2000; Schoenbaum, Roesch, & Stalnaker, 2006). OFC dysfunction and connectivity play a role in the deviant behaviors associated with addictive disorders (Kim et al., 2019; London, Ernst, Grant, Bonson, & Weinstein, 2000; Volkow & Fowler, 2000). An 18F-fluorodeoxyglucose positron emission tomography study revealed increased glucose metabolism in the OFC and striatum of the IGD group; these changes were related to the dopaminergic system (Park et al., 2010). Individuals with IGD had decreased gray matter volume in the OFC (Jin et al., 2016; Weng et al., 2013) and decreased cortical thickness in the OFC (Hong et al., 2013), suggesting changes in the neural mechanism that underlies cognitive behavioral performance. The positive correlation between the P3 amplitudes and regional activity of resting-state fMRI in the prefrontal, temporal, and occipital cortices suggests that cognitive processing of tasks is inefficient in individuals with IGD when their auditory and visual processing integration capacity decreases during the resting state. Also, we found that higher depression or anxiety scores in individuals with IGD correlated with lower ReHo and fALFF values of regional resting-state fMRI activity in the occipital area. We found lower ReHo and fALFF values of the occipital regions in individuals with IGD correlated with lower the P3 amplitudes. Additionally, we found that individuals with IGD showed lower ReHo and fALFF values in the occipital area. These findings suggest that the lower ReHo and fALFF values of the fMRI activity in the occipital regions, which are also associated with cognitive functions as well as sensory systems in individuals with IGD, are associated with the characteristics of individuals with IGD.

Furthermore, the P3 amplitudes of ERPs in individuals with IGD were negatively correlated with the regional ReHo activity in the left hippocampus and right amygdala. Interactions between the hippocampus and amygdala are important in emotional memory and learning (Robbins, Ersche, & Everitt, 2008; Volkow et al., 2010). Neuroimaging studies have revealed that the hippocampus and amygdala are associated with cravings in response to addiction-related cues (Childress et al., 1999; Grant et al., 1996; Sun et al., 2012). There is evidence that exposure to drug-related cues is associated with the activation of dopamine transmission in the amygdala and hippocampus (Fotros et al., 2013; Weiss et al., 2000). Patients with IGD had larger volumes in the precuneus and hippocampus/amygdala; hippocampal volume is reportedly correlated with symptom severity in IGD and may be influenced by internet gaming-related cues (Yoon et al., 2017). Compared with HCs, IGD patients reportedly exhibited higher resting-state functional connectivity between the amygdala and dorsolateral prefrontal cortex, which was negatively associated with depression symptom severity within the IGD group (Liu et al., 2018). A task fMRI study showed that IGD patients with major depressive disorder had greater relative activation in the hippocampus during an attention task, compared with the pure IGD group and HCs; this finding suggested that IGD patients with major depressive disorder could not suppress brain activity within the DMN (Han, Kim, Bae, Renshaw, & Anderson, 2016).

The findings of negative correlations between P3 amplitudes and ReHo activation in the hippocampus and amygdala suggest that dysfunctional cognitive processing in individuals with IGD is associated with regional activity in the hippocampus and amygdala that are involved in conditioned learning and memory. Cognitive impairment in individuals with IGD is associated with the weakening of hippocampus and amygdala functionality in response to the cumulative experience of internet gaming habits and emotional memory. The hippocampus and amygdala are involved with emotional processing and may modulate or interact with other brain regions; therefore, careful interpretation and further investigations are needed.

Some limitations of this study should be noted. First, because the current study was cross-sectional, it is difficult to establish causality or directionality. A longitudinal study may help to clarify causality. Second, because the participants were all younger men, our findings may be not generalizable to other populations. Third, because the current study has not found that individuals with IGD have any cognitive or sensory difficulties at the level of behavior, this study should be interpreted with some caution. Fourth, because BDI or BAI scores were uncontrollable, the effects of depression and anxiety are difficult to exclude completely, but none of the individuals with IGD who participated in this study had psychiatric comorbidities. However, the current study had a few notable strengths. This is the first multimodal study to combine resting-state fMRI with ERPs during a cognitive task in individuals with IGD.

In summary, this multimodal study of combined regional activity during resting-state fMRI and auditory oddball task ERPs revealed that resting-state regional brain hemodynamics are associated with ERP neuronal activities, particularly in brain areas involved in cognition, sensory, and emotional modulation and integration. These findings suggest that individuals with IGD patients have an imbalance between top-down and bottom-up processing, which hinders efficient cognitive and sensory processing in these patients. Therefore, we suggest that characteristics of IGD with high levels of game-related conditions and loss of control make it difficult for IGD patients to maintain a proper gaming life balance, which may lead to continuous game behavior. We expect that these findings will broaden the understanding of neural mechanisms that underlie IGD pathophysiology and will facilitate clinical interventions to improve the mental well-being of individuals who may develop IGD.

Funding sources

This research was supported by a grant from the Korea Mental Health R&D Project, funded by the Ministry of Health & Welfare, Republic of Korea (HI22C0404), from the National Research Foundation of Korea (2021R1F1A1046081), from the Korea Brain Research Institute (21-BR-03-03), and from the Basic Science Research Program through the NRF funded by the Ministry of Education (NRF-2020R1I1A1A01054095). We thank all the participants for their cooperation.

Authors’ contributions

MP and J-SC contributed to analyzing data, interpretation of the findings and writing the manuscript. MP and AC contributed to data collection. MP, JHJ, SYY, HK and J-SC contributed to study conception and design, interpretation of the findings, critical revision of the manuscript, and supervision. All authors critically reviewed content and approved the final manuscript.

Conflicts of interest

The authors declare no competing interests.

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Dr. Zsolt Demetrovics
Institute of Psychology, ELTE Eötvös Loránd University
Address: Izabella u. 46. H-1064 Budapest, Hungary
Phone: +36-1-461-2681
E-mail: jba@ppk.elte.hu

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2022  
Web of Science  
Total Cites
WoS
5713
Journal Impact Factor 7.8
Rank by Impact Factor

Psychiatry (SCIE) 18/155
Psychiatry (SSCI) 13/144

Impact Factor
without
Journal Self Cites
7.2
5 Year
Impact Factor
8.9
Journal Citation Indicator 1.42
Rank by Journal Citation Indicator

Psychiatry 35/264

Scimago  
Scimago
H-index
69
Scimago
Journal Rank
1.918
Scimago Quartile Score Clinical Psychology Q1
Medicine (miscellaneous) Q1
Psychiatry and Mental Health Q1
Scopus  
Scopus
Cite Score
11.1
Scopus
Cite Score Rank
Clinical Psychology 10/292 (96th PCTL)
Psychiatry and Mental Health 30/531 (94th PCTL)
Medicine (miscellaneous) 25/309 (92th PCTL)
Scopus
SNIP
1.966

 

 
2021  
Web of Science  
Total Cites
WoS
5223
Journal Impact Factor 7,772
Rank by Impact Factor Psychiatry SCIE 26/155
Psychiatry SSCI 19/142
Impact Factor
without
Journal Self Cites
7,130
5 Year
Impact Factor
9,026
Journal Citation Indicator 1,39
Rank by Journal Citation Indicator

Psychiatry 34/257

Scimago  
Scimago
H-index
56
Scimago
Journal Rank
1,951
Scimago Quartile Score Clinical Psychology (Q1)
Medicine (miscellaneous) (Q1)
Psychiatry and Mental Health (Q1)
Scopus  
Scopus
Cite Score
11,5
Scopus
CIte Score Rank
Clinical Psychology 5/292 (D1)
Psychiatry and Mental Health 20/529 (D1)
Medicine (miscellaneous) 17/276 (D1)
Scopus
SNIP
2,184

2020  
Total Cites 4024
WoS
Journal
Impact Factor
6,756
Rank by Psychiatry (SSCI) 12/143 (Q1)
Impact Factor Psychiatry 19/156 (Q1)
Impact Factor 6,052
without
Journal Self Cites
5 Year 8,735
Impact Factor
Journal  1,48
Citation Indicator  
Rank by Journal  Psychiatry 24/250 (Q1)
Citation Indicator   
Citable 86
Items
Total 74
Articles
Total 12
Reviews
Scimago 47
H-index
Scimago 2,265
Journal Rank
Scimago Clinical Psychology Q1
Quartile Score Psychiatry and Mental Health Q1
  Medicine (miscellaneous) Q1
Scopus 3593/367=9,8
Scite Score  
Scopus Clinical Psychology 7/283 (Q1)
Scite Score Rank Psychiatry and Mental Health 22/502 (Q1)
Scopus 2,026
SNIP  
Days from  38
submission  
to 1st decision  
Days from  37
acceptance  
to publication  
Acceptance 31%
Rate  

2019  
Total Cites
WoS
2 184
Impact Factor 5,143
Impact Factor
without
Journal Self Cites
4,346
5 Year
Impact Factor
5,758
Immediacy
Index
0,587
Citable
Items
75
Total
Articles
67
Total
Reviews
8
Cited
Half-Life
3,3
Citing
Half-Life
6,8
Eigenfactor
Score
0,00597
Article Influence
Score
1,447
% Articles
in
Citable Items
89,33
Normalized
Eigenfactor
0,7294
Average
IF
Percentile
87,923
Scimago
H-index
37
Scimago
Journal Rank
1,767
Scopus
Scite Score
2540/376=6,8
Scopus
Scite Score Rank
Cllinical Psychology 16/275 (Q1)
Medicine (miscellenous) 31/219 (Q1)
Psychiatry and Mental Health 47/506 (Q1)
Scopus
SNIP
1,441
Acceptance
Rate
32%

 

Journal of Behavioral Addictions
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 990 EUR/article for articles submitted after 30 April 2023 (850 EUR for articles submitted prior to this date)
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%.
Subscription Information Gold Open Access

Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
Volumes
per Year
1
Issues
per Year
4
Founder Eötvös Loránd Tudományegyetem
Founder's
Address
H-1053 Budapest, Hungary Egyetem tér 1-3.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2062-5871 (Print)
ISSN 2063-5303 (Online)

Senior editors

Editor(s)-in-Chief: Zsolt DEMETROVICS

Assistant Editor(s): Csilla ÁGOSTON

Associate Editors

  • Stephanie ANTONS (Universitat Duisburg-Essen, Germany)
  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Beáta BŐTHE (University of Montreal, Canada)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Ruth J. van HOLST (Amsterdam UMC, The Netherlands)
  • Daniel KING (Flinders University, Australia)
  • Gyöngyi KÖKÖNYEI (ELTE Eötvös Loránd University, Hungary)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)

Editorial Board

  • Max W. ABBOTT (Auckland University of Technology, New Zealand)
  • Elias N. ABOUJAOUDE (Stanford University School of Medicine, USA)
  • Hojjat ADELI (Ohio State University, USA)
  • Alex BALDACCHINO (University of Dundee, United Kingdom)
  • Alex BLASZCZYNSKI (University of Sidney, Australia)
  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Kenneth BLUM (University of Florida, USA)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • Wim VAN DEN BRINK (University of Amsterdam, The Netherlands)
  • Gerhard BÜHRINGER (Technische Universität Dresden, Germany)
  • Sam-Wook CHOI (Eulji University, Republic of Korea)
  • Damiaan DENYS (University of Amsterdam, The Netherlands)
  • Jeffrey L. DEREVENSKY (McGill University, Canada)
  • Naomi FINEBERG (University of Hertfordshire, United Kingdom)
  • Marie GRALL-BRONNEC (University Hospital of Nantes, France)
  • Jon E. GRANT (University of Minnesota, USA)
  • Mark GRIFFITHS (Nottingham Trent University, United Kingdom)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • Heather HAUSENBLAS (Jacksonville University, USA)
  • Tobias HAYER (University of Bremen, Germany)
  • Susumu HIGUCHI (National Hospital Organization Kurihama Medical and Addiction Center, Japan)
  • David HODGINS (University of Calgary, Canada)
  • Eric HOLLANDER (Albert Einstein College of Medicine, USA)
  • Jaeseung JEONG (Korea Advanced Institute of Science and Technology, Republic of Korea)
  • Yasser KHAZAAL (Geneva University Hospital, Switzerland)
  • Orsolya KIRÁLY (Eötvös Loránd University, Hungary)
  • Emmanuel KUNTSCHE (La Trobe University, Australia)
  • Hae Kook LEE (The Catholic University of Korea, Republic of Korea)
  • Michel LEJOXEUX (Paris University, France)
  • Anikó MARÁZ (Humboldt-Universität zu Berlin, Germany)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Astrid MÜLLER  (Hannover Medical School, Germany)
  • Frederick GERARD MOELLER (University of Texas, USA)
  • Daniel Thor OLASON (University of Iceland, Iceland)
  • Nancy PETRY (University of Connecticut, USA)
  • Bettina PIKÓ (University of Szeged, Hungary)
  • Afarin RAHIMI-MOVAGHAR (Teheran University of Medical Sciences, Iran)
  • József RÁCZ (Hungarian Academy of Sciences, Hungary)
  • Rory C. REID (University of California Los Angeles, USA)
  • Marcantanio M. SPADA (London South Bank University, United Kingdom)
  • Daniel SPRITZER (Study Group on Technological Addictions, Brazil)
  • Dan J. STEIN (University of Cape Town, South Africa)
  • Sherry H. STEWART (Dalhousie University, Canada)
  • Attila SZABÓ (Eötvös Loránd University, Hungary)
  • Ferenc TÚRY (Semmelweis University, Hungary)
  • Alfred UHL (Austrian Federal Health Institute, Austria)
  • Róbert URBÁN  (ELTE Eötvös Loránd University, Hungary)
  • Johan VANDERLINDEN (University Psychiatric Center K.U.Leuven, Belgium)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Aviv M. WEINSTEIN  (Ariel University, Israel)
  • Kimberly YOUNG (Center for Internet Addiction, USA)

 

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