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. 2024 Oct 30;70:101472. doi: 10.1016/j.dcn.2024.101472

Abnormal Granger causal connectivity based on altered gray matter volume and associated neurotransmitters of adolescents with internet gaming disorder revealed by a multimodal neuroimaging study

Xiaoyu Niu a,b,c, Mengzhe Zhang b,c, Xinyu Gao b,c, Jinghan Dang b,c, Jieping Sun b,c, Qiuying Tao b,c, Yan Lang d, Weijian Wang b,c, Yarui Wei b,c, Shaoqiang Han b,c, Huayan Xu a, Yingkun Guo a,⁎, Jingliang Cheng b,c,⁎⁎, Yong Zhang b,c,⁎⁎
PMCID: PMC11566705  PMID: 39486388

Abstract

Although prior studies have revealed alterations in gray matter volume (GMV) among individuals with internet gaming disorder (IGD). The brain's multifaceted functions hinge crucially on the intricate connections and communication among distinct regions. However, the intricate interaction of information between brain regions with altered GMV and other regions, and how they synchronize with various neurotransmitter systems, remains enigmatic. Therefore, we aimed to integrate structural, functional and molecular data to explore the GMV-based Granger causal connectivity abnormalities and their correlated neurotransmitter systems in IGD adolescents. Voxel-based morphometry (VBM) analysis was firstly performed to investigate GMV differences between 37 IGD adolescents and 35 matched controls. Brain regions with altered GMV were selected as seeds for further Granger causality analysis (GCA). Two-sample t tests were performed using the SPM12 toolkit to compare the GMV and Granger causal connectivity between IGD and control groups (GRF corrected, Pvoxel<0.005, Pcluster<0.05). Then, GMV-based Granger causal connectivity was spatially correlated with PET- and SPECT-derived maps covering multifarious neurotransmitter systems. Multiple comparison correction was performed using false discovery rate (FDR). Compared with controls, IGD adolescents showed higher GMV in the caudate nucleus and lingual gyrus. For the GCA, IGD adolescents showed higher Granger causal connectivity from insula, putamen, supplementary motor area (SMA) and middle cingulum cortex (MCC) to the caudate nucleus, and lower Granger causal connectivity from superior/inferior parietal gyrus (SPG/IPG) and middle occipital gyrus (MOG) to the lingual gyrus. Besides, GMV-based Granger causal connectivity of IGD adolescents were associated with the dopaminergic, serotonergic, GABAergic and noradrenaline systems. This study revealed that the caudate nucleus and lingual gyrus may be the key sites of neuroanatomical changes in IGD adolescents, and whole-brain Granger causal connectivity abnormalities based on altered GMV involved large brain networks including reward, cognitive control, and visual attention networks, and these abnormalities are associated with a variety of neurotransmitter systems, which may be associated with higher reward sensitivity, cognitive control, and attention control dysfunction.

Keywords: Internet gaming disorder (IGD), Adolescents, Gray matter volume (GMV), Voxel-based morphometry (VBM), Granger causality analysis (GCA), Neurotransmitter

1. Introduction

As the availability and use of computer technology has greatly increased, digital games have become a popular form of entertainment for many individuals, providing a relaxing and enjoyable escape from daily routines(Paschke et al., 2021). However, some users indulge in excessive and uncontrolled gaming behaviors, which can have significant negative impacts on their social, work, and/or academic life. This excessive gaming, known as internet gaming disorder (IGD), is characterized by a loss of control over gaming activities, preoccupation with gaming, and continuation of gaming despite negative consequences(Petry and O'Brien, 2013).

Adolescence is a period of significant neurobiological and psychological changes, marked by the maturation of the brain and an increased susceptibility to mental illnesses(Schettler et al., 2022). The neural reward system becomes more responsive to motivational stimuli, while the cognitive control system is still developing(Casey and Jones, 2010). This imbalance makes adolescents particularly vulnerable to addictive behaviors such as IGD. The excessive and uncontrolled engagement in online gaming can have profound adverse effects on individuals, families, and society, affecting academic performance, social skills, and overall well-being. Given the severity of the problem, it is imperative to enhance our understanding of the neurobiological basis of adolescent IGD.

With the development of imaging medicine, advanced multimodal neuroimaging techniques have revolutionized our understanding of the nervous system. These techniques, which encompass structural, functional, and molecular imaging, allow for the in vivo characterization of structural and functional abnormalities within the nervous system. This has been instrumental in gaining insights into the neurobiological mechanisms underlying psychiatric disorders and in identifying new therapeutic targets(Chen et al., 2023); (Kwon et al., 2024); (Mestre-Bach et al., 2023); (Park et al., 2023); (von Deneen et al., 2022). One of the most widely used anatomic morphometric measurement methods is the voxel-based morphometric method (VBM). This method enables the precise measurement of gray matter volume (GMV) in the brain, providing a valuable tool for exploring the etiology and pathogenesis of IGD(Paulekiene et al., 2022). Earlier studies employing structural magnetic resonance imaging (MRI) have revealed interesting findings. For instance, individuals who frequently engage in online gaming have been found to exhibit higher GMV in the left striatum. This increase is negatively correlated with thinking time during tasks like the Cambridge gambling task, suggesting altered reward processing and adaptive neuroplasticity in this population(Kühn et al., 2011). However, it's important to note that no single neuron operates in isolation. The various functions of the brain are not solely determined by the independent role of each brain region, but rather by the intricate connections and communication between different regions. Functional connectivity (FC) algorithms, based on resting-state functional magnetic resonance imaging (rs-fMRI), can calculate the Pearson correlation of brain blood oxygen level dependence (BOLD) signals(X. (Hou et al., 2023). This allows for the characterization of the synchronization of signals between brain regions. Studies have shown evidence of abnormal patterns of FC between regions with altered GMV in adolescents with IGD and other regions of the brain(Yuan et al., 2017). These findings highlight the importance of considering both structural and functional abnormalities when examining the neurobiological mechanisms underlying psychiatric disorders.

However, while the above studies demonstrated abnormalities in brain structure and functional connectivity patterns in adolescent IGD, previous traditional FC analyses were only able to assess Pearson correlations between region of interest (ROI) and other brain regions. It is difficult to characterize whether there is a causal relationship between two time series and how they affect each other. In contrast, Granger causality analysis (GCA) is a linear regression based metric that can detect causal relationships between brain regions, reveal the direction of information flow, and calculate their relative causal strength(Yang et al., 2023) GCA has been widely used in the study of brain functional networks in psychiatric diseases, exploring the flow of information between brain regions, and greatly deepening people's understanding of the neural mechanisms of diseases(Hao et al., 2022); (Huang et al., 2023); (Qin et al., 2022).

Besides, early theories of addiction framed it as a pathological hijacking of the neural processes related to normal reward learning(Kauer, 2004), potentially tied to synapse-specific changes known as synaptic plasticity(Kauer and Malenka, 2007). Prior research has implied that IGD is linked to dopamine system dysfunction, which includes decreased availability of the dopamine D2 receptor (D2R) in the dorsal striatum and reduced levels of the dopamine transporter (DAT)(H. (Hou et al., 2012); S. H. (Kim et al., 2011). A recent study also indicated that adolescents with IGD exhibit elevated levels of the glutamine-glutamine complex (Glx) in the striatum, potentially indicating overactivation of the reward system(Klar et al., 2024). Existing in vivo neuroimaging studies, while valuable, have been limited in their ability to connect with the molecular underpinnings of neurological dysfunction associated with IGD. To advance the field, it is crucial to integrate neuroimaging findings with molecular biology research, enabling a deeper understanding of the complex interactions that underlie this disorder. This integrated approach could lead to more targeted and effective therapeutic strategies for individuals suffering from IGD. The JuSpace toolbox (available at https://github.com/juryxy/JuSpace) is a recently introduced tool designed for spatial correlation analysis between brain image data and neurotransmitter maps, facilitating the examination of the molecular basis of abnormal brain functions(Dukart et al., 2021). It has found widespread application in the investigation of the pathological mechanisms underlying mental illnesses(Hirjak et al., 2022); M. (Zhang et al., 2023).

Therefore, this study first used VBM analysis to investigate the abnormal GMV of IGD adolescents compared with healthy control (HC). Next, the brain regions with altered GMV were selected as seeds, and the Granger causal connectivity of the whole brain with abnormal brain structure was explored by using GCA method, so as to explore the functional interaction between various brain regions. Besides, this study further combined with molecular imaging to explore the relationship between the whole brain Granger causal connectivity of the GMV-altered brain regions of IGD adolescents and the distribution of neurotransmitters, receptors and transporters in the brain, so as to conduct in-depth research on the neural mechanism of IGD in order to provide more accurate targets for treatment. This study combined structural, functional and molecular data to explore the Granger causal connectivity between the brain regions with altered GMV of IGD adolescents and the whole brain, and further explore the complex relationship between brain functional abnormalities and neurotransmitters, in order to comprehensively understand the neuroimaging characteristics of IGD, and provide basis for the accurate diagnosis and treatment of IGD adolescents.

Based on previous studies, our hypothesis for the current investigation centers on the observed alterations in GMV within brain regions associated with reward processing in IGD adolescents. Specifically, we postulate that these alterations in GMV would be accompanied by abnormal Granger causal connectivity between the affected brain regions and other regions involved in reward processing. Furthermore, we anticipate significant associations between these Granger causal connectivity patterns based on altered GMV and neurotransmitter systems, particularly the dopaminergic.

2. Materials and methods

2.1. Participants

37 adolescents with IGD were recruited from the psychiatric clinic of the First Affiliated Hospital of Zhengzhou University and 35 control subjects were recruited from the community. All participants were first assessed for IGD diagnosis by an experienced psychiatrist via the fifth edition of the diagnostic and statistical manual of mental disorders (DSM-V) diagnostic criteria. The criteria include: (1) Preoccupation with online gaming to the extent that it dominates daily activities; (2) Withdrawal symptoms (e.g., irritability, anxiety, or sadness) when internet gaming is discontinued, without evidence of pharmacologic withdrawal; (3) Needing to spend increasing amounts of time engaging in online gaming (tolerance); (4) A sense of loss of control over gaming; (5) Loss of interest in previous hobbies and entertainment; (6) Continuation of online gaming despite knowledge of having a psychological problem; (7) Lies about the amount of time spent gaming to family members, therapists, or others; (8) Using online gaming to escape or relieve a negative mood (e.g., feelings of helplessness, guilt, or anxiety); (9) Jeopardizing or losing a significant relationship, job, or educational or career opportunity because of online gaming. The severity of IGD was assessed by the Young’s Internet Addiction Test (IAT).

Inclusion criteria for the IGD group include: (1) Meeting five or more of the DSM-V diagnostic criteria for IGD; (2) Scoring 50 or above on the IAT; (3) Being of Han ethnicity and right-handed; (4) Being a male between the ages of 10 and 19; (5) Not having taken any psychotropic medications or undergone any clinical treatment for mental illness within the past month, and not having used any addictive substances (such as nicotine, cocaine, etc.). Exclusion criteria for the IGD group include: (1) Having a history of any other substance dependence or behavioral addiction apart from online gaming addiction; (2) Having comorbid Axis-I psychiatric disorders (e.g., depression, anxiety, schizophrenia, attention-deficit hyperactivity disorder, mental retardation); (3) Having a history of other physical illnesses or brain organic lesions; (4) Having taken antipsychotics or sedatives within the past 2 weeks; (5) Having contraindications for MRI scanning.

Inclusion criteria for the control group include: (1) Not meeting the DSM-V diagnostic criteria for IGD; (2) Scoring below 50 on the IAT; (3) Being of Han ethnicity and right-handed; (4) Being a male between the ages of 10 and 19. Exclusion criteria for the control group include: (1) Having taken sedatives within the past 2 weeks; (2) Having a history of any mental illness, physical illness, or brain organic lesions; (3) Having contraindications for MRI scanning.

This study was carried out in accordance with the Declaration of Helsinki and approved by the Local Medical Ethics Committee of the First Affiliated Hospital of Zhengzhou University (2022-KY-0438). Informed consent was obtained from all participants.

2.2. MRI data acquisition

MRI data were acquired using the 3 T Magnetom Prisma MRI scanner (Siemens Healthcare, Erlangen, Germany) with 64 channel head coils. Participants were asked to lie still on their back on the scanning bed with their eyes closed and keep awake. Foam pads and earplugs were used to minimize head movement and canner noise. High-resolution T1-weighted volumetric 3D images were acquired using a rapid acquisition gradient echo sequence with the following parameters: repetition time/echo time (TR/TE) = 2300/2.32ms, field of view (FOV) = 240 × 240 mm2, slices = 176, and voxel size = 0.9 × 0.9 × 0.9 mm3. The rs-fMRI images were acquired using a simultaneous multi-slice (SMS) echo-planar imaging sequence (TR/TE = 1000/30ms, flip angle = 70°, FOV = 220 × 220 mm2, voxel size = 2 × 2 × 2.2 mm3, slices = 52, slice thickness = 2.2 mm, and 400 volumes totally).

2.3. Data analysis

2.3.1. Clinical data analysis

For comparisons between two groups in demographics and clinical characteristics (i.e. age, years of education and IAT scores), two sample t tests were performed using IBM SPSS Statistics software (version 26.0). P value<0.05 was considered statistically significant.

2.3.2. Structural data preprocessing and VBM analysis

Each image was initially checked for quality manually. Computational Anatomy Toolbox 12 (CAT12) (http://www.neuro.uni-jena.de/cat/), an extension of Statistical Parametric Mapping 12 (SPM12) (http://www.fil.ion.ucl.ac.uk/spm/software/spm12/), was used to calculate anatomy. VBM analysis was conducted using the CAT12 toolkit based on MatLab 2018a. All the structural images were spatially normalized using the DARTEL algorithm(Ashburner, 2007) and segmented into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF)(Ashburner and Friston, 2005). After data preprocessing, the GM images were smoothed with an 8 mm full-width at half-maximum (FWHM) Gaussian kernel.

GMV comparison was performed between IGD and HC groups using the general linear model (GLM) in SPM12 with age and total intracranial volume (TIV) as covariates [Gaussian random field theory (GRF) corrected, Pvoxel<0.005, Pcluster<0.05].

2.3.3. Functional data preprocessing and GCA analysis

Functional data were preprocessed using the Data Processing Assistant for Resting-State fMRI (DPARSF). The preprocessing main steps were as follows: 1) removing first 10 volumes for signal equilibrium; 2) slice timing; 3) realignment (excluding participants with a maximum head motion >2.5 mm or rotation>2.5◦). No participant was excluded in this step; 4) spatial normalization into Montreal Neurological Institute (MNI) EPI templates and resampling to 3 ×3×3 mm3; 5) smoothing with an 8 mm FWHM Gaussian kernel; 6) removing linear trends and temporally bandpass filtering (0.01–0.08 Hz); 7) regression of 24 head motion parameters, white matter and cerebrospinal fluid signals(Friston et al., 1996).

Based on the results of VBM, brain regions with significant differences in GMV between the two groups were selected as seed-based spherical ROI. Voxel-wise bivariate coefficient GCA was conducted to assess the causal effect between seeds and other voxels of the whole brain using Resting-State fMRI Data Analysis Toolkit (REST). The signed-path coefficient of linear regression was used to evaluate Granger causality effect. Positive signed-path coefficients may indicate excitatory effects, while negative signed-path coefficients may indicate inhibitory effects. Seed-to-whole brain and whole brain-to-seed causal effects were calculated for each seed separately. Seed-to-whole brain analyses were used to estimate the excitatory or inhibitory effects from seeds to other voxels of the whole brain, while brain-to-seed analyses were used to estimate the positive or negative feedback effects from other voxels of the whole brain to seeds.

Two sample t tests were conducted on the causal effects between IGD and HC groups with age and framewise displacement (FD) as covariates [GRF corrected, Pvoxel<0.005, Pcluster<0.05].

2.3.4. Spatial correlation between Granger causal connectivity and neurotransmitter maps

To investigate the molecular basis underlying GMV-based Granger causal connectivity induced by IGD, Granger causal connectivity maps derived from GCA analysis were used as input for spatial correlation with the PET- and SPECT-derived maps in JuSpace toolbox (https://github.com/ juryxy/JuSpace). The Granger causal connectivity maps were voxel-based T-statistical maps generated by calculating the Granger causal connectivity of the IGD and HC groups through GCA analysis and then performing the two sample t tests, which represent the Granger causal connectivity difference between the two groups. The PET- and SPECT-derived maps were derived from independent datasets (from adults)(Dukart et al., 2021) provided by JuSpace toolbox consisted of a variety of neurotransmitter systems including the serotonergic system [serotonin receptors (5-HT1a, 5-HT1b, 5-HT2a), serotonin transporter (SERT DASB HC30 and SERT MADAM)], the dopaminergic system [dopamine D1 (D1), dopamine D2 (D2), dopamine transporter (DAT), dopamine synthesis capacity (F-DOPA)], the GABAergic system [gamma-aminobutyric acid type a (GABAa)], the μ-opioid receptor system (MU) and the noradrenaline system [noradrenaline transporter (NAT)]. We used the JuSpace toolbox to compute independent z-score maps obtained from Spearman correlation coefficients between Granger causal connectivity data and the above neurotransmitter maps included in the toolbox (based on the Neuromorphometrics atlas; exact P-values with 10000 permutations; adjusted for spatial autocorrelation). False discovery rate (FDR) was used to correct for multiple comparisons.

3. Results

3.1. Characteristics of Participants

No significant differences were observed in age and years of education between two groups. The IGD group reported significantly higher IAT scores as compared to the control group. Detailed demographic and clinical information were presented in

Table 1.

Demographic and clinical characteristics of participants.

IGD HC t P value
Sex, M/F 37/0 35/0 - -
Age, years 14.35±1.50 14.74±3.21 −0.657 0.514
Years of education 8.73±1.59 8.83±3.15 −0.167 0.868
IAT Score 61.97±9.20 27.86±4.96 19.724 <0.001
Handedness, R/L 37/0 35/0 - -
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Note: Values are presented as mean ± SD; IGD, Internet gaming disorder; HC, healthy control; IAT, Internet addiction test; M, male; F, female; R, right; L, left.

3.2. Structural differences between two groups

Compared with the HCs, the IGD adolescents showed significantly higher GMV in the left caudate nucleus and right lingual gyrus [GRF corrected, Pvoxel<0.005, Pcluster<0.05], which were selected as seeds for the subsequent GCA (Fig. 1 and Table 2). No significantly lower GMV was observed for the IGD adolescents relative to the HCs.

Fig. 1.

Fig. 1

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Brain regions with significant group differences in GMV. Group differences of GMV between the IGD and HC groups were identified using two sample t tests (GRF corrected, Pvoxel<0.005, Pcluster<0.05). Abbreviations: GMV, gray matter volume; IGD, internet gaming disorder; HC, healthy control; GRF, gaussian random field theory; * indicates statistically significant differences.

Table 2.

Brain regions with significant group differences in GMV.

Brain Regions Peak MNI Coordinates Number of cluster voxels t-values
（x, y, z）
Left Caudate nucleus −21, −9, 18 72 4.16
Right Lingual gyrus 15, −74, 0 289 4.10
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Abbreviations: GMV, gray matter volume; MNI, Montreal Neurological Institute.

3.3. Voxel-wise GCA: whole-brain-to-seed analysis

For the left caudate nucleus seed: compared with HCs, the IGD adolescents showed significantly higher Granger causal connectivity from bilateral insula, putamen, supplementary motor area (SMA) and middle cingulum cortex (MCC) to left caudate nucleus [GRF corrected, Pvoxel<0.005, Pcluster<0.05]. For the right lingual gyrus seed: compared with HCs, the IGD adolescents showed significantly lower Granger causal connectivity from left superior/inferior parietal gyrus (SPG/IPG) and middle occipital gyrus (MOG) to the right lingual gyrus [GRF corrected, Pvoxel<0.005, Pcluster<0.05] (Fig. 2, Fig. 3 and Table 3).

Fig. 2.

Fig. 2

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Schematic diagram of altered Granger causal connectivity in the IGD group compared with the HC group (GRF corrected, Pvoxel<0.005, Pcluster<0.05). The red nodes indicate the brain regions with altered GMV (i.e. Caudate.L and LG.R). The red arrows represent higher Granger causal connectivity from input brain regions (Insula, Putamen, SMA, MCC) to the Caudate.L, as shown in (A). The blue arrows represent lower Granger causal connectivity from input brain regions (SPG, IPG, MOG) to the LG.R, as shown in (B). Abbreviations: IGD, internet gaming disorder; HC, healthy control; GRF, gaussian random field theory; GMV, gray matter volume; LG, lingual gyrus; SMA, supplementary motor area; MCC, middle cingulum cortex; SPG, superior parietal gyrus; IPG, inferior parietal gyrus; MOG, middle occipital gyrus; L, left; R, right.

Fig. 3.

Fig. 3

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Results of whole-brain GCA analysis based on the left caudate nucleus are shown in (A). The red area indicates the brain regions where the IGD adolescents have higher Granger causal connectivity from the whole brain to the left caudate nucleus relative to HC (GRF corrected, Pvoxel<0.005, Pcluster<0.05). Results of whole-brain GCA analysis based on the right lingual gyrus are shown in (B). The blue area indicates the brain regions where the IGD adolescents has lower Granger causal connectivity from the whole brain to the right lingual gyrus relative to HC (GRF corrected, Pvoxel<0.005, Pcluster<0.05). GCA, Granger connectivity analysis; IGD, internet gaming disorder; HC, healthy control; GRF, gaussian random field theory; L, left; R, right.

Table 3.

Brain regions with significant group differences in the Granger causal connectivity.

Brain Regions Peak MNI Coordinates Number of cluster voxels t-values
（x, y, z）
Higher Granger causal connectivity from the whole brain to the left caudate nucleus
Insula.L −30, 12, 12 200 6.82
Putamen.L −29, 14, 2 85 7.10
Insula.R 30, 21, 12 165 7.23
Putamen.R 29, −7, 2 146 7.78
SMA.R 9, −7, 63 123 4.89
SMA.L −4, −16, 52 60 4.85
MCC.R 5, 7, 45 33 4.79
Granger causal connectivity from the left caudate nucleus to the whole brain
None
Lower Granger causal connectivity from the whole brain to the right lingual gyrus
IPG.L −30, −78, 45 67 −5.10
SPG.L −27, −76, 45 55 −4.54
MOG.L −30, −75, 42 42 −4.75
Granger causal connectivity from the right lingual gyrus to the whole brain
None
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Abbreviations: SMA, supplementary motor area; MCC, middle cingulum cortex; SPG, superior parietal gyrus; IPG, inferior parietal gyrus; MOG, middle occipital gyrus; L, left; R, right; MNI, Montreal Neurological Institute.

3.4. Voxel-wise GCA: seed-to-whole-brain analysis

Seed-to-whole-brain analysis exhibited that there was no abnormal Granger causal connectivity from the seeds (left caudate nucleus and right lingual gyrus) to the whole brain in IGD adolescents.

3.5. Association between Granger causal connectivity and neurotransmitter activity maps

Granger causal connectivity from the whole brain to the left caudate nucleus alterations in IGD adolescents as compared to controls were significantly associated with spatial distribution of NAT (rho=-0.13, p=0.002), 5-HT1b (rho=-0.10, p=0.002), 5-HT2a (rho=-0.08, p=0.003) and D2 receptors (rho=-0.09, p=0.026) (FDR corrected). Granger causal connectivity from the whole brain to the right lingual gyrus alterations in IGD adolescents as compared to controls were significantly associated with spatial distribution of 5-HT2a (rho=-0.07, p=0.023) and GAGBa (rho=-0.06, p=0.022) receptors (FDR corrected) (Fig. 3).

Fig. 4.

Fig. 4

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Barplots of cross-modal correlations between neurotransmitter systems and seed-based Granger causal connectivity maps. The ordinate is the Fisher's z-transformed correlation coefficients, and the abscissa shows respective neurotransmitters. (A) Correlations between brain maps of differences in the Granger causal connectivity based on left caudate nucleus between IGD and HC groups and neurotransmitters. (B) Correlations between brain maps of differences in the Granger causal connectivity based on right lingual gyrus between IGD and HC groups and neurotransmitters. The ‘*’ represented the correlation was significant (P<0.05 for permutation, FDR corrected). The two SERT-labelled bars each are from left to right SERT DASB HC30 and SERT MADAM. IGD, internet gaming disorder; HC, healthy control.

4. Discussion

This study explored the abnormal brain Granger causal connectivity based on altered GMV of IGD adolescents and its potential associated neurotransmitters. Three main findings emerged: First, IGD adolescents compared to controls showed higher GMV in the left caudate nucleus and right lingual gyrus. Second, IGD adolescents showed higher Granger causal connectivity from bilateral insula, putamen, supplementary motor area (SMA) and middle cingulum cortex (MCC) to the left caudate nucleus. Besides, IGD adolescents showed significantly lower Granger causal connectivity from left superior/inferior parietal gyrus (SPG/IPG) and middle occipital gyrus (MOG) to the right lingual gyrus. Third, altered Granger causal connectivity based on the left caudate nucleus were significantly associated with spatial distribution of noradrenaline system (NAT), dopaminergic system (D2), and serotonergic systems (5-HT1b, 5-HT2a). Altered Granger causal connectivity based on the right lingual gyrus were significantly associated with 5-HT2a and GABAergic system (GAGBa).

4.1. Alterations of GMV in adolescents with IGD

This study found that IGD is associated with neuroanatomical alterations in the left caudate nucleus and the right lingual gyrus. GMV abnormalities in the caudate nucleus have been widely reported in previous structural MRI studies of addiction, including substance use disorders(Chang et al., 2007); (Jacobsen et al., 2001), gambling addiction(Koehler et al., 2015), and IGD(Cai et al., 2016); (Yuan et al., 2017). As an important part of the striatum, the caudate nucleus plays a key role in reward-based behavioral learning, reward processing and reinforcement. Besides, the caudate nucleus is intricately involved in motivation and pleasure and in the development and maintenance of addictive behaviors(Ma et al., 2012); (Vanderschuren and Everitt, 2005). A financial decision-making task has revealed that the left caudate nucleus of individuals with Internet addiction showed significantly higher activation when choosing risky options(J. W. (Seok et al., 2015). These findings together with the present study suggested that higher GMV in the left caudate nucleus may be associated with increased sensitivity to reward anticipation in IGD(J.-W. (Seok and Sohn, 2018).

In addition, we found that the GMV in the right lingual gyrus was higher in IGD adolescents than in the control group. Located between the calcarine sulcus and the fusiform gyrus, the lingual gyrus is a vital component of the occipital lobe, which has been reported to be involved in visual recognition and processing, and is thought to play an important role in episodic memory consolidation(Jung et al., 2014); (Kukolja et al., 2016). Due to long-term viewing of computer game screens, a large amount of visual memory information is stored in the lingual gyrus of IGD adolescents, which may be one of the reasons for the gradual increase in the GMV of the lingual gyrus. In addition, one study found that IGD participants had lower activity in the left lingual gyrus when faced with a winning outcome, which may be related to IGD participants' long-term gaze at the computer and insensitivity to visual stimuli presented by the screen(J. (Zhang et al., 2020). Therefore, we speculate that another reason for higher GMV in IGD adolescents may be to compensate for hypoactivity.

Taken together, GMV alterations in the left caudate nucleus and the right lingual gyrus may be the key sites of neuroanatomical changes in IGD.

4.2. Alterations of brain Granger causal connectivity based on altered GMV in adolescents with IGD

To investigate the relationship between structural alterations and aberrant whole-brain Granger causal connectivity of IGD, we performed a seed-based resting-state GCA. The GCA seeded with the left caudate nucleus revealed that IGD adolescents showed higher Granger causal connectivity from bilateral insula, putamen, SMA and MCC to the left caudate nucleus. Like the caudate nucleus, the putamen is an important part of the dorsal striatum, a core region involved in craving, compulsive behavior and habit formation(Everitt and Robbins, 2005); (Koob and Volkow, 2010); (Volkow et al., 2006). One card-guessing task fMRI study observed that IGD subjects showed higher activation in the bilateral striatum when facing a winning outcome, suggesting that individuals with IGD showed greater reward sensitivity than controls(Guangheng (Dong et al., 2011); G. (Dong et al., 2017). The current study found higher Granger causal connectivity from bilateral putamen to the left caudate nucleus in IGD adolescents, which may suggest that increased interactions in brain regions associated with the reward system contribute to higher reward sensitivity in IGD.

The insula is a multimodal structure in which the anterior part plays a pivotal role in salient attribution and executive control, and the posterior part is mainly involved in interoceptive and exteroceptive processing, such as receiving and integrating bodily signals and external stimuli to influence behaviors(Cauda et al., 2011); (Paulus and Stewart, 2014); J. T. (Zhang et al., 2016). It has been suggested that the insula may be involved in the initiation and maintenance of addiction by translating interoceptive signals into feelings of craving, anticipation, or impulsiveness that a person subjectively experiences(Naqvi and Bechara, 2009); (Verdejo-Garcia et al., 2012). A study on resting-state functional connectivity (rsFC) suggests that in comparison with control group, individuals with IGD showed higher rsFC between left anterior insula and the dorsal striatum(J. T. (Zhang et al., 2016). Similar to the previous study, higher Granger causal connectivity from the bilateral insula to the left caudate nucleus was observed in present research, possibly indicating that the insula translates interoceptive signals into feelings of "urge" or "desire" that may become subjective experiences, making the activity of the reward system more sensitive and thus increasing drive and motivation for online gaming.

The SMA and MCC are main components of the cognitive control network, whose primary functions include reward evaluation, executive control and decision making, which are closely associated with addiction(Vogt, 2014); (Wang et al., 2019). Hyperactivity in brain regions associated with cognitive control network function, including the DLPFC and cingulate cortex was observed when the individuals with Internet addiction were exposed to internet-related cues(Ko et al., 2013). Additionally, Wang found that individuals with Internet addiction showed higher local functional connectivity values in the right DLPFC, bilateral MCC and the left cerebellum(Wang et al., 2019). Higher Granger causal connectivity from bilateral SMA and MCC to the left caudate nucleus in IGD adolescents was observed in the present study, we hypothesized that they need to mobilize more cognitive control to downregulate their higher cravings.

Furthermore, the GCA seeded with the right lingual gyrus revealed that IGD adolescents showed lower Granger causal connectivity from left SPG/IPG and MOG to the right lingual gyrus. All of above regions are vital components of the visual attention network(Goodale and Milner, 1992); (Igelström and Graziano, 2017), involved in the processes of selective and sustained attention, attentional control, visual space attention and visual information processing(Müller and Kleinschmidt, 2003). Weaker local functional connectivity density (lFCD) values in the right IPG, bilateral calcarine and lingual gyrus were revealed in Internet addiction adolescents. Abnormalities in visual attention network (VAN) is thought to be a key determinant in impairment of attention(Jiang et al., 2018). Lower Granger causal connectivity in these regions observed in this study may be related to the dysfunction of attention control in IGD adolescents.

4.3. Association between altered Granger causal connectivity and neurotransmitter activity maps

Our study found left caudate nucleus-based Granger causal connectivity alterations were significantly associated with dopaminergic system (D2), serotonergic systems (5-HT1b, 5-HT2a) and noradrenaline system (NAT) activity maps, and right lingual gyrus-based Granger causal connectivity alterations were significantly associated with 5-HT2a and GABAergic system (GABAa) activity maps. Human neuroimaging studies have shown that addiction is associated with a significant reduction in striatal dopamine transmission, which is measured by dopamine D2 receptor binding and presynaptic dopamine release(Trifilieff and Martinez, 2014). Reduced D2 receptor binding in the striatum is a consistent finding in addiction imaging studies(Trifilieff and Martinez, 2014). A previous PET study using the radiolabeled ligand [11 C] raclopride found that IGD participants showed reduced levels of dopamine D2 receptor availability in the dorsal striatum including caudate nucleus and putamen(S. H. (Kim et al., 2011). Reduced availability of dopaminergic receptors has been linked to reward deficiency syndrome, which describes an increased need for high levels of arousal and stimulation in people with genetic alterations in dopaminergic neurons in the reward pathway(Comings and Blum, 2000). People with this syndrome may seek out drugs or gambling behaviors to normalize low dopaminergic activity in the brain. Playing online games may stimulate the reward pathway, which induces feelings of excitement and pleasure.

In addition to changes in the dopaminergic system, other neurochemical systems also play crucial roles in addiction including the serotonergic system, noradrenaline system and GABAergic system. Abnormally low levels of serotonin increase susceptibility to reward-seeking behaviors and contribute to the maintenance of addictive behaviors(Kirby et al., 2011). One study found a significant correlation between low levels of dopamine D2 receptors in the striatum and reduced serotonin 5-HT2a receptors in individuals with IGD, which may indicate a possible interaction between two receptors(Tian et al., 2014). Furthermore, previous research has revealed that the release of glucocorticoids and NA facilitates the transition of addictive behavior from an initial reward-driven pattern to a habitual, compulsive pattern(Dias-Ferreira et al., 2009); (Schwabe et al., 2012). Additionally, numerous studies have shown a link between GABAa receptor activity and addictive disorders such as alcohol(Olsen and Liang, 2017), nicotine dependence(Agrawal et al., 2008), heroin dependence(Y. S. (Kim et al., 2015) and cannabis abuse(Agrawal et al., 2008). Especially in the study of alcohol addiction, GABAergic system was considered to be a special target of alcohol(Behar et al., 1999); (Lovinger and Roberto, 2013). GABAa receptor availability was reduced in individuals with alcohol addiction compared to controls(Lingford-Hughes et al., 2012), which is thought to be related to synaptic plasticity and pathological memory phenomena(Hyman, 2005). Taken together, IGD is caused by complex interactions of multiple receptor and neurotransmitter systems and is not suitable for analysis of isolated receptor systems.

5. Strengths and limitations

The main strength of this study is a multimodal analysis of structural, functional and molecular data in IGD adolescents, which contributes to a comprehensive understanding of the neural mechanisms of IGD. The JuSpace toolbox enabled us to observe the underlying molecular signatures of functional changes associated with brain structural abnormalities in IGD adolescents. Nevertheless, this study has several limitations to be noted. First, since there are no widely used brain templates for adolescents (age range 10–19 years old), adult brain templates were used in this study. In addition, JuSpace's neurotransmitter atlas templates were derived from independent datasets (from adults). We expect to develop brain templates and neurotransmitter maps for adolescents in the future to validate or supplement this study. Second, this study was cross-sectional. Third, only male adolescents included in the current study. Previous studies have reported gender differences in neural activity of IGD. While restricting this study to male participants would have maximized the homogeneity of the study population, this selection could have limited the scope of the present findings. At last, the sample size of this study was relatively small, and large sample size studies are needed to verify the stability of these results in the future.

Conclusion

This study found that IGD adolescents showed neuroanatomical alterations in the left caudate nucleus and right lingual gyrus, which may be the key sites of neuroanatomical changes in IGD adolescents. Besides, whole-brain Granger causal connectivity abnormalities based on altered GMV involved large brain networks including reward, cognitive control, and visual attention networks, and these abnormalities are associated with a variety of neurotransmitter systems, which may be associated with increased reward sensitivity, cognitive control, and attention control dysfunction. The combination of structural, functional and molecular data based on multimodal neuroimaging methods contributes to a comprehensive understanding of the neuropathological mechanisms of IGD.

Funding information

This study was supported by the National Natural Science Foundation of China (82120108015, 82102020, 82071874, 81971586, 82271981, 81601467, 81871327) and the Funding for Scientific Research and Innovation Team of The First Affiliated Hospital of Zhengzhou University (QNCXTD2023007).

CRediT authorship contribution statement

Shaoqiang Han: Supervision, Data curation. Yan Lang: Supervision, Resources, Data curation. Qiuying Tao: Supervision, Data curation. Jieping Sun: Supervision, Data curation. Jinghan Dang: Supervision, Data curation. Yingkun Guo: Funding acquisition, Supervision, Writing – review & editing. Xinyu Gao: Supervision, Data curation. Huayan Xu: Funding acquisition, Validation. Mengzhe Zhang: Supervision, Data curation. Yong Zhang: Supervision, Funding acquisition, Data curation, Conceptualization. Xiaoyu Niu: Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Data curation, Conceptualization. Jingliang Cheng: Supervision, Resources, Project administration. Yarui Wei: Supervision, Data curation. Weijian Wang: Supervision, Resources, Data curation.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

We are grateful to all participants for their understanding and voluntary participation.

Author contributions

Xiaoyu Niu: Experiment design, data acquisition and analysis, manuscript writing and revising. Mengzhe Zhang, Xinyu Gao: Data acquisition and analysis, manuscript revising. Jinghan Dang, Jieping Sun, Qiuying Tao: Data analysis. Weijian Wang, Yan Lang: Data acquisition. Yarui Wei, Shaoqiang Han, Huayan Xu: Experiment design, manuscript revising. Yong Zhang, Jingliang Cheng, Yingkun Guo: Experiment design, funding acquisition, manuscript revising.

Data statement

The datasets generated and/or analyzed during the current study are not publicly available due to confidentiality but are available from the corresponding author on reasonable request.

Contributor Information

Yingkun Guo, Email: gykpanda@163.com.

Jingliang Cheng, Email: cjr.chjl@vip.163.com.

Yong Zhang, Email: zzuzhangyong2013@163.com.

Data Availability

Data will be made available on request.

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Data Availability Statement

Data will be made available on request.

Articles from Developmental Cognitive Neuroscience are provided here courtesy of Elsevier

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