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Journal of Behavioral Addictions logoLink to Journal of Behavioral Addictions
. 2025 Aug 16;14(3):1343–1354. doi: 10.1556/2006.2025.00063

How impulsivity and sensation seeking drive IGD tendency in healthy young adults: A resting-state MRI study

Jingzhen He 1, Haichao Zhao 1, Xu Lei 1, Jiang Qiu 1, Tingyong Feng 1, Hong Chen 1, Ofir Turel 2,3, Antoine Bechara 4, Qinghua He 1,5,*
PMCID: PMC12486288  PMID: 40818056

Abstract

Background and Aims

Internet Gaming Disorder (IGD) is a psychological condition that impairs various aspects of life, with adolescents being particularly vulnerable due to developmental factors and heightened digital exposure. This study aims to explore the interplay between behavioral and neural bases of the development of IGD, offering insights for possible prevention strategies.

Methods

A large sample of young adults (n = 1,205, age (SD) = 18.80 (1.26)) was assessed for impulsivity, sensation seeking, and IGD tendency. Two year later, impulsivity and IGD tendency were evaluated again, to expose possible long-term effects. Resting-state MRI data were collected to measure the topological properties of functional networks through graph theory analysis, encompassing global efficiency, local efficiency, and degree centrality.

Results

Sensation seeking was positively associated with impulsivity, which, in turn, was positively linked to IGD tendency. A cross-lagged effect between impulsivity and IGD tendency was evident over a two-year timeframe. Furthermore, whole-brain local efficiency was a positive predictor of impulsivity, with centrality and efficiency of the right thalamus, along with local efficiency of the right lingual gyrus, demonstrating positive correlations with IGD tendency.

Conclusion

This study revealed that sensation seeking indirectly affects IGD tendency through impulsivity, which also directly impacts IGD. Neural correlates of impulsivity included local efficiency, and of IGD tendency, included the right thalamus and lingual gyrus. These findings offer insights into IGD mechanisms and possible prevention approaches.

Keywords: sensation seeking, impulsivity, internet gaming disorder, cross-lagged panel model, resting-state MRI, brain topology

Introduction

Adolescents are highly susceptible to Internet Gaming Disorder (IGD) due to cognitive challenges, such as impaired impulse control, and inadequate emotional regulation, posed by their development trajectory, whereby their reward system is fully developed, but their self-control systems are not yet mature (King & Delfabbro, 2014, 2016; Tsui & Cheng, 2021; von Deneen et al., 2022). A meta-analysis involving 226,247 participants across 17 countries revealed a global prevalence of gaming disorders at 3.05%, with significantly higher rates among adolescent samples compared to other samples (Stevens, Dorstyn, Delfabbro, & King, 2021). This is an important issue to tackle, because IGD can have many negative consequences, including financial strain, physical discomfort, disruption of daily life (King & Delfabbro, 2018; Liao et al., 2020), obesity, poor nutritional habits, and weight control behaviors in children and adolescents (Che Mokhtar & McGee, 2025). Given these adverse impacts, investigating susceptibility to IGD among healthy young adults is essential for uncovering the potential developmental mechanisms of the disorder, and offering insights for timely intervention and prevention strategies.

Impulsivity, a personality trait characterized by rapid, unplanned responses to stimuli without considering potential consequences (DeYoung & Rueter, 2010), is closely linked to IGD (Ryu et al., 2018; Şalvarlı & Griffiths, 2019; Zhu et al., 2023). Individuals with IGD often exhibit elevated levels of trait impulsivity, which means that trait impulsivity is an important marker for people's susceptibility to IGD (Andrade et al., 2024; Şalvarlı & Griffiths, 2019). Specifically, impaired control over gaming, which manifests from impulsivity, is both a diagnostic marker and antecedent risk factor (American Psychiatric Association, 2013). Indeed, pre-existing impulse control deficits significantly predict subsequent IGD onset (Gentile et al., 2011). This link also manifests neurally. Individuals with IGD exhibit neurobiological changes akin to those seen in other forms of addiction, including diminished impulse control and impaired decision-making abilities (Weinstein & Lejoyeux, 2020).

Sensetaion seeking is another individual difference that has been linked to IGD in adolescents (Hu, Zhen, Yu, Zhang, & Zhang, 2017; Tian et al., 2018). It is a core personality trait involving the pursuit of novel and intense experiences despite risks (Zuckerman, 1994). It has been linked to increased gaming duration and a higher likelihood of developing IGD (Hamid, Abo Hamza, Hussain, & AlAhmadi, 2022). Notably, impulsivity and sensation seeking play distinct roles in addictive behaviors. Impulsivity emphasizes an individual's tendency to respond quickly without anticipating the consequences of their actions (Whiteside & Lynam, 2001), whereas sensation seeking focuses on the motivation to actively pursue novel, complex, or intense stimuli (Zuckerman, 1994). Moreover, these traits are clearly separable in terms of their factor structure (Cross, Copping, & Campbell, 2011), genetic correlations (Harden, Quinn, & Tucker‐Drob, 2012), and their predictive pathways to addictive behaviors (Sharma, Markon, & Clark, 2014). These traits can also play different roles in IGD progression: sensation seeking may drive initial engagement with gaming through enhanced reward sensitivity, while impulsivity may maintain the cycle of addiction through deficits in impulse control (Brand, Young, Laier, Wölfling, & Potenza, 2016). Hence, we consider here both impulsivity and sensation seeking as risk factors for IGD.

To supplement this perspective, we also consider the human brain as a set of intricately organized networks with optimized and balanced topological structures that manifest in the abovementioned traits (Mo et al., 2024; Zhang, Xu, Ma, Qian, & Zhu, 2024). By utilizing graph theory analysis, the brain network can be assessed through a concise set of neurobiologically meaningful measurements (Deuker et al., 2009; Olaf Sporns, 2011; O. Sporns, 2018). This analysis offers a robust framework for delineating the topological organization of brain networks, that allows efficient information transfer (He & Evans, 2010). Such topological structures can underlie IGD and associated traits. For instance, disruptions in regions such as the prefrontal lobe, subcortical region, parietal lobe, and occipital lobe are linked to IGD (Wee et al., 2014). Specifically, IGD has been associated with increased nodal centrality in regions responsible for reward processing, emotional memory, and sensorimotor functions (Chen, Li, Wang, Du, & Dong, 2020). Furthermore, individuals with IGD have increased global efficacy and decreased local efficacy (Park et al., 2017). Such abnormalities can be detected with graph theory analysis. This can therefore aid in the understanding of the neural mechanisms underlying IGD. Furthermore, through its exploratory approach, graph theory analysis can uncover the complex relationships between network structures and individual behaviors, providing new perspectives and directions for future research.

The present study integrates the abovementioned perspectives to uncover novel insights on risk factors for IGD. It aims to investigate the relationship between impulsivity, sensation seeking, and IGD tendency in healthy young adults, as well as their longitudinal links. for establishing some causality. It focuses on young adults, a demographic that has been identified in previous research as potentially at high risk for developing IGD (Kuss & Griffiths, 2012; Paulus, Ohmann, Von Gontard, & Popow, 2018; Wartberg, Kriston, & Thomasius, 2020). By adopting a longitudinal approach, we aim to better elucidate and explore the behavioral influences and neural basis underlying the development of IGD. We hypothesize positive correlations between sensation seeking and impulsivity, and their association with IGD tendency. A two-year cross lagged panel model analysis is expected to reveal a significant cross lagged effect between impulsivity and IGD tendency. Furthermore, we aim to identify neural predictors related to brain connectivity that influence these traits, revealing potential mechanisms of IGD development. We propose that global and local efficiency of brain networks will correlate with sensation seeking, impulsivity, and IGD tendency. Specific nodal properties are expected to relate to these traits in regions tied to reward, visual, and sensory motor processing. We provide a schematic overview in Fig. 1.

Fig. 1.

Fig. 1.

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A schematic overview of the research process and analytical procedure. UPPS-P represents scores for Urgency, Premeditation (lack of), Perseverance (lack of), Sensation Seeking, and Positive Urgency impulsive behavior; SS represents the sensation seeking score, and IGD represents the internet gaming disorder score

Methods

Participants

From September 2019 to October 2020, a total of 1,245 college students (freshmen and sophomores from a university in China) completed questionnaire surveys and underwent MRI scans. They were recruited via posters, in-person outreach, and invitations. After excluding participants with poor scan quality (determined through visual examination) or excessive head movements (defined as mean displacement > 2 mm or scrubbed timepoints > 25%), the remaining 1,205 met the inclusion criteria (behavior-brain cross-sectional sample). Two years later, between October 2022 and December 2022, the original participants were invited to complete a follow-up survey. Out of the original group, 500 responded and completed the questionnaire, with 485 meeting the data requirements (behavior longitudinal sample). The inclusion criteria were: 1) No history of neurological or psychiatric disorders; 2) No severe physical disabilities or illnesses; 3) No metal implants in the body; 4)No claustrophobia. The exclusion criteria were: 1) A history of neurological or psychiatric disorders; 2) A recent history of surgery or trauma; 3) The presence of metal implants in the body; 4) Severe claustrophobia.

Behavior measures

Impulsive behavior

The UPPS (Urgency, Premeditation, Perseverance and Sensation Seeking) scale (Whiteside & Lynam, 2001), measures impulsivity through four dimensions: Negative Urgency, Lack of Preparedness, Lack of Persistence, and Sensation Seeking. In 2007, a Proactive Urgency scale was added to account for impulsive behavior under extreme positive emotions (Cyders et al., 2007). This updated version, known as the UPPS-P Impulsive Behavior Scale, is widely used to assess impulsivity. It has a total of 59 items (some reverse coded) rated on a 4-point Likert scale: from 3 (strongly agree) to 0 (strongly disagree). After re-coding reverse-coded items, the sum of all items (range from 0 to 236) represents impulsivity, with higher scores representing stronger impulsivity. The Cronbach's Alpha for the UPPS-P Impulsive Behavior Scale is 0.915 for the behavior-brain cross-sectional sample (collected at T1) and 0.920 for the behavior longitudinal sample (collected at T2) in this study, demonstrating a good reliability.

Sensation seeking

The SSS (Sensation Seeking Scale, SSS) evaluates people's willingness and behavior towards sensation seeking through four dimensions: Thrill and Adventure Seeking (TAS), Experience Seeking (ES), Disinhibition (DIS), and Boredom Susceptibility (BS), with a total of 40 items (Zuckerman, 1971, 2007). Each item is rated as 0 or 1, resulting in a total score range of 0–40. A higher score indicates a stronger tendency towards sensation seeking. In this study, the Cronbach's Alpha for the Sensation Seeking Scale in the behavior-brain cross-sectional sample is 0.773, and in the behavior longitudinal sample, it is 0.760.

Internet Gaming Disorder

The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V) was used as the scoring standard for IGD. It consists of 9 items (American Psychiatric Association, 2013): (1) preoccupation; (2) withdrawal; (3) tolerance; (4) impaired control; (5) loss of interest in other activities and hobbies; (6) continued play, despite knowing the consequence of its negative impact on life; (7) a tendency to lie about the frequency of gaming; (8) a desire to escape from psychological problems; and (9) loss of opportunity in work or study or significant relationships. All nine diagnostic criteria contribute equally to the diagnosis of IGD (Yao, Potenza, & Zhang, 2017). Each item received a score of 1 for meeting the described criteria and 0 for not meeting it, resulting in a total score range of 0–9. An IGD score of 5 or higher indicates the possible presence of Internet Gaming Disorder, with higher scores reflecting a greater severity or tendency toward IGD. This is not a mental disorder at this point based on DSM-V, as it primarily pertains to a pattern of gaming behavior that may not meet clinical criteria for diagnosis. However, it can still have significant behavioral implications in non-clinical populations. The Cronbach's Alpha for the IGD tendency in the behavior-brain cross-sectional sample was 0.754, and 0.758 in the behavior longitudinal sample.

Image acquisition

All participants in this study underwent a resting-state functional magnetic resonance imaging scan lasting 8 min using a 3T Siemens Prisma scanner (Erlangen, Germany). Participants were asked to keep their eyes open and maintain a relaxed state without focusing on any specific thoughts during the fMRI scan. A gradient echo planar imaging sequence was used to obtain 240 functional volumes. The scanning parameters are as follows: repetition time (TR) = 2,000 ms, echo time (TE) = 30 ms, field of view (FOV) = 224 × 224 mm2, flip angle (FA) = 90°, slices = 62, thickness = 2 mm, slice gap = 0.3 mm, voxel size = 2 × 2 × 2 mm3. In addition, the magnetization prepared rapid acquisition gradient echo (MPRAGE) sequence was used to obtain high-resolution T1 weighted structural images. The scanning parameters are as follows: TR = 2,530 ms, TE = 2.98 ms, FOV = 224 × 256 mm2, resolution matrix = 448 × 512, FA = 7°, slices = 192, thickness = 1.0 mm, inversion time = 1,100 ms, voxel size = 0.5 × 0.5 × 1 mm3.

Data preprocessing

The preprocessing of fMRI data was achieved through the SPM12 and CONN v20.b toolboxes. After temporal correction, field-based deformation correction (scanning parameters for the field map: slice thickness = 2 mm, slice gap = 2.3 mm, TE = 4.92 ms, TR = 620 ms, flip angle = 60°, matrix size = 112 × 112, pixel bandwidth = 565 Hz/Px), and motion correction, spatial standardization was performed to align high-resolution anatomical images of individuals to functional images, which were then cut into gray matter, white matter, cerebrospinal fluid, and other parts. Subsequently, the images were standardized into the Montreal Neurological Institute (MNI) space using the Dartel processing flow, then smoothed using a 6 mm FWHM. Denoising processing involves regressing the head motion parameters using the Friston 24 parameters method (Friston, Williams, Howard, Frackowiak, & Turner, 1996), and further removing individual physiological noise and head motion artifacts using the aCompCor method (Muschelli et al., 2014), which extracts the first five principal components from both white matter and cerebrospinal fluid signals. Finally, this study also performed linear drift detrending and filtering (0.008–0.09) on fMRI data to prepare for calculating the global brain functional connectivity matrix and topological properties.

Statistical analysis

The statistical analysis of the data in this study was conducted using the following tools: SPSS 26.0, MATLAB R2023a, GRETNA 2.0 and Mplus 8.3. This study calculated the Spearman correlation coefficient and p-value to evaluate the correlation between behavior (UPPS-P impulsive behavior, sensation seeking and IGD tendency) and the correlation between behavior and topological properties of functional networks. When conducting correlation analysis between behavior and nodal metrics, Bonferroni correction was used to adjust the p-value. The path test based on behavioral data, along with longitudinal analyses using a cross-lagged panel model (CLPM) and structural equation modeling (SEM) constructed from global metrics of functional networks and behavior, was conducted using Mplus 8.3, excluding models with insignificant path tests and unsatisfactory model fit. The model fit was tested using several indices, including the chi-square test (with a p-value >0.05, indicating no significant lack of fit), the chi-square degrees of freedom ratio, the Comparative Fit Index (CFI), which values close to 1 suggest a good fit, the Tucker-Lewis Index (TLI), where values above 0.90 indicate satisfactory fit, the Root Mean Square Error of Approximation (RMSEA), with values below 0.05 indicating a close fit, and the Standardized Root Mean Square Residual (SRMR), where values less than 0.08 reflect a good fit. The significance level was set at α = 0.05 (two-sided).

This study used the GRETNA 2.0 to calculate brain topological properties on preprocessed and filtered resting-state brain imaging data, the first step is to calculate the functional connectivity matrix of the entire brain. This study identified 46 thresholds with an interval of 0.01 between 0.05 and 0.5. We utilized the Area Under the Curve (AUC) method to integrate the connectivity data generated from various sparsity thresholds, thereby providing a robust evaluation of the topological metrics. In our analysis, the absolute values of the signs were used for graph theory analysis of the brain functional connectivity network. Then, the small world attribute was calculated, and whether the sample satisfies the small world attribute is achieved through a single sample t-test based on the sigma value of 1.1, which is the basis for further graph theory analysis. The global metrics, including global efficiency, local efficiency, and clustering coefficient were calculated next. In a brain network, nodes typically denote brain regions, while edges represent structural or functional connections (Friston, 1994). Graph theory analysis encompasses degree centrality, clustering coefficients, global efficiency, local efficiency, path length, among others (Bullmore & Sporns, 2009). This study calculated local nodal metrics including betweenness centrality, degree centrality, nodal efficiency, node local efficiency and node clustering coefficient using the brain region partitioning method of AAL90. The specific network metrics used to analyze the topological structure of the networks, as outlined in Supplementary Material 2. Finally, this study used the BrainNet Viewer 1.7 toolkit to draw node distribution models correlated with IGD tendency in the behavior-brain cross-sectional sample and behavior longitudinal sample.

Ethics

The study was approved by the local ethics committee and all participants provided written informed consent before participating in the experiment.

Results

Behavior results

Participant characteristics

Table 1 displays the demographic information and behavioral scores. In the first data collection stage (T1), a total of 1,205 participants were included in the dataset, with females accounting for 66.89% and an average (SD) age of 18.80 (1.26) years old. In the second data collection stage (T2), a total of 485 participants were included in the dataset, with females accounting for 74.23% and an average age (SD) of 20.81 (0.93) years old. Additionally, the results showed a significant increase in IGD score over the two years (Z = −6.238, p < 0.001). The results of the common method bias can be found in Supplementary Material 1.

Table 1.

Demographic and behavioral information (cross-sectional and longitudinal sample) at the first and second data collection points

Characteristics First Data Collection (T1) Second Data Collection (T2)
Behavior-brain cross-sectional sample (n = 1,205)
Females, n (%) 806 (66.89%)
Age, years (SD) 18.80 (1.26)
UPPS-P, mean (SD) 136.87 (19.52)
SS, mean (SD) 16.79 (5.36)
IGD, mean (SD) 0.73 (1.38)
Behavior longitudinal sample (n = 485)
Females, n (%) 360 (74.23%) 360 (74.23%)
Age, years (SD) 18.81 (0.93) 20.81 (0.93)
UPPS-P, mean (SD) 135.88 (19.91) 134.46 (19.82)
SS, mean (SD) 16.04 (5.22) NA
IGD, mean (SD) 0.69 (1.35) 1.21 (1.86)
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Note: UPPS-P, Impulsive Behavior (Urgency, Premeditation, Perseverance, Sensation Seeking, Positive Urgency); SS, Sensation Seeking; T1, the time of first data collection; T2, the time of second data collection; NA, not available.

Behavioral correlation analysis results

The correlation results of different behavior score are shown in Table 2. The results showed that in the behavior-brain cross-sectional sample, UPPS-P impulsive behavior was significantly positively correlated with sensation seeking and IGD tendency, while the correlation between sensation seeking and IGD tendency was not significant. Similarly, in the behavior longitudinal sample at T1, UPPS-P impulsive behavior was also significantly positively correlated with sensation seeking and IGD tendency, but the correlation between sensation seeking and IGD tendency was not significant. In addition, UPPS-P impulsive behavior at T1 was significantly positively correlated with UPPS-P impulsive behavior at T2 and IGD tendency at T2. IGD tendency at T1 was significantly positively correlated with IGD tendency at T2.

Table 2.

Behavioral correlation analysis results of behavior-brain cross-sectional and behavior longitudinal sample at T1 and T2

Characteristics UPPS-P (T1) SS (T1) IGD (T1) UPPS-P (T2) IGD (T2)
Behavior-brain cross-sectional sample (n = 1,205)
UPPS-P (T1) 1
SS (T1) 0.361*** 1
IGD (T1) 0.185*** 0.057 1
Behavior longitudinal sample (n = 485)
UPPS-P (T1) 1
SS (T1) 0.315*** 1
IGD (T1) 0.199*** 0.038 1
UPPS-P (T2) 0.732*** 0.278*** 0.225*** 1
IGD (T2) 0.174*** 0.012 0.414*** 0.233*** 1
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Note: UPPS-P, Impulsive Behavior (Urgency, Premeditation, Perseverance, Sensation Seeking, Positive Urgency); SS, Sensation Seeking; T1, the time of first data collection; T2, the time of second data collection; ***p < 0.001.

Path analysis results

Structural Equation Modeling analysis were conducted to investigate the cross-sectional and longitudinal relationship among UPPS-P impulsive behavior, sensation seeking, and IGD tendency. The findings presented in Table 3 indicated that, within the behavior-brain cross-sectional sample, the indirect impact of sensation seeking on the relationship between UPPS-P impulsive behavior and IGD tendency was found to be non-significant, while the direct association between UPPS-P impulsive behavior and IGD tendency was significant. The indirect effect of UPPS-P impulsive behavior on the link between sensation seeking and IGD tendency was significant, whereas the direct effect between sensation seeking and IGD tendency was not significant. These results suggest that UPPS-P impulsive behavior may serve as a crucial predictor of IGD tendency.

Table 3.

Path testing of behavior-brain cross-sectional and behavior longitudinal sample

Path β SE p
Behavior-brain cross-sectional sample (n = 1,205)
UPPS-P → SS → IGD −0.010 0.011 0.384
SS → UPPS-P → IGD 0.076 0.012 <0.001
UPPS-P → SS 0.359 0.027 <0.001
SS → IGD −0.027 0.031 0.380
UPPS-P → IGD 0.210 0.030 <0.001
Behavior longitudinal sample (n = 485)
UPPS-P (T1) → UPPS-P (T2) 0.714 0.032 <0.001
UPPS-P (T1) → IGD (T2) 0.116 0.046 0.011
IGD (T1) → IGD (T2) 0.081 0.029 0.005
IGD (T1) → UPPS-P (T2) 0.397 0.059 <0.001
UPPS-P (T1) ↔ IGD (T1) 0.206 0.045 <0.001
UPPS-P (T2) ↔ IGD (T2) 0.104 0.033 0.002
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Note: UPPS-P, Impulsive Behavior (Urgency, Premeditation, Perseverance, Sensation Seeking, Positive Urgency); SS, Sensation Seeking; T1, the time of first data collection; T2, the time of second data collection.

In the behavior longitudinal sample, UPPS-P impulsive behavior at T1 significantly predicted both UPPS-P impulsive behavior and IGD tendency at T2. Similarly, IGD tendency at T1 significantly predicted both IGD tendency and UPPS-P impulsive behavior at T2. The cross-lagged panel model illustrating the relationship between UPPS-P impulsive behavior and IGD tendency is shown in Fig. 2. These findings suggested that accounting for temporal dynamics, UPPS-P impulsive behavior could impact IGD tendency, indicating a positive predictive relationship.

Fig. 2.

Fig. 2.

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The cross-lagged panel model of UPPS-P and IGD. Note. UPPS-P, Impulsive Behavior (Urgency, Premeditation, Perseverance, Sensation Seeking, Positive Urgency); SS, Sensation Seeking; T1, the time of first data collection; T2, the time of second data collection; ***p < 0.001, **p < 0.01, *p < 0.05.

Resting-state MRI results

Global metrics of functional networks

A one sample t-test based on sigma values was conducted to analyze the small world attributes of behavior-brain cross-sectional and longitudinal samples. The results showed that both samples met the small world attributes (behavior-brain cross-sectional sample: t1204 = −172.293, p < 0.001; behavior longitudinal sample: t484 = −112.265, p < 0.001). To explore the role of the brain's topological properties in UPPS-P impulsive behavior, sensation seeking, and IGD tendency, we investigated the neuro-behavioral correlation of the global efficiency, local efficiency, and clustering co-efficacy with UPPS-P impulsive behavior, sensation seeking, and IGD tendency. As shown in Table 4, the results showed that in the behavior-brain cross-sectional sample, global efficiency, and local efficiency were correlated with IGD tendency. In the behavior longitudinal sample, global efficiency and local efficiency were not only correlated with IGD tendency, but also with UPPS-P impulsive behavior.

Table 4.

Analysis results of the correlation between behavior and global local metrics of behavior-brain cross-sectional sample and behavior longitudinal sample

Global metrics UPPS-P SS IGD
Behavior-brain cross-sectional sample (n = 1,205)
Eg 0.013 0.016 0.097**
Eloc 0.042 0.027 0.112***
Lp 0.046 0.016 0.050
Behavior longitudinal sample (n = 485)
Eg 0.087 0.042 0.093*
Eloc 0.110* 0.062 0.107*
Lp 0.040 0.044 0.034
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Note: UPPS-P, Impulsive Behavior (Urgency, Premeditation, Perseverance, Sensation Seeking, Positive Urgency); SS, Sensation Seeking; Eg, Global Efficiency; Eloc, Local Efficiency; Lp, Clustering Coefficiency; ***p < 0.01, **p < 0.01, *p < 0.05.

Behavior-brain longitudinal model

Based on the correlation analysis between behavior and global metrics, in order to further predict the roles of whole-brain local efficiency, sensory seeking, and impulsivity in IGD tendency, we constructed a mediation model based on the longitudinal relationship between UPPS-P impulsive behavior and IGD tendency, as shown in Fig. 3. The model fit results show that, χ2/v = 2.068, p = 0.150, CFI = 0.987, TLI = 0.921, RMSEA = 0.047, SRMR = 0.017, which indicates the model has a good fit. The findings revealed that sensation seeking and local efficiency at T1 positively predicted UPPS-P impulsive behavior at T1, which in turn positively predicted IGD tendency at T2.

Fig. 3.

Fig. 3.

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The behavior longitudinal model of SS (T1), UPPS-P (T1), Eloc (Area under the curve) and IGD (T2). Note. UPPS-P, Impulsive Behavior (Urgency, Premeditation, Perseverance, Sensation Seeking, Positive Urgency); SS, Sensation Seeking; Eloc, Local Efficiency; T1, the time of first data collection; T2, the time of second data collection; ***p < 0.001, *p < 0.05.

Local nodal metrics within functional networks

To explore the potential role of nodal topological properties of functional networks in UPPS-P impulsive behavior, sensation seeking and IGD tendency, we further calculated the Spearman correlation coefficient between aforementioned behavioral performances and betweenness centrality, degree centrality, nodal efficiency, node local efficiency, and node clustering coefficient. The brain regions of node metrics correlated with IGD tendency are shown in Fig. 4. Results showed that centrality and efficiency of right thalamus, and local efficiency of right lingual gyrus were overlapping regions of both behavior-brain cross-sectional sample and behavior longitudinal sample. The specific correlation coefficients and p-values for the overlapping brain regions are shown in Table 5, while the information for the remaining brain regions in Fig. 4 is provided in Supplementary table S1.

Fig. 4.

Fig. 4.

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The different brain regions of Dc, Ne, Nle and Ncp that significantly correlated with IGD tendency. Note. All p-values were obtained after Bonferroni correction; A: behavior-brain cross-sectional sample, B: behavior longitudinal sample; Dc: Degree Centrality; Ne: Nodal Efficiency; Nle: Node Local Efficiency; NCp: Node Clustering Coefficient; The brain regions highlighted by the red circles indicate significant correlations between behavior and brain structure in both cross-sectional and longitudinal samples. The brain regions highlighted by the red circles represent overlapping areas in cross-sectional and longitudinal samples. Node sizes convey no quantitative information.

Table 5.

The overlapping regions between the different node metrics and IGD tendency of behavior-brain cross-sectional and behavior longitudinal samples

Node metrics Overlapping regions Abbrevia-tion IGD
r p
Dc
Behavior-brain cross-sectional sample (n = 1,205) Thalamus THA.R 0.154 <0.001
Behavior longitudinal sample (n = 485) 0.163 0.028
Ne
Behavior-brain cross-sectional sample (n = 1,205) Thalamus THA.R 0.154 <0.001
Behavior longitudinal sample (n = 485) 0.163 0.028
Nle
Behavior-brain cross-sectional sample (n = 1,205) Lingual gyrus LING.R 0.143 <0.001
Behavior longitudinal sample (n = 485) 0.180 0.006
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Note: Dc, Degree Centrality; Ne, Nodal Efficiency; Nle, Node Local Efficiency; All p-values were obtained after Bonferroni correction.

Discussion

This study investigates the impact of impulsivity and sensation seeking on IGD tendency, as well as the neural underpinnings of brain functional networks in healthy young adults. The findings reveal significant positive associations between impulsivity and IGD tendency. The cross-lagged panel model revealed that impulsivity distinctly and positively predicts IGD tendency two years later. While sensation seeking does not directly affect IGD tendency, it positively predicts impulsivity, which subsequently influences IGD tendency. Furthermore, we found positive correlation between impulsivity and topological properties of resting-state functional networks. Additionally, we found that both the centrality and efficiency of the thalamus, as well as the local efficiency of the lingual gyrus, positively predict IGD tendency in both cross-sectional and longitudinal samples. These findings highlight the crucial roles of the thalamus and lingual gyrus in IGD tendency, with the thalamus potentially playing a pivotal role in predisposing individuals to IGD.

Specifically, the study found that higher impulsivity predicts higher IGD score, aligning with previous research showing a positive correlation between impulsivity and IGD (Ryu et al., 2018; Şalvarlı & Griffiths, 2019; Zhu et al., 2023). A review study indicates that individuals with IGD exhibit high impulsivity traits and abnormalities in brain regions related to impulsivity and decision-making, identifying impulsivity as a risk factor for IGD (Li, Turel, & He, 2023; Wen et al., 2025). Individuals with IGD largely exhibit typical neurobiological changes associated with other addictions, including reduced activity and impaired decision-making in the areas of impulse control (Weinstein & Lejoyeux, 2020). Impulsivity is a core marker of behavioral addiction, while sensation seeking may not be (Billieux, Maurage, Lopez-Fernandez, Kuss, & Griffiths, 2015). Research findings suggest a weak association between sensation seeking and IGD, as no statistically significant differences in sensation seeking levels were observed between individuals with IGD and healthy controls (K. Müller, Dreier, Beutel, & Wölfling, 2016). The present study found that even among healthy young adults, impulsivity predicted IGD tendency, suggesting it is a risk factor. The cross-lagged panel model showed impulsivity positively influencing IGD tendency over two years, indicating it is a long-term and sustained factor in IGD development. Impulsivity, a trait involving quick decision-making without considering consequences, may lead to IGD due to impaired self-control (Hammad & Al-Shahrani, 2024; S. M. Müller, Antons, & Brand, 2023).

The study confirmed that brain networks in both cross-sectional and longitudinal behavior samples exhibit small-world properties, aligning with previous research (Chen et al., 2020; Jiang et al., 2013; Tan, Chen, Liao, & Qian, 2019). Global efficiency is a measure of the efficiency of parallel information transfer in the network, while local efficiency is a measure of the fault tolerance of the network (Liu et al., 2008). Global efficiency and local efficiency are important metrics for measuring network integration (Achard & Bullmore, 2007; Rubinov & Sporns, 2010). A study showed a significant increase in global efficiency among adolescents with IGD (Park et al., 2017). The present study found correlations between brain network efficiencies, impulsivity and IGD tendency, with sensation seeking and local efficiency indirectly affecting IGD tendency through impulsivity over two years. The positive correlation between sensation seeking and impulsivity has been suggested in previous studies (Wasserman, Mathias, Hill‐Kapturczak, Karns‐Wright, & Dougherty, 2020). A study found that Parkinson's impulse control disorder showed improved local efficiency in emotional and reward-related decision-making areas, suggesting a potential positive correlation between local efficiency and impulsivity (Navalpotro-Gomez et al., 2020). This means that there may be a positive correlation between local efficiency and impulsivity. However, in further analysis of node metrics, we did not find associations between local efficiency and impulsivity.

The lingual gyrus is a tongue-shaped structure that lies on the medial aspect of the occipital lobe along the inferomedial (tentorial) surface (Flores, 2002). It mainly involves basic and advanced visual processing (Palejwala et al., 2021). A voxel-based morphometry (VBM) study found that adolescents with IGD exhibited reduced gray matter density in the lingual gyrus (Y. Zhou et al., 2011). Our study found that the local efficiency of the right lingual gyrus significantly positively predicts IGD tendency, revealing a potential role of this region in the development of IGD, though further research is needed to substantiate these findings. In addition, this study also found that thalamus's centrality and efficiency significantly positively predict IGD tendency. Degree centrality refers to the number of connections that connect a node to other nodes in the network, which can evaluate the importance of a node (Bullmore & Sporns, 2009; Rubinov & Sporns, 2010). Nodal efficiency corresponds to global efficiency, which can indicate the information transmission efficiency of a node in the network (Latora & Marchiori, 2001). In previous studies, the authors found through techniques such as diffusion tensor imaging and diffusion-weighted MRI that IGD increases white matter integrity in the thalamus (Dong, DeVito, Huang, & Du, 2012; Dong et al., 2018). An fMRI study showed abnormalities in the thalamus-cortical circuits in patients with IGD, which impaired the goal-directed system and made them more dependent on the habitual system, leading to an imbalance between the goal-directed and habitual systems (W. Zhou et al., 2021). These findings suggest that the number of connecting nodes and information transmission rate in the right thalamus may be important metrics for predicting IGD tendency, highlighting the thalamus's potential role in IGD development.

To enhance the practical applications of these findings, future research should develop and evaluate targeted intervention programs addressing impulsivity in young adults. When doing so, caution should be exercised as the IGD scores in our sample were relatively low. Thus, generalizability to cases with much higher IGD scores (e.g., people diagnosed with IGD vs heathy young adults) should be examined in future research. In that sense, our findings can highlight possible prevention strategies; possible intervention strategies should be examined in future research with diagnosed IGD cases. Specific prevention strategies could include for example, applications that help with enhancing the regulatory function of the thalamic-cortical circuit. A mobile application could be developed, such as an adaptive Go/No-Go task, to strengthen the connection between the thalamus and the dorsolateral prefrontal cortex, thereby training individuals' inhibitory control abilities (Sitaram et al., 2017). As another example, to address the hyperactivity of the lingual gyrus, a graded exposure therapy based on virtual reality (VR) could be implemented (Lindner et al., 2021), gradually simulating gaming scenarios to reduce the abnormal local efficiency of the lingual gyrus, while incorporating real-time monitoring through functional near-infrared spectroscopy (fNIRS) to reward individuals who demonstrate activation attenuation. As another example, emotional regulation training can employ cognitive behavioral therapy (CBT) techniques, specifically designed to target impulsive characteristics, to assist individuals in making more effective decisions and managing their emotions when faced with impulsive urges. The efficacy of such intervention, though, should be examined in future research.

This study also has several limitations that point to future research opportunities. Firstly, participants were college students, and most of them have not met the DSM-V diagnostic criteria for IGD. Therefore, our study is based on a large sample of IGD tendency, attempting to reveal the potential development mechanism of IGD. Future research can test the generalizability of our findings with samples with higher IGD scores or with more formal IGD diagnoses. Secondly, the predominance of female participants limits the interpretation and generalizability of the results. Future research should test the generalizability of our results with gender-balanced samples. Additionally, using both qualitative and quantitative methods will offer a broader understanding of IGD phenomena. Thirdly, the results of the functional network topology attributes and IGD tendency are exploratory, and further research is needed to investigate the potential role of each node in the development of IGD. Finally, resting-state functional connectivity is obtained when participants are at rest, which may limit our understanding of the dynamic changes in the brain during specific tasks, as the connection patterns of the brain network can vary during task execution.

Conclusion

This study elucidated the relationship between sensation seeking, impulsivity, and IGD tendency using cross-sectional and two-year longitudinal data, by offering a behavioral-neural model of IGD trajectories. The behavioral findings revealed that sensation seeking indirectly influences IGD tendency through impulsivity, with impulsivity also demonstrating a significant direct impact on IGD tendency, including a cross-lagged effect. Neural results indicated that local efficiency positively correlates with impulsivity, while centrality and efficiency of the right thalamus, as well as local efficiency of the right lingual gyrus, are positively associated with IGD tendency. In conclusion, this research integrated behavioral and neural investigations into potential pathways leading to IGD susceptibility, offering insights into the underlying developmental mechanisms of IGD and providing valuable insights for possible prevention strategies.

Supplementary material

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Acknowledgments

We deeply appreciate all the graduate students and mentors of Southwest University who have contributed to the collection of large sample resting-state MRI data and behavioral data, and we also appreciate the support and dedication of the undergraduate of Southwest University who participated in this study.

Funding Statement

Funding sources: This work was supported by research grants from the National Natural Science Foundation of China (31972906), Fundamental Research Funds for the Central Universities (SWU2209235), and the Innovation Research 2035 Pilot Plan of Southwest University (SWUPilotPlan006).

Footnotes

Authors' contribution: JZH: conceptualization, formal analysis, methodology, writing-original draft, writing-review & editing; HCZ: conceptualization, methodology; XL: conceptualization; JQ: methodology; TYF: conceptualization, methodology; HC: methodology; OT: methodology; AB: methodology; QHH: conceptualization, data curation, funding acquisition, methodology, writing-review & editing.

Conflict of interest: None.

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