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•Dispositional envy was associated with SVA.•SVA was positively correlated with GMV in OFC and cerebellum.•SVA exhibited specific transcriptomic and cellular signatures. Short video addiction (SVA) has emerged as a growing behavioral and social issue, driven by the widespread use of digital platforms that provide highly engaging, personalized, and brief video content. We investigated the neuroanatomical and functional substrates of SVA symptoms, alongside brain transcriptomic and cellular characteristics, using Inter-Subject Representational Similarity Analysis (IS-RSA) and transcriptomic approaches. Behaviorally, we found that dispositional envy was associated with SVA. Structurally, SVA was positively correlated with increased morphological volumes in the orbitofrontal cortex (OFC) and bilateral cerebellum. Functionally, the dorsolateral prefrontal cortex (DLPFC), posterior cingulate cortex (PCC), cerebellum, and temporal pole (TP) exhibited heightened spontaneous activity, which was positively correlated with SVA severity. Transcriptomic and cellular analyses also showed specific genes linked to gray matter volume (GMV) associated with SVA, with predominant expression in excitatory and inhibitory neurons. These genes showed distinct spatiotemporal expression patterns in the cerebellum during adolescence. This study offers a comprehensive framework integrating structural, functional, and neurochemical evidence to highlight the neural-transcriptomic underpinnings of SVA symptoms in a non-clinical population.

•Baseline morphological characteristics (e.g., FPN/DMN/ hippocampal morphological patterns) and functional characteristics (e.g., FPN, DMN, VAN, and SMN) prospectively predict short video addiction (SVA) symptoms.•Double Dissociation Mechanism: hippocampal morphological patterns predicted SVAS via follow-up fear of missing out (FoMO), while ReHo of DMN operates through follow up negative affect (NA), revealing distinct neuropsychological pathways.•Transcriptomic Signatures: SVA exhibits specific transcriptomic and cellular signatures. Short video addiction symptoms (SVAS) have become increasingly prevalent, yet their longitudinal neurobiological basis remains unclear. In a multi-site longitudinal study (n = 280), we examined whether baseline brain features and dispositional traits—negative affect (NA) and fear of missing out (FoMO)—predict future SVAS. Participants completed self-report measures and MRI scans at baseline, with follow-up assessments conducted after 5 months to 5 years. Behaviorally, both baseline and follow-up NA and FoMO significantly predicted SVAS. Structurally, baseline gray matter volume (GMV) in the frontal-parietal network (FPN), default mode network (DMN), and hippocampal morphological patterns predicted follow-up SVAS severity. Functionally, baseline regional homogeneity (ReHo) in the FPN, DMN, ventral attention network (VAN), and sensorimotor network (SMN) also predicted SVAS. Parallel multiple mediation analyses revealed a dissociable neural architecture: hippocampal morphological patterns predicted SVAS via the unique indirect effect of follow-up FoMO, whereas DMN functional profiles (e.g., ReHo) predicted SVAS via follow-up NA. Notably, the VAN served as an integrative hub, exerting its influence via the unique indirect effects of both follow-up NA and FoMO. Transcriptomic analyses linked SVAS-related ReHo to two gene sets, namely positively correlated (SVAS-ReHo⁺) and negatively correlated (SVAS-ReHo⁻) genes. SVAS-ReHo⁺ genes were enriched in RNA processing and vascular signaling and expressed in endothelial cells; SVAS-ReHo⁻ genes were enriched in synaptic transmission and expressed in excitatory and inhibitory neurons. Spatial-temporal patterns showed SVAS-ReHo⁺ genes were expressed in subcortical regions across adolescence, whereas SVAS-ReHo⁻ genes were prominent in cortical-limbic areas during postnatal development. Functional decoding linked SVAS-ReHo⁺ genes to sensorimotor function and metabolism, and SVAS-ReHo⁻ genes to emotion and psychiatric risk. Together, these findings highlight dissociable structural, functional, and molecular pathways through which FoMO and NA contribute to short video addiction development.

Studies of Internet gaming disorder (IGD) suggest an imbalanced relationship between cognitive control and reward processing in people with IGD. However, it remains unclear how these two systems interact with each other, and whether they could serve as neurobiological markers for IGD. Fifty IGD subjects and matched individuals with recreational game use (RGU) were selected and compared when they were performing a cue-craving task. Regions of interests [anterior cingulate cortex (ACC), lentiform nucleus] were selected based on the comparison between brain responses to gaming-related cues and neutral cues. Directional connectivities among these brain regions were determined using Bayesian estimation. We additionally examined the posterior cingulate cortex (PCC) in a separate analysis based on data implicating the PCC in craving in addiction. During fixed-connectivity analyses, IGD subjects showed blunted ACC-to-lentiform and lentiform-to-ACC connectivity relative to RGU subjects, especially in the left hemisphere. When facing gaming cues, IGD subjects trended toward lower left-hemispheric modulatory effects in ACC-to-lentiform connectivity than RGU subjects. Self-reported cue-related craving prior to scanning correlated inversely with left-hemispheric modulatory effects in ACC-to-lentiform connectivity. The results suggesting that prefrontal-to-lentiform connectivity is impaired in IGD provides a possible neurobiological mechanism for difficulties in controlling gaming-cue-elicited cravings. Reduced connectivity ACC-lentiform connectivity may be a useful neurobiological marker for IGD.

•The high-risk individuals with IGD among young adults were identified.•The high-risk individuals have abnormal functional connectome gradient at baseline.•The role of impulsivity was highlighted in the development of IGD. Adolescents and young adults are considered a high-risk group for internet gaming disorder (IGD). Early screening for high-risk individuals with IGD and exploring the underlying neural mechanisms is an effective strategy to reduce the harm of IGD. We recruited 219 non-internet gaming addicted college students and evaluated them with magnetic resonance imaging, followed by a two-year longitudinal follow-up. We used functional connectome gradient (FCG) to capture the macroscopic hierarchical organization of human brain. Canonical correlation analysis was employed to identify components mapping relationships between FCG and behavioral scores. Consequently, K-means clustering was used to define distinct subtypes. The risk of developing IGD and FCG patterns were compared among the subtypes. Three subtypes were identified and subtype 3 exhibited the highest risk for developing IGD according to the occurrence rates of IGD two years later: (1) subtype 1 (5.3 %, 4 participants), (2) subtype 2 (10.8 %, 9 participants), (3) subtype 3 (20 %, 12 participants). The abnormal FCG in the inferior frontal gyrus and posterior cingulate cortex at baseline were observed in subtype 3, which were correlated with impulsivity. These findings advanced understanding of the biological and behavioral heterogeneity associated with developing of IGD, and represented a promising step toward the prediction of high-risk individuals.

•SVA symptoms were negatively correlated with LA.•Drift rate mediated the association between SVA symptoms and LA.•Brain activation patterns within cognitive control and motor networks shaped decision-making biases related to addiction. Excessive use of short-video platforms not only impairs decision-making processes but also predisposes individuals to addictive behaviors. This study investigated the relationship between short-video addiction (SVA) symptoms and loss aversion (LA), delving into the underlying computational and neural mechanisms using the drift diffusion model (DDM) and the inter-subject representational similarity analysis (IS-RSA). Behavioral analyses revealed a significant negative correlation between SVA symptoms and the LA coefficient (lnλ). Additionally, the DDM-based drift rate (v) was found to mediate this relationship. Neuroimaging analyses further indicated that SVA symptoms were negatively associated with gain-related activity in the right precuneus, while positively correlating with loss-related activity in the right cerebellum and left postcentral gyrus. Notably, precuneus activation during gain processing mediated the relationship between SVA symptoms and both lnλ and drift rate. IS-RSA revealed that inter-subject variations in SVA symptoms were significantly associated with distinct activation patterns related to gain processing in the frontoparietal network (e.g., frontal pole, inferior frontal gyrus, and supramarginal gyrus) and motor network (e.g., precentral), as well as loss-related activation patterns in the motor networks (e.g., postcentral and pre-supplementary motor area). Similar patterns emerged when examining simultaneous gain and loss-related activation patterns. Mediation analyses further demonstrated that functional activation patterns in the motor network mediated the relationships between inter-subject variations in SVA symptoms and both loss-aversion and psychological processing patterns (e.g., decision threshold, drift rate, and non-decision time). These findings provide novel insights into the cognitive and neural mechanisms underlying the influence of SVA symptoms on loss aversion, and suggest the critical roles of evidence accumulation speed and specific brain activation patterns—particularly within the cognitive control and motor network—in shaping decision-making biases associated with addiction.

Internet gaming disorder (IGD) is a type of behavioural addictions. One of the key features of addiction is the excessive exposure to addictive objectives (e.g. drugs) reduces the sensitivity of the brain reward system to daily rewards (e.g. money). This is thought to be mediated via the signals expressed as dopaminergic reward prediction error (RPE). Emerging evidence highlights blunted RPE signals in drug addictions. However, no study has examined whether IGD also involves alterations in RPE signals that are observed in other types of addictions. To fill this gap, we used functional magnetic resonance imaging data from 45 IGD and 42 healthy controls (HCs) during a reward-related prediction-error task and utilised a psychophysiological interaction (PPI) analysis to characterise the underlying neural correlates of RPE and related functional connectivity. Relative to HCs, IGD individuals showed impaired reinforcement learning, blunted RPE signals in multiple regions of the brain reward system, including the right caudate, left orbitofrontal cortex (OFC), and right dorsolateral prefrontal cortex (DLPFC). Moreover, the PPI analysis revealed a pattern of hyperconnectivity between the right caudate, right putamen, bilateral DLPFC, and right dorsal anterior cingulate cortex (dACC) in the IGD group. Finally, linear regression suggested that the connection between the right DLPFC and right dACC could significantly predict the variation of RPE signals in the left OFC. These results highlight disrupted RPE signalling and hyperconnectivity between regions of the brain reward system in IGD. Reinforcement learning deficits may be crucial underlying characteristics of IGD pathophysiology.

Individual with internet gaming disorder (IGD) often experience a high level of loneliness, and neuroimaging studies have demonstrated that amygdala function is associated with both IGD and loneliness. However, the neurobiological basis underlying these relationships remains unclear. In the current study, Granger causal analysis was performed to investigate amygdalar subdivision-based resting-state effective connectivity differences between 111 IGD subjects and 120 matched participants with recreational game use (RGUs). We further correlated neuroimaging findings with clinical measures. Mediation analysis was conducted to explore whether amygdalar subdivision-based effective connectivity mediated the relationship between IGD severity and loneliness. Compared with RGUs, IGD subjects showed inhibitory effective connections from the left pregenual anterior cingulate cortex (pACC) to the left laterobasal amygdala (LBA) and from the right medial prefrontal cortex (mPFC) to the left LBA, as well as an excitatory effective connection from the left middle prefrontal gyrus (MFG) to the right superficial amygdala. Further analyses demonstrated that the left pACC-left LBA effective connection was negatively correlated with both Internet Addiction Test and UCLA Loneliness scores, and it mediated the relationship between the two. IGD subjects and RGUs showed different connectivity patterns involving amygdalar subdivisions. These findings support a neurobiological mechanism for the relationship between IGD and loneliness, and suggest targets for therapeutic approaches that could be used to treat IGD.

Internet gaming disorder (IGD) and tobacco use disorder (TUD) are globally common, non‐substance‐related disorders and substance‐related disorders worldwide, respectively. Recognizing the commonalities between IGD and TUD will deepen understanding of the underlying mechanisms of addictive behavior and excessive online gaming. Using node strength, 141 resting‐state data were collected in this study to compute network homogeneity. The participants included participants with IGD (PIGD: n = 34, male = 29, age: 15–25 years), participants with TUD (PTUD: n = 33, male = 33, age: 19–42 years), and matched healthy controls (control‐for‐IGD: n = 41, male = 38, age: 17–32 years; control‐for‐TUD: n = 33, age: 21–27 years). PIGD and PTUD exhibited common enhanced node strength between the subcortical and motor networks. Additionally, a common enhanced resting‐state functional connectivity (RSFC) was found between the right thalamus and right postcentral gyrus in PIGD and PTUD. Node strength and RSFC were used to distinguish PIGD and PTUD from their respective healthy controls. Interestingly, models trained on PIGD versus controls could classify PTUD versus controls and vice versa, suggesting that these disorders share common neurological patterns. Enhanced connectivity may indicate a greater association between rewards and behaviors, inducing addiction behaviors without flexible and complex regulation. This study discovered that the connectivity between the subcortical and motor networks is a potential biological target for developing addiction treatment in the future. Both IGD and TUD showed enhanced node strength between the subcortical and motor networks. The node strength and RSFC significantly classified IGD and TUD from controls. IGD and TUD shared neurological patterns between the subcortical and motor networks.

This study explored the relationship between childhood trauma and short-video addiction (SVA) symptoms, alongside exploring the potential morphological substrate underlying this association. Among three independent datasets (Dataset 1: n = 980; Dataset 2: n = 1260; Dataset 3: n = 190), we found consistent positive correlations between childhood trauma and SVA symptoms, particularly in emotion abuse and physical neglect sub-dimensions. Additionally, we identified differential patterns in high- and low-risk addiction groups, with emotional neglect playing a critical role in the development of addiction, especially among low-risk individuals. Structural MRI data were collected in dataset 3, univariate analysis revealed that gray matter volumes (GMV) in the dorsolateral prefrontal cortex (dlPFC), orbitofrontal cortex, dorsomedial prefrontal cortex, frontal pole, and superior temporal gyrus were positively correlated with SVA symptoms. Within this dataset, mediation analysis showed that GMV in the dlPFC mediated the effect of physical neglect on SVA symptoms, suggesting that childhood trauma, particularly physical neglect, influences SVA symptoms through GMV strength in prefrontal cortex. These findings emphasize the critical role of childhood trauma in the development of short video addiction and underscore the need for targeted interventions addressing both psychological and neural aspects of addiction.

Internet addiction refers to problematic patterns of internet use that continually alter the neural organization and brain networks that control impulsive behaviors and inhibitory functions. Individuals with elevated tendencies to develop internet addiction represent the transition between healthy and clinical conditions and may progress to behavioral addictive disorders. In this network neuroscience study, we used resting‐state functional magnetic resonance imaging (rs‐fMRI) to examine how and whether individual variations in the tendency of developing internet addiction rewire functional connectivity and diminish the amplitude of spontaneous low‐frequency fluctuations in healthy brains. The influence of neurocognitive aging (aged over 60 years) on executive‐cerebellar networks responsible for internet addictive behavior was also investigated. Our results revealed that individuals with an elevated tendency of developing internet addiction had disrupted executive‐cerebellar networks but increased occipital‐putamen connectivity, probably resulting from addiction‐sensitive cognitive control processes and bottom‐up sensory plasticity. Neurocognitive aging alleviated the effects of reduced mechanisms of prefrontal and cerebellar connectivity, suggesting age‐related modulation of addiction‐associated brain networks in response to compulsive internet use. Our findings highlight age‐related and individual differences in altered functional connectivity and the brain networks of individuals at a high risk of developing internet addictive disorders. These results offer novel network‐based preclinical markers of internet addictive behaviors for individuals of different ages. Internet addiction refers to problematic patterns of internet use that continually alter neural organization and brain networks. In this network neuroscience study, we used resting‐state functional magnetic resonance imaging (rs‐fMRI) to examine how and whether individual variations in the tendency of developing internet addiction rewire functional connectivity and diminish the amplitude of spontaneous low‐frequency fluctuations in healthy brains. Our findings not only highlight age‐related and individual differences in altered functional connectivity and the brain networks of individuals at a high risk of developing internet addictive disorders but also offer novel network‐based preclinical markers of internet addictive behaviors for individuals of different ages.