White matter microstructural changes in internet addiction disorder: A systematic review of diffusion tensor imaging studies

Differential neural reward processes in internet addiction: A systematic review of brain imaging research

OBJECTIVES

This systematic review aims to examine the neural correlates of reward processing in various forms of Internet Addiction (IA) among adults, including generalized IA and specific conditions such as Internet Gaming Disorder (IGD). The study seeks to identify distinct patterns of altered connectivity and activation in reward-related brain regions across different IA subtypes.

METHODS

We analyzed findings from 44 neuroimaging studies, aligning with the Research Domain Criteria (RDoC) framework. The review focused on three key aspects of reward processing: responsiveness, learning, and valuation. Studies included both structural and functional neuroimaging data from adult populations with various forms of IA.

RESULTS

Findings suggest distinct patterns of altered connectivity and activation in reward-related brain regions across different IA subtypes. IGD is associated with widespread abnormalities in both structural and functional connectivity within the reward network, whereas excess social media use primarily affects the amygdala-striatal system. However, methodological limitations, including variability in IA definitions, lack of comparative studies between IA subtypes, and predominance of cross-sectional designs, hinder definitive conclusions.

CONCLUSION

This review underscores the need for a nuanced approach to IA, recognizing potentially distinct neural mechanisms across subtypes. Such insights could inform the development of targeted interventions and enhance the clinical utility of IA research and treatment. Future research should address current methodological limitations to provide more definitive conclusions about the neurobiological underpinnings of various forms of IA.

Application of machine learning in predicting adolescent Internet behavioral addiction

Objective

To explore the risk factors affecting adolescents' Internet addiction behavior and build a prediction model for adolescents' Internet addiction behavior based on machine learning algorithms.

Methods

A total of 4461 high school students in Chongqing were selected using stratified cluster sampling, and questionnaires were administered. Based on the presence of Internet addiction behavior, students were categorized into an Internet addiction group (n=1210) and a non-Internet addiction group (n=3115). Gender, age, residence type, and other data were compared between the groups, and independent risk factors for adolescent Internet addiction were analyzed using a logistic regression model. Six methods-multi-level perceptron, random forest, K-nearest neighbor, support vector machine, logistic regression, and extreme gradient boosting-were used to construct the model. The model's indicators under each algorithm were compared, evaluated with a confusion matrix, and the optimal model was selected.

Result

The proportion of male adolescents, urban household registration, and scores on the family function, planning, action, and cognitive subscales, along with psychoticism, introversion-extroversion, neuroticism, somatization, obsessive-compulsiveness, interpersonal sensitivity, depression, anxiety, hostility, paranoia, and psychosis, were significantly higher in the Internet addiction group than in the non-Internet addiction group (P < 0.05). No significant differences were found in age or only-child status (P > 0.05). Statistically significant variables were analyzed using a logistic regression model, revealing that gender, household registration type, and scores on planning, action, introversion-extroversion, psychoticism, neuroticism, cognitive, obsessive-compulsive, depression, and hostility scales are independent risk factors for adolescent Internet addiction. The area under the curve (AUC) for multi-level perceptron, random forest, K-nearest neighbor, support vector machine, logistic regression, and extreme gradient boosting models were 0.843, 0.817, 0.778, 0.846, 0.847, and 0.836, respectively, with extreme gradient boosting showing the best predictive performance among these models.

Conclusion

The detection rate of Internet addiction is higher in males than in females, and adolescents with impulsive, extroverted, psychotic, neurotic, obsessive, depressive, and hostile traits are more prone to developing Internet addiction. While the overall performance of the machine learning models for predicting adolescent Internet addiction is moderate, the extreme gradient boosting method outperforms others, effectively identifying risk factors and enabling targeted interventions.

Research landscape and trends of internet addiction disorder: A comprehensive bibliometric analysis of publications in the past 20 years

Background

Internet addiction disorder (IAD) has emerged as a significant public health concern in the digital age, with implications for mental health and social wellbeing. Despite growing recognition, IAD remains a relatively nascent field within academic research.

Methods

We conducted a comprehensive bibliometric analysis to explore the global research landscape and trends of IAD. Our methodology involved analyzing author analysis, journal analysis, keywords, and citations in publications related to IAD from 2004 to 2024.

Results

We identified "internet addiction," "internet gaming disorder," and "adolescent" as the most frequently occurring keywords, highlighting significant research areas within IAD. The analysis revealed that terms like "social media addiction," "problematic smartphone use," and "COVID-19" have gained prominence in recent years, reflecting the evolving nature of digital technology's impact on mental health. Clustering analysis illustrated the interdisciplinary nature of IAD research, integrating insights from psychology, sociology, network science, and psychiatry. Citation analysis identified highly influential papers, such as Kuss and Griffiths' review on social networking addiction and Brand et al.'s I-PACE model for internet-use disorders.

Conclusions

Our findings highlighted the importance of continuing interdisciplinary research to address the multifaceted challenges of IAD. Future research should focus on the intersections of digital behaviors with mental health, personality traits, and social dynamics to develop comprehensive strategies for prevention and intervention.

Correlation of diffusion tensor tractography with obstructive sleep apnea severity

INTRODUCTION

Using correlation tractography, this study aimed to find statistically significant correlations between white matter (WM) tracts in participants with obstructive sleep apnea (OSA) and OSA severity. We hypothesized that changes in certain WM tracts could be related to OSA severity.

METHODS

We enrolled 40 participants with OSA who underwent diffusion tensor imaging (DTI) using a 3.0 Tesla MRI scanner. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and quantitative anisotropy (QA)-values were used in the connectometry analysis. The apnea-hypopnea index (AHI) is a representative measure of the severity of OSA. Diffusion MRI connectometry that was used to derive correlational tractography revealed changes in the values of FA, MD, AD, RD, and QA when correlated with the AHI. A false-discovery rate threshold of 0.05 was used to select tracts to conduct multiple corrections.

RESULTS

Connectometry analysis revealed that the AHI in participants with OSA was negatively correlated with FA values in WM tracts that included the cingulum, corpus callosum, cerebellum, inferior longitudinal fasciculus, fornices, thalamic radiations, inferior fronto-occipital fasciculus, superior and posterior corticostriatal tracts, medial lemnisci, and arcuate fasciculus. However, there were no statistically significant results in the WM tracts, in which FA values were positively correlated with the AHI. In addition, connectometry analysis did not reveal statistically significant results in WM tracts, in which MD, AD, RD, and QA values were positively or negatively correlated with the AHI.

CONCLUSION

Several WM tract changes were correlated with OSA severity. However, WM changes in OSA likely involve tissue edema and not neuronal changes, such as axonal loss. Connectometry analyses are valuable tools for detecting WM changes in sleep disorders.

Neural Network Dynamics and Brain Oscillations Underlying Aberrant Inhibitory Control in Internet Addiction

Previous studies have reported a role of alterations in the brain's inhibitory control mechanism in addiction. Mounting evidence from neuroimaging studies indicates that its key components can be evaluated with brain oscillations and connectivity during inhibitory control. In this study, we developed an internet-related stop-signal task with electroencephalography (EEG) signal recorded to investigate inhibitory control. Healthy controls and participants with Internet addiction were recruited to participate in the internet-related stop-signal task with 19-channel EEG signal recording, and the corresponding event-related potentials and spectral perturbations were analyzed. Brain effective connections were also evaluated using direct directed transfer function. The results showed that, relative to the healthy controls, participants with Internet addiction had increased Stop-P3 during inhibitory control, suggesting that they have an altered neural mechanism in impulsive control. Furthermore, participants with Internet addiction showed increased low-frequency synchronization and decreased alpha and beta desynchronization in the middle and right frontal regions compared to healthy controls. Aberrant brain effective connectivity was also observed, with increased occipital-parietal and intra-occipital connections, as well as decreased frontal-paracentral connection in participants with Internet addiction. These results suggest that physiological signals are essential in future implementations of cognitive assessment of Internet addiction to further investigate the underlying mechanisms and effective biomarkers.

The effects of the dialysis on the white matter tracts in patients with end-stage renal disease using differential tractography study

This study aimed to determine whether white matter tracts correlate with kidney function using correlation tractography, and to investigate the effects of dialysis on white matter tracts in patients with end-stage renal disease (ESRD) using differential tractography. Ten patients with ESRD, who had a glomerular filtration rate of < 15 mL/min/1.73 m2, were enrolled in this prospective study. Diffusion tensor imaging (DTI) was performed both before and after dialysis. We discovered that white matter tracts correlated with the estimated glomerular filtration rate based on pre- and post-dialysis DTI using correlation tractography and investigated the differences in the white matter tracts between pre- and post-dialysis DTI in patients with ESRD using differential tractography. Correlation tractography revealed no quantitative anisotropy of the white matter tracts that correlated with the estimated glomerular filtration rate in pre- and post-dialysis patients with ESRD. Differential tractography revealed significant differences in several white matter tracts, particularly the cingulum, thalamic radiation, corpus callosum, and superior longitudinal fasciculus, between pre- and post-dialysis DTI, which revealed increased diffusion density after dialysis. We demonstrated the significant effects of dialysis on several white matter tracts in patients with ESRD using differential tractography, which showed increased diffusion density after dialysis. In this study, we confirmed the effects of dialysis on brain structure, especially white matter tracts.

Correlation of Diffusion Tensor Tractography with Restless Legs Syndrome Severity

This prospective study investigated white matter tracts associated with restless legs syndrome (RLS) severity in 69 patients with primary RLS using correlational tractography based on diffusion tensor imaging. Fractional anisotropy (FA) and quantitative anisotropy (QA) were analyzed separately to understand white matter abnormalities in RLS patients. Connectometry analysis revealed positive correlations between RLS severity and FA values in various white matter tracts, including the left and right cerebellum, corpus callosum forceps minor and major, corpus callosum body, right cingulum, and frontoparietal tract. In addition, connectometry analysis revealed that the FA of the middle cerebellar peduncle, left inferior longitudinal fasciculus, left corticospinal tract, corpus callosum forceps minor, right cerebellum, left frontal aslant tract, left dentatorubrothalamic tract, right inferior longitudinal fasciculus, left corticostriatal tract superior, and left cingulum parahippocampoparietal tract was negatively correlated with RLS severity in patients with RLS. However, there were no significant correlations between QA values and RLS severity. It is implied that RLS symptoms may be potentially reversible with appropriate treatment. This study highlights the importance of considering white matter alterations in understanding the pathophysiology of RLS and in developing effective treatment strategies.