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Tseng YL, Su YK, Chou WJ, Miyakoshi M, Tsai CS, Li CJ, Lee SY, Wang LJ. Neural Network Dynamics and Brain Oscillations Underlying Aberrant Inhibitory Control in Internet Addiction. IEEE Trans Neural Syst Rehabil Eng 2024; 32:946-955. [PMID: 38335078 DOI: 10.1109/tnsre.2024.3363756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2024]
Abstract
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.
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Su YK, Wang LJ, Chuang TM, Peng PC, Chou WJ, Tseng YL. Altered Inhibitory Control Mechanism of Internet Addiction: An Electroencephalogram Study of Brain Oscillations and Connectivity. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083509 DOI: 10.1109/embc40787.2023.10340509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The development of the Internet has changed people's lives and has resulted in a new type of addictive behavior. In the past decade, Internet game addiction has been identified as a mental illness. Considering internet game addiction as the only cause of mental illness is limited in its view, as internet games, social platforms and other internet multimedia are also widely used. Thus, other internet-related behaviors, that maybe addictive, should also be included. Previous neuroimaging studies have reported a role of alteration in brain's inhibitory control mechanism in addiction. However, the results are still diverse with inconsistent findings. In this study, we used an Internet-related stop signal task with EEG signals recorded to study the relationship between internet addiction through brain oscillations and functional connectivity. We also compared the differences in the brain connectivity between addicted and non-addicted participants using phase lag index. We found that the brain connectivity in participants addicted to the internet is significantly greater than that of nonaddicted users.Clinical Relevance- In this study, we assessed brain functional networks of participants with Internet Gaming Disorder and internet addiction.
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Park S, Ha J, Ahn W, Kim L. Measurement of craving among gamers with internet gaming disorder using repeated presentations of game videos: a resting-state electroencephalography study. BMC Public Health 2023; 23:816. [PMID: 37143023 PMCID: PMC10158347 DOI: 10.1186/s12889-023-15750-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 04/25/2023] [Indexed: 05/06/2023] Open
Abstract
BACKGROUND Internet gaming disorder (IGD) is receiving increasing attention owing to its effects on daily living and psychological function. METHODS In this study, electroencephalography was used to compare neural activity triggered by repeated presentation of a stimulus in healthy controls (HCs) and those with IGD. A total of 42 adult men were categorized into two groups (IGD, n = 21) based on Y-IAT-K scores. Participants were required to watch repeated presentations of video games while wearing a head-mounted display, and the delta (D), theta (T), alpha (A), beta (B), and gamma (G) activities in the prefrontal (PF), central (C), and parieto-occipital (PO) regions were analyzed. RESULTS The IGD group exhibited higher absolute powers of DC, DPO, TC, TPO, BC, and BPO than HCs. Among the IGD classification models, a neural network achieves the highest average accuracy of 93% (5-fold cross validation) and 84% (test). CONCLUSIONS These findings may significantly contribute to a more comprehensive understanding of the neurological features associated with IGD and provide potential neurological markers that can be used to distinguish between individuals with IGD and HCs.
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Affiliation(s)
- Sangin Park
- Industry-Academy Cooperation Team, Hanyang University, 222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, South Korea
| | - Jihyeon Ha
- Center for Bionics, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil Seongbuk-gu, Seoul, 02792, South Korea
| | - Wonbin Ahn
- Applied AI Research Lab, LG AI Research, 128, Yeoui-daero, Yeongdeungpo-gu, Seoul, 07796, South Korea
| | - Laehyun Kim
- Center for Bionics, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil Seongbuk-gu, Seoul, 02792, South Korea.
- Department of HY-KIST Bio-convergence, Hanyang University, 222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, South Korea.
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FoMO and the brain: Loneliness and problematic social networking site use mediate the association between the topology of the resting-state EEG brain network and fear of missing out. COMPUTERS IN HUMAN BEHAVIOR 2023. [DOI: 10.1016/j.chb.2022.107624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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EEG Signals Based Internet Addiction Diagnosis Using Convolutional Neural Networks. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12136297] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Internet addiction (IA), as a new and often unrecognized psychosocial disorder, endangers people’s health and their lives. However, the common biometric analysis based on the combination of EEG signals and results of questionnaires is not quantitative, and thus difficult to ensure a specific biomarker. This work aims to develop a deep learning algorithm (no need to identify biomarkers) used for diagnosing IA and evaluating therapy efficacy. Herein, a five-layer CNN model combined with a fast Fourier transform is proposed to diagnose IA quantitatively. This algorithm is validated in the Lemon dataset by using it to process raw data, full spectral power, and alpha-beta-gamma spectral power (related to IA). In contrast to alpha-beta-gamma spectral power, the results based on full spectral power show better performance (87.59% accuracy, 88.80% sensitivity, and 86.41% specificity), which confirms that the proposed algorithm can diagnose IA without biomarkers. In addition, this proposed CNN model presents obvious advantages in processing raw data, achieving 81.1% accuracy. Such results verify that this method can contribute to the reduction of diagnosis time and be potentially used in real-time health monitoring systems. This work provides a quantitative approach to diagnose IA and evaluate therapy efficacy, as a general strategy, and can be widely used in other disorder diagnoses that affect EEG signals, such as psychiatric disorders, substance dependence, and depression.
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Lu J, Xie J, Chen J, Zeng Y, Jiang Z, Wang Y, Zheng H. More utilitarian judgment in Internet addiction? An exploration using process dissociation and the CNI model. Brain Behav 2022; 12:e2510. [PMID: 35114077 PMCID: PMC8933780 DOI: 10.1002/brb3.2510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 12/12/2021] [Accepted: 01/11/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Internet addiction (IA), which is disadvantageous for decision making, such as moral judgment, is a pernicious threat to contemporary societies. However, few studies consider social cognition abilities as an important variable in IA. OBJECTIVES This study explores the psychological mechanism of IA facing the moral dilemma. METHODS Forty participants with IA and 89 healthy participants were recruited. They finished the Internet Addiction Test and completed the moral judgment task. The process dissociation (PD) method and the consequences, norms, and generalized inaction (CNI) model were used to analyze moral judgment data. RESULTS Compared with the healthy control (HC) group, the traditional analysis showed that the IA group made more utilitarian judgment regarding moral dilemmas. PD analysis showed that the IA group had decreased deontological inclination, without utilitarian inclination. The CNI model further showed that the sensitivity of the IA group to moral rules was significantly lower than that of the HC group, while there was no significant difference between groups in the sensitivity to the consequences and the general preference for action. CONCLUSIONS Individuals with IA make more utilitarian judgment when faced with a moral dilemma, which is related to their weak sensitivity to moral norms.
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Affiliation(s)
- Jianxia Lu
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.,School of Rehabilitation, Jiangsu Vocational College of Medicine, Yancheng, China
| | - Junjie Xie
- School of Psychology, Nanjing Normal University, Nanjing, China
| | - Jin Chen
- School of Rehabilitation, Jiangsu Vocational College of Medicine, Yancheng, China
| | - Yan Zeng
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, China
| | - Zhongli Jiang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yunqiang Wang
- School of Psychology, Nanjing Normal University, Nanjing, China
| | - Hui Zheng
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Kang X, Handayani DOD, Chong PP, Acharya UR. Profiling of pornography addiction among children using EEG signals: A systematic literature review. Comput Biol Med 2020; 125:103970. [PMID: 32892114 DOI: 10.1016/j.compbiomed.2020.103970] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 08/09/2020] [Accepted: 08/09/2020] [Indexed: 01/15/2023]
Abstract
Nowadays human behavior has been affected with the advent of new digital technologies. Due to the rampant use of the Internet by children, many have been addicted to pornography. This addiction has negatively affected the behaviors of children including increased impulsiveness, learning ability to attention, poor decision-making, memory problems, and deficit in emotion regulation. The children with porn addiction can be identified by parents and medical practitioners as third-party observers. This systematic literature review (SLR) is conducted to increase the understanding of porn addiction using electroencephalogram (EEG) signals. We have searched five different databases namely IEEE, ACM, Science Direct, Springer and National Center for Biotechnology Information (NCBI) using addiction, porn, and EEG as keywords along with 'OR 'operation in between the expressions. We have selected 46 studies in this work by screening 815,554 papers from five databases. Our results show that it is possible to identify children with porn addiction using EEG signals.
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Affiliation(s)
- Xiaoxi Kang
- Master of Computer Science, Taylor's University, 1, Jalan Taylors, 47500, Subang Jaya, Selangor, Malaysia.
| | - Dini Oktarina Dwi Handayani
- School of Computer Science & Engineering, Faculty of Innovation & Technology, Taylor's University, 1, Jalan Taylors, 47500, Subang Jaya, Selangor, Malaysia.
| | - Pei Pei Chong
- School of Biosciences, Faculty of Health and Medical Sciences, Taylor's University, 1 Jalan Taylors, 47500, Subang Jaya, Selangor, Malaysia.
| | - U Rajendra Acharya
- Ngee Ann, Singapore University of Social Science, University of Malaya, Malaysia; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan.
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Burleigh TL, Griffiths MD, Sumich A, Wang GY, Kuss DJ. Gaming disorder and internet addiction: A systematic review of resting-state EEG studies. Addict Behav 2020; 107:106429. [PMID: 32283445 DOI: 10.1016/j.addbeh.2020.106429] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 04/02/2020] [Accepted: 04/03/2020] [Indexed: 02/07/2023]
Abstract
Neurophysiological studies of Gaming Disorder (GD) and internet addiction (IA) are providing important insight into neurocognitive mechanisms underpinning these disorders, which will enable more accurate diagnostic classification. Electroencephalography (EEG) has been widely used to investigate addictive behaviours, and offers advantages of accessibility, low cost, and excellent temporal resolution. The present systematic review evaluates resting-state EEG studies in GD and IA. Papers (n = 7293) were identified in the PsychARTICLES, PsychINFO, Scopus, and Pubmed databases. Following inclusion/exclusion criteria, ten studies remained for evaluation. Results suggest individuals with GD have raised delta and theta activity and reduced beta activity, with coherence analysis suggesting altered brain activity in the mid-to-high frequency range. IA individuals demonstrate raised gamma activity and reduced beta and delta activity. Results suggest that the altered brain activity found in GD/IA may represent distinct underlying neurophysiological markers or traits, lending further support to their unique constructs. Results are also discussed in relation to relevant psychometric measurements and similar (higher frequency) activity found in substance addiction. Future research should focus on replicating the findings in a wider variety of cultural contexts to support the neurophysiological basis of classifying GD and IA.
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Wang H, Sun Y, Lan F, Liu Y. Altered brain network topology related to working memory in internet addiction. J Behav Addict 2020; 9:325-338. [PMID: 32644933 PMCID: PMC8939409 DOI: 10.1556/2006.2020.00020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 03/28/2020] [Accepted: 04/15/2020] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND AND AIMS The working memory (WM) ability of internet addicts and the topology underlying the WM processing in internet addiction (IA) are poorly understood. In this study, we employed a graph theoretical framework to characterize the topological properties of the IA brain network in the source cortical space during WM task. METHODS A sample of 24 subjects with IA and 23 matched healthy controls (HCs) performed visual 2-back task. Exact Low Resolution Electromagnetic Tomography was adopted to project the pre-processed EEG signals into source space. Subsequently, Lagged phase synchronization was calculated between all pairs of Brodmann areas, the graph theoretical approaches were then employed to estimate the brain topological properties of all participants during the WM task. RESULTS We found better WM behavioral performance in IA subjects compared with the HCs. Moreover, compared to the HC group, more integrated and hierarchical brain network was revealed in the IA subjects in alpha band. And altered regional centrality was mainly resided in frontal and limbic lobes. In addition, significant relationships between the IA severity and the significant altered graph indices were found. CONCLUSIONS In conclusion, these findings provide evidence to support the notion that altered topological configuration may underline changed WM function observed in IA.
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Affiliation(s)
- Hongxia Wang
- School of Psychology, Liaoning Normal University, Da Lian, 116029, China,Department of Psychology, Renmin University of China, Beijing, 100872, China
| | - Yan Sun
- School of Psychology, Liaoning Normal University, Da Lian, 116029, China,Corresponding author’s e-mail:
| | - Fan Lan
- School of Psychology, Liaoning Normal University, Da Lian, 116029, China
| | - Yan Liu
- School of Psychology, Liaoning Normal University, Da Lian, 116029, China
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