Differential resting-state EEG patterns associated with comorbid depression in Internet addiction

OBJECTIVE Many researchers have reported a relationship between Internet addiction and depression. In the present study, we compared the resting-state quantitative electroencephalography (QEEG) activity of treatment-seeking patients with comorbid Internet addiction and depression with those of treatment-seeking patients with Internet addiction without depression, and healthy controls to investigate the neurobiological markers that differentiate pure Internet addiction from Internet addiction with comorbid depression. METHOD Thirty-five patients diagnosed with Internet addiction and 34 age-, sex-, and IQ-matched healthy controls were enrolled in this study. Patients with Internet addiction were divided into two groups according to the presence (N=18) or absence (N=17) of depression. Resting-state, eye-closed QEEG was recorded, and the absolute and relative power of the brain were analyzed. RESULTS The Internet addiction group without depression had decreased absolute delta and beta powers in all brain regions, whereas the Internet addiction group with depression had increased relative theta and decreased relative alpha power in all regions. These neurophysiological changes were not related to clinical variables. CONCLUSION The current findings reflect differential resting-state QEEG patterns between both groups of participants with Internet addiction and healthy controls and also suggest that decreased absolute delta and beta powers are neurobiological markers of Internet addiction.

[1]  Catriona M. Morrison,et al.  The Relationship between Excessive Internet Use and Depression: A Questionnaire-Based Study of 1,319 Young People and Adults , 2010, Psychopathology.

[2]  A. Beck,et al.  An inventory for measuring clinical anxiety: psychometric properties. , 1988, Journal of consulting and clinical psychology.

[3]  Michael A. DiSano,et al.  Intracranial EEG Reveals a Time- and Frequency-Specific Role for the Right Inferior Frontal Gyrus and Primary Motor Cortex in Stopping Initiated Responses , 2009, The Journal of Neuroscience.

[4]  Chin-Chung Tsai,et al.  Internet Addiction of Adolescents in Taiwan: An Interview Study , 2003, Cyberpsychology Behav. Soc. Netw..

[5]  Robert J Barry,et al.  Age and sex effects in the EEG: development of the normal child , 2001, Clinical Neurophysiology.

[6]  J. Polich,et al.  Meditation states and traits: EEG, ERP, and neuroimaging studies. , 2013 .

[7]  R. Buckner,et al.  Evidence for the Default Network's Role in Spontaneous Cognition , 2010 .

[8]  M. First,et al.  Structured clinical interview for DSM-IV axis I disorders : SCID-I : clinical version : scoresheet , 1997 .

[9]  I. Lyoo,et al.  Attention deficit hyperactivity symptoms and Internet addiction , 2004, Psychiatry and clinical neurosciences.

[10]  C. Ko,et al.  Psychiatric Comorbidity of Internet Addiction in College Students: An Interview Study , 2008, CNS Spectrums.

[11]  M. Mintun,et al.  Brain work and brain imaging. , 2006, Annual review of neuroscience.

[12]  Patricia L. Gibbs Reality in cyberspace: analysands' use of the Internet and ordinary everyday psychosis. , 2007, Psychoanalytic review.

[13]  A. Beck,et al.  An inventory for measuring depression. , 1961, Archives of general psychiatry.

[14]  Jung-Seok Choi,et al.  Resting-state beta and gamma activity in Internet addiction. , 2013, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[15]  Bernd Saletu,et al.  Differences in brain function between relapsing and abstaining alcohol-dependent patients, evaluated by EEG mapping. , 2004, Alcohol and alcoholism.

[16]  S. Pallanti,et al.  Internet addiction: a descriptive clinical study focusing on comorbidities and dissociative symptoms. , 2009, Comprehensive psychiatry.

[17]  Kyunghee Kim,et al.  Internet addiction in Korean adolescents and its relation to depression and suicidal ideation: a questionnaire survey. , 2006, International journal of nursing studies.

[18]  M. Matsuura,et al.  Relationship between Quantitative Electroencephalogram and Interferon-α-Induced Depression in Chronic Hepatitis C Patients , 2013, Neuropsychobiology.

[19]  Fahmeed Hyder,et al.  Energetic basis of brain activity: implications for neuroimaging , 2004, Trends in Neurosciences.

[20]  D. Black,et al.  Clinical features, psychiatric comorbidity, and health-related quality of life in persons reporting compulsive computer use behavior. , 1999, The Journal of clinical psychiatry.

[21]  L. Schneider,et al.  Quantitative, waking EEG research on depression , 1990, Biological Psychiatry.

[22]  Jack H. Mendelson,et al.  EEG alpha activity increases during transient episodes of ethanol-induced euphoria , 1986, Pharmacology Biochemistry and Behavior.

[23]  M. Schuckit,et al.  EEG spectral characteristics following ethanol administration in young men. , 1989, Electroencephalography and clinical neurophysiology.

[24]  Christopher R. Brown,et al.  EEG differences in children between eyes-closed and eyes-open resting conditions , 2009, Clinical Neurophysiology.

[25]  Miro Jakovljević,et al.  Quantitative electroencephalography in schizophrenia and depression. , 2011, Psychiatria Danubina.

[26]  M. Greicius,et al.  Default-Mode Activity during a Passive Sensory Task: Uncoupled from Deactivation but Impacting Activation , 2004, Journal of Cognitive Neuroscience.

[27]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[28]  P. Keck,et al.  Psychiatric features of individuals with problematic internet use. , 2000, Journal of affective disorders.

[29]  Sean O'Connor,et al.  Theta power in the EEG of alcoholics. , 2003, Alcoholism, clinical and experimental research.

[30]  G. Adler,et al.  Mild Cognitive Impairment in Old-Age Depression Is Associated with Increased EEG Slow-Wave Power , 1999, Neuropsychobiology.

[31]  B. Muthén,et al.  Antidepressant response trajectories and quantitative electroencephalography (QEEG) biomarkers in major depressive disorder. , 2010, Journal of psychiatric research.

[32]  Mark Griffiths,et al.  Psychology of Computer Use: XLIII. Some Comments on ‘Addictive Use of the Internet’ by Young , 1997, Psychological reports.

[33]  Christine L Larson,et al.  Functional coupling of simultaneous electrical and metabolic activity in the human brain , 2004, Human brain mapping.

[34]  Lixuan Zhang,et al.  A Comparative Study of Internet Addiction between the United States and China , 2008, Cyberpsychology Behav. Soc. Netw..

[35]  M. Kaess,et al.  The Association between Pathological Internet Use and Comorbid Psychopathology: A Systematic Review , 2012, Psychopathology.

[36]  H. Begleiter,et al.  Alcoholism and Human Electrophysiology , 2003, Alcohol research & health : the journal of the National Institute on Alcohol Abuse and Alcoholism.

[37]  I. Lyoo,et al.  Depression and Internet Addiction in Adolescents , 2007, Psychopathology.

[38]  K. Young,et al.  Psychology of Computer Use: XL. Addictive Use of the Internet: A Case That Breaks the Stereotype , 1996, Psychological reports.

[39]  S. Berman,et al.  The emerging use of technology for the treatment of depression and other neuropsychiatric disorders. , 2011, Annals of clinical psychiatry : official journal of the American Academy of Clinical Psychiatrists.

[40]  D. Liley,et al.  Drug-induced modification of the system properties associated with spontaneous human electroencephalographic activity. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[41]  E. Hardie,et al.  Excessive internet use: the role of personality, loneliness and social support networks in internet addiction , 2007 .

[42]  Kimberly Young,et al.  The Relationship Between Depression and Internet Addiction , 1998, Cyberpsychology Behav. Soc. Netw..