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      EEG correlates associated with the severity of gambling disorder and serum BDNF levels in patients with gambling disorder

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          Abstract

          Background and aims

          This study aimed to evaluate the association between the severity of pathological gambling, serum brain-derived neurotrophic factor (BDNF) level, and the characteristics of quantitative electroencephalography (EEG) in patients with gambling disorder.

          Methods

          A total of 55 male patients aged 18–65 with gambling disorder participated. The severity of pathological gambling was assessed with the nine-item Problem Gambling Severity Index from the Canadian Problem Gambling Index (CPGI-PGSI). The Beck Depression Inventory and Lubben Social Network Scale were also assessed. Serum BDNF levels were assessed from blood samples. The resting-state EEG was recorded while the eyes were closed, and the absolute power of five frequency bands was analyzed: delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), and gamma (30–50 Hz).

          Results

          Serum BDNF level was positively correlated with theta power in the right parietal region (P4, r = .403, p = .011), beta power in the right parietal region (P4, r = .456, p = .010), and beta power in the right temporal region (T8, r = .421, p = .008). Gambling severity (CPGI-PGSI) was positively correlated with absolute beta power in the left frontal region (F7, r = .284, p = .043) and central region [(C3, r = .292, p = .038), (C4, r = .304, p = .030)].

          Conclusions

          These findings support the hypothesis that right-dominant lateralized correlations between BDNF and beta and theta power reflect right-dominant brain activation in addiction. The positive correlations between beta power and the severity of gambling disorder may be associated with hyperexcitability and increased cravings. These findings contribute to a better understanding of brain-based electrophysiological changes and BDNF levels in patients with pathological gambling.

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          Most cited references52

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

            We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
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              Removing electroencephalographic artifacts by blind source separation.

              Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.
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                Author and article information

                Journal
                jba
                2006
                Journal of Behavioral Addictions
                J Behav Addict
                Akadémiai Kiadó (Budapest )
                2062-5871
                2063-5303
                05 June 2018
                June 2018
                : 7
                : 2
                : 331-338
                Affiliations
                [ 1 ]Department of Psychiatry, Dankook University Hospital , Cheonan, Republic of Korea
                [ 2 ]Department of Psychiatry, Korea Institute on Behavioral Addictions, True Mind Mental Health Clinic , Seoul, Republic of Korea
                [ 3 ]Department of Psychiatry, Korea Institute on Neuromodulation, Easybrain Center , Seoul, Republic of Korea
                [ 4 ]Department of Psychiatry, Catholic University of Daegu School of Medicine , Daegu, Republic of Korea
                Author notes
                [* ]Corresponding authors: Jaewon Lee, MD, PhD; Department of Psychiatry, Korea Institute on Neuromodulation, EasyBrain Center, 1330-9 Seocho-dong, Seocho-gu, Seoul, Republic of Korea; Phone: +82 2 583 9081; Fax: +82 2 583 9082; E-mail: sonton21@ 123456gmail.com ; Jun Won Kim, MD, PhD; Department of Psychiatry, Catholic University of Daegu School of Medicine, 33 Duryugongwon-ro 17-gil, Nam-Gu, Daegu 42472, Republic of Korea; Phone: +82 53 650 4332; Fax: +82 53 623 1694; E-mail: f_affection@ 123456naver.com
                Article
                10.1556/2006.7.2018.43
                9a8e47f6-00c3-487c-a49e-b0f2a8454d51
                © 2018 The Author(s)

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium for non-commercial purposes, provided the original author and source are credited, a link to the CC License is provided, and changes – if any – are indicated.

                History
                : 21 December 2017
                : 08 April 2018
                : 14 April 2018
                Page count
                Figures: 2, Tables: 1, Equations: 0, References: 50, Pages: 8
                Funding
                Funding sources: This work was supported by a grant from the Korea Healthcare Technology R&D Project, Ministry for Health and Welfare, South Korea (no. A120157).
                Categories
                FULL-LENGTH REPORT

                Evolutionary Biology,Medicine,Psychology,Educational research & Statistics,Social & Behavioral Sciences
                BDNF,gambling disorder,resting state,quantitative electroencephalography

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