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Guerrero-Aranda A, Alvarado-Rodríguez FJ, Enríquez-Zaragoza A, Carmona-Huerta J, González-Garrido AA. Assessment of Classical and Non-Classical Quantitative Electroencephalographic Measures in Patients with Substance Use Disorders. Clin EEG Neurosci 2024; 55:296-304. [PMID: 37849312 DOI: 10.1177/15500594231208245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Abstract
Background: People diagnosed with substance use disorders (SUDs) are at risk for impairment of brain function and structure. However, physicians still do not have any clinical biomarker of brain impairment that helps diagnose or treat these patients when needed. The most common method to study these patients is the classical electroencephalographic (EEG) analyses of absolute and relative powers, but this has limited individual clinical applicability. Other non-classical measures such as frequency band ratios and entropy show promise in these patients. Therefore, there is a need to expand the use of quantitative (q)EEG beyond classical measures in clinical populations. Our aim is to assess a group of classical and non-classical qEEG measures in a population with SUDs. Methods: We selected 56 non-medicated and drug-free adult patients (30 males) diagnosed with SUDs and admitted to Rehabilitation Clinics. According to qualitative EEG findings, patients were divided into four groups. We estimated the absolute and relative powers and calculated the entropy, and the alpha/(delta + theta) ratio. Results: Our findings showed a significant variability of absolute and relative powers among patients with SUDs. We also observed a decrease in the EEG-based entropy index and alpha/(theta + delta) ratio, mainly in posterior regions, in the patients with abnormal qualitative EEG. Conclusions: Our findings support the view that the power spectrum is not a reliable biomarker on an individual level. Thus, we suggest shifting the approach from the power spectrum toward other potential methods and designs that may offer greater clinical possibilities.
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Affiliation(s)
- Alioth Guerrero-Aranda
- University Center "Los Valles", University of Guadalajara, Ameca, México
- Department of EEG and Brain Mapping, Teleeg, México
| | | | | | - Jaime Carmona-Huerta
- University Center of Health Sciences, University of Guadalajara, Guadalajara, México
- Jalisco Institute of Mental Health, Salme, México
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Zhao L, Zhang Y, Yu X, Wu H, Wang L, Li F, Duan M, Lai Y, Liu T, Dong L, Yao D. Quantitative signal quality assessment for large-scale continuous scalp electroencephalography from a big data perspective. Physiol Meas 2023; 44. [PMID: 35952665 DOI: 10.1088/1361-6579/ac890d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 08/11/2022] [Indexed: 11/12/2022]
Abstract
Objective. Despite electroencephalography (EEG) being a widely used neuroimaging technique with an excellent temporal resolution, in practice, the signals are heavily contaminated by artifacts masking responses of interest in an experiment. It is thus essential to guarantee a prompt and effective detection of artifacts that provides quantitative quality assessment (QA) on raw EEG data. This type of pipeline is crucial for large-scale EEG studies. However, current EEG QA studies are still limited.Approach. In this study, combined from a big data perspective, we therefore describe a quantitative signal quality assessment pipeline, a stable and general threshold-based QA pipeline that automatically integrates artifact detection and new QA measures to assess continuous resting-state raw EEG data. One simulation dataset and two resting-state EEG datasets from 42 healthy subjects and 983 clinical patients were utilized to calibrate the QA pipeline.Main Results. The results demonstrate that (1) the QA indices selected are sensitive: they almost strictly and linearly decrease as the noise level increases; (2) stable, replicable QA thresholds are valid for other experimental and clinical EEG datasets; and (3) use of the QA pipeline on these datasets reveals that high-frequency noises are the most common noises in EEG practice. The QA pipeline is also deployed in the WeBrain cloud platform (https://webrain.uestc.edu.cn/, the Chinese EEG Brain Consortium portal).Significance. These findings suggest that the proposed QA pipeline may be a stable and promising approach for quantitative EEG signal quality assessment in large-scale EEG studies.
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Affiliation(s)
- Lingling Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Yufan Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Xue Yu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Hanxi Wu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Lei Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, People's Republic of China
| | - Mingjun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.,Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu 611731, People's Republic of China
| | - Yongxiu Lai
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, People's Republic of China
| | - Tiejun Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, People's Republic of China.,Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu 611731, People's Republic of China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, People's Republic of China.,Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu 611731, People's Republic of China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, People's Republic of China.,Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu 611731, People's Republic of China
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Liu Y, Chen Y, Fraga-González G, Szpak V, Laverman J, Wiers RW, Richard Ridderinkhof K. Resting-state EEG, Substance use and Abstinence After Chronic use: A Systematic Review. Clin EEG Neurosci 2022; 53:344-366. [PMID: 35142589 DOI: 10.1177/15500594221076347] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Resting-state EEG reflects intrinsic brain activity and its alteration represents changes in cognition that are related to neuropathology. Thereby, it provides a way of revealing the neurocognitive mechanisms underpinning chronic substance use. In addition, it is documented that some neurocognitive functions can recover following sustained abstinence. We present a systematic review to synthesize how chronic substance use is associated with resting-state EEG alterations and whether these spontaneously recover from abstinence. A literature search in Medline, PsycINFO, Embase, CINAHL, Web of Science, and Scopus resulted in 4088 articles, of which 57 were included for evaluation. It covered the substance of alcohol (18), tobacco (14), cannabis (8), cocaine (6), opioids (4), methamphetamine (4), and ecstasy (4). EEG analysis methods included spectral power, functional connectivity, and network analyses. It was found that long-term substance use with or without substance use disorder diagnosis was associated with broad intrinsic neural activity alterations, which were usually expressed as neural hyperactivation and decreased neural communication between brain regions. Some studies found the use of alcohol, tobacco, cocaine, cannabis, and methamphetamine was positively correlated with these changes. These alterations can partly recover from abstinence, which differed between drugs and may reflect their neurotoxic degree. Moderating factors that may explain results inconsistency are discussed. In sum, resting-state EEG may act as a potential biomarker of neurotoxic effects of chronic substance use. Recovery effects awaits replication in larger samples with prolonged abstinence. Balanced sex ratio, enlarged sample size, advanced EEG analysis methods, and transparent reporting are recommended for future studies.
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Affiliation(s)
- Yang Liu
- 12544Department of Psychology, School of Education, Shanghai Normal University, Shanghai, China
| | - Yujie Chen
- 12544Department of Psychology, School of Education, Shanghai Normal University, Shanghai, China
| | - Gorka Fraga-González
- 27217Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Veronica Szpak
- 1234Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
| | - Judith Laverman
- 1234Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
| | - Reinout W Wiers
- 1234Addiction Development and Psychopathology (ADAPT)-Lab, Department of Psychology and Centre for Urban Mental Health, University of Amsterdam, Amsterdam, Netherlands
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Lovell ME, Bruno R, Johnston J, Matthews A, McGregor I, Allsop DJ, Lintzeris N. Cognitive, physical, and mental health outcomes between long-term cannabis and tobacco users. Addict Behav 2018; 79:178-188. [PMID: 29291509 DOI: 10.1016/j.addbeh.2017.12.009] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 11/17/2017] [Accepted: 12/07/2017] [Indexed: 11/30/2022]
Abstract
INTRODUCTION Cannabis intoxication adversely affects health, yet persistent effects following short-term abstinence in long-term cannabis users are unclear. This matched-subjects, cross-sectional study compared health outcomes of long-term cannabis and long-term tobacco-only users, relative to population norms. METHODS Nineteen long-term (mean 32.3years of use, mean age 55.7years), abstinent (mean 15h) cannabis users and 16 long-term tobacco users (mean 37.1years of use, mean age 52.9years), matched for age, educational attainment, and lifetime tobacco consumption, were compared on measures of learning and memory, response inhibition, information-processing, sustained attention, executive control, and mental and physical health. RESULTS Cannabis users exhibited poorer overall learning and delayed recall and greater interference and forgetting than tobacco users, and exhibited poorer recall than norms. Inhibition and executive control were similar between groups, but cannabis users had slower reaction times during information processing and sustained attention tasks. Cannabis users had superior health satisfaction and psychological, somatic, and general health than tobacco users and had similar mental and physical health to norms whilst tobacco users had greater stress, role limitations from emotional problems, and poorer health satisfaction. CONCLUSIONS Long-term cannabis users may exhibit deficits in some cognitive domains despite short-term abstinence and may therefore benefit from interventions to improve cognitive performance. Tobacco alone may contribute to adverse mental and physical health outcomes, which requires appropriate control in future studies.
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Affiliation(s)
- M E Lovell
- School of Medicine (Psychology), University of Tasmania, Hobart, Tasmania 7000, Australia.
| | - R Bruno
- School of Medicine (Psychology), University of Tasmania, Hobart, Tasmania 7000, Australia
| | - J Johnston
- University Centre for Rural Health - North Coast, University of Sydney, Lismore, New South Wales 2480, Australia
| | - A Matthews
- School of Medicine (Psychology), University of Tasmania, Hobart, Tasmania 7000, Australia
| | - I McGregor
- Lambert Initiative for Cannabinoid Therapeutics, School of Psychology, Brain and Mind Centre, University of Sydney, New South Wales 2006, Australia
| | - D J Allsop
- Lambert Initiative for Cannabinoid Therapeutics, School of Psychology, Brain and Mind Centre, University of Sydney, New South Wales 2006, Australia
| | - N Lintzeris
- Discipline of Addictive Medicine, Sydney Medical School, University of Sydney, Sydney, New South Wales 2006, Australia; The Langton Centre, South East Sydney Local Health District (SESLHD), Drug and Alcohol Services, 2010, Australia
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