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Sampedro-Piquero P, Buades-Sitjar F, Capilla A, Zancada-Menéndez C, González-Baeza A, Moreno-Fernández RD. Risky alcohol use during youth: Impact on emotion, cognitive networks, and resting-state EEG activity. Prog Neuropsychopharmacol Biol Psychiatry 2024; 132:110994. [PMID: 38514039 DOI: 10.1016/j.pnpbp.2024.110994] [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] [Received: 12/06/2023] [Revised: 03/14/2024] [Accepted: 03/17/2024] [Indexed: 03/23/2024]
Abstract
The identification of the risk factors of alcohol consumption in youths is crucial for early interventions focused on reducing harmful alcohol use. In our study, 82 college students (40 healthy control (CO group) and 42 with risky alcohol use (RAU group) determined by AUDIT questionnaire) between the ages of 18 and 25 years underwent a comprehensive neuropsychological assessment covering emotional and cognitive functioning. Their resting-state activity was also recorded with an EEG for 10 min with their eyes open (EO) and 10 min with their eyes closed (EC) and analyzed using the Fitting Oscillations & One-Over-F (FOOOF) paradigm. After adjusting for sex, those in the RAU group had higher emotional dysregulation and impulsivity traits. The RAU girls presented more emotional regulation problems, such as dysregulation and negative urgency compared with the RAU boys. The RAU youths had significantly worse functioning in several cognitive domains, such as sustained attention, verbal memory, and executive functions. Cognitive network analysis revealed a different pattern of connections in each group showing that in the RAU group, the verbal memory domain had the highest connection with other cognitive functions. The EEG analyses did not reveal any significant differences between the CO and the RAU groups. However, we observed only in the EO condition that boys the from the RAU group displayed a higher theta/beta ratio than the RAU girls, whereas these differences were not observed within the CO group. Our findings highlight the need to explore more deeply the emotional, cognitive and brain changes underlying the RAU in young people.
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Affiliation(s)
- P Sampedro-Piquero
- Departamento de Psicología Biológica y de la Salud, Facultad de Psicología, Universidad Autónoma de Madrid, Spain.
| | - F Buades-Sitjar
- Departamento de Psicología Biológica y de la Salud, Facultad de Psicología, Universidad Autónoma de Madrid, Spain
| | - A Capilla
- Departamento de Psicología Biológica y de la Salud, Facultad de Psicología, Universidad Autónoma de Madrid, Spain
| | - C Zancada-Menéndez
- Facultad de Ciencias de la Salud, Universidad Internacional de La Rioja (UNIR), Logroño, Spain
| | - A González-Baeza
- Departamento de Psicología Biológica y de la Salud, Facultad de Psicología, Universidad Autónoma de Madrid, Spain
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2
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Barr PB, Neale Z, Chatzinakos C, Schulman J, Mullins N, Zhang J, Chorlian DB, Kamarajan C, Kinreich S, Pandey AK, Pandey G, Saenz de Viteri S, Acion L, Bauer L, Bucholz KK, Chan G, Dick DM, Edenberg HJ, Foroud T, Goate A, Hesselbrock V, Johnson EC, Kramer J, Lai D, Plawecki MH, Salvatore JE, Wetherill L, Agrawal A, Porjesz B, Meyers JL. Clinical, genomic, and neurophysiological correlates of lifetime suicide attempts among individuals with an alcohol use disorder. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.04.28.23289173. [PMID: 37162915 PMCID: PMC10168504 DOI: 10.1101/2023.04.28.23289173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Research has identified clinical, genomic, and neurophysiological markers associated with suicide attempts (SA) among individuals with psychiatric illness. However, there is limited research among those with an alcohol use disorder (AUD), despite their disproportionately higher rates of SA. We examined lifetime SA in 4,068 individuals with DSM-IV alcohol dependence from the Collaborative Study on the Genetics of Alcoholism (23% lifetime suicide attempt; 53% female; mean age: 38). Within participants with an AUD diagnosis, we explored risk across other clinical conditions, polygenic scores (PGS) for comorbid psychiatric problems, and neurocognitive functioning for lifetime suicide attempt. Participants with an AUD who had attempted suicide had greater rates of trauma exposure, major depressive disorder, post-traumatic stress disorder, and other substance use disorders compared to those who had not attempted suicide. Polygenic scores for suicide attempt, depression, and PTSD were associated with reporting a suicide attempt (ORs = 1.22 - 1.44). Participants who reported a SA also had decreased right hemispheric frontal-parietal theta and decreased interhemispheric temporal-parietal alpha electroencephalogram resting-state coherences relative to those who did not, but differences were small. Overall, individuals with an AUD who report a lifetime suicide attempt appear to experience greater levels of trauma, have more severe comorbidities, and carry polygenic risk for a variety of psychiatric problems. Our results demonstrate the need to further investigate suicide attempts in the presence of substance use disorders.
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Affiliation(s)
- Peter B. Barr
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY
- VA New York Harbor Healthcare System, Brooklyn, NY
- Institute for Genomics in Health (IGH), SUNY Downstate Health Sciences University, Brooklyn, NY
- Department of Epidemiology and Biostatistics, School of Public Health, SUNY Downstate Health Sciences University, Brooklyn, NY
| | - Zoe Neale
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY
- VA New York Harbor Healthcare System, Brooklyn, NY
- Institute for Genomics in Health (IGH), SUNY Downstate Health Sciences University, Brooklyn, NY
| | - Chris Chatzinakos
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY
- VA New York Harbor Healthcare System, Brooklyn, NY
- Institute for Genomics in Health (IGH), SUNY Downstate Health Sciences University, Brooklyn, NY
| | | | - Niamh Mullins
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Jian Zhang
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY
| | - David B. Chorlian
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY
| | - Chella Kamarajan
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY
| | - Sivan Kinreich
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY
| | - Ashwini K. Pandey
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY
| | - Gayathri Pandey
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY
| | | | - Laura Acion
- Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, IA
| | - Lance Bauer
- Department of Psychiatry, School of Medicine, University of Connecticut, Farmington, CT
| | - Kathleen K. Bucholz
- Department of Psychiatry, School of Medicine, Washington University in St. Louis, St Louis, MO
| | - Grace Chan
- Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, IA
- Department of Psychiatry, School of Medicine, University of Connecticut, Farmington, CT
| | - Danielle M. Dick
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ
- Rutgers Addiction Research Center, Rutgers University, Piscataway, NJ
| | - Howard J. Edenberg
- Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, IN
- Department of Biochemistry and Molecular Biology, School of Medicine, Indiana University, Indianapolis, IN
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, IN
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN
| | - Alison Goate
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Victor Hesselbrock
- Department of Psychiatry, School of Medicine, University of Connecticut, Farmington, CT
| | - Emma C. Johnson
- Department of Psychiatry, School of Medicine, Washington University in St. Louis, St Louis, MO
| | - John Kramer
- Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, IA
| | - Dongbing Lai
- Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, IN
| | - Martin H. Plawecki
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN
| | - Jessica E. Salvatore
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ
| | - Leah Wetherill
- Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, IN
| | - Arpana Agrawal
- Department of Psychiatry, School of Medicine, Washington University in St. Louis, St Louis, MO
| | - Bernice Porjesz
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY
| | - Jacquelyn L. Meyers
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY
- VA New York Harbor Healthcare System, Brooklyn, NY
- Institute for Genomics in Health (IGH), SUNY Downstate Health Sciences University, Brooklyn, NY
- Department of Epidemiology and Biostatistics, School of Public Health, SUNY Downstate Health Sciences University, Brooklyn, NY
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3
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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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4
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Ardinger CE, Lapish CC, Linsenbardt DN. Repeated Binge Alcohol Drinking Leads to Reductions in Corticostriatal Theta Coherence in Female but not Male Mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.07.581791. [PMID: 38496601 PMCID: PMC10942409 DOI: 10.1101/2024.03.07.581791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Decreased functional connectivity between the striatum and frontal cortex is observed in individuals with alcohol use disorder (AUD), and predicts the probability of relapse in abstinent individuals with AUD. To further our understanding of how repeated alcohol (ethanol; EtOH) consumption impacts the corticostriatal circuit, extracellular electrophysiological recordings (local field potentials; LFPs) were gathered from the nucleus accumbens (NAc) and prefrontal cortex (PFC) of C57BL/6J mice voluntarily consuming EtOH or water using a 'drinking-in-the-dark' (DID) procedure. Following a three-day acclimation period wherein only water access was provided during DID, mice were given 15 consecutive days of access to EtOH. Each session consisted of a 30-minute baseline period where water was available and was followed immediately by a 2-hour period where sippers containing water were replaced with new sippers containing either unsweetened 20% (v/v) EtOH (days 4-18; DID) or water (days 1-3; acclimation). Our analyses focused primarily on theta coherence during bouts of drinking, as differences in this band are associated with several behavioral markers of AUD. Both sexes displayed decreases in theta coherence during the first day of binge EtOH consumption. However, only females displayed further decreases in theta coherence on the 14th day of EtOH access. No differences in theta coherence were observed between the first and final bout on any EtOH drinking days. These results provide additional support for decreases in the functional coupling of corticostriatal circuits as a consequence of alcohol consumption and suggests that female mice are uniquely vulnerable to these effects following repeated EtOH drinking.
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Affiliation(s)
- Cherish E Ardinger
- Addiction Neuroscience, Department of Psychology and Indiana Alcohol Research Center, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, 46202
| | - Christopher C Lapish
- Addiction Neuroscience, Department of Psychology and Indiana Alcohol Research Center, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, 46202
- Indiana University School of Medicine Stark Neuroscience Institute, Indianapolis, Indiana, 46202
| | - David N Linsenbardt
- Department of Neurosciences School of Medicine and Health Sciences Center, University of New Mexico, Albuquerque, New Mexico, 87131
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5
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Barr P, Neale Z, Chatzinakos C, Schulman J, Mullins N, Zhang J, Chorlian D, Kamarajan C, Kinreich S, Pandey A, Pandey G, de Viteri SS, Acion L, Bauer L, Bucholz K, Chan G, Dick D, Edenberg H, Foroud T, Goate A, Hesselbrock V, Johnson E, Kramer J, Lai D, Plawecki M, Salvatore J, Wetherill L, Agrawal A, Porjesz B, Meyers J. Clinical, genomic, and neurophysiological correlates of lifetime suicide attempts among individuals with alcohol dependence. RESEARCH SQUARE 2024:rs.3.rs-3894892. [PMID: 38405959 PMCID: PMC10889049 DOI: 10.21203/rs.3.rs-3894892/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Research has identified clinical, genomic, and neurophysiological markers associated with suicide attempts (SA) among individuals with psychiatric illness. However, there is limited research among those with an alcohol use disorder (AUD), despite their disproportionately higher rates of SA. We examined lifetime SA in 4,068 individuals with DSM-IV alcohol dependence from the Collaborative Study on the Genetics of Alcoholism (23% lifetime suicide attempt; 53% female; 17% Admixed African American ancestries; mean age: 38). We 1) conducted a genome-wide association study (GWAS) of SA and performed downstream analyses to determine whether we could identify specific biological pathways of risk, and 2) explored risk in aggregate across other clinical conditions, polygenic scores (PGS) for comorbid psychiatric problems, and neurocognitive functioning between those with AD who have and have not reported a lifetime suicide attempt. The GWAS and downstream analyses did not produce any significant associations. Participants with an AUD who had attempted suicide had greater rates of trauma exposure, major depressive disorder, post-traumatic stress disorder, and other substance use disorders compared to those who had not attempted suicide. Polygenic scores for suicide attempt, depression, and PTSD were associated with reporting a suicide attempt (ORs = 1.22-1.44). Participants who reported a SA also had decreased right hemispheric frontal-parietal theta and decreased interhemispheric temporal-parietal alpha electroencephalogram resting-state coherences relative to those who did not, but differences were small. Overall, individuals with alcohol dependence who report SA appear to experience a variety of severe comorbidities and elevated polygenic risk for SA. Our results demonstrate the need to further investigate suicide attempts in the presence of substance use disorders.
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Affiliation(s)
- Peter Barr
- SUNY Downstate Health Sciences University
| | - Zoe Neale
- SUNY Downstate Health Sciences University
| | | | | | | | | | | | | | | | - Ashwini Pandey
- State University of New York Downstate Health Sciences University
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Jacquelyn Meyers
- State University of New York (SUNY) Downstate Health Sciences University
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6
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Meyers JL, McCutcheon VV, Horne-Osipenko KA, Waters LR, Barr P, Chan G, Chorlian DB, Johnson EC, Kuo SIC, Kramer JR, Dick DM, Kuperman S, Kamarajan C, Pandey G, Singman D, de Viteri SSS, Salvatore JE, Bierut LJ, Foroud T, Goate A, Hesselbrock V, Nurnberger J, Plaweck MH, Schuckit MA, Agrawal A, Edenberg HJ, Bucholz KK, Porjesz B. COVID-19 pandemic stressors are associated with reported increases in frequency of drunkenness among individuals with a history of alcohol use disorder. Transl Psychiatry 2023; 13:311. [PMID: 37803048 PMCID: PMC10558437 DOI: 10.1038/s41398-023-02577-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 07/25/2023] [Accepted: 07/31/2023] [Indexed: 10/08/2023] Open
Abstract
Some sources report increases in alcohol use have been observed since the start of the COVID-19 pandemic, particularly among women. Cross-sectional studies suggest that specific COVID-19-related stressful experiences (e.g., social disconnection) may be driving such increases in the general population. Few studies have explored these topics among individuals with a history of Alcohol Use Disorders (AUD), an especially vulnerable population. Drawing on recent data collected by the Collaborative Study on the Genetics of Alcoholism (COGA; COVID-19 study N = 1651, 62% women, age range: 30-91) in conjunction with AUD history data collected on the sample since 1990, we investigated associations of COVID-19 related stressors and coping activities with changes in drunkenness frequency since the start of the pandemic. Analyses were conducted for those without a history of AUD (N: 645) and three groups of participants with a history of AUD prior to the start of the pandemic: (1) those experiencing AUD symptoms (N: 606), (2) those in remission who were drinking (N: 231), and (3) those in remission who were abstinent (had not consumed alcohol for 5+ years; N: 169). Gender-stratified models were also examined. Exploratory analyses examined the moderating effects of 'problematic alcohol use' polygenic risk scores (PRS) and neural connectivity (i.e., posterior interhemispheric alpha EEG coherence) on associations between COVID-19 stressors and coping activities with changes in the frequency of drunkenness. Increases in drunkenness frequency since the start of the pandemic were higher among those with a lifetime AUD diagnosis experiencing symptoms prior to the start of the pandemic (14% reported increased drunkenness) when compared to those without a history of AUD (5% reported increased drunkenness). Among individuals in remission from AUD prior to the start of the pandemic, rates of increased drunkenness were 10% for those who were drinking pre-pandemic and 4% for those who had previously been abstinent. Across all groups, women reported nominally greater increases in drunkenness frequency when compared with men, although only women experiencing pre-pandemic AUD symptoms reported significantly greater rates of increased drunkenness since the start of the pandemic compared to men in this group (17% of women vs. 5% of men). Among those without a prior history of AUD, associations between COVID-19 risk and protective factors with increases in drunkenness frequency were not observed. Among all groups with a history of AUD (including those with AUD symptoms and those remitted from AUD), perceived stress was associated with increases in drunkenness. Among the remitted-abstinent group, essential worker status was associated with increases in drunkenness. Gender differences in these associations were observed: among women in the remitted-abstinent group, essential worker status, perceived stress, media consumption, and decreased social interactions were associated with increases in drunkenness. Among men in the remitted-drinking group, perceived stress was associated with increases in drunkenness, and increased relationship quality was associated with decreases in drunkenness. Exploratory analyses indicated that associations between family illness or death with increases in drunkenness and increased relationship quality with decreases in drunkenness were more pronounced among the remitted-drinking participants with higher PRS. Associations between family illness or death, media consumption, and economic hardships with increases in drunkenness and healthy coping with decreases in drunkenness were more pronounced among the remitted-abstinent group with lower interhemispheric alpha EEG connectivity. Our results demonstrated that only individuals with pre-pandemic AUD symptoms reported greater increases in drunkenness frequency since the start of the COVID-19 pandemic compared to those without a lifetime history of AUD. This increase was more pronounced among women than men in this group. However, COVID-19-related stressors and coping activities were associated with changes in the frequency of drunkenness among all groups of participants with a prior history of AUD, including those experiencing AUD symptoms, as well as abstinent and non-abstinent participants in remission. Perceived stress, essential worker status, media consumption, social connections (especially for women), and relationship quality (especially for men) are specific areas of focus for designing intervention and prevention strategies aimed at reducing pandemic-related alcohol misuse among this particularly vulnerable group. Interestingly, these associations were not observed for individuals without a prior history of AUD, supporting prior literature that demonstrates that widespread stressors (e.g., pandemics, terrorist attacks) disproportionately impact the mental health and alcohol use of those with a prior history of problems.
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Affiliation(s)
- Jacquelyn L Meyers
- Department of Psychiatry and Behavioral Sciences, State University of New York Downstate Medical Center, Brooklyn, NY, USA.
| | - Vivia V McCutcheon
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Kristina A Horne-Osipenko
- Department of Psychiatry and Behavioral Sciences, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Lawrence R Waters
- Department of Psychiatry and Behavioral Sciences, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Peter Barr
- Department of Psychiatry and Behavioral Sciences, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Grace Chan
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - David B Chorlian
- Department of Psychiatry and Behavioral Sciences, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Emma C Johnson
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Sally I-Chun Kuo
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ, USA
| | - John R Kramer
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Danielle M Dick
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ, USA
| | - Samuel Kuperman
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Chella Kamarajan
- Department of Psychiatry and Behavioral Sciences, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Gayathri Pandey
- Department of Psychiatry and Behavioral Sciences, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Dzov Singman
- Department of Psychiatry and Behavioral Sciences, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Stacey Subbie-Saenz de Viteri
- Department of Psychiatry and Behavioral Sciences, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Jessica E Salvatore
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ, USA
| | - Laura J Bierut
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Tatiana Foroud
- Departments of Genetics and Genomic Sciences, Neuroscience, and Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alison Goate
- Departments of Genetics and Genomic Sciences, Neuroscience, and Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Victor Hesselbrock
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - John Nurnberger
- Department of Biochemistry and Molecular Biology and Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Martin H Plaweck
- Department of Biochemistry and Molecular Biology and Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Marc A Schuckit
- Department of Psychiatry, University of California San Diego Medical School, La Jolla, CA, USA
| | - Arpana Agrawal
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Howard J Edenberg
- Department of Biochemistry and Molecular Biology and Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kathleen K Bucholz
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Bernice Porjesz
- Department of Psychiatry and Behavioral Sciences, State University of New York Downstate Medical Center, Brooklyn, NY, USA
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7
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Cavicchioli M, Galbiati A, Tobia V, Ogliari A. Genetic factors linked to aberrant neural activity of individuals with substance use disorder phenotypes: A systematic review and meta-analysis of EEG studies. J Addict Dis 2023:1-12. [PMID: 37423772 DOI: 10.1080/10550887.2023.2232252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Background: Alterations in EEG activity have been considered valid endophenotypes of substance use disorders (SUDs). Empirical evidence has supported the association between genetic factors (e.g., genes, single nucleotide polymorphisms [SNPs]) and SUDs, considering both clinical samples and individuals with a positive family history of SUDs [F+SUD]). Nevertheless, the relationship between genetic factors and intermediate phenotypes (i.e., altered EEG activity) among individuals with SUD phenotypes remains unclear.Objective(s): The current study aims at summarizing genetic factors linked to aberrant EEG activity among individuals with SUDs and those with F+SUD.Methods: Sixteen studies (5 [N = 986] + 11 from the Collaborative Studies On Genetics of Alcoholism [COGA] sample [432 ≤ N ≤ 8810]) were included for a qualitative systematic review. Thirteen studies (5 + 8 studies from the COGA sample) were used for multi-level meta-analytic procedures.Results: Qualitative analyses highlighted a multivariate genetic architecture linked to alterations in EEG waves among individuals with SUD phenotypes (i.e., augmented resting-state beta waves; reduced resting-state alpha waves; reduced resting-state and task-dependent theta waves). The most recurrent genetic factors were involved in cellular energy homeostasis, modulation of inhibitory and excitatory neural activity together with neural cell growth. Meta-analytic results showed a moderate association between genetic factors and altered resting-state and task-dependent EEG activity. Meta-analytic results also suggested non-additive genetic effects on altered EEG activity.Conclusions: Complex genetic interactions mediating neural activity and brain development might constitute a causal pathway toward intermediate phenotypes associated with phenotypic features, which in turn are linked to SUDs.
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Affiliation(s)
- Marco Cavicchioli
- Department of Psychology, Vita-Salute San Raffaele University, Milan, Italy
| | - Andrea Galbiati
- Vita-Salute San Raffaele University, Milan, Italy
- IRCCS San Raffaele Scientific Institute, Neurology - Sleep Disorders Center, Milan, Italy
| | - Valentina Tobia
- Child in Mind Lab, Vita-Salute San Raffaele University, Milan, Italy
| | - Anna Ogliari
- Child in Mind Lab, Vita-Salute San Raffaele University, Milan, Italy
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8
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Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023; 21:241. [PMID: 37400814 DOI: 10.1186/s12916-023-02941-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
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Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
| | - Bowen Hu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kuan Miao
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Xingmei Gu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Yancheng Tang
- School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xin Dai
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangdong, China
| | - Artemiy Leonov
- School of Psychology, Clark University, Worcester, MA, USA
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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9
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Xiong X, Feng J, Zhang Y, Wu D, Yi S, Wang C, Liu R, He J. Improved HHT-microstate analysis of EEG in nicotine addicts. Front Neurosci 2023; 17:1174399. [PMID: 37292161 PMCID: PMC10244792 DOI: 10.3389/fnins.2023.1174399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 05/08/2023] [Indexed: 06/10/2023] Open
Abstract
Background Substance addiction is a chronic disease which causes great harm to modern society and individuals. At present, many studies have applied EEG analysis methods to the substance addiction detection and treatment. As a tool to describe the spatio-temporal dynamic characteristics of large-scale electrophysiological data, EEG microstate analysis has been widely used, which is an effective method to study the relationship between EEG electrodynamics and cognition or disease. Methods To study the difference of EEG microstate parameters of nicotine addicts at each frequency band, we combine an improved Hilbert Huang Transformation (HHT) decomposition with microstate analysis, which is applied to the EEG of nicotine addicts. Results After using improved HHT-Microstate method, we notice that there is significant difference in EEG microstates of nicotine addicts between viewing smoke pictures group (smoke) and viewing neutral pictures group (neutral). Firstly, there is a significant difference in EEG microstates at full-frequency band between smoke and neutral group. Compared with the FIR-Microstate method, the similarity index of microstate topographic maps at alpha and beta bands had significant differences between smoke and neutral group. Secondly, we find significant class × group interactions for microstate parameters at delta, alpha and beta bands. Finally, the microstate parameters at delta, alpha and beta bands obtained by the improved HHT-microstate analysis method are selected as features for classification and detection under the Gaussian kernel support vector machine. The highest accuracy is 92% sensitivity is 94% and specificity is 91%, which can more effectively detect and identify addiction diseases than FIR-Microstate and FIR-Riemann methods. Conclusion Thus, the improved HHT-Microstate analysis method can effectively identify substance addiction diseases and provide new ideas and insights for the brain research of nicotine addiction.
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Affiliation(s)
- Xin Xiong
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
| | - Jiannan Feng
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
| | - Yaru Zhang
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
| | - Di Wu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
| | - Sanli Yi
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
| | - Chunwu Wang
- College of Physics and Electronic Engineering, Hanshan Normal University, Chaozhou, China
| | - Ruixiang Liu
- Department of Clinical Psychology, Second People's Hospital of Yunnan, Kunming, China
| | - Jianfeng He
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
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10
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Marvi N, Haddadnia J, Fayyazi Bordbar MR. An automated drug dependence detection system based on EEG. Comput Biol Med 2023; 158:106853. [PMID: 37030264 DOI: 10.1016/j.compbiomed.2023.106853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 02/13/2023] [Accepted: 03/30/2023] [Indexed: 04/05/2023]
Abstract
OBJECTIVE Substance abuse causes damage to the brain structure and function. This research aim is to design an automated drug dependence detection system based on EEG signals in a Multidrug (MD) abuser. METHODS EEG signals were recorded from participants categorized into MD-dependents (n = 10) and Healthy Control (HC) (n = 12). The Recurrence Plot investigates the dynamic characteristics of the EEG signal. The entropy index (ENTR) measured from the Recurrence Quantification Analysis was considered the complexity index of the delta, theta, alpha, beta, gamma, and all-band EEG signals. Statistical analysis was performed by t-test. The support vector machine technique was used for the data classification. RESULTS The results show decreased ENTR indices in the delta, alpha, beta, gamma, and all-band EEG signal and increased theta band in MD abusers compared to the HC group. That indicated the reduction of complexity in the delta, alpha, beta, gamma, and all-band EEG signals in the MD group. Additionally, the SVM classifier distinguished the MD group from the HC group with 90% accuracy, 89.36% sensitivity, 90.7% specificity, and 89.8% F1 score. CONCLUSIONS AND SIGNIFICANCE The nonlinear analysis of brain data was used to build an automatic diagnostic aid system that could identify HC people apart from those who abuse MD.
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11
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Ma HL, Zeng TA, Jiang L, Zhang M, Li H, Su R, Wang ZX, Chen DM, Xu M, Xie WT, Dang P, Bu XO, Zhang T, Wang TZ. Altered resting-state network connectivity patterns for predicting attentional function in deaf individuals: An EEG study. Hear Res 2023; 429:108696. [PMID: 36669260 DOI: 10.1016/j.heares.2023.108696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 12/22/2022] [Accepted: 01/12/2023] [Indexed: 01/16/2023]
Abstract
Multiple aspects of brain development are influenced by early sensory loss such as deafness. Despite growing evidence of changes in attentional functions for prelingual profoundly deaf, the brain mechanisms underlying these attentional changes remain unclear. This study investigated the relationships between differential attention and the resting-state brain network difference in deaf individuals from the perspective of brain network connectivity. We recruited 36 deaf individuals and 34 healthy controls (HC). We recorded each participant's resting-state electroencephalogram (EEG) and the event-related potential (ERP) data from the Attention Network Test (ANT). The coherence (COH) method and graph theory were used to build brain networks and analyze network connectivity. First, the ERPs of analysis in task states were investigated. Then, we correlated the topological properties of the network functional connectivity with the ERPs. The results revealed a significant correlation between frontal-occipital connection in the resting state and the amplitude of alert N1 amplitude in the alpha band. Specifically, clustering coefficients and global and local efficiency correlate negatively with alert N1 amplitude, whereas the characteristic path length positively correlates with alert N1 amplitude. In addition, deaf individuals exhibited weaker frontal-occipital connections compared to the HC group. In executive control, the deaf group had longer reaction times and larger P3 amplitudes. However, the orienting function did not significantly differ from the HC group. Finally, the alert N1 amplitude in the ANT task for deaf individuals was predicted using a multiple linear regression model based on resting-state EEG network properties. Our results suggest that deafness affects the performance of alerting and executive control while orienting functions develop similarly to hearing individuals. Furthermore, weakened frontal-occipital connections in the deaf brain are a fundamental cause of altered alerting functions in the deaf. These results reveal important effects of brain networks on attentional function from the perspective of brain connections and provide potential physiological biomarkers to predicting attention.
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Affiliation(s)
- Hai-Lin Ma
- Faculty of Education, Shaanxi Normal University, No.199, Chang'an Road, Yanta District, Xi 'an, Shaanxi 710062, China; Plateau Brain Science Research Center, Tibet University /South China Normal University, 850012/Guangzhou, Lhasa 510631, China
| | - Tong-Ao Zeng
- Plateau Brain Science Research Center, Tibet University /South China Normal University, 850012/Guangzhou, Lhasa 510631, China
| | - Lin Jiang
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Mei Zhang
- College of Special Education, Leshan Normal University, Leshan 614000, China
| | - Hao Li
- Plateau Brain Science Research Center, Tibet University /South China Normal University, 850012/Guangzhou, Lhasa 510631, China
| | - Rui Su
- Plateau Brain Science Research Center, Tibet University /South China Normal University, 850012/Guangzhou, Lhasa 510631, China
| | - Zhi-Xin Wang
- Plateau Brain Science Research Center, Tibet University /South China Normal University, 850012/Guangzhou, Lhasa 510631, China; Department of Psychology, Shandong Normal University, No. 88East Wenhua Road, Jinan, Shandong 250014, China
| | - Dong-Mei Chen
- Plateau Brain Science Research Center, Tibet University /South China Normal University, 850012/Guangzhou, Lhasa 510631, China
| | - Meng Xu
- Plateau Brain Science Research Center, Tibet University /South China Normal University, 850012/Guangzhou, Lhasa 510631, China
| | - Wen-Ting Xie
- Plateau Brain Science Research Center, Tibet University /South China Normal University, 850012/Guangzhou, Lhasa 510631, China
| | - Peng Dang
- Plateau Brain Science Research Center, Tibet University /South China Normal University, 850012/Guangzhou, Lhasa 510631, China
| | - Xiao-Ou Bu
- Plateau Brain Science Research Center, Tibet University /South China Normal University, 850012/Guangzhou, Lhasa 510631, China; Faculty of Education, East China Normal University, Shanghai 200062, China
| | - Tao Zhang
- Mental Health Education Center and School of Science, Xihua University, Chengdu 610039, China,.
| | - Ting-Zhao Wang
- Faculty of Education, Shaanxi Normal University, No.199, Chang'an Road, Yanta District, Xi 'an, Shaanxi 710062, China.
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12
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Chen Z, Liu X, Yang Q, Wang YJ, Miao K, Gong Z, Yu Y, Leonov A, Liu C, Feng Z, Chuan-Peng H. Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review. JAMA Netw Open 2023; 6:e231671. [PMID: 36877519 PMCID: PMC9989906 DOI: 10.1001/jamanetworkopen.2023.1671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
IMPORTANCE Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. OBJECTIVE To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. EVIDENCE REVIEW PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. FINDINGS A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). CONCLUSIONS AND RELEVANCE This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.
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Affiliation(s)
- Zhiyi Chen
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Xuerong Liu
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Qingwu Yang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Yan-Jiang Wang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Kuan Miao
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Zheng Gong
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Yang Yu
- School of Psychology, Third Military Medical University, Chongqing, China
| | - Artemiy Leonov
- Department of Psychology, Clark University, Worcester, Massachusetts
| | - Chunlei Liu
- School of Psychology, Qufu Normal University, Qufu, China
| | - Zhengzhi Feng
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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13
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Shen M, Wen P, Song B, Li Y. Detection of alcoholic EEG signals based on whole brain connectivity and convolution neural networks. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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14
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Behzadfar N, Dorvashi M, Shahgholian G. An efficient method for classification of alcoholic and normal electroencephalogram signals based on selection of an appropriate feature. JOURNAL OF MEDICAL SIGNALS & SENSORS 2023. [DOI: 10.4103/jmss.jmss_183_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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15
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Alarefi A, Alhusaini N, Wang X, Tao R, Rui Q, Gao G, Pang L, Qiu B, Zhang X. Alcohol dependence inpatients classification with GLM and hierarchical clustering integration using fMRI data of alcohol multiple scenario cues. Exp Brain Res 2022; 240:2595-2605. [PMID: 36029312 DOI: 10.1007/s00221-022-06447-y] [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: 03/27/2022] [Accepted: 08/18/2022] [Indexed: 11/25/2022]
Abstract
Alterations in brain reactions to alcohol-related cues are a neurobiological characteristic of alcohol dependence (AD) and a prospective target for achieving substantial treatment effects. However, a robust prediction of the differences in inpatients' brain responses to alcohol cues during the treatment process is still required. This study offers a data-driven approach for classifying AD inpatients undertaking alcohol treatment protocols based on their brain responses to alcohol imagery with and without drinking actions. The brain activity of thirty inpatients with AD undergoing treatment was scanned using functional magnetic resonance imaging (fMRI) while seeing alcohol and matched non-alcohol images. The mean values of brain regions of interest (ROI) for alcohol-related brain responses were obtained using general linear modeling (GLM) and subjected to hierarchical clustering analysis. The proposed classification technique identified two distinct subgroups of inpatients. For the two types of cues, subgroup one exhibited significant activation in a wide range of brain regions, while subgroup two showed mainly decreased activation. The proposed technique may aid in detecting the vulnerability of the classified inpatient subgroups, which can suggest allocating the inpatients in the classified subgroups to more effective therapies and developing prognostic future relapse markers in AD.
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Affiliation(s)
- Abdulqawi Alarefi
- Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, 230027, China
| | - Naji Alhusaini
- School of Computer and Information Engineering, Chuzhou University, Chuzhou, 239099, Anhui, China.,School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230009, China
| | - Xunshi Wang
- Hefei Medical Research Center on Alcohol Addiction, Affiliated Psychological Hospital of Anhui Medical University, Hefei Fourth People's Hospital, Anhui Mental Health Center, Hefei, 230017, China
| | - Rui Tao
- Hefei Medical Research Center on Alcohol Addiction, Affiliated Psychological Hospital of Anhui Medical University, Hefei Fourth People's Hospital, Anhui Mental Health Center, Hefei, 230017, China
| | - Qinqin Rui
- Hefei Medical Research Center on Alcohol Addiction, Affiliated Psychological Hospital of Anhui Medical University, Hefei Fourth People's Hospital, Anhui Mental Health Center, Hefei, 230017, China
| | - Guoqing Gao
- Hefei Medical Research Center on Alcohol Addiction, Affiliated Psychological Hospital of Anhui Medical University, Hefei Fourth People's Hospital, Anhui Mental Health Center, Hefei, 230017, China
| | - Liangjun Pang
- Hefei Medical Research Center on Alcohol Addiction, Affiliated Psychological Hospital of Anhui Medical University, Hefei Fourth People's Hospital, Anhui Mental Health Center, Hefei, 230017, China
| | - Bensheng Qiu
- Centers for Biomedical Engineering, School of Information Science and Technology, University of Science and Technology of China, Hefei, 230027, Anhui, China
| | - Xiaochu Zhang
- Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, 230027, China. .,Hefei Medical Research Center on Alcohol Addiction, Affiliated Psychological Hospital of Anhui Medical University, Hefei Fourth People's Hospital, Anhui Mental Health Center, Hefei, 230017, China. .,Centers for Biomedical Engineering, School of Information Science and Technology, University of Science and Technology of China, Hefei, 230027, Anhui, China. .,Application Technology Center of Physical Therapy to Brain Disorders, Institute of Advanced Technology, University of Science & Technology of China, Hefei, 230031, China.
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16
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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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Khan DM, Yahya N, Kamel N, Faye I. Effective Connectivity in Default Mode Network for Alcoholism Diagnosis. IEEE Trans Neural Syst Rehabil Eng 2021; 29:796-808. [PMID: 33900918 DOI: 10.1109/tnsre.2021.3075737] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Alcohol Use Disorder (AUD) is a chronic relapsing brain disease characterized by excessive alcohol use, loss of control over alcohol intake, and negative emotional states under no alcohol consumption. The key factor in successful treatment of AUD is the accurate diagnosis for better medical and therapy management. Conventionally, for individuals to be diagnosed with AUD, certain criteria as outlined in the Diagnostic and Statistical Manual of Mental Disorders (DSM) should be met. However, this process is subjective in nature and could be misleading due to memory problems and dishonesty of some AUD patients. In this paper, an assessment scheme for objective diagnosis of AUD is proposed. For this purpose, EEG recording of 31 healthy controls and 31 AUD patients are used for the calculation of effective connectivity (EC) between the various regions of the brain Default Mode Network (DMN). The EC is estimated using partial directed coherence (PDC) which are then used as input to a 3D Convolutional Neural Network (CNN) for binary classification of AUD cases. Using 5-fold cross validation, the classification of AUD vs. HC effective connectivity matrices using the proposed 3D-CNN gives an accuracy of 87.85 ± 4.64 %. For further validation, 32 and 30 subjects are randomly selected for training and testing, respectively, giving 100% correct classification of all the testing subjects.
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Kinreich S, Meyers JL, Maron-Katz A, Kamarajan C, Pandey AK, Chorlian DB, Zhang J, Pandey G, Subbie-Saenz de Viteri S, Pitti D, Anokhin AP, Bauer L, Hesselbrock V, Schuckit MA, Edenberg HJ, Porjesz B. Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study. Mol Psychiatry 2021; 26:1133-1141. [PMID: 31595034 PMCID: PMC7138692 DOI: 10.1038/s41380-019-0534-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 09/11/2019] [Accepted: 09/20/2019] [Indexed: 11/09/2022]
Abstract
Predictive models have succeeded in distinguishing between individuals with Alcohol use Disorder (AUD) and controls. However, predictive models identifying who is prone to develop AUD and the biomarkers indicating a predisposition to AUD are still unclear. Our sample (n = 656) included offspring and non-offspring of European American (EA) and African American (AA) ancestry from the Collaborative Study of the Genetics of Alcoholism (COGA) who were recruited as early as age 12 and were unaffected at first assessment and reassessed years later as AUD (DSM-5) (n = 328) or unaffected (n = 328). Machine learning analysis was performed for 220 EEG measures, 149 alcohol-related single nucleotide polymorphisms (SNPs) from a recent large Genome-wide Association Study (GWAS) of alcohol use/misuse and two family history (mother DSM-5 AUD and father DSM-5 AUD) features using supervised, Linear Support Vector Machine (SVM) classifier to test which features assessed before developing AUD predict those who go on to develop AUD. Age, gender, and ancestry stratified analyses were performed. Results indicate significant and higher accuracy rates for the AA compared with the EA prediction models and a higher model accuracy trend among females compared with males for both ancestries. Combined EEG and SNP features model outperformed models based on only EEG features or only SNP features for both EA and AA samples. This multidimensional superiority was confirmed in a follow-up analysis in the AA age groups (12-15, 16-19, 20-30) and EA age group (16-19). In both ancestry samples, the youngest age group achieved higher accuracy score than the two other older age groups. Maternal AUD increased the model's accuracy in both ancestries' samples. Several discriminative EEG measures and SNPs features were identified, including lower posterior gamma, higher slow wave connectivity (delta, theta, alpha), higher frontal gamma ratio, higher beta correlation in the parietal area, and 5 SNPs: rs4780836, rs2605140, rs11690265, rs692854, and rs13380649. Results highlight the significance of sampling uniformity followed by stratified (e.g., ancestry, gender, developmental period) analysis, and wider selection of features, to generate better prediction scores allowing a more accurate estimation of AUD development.
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Affiliation(s)
- Sivan Kinreich
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA.
| | - Jacquelyn L Meyers
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Adi Maron-Katz
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Chella Kamarajan
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Ashwini K Pandey
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - David B Chorlian
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Jian Zhang
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Gayathri Pandey
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | | | - Dan Pitti
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Andrey P Anokhin
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Lance Bauer
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Victor Hesselbrock
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Marc A Schuckit
- Department of Psychiatry, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Howard J Edenberg
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Bernice Porjesz
- Department of Psychiatry, State University of New York Downstate Medical Center, Brooklyn, NY, USA
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19
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Vanneste S, De Ridder D. Chronic pain as a brain imbalance between pain input and pain suppression. Brain Commun 2021; 3:fcab014. [PMID: 33758824 PMCID: PMC7966784 DOI: 10.1093/braincomms/fcab014] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 11/19/2020] [Accepted: 11/23/2020] [Indexed: 12/20/2022] Open
Abstract
Chronic pain is pain that persists beyond the expected period of healing. The subjective experience of chronic pain results from pathological brain network interactions, rather than from persisting physiological sensory input of nociceptors. We hypothesize that pain is an imbalance between pain evoking dorsal anterior cingulate cortex and somatosensory cortex and pain suppression (i.e. pregenual anterior cingulate cortex). This imbalance can be measured objectively by current density ratios between pain input and pain inhibition. A balance between areas involved in pain input and pain suppression requires communication, which can be objectively identified by connectivity measures, both functional and effective connectivity. In patients with chronic neuropathic pain, electroencephalography is performed with source localization demonstrating that pain is reflected by an abnormal ratio between the dorsal anterior cingulate cortex, somatosensory cortex and pregenual anterior cingulate cortex. Functional connectivity demonstrates decreased communication between these areas, and effective connectivity puts the culprit at the dorsal anterior cingulate cortex, suggesting that the problem is related to abnormal behavioral relevance attached to the pain. In conclusion, chronic pain can be considered as an imbalance between pain input and pain suppression.
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Affiliation(s)
- Sven Vanneste
- Global Brain Health Institute, Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Dirk De Ridder
- Department of Surgical Sciences, Section of Neurosurgery, Dunedin School of Medicine, University of Otago, 9016 Dunedin, New Zealand
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20
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Bavkar S, Iyer B, Deosarkar S. Optimal EEG channels selection for alcoholism screening using EMD domain statistical features and harmony search algorithm. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2020.11.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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21
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Almeida-Antunes N, Crego A, Carbia C, Sousa SS, Rodrigues R, Sampaio A, López-Caneda E. Electroencephalographic signatures of the binge drinking pattern during adolescence and young adulthood: A PRISMA-driven systematic review. NEUROIMAGE-CLINICAL 2020; 29:102537. [PMID: 33418172 PMCID: PMC7803655 DOI: 10.1016/j.nicl.2020.102537] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 11/13/2020] [Accepted: 12/14/2020] [Indexed: 01/10/2023]
Abstract
Controls and binge drinkers (BDs) do not differ in their behavioral performance. BDs show increased neural activity during attention, working memory and inhibition. Augmented P3 amplitude in BDs was the most solid electrophysiological finding. Evidence does not support specific gender vulnerabilities to the effects of BD. Memory, emotional processing and decision-making processes need further exploration.
Research on neurophysiological impairments associated with binge drinking (BD), an excessive but episodic alcohol use pattern, has significantly increased over the last decade. This work is the first to systematically review –following PRISMA guidelines- the empirical evidence regarding the effects of BD on neural activity –assessed by electroencephalography- of adolescents and young adults. A systematic review was conducted in 34 studies (N = 1723). Results indicated that binge drinkers (BDs) showed similar behavioral performance as non/low drinkers. The most solid electrophysiological finding was an augmented P3 amplitude during attention, working memory and inhibition tasks. This increased neural activity suggests the recruitment of additional resources to perform the task at adequate/successful levels, which supports the neurocompensation hypothesis. Similar to alcoholics, BDs also displayed increased reactivity to alcohol-related cues, augmented resting-state electrophysiological signal and reduced activity during error detection –which gives support to the continuum hypothesis. Evidence does not seem to support greater vulnerability to BD in females. Replication and longitudinal studies are required to account for mixed results and to elucidate the extent/direction of the neural impairments associated with BD.
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Affiliation(s)
- Natália Almeida-Antunes
- Psychological Neuroscience Laboratory (PNL), Research Center in Psychology (CIPsi), School of Psychology, University of Minho, Campus de Gualtar, Portugal
| | - Alberto Crego
- Psychological Neuroscience Laboratory (PNL), Research Center in Psychology (CIPsi), School of Psychology, University of Minho, Campus de Gualtar, Portugal
| | - Carina Carbia
- APC Microbiome Ireland, University College Cork, Cork, Ireland
| | - Sónia S Sousa
- Psychological Neuroscience Laboratory (PNL), Research Center in Psychology (CIPsi), School of Psychology, University of Minho, Campus de Gualtar, Portugal
| | - Rui Rodrigues
- Psychological Neuroscience Laboratory (PNL), Research Center in Psychology (CIPsi), School of Psychology, University of Minho, Campus de Gualtar, Portugal
| | - Adriana Sampaio
- Psychological Neuroscience Laboratory (PNL), Research Center in Psychology (CIPsi), School of Psychology, University of Minho, Campus de Gualtar, Portugal
| | - Eduardo López-Caneda
- Psychological Neuroscience Laboratory (PNL), Research Center in Psychology (CIPsi), School of Psychology, University of Minho, Campus de Gualtar, Portugal.
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22
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Prabhakar SK, Rajaguru H. Alcoholic EEG signal classification with Correlation Dimension based distance metrics approach and Modified Adaboost classification. Heliyon 2020; 6:e05689. [PMID: 33364482 PMCID: PMC7750377 DOI: 10.1016/j.heliyon.2020.e05689] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 09/14/2020] [Accepted: 12/04/2020] [Indexed: 11/16/2022] Open
Abstract
The basic function of the brain is severely affected by alcoholism. For the easy depiction and assessment of the mental condition of a human brain, Electroencephalography (EEG) signals are highly useful as it can record and measure the electrical activities of the brain much to the satisfaction of doctors and researchers. Utilizing the standard conventional techniques is quite hectic to derive the useful information as these signals are highly non-linear and non-stationary in nature. While recording the EEG signals, the activities of the neurons are recorded from various scalp regions which has varied characteristics and has a very low magnitude. Therefore, human interpretation of such signals is very difficult and consumes a lot of time. Hence, with the advent of Computer Aided Diagnosis (CAD) Techniques, identifying the normal versus alcoholic EEG signals has been of great utility in the medical field. In this work, we perform the initial clustering of the alcoholic EEG signals by means of using Correlation Dimension (CD) for easy feature extraction and then the suitable features are selected in it by means of employing various distance metrics like correlation distance, city block distance, cosine distance and chebyshev distance. Proceeding in such a methodology aids and assures that a good discrimination could be achieved between normal and alcoholic EEG signals using non-linear features. Finally, classification is then carried out with the suitable classifiers chosen such as Adaboost.RT classifier, the proposed Modified Adaboost.RT classifier by means of introducing Ridge and Lasso based soft thresholding technique, Random Forest with bootstrap resampling technique, Artificial Neural Networks (ANN) such as Radial Basis Functions (RBF) and Multi-Layer Perceptron (MLP), Support Vector Machine (SVM) with Linear, Polynomial and RBF Kernel, Naïve Bayesian Classifier (NBC), K-means classifier, and K Nearest Neighbor (KNN) Classifier and the results are analyzed. Results report a comparatively high classification accuracy of about 98.99% when correlation distance metrics are utilized with CD and the proposed Modified Adaboost.RT classifier using Ridge based soft thresholding technique.
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Affiliation(s)
- Sunil Kumar Prabhakar
- Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, South Korea
| | - Harikumar Rajaguru
- Department of ECE, Bannari Amman Institute of Technology, Sathyamangalam, 638402, India
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23
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Erguzel TT, Uyulan C, Unsalver B, Evrensel A, Cebi M, Noyan CO, Metin B, Eryilmaz G, Sayar GH, Tarhan N. Entropy: A Promising EEG Biomarker Dichotomizing Subjects With Opioid Use Disorder and Healthy Controls. Clin EEG Neurosci 2020; 51:373-381. [PMID: 32043373 DOI: 10.1177/1550059420905724] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Electroencephalography (EEG) signals are known to be nonstationary and often multicomponential signals containing information about the condition of the brain. Since the EEG signal has complex, nonlinear, nonstationary, and highly random behaviour, numerous linear feature extraction methods related to the short-time windowing technique do not satisfy higher classification accuracy. Since biosignals are highly subjective, the symptoms may appear at random in the time scale and very small variations in EEG signals may depict a definite type of brain abnormality it is valuable and vital to extract and analyze the EEG signal parameters using computers. The challenge is to design and develop signal processing algorithms that extract this subtle information and use it for diagnosis, monitoring, and treatment of subjects suffering from psychiatric disorders. For this purpose, finite impulse response-based filtering process was employed rather than traditional time and frequency domain methods. Finite impulse response subbands were analyzed further to obtain feature vectors of different entropy markers and these features were fed into a classifier namely multilayer perceptron. The performances of the classifiers were finally compared considering overall classification accuracies, area under receiver operating characteristic curve scores. Our results underline the potential benefit of the introduced methodology is promising and is to be treated as a clinical interface in dichotomizing substance use disorders subjects and for other medical data analysis studies. The results also indicate that entropy estimators can distinguish normal and opioid use disorder subjects. EEG data and theta frequency band have distinctive capability for almost all types of entropies while nonextensive Tsallis entropy outperforms compared with other types of entropies.
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Affiliation(s)
- Turker Tekin Erguzel
- Department of Software Engineering, Faculty of Engineering and Natural Sciences, Uskudar University, Istanbul, Turkey
| | - Caglar Uyulan
- Department of Mechatronics, Faculty of Engineering, Bulent Evevit University, Zonguldak, Turkey
| | - Baris Unsalver
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | - Alper Evrensel
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | - Merve Cebi
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey
| | - Cemal Onur Noyan
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | - Baris Metin
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | - Gul Eryilmaz
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | - Gokben Hizli Sayar
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | - Nevzat Tarhan
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
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24
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Rodríguez-González V, Gómez C, Shigihara Y, Hoshi H, Revilla-Vallejo M, Hornero R, Poza J. Consistency of local activation parameters at sensor- and source-level in neural signals. J Neural Eng 2020; 17:056020. [PMID: 33055364 DOI: 10.1088/1741-2552/abb582] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
OBJECTIVE Although magnetoencephalography and electroencephalography (M/EEG) signals at sensor level are robust and reliable, they suffer from different degrees of distortion due to changes in brain tissue conductivities, known as field spread and volume conduction effects. To estimate original neural generators from M/EEG activity acquired at sensor level, diverse source localisation algorithms have been proposed; however, they are not exempt from limitations and usually involve time-consuming procedures. Connectivity and network-based M/EEG analyses have been found to be affected by field spread and volume conduction effects; nevertheless, the influence of the aforementioned effects on widely used local activation parameters has not been assessed yet. The goal of this study is to evaluate the consistency of various local activation parameters when they are computed at sensor- and source-level. APPROACH Six spectral (relative power, median frequency, and individual alpha frequency) and non-linear parameters (Lempel-Ziv complexity, sample entropy, and central tendency measure) are computed from M/EEG signals at sensor- and source-level using four source inversion methods: weighted minimum norm estimate (wMNE), standardised low-resolution brain electromagnetic tomography (sLORETA), linear constrained minimum variance (LCMV), and dynamical statistical parametric mapping (dSPM). MAIN RESULTS Our results show that the spectral and non-linear parameters yield similar results at sensor- and source-level, showing high correlation values between them for all the source inversion methods evaluated and both modalities of signal, EEG and MEG. Furthermore, the correlation values remain high when performing coarse-grained spatial analyses. SIGNIFICANCE To the best of our knowledge, this is the first study analysing how field spread and volume conduction effects impact on local activation parameters computed from resting-state neural activity. Our findings evidence that local activation parameters are robust against field spread and volume conduction effects and provide equivalent information at sensor- and source-level even when performing regional analyses.
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25
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Real-Time Stress Assessment Using Sliding Window Based Convolutional Neural Network. SENSORS 2020; 20:s20164400. [PMID: 32784531 PMCID: PMC7472011 DOI: 10.3390/s20164400] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 06/20/2020] [Accepted: 06/22/2020] [Indexed: 01/08/2023]
Abstract
Mental stress has been identified as a significant cause of several bodily disorders, such as depression, hypertension, neural and cardiovascular abnormalities. Conventional stress assessment methods are highly subjective and tedious and tend to lack accuracy. Machine-learning (ML)-based computer-aided diagnosis systems can be used to assess the mental state with reasonable accuracy, but they require offline processing and feature extraction, rendering them unsuitable for real-time applications. This paper presents a real-time mental stress assessment approach based on convolutional neural networks (CNNs). The CNN-based approach afforded real-time mental stress assessment with an accuracy as high as 96%, the sensitivity of 95%, and specificity of 97%. The proposed approach is compared with state-of-the-art ML techniques in terms of accuracy, time utilisation, and quality of features.
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26
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Belkacem AN, Jamil N, Palmer JA, Ouhbi S, Chen C. Brain Computer Interfaces for Improving the Quality of Life of Older Adults and Elderly Patients. Front Neurosci 2020; 14:692. [PMID: 32694979 PMCID: PMC7339951 DOI: 10.3389/fnins.2020.00692] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 06/08/2020] [Indexed: 02/01/2023] Open
Abstract
All people experience aging, and the related physical and health changes, including changes in memory and brain function. These changes may become debilitating leading to an increase in dependence as people get older. Many external aids and tools have been developed to allow older adults and elderly patients to continue to live normal and comfortable lives. This mini-review describes some of the recent studies on cognitive decline and motor control impairment with the goal of advancing non-invasive brain computer interface (BCI) technologies to improve health and wellness of older adults and elderly patients. First, we describe the state of the art in cognitive prosthetics for psychiatric diseases. Then, we describe the state of the art of possible assistive BCI applications for controlling an exoskeleton, a wheelchair and smart home for elderly people with motor control impairments. The basic age-related brain and body changes, the effects of age on cognitive and motor abilities, and several BCI paradigms with typical tasks and outcomes are thoroughly described. We also discuss likely future trends and technologies to assist healthy older adults and elderly patients using innovative BCI applications with minimal technical oversight.
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Affiliation(s)
- Abdelkader Nasreddine Belkacem
- Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Nuraini Jamil
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Jason A. Palmer
- Department of Neurological Diagnosis and Restoration, Osaka University, Suita, Japan
| | - Sofia Ouhbi
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Chao Chen
- Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, China
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27
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Mora-Sánchez A, Pulini AA, Gaume A, Dreyfus G, Vialatte FB. A brain-computer interface for the continuous, real-time monitoring of working memory load in real-world environments. Cogn Neurodyn 2020; 14:301-321. [PMID: 32399073 PMCID: PMC7203264 DOI: 10.1007/s11571-020-09573-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 01/31/2020] [Accepted: 02/17/2020] [Indexed: 12/12/2022] Open
Abstract
We developed a brain-computer interface (BCI) able to continuously monitor working memory (WM) load in real-time (considering the last 2.5 s of brain activity). The BCI is based on biomarkers derived from spectral properties of non-invasive electroencephalography (EEG), subsequently classified by a linear discriminant analysis classifier. The BCI was trained on a visual WM task, tested in a real-time visual WM task, and further validated in a real-time cross task (mental arithmetic). Throughout each trial of the cross task, subjects were given real or sham feedback about their WM load. At the end of the trial, subjects were asked whether the feedback provided was real or sham. The high rate of correct answers provided by the subjects validated not only the global behaviour of the WM-load feedback, but also its real-time dynamics. On average, subjects were able to provide a correct answer 82% of the time, with one subject having 100% accuracy. Possible cognitive and motor confounding factors were disentangled to support the claim that our EEG-based markers correspond indeed to WM.
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Affiliation(s)
- Aldo Mora-Sánchez
- Brain Plasticity Unit, CNRS, UMR8249, Paris, 75005 France
- ESPCI Paris, PSL Research University, Paris, 75005 France
| | - Alfredo-Aram Pulini
- Brain Plasticity Unit, CNRS, UMR8249, Paris, 75005 France
- ESPCI Paris, PSL Research University, Paris, 75005 France
| | - Antoine Gaume
- Brain Plasticity Unit, CNRS, UMR8249, Paris, 75005 France
- ESPCI Paris, PSL Research University, Paris, 75005 France
| | - Gérard Dreyfus
- ESPCI Paris, PSL Research University, Paris, 75005 France
| | - François-Benoît Vialatte
- Brain Plasticity Unit, CNRS, UMR8249, Paris, 75005 France
- ESPCI Paris, PSL Research University, Paris, 75005 France
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28
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Kamarajan C, Ardekani BA, Pandey AK, Chorlian DB, Kinreich S, Pandey G, Meyers JL, Zhang J, Kuang W, Stimus AT, Porjesz B. Random Forest Classification of Alcohol Use Disorder Using EEG Source Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures. Behav Sci (Basel) 2020; 10:bs10030062. [PMID: 32121585 PMCID: PMC7139327 DOI: 10.3390/bs10030062] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 02/26/2020] [Accepted: 02/28/2020] [Indexed: 12/16/2022] Open
Abstract
: Individuals with alcohol use disorder (AUD) manifest a variety of impairments that can be attributed to alterations in specific brain networks. The current study aims to identify features of EEG-based functional connectivity, neuropsychological performance, and impulsivity that can classify individuals with AUD (N = 30) from unaffected controls (CTL, N = 30) using random forest classification. The features included were: (i) EEG source functional connectivity (FC) of the default mode network (DMN) derived using eLORETA algorithm, (ii) neuropsychological scores from the Tower of London test (TOLT) and the visual span test (VST), and (iii) impulsivity factors from the Barratt impulsiveness scale (BIS). The random forest model achieved a classification accuracy of 80% and identified 29 FC connections (among 66 connections per frequency band), 3 neuropsychological variables from VST (total number of correctly performed trials in forward and backward sequences and average time for correct trials in forward sequence) and all four impulsivity scores (motor, non-planning, attentional, and total) as significantly contributing to classifying individuals as either AUD or CTL. Although there was a significant age difference between the groups, most of the top variables that contributed to the classification were not significantly correlated with age. The AUD group showed a predominant pattern of hyperconnectivity among 25 of 29 significant connections, indicating aberrant network functioning during resting state suggestive of neural hyperexcitability and impulsivity. Further, parahippocampal hyperconnectivity with other DMN regions was identified as a major hub region dysregulated in AUD (13 connections overall), possibly due to neural damage from chronic drinking, which may give rise to cognitive impairments, including memory deficits and blackouts. Furthermore, hypoconnectivity observed in four connections (prefrontal nodes connecting posterior right-hemispheric regions) may indicate a weaker or fractured prefrontal connectivity with other regions, which may be related to impaired higher cognitive functions. The AUD group also showed poorer memory performance on the VST task and increased impulsivity in all factors compared to controls. Features from all three domains had significant associations with one another. These results indicate that dysregulated neural connectivity across the DMN regions, especially relating to hyperconnected parahippocampal hub as well as hypoconnected prefrontal hub, may potentially represent neurophysiological biomarkers of AUD, while poor visual memory performance and heightened impulsivity may serve as cognitive-behavioral indices of AUD.
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Affiliation(s)
- Chella Kamarajan
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (D.B.C.); (S.K.); (G.P.); (J.L.M.); (J.Z.); (W.K.); (A.T.S.); (B.P.)
- Correspondence: ; Tel.: +1-718-270-2913
| | - Babak A. Ardekani
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA;
- Department of Psychiatry, NYU School of Medicine, New York, NY 10016, USA
| | - Ashwini K. Pandey
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (D.B.C.); (S.K.); (G.P.); (J.L.M.); (J.Z.); (W.K.); (A.T.S.); (B.P.)
| | - David B. Chorlian
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (D.B.C.); (S.K.); (G.P.); (J.L.M.); (J.Z.); (W.K.); (A.T.S.); (B.P.)
| | - Sivan Kinreich
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (D.B.C.); (S.K.); (G.P.); (J.L.M.); (J.Z.); (W.K.); (A.T.S.); (B.P.)
| | - Gayathri Pandey
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (D.B.C.); (S.K.); (G.P.); (J.L.M.); (J.Z.); (W.K.); (A.T.S.); (B.P.)
| | - Jacquelyn L. Meyers
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (D.B.C.); (S.K.); (G.P.); (J.L.M.); (J.Z.); (W.K.); (A.T.S.); (B.P.)
| | - Jian Zhang
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (D.B.C.); (S.K.); (G.P.); (J.L.M.); (J.Z.); (W.K.); (A.T.S.); (B.P.)
| | - Weipeng Kuang
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (D.B.C.); (S.K.); (G.P.); (J.L.M.); (J.Z.); (W.K.); (A.T.S.); (B.P.)
| | - Arthur T. Stimus
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (D.B.C.); (S.K.); (G.P.); (J.L.M.); (J.Z.); (W.K.); (A.T.S.); (B.P.)
| | - Bernice Porjesz
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (D.B.C.); (S.K.); (G.P.); (J.L.M.); (J.Z.); (W.K.); (A.T.S.); (B.P.)
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O'Halloran L, Rueda‐Delgado LM, Jollans L, Cao Z, Boyle R, Vaughan C, Coey P, Whelan R. Inhibitory-control event-related potentials correlate with individual differences in alcohol use. Addict Biol 2020; 25:e12729. [PMID: 30919532 DOI: 10.1111/adb.12729] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 12/28/2018] [Accepted: 01/23/2019] [Indexed: 12/16/2022]
Abstract
Impulsivity is a multidimensional construct that is related to different aspects of alcohol use, abuse, and dependence. Inhibitory control, one facet of impulsivity, can be assayed using the stop-signal task (SST) and quantified behaviorally via the stop-signal reaction time (SSRT) and electrophysiologically using event-related potentials (ERPs). Research on the relationship between alcohol use and SSRTs, and between alcohol use and inhibitory-control ERPs, is mixed. Here, adult alcohol users (n = 79), with a wide range of scores on the Alcohol Use Disorders Identification Test (AUDIT), completed the SST under electroencephalography (EEG) (70% of participants had AUDIT total scores greater than or equal to 8). Other measures, including demographic, self-report, and task-based measures of impulsivity, personality, and psychological factors, were also recorded. A machine-learning method with penalized linear regression was used to correlate individual differences in alcohol use with impulsivity measures. Four separate models were tested, with out-of-sample validation used to quantify performance. ERPs alone statistically predicted alcohol use (cross-validated r = 0.28), with both early and late ERP components contributing to the model (larger N2, but smaller P3, amplitude). Behavioral data from a wide range of impulsivity measures were also associated with alcohol use (r = 0.37). SSRT was a relatively weak statistical predictor, whereas the Stroop interference effect was relatively strong. The addition of nonimpulsivity behavioral measures did not improve the correlation (r = 0.34) and was similar when ERPs were combined with non-ERP data (r = 0.29). These findings show that inhibitory control ERPs are robustly correlated individual differences in alcohol use.
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Affiliation(s)
| | | | - Lee Jollans
- School of PsychologyTrinity College Dublin Dublin Ireland
| | - Zhipeng Cao
- School of PsychologyTrinity College Dublin Dublin Ireland
| | - Rory Boyle
- School of PsychologyTrinity College Dublin Dublin Ireland
| | | | - Phillip Coey
- School of PsychologyTrinity College Dublin Dublin Ireland
| | - Robert Whelan
- School of PsychologyTrinity College Dublin Dublin Ireland
- Global Brain Health InstituteTrinity College Dublin Dublin Ireland
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30
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Anuragi A, Sisodia DS. Empirical wavelet transform based automated alcoholism detecting using EEG signal features. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101777] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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31
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Kamarajan C, Ardekani BA, Pandey AK, Kinreich S, Pandey G, Chorlian DB, Meyers JL, Zhang J, Bermudez E, Stimus AT, Porjesz B. Random Forest Classification of Alcohol Use Disorder Using fMRI Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures. Brain Sci 2020; 10:brainsci10020115. [PMID: 32093319 PMCID: PMC7071377 DOI: 10.3390/brainsci10020115] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 02/12/2020] [Accepted: 02/18/2020] [Indexed: 12/22/2022] Open
Abstract
Individuals with alcohol use disorder (AUD) are known to manifest a variety of neurocognitive impairments that can be attributed to alterations in specific brain networks. The current study aims to identify specific features of brain connectivity, neuropsychological performance, and impulsivity traits that can classify adult males with AUD (n = 30) from healthy controls (CTL, n = 30) using the Random Forest (RF) classification method. The predictor variables were: (i) fMRI-based within-network functional connectivity (FC) of the Default Mode Network (DMN), (ii) neuropsychological scores from the Tower of London Test (TOLT), and the Visual Span Test (VST), and (iii) impulsivity factors from the Barratt Impulsiveness Scale (BIS). The RF model, with a classification accuracy of 76.67%, identified fourteen DMN connections, two neuropsychological variables (memory span and total correct scores of the forward condition of the VST), and all impulsivity factors as significantly important for classifying participants into either the AUD or CTL group. Specifically, the AUD group manifested hyperconnectivity across the bilateral anterior cingulate cortex and the prefrontal cortex as well as between the bilateral posterior cingulate cortex and the left inferior parietal lobule, while showing hypoconnectivity in long-range anterior-posterior and interhemispheric long-range connections. Individuals with AUD also showed poorer memory performance and increased impulsivity compared to CTL individuals. Furthermore, there were significant associations among FC, impulsivity, neuropsychological performance, and AUD status. These results confirm the previous findings that alterations in specific brain networks coupled with poor neuropsychological functioning and heightened impulsivity may characterize individuals with AUD, who can be efficiently identified using classification algorithms such as Random Forest.
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Affiliation(s)
- Chella Kamarajan
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (S.K.); (G.P.); (D.B.C.); (J.L.M.); (J.Z.); (A.T.S.); (B.P.)
- Correspondence: ; Tel.: +1-718-270-2913
| | - Babak A. Ardekani
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA;
- Department of Psychiatry, NYU School of Medicine, New York, NY 10016, USA;
| | - Ashwini K. Pandey
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (S.K.); (G.P.); (D.B.C.); (J.L.M.); (J.Z.); (A.T.S.); (B.P.)
| | - Sivan Kinreich
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (S.K.); (G.P.); (D.B.C.); (J.L.M.); (J.Z.); (A.T.S.); (B.P.)
| | - Gayathri Pandey
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (S.K.); (G.P.); (D.B.C.); (J.L.M.); (J.Z.); (A.T.S.); (B.P.)
| | - David B. Chorlian
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (S.K.); (G.P.); (D.B.C.); (J.L.M.); (J.Z.); (A.T.S.); (B.P.)
| | - Jacquelyn L. Meyers
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (S.K.); (G.P.); (D.B.C.); (J.L.M.); (J.Z.); (A.T.S.); (B.P.)
| | - Jian Zhang
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (S.K.); (G.P.); (D.B.C.); (J.L.M.); (J.Z.); (A.T.S.); (B.P.)
| | - Elaine Bermudez
- Department of Psychiatry, NYU School of Medicine, New York, NY 10016, USA;
| | - Arthur T. Stimus
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (S.K.); (G.P.); (D.B.C.); (J.L.M.); (J.Z.); (A.T.S.); (B.P.)
| | - Bernice Porjesz
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (S.K.); (G.P.); (D.B.C.); (J.L.M.); (J.Z.); (A.T.S.); (B.P.)
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Khajehpour H, Mohagheghian F, Ekhtiari H, Makkiabadi B, Jafari AH, Eqlimi E, Harirchian MH. Computer-aided classifying and characterizing of methamphetamine use disorder using resting-state EEG. Cogn Neurodyn 2019; 13:519-530. [PMID: 31741689 PMCID: PMC6825232 DOI: 10.1007/s11571-019-09550-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 07/18/2019] [Accepted: 08/01/2019] [Indexed: 12/17/2022] Open
Abstract
Methamphetamine (meth) is potently addictive and is closely linked to high crime rates in the world. Since meth withdrawal is very painful and difficult, most abusers relapse to abuse in traditional treatments. Therefore, developing accurate data-driven methods based on brain functional connectivity could be helpful in classifying and characterizing the neural features of meth dependence to optimize the treatments. Accordingly, in this study, computation of functional connectivity using resting-state EEG was used to classify meth dependence. Firstly, brain functional connectivity networks (FCNs) of 36 meth dependent individuals and 24 normal controls were constructed by weighted phase lag index, in six frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-15 Hz), beta (15-30 Hz), gamma (30-45 Hz) and wideband (1-45 Hz).Then, significant differences in graph metrics and connectivity values of the FCNs were used to distinguish the two groups. Support vector machine classifier had the best performance with 93% accuracy, 100% sensitivity, 83% specificity and 0.94 F-score for differentiating between MDIs and NCs. The best performance yielded when selected features were the combination of connectivity values and graph metrics in the beta frequency band.
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Affiliation(s)
- Hassan Khajehpour
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Fahimeh Mohagheghian
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences (SBMU), Tehran, Iran
| | - Hamed Ekhtiari
- Laureate Institute for Brain Research (LIBR), Tulsa, OK USA
- Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Bahador Makkiabadi
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Amir Homayoun Jafari
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Ehsan Eqlimi
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Mohammad Hossein Harirchian
- Iranian Center of Neurological Research, Neuroscience Institute, Tehran University of Medical Sciences (TUMS), Tehran, Iran
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Si Y, Jiang L, Tao Q, Chen C, Li F, Jiang Y, Zhang T, Cao X, Wan F, Yao D, Xu P. Predicting individual decision-making responses based on the functional connectivity of resting-state EEG. J Neural Eng 2019; 16:066025. [DOI: 10.1088/1741-2552/ab39ce] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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34
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Afshani F, Shalbaf A, Shalbaf R, Sleigh J. Frontal-temporal functional connectivity of EEG signal by standardized permutation mutual information during anesthesia. Cogn Neurodyn 2019; 13:531-540. [PMID: 31741690 DOI: 10.1007/s11571-019-09553-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 07/28/2019] [Accepted: 08/16/2019] [Indexed: 01/01/2023] Open
Abstract
Quantifying brain dynamics during anesthesia is an important challenge for understanding the neurophysiological mechanisms of anesthetic drug effect. Several single channel Electroencephalogram (EEG) indices have been proposed for monitoring anesthetic drug effect. The most commonly used single channel commercial index is the Bispectral index (BIS). However, this monitor has shown some drawbacks. In this study, a nonlinear functional connectivity measure named Standardized Permutation Mutual Information (SPMI) is proposed to describe communication between two-channel EEG signals at frontal and temporal brain regions during a controlled propofol-induced anesthesia and recovery design from eight subjects. The SPMI index has higher correlation with estimated propofol effect-site concentration and has better ability to distinguish three anesthetic states of patient than the other functional connectivity indexes (cross-correlation, coherence, phase analysis) and also the BIS index. Moreover, the SPMI index has a faster reaction to the effect of drug concentration, less variability at the consciousness state and better robustness to noise than BIS.
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Affiliation(s)
- Fahimeh Afshani
- 1Department of Biomedical Engineering, Electronic Branch, Islamic Azad University, Tehran, Iran
| | - Ahmad Shalbaf
- 2Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Reza Shalbaf
- 3Institute for Cognitive Science Studies, Tehran, Iran
| | - Jamie Sleigh
- 4Department of Anesthesia, Waikato Hospital, Hamilton, New Zealand
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35
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Alcohol use disorder detection using EEG Signal features and flexible analytical wavelet transform. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.10.017] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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36
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Analysis of heart rate signals during meditation using visibility graph complexity. Cogn Neurodyn 2019; 13:45-52. [PMID: 30728870 DOI: 10.1007/s11571-018-9501-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 07/19/2018] [Accepted: 08/22/2018] [Indexed: 12/24/2022] Open
Abstract
In the dynamics analysis of heart rate, the complexity of visibility graphs (VGs) is seen as a sign of short term variability in signals. The present study was conducted to investigate the possible impact of meditation on heart rate signals complexity using VG method. In this study, existing heart rate signals in Physionet database were used. The dynamics of the signals were then studied both before and during meditation by examining the complexity of VGs using graph index complexity (GIC). Generally, the obtained results showed that the heart rate signals were more complex during meditation. The simple process of calculating the GIC of VG and its adaptability to the chaotic nature of the biological signals can help in estimating the heart rate complexity in meditation.
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37
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Li F, Liang Y, Zhang L, Yi C, Liao Y, Jiang Y, Si Y, Zhang Y, Yao D, Yu L, Xu P. Transition of brain networks from an interictal to a preictal state preceding a seizure revealed by scalp EEG network analysis. Cogn Neurodyn 2019; 13:175-181. [PMID: 30956721 DOI: 10.1007/s11571-018-09517-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2018] [Revised: 11/29/2018] [Accepted: 12/19/2018] [Indexed: 11/29/2022] Open
Abstract
Epilepsy is a neurological disorder in the brain that is characterized by unprovoked seizures. Epileptic seizures are attributed to abnormal synchronous neuronal activity in the brain. To detect the seizure as early as possible, the identification of specific electroencephalogram (EEG) dynamics is of great importance in investigating the transition of brain activity as the epileptic seizure approaches. In this study, we investigated the transition of brain activity from interictal to preictal states preceding a seizure by combining EEG network and clustering analyses together in different frequency bands. The findings of this study demonstrated the best clustering performance of k-medoids in the beta band; in addition, compared to the interictal state, the preictal state experienced increased synchronization of EEG network connectivity, characterized by relatively higher network properties. These findings can provide helpful insight into the mechanism of epilepsy, which can also be used in the prediction of epileptic seizures and subsequent intervention.
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Affiliation(s)
- Fali Li
- 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Yi Liang
- 2Department of Neurology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China.,3Department of Neurology, Affiliated Hospital of University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731 Sichuan China
| | - Luyan Zhang
- 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Chanlin Yi
- 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuanyuan Liao
- 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuanling Jiang
- 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Yajing Si
- 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Yangsong Zhang
- 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,4School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China
| | - Dezhong Yao
- 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,5School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Liang Yu
- 2Department of Neurology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China.,3Department of Neurology, Affiliated Hospital of University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731 Sichuan China
| | - Peng Xu
- 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,5School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
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38
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Ferreri F, Bourla A, Mouchabac S, Karila L. e-Addictology: An Overview of New Technologies for Assessing and Intervening in Addictive Behaviors. Front Psychiatry 2018; 9:51. [PMID: 29545756 PMCID: PMC5837980 DOI: 10.3389/fpsyt.2018.00051] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Accepted: 02/06/2018] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND New technologies can profoundly change the way we understand psychiatric pathologies and addictive disorders. New concepts are emerging with the development of more accurate means of collecting live data, computerized questionnaires, and the use of passive data. Digital phenotyping, a paradigmatic example, refers to the use of computerized measurement tools to capture the characteristics of different psychiatric disorders. Similarly, machine learning-a form of artificial intelligence-can improve the classification of patients based on patterns that clinicians have not always considered in the past. Remote or automated interventions (web-based or smartphone-based apps), as well as virtual reality and neurofeedback, are already available or under development. OBJECTIVE These recent changes have the potential to disrupt practices, as well as practitioners' beliefs, ethics and representations, and may even call into question their professional culture. However, the impact of new technologies on health professionals' practice in addictive disorder care has yet to be determined. In the present paper, we therefore present an overview of new technology in the field of addiction medicine. METHOD Using the keywords [e-health], [m-health], [computer], [mobile], [smartphone], [wearable], [digital], [machine learning], [ecological momentary assessment], [biofeedback] and [virtual reality], we searched the PubMed database for the most representative articles in the field of assessment and interventions in substance use disorders. RESULTS We screened 595 abstracts and analyzed 92 articles, dividing them into seven categories: e-health program and web-based interventions, machine learning, computerized adaptive testing, wearable devices and digital phenotyping, ecological momentary assessment, biofeedback, and virtual reality. CONCLUSION This overview shows that new technologies can improve assessment and interventions in the field of addictive disorders. The precise role of connected devices, artificial intelligence and remote monitoring remains to be defined. If they are to be used effectively, these tools must be explained and adapted to the different profiles of physicians and patients. The involvement of patients, caregivers and other health professionals is essential to their design and assessment.
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Affiliation(s)
- Florian Ferreri
- Sorbonne Université, UPMC, Department of Adult Psychiatry and Medical Psychology, APHP, Saint-Antoine Hospital, Paris, France
| | - Alexis Bourla
- Sorbonne Université, UPMC, Department of Adult Psychiatry and Medical Psychology, APHP, Saint-Antoine Hospital, Paris, France
| | - Stephane Mouchabac
- Sorbonne Université, UPMC, Department of Adult Psychiatry and Medical Psychology, APHP, Saint-Antoine Hospital, Paris, France
| | - Laurent Karila
- Université Paris Sud - INSERM U1000, Addiction Research and Treatment Center, APHP, Paul Brousse Hospital, Villejuif, France
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Mumtaz W, Vuong PL, Malik AS, Rashid RBA. A review on EEG-based methods for screening and diagnosing alcohol use disorder. Cogn Neurodyn 2017; 12:141-156. [PMID: 29564024 DOI: 10.1007/s11571-017-9465-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Revised: 11/20/2017] [Accepted: 11/29/2017] [Indexed: 01/28/2023] Open
Abstract
The screening test for alcohol use disorder (AUD) patients has been of subjective nature and could be misleading in particular cases such as a misreporting the actual quantity of alcohol intake. Although the neuroimaging modality such as electroencephalography (EEG) has shown promising research results in achieving objectivity during the screening and diagnosis of AUD patients. However, the translation of these findings for clinical applications has been largely understudied and hence less clear. This study advocates the use of EEG as a diagnostic and screening tool for AUD patients that may help the clinicians during clinical decision making. In this context, a comprehensive review on EEG-based methods is provided including related electrophysiological techniques reported in the literature. More specifically, the EEG abnormalities associated with the conditions of AUD patients are summarized. The aim is to explore the potentials of objective techniques involving quantities/features derived from resting EEG, event-related potentials or event-related oscillations data.
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Affiliation(s)
- Wajid Mumtaz
- 1Department of Electrical and Electronic Engineering, Center for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Malaysia
| | - Pham Lam Vuong
- 1Department of Electrical and Electronic Engineering, Center for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Malaysia
| | - Aamir Saeed Malik
- 1Department of Electrical and Electronic Engineering, Center for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Malaysia
| | - Rusdi Bin Abd Rashid
- 2Universiti Malaya, Aras 21, Wisma R&D Universiti Malaya, Jalan Pantai Bharu, 59200 Kuala Lumpur, Malaysia
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López-Caneda E, Cadaveira F, Correas A, Crego A, Maestú F, Rodríguez Holguín S. The Brain of Binge Drinkers at Rest: Alterations in Theta and Beta Oscillations in First-Year College Students with a Binge Drinking Pattern. Front Behav Neurosci 2017; 11:168. [PMID: 28959193 PMCID: PMC5604281 DOI: 10.3389/fnbeh.2017.00168] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Accepted: 08/23/2017] [Indexed: 12/23/2022] Open
Abstract
Background: Previous studies have reported anomalous resting brain activity in the electroencephalogram (EEG) of alcoholics, often reflected as increased power in the beta and theta frequency bands. The effects of binge drinking, the most common pattern of excessive alcohol consumption during adolescence and youth, on brain activity at rest is still poorly known. In this study, we sought to assess the pattern of resting-state EEG oscillations in college-aged binge drinkers (BDs). Methods: Resting-state brain activity during eyes-open and eyes-closed conditions was recorded from 60 channels in 80 first-year undergraduate students (40 controls and 40 BDs). Cortical sources activity of EEG rhythms was estimated using exact Low-Resolution Electromagnetic Tomography (eLORETA) analysis. Results: EEG-source localization analysis revealed that BDs showed, in comparison with controls, significantly higher intracranial current density in the beta frequency band over the right temporal lobe (parahippocampal and fusiform gyri) during eyes-open resting state as well as higher intracranial current density in the theta band over the bilateral occipital cortex (cuneus and lingual gyrus) during eyes-closed resting condition. Conclusions: These findings are in line with previous results observing increased beta and/or theta power following chronic or heavy alcohol drinking in alcohol-dependent subjects and BDs. Increased tonic beta and theta oscillations are suggestive of an augmented cortical excitability and of potential difficulties in the information processing capacity in young BDs. Furthermore, enhanced EEG power in these frequency bands may respond to a neuromaturational delay as a result of excessive alcohol consumption during this critical brain developmental period.
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Affiliation(s)
- Eduardo López-Caneda
- Neuropsychophysiology Lab, Research Center in Psychology (CIPsi), School of Psychology, University of MinhoBraga, Portugal
| | - Fernando Cadaveira
- Department of Clinical Psychology and Psychobiology, University of Santiago de CompostelaSantiago de Compostela, Spain
| | - Angeles Correas
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical TechnologyMadrid, Spain
| | - Alberto Crego
- Neuropsychophysiology Lab, Research Center in Psychology (CIPsi), School of Psychology, University of MinhoBraga, Portugal
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical TechnologyMadrid, Spain.,Department of Basic Psychology II, Complutense University of MadridMadrid, Spain
| | - Socorro Rodríguez Holguín
- Department of Clinical Psychology and Psychobiology, University of Santiago de CompostelaSantiago de Compostela, Spain
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