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Vilar-Ribó L, Cabana-Domínguez J, Alemany S, Llonga N, Arribas L, Grau-López L, Daigre C, Cormand B, Fernàndez-Castillo N, Ramos-Quiroga JA, Soler Artigas M, Ribasés M. Disentangling heterogeneity in substance use disorder: Insights from genome-wide polygenic scores. Transl Psychiatry 2024; 14:221. [PMID: 38811559 DOI: 10.1038/s41398-024-02923-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 05/31/2024] Open
Abstract
Substance use disorder (SUD) is a global health problem with a significant impact on individuals and society. The presentation of SUD is diverse, involving various substances, ages at onset, comorbid conditions, and disease trajectories. Current treatments for SUD struggle to address this heterogeneity, resulting in high relapse rates. SUD often co-occurs with other psychiatric and mental health-related conditions that contribute to the heterogeneity of the disorder and predispose to adverse disease trajectories. Family and genetic studies highlight the role of genetic and environmental factors in the course of SUD, and point to a shared genetic liability between SUDs and comorbid psychopathology. In this study, we aimed to disentangle SUD heterogeneity using a deeply phenotyped SUD cohort and polygenic scores (PGSs) for psychiatric disorders and related traits. We explored associations between PGSs and various SUD-related phenotypes, as well as PGS-environment interactions using information on lifetime emotional, physical, and/or sexual abuse. Our results identify clusters of individuals who exhibit differences in their phenotypic profile and reveal different patterns of associations between SUD-related phenotypes and the genetic liability for mental health-related traits, which may help explain part of the heterogeneity observed in SUD. In our SUD sample, we found associations linking the genetic liability for attention-deficit hyperactivity disorder (ADHD) with lower educational attainment, the genetic liability for post-traumatic stress disorder (PTSD) with higher rates of unemployment, the genetic liability for educational attainment with lower rates of criminal records and unemployment, and the genetic liability for well-being with lower rates of outpatient treatments and fewer problems related to family and social relationships. We also found evidence of PGS-environment interactions showing that genetic liability for suicide attempts worsened the psychiatric status in SUD individuals with a history of emotional physical and/or sexual abuse. Collectively, these data contribute to a better understanding of the role of genetic liability for mental health-related conditions and adverse life experiences in SUD heterogeneity.
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Affiliation(s)
- Laura Vilar-Ribó
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Judit Cabana-Domínguez
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Silvia Alemany
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Natalia Llonga
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - Lorena Arribas
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Lara Grau-López
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
- Addiction and Dual Diagnosis Unit, Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Constanza Daigre
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
- Addiction and Dual Diagnosis Unit, Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Bru Cormand
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
- Institut de Recerca Sant Joan de Déu (IRSJD), Esplugues de Llobregat, Catalonia, Spain
- Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Catalonia, Spain
| | - Noèlia Fernàndez-Castillo
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
- Institut de Recerca Sant Joan de Déu (IRSJD), Esplugues de Llobregat, Catalonia, Spain
- Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Catalonia, Spain
| | - Josep Antoni Ramos-Quiroga
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
- Addiction and Dual Diagnosis Unit, Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - María Soler Artigas
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain.
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia, Spain.
| | - Marta Ribasés
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain.
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia, Spain.
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Lee JY, Song MS, Yoo SY, Jang JH, Lee D, Jung YC, Ahn WY, Choi JS. Multimodal-based machine learning approach to classify features of internet gaming disorder and alcohol use disorder: A sensor-level and source-level resting-state electroencephalography activity and neuropsychological study. Compr Psychiatry 2024; 130:152460. [PMID: 38335572 DOI: 10.1016/j.comppsych.2024.152460] [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: 12/01/2023] [Revised: 01/17/2024] [Accepted: 02/03/2024] [Indexed: 02/12/2024] Open
Abstract
OBJECTIVES Addictions have recently been classified as substance use disorder (SUD) and behavioral addiction (BA), but the concept of BA is still debatable. Therefore, it is necessary to conduct further neuroscientific research to understand the mechanisms of BA to the same extent as SUD. The present study used machine learning (ML) algorithms to investigate the neuropsychological and neurophysiological aspects of addictions in individuals with internet gaming disorder (IGD) and alcohol use disorder (AUD). METHODS We developed three models for distinguishing individuals with IGD from those with AUD, individuals with IGD from healthy controls (HCs), and individuals with AUD from HCs using ML algorithms, including L1-norm support vector machine, random forest, and L1-norm logistic regression (LR). Three distinct feature sets were used for model training: a unimodal-electroencephalography (EEG) feature set combined with sensor- and source-level feature; a unimodal-neuropsychological feature (NF) set included sex, age, depression, anxiety, impulsivity, and general cognitive function, and a multimodal (EEG + NF) feature set. RESULTS The LR model with the multimodal feature set used for the classification of IGD and AUD outperformed the other models (accuracy: 0.712). The important features selected by the model highlighted that the IGD group had differential delta and beta source connectivity between right intrahemispheric regions and distinct sensor-level EEG activities. Among the NFs, sex and age were the important features for good model performance. CONCLUSIONS Using ML techniques, we demonstrated the neurophysiological and neuropsychological similarities and differences between IGD (a BA) and AUD (a SUD).
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Affiliation(s)
- Ji-Yoon Lee
- Department of Health Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
| | - Myeong Seop Song
- Department of Psychology, Seoul National University, Seoul, Republic of Korea
| | - So Young Yoo
- Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea
| | - Joon Hwan Jang
- Department of Psychiatry, Seoul National University Health Service Center, Seoul, Republic of Korea; Department of Human Systems Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Deokjong Lee
- Department of Psychiatry, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea; Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Young-Chul Jung
- Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea; Department of Psychiatry, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Woo-Young Ahn
- Department of Psychology, Seoul National University, Seoul, Republic of Korea; Department of Brain and Cognitive Sciences, Seoul National University, Seoul, Republic of Korea.
| | - Jung-Seok Choi
- Department of Psychiatry, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
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Kurihara K, Shinzato H, Takaesu Y, Kondo T. Drinking behavior patterns may be associated with persistent depressive symptoms after alcohol abstinence in alcohol use disorder. Neuropsychopharmacol Rep 2024. [PMID: 38463015 DOI: 10.1002/npr2.12429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 02/15/2024] [Accepted: 02/25/2024] [Indexed: 03/12/2024] Open
Abstract
AIM This study examined the association between drinking behavior patterns and depressive symptoms after alcohol abstinence in patients with alcohol use disorder (AUD). METHOD We recruited 102 AUD inpatients with baseline depressive symptoms, indicated by scores ≥6 on the Quick Inventory of Depressive Symptomatology Self-Report Japanese Version (QIDS-SR-J) pre-detoxification. Post-4-week abstinence, remission was defined as QIDS-SR-J scores <6. Patients were classified into remitted (n = 51) and persistent (n = 51) groups. Comparative analyses were conducted using patient profiles and the Drinking Behavior Pattern 20-item Questionnaire (DBP-20). Logistic regression identified factors related to post-abstinence persistent depression. Receiver operating characteristic curve analysis determined DBP-20 cutoff scores differentiating between persistent and remitted depression. RESULTS The persistent group exhibited higher scores in the DBP-20 "coping with negative affect" subscale. Logistic regression showed low education, unemployment, and using alcohol for coping as significant factors for persistent depression. Conversely, an automatic drinking pattern indicated natural remission post-abstinence. A subscale score of ≥8 in alcohol use for coping, especially among unemployed patients, predicted persistent depression (sensitivity 86.8%, positive predictive value 73.3%). CONCLUSION Unemployed patients with AUD using alcohol to cope with negative affect may experience residual depression even after detoxification. In contrast, patients with AUD with predominantly automatic drinking behavior may exhibit natural remission post-abstinence.
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Affiliation(s)
- Kazuhiro Kurihara
- Department of Neuropsychiatry, Graduate School of Medicine, University of the Ryukyus, Okinawa, Japan
| | - Hotaka Shinzato
- Department of Neuropsychiatry, Graduate School of Medicine, University of the Ryukyus, Okinawa, Japan
| | - Yoshikazu Takaesu
- Department of Neuropsychiatry, Graduate School of Medicine, University of the Ryukyus, Okinawa, Japan
| | - Tsuyoshi Kondo
- Department of Neuropsychiatry, Graduate School of Medicine, University of the Ryukyus, Okinawa, Japan
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Lai D, Kuo SIC, Wetherill L, Aliev F, Zhang M, Abreu M, Schwantes-An TH, Dick D, Francis MW, Johnson EC, Kamarajan C, Kinreich S, Kuperman S, Meyers J, Nurnberger JI, Liu Y, Edenberg HJ, Porjesz B, Agrawal A, Foroud T, Schuckit M, Plawecki MH, Bucholz KK, McCutcheon VV. Associations between alcohol use disorder polygenic score and remission in participants from high-risk families and the Indiana Biobank. ALCOHOL, CLINICAL & EXPERIMENTAL RESEARCH 2024; 48:283-294. [PMID: 38054532 PMCID: PMC10922306 DOI: 10.1111/acer.15239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/29/2023] [Accepted: 11/29/2023] [Indexed: 12/07/2023]
Abstract
BACKGROUND In the United States, ~50% of individuals who meet criteria for alcohol use disorder (AUD) during their lifetimes do not remit. We previously reported that a polygenic score for AUD (PGSAUD ) was positively associated with AUD severity as measured by DSM-5 lifetime criterion count, and AUD severity was negatively associated with remission. Thus, we hypothesized that PGSAUD would be negatively associated with remission. METHODS Individuals of European (EA) and African ancestry (AA) from the Collaborative Study on the Genetics of Alcoholism (COGA) who met lifetime criteria for AUD, and two EA cohorts ascertained for studies of liver diseases and substance use disorders from the Indiana Biobank were included. In COGA, 12-month remission was defined as any period of ≥12 consecutive months without meeting AUD criteria except craving and was further categorized as abstinent and non-abstinent. In the Indiana Biobank, remission was defined based on ICD codes and could not be further distinguished as abstinent or non-abstinent. Sex and age were included as covariates. COGA analyses included additional adjustment for AUD severity, family history of remission, and AUD treatment history. RESULTS In COGA EA, PGSAUD was negatively associated with 12-month and non-abstinent remission (p ≤ 0.013, βs between -0.15 and -0.10) after adjusting for all covariates. In contrast to the COGA findings, PGSAUD was positively associated with remission (p = 0.004, β = 0.28) in the Indiana Biobank liver diseases cohort but not in the Indiana Biobank substance use disorder cohort (p = 0.17, β = 0.15). CONCLUSIONS PGSAUD was negatively associated with 12-month and non-abstinent remission in COGA EA, independent of behavioral measures of AUD severity and family history of remission. The discrepant results in COGA and the Indiana Biobank could reflect different ascertainment strategies: the Indiana Biobank participants were older and had higher rates of liver disease, suggesting that these individuals remitted due to alcohol-related health conditions that manifested in later life.
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Affiliation(s)
- Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN
| | - Sally I-Chun Kuo
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ
| | - Leah Wetherill
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN
| | - Fazil Aliev
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ
| | - Michael Zhang
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN
| | - Marco Abreu
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN
| | - Tae-Hwi Schwantes-An
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN
| | - Danielle Dick
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ
| | | | - Emma C. Johnson
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO
| | - Chella Kamarajan
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Science University, NY
| | - Sivan Kinreich
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Science University, NY
| | - Samuel Kuperman
- Department of Psychiatry, University of Iowa Roy J and Lucille A Carver College of Medicine, Iowa City, IA
| | - Jacquelyn Meyers
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Science University, NY
| | - John I Nurnberger
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN
| | - Yunlong Liu
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN
| | - Howard J Edenberg
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN
| | - Bernice Porjesz
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Science University, NY
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN
| | - Marc Schuckit
- Department of Psychiatry, University of California, San Diego Medical School, San Diego, CA
| | - Martin H. Plawecki
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN
| | - Kathleen K. Bucholz
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO
| | - Vivia V. McCutcheon
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO
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Buchman DZ, Imahori D, Lo C, Hui K, Walker C, Shaw J, Davis KD. The Influence of Using Novel Predictive Technologies on Judgments of Stigma, Empathy, and Compassion among Healthcare Professionals. AJOB Neurosci 2024; 15:32-45. [PMID: 37450417 DOI: 10.1080/21507740.2023.2225470] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
BACKGROUND Our objective was to evaluate whether the description of a machine learning (ML) app or brain imaging technology to predict the onset of schizophrenia or alcohol use disorder (AUD) influences healthcare professionals' judgments of stigma, empathy, and compassion. METHODS We randomized healthcare professionals (N = 310) to one vignette about a person whose clinician seeks to predict schizophrenia or an AUD, using a ML app, brain imaging, or a psychosocial assessment. Participants used scales to measure their judgments of stigma, empathy, and compassion. RESULTS Participants randomized to the ML vignette endorsed less anger and more fear relative to the psychosocial vignette, and the brain imaging vignette elicited higher pity ratings. The brain imaging and ML vignettes evoked lower personal responsibility judgments compared to the psychosocial vignette. Physicians and nurses reported less empathy than clinical psychologists. CONCLUSIONS The use of predictive technologies may reinforce essentialist views about mental health and substance use that may increase specific aspects of stigma and reduce others.
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Affiliation(s)
- Daniel Z Buchman
- Centre for Addiction and Mental Health
- Dalla Lana School of Public Health, University of Toronto
- University of Toronto Joint Centre for Bioethics
| | | | - Christopher Lo
- Dalla Lana School of Public Health, University of Toronto
- Temerty Faculty of Medicine, University of Toronto
- College of Healthcare Sciences, James Cook University, Singapore
| | - Katrina Hui
- Centre for Addiction and Mental Health
- Temerty Faculty of Medicine, University of Toronto
| | | | - James Shaw
- University of Toronto Joint Centre for Bioethics
- Temerty Faculty of Medicine, University of Toronto
| | - Karen D Davis
- Temerty Faculty of Medicine, University of Toronto
- Krembil Brain Institute, University Health Network
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Agrawal A, Brislin SJ, Bucholz KK, Dick D, Hart RP, Johnson EC, Meyers J, Salvatore J, Slesinger P, Almasy L, Foroud T, Goate A, Hesselbrock V, Kramer J, Kuperman S, Merikangas AK, Nurnberger JI, Tischfield J, Edenberg HJ, Porjesz B. The Collaborative Study on the Genetics of Alcoholism: Overview. GENES, BRAIN, AND BEHAVIOR 2023; 22:e12864. [PMID: 37736010 PMCID: PMC10550790 DOI: 10.1111/gbb.12864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 07/21/2023] [Accepted: 08/23/2023] [Indexed: 09/23/2023]
Abstract
Alcohol use disorders (AUD) are commonly occurring, heritable and polygenic disorders with etiological origins in the brain and the environment. To outline the causes and consequences of alcohol-related milestones, including AUD, and their related psychiatric comorbidities, the Collaborative Study on the Genetics of Alcoholism (COGA) was launched in 1989 with a gene-brain-behavior framework. COGA is a family based, diverse (~25% self-identified African American, ~52% female) sample, including data on 17,878 individuals, ages 7-97 years, in 2246 families of which a proportion are densely affected for AUD. All participants responded to questionnaires (e.g., personality) and the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) which gathers information on psychiatric diagnoses, conditions and related behaviors (e.g., parental monitoring). In addition, 9871 individuals have brain function data from electroencephalogram (EEG) recordings while 12,009 individuals have been genotyped on genome-wide association study (GWAS) arrays. A series of functional genomics studies examine the specific cellular and molecular mechanisms underlying AUD. This overview provides the framework for the development of COGA as a scientific resource in the past three decades, with individual reviews providing in-depth descriptions of data on and discoveries from behavioral and clinical, brain function, genetic and functional genomics data. The value of COGA also resides in its data sharing policies, its efforts to communicate scientific findings to the broader community via a project website and its potential to nurture early career investigators and to generate independent research that has broadened the impact of gene-brain-behavior research into AUD.
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Affiliation(s)
- Arpana Agrawal
- Department of PsychiatryWashington University School of Medicine in St. LouisSt. LouisMissouriUSA
| | - Sarah J. Brislin
- Department of PsychiatryRutgers Robert Wood Johnson Medical SchoolPiscatawayNew JerseyUSA
| | - Kathleen K. Bucholz
- Department of PsychiatryWashington University School of Medicine in St. LouisSt. LouisMissouriUSA
| | - Danielle Dick
- Department of PsychiatryRutgers Robert Wood Johnson Medical SchoolPiscatawayNew JerseyUSA
| | - Ronald P. Hart
- Department of Cell Biology and NeuroscienceRutgers UniversityPiscatawayNew JerseyUSA
| | - Emma C. Johnson
- Department of PsychiatryWashington University School of Medicine in St. LouisSt. LouisMissouriUSA
| | - Jacquelyn Meyers
- Department of Psychiatry and Behavioral SciencesSUNY Downstate Health Sciences UniversityBrooklynNew YorkUSA
| | - Jessica Salvatore
- Department of PsychiatryRutgers Robert Wood Johnson Medical SchoolPiscatawayNew JerseyUSA
| | - Paul Slesinger
- Department of Neuroscience & Friedman Brain InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Laura Almasy
- Department of Biomedical and Health InformaticsChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Department of Genetics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Tatiana Foroud
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Alison Goate
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeurologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Victor Hesselbrock
- Department of PsychiatryUniversity of Connecticut School of MedicineFarmingtonConnecticutUSA
| | - John Kramer
- Department of PsychiatryUniversity of Iowa Carver College of MedicineIowa CityIowaUSA
| | - Samuel Kuperman
- Department of PsychiatryUniversity of Iowa Carver College of MedicineIowa CityIowaUSA
| | - Alison K. Merikangas
- Department of Biomedical and Health InformaticsChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Department of Genetics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Jay Tischfield
- Department of GeneticsRutgers UniversityPiscatawayNew JerseyUSA
| | - Howard J. Edenberg
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Biochemistry and Molecular BiologyIndiana University School of MedicineIndianapolisIndianaUSA
| | - Bernice Porjesz
- Department of Psychiatry and Behavioral SciencesSUNY Downstate Health Sciences UniversityBrooklynNew YorkUSA
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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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8
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Kamarajan C, Pandey AK, Chorlian DB, Meyers JL, Kinreich S, Pandey G, Subbie-Saenz de Viteri S, Zhang J, Kuang W, Barr PB, Aliev F, Anokhin AP, Plawecki MH, Kuperman S, Almasy L, Merikangas A, Brislin SJ, Bauer L, Hesselbrock V, Chan G, Kramer J, Lai D, Hartz S, Bierut LJ, McCutcheon VV, Bucholz KK, Dick DM, Schuckit MA, Edenberg HJ, Porjesz B. Predicting Alcohol-Related Memory Problems in Older Adults: A Machine Learning Study with Multi-Domain Features. Behav Sci (Basel) 2023; 13:bs13050427. [PMID: 37232664 DOI: 10.3390/bs13050427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 05/08/2023] [Accepted: 05/11/2023] [Indexed: 05/27/2023] Open
Abstract
Memory problems are common among older adults with a history of alcohol use disorder (AUD). Employing a machine learning framework, the current study investigates the use of multi-domain features to classify individuals with and without alcohol-induced memory problems. A group of 94 individuals (ages 50-81 years) with alcohol-induced memory problems (the memory group) were compared with a matched control group who did not have memory problems. The random forests model identified specific features from each domain that contributed to the classification of the memory group vs. the control group (AUC = 88.29%). Specifically, individuals from the memory group manifested a predominant pattern of hyperconnectivity across the default mode network regions except for some connections involving the anterior cingulate cortex, which were predominantly hypoconnected. Other significant contributing features were: (i) polygenic risk scores for AUD, (ii) alcohol consumption and related health consequences during the past five years, such as health problems, past negative experiences, withdrawal symptoms, and the largest number of drinks in a day during the past twelve months, and (iii) elevated neuroticism and increased harm avoidance, and fewer positive "uplift" life events. At the neural systems level, hyperconnectivity across the default mode network regions, including the connections across the hippocampal hub regions, in individuals with memory problems may indicate dysregulation in neural information processing. Overall, the study outlines the importance of utilizing multidomain features, consisting of resting-state brain connectivity data collected ~18 years ago, together with personality, life experiences, polygenic risk, and alcohol consumption and related consequences, to predict the alcohol-related memory problems that arise in later life.
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Affiliation(s)
- Chella Kamarajan
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Ashwini K Pandey
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - David B Chorlian
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Jacquelyn L Meyers
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Sivan Kinreich
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Gayathri Pandey
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Stacey Subbie-Saenz de Viteri
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Jian Zhang
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Weipeng Kuang
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Peter B Barr
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Fazil Aliev
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Andrey P Anokhin
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | | | - Samuel Kuperman
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
| | - Laura Almasy
- The Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alison Merikangas
- The Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sarah J Brislin
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Lance Bauer
- Department of Psychiatry, University of Connecticut, Farmington, CT 06030, USA
| | - Victor Hesselbrock
- Department of Psychiatry, University of Connecticut, Farmington, CT 06030, USA
| | - Grace Chan
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
- Department of Psychiatry, University of Connecticut, Farmington, CT 06030, USA
| | - John Kramer
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
| | - Dongbing Lai
- Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Sarah Hartz
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Laura J Bierut
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Vivia V McCutcheon
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Kathleen K Bucholz
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Danielle M Dick
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Marc A Schuckit
- Department of Psychiatry, University of California, San Diego, CA 92103, USA
| | | | - Bernice Porjesz
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
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9
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Wen X, Yue L, Du Z, Li L, Zhu Y, Yu D, Yuan K. Implications of neuroimaging findings in addiction. PSYCHORADIOLOGY 2023; 3:kkad006. [PMID: 38666116 PMCID: PMC10917371 DOI: 10.1093/psyrad/kkad006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 04/14/2023] [Accepted: 04/28/2023] [Indexed: 04/28/2024]
Affiliation(s)
- Xinwen Wen
- School of Life Science and Technology, Xidian University, Xi'an 710126, China
| | - Lirong Yue
- School of Life Science and Technology, Xidian University, Xi'an 710126, China
| | - Zhe Du
- School of Life Science and Technology, Xidian University, Xi'an 710126, China
| | - Linling Li
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen 518060, China
| | - Yuanqiang Zhu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China
| | - Dahua Yu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
| | - Kai Yuan
- School of Life Science and Technology, Xidian University, Xi'an 710126, China
- Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, Xidian University, Xi'an 710126, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an 710126, China
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10
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The potential of electroencephalography coherence to predict the outcome of repetitive transcranial magnetic stimulation in insomnia disorder. J Psychiatr Res 2023; 160:56-63. [PMID: 36774831 DOI: 10.1016/j.jpsychires.2023.02.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/27/2023] [Accepted: 02/04/2023] [Indexed: 02/09/2023]
Abstract
BACKGROUND It is unknown whether repetitive Transcranial Magnetic Stimulation (rTMS) could improve sleep quality by modulating electroencephalography (EEG) connectivity of insomnia disorder (ID) patients. Great heterogeneity had been found in the clinical outcomes of rTMS for ID. The study aimed to investigate the potential mechanisms of rTMS therapy for ID and develop models to predict clinical outcomes. METHODS In Study 1, 50 ID patients were randomly divided into active and sham groups, and subjected to 20 sessions of treatment with 1 Hz rTMS over the left dorsolateral prefrontal cortex. EEG during awake, Polysomnography, and clinical assessment were collected and analyzed before and after rTMS. In Study 2, 120 ID patients were subjected to active rTMS stimulation and were then separated into optimal and sub-optimal groups due to the median of Pittsburgh Sleep Quality Index reduction rate. Machine learning models were developed based on baseline EEG coherence to predict rTMS treatment effects. RESULTS In Study 1, decreased EEG coherence in theta and alpha bands were observed after rTMS treatment, and changes in theta band (F7-O1) coherence were correlated with changes in sleep efficiency. In Study 2, baseline EEG coherence in theta, alpha, and beta bands showed the potential to predict the treatment effects of rTMS for ID. CONCLUSION rTMS improved sleep quality of ID patients by modulating the abnormal EEG coherence. Baseline EEG coherence between certain channels in theta, alpha, and beta bands could act as potential biomarkers to predict the therapeutic effects.
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11
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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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12
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Bel-Bahar TS, Khan AA, Shaik RB, Parvaz MA. A scoping review of electroencephalographic (EEG) markers for tracking neurophysiological changes and predicting outcomes in substance use disorder treatment. Front Hum Neurosci 2022; 16:995534. [PMID: 36325430 PMCID: PMC9619053 DOI: 10.3389/fnhum.2022.995534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 09/20/2022] [Indexed: 11/24/2022] Open
Abstract
Substance use disorders (SUDs) constitute a growing global health crisis, yet many limitations and challenges exist in SUD treatment research, including the lack of objective brain-based markers for tracking treatment outcomes. Electroencephalography (EEG) is a neurophysiological technique for measuring brain activity, and although much is known about EEG activity in acute and chronic substance use, knowledge regarding EEG in relation to abstinence and treatment outcomes is sparse. We performed a scoping review of longitudinal and pre-post treatment EEG studies that explored putative changes in brain function associated with abstinence and/or treatment in individuals with SUD. Following PRISMA guidelines, we identified studies published between January 2000 and March 2022 from online databases. Search keywords included EEG, addictive substances (e.g., alcohol, cocaine, methamphetamine), and treatment related terms (e.g., abstinence, relapse). Selected studies used EEG at least at one time point as a predictor of abstinence or other treatment-related outcomes; or examined pre- vs. post-SUD intervention (brain stimulation, pharmacological, behavioral) EEG effects. Studies were also rated on the risk of bias and quality using validated instruments. Forty-four studies met the inclusion criteria. More consistent findings included lower oddball P3 and higher resting beta at baseline predicting negative outcomes, and abstinence-mediated longitudinal decrease in cue-elicited P3 amplitude and resting beta power. Other findings included abstinence or treatment-related changes in late positive potential (LPP) and N2 amplitudes, as well as in delta and theta power. Existing studies were heterogeneous and limited in terms of specific substances of interest, brief times for follow-ups, and inconsistent or sparse results. Encouragingly, in this limited but maturing literature, many studies demonstrated partial associations of EEG markers with abstinence, treatment outcomes, or pre-post treatment-effects. Studies were generally of good quality in terms of risk of bias. More EEG studies are warranted to better understand abstinence- or treatment-mediated neural changes or to predict SUD treatment outcomes. Future research can benefit from prospective large-sample cohorts and the use of standardized methods such as task batteries. EEG markers elucidating the temporal dynamics of changes in brain function related to abstinence and/or treatment may enable evidence-based planning for more effective and targeted treatments, potentially pre-empting relapse or minimizing negative lifespan effects of SUD.
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Affiliation(s)
- Tarik S. Bel-Bahar
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Anam A. Khan
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Riaz B. Shaik
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Muhammad A. Parvaz
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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13
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Insights on rehabilitation programs, women, families, and COVID19. Transl Psychiatry 2022; 12:242. [PMID: 35680850 PMCID: PMC9184496 DOI: 10.1038/s41398-022-02008-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 05/23/2022] [Accepted: 05/27/2022] [Indexed: 11/11/2022] Open
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14
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Smart K, Worhunsky PD, Scheinost D, Angarita GA, Esterlis I, Carson RE, Krystal JH, O'Malley SS, Cosgrove KP, Hillmer AT. Multimodal neuroimaging of metabotropic glutamate 5 receptors and functional connectivity in alcohol use disorder. Alcohol Clin Exp Res 2022; 46:770-782. [PMID: 35342968 PMCID: PMC9117461 DOI: 10.1111/acer.14816] [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: 11/17/2021] [Revised: 03/15/2022] [Accepted: 03/19/2022] [Indexed: 12/01/2022]
Abstract
BACKGROUND People recovering from alcohol use disorder (AUD) show altered resting brain connectivity. The metabotropic glutamate 5 (mGlu5) receptor is an important regulator of synaptic plasticity potentially linked with synchronized brain activity and a target of interest in treating AUD. The goal of this work was to assess potential relationships of brain connectivity at rest with mGlu5 receptor availability in people with AUD at two time points early in abstinence. METHODS Forty-eight image data sets were acquired with a multimodal neuroimaging battery that included resting-state functional magnetic resonance imaging (fMRI) and mGlu5 receptor positron emission tomography (PET) with the radiotracer [18 F]FPEB. Participants with AUD (n = 14) were scanned twice, at approximately 1 and 4 weeks after beginning supervised abstinence. [18 F]FPEB PET results were published previously. Primary comparisons of fMRI outcomes were performed between the AUD group and healthy controls (HCs; n = 23) and assessed changes over time within the AUD group. Relationships between resting-state connectivity measures and mGlu5 receptor availability were explored within groups. RESULTS Compared to HCs, global functional connectivity of the orbitofrontal cortex was higher in the AUD group at 4 weeks of abstinence (p = 0.003), while network-level functional connectivity within the default mode network (DMN) was lower (p < 0.04). Exploratory multimodal analyses showed that mGlu5 receptor availability was correlated with global connectivity across all brain regions (HCs, r = 0.41; AUD group at 1 week of abstinence, r = 0.50 and at 4 weeks, r = 0.46; all p < 0.0001). Furthermore, a component of cortical and striatal mGlu5 availability was correlated with connectivity between the DMN and salience networks in HCs (r = 0.60, p = 0.003) but not in the AUD group (p > 0.3). CONCLUSIONS These preliminary findings of altered global and network connectivity during the first month of abstinence from drinking may reflect the loss of efficient network function, while exploratory relationships with mGlu5 receptor availability suggest a potential glutamatergic relationship with network coherence.
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Affiliation(s)
- Kelly Smart
- Yale PET Center, Yale School of Medicine, New Haven, Connecticut, USA.,Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Patrick D Worhunsky
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA.,Department of Biomedical Engineering, Yale School of Engineering & Applied Science, New Haven, Connecticut, USA
| | - Gustavo A Angarita
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, USA
| | - Irina Esterlis
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, USA
| | - Richard E Carson
- Yale PET Center, Yale School of Medicine, New Haven, Connecticut, USA.,Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA.,Department of Biomedical Engineering, Yale School of Engineering & Applied Science, New Haven, Connecticut, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, USA
| | | | - Kelly P Cosgrove
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA.,Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, USA
| | - Ansel T Hillmer
- Yale PET Center, Yale School of Medicine, New Haven, Connecticut, USA.,Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA.,Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, USA.,Department of Biomedical Engineering, Yale School of Engineering & Applied Science, New Haven, Connecticut, USA
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15
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Roberts W, Zhao Y, Verplaetse T, Moore KE, Peltier MR, Burke C, Zakiniaeiz Y, McKee S. Using machine learning to predict heavy drinking during outpatient alcohol treatment. Alcohol Clin Exp Res 2022; 46:657-666. [PMID: 35420710 PMCID: PMC9180421 DOI: 10.1111/acer.14802] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 02/15/2022] [Accepted: 02/22/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Accurate clinical prediction supports the effective treatment of alcohol use disorder (AUD) and other psychiatric disorders. Traditional statistical techniques have identified patient characteristics associated with treatment outcomes. However, less work has focused on systematically leveraging these associations to create optimal predictive models. The current study demonstrates how machine learning can be used to predict clinical outcomes in people completing outpatient AUD treatment. METHOD We used data from the COMBINE multisite clinical trial (n = 1383) to develop and test predictive models. We identified three priority prediction targets, including (1) heavy drinking during the first month of treatment, (2) heavy drinking during the last month of treatment, and (3) heavy drinking between weekly/bi-weekly sessions. Models were generated using the random forest algorithm. We used "leave sites out" partitioning to externally validate the models in trial sites that were not included in the model training. Stratified model development was used to test for sex differences in the relative importance of predictive features. RESULTS Models predicting heavy alcohol use during the first and last months of treatment showed internal cross-validation area under the curve (AUC) scores ranging from 0.67 to 0.74. AUC was comparable in the external validation using data from held-out sites (AUC range = 0.69 to 0.72). The model predicting between-session heavy drinking showed strong classification accuracy in internal cross-validation (AUC = 0.89) and external test samples (AUC range = 0.80 to 0.87). Stratified analyses showed substantial sex differences in optimal feature sets. CONCLUSION Machine learning techniques can predict alcohol treatment outcomes using routinely collected clinical data. This technique has the potential to greatly improve clinical prediction accuracy without requiring expensive or invasive assessment methods. More research is needed to understand how best to deploy these models.
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Affiliation(s)
- Walter Roberts
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA.,Department of Psychology, East Tennessee State University, Johnson City, Tennessee, USA
| | - Yize Zhao
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Terril Verplaetse
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Kelly E Moore
- Department of Psychology, East Tennessee State University, Johnson City, Tennessee, USA
| | - MacKenzie R Peltier
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA.,Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, USA
| | - Catherine Burke
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Yasmin Zakiniaeiz
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Sherry McKee
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
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16
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Ezhumalai S, Muralidhar D, Murthy P. Occupational factors associated with long-term abstinence among persons treated for alcohol dependence: A follow-up study. Indian J Occup Environ Med 2022; 26:122-128. [PMID: 35991207 PMCID: PMC9384881 DOI: 10.4103/ijoem.ijoem_37_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/05/2022] [Accepted: 03/12/2022] [Indexed: 11/19/2022] Open
Abstract
Background Several studies have examined the occupational factors associated with alcohol use and dependence. However, there are very few studies that investigated the role of occupational factors associated with long-term abstinence among persons treated for alcohol dependence in India. Aim To examine the occupational factors associated with long-term abstinence among persons treated for alcohol dependence. Methods Sixty in-patients treated for alcohol dependence were selected using inclusion criteria from the Government-run de-addiction center, tertiary care teaching hospital, Bangalore. All patients were followed up periodically for 1 year. The semi-structured interview schedule was used for collecting data on occupational factors associated with long-term abstinence. Descriptive statistics, Chi-square test, and Fisher exact test were used for data analysis. Results There was a positive trend showing self-employed (pf = 1.74, P = 0.45), having skilled work (pf = 1.52, P = 0.72), regular pattern of employment (pf = 1.21, P =.60), monthly mode of income (pf = 1.43, P =.76) were factors associated with abstinence. Among eight occupational variables, employment status (x 2 = 4.0, P =.04) and having well-defined working hours ((pf = 6.18, P =.04) were significantly associated with long-term abstinence among persons treated for alcohol dependence. Conclusion Occupational factors seem to influence the outcome in alcohol dependence and appropriate vocational interventions would be effective in promoting long-term abstinence.
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Affiliation(s)
- Sinu Ezhumalai
- Department of Psychiatric Social Work, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India,Address for correspondence: Dr. Sinu Ezhumalai, Department of Psychiatric Social Work, National Institute of Mental Health and Neurosciences, Bangaluru - 560029, Karnataka, India. E-mail:
| | - D. Muralidhar
- Department of Psychiatric Social Work, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India
| | - Pratima Murthy
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India
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De Angelis F, Wendt FR, Pathak GA, Tylee DS, Goswami A, Gelernter J, Polimanti R. Drinking and smoking polygenic risk is associated with childhood and early-adulthood psychiatric and behavioral traits independently of substance use and psychiatric genetic risk. Transl Psychiatry 2021; 11:586. [PMID: 34775470 PMCID: PMC8590689 DOI: 10.1038/s41398-021-01713-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 10/22/2021] [Accepted: 10/29/2021] [Indexed: 11/09/2022] Open
Abstract
Alcohol drinking and tobacco smoking are hazardous behaviors associated with a wide range of adverse health outcomes. In this study, we explored the association of polygenic risk scores (PRS) related to drinks per week, age of smoking initiation, smoking initiation, cigarettes per day, and smoking cessation with 433 psychiatric and behavioral traits in 4498 children and young adults (aged 8-21) of European ancestry from the Philadelphia neurodevelopmental cohort. After applying a false discovery rate multiple testing correction accounting for the number of PRS and traits tested, we identified 36 associations related to psychotic symptoms, emotion and age recognition social competencies, verbal reasoning, anxiety-related traits, parents' education, and substance use. These associations were independent of the genetic correlations among the alcohol-drinking and tobacco-smoking traits and those with cognitive performance, educational attainment, risk-taking behaviors, and psychopathology. The removal of participants endorsing substance use did not affect the associations of each PRS with psychiatric and behavioral traits identified as significant in the discovery analyses. Gene-ontology enrichment analyses identified several neurobiological processes underlying mechanisms of the PRS associations we report. In conclusion, we provide novel insights into the genetic overlap of smoking and drinking behaviors in children and young adults, highlighting their independence from psychopathology and substance use.
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Affiliation(s)
- Flavio De Angelis
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT, USA
- Veteran Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Frank R Wendt
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT, USA
- Veteran Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Gita A Pathak
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT, USA
- Veteran Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Daniel S Tylee
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT, USA
- Veteran Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Aranyak Goswami
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT, USA
- Veteran Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT, USA
- Veteran Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT, USA.
- Veteran Affairs Connecticut Healthcare System, West Haven, CT, USA.
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