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Wang B, Irizar H, Thygesen JH, Zartaloudi E, Austin-Zimmerman I, Bhat A, Harju-Seppänen J, Pain O, Bass N, Gkofa V, Alizadeh BZ, van Amelsvoort T, Arranz MJ, Bender S, Cahn W, Stella Calafato M, Crespo-Facorro B, Di Forti M, Giegling I, de Haan L, Hall J, Hall MH, van Haren N, Iyegbe C, Kahn RS, Kravariti E, Lawrie SM, Lin K, Luykx JJ, Mata I, McDonald C, McIntosh AM, Murray RM, Picchioni M, Powell J, Prata DP, Rujescu D, Rutten BPF, Shaikh M, Simons CJP, Toulopoulou T, Weisbrod M, van Winkel R, Kuchenbaecker K, McQuillin A, Bramon E. Psychosis Endophenotypes: A Gene-Set-Specific Polygenic Risk Score Analysis. Schizophr Bull 2023; 49:1625-1636. [PMID: 37582581 PMCID: PMC10686343 DOI: 10.1093/schbul/sbad088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/17/2023]
Abstract
BACKGROUND AND HYPOTHESIS Endophenotypes can help to bridge the gap between psychosis and its genetic predispositions, but their underlying mechanisms remain largely unknown. This study aims to identify biological mechanisms that are relevant to the endophenotypes for psychosis, by partitioning polygenic risk scores into specific gene sets and testing their associations with endophenotypes. STUDY DESIGN We computed polygenic risk scores for schizophrenia and bipolar disorder restricted to brain-related gene sets retrieved from public databases and previous publications. Three hundred and seventy-eight gene-set-specific polygenic risk scores were generated for 4506 participants. Seven endophenotypes were also measured in the sample. Linear mixed-effects models were fitted to test associations between each endophenotype and each gene-set-specific polygenic risk score. STUDY RESULTS After correction for multiple testing, we found that a reduced P300 amplitude was associated with a higher schizophrenia polygenic risk score of the forebrain regionalization gene set (mean difference per SD increase in the polygenic risk score: -1.15 µV; 95% CI: -1.70 to -0.59 µV; P = 6 × 10-5). The schizophrenia polygenic risk score of forebrain regionalization also explained more variance of the P300 amplitude (R2 = 0.032) than other polygenic risk scores, including the genome-wide polygenic risk scores. CONCLUSIONS Our finding on reduced P300 amplitudes suggests that certain genetic variants alter early brain development thereby increasing schizophrenia risk years later. Gene-set-specific polygenic risk scores are a useful tool to elucidate biological mechanisms of psychosis and endophenotypes, offering leads for experimental validation in cellular and animal models.
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Affiliation(s)
- Baihan Wang
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Haritz Irizar
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Johan H Thygesen
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Eirini Zartaloudi
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
| | - Isabelle Austin-Zimmerman
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Anjali Bhat
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
| | - Jasmine Harju-Seppänen
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
| | - Oliver Pain
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Nick Bass
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
| | - Vasiliki Gkofa
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
| | - Behrooz Z Alizadeh
- University of Groningen, University Medical Center Groningen, University Center for Psychiatry, Rob Giel Research Center, Groningen, The Netherlands
- Department of Epidemiology, University Medical Center Groningen, Groningen, The Netherlands
| | - Therese van Amelsvoort
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Maria J Arranz
- Fundació Docència i Recerca Mutua Terrassa, Terrassa, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Institut de Recerca Biomédica Sant Pau (IIB-Sant Pau), Barcelona, Spain
| | - Stephan Bender
- Department of Child and Adolescent Psychiatry, Psychosomatic Medicine and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Wiepke Cahn
- Department of Psychiatry, Brain Centre Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Altrecht, General Mental Health Care, Utrecht, The Netherlands
| | - Maria Stella Calafato
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
| | - Benedicto Crespo-Facorro
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Sevilla, Spain
- Department of Psychiatry, University Hospital Virgen del Rocio, School of Medicine, University of Sevilla–IBiS, Sevilla, Spain
| | - Marta Di Forti
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | | | - Ina Giegling
- Comprehensive Centers for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Lieuwe de Haan
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Arkin, Institute for Mental Health, Amsterdam, The Netherlands
| | - Jeremy Hall
- Neuroscience and Mental Health Innovation Institute, School of Medicine, Cardiff University, Hadyn Ellis Building, Mandy Road, Cardiff, UK
| | - Mei-Hua Hall
- Psychosis Neurobiology Laboratory, Harvard Medical School, McLean Hospital, Belmont, MA, USA
| | - Neeltje van Haren
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia’s Children Hospital, Rotterdam, The Netherlands
| | - Conrad Iyegbe
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - René S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eugenia Kravariti
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Stephen M Lawrie
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jurjen J Luykx
- Department of Psychiatry, Brain Centre Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Translational Neuroscience, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Ignacio Mata
- Fundacion Argibide, Pamplona, Spain
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
| | - Colm McDonald
- The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, University of Galway, Galway, Ireland
| | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Robin M Murray
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | | | - Marco Picchioni
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- St Magnus Hospital, Surrey, UK
| | - John Powell
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Diana P Prata
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciencias da Universidade de Lisboa, Portugal
| | - Dan Rujescu
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Division of General Psychiatry, Medical University of Vienna, Austria
| | - Bart P F Rutten
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Madiha Shaikh
- North East London Foundation Trust, London, UK
- Research Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Claudia J P Simons
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
- GGzE Institute for Mental Health Care, Eindhoven, The Netherlands
| | - Timothea Toulopoulou
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Interdisciplinary Program in Neuroscience, Aysel Sabuncu Brain Research Center, Bilkent University, Ankara, Türkiye
- National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Türkiye
- Department of Psychology, Bilkent University, Ankara, Türkiye
- School of Medicine, Department of Psychiatry, National and Kapodistrian University of Athens, Athens, Greece
- Department of Psychiatry and Behavioral Health System, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Matthias Weisbrod
- Department of General Psychiatry, Center of Psychosocial Medicine, University of Heidelberg, Germany
- SRH Klinikum, Karlsbad-Langensteinbach, Germany
| | - Ruud van Winkel
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
- KU Leuven, Department of Neuroscience, Research Group Psychiatry, Leuven, Belgium
| | - Karoline Kuchenbaecker
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
- UCL Genetics Institute, Division of Biosciences, University College London, London, UK
| | - Andrew McQuillin
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
| | - Elvira Bramon
- Department of Mental Health Neuroscience, Division of Psychiatry, University College London, London, UK
- Institute of Cognitive Neuroscience, University College London, London, UK
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Wang B, Otten LJ, Schulze K, Afrah H, Varney L, Cotic M, Saadullah Khani N, Linden JF, Kuchenbaecker K, McQuillin A, Hall MH, Bramon E. Is auditory processing measured by the N100 an endophenotype for psychosis? A family study and a meta-analysis. Psychol Med 2023:1-14. [PMID: 37997703 DOI: 10.1017/s0033291723003409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2023]
Abstract
BACKGROUND The N100, an early auditory event-related potential, has been found to be altered in patients with psychosis. However, it is unclear if the N100 is a psychosis endophenotype that is also altered in the relatives of patients. METHODS We conducted a family study using the auditory oddball paradigm to compare the N100 amplitude and latency across 243 patients with psychosis, 86 unaffected relatives, and 194 controls. We then conducted a systematic review and a random-effects meta-analysis pooling our results and 14 previously published family studies. We compared data from a total of 999 patients, 1192 relatives, and 1253 controls in order to investigate the evidence and degree of N100 differences. RESULTS In our family study, patients showed reduced N100 amplitudes and prolonged N100 latencies compared to controls, but no significant differences were found between unaffected relatives and controls. The meta-analysis revealed a significant reduction of the N100 amplitude and delay of the N100 latency in both patients with psychosis (standardized mean difference [s.m.d.] = -0.48 for N100 amplitude and s.m.d. = 0.43 for N100 latency) and their relatives (s.m.d. = - 0.19 for N100 amplitude and s.m.d. = 0.33 for N100 latency). However, only the N100 latency changes in relatives remained significant when excluding studies with affected relatives. CONCLUSIONS N100 changes, especially prolonged N100 latencies, are present in both patients with psychosis and their relatives, making the N100 a promising endophenotype for psychosis. Such changes in the N100 may reflect changes in early auditory processing underlying the etiology of psychosis.
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Affiliation(s)
- Baihan Wang
- Division of Psychiatry, University College London, London, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Leun J Otten
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Katja Schulze
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Hana Afrah
- Division of Psychiatry, University College London, London, UK
| | - Lauren Varney
- Division of Psychiatry, University College London, London, UK
| | - Marius Cotic
- Division of Psychiatry, University College London, London, UK
- Department of Genetics & Genomic Medicine, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | | | - Jennifer F Linden
- Ear Institute, University College London, London, UK
- Department of Neuroscience, Physiology & Pharmacology, University College London, London, UK
| | - Karoline Kuchenbaecker
- Division of Psychiatry, University College London, London, UK
- Division of Biosciences, UCL Genetics Institute, University College London, London, UK
| | | | - Mei-Hua Hall
- Psychosis Neurobiology Laboratory, Harvard Medical School, McLean Hospital, Belmont, MA, USA
| | - Elvira Bramon
- Division of Psychiatry, University College London, London, UK
- Institute of Cognitive Neuroscience, University College London, London, UK
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Brett J, Knock E, Korthuis PT, Liknaitzky P, Murnane KS, Nicholas CR, Patterson JC, Stauffer CS. Exploring psilocybin-assisted psychotherapy in the treatment of methamphetamine use disorder. Front Psychiatry 2023; 14:1123424. [PMID: 36998623 PMCID: PMC10043240 DOI: 10.3389/fpsyt.2023.1123424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 02/27/2023] [Indexed: 03/18/2023] Open
Abstract
Methamphetamine use disorder is a chronic relapsing condition associated with substantial mental, physical, and social harms and increasing rates of mortality. Contingency management and psychotherapy interventions are the mainstays of treatment but are modestly effective with high relapse rates, while pharmacological treatments have shown little to no efficacy. Psilocybin-assisted psychotherapy is emerging as a promising treatment for a range of difficult-to-treat conditions, including substance use disorders; however, no studies have yet been published looking at psilocybin-assisted psychotherapy in the treatment of methamphetamine use disorder. Here we review the rationale for psilocybin-assisted psychotherapy as a potential treatment for this indication, and describe practical considerations based on our early experience designing and implementing four separate clinical trials of psilocybin-assisted psychotherapy for methamphetamine use disorder.
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Affiliation(s)
- Jonathan Brett
- Department of Clinical Pharmacology, St. Vincent’s Hospital, Sydney, NSW, Australia
- School of Population Health, Medicines Intelligence Centre of Research Excellence, University of New South Wales, Sydney, NSW, Australia
| | - Elizabeth Knock
- Alcohol and Drug Service, St. Vincent’s Hospital, Sydney, NSW, Australia
| | - P. Todd Korthuis
- Section of Addiction Medicine, Oregon Health & Science University, Portland, OR, United States
| | - Paul Liknaitzky
- Department of Psychiatry, School of Clinical Sciences, Monash University, Caulfield, VIC, Australia
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Caulfield, VIC, Australia
| | - Kevin S. Murnane
- Louisiana Addiction Research Center, Department of Pharmacology, Toxicology & Neuroscience, Shreveport, LA, United States
- Department of Psychiatry and Behavioral Medicine, Louisiana State University Health, Shreveport, LA, United States
| | - Christopher R. Nicholas
- Department of Family Medicine and Community Health, University of Wisconsin-Madison, Madison, WI, United States
| | - James C. Patterson
- Louisiana Addiction Research Center, Department of Pharmacology, Toxicology & Neuroscience, Shreveport, LA, United States
- Department of Psychiatry and Behavioral Medicine, Louisiana State University Health, Shreveport, LA, United States
| | - Christopher S. Stauffer
- Department of Mental Health, Veterans Affairs Portland Health Care System, Portland, OR, United States
- Social Neuroscience and Psychotherapy Lab, Department of Psychiatry, Oregon Health and Science University, Portland, OR, United States
- *Correspondence: Christopher S. Stauffer,
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Wang B, Zartaloudi E, Linden JF, Bramon E. Neurophysiology in psychosis: The quest for disease biomarkers. Transl Psychiatry 2022; 12:100. [PMID: 35277479 DOI: 10.1038/s41398-022-01860-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 02/14/2022] [Accepted: 02/18/2022] [Indexed: 01/11/2023] Open
Abstract
Psychotic disorders affect 3% of the population at some stage in life, are a leading cause of disability, and impose a great economic burden on society. Major breakthroughs in the genetics of psychosis have not yet been matched by an understanding of its neurobiology. Biomarkers of perception and cognition obtained through non-invasive neurophysiological tools, especially EEG, offer a unique opportunity to gain mechanistic insights. Techniques for measuring neurophysiological markers are inexpensive and ubiquitous, thus having the potential as an accessible tool for patient stratification towards early treatments leading to better outcomes. In this paper, we review the literature on neurophysiological markers for psychosis and their relevant disease mechanisms, mainly covering event-related potentials including P50/N100 sensory gating, mismatch negativity, and the N100 and P300 waveforms. While several neurophysiological deficits are well established in patients with psychosis, more research is needed to study neurophysiological markers in their unaffected relatives and individuals at clinical high risk. We need to harness EEG to investigate markers of disease risk as key steps to elucidate the aetiology of psychosis and facilitate earlier detection and treatment.
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Tamminga CA, Clementz BA, Pearlson G, Keshavan M, Gershon ES, Ivleva EI, McDowell J, Meda SA, Keedy S, Calhoun VD, Lizano P, Bishop JR, Hudgens-Haney M, Alliey-Rodriguez N, Asif H, Gibbons R. Biotyping in psychosis: using multiple computational approaches with one data set. Neuropsychopharmacology 2021; 46:143-155. [PMID: 32979849 PMCID: PMC7689458 DOI: 10.1038/s41386-020-00849-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 08/24/2020] [Accepted: 08/26/2020] [Indexed: 12/15/2022]
Abstract
Focusing on biomarker identification and using biomarkers individually or in clusters to define biological subgroups in psychiatry requires a re-orientation from behavioral phenomenology to quantifying brain features, requiring big data approaches for data integration. Much still needs to be accomplished, not only to refine but also to build support for the application and customization of such an analytical phenotypic approach. In this review, we present some of what Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) has learned so far to guide future applications of multivariate phenotyping and their analyses to understanding psychosis. This paper describes several B-SNIP projects that use phenotype data and big data computations to generate novel outcomes and glimpse what phenotypes contribute to disease understanding and, with aspiration, to treatment. The source of the phenotypes varies from genetic data, structural neuroanatomic localization, immune markers, brain physiology, and cognition. We aim to see guiding principles emerge and areas of commonality revealed. And, we will need to demonstrate not only data stability but also the usefulness of biomarker information for subgroup identification enhancing target identification and treatment development.
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Affiliation(s)
- Carol A. Tamminga
- grid.267313.20000 0000 9482 7121Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX 75390 USA
| | - Brett A. Clementz
- grid.213876.90000 0004 1936 738XDepartments of Psychology, Neuroscience, and BioImaging Research Center, University of Georgia, Athens, GA 30602 USA
| | - Godfrey Pearlson
- grid.277313.30000 0001 0626 2712Olin Neuropsychiatry Research Center, Institute of Living at Hartford Hospital, Hartford, CT USA ,grid.47100.320000000419368710Departments of Psychiatry & Neuroscience, Yale University, New Haven, CT USA
| | - Macheri Keshavan
- grid.239395.70000 0000 9011 8547Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, United States
| | - Elliot S. Gershon
- grid.170205.10000 0004 1936 7822Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL 60637 USA
| | - Elena I. Ivleva
- grid.267313.20000 0000 9482 7121Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX 75390 USA
| | - Jennifer McDowell
- grid.213876.90000 0004 1936 738XDepartments of Psychology, Neuroscience, and BioImaging Research Center, University of Georgia, Athens, GA 30602 USA
| | - Shashwath A. Meda
- grid.277313.30000 0001 0626 2712Olin Neuropsychiatry Research Center, Institute of Living at Hartford Hospital, Hartford, CT USA ,grid.47100.320000000419368710Departments of Psychiatry & Neuroscience, Yale University, New Haven, CT USA
| | - Sarah Keedy
- grid.170205.10000 0004 1936 7822Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL 60637 USA
| | - Vince D. Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia USA
| | - Paulo Lizano
- grid.239395.70000 0000 9011 8547Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, United States
| | - Jeffrey R. Bishop
- grid.17635.360000000419368657Department of Experimental and Clinical Pharmacology, University of Minnesota College of Pharmacy, Minneapolis, United States ,grid.17635.360000000419368657Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN 55455 USA
| | - Matthew Hudgens-Haney
- grid.267313.20000 0000 9482 7121Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX 75390 USA
| | - Ney Alliey-Rodriguez
- grid.170205.10000 0004 1936 7822Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL 60637 USA
| | - Huma Asif
- grid.170205.10000 0004 1936 7822Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL 60637 USA
| | - Robert Gibbons
- grid.170205.10000 0004 1936 7822Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL 60637 USA ,grid.170205.10000 0004 1936 7822Departments of Medicine and Public Health Sciences, University of Chicago, Chicago, Ill USA
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Delfin C, Ruzich E, Wallinius M, Björnsdotter M, Andiné P. Trait Disinhibition and NoGo Event-Related Potentials in Violent Mentally Disordered Offenders and Healthy Controls. Front Psychiatry 2020; 11:577491. [PMID: 33362599 PMCID: PMC7759527 DOI: 10.3389/fpsyt.2020.577491] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 11/17/2020] [Indexed: 12/21/2022] Open
Abstract
Trait disinhibition may function as a dispositional liability toward maladaptive behaviors relevant in the treatment of mentally disordered offenders (MDOs). Reduced amplitude and prolonged latency of the NoGo N2 and P3 event-related potentials have emerged as promising candidates for transdiagnostic, biobehavioral markers of trait disinhibition, yet no study has specifically investigated these two components in violent, inpatient MDOs. Here, we examined self-reported trait disinhibition, experimentally assessed response inhibition, and NoGo N2 and P3 amplitude and latency in male, violent MDOs (N = 27) and healthy controls (N = 20). MDOs had a higher degree of trait disinhibition, reduced NoGo P3 amplitude, and delayed NoGo P3 latency compared to controls. The reduced NoGo P3 amplitude and delayed NoGo P3 latency in MDOs may stem from deficits during monitoring or evaluation of behavior. NoGo P3 latency was associated with increased trait disinhibition in the whole sample, suggesting that trait disinhibition may be associated with reduced neural efficiency during later stages of outcome monitoring or evaluation. Findings for NoGo N2 amplitude and latency were small and non-robust. With several limitations in mind, this is the first study to demonstrate attenuated NoGo P3 amplitude and delayed NoGo P3 latency in violent, inpatient MDOs compared to healthy controls.
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Affiliation(s)
- Carl Delfin
- Centre for Ethics, Law and Mental Health, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Research Department, Regional Forensic Psychiatric Clinic, Växjö, Sweden
| | - Emily Ruzich
- MedTech West, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Märta Wallinius
- Centre for Ethics, Law and Mental Health, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Research Department, Regional Forensic Psychiatric Clinic, Växjö, Sweden
- Lund Clinical Research on Externalizing and Developmental Psychopathology, Child and Adolescent Psychiatry, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Malin Björnsdotter
- Centre for Ethics, Law and Mental Health, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Affective Psychiatry, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Peter Andiné
- Centre for Ethics, Law and Mental Health, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Forensic Psychiatric Clinic, Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Forensic Psychiatry, National Board of Forensic Medicine, Gothenburg, Sweden
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Zartaloudi E, Laws KR, Bramon E. Endophenotypes of executive functions in obsessive compulsive disorder? A meta-analysis in unaffected relatives. Psychiatr Genet 2019; 29:211-9. [PMID: 31625982 DOI: 10.1097/YPG.0000000000000241] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Endophenotypes are mediator traits between genetic influences and clinical phenotypes. Meta-analyses have consistently shown modest impairments of executive functioning in obsessive compulsive disorder (OCD) patients compared to healthy controls. Similar deficits have also been reported in unaffected relatives of OCD patients, but have not been quantified. We conducted the first meta-analysis combining all studies investigating executive functioning in unaffected relatives of individuals with OCD to quantify any deficits. A search of Pubmed, Medline and PsychInfo databases identified 21 suitable papers comprising 707 unaffected relatives of OCD patients and 842 healthy controls. Effect sizes were calculated using random effects models. Unaffected relatives displayed a significant impairment in global executive functioning. Analyses of specific executive functioning subdomains revealed impairments in: planning, visuospatial working memory and verbal fluency. Deficits in executive functioning are promising endophenotypes for OCD. To identify further biomarkers of disease risk/resilience in OCD, we suggest examining specific executive functioning domains.
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Abstract
Social anxiety disorder (SAD) is serious psychiatric condition with a genetic background. Insight into the neurobiological alterations underlying the disorder is essential to develop effective interventions that could relieve SAD-related suffering. In this expert review, we consider recent neuroimaging work on SAD. First, we focus on new results from magnetic resonance imaging studies dedicated to outlining biomarkers of SAD, including encouraging findings with respect to structural and functional brain alterations associated with the disorder. Furthermore, we highlight innovative studies in the field of neuroprediction and studies that established the effects of treatment on brain characteristics. Next, we describe novel work aimed to delineate endophenotypes of SAD, providing insight into the genetic susceptibility to develop the disorder. Finally, we outline outstanding questions and point out directions for future research.
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Affiliation(s)
- Janna Marie Bas-Hoogendam
- Developmental and Educational Psychology, Institute of Psychology, Leiden University, Wassenaarseweg 52, 2333 AK Leiden, The Netherlands
- Leiden Institute for Brain and Cognition, c/o LUMC, postzone C2-S, P.O.Box 9600, 2300 RC Leiden, The Netherlands
- Department of Psychiatry, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - P. Michiel Westenberg
- Developmental and Educational Psychology, Institute of Psychology, Leiden University, Wassenaarseweg 52, 2333 AK Leiden, The Netherlands
- Leiden Institute for Brain and Cognition, c/o LUMC, postzone C2-S, P.O.Box 9600, 2300 RC Leiden, The Netherlands
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10
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Bas-Hoogendam JM, van Steenbergen H, van der Wee NJA, Westenberg PM. Amygdala hyperreactivity to faces conditioned with a social-evaluative meaning- a multiplex, multigenerational fMRI study on social anxiety endophenotypes. Neuroimage Clin 2020; 26:102247. [PMID: 32247196 PMCID: PMC7125356 DOI: 10.1016/j.nicl.2020.102247] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 03/12/2020] [Accepted: 03/14/2020] [Indexed: 12/31/2022]
Abstract
Social anxiety disorder (SAD) runs in families, but the neurobiological pathways underlying the genetic susceptibility towards SAD are largely unknown. Here, we employed an endophenotype approach, and tested the hypothesis that amygdala hyperreactivity to faces conditioned with a social-evaluative meaning is a candidate SAD endophenotype. We used data from the multiplex, multigenerational Leiden Family Lab study on Social Anxiety Disorder (eight families, n = 105) and investigated amygdala activation during a social-evaluative conditioning paradigm with high ecological validity in the context of SAD. Three neutral faces were repeatedly presented in combination with socially negative, positive or neutral sentences. We focused on two endophenotype criteria: co-segregation of the candidate endophenotype with the disorder within families, and heritability. Analyses of the fMRI data were restricted to the amygdala as a region of interest, and association analyses revealed that bilateral amygdala hyperreactivity in response to the conditioned faces co-segregated with social anxiety (SA; continuous measure) within the families; we found, however, no relationship between SA and brain activation in response to more specific fMRI contrasts. Furthermore, brain activation in a small subset of voxels within these amygdala clusters was at least moderately heritable. Taken together, these findings show that amygdala engagement in response to conditioned faces with a social-evaluative meaning qualifies as a neurobiological candidate endophenotype of social anxiety. Thereby, these data shed light on the genetic vulnerability to develop SAD.
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Affiliation(s)
- Janna Marie Bas-Hoogendam
- Institute of Psychology, Leiden University, Wassenaarseweg 52, 2333 AK Leiden, The Netherlands; Department of Psychiatry, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands; Leiden Institute for Brain and Cognition, Leiden, The Netherlands.
| | - Henk van Steenbergen
- Institute of Psychology, Leiden University, Wassenaarseweg 52, 2333 AK Leiden, The Netherlands; Leiden Institute for Brain and Cognition, Leiden, The Netherlands.
| | - Nic J A van der Wee
- Department of Psychiatry, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands; Leiden Institute for Brain and Cognition, Leiden, The Netherlands.
| | - P Michiel Westenberg
- Institute of Psychology, Leiden University, Wassenaarseweg 52, 2333 AK Leiden, The Netherlands; Leiden Institute for Brain and Cognition, Leiden, The Netherlands.
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11
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Ebdrup BH, Axelsen MC, Bak N, Fagerlund B, Oranje B, Raghava JM, Nielsen MØ, Rostrup E, Hansen LK, Glenthøj BY. Accuracy of diagnostic classification algorithms using cognitive-, electrophysiological-, and neuroanatomical data in antipsychotic-naïve schizophrenia patients. Psychol Med 2019; 49:2754-2763. [PMID: 30560750 PMCID: PMC6877469 DOI: 10.1017/s0033291718003781] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 11/13/2018] [Accepted: 11/20/2018] [Indexed: 11/10/2022]
Abstract
BACKGROUND A wealth of clinical studies have identified objective biomarkers, which separate schizophrenia patients from healthy controls on a group level, but current diagnostic systems solely include clinical symptoms. In this study, we investigate if machine learning algorithms on multimodal data can serve as a framework for clinical translation. METHODS Forty-six antipsychotic-naïve, first-episode schizophrenia patients and 58 controls underwent neurocognitive tests, electrophysiology, and magnetic resonance imaging (MRI). Patients underwent clinical assessments before and after 6 weeks of antipsychotic monotherapy with amisulpride. Nine configurations of different supervised machine learning algorithms were applied to first estimate the unimodal diagnostic accuracy, and next to estimate the multimodal diagnostic accuracy. Finally, we explored the predictability of symptom remission. RESULTS Cognitive data significantly classified patients from controls (accuracies = 60-69%; p values = 0.0001-0.009). Accuracies of electrophysiology, structural MRI, and diffusion tensor imaging did not exceed chance level. Multimodal analyses with cognition plus any combination of one or more of the remaining three modalities did not outperform cognition alone. None of the modalities predicted symptom remission. CONCLUSIONS In this multivariate and multimodal study in antipsychotic-naïve patients, only cognition significantly discriminated patients from controls, and no modality appeared to predict short-term symptom remission. Overall, these findings add to the increasing call for cognition to be included in the definition of schizophrenia. To bring about the full potential of machine learning algorithms in first-episode, antipsychotic-naïve schizophrenia patients, careful a priori variable selection based on independent data as well as inclusion of other modalities may be required.
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Affiliation(s)
- Bjørn H. Ebdrup
- Centre for Neuropsychiatric Schizophrenia Research & Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, University of Copenhagen, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Martin C. Axelsen
- Centre for Neuropsychiatric Schizophrenia Research & Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, University of Copenhagen, Copenhagen, Denmark
- Cognitive Systems, DTU Compute, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Nikolaj Bak
- Centre for Neuropsychiatric Schizophrenia Research & Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, University of Copenhagen, Copenhagen, Denmark
| | - Birgitte Fagerlund
- Centre for Neuropsychiatric Schizophrenia Research & Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, University of Copenhagen, Copenhagen, Denmark
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Bob Oranje
- Centre for Neuropsychiatric Schizophrenia Research & Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, University of Copenhagen, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jayachandra M. Raghava
- Centre for Neuropsychiatric Schizophrenia Research & Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Glostrup, Denmark
| | - Mette Ø. Nielsen
- Centre for Neuropsychiatric Schizophrenia Research & Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, University of Copenhagen, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Egill Rostrup
- Centre for Neuropsychiatric Schizophrenia Research & Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, University of Copenhagen, Copenhagen, Denmark
| | - Lars K. Hansen
- Cognitive Systems, DTU Compute, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Birte Y. Glenthøj
- Centre for Neuropsychiatric Schizophrenia Research & Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, University of Copenhagen, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
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