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Chekroud AM, Hawrilenko M, Loho H, Bondar J, Gueorguieva R, Hasan A, Kambeitz J, Corlett PR, Koutsouleris N, Krumholz HM, Krystal JH, Paulus M. Illusory generalizability of clinical prediction models. Science 2024; 383:164-167. [PMID: 38207039 DOI: 10.1126/science.adg8538] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 11/10/2023] [Indexed: 01/13/2024]
Abstract
It is widely hoped that statistical models can improve decision-making related to medical treatments. Because of the cost and scarcity of medical outcomes data, this hope is typically based on investigators observing a model's success in one or two datasets or clinical contexts. We scrutinized this optimism by examining how well a machine learning model performed across several independent clinical trials of antipsychotic medication for schizophrenia. Models predicted patient outcomes with high accuracy within the trial in which the model was developed but performed no better than chance when applied out-of-sample. Pooling data across trials to predict outcomes in the trial left out did not improve predictions. These results suggest that models predicting treatment outcomes in schizophrenia are highly context-dependent and may have limited generalizability.
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Affiliation(s)
- Adam M Chekroud
- Spring Health, New York City, NY 10010, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
| | | | - Hieronimus Loho
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
| | | | | | - Alkomiet Hasan
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Augsburg, 86159 Augsburg, Germany
| | - Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, University of Cologne, Faculty of Medicine and University Hospital of Cologne, Cologne, Germany
| | - Philip R Corlett
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany
| | - Harlan M Krumholz
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT 06520, USA
| | - John H Krystal
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Martin Paulus
- Laureate Institute for Brain Research, Tulsa, OK 74136, USA
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Bondar J, Babich Morrow C, Gueorguieva R, Brown M, Hawrilenko M, Krystal JH, Corlett PR, Chekroud AM. Clinical and Financial Outcomes Associated With a Workplace Mental Health Program Before and During the COVID-19 Pandemic. JAMA Netw Open 2022; 5:e2216349. [PMID: 35679044 PMCID: PMC9185188 DOI: 10.1001/jamanetworkopen.2022.16349] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
IMPORTANCE Investment in workplace wellness programs is increasing despite concerns about lack of clinical benefit and return on investment (ROI). In contrast, outcomes from workplace mental health programs, which treat mental health difficulties more directly, remain mostly unknown. OBJECTIVE To determine whether participation in an employer-sponsored mental health benefit was associated with improvements in depression and anxiety, workplace productivity, and ROI as well as to examine factors associated with clinical improvement. DESIGN, SETTING, AND PARTICIPANTS This cohort study included participants in a US workplace mental health program implemented by 66 employers across 40 states from January 1, 2018, to January 1, 2021. Participants were employees who enrolled in the mental health benefit program and had at least moderate anxiety or depression, at least 1 appointment, and at least 2 outcome assessments. INTERVENTION A digital platform that screened individuals for common mental health conditions and provided access to self-guided digital content, care navigation, and video and in-person psychotherapy and/or medication management. MAIN OUTCOMES AND MEASURES Primary outcomes were the Patient Health Questionnaire-9 for depression (range, 0-27) score and the Generalized Anxiety Disorder 7-item scale (range, 0-21) score. The ROI was calculated by comparing the cost of treatment to salary costs for time out of the workplace due to mental health symptoms, measured with the Sheehan Disability Scale. Data were collected through 6 months of follow-up and analyzed using mixed-effects regression. RESULTS A total of 1132 participants (520 of 724 who reported gender [71.8%] were female; mean [SD] age, 32.9 [8.8] years) were included. Participants reported improvements from pretreatment to posttreatment in depression (b = -6.34; 95% CI, -6.76 to -5.91; Cohen d = -1.11; 95% CI, -1.18 to -1.03) and anxiety (b = -6.28; 95% CI, -6.77 to -5.91; Cohen d = -1.21; 95% CI, -1.30 to -1.13). Symptom change per log-day of treatment was similar post-COVID-19 vs pre-COVID-19 for depression (b = 0.14; 95% CI, -0.10 to 0.38) and anxiety (b = 0.08; 95% CI, -0.22 to 0.38). Workplace salary savings at 6 months at the federal median wage was US $3440 (95% CI, $2730-$4151) with positive ROI across all wage groups. CONCLUSIONS AND RELEVANCE Results of this cohort study suggest that an employer-sponsored workplace mental health program was associated with large clinical effect sizes for employees and positive financial ROI for employers.
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Affiliation(s)
- Julia Bondar
- Spring Health, Spring Care Inc, New York, New York
| | | | - Ralitza Gueorguieva
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut
| | | | | | - John H. Krystal
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Philip R. Corlett
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Adam M. Chekroud
- Spring Health, Spring Care Inc, New York, New York
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
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Degli Esposti M, Ziauddeen H, Bowes L, Reeves A, Chekroud AM, Humphreys DK, Ford T. Trends in inpatient care for psychiatric disorders in NHS hospitals across England, 1998/99-2019/20: an observational time series analysis. Soc Psychiatry Psychiatr Epidemiol 2022; 57:993-1006. [PMID: 34951652 PMCID: PMC8705084 DOI: 10.1007/s00127-021-02215-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [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: 07/19/2021] [Accepted: 12/05/2021] [Indexed: 11/29/2022]
Abstract
PURPOSE It is unclear how hospitals are responding to the mental health needs of the population in England, against a backdrop of diminishing resources. We aimed to document patterns in hospital activity by psychiatric disorder and how these have changed over the last 22 years. METHODS In this observational time series analysis, we used routinely collected data on all NHS hospitals in England from 1998/99 to 2019/20. Trends in hospital admissions and bed days for psychiatric disorders were smoothed using negative binomial regression models with year as the exposure and rates (per 1000 person-years) as the outcome. When linear trends were not appropriate, we fitted segmented negative binomial regression models with one change-point. We stratified by gender and age group [children (0-14 years); adults (15 years +)]. RESULTS Hospital admission rates and bed days for all psychiatric disorders decreased by 28.4 and 38.3%, respectively. Trends were not uniform across psychiatric disorders or age groups. Admission rates mainly decreased over time, except for anxiety and eating disorders which doubled over the 22-year period, significantly increasing by 2.9% (AAPC = 2.88; 95% CI: 2.61-3.16; p < 0.001) and 3.4% (AAPC = 3.44; 95% CI: 3.04-3.85; p < 0.001) each year. Inpatient hospital activity among children showed more increasing and pronounced trends than adults, including an increase of 212.9% for depression, despite a 63.8% reduction for adults with depression during the same period. CONCLUSION In the last 22 years, there have been overall reductions in hospital activity for psychiatric disorders. However, some disorders showed pronounced increases, pointing to areas of growing need for inpatient psychiatric care, especially among children.
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Affiliation(s)
- Michelle Degli Esposti
- Department of Social Policy and Intervention, University of Oxford, Barnett House, 32 Wellington Square, Oxford, OX1 2ER, UK.
| | - Hisham Ziauddeen
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 3EB UK
| | - Lucy Bowes
- Department of Experimental Psychology, University of Oxford, Oxford, OX2 6GG UK
| | - Aaron Reeves
- Department of Social Policy and Intervention, University of Oxford, Barnett House, 32 Wellington Square, Oxford, OX1 2ER UK
| | - Adam M. Chekroud
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06510 USA
| | - David K. Humphreys
- Department of Social Policy and Intervention, University of Oxford, Barnett House, 32 Wellington Square, Oxford, OX1 2ER UK
| | - Tamsin Ford
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 3EB UK
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Chekroud AM, Bondar J, Delgadillo J, Doherty G, Wasil A, Fokkema M, Cohen Z, Belgrave D, DeRubeis R, Iniesta R, Dwyer D, Choi K. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry 2021; 20:154-170. [PMID: 34002503 PMCID: PMC8129866 DOI: 10.1002/wps.20882] [Citation(s) in RCA: 138] [Impact Index Per Article: 46.0] [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] [Indexed: 12/14/2022] Open
Abstract
For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real-world clinical practice. Relatively few retrospective studies to-date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.
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Affiliation(s)
- Adam M Chekroud
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Spring Health, New York City, NY, USA
| | | | - Jaime Delgadillo
- Clinical Psychology Unit, Department of Psychology, University of Sheffield, Sheffield, UK
| | - Gavin Doherty
- School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland
| | - Akash Wasil
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Marjolein Fokkema
- Department of Methods and Statistics, Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Zachary Cohen
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Robert DeRubeis
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel Iniesta
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich, Germany
| | - Karmel Choi
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Browning M, Carter CS, Chatham C, Den Ouden H, Gillan CM, Baker JT, Chekroud AM, Cools R, Dayan P, Gold J, Goldstein RZ, Hartley CA, Kepecs A, Lawson RP, Mourao-Miranda J, Phillips ML, Pizzagalli DA, Powers A, Rindskopf D, Roiser JP, Schmack K, Schiller D, Sebold M, Stephan KE, Frank MJ, Huys Q, Paulus M. Realizing the Clinical Potential of Computational Psychiatry: Report From the Banbury Center Meeting, February 2019. Biol Psychiatry 2020; 88:e5-e10. [PMID: 32113656 DOI: 10.1016/j.biopsych.2019.12.026] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 12/30/2019] [Accepted: 12/30/2019] [Indexed: 12/31/2022]
Affiliation(s)
- Michael Browning
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; Oxford Health National Health Service Foundation Trust, Warneford Hospital, Oxford, United Kingdom.
| | - Cameron S Carter
- Department of Psychiatry, University of California, Davis, Davis, California; Department of Psychology, University of California, Davis, Davis, California
| | - Christopher Chatham
- Department of Neuroscience and Rare Diseases, Roche Pharma Research and Early Development, Roche Innovation Center, Basel, Switzerland
| | - Hanneke Den Ouden
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Claire M Gillan
- School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Justin T Baker
- McLean Institute for Technology in Psychiatry, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | | | - Roshan Cools
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands; Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Peter Dayan
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - James Gold
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - Rita Z Goldstein
- Department of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York
| | | | - Adam Kepecs
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, New York
| | - Rebecca P Lawson
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - Janaina Mourao-Miranda
- Centre for Medical Image Computing, University College London, London, United Kingdom; Department of Computer Science, Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
| | - Mary L Phillips
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Diego A Pizzagalli
- Department of Psychiatry, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Albert Powers
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - David Rindskopf
- Educational Psychology, Graduate School and University Center of the City University of New York, New York, New York
| | - Jonathan P Roiser
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Katharina Schmack
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, New York
| | - Daniela Schiller
- Department of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Miriam Sebold
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Klaas Enno Stephan
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom; Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich, Zurich, Switzerland; Eidgenössische Technische Hochschule Zürich, Zurich, Switzerland; Max Planck Institute for Metabolism Research, Cologne, Germany
| | - Michael J Frank
- J. & Nancy D. Carney Institute for Brain Science, Department of Cognitive, Linguistic & Psychological Sciences, Brown University, Providence, Rhode Island
| | - Quentin Huys
- Department of Computer Science, Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom; Division of Psychiatry, University College London, London, United Kingdom; Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich, Zurich, Switzerland; Eidgenössische Technische Hochschule Zürich, Zurich, Switzerland
| | - Martin Paulus
- Laureate Institute for Brain Research, Tulsa, Oklahoma
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Bondar J, Caye A, Chekroud AM, Kieling C. Symptom clusters in adolescent depression and differential response to treatment: a secondary analysis of the Treatment for Adolescents with Depression Study randomised trial. Lancet Psychiatry 2020; 7:337-343. [PMID: 32199509 DOI: 10.1016/s2215-0366(20)30060-2] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [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: 12/02/2019] [Revised: 01/31/2020] [Accepted: 02/06/2020] [Indexed: 10/24/2022]
Abstract
BACKGROUND Better understanding of the heterogeneity of treatment responses could help to improve care for adolescents with depression. We analysed data from a clinical trial to assess whether specific symptom clusters responded differently to various treatments. METHODS For this secondary analysis, we used data from the Treatment for Adolescents with Depression Study (TADS), in which 439 US adolescents aged 12-17 with a DSM-IV diagnosis of major depressive disorder and a minimum score of 45 on the Children's Depression Rating Scale-Revised (CDRS-R) were randomly assigned (1:1:1:1) to treatment with fluoxetine, cognitive behavioural therapy (CBT), fluoxetine plus CBT, or pill placebo. Our analysis focuses on the acute phase of the trial (ie, the first 12 weeks). Groups of co-occurring symptoms were established by clustering scores for each CDRS-R item at baseline with Ward's method, with Euclidean distances for hierarchical agglomerative clustering. We then used a linear mixed-effects model to investigate the relationship between symptom clusters and treatment efficacy, with the sum of symptom scores within each cluster as the dependent measure. As fixed effects, we entered cluster, time, and treatment assignment, with all two-way and three-way interactions, into the model. The random effect providing better fit was established to be a by-subject random slope for cluster based on improvement in the Schwarz-Bayesian information criterion. OUTCOMES We identified two symptom clusters: cluster 1 comprised depressed mood, difficulty having fun, irritability, social withdrawal, sleep disturbance, impaired schoolwork, excessive fatigue, and low self-esteem, and cluster 2 comprised increased appetite, physical complaints, excessive weeping, decreased appetite, excessive guilt, morbid ideation, and suicidal ideation. For cluster 1 symptoms, CDRS-R scores were reduced by 5·8 points (95% CI 2·8-8·9) in adolescents treated with fluoxetine plus CBT, and by 4·1 points (1·1-7·1) in those treated with fluoxetine, compared with those given placebo. For cluster 2 symptoms, no significant differences in improvements in CDRS-R scores were detected between the active treatment and placebo groups. INTERPRETATION Response to fluoxetine and CBT among adolescents with depression is heterogeneous. Clinicians should consider clinical profile when selecting therapeutic modality. The contrast in response patterns between symptom clusters could provide opportunities to improve treatment efficacy by gearing the development of new therapies towards the resolution of specific symptoms. FUNDING Conselho Nacional de Desenvolvimento Científico e Tecnológico.
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Affiliation(s)
- Julia Bondar
- Department of Psychiatry, School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Child & Adolescent Psychiatry Division, Hospital de Clínicas de Porto Alegre, Brazil, Porto Alegre, Brazil
| | - Arthur Caye
- Department of Psychiatry, School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Child & Adolescent Psychiatry Division, Hospital de Clínicas de Porto Alegre, Brazil, Porto Alegre, Brazil
| | - Adam M Chekroud
- Spring Health, New York, NY, USA; Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Christian Kieling
- Department of Psychiatry, School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Child & Adolescent Psychiatry Division, Hospital de Clínicas de Porto Alegre, Brazil, Porto Alegre, Brazil.
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Affiliation(s)
- Adam M Chekroud
- Spring Health, New York City, New York.,Department of Psychiatry, Yale University, New Haven, Connecticut
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Rutledge RB, Chekroud AM, Huys QJ. Machine learning and big data in psychiatry: toward clinical applications. Curr Opin Neurobiol 2019; 55:152-159. [PMID: 30999271 DOI: 10.1016/j.conb.2019.02.006] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 01/29/2019] [Accepted: 02/07/2019] [Indexed: 12/21/2022]
Abstract
Psychiatry is a medical field concerned with the treatment of mental illness. Psychiatric disorders broadly relate to higher functions of the brain, and as such are richly intertwined with social, cultural, and experiential factors. This makes them exquisitely complex phenomena that depend on and interact with a large number of variables. Computational psychiatry provides two ways of approaching this complexity. Theory-driven computational approaches employ mechanistic models to make explicit hypotheses at multiple levels of analysis. Data-driven machine-learning approaches can make predictions from high-dimensional data and are generally agnostic as to the underlying mechanisms. Here, we review recent advances in the use of big data and machine-learning approaches toward the aim of alleviating the suffering that arises from psychiatric disorders.
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Affiliation(s)
- Robb B Rutledge
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, England, United Kingdom; Wellcome Centre for Human Neuroimaging, University College London, London, England, United Kingdom
| | - Adam M Chekroud
- Department of Psychiatry, Yale University, New Haven, CT, United States; Spring Health, New York, NY, United States
| | - Quentin Jm Huys
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, England, United Kingdom; Division of Psychiatry, University College London, London, England, United Kingdom; Camden and Islington NHS Foundation Trust, London, England, United Kingdom.
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Chekroud SR, Gueorguieva R, Zheutlin AB, Paulus M, Krumholz HM, Krystal JH, Chekroud AM. Physical activity and mental health - Author's reply. Lancet Psychiatry 2018; 5:874. [PMID: 30245184 DOI: 10.1016/s2215-0366(18)30354-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 09/07/2018] [Indexed: 11/18/2022]
Affiliation(s)
- Sammi R Chekroud
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Ralitza Gueorguieva
- Department of Biostatistics, Yale University, New Haven, CT 06510, USA; Section of Cardiovascular Medicine, and School of Medicine, Yale University, New Haven, CT 06510, USA
| | | | - Martin Paulus
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Harlan M Krumholz
- Section of Cardiovascular Medicine, and School of Medicine, Yale University, New Haven, CT 06510, USA
| | - John H Krystal
- Department of Psychiatry, Yale University, New Haven, CT 06510, USA; Psychiatry and Behavioral Health Services, Yale-New Haven Hospital, New Haven, CT, USA; Clinical Neuroscience Division, National Center for PTSD, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Adam M Chekroud
- Section of Cardiovascular Medicine, and School of Medicine, Yale University, New Haven, CT 06510, USA; Department of Psychiatry, Yale University, New Haven, CT 06510, USA; Spring Health, New York City, NY, USA.
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Chekroud AM. Anticipating Suicide Will Be Hard, But This Is Progress. Am J Psychiatry 2018; 175:921-922. [PMID: 30269536 DOI: 10.1176/appi.ajp.2018.18060714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Adam M Chekroud
- From Spring Health, New York City, and the Department of Psychiatry, Yale University, New Haven, Conn
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Affiliation(s)
- Sammi R Chekroud
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, England
| | - Adam M Chekroud
- Department of Psychiatry, Yale University, New Haven, Connecticut.,Spring Health, New York, New York
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Chekroud SR, Gueorguieva R, Zheutlin AB, Paulus M, Krumholz HM, Krystal JH, Chekroud AM. Association between physical exercise and mental health in 1·2 million individuals in the USA between 2011 and 2015: a cross-sectional study. Lancet Psychiatry 2018; 5:739-746. [PMID: 30099000 DOI: 10.1016/s2215-0366(18)30227-x] [Citation(s) in RCA: 488] [Impact Index Per Article: 81.3] [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: 04/18/2018] [Revised: 05/30/2018] [Accepted: 06/05/2018] [Indexed: 12/15/2022]
Abstract
BACKGROUND Exercise is known to be associated with reduced risk of all-cause mortality, cardiovascular disease, stroke, and diabetes, but its association with mental health remains unclear. We aimed to examine the association between exercise and mental health burden in a large sample, and to better understand the influence of exercise type, frequency, duration, and intensity. METHODS In this cross-sectional study, we analysed data from 1 237 194 people aged 18 years or older in the USA from the 2011, 2013, and 2015 Centers for Disease Control and Prevention Behavioral Risk Factors Surveillance System survey. We compared the number of days of bad self-reported mental health between individuals who exercised and those who did not, using an exact non-parametric matching procedure to balance the two groups in terms of age, race, gender, marital status, income, education level, body-mass index category, self-reported physical health, and previous diagnosis of depression. We examined the effects of exercise type, duration, frequency, and intensity using regression methods adjusted for potential confounders, and did multiple sensitivity analyses. FINDINGS Individuals who exercised had 1·49 (43·2%) fewer days of poor mental health in the past month than individuals who did not exercise but were otherwise matched for several physical and sociodemographic characteristics (W=7·42 × 1010, p<2·2 × 10-16). All exercise types were associated with a lower mental health burden (minimum reduction of 11·8% and maximum reduction of 22·3%) than not exercising (p<2·2 × 10-16 for all exercise types). The largest associations were seen for popular team sports (22·3% lower), cycling (21·6% lower), and aerobic and gym activities (20·1% lower), as well as durations of 45 min and frequencies of three to five times per week. INTERPRETATION In a large US sample, physical exercise was significantly and meaningfully associated with self-reported mental health burden in the past month. More exercise was not always better. Differences as a function of exercise were large relative to other demographic variables such as education and income. Specific types, durations, and frequencies of exercise might be more effective clinical targets than others for reducing mental health burden, and merit interventional study. FUNDING Cloud computing resources were provided by Microsoft.
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Affiliation(s)
- Sammi R Chekroud
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Ralitza Gueorguieva
- Department of Biostatistics, Yale University, New Haven, CT, USA; School of Medicine, Yale University, New Haven, CT, USA
| | | | - Martin Paulus
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Harlan M Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA; Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
| | - John H Krystal
- Department of Psychiatry, Yale University, New Haven, CT, USA; Psychiatry and Behavioral Health Services, Yale-New Haven Hospital, New Haven, CT, USA; Clinical Neuroscience Division, National Center for PTSD, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Adam M Chekroud
- School of Medicine, Yale University, New Haven, CT, USA; Department of Psychiatry, Yale University, New Haven, CT, USA; Spring Health, New York City, NY, USA.
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Chekroud AM, Foster D, Zheutlin AB, Gerhard DM, Roy B, Koutsouleris N, Chandra A, Esposti MD, Subramanyan G, Gueorguieva R, Paulus M, Krystal JH. Predicting Barriers to Treatment for Depression in a U.S. National Sample: A Cross-Sectional, Proof-of-Concept Study. Psychiatr Serv 2018; 69:927-934. [PMID: 29962307 PMCID: PMC7232987 DOI: 10.1176/appi.ps.201800094] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [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] [Indexed: 10/28/2022]
Abstract
OBJECTIVE Even though safe and effective treatments for depression are available, many individuals with a diagnosis of depression do not obtain treatment. This study aimed to develop a tool to identify persons who might not initiate treatment among those who acknowledge a need. METHODS Data were aggregated from the 2008-2014 U.S. National Survey on Drug Use and Health (N=391,753), including 20,785 adults given a diagnosis of depression by a health care provider in the 12 months before the survey. Machine learning was applied to self-report survey items to develop strategies for identifying individuals who might not get needed treatment. RESULTS A derivation cohort aggregated between 2008 and 2013 was used to develop a model that identified the 30.6% of individuals with depression who reported needing but not getting treatment. When applied to independent responses from the 2014 cohort, the model identified 72% of those who did not initiate treatment (p<.01), with a balanced accuracy that was also significantly above chance (71%, p<.01). For individuals who did not get treatment, the model predicted 10 (out of 15) reasons that they endorsed as barriers to treatment, with balanced accuracies between 53% and 65% (p<.05 for all). CONCLUSIONS Considerable work is needed to improve follow-up and retention rates after the critical initial meeting in which a patient is given a diagnosis of depression. Routinely collected information about patients with depression could identify those at risk of not obtaining needed treatment, which may inform the development and implementation of interventions to reduce the prevalence of untreated depression.
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Affiliation(s)
- Adam M Chekroud
- Dr. Chekroud, Dr. Gerhard, Dr. Gueorguieva, and Dr. Krystal are with the Department of Psychiatry and Dr. Roy is with the Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut. Dr. Chekroud is also with Spring Health, New York, where Mr. Chandra and Dr. Subramanyan are affiliated. Dr. Gueorguieva is also with the Department of Biostatistics, Yale University, New Haven, Connecticut. Mr. Foster is with Applied Data Science Partners, London. Dr. Zheutlin is with the Center for Genomic Medicine, Massachusetts General Hospital, Boston. Dr. Koutsouleris is with the Department of Psychiatry and Psychotherapy, Ludwig Maximilian University, Munich. Ms. Degli Esposti is with the Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom. Dr. Paulus is with the Laureate Institute for Brain Research, Tulsa, Oklahoma
| | - David Foster
- Dr. Chekroud, Dr. Gerhard, Dr. Gueorguieva, and Dr. Krystal are with the Department of Psychiatry and Dr. Roy is with the Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut. Dr. Chekroud is also with Spring Health, New York, where Mr. Chandra and Dr. Subramanyan are affiliated. Dr. Gueorguieva is also with the Department of Biostatistics, Yale University, New Haven, Connecticut. Mr. Foster is with Applied Data Science Partners, London. Dr. Zheutlin is with the Center for Genomic Medicine, Massachusetts General Hospital, Boston. Dr. Koutsouleris is with the Department of Psychiatry and Psychotherapy, Ludwig Maximilian University, Munich. Ms. Degli Esposti is with the Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom. Dr. Paulus is with the Laureate Institute for Brain Research, Tulsa, Oklahoma
| | - Amanda B Zheutlin
- Dr. Chekroud, Dr. Gerhard, Dr. Gueorguieva, and Dr. Krystal are with the Department of Psychiatry and Dr. Roy is with the Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut. Dr. Chekroud is also with Spring Health, New York, where Mr. Chandra and Dr. Subramanyan are affiliated. Dr. Gueorguieva is also with the Department of Biostatistics, Yale University, New Haven, Connecticut. Mr. Foster is with Applied Data Science Partners, London. Dr. Zheutlin is with the Center for Genomic Medicine, Massachusetts General Hospital, Boston. Dr. Koutsouleris is with the Department of Psychiatry and Psychotherapy, Ludwig Maximilian University, Munich. Ms. Degli Esposti is with the Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom. Dr. Paulus is with the Laureate Institute for Brain Research, Tulsa, Oklahoma
| | - Danielle M Gerhard
- Dr. Chekroud, Dr. Gerhard, Dr. Gueorguieva, and Dr. Krystal are with the Department of Psychiatry and Dr. Roy is with the Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut. Dr. Chekroud is also with Spring Health, New York, where Mr. Chandra and Dr. Subramanyan are affiliated. Dr. Gueorguieva is also with the Department of Biostatistics, Yale University, New Haven, Connecticut. Mr. Foster is with Applied Data Science Partners, London. Dr. Zheutlin is with the Center for Genomic Medicine, Massachusetts General Hospital, Boston. Dr. Koutsouleris is with the Department of Psychiatry and Psychotherapy, Ludwig Maximilian University, Munich. Ms. Degli Esposti is with the Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom. Dr. Paulus is with the Laureate Institute for Brain Research, Tulsa, Oklahoma
| | - Brita Roy
- Dr. Chekroud, Dr. Gerhard, Dr. Gueorguieva, and Dr. Krystal are with the Department of Psychiatry and Dr. Roy is with the Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut. Dr. Chekroud is also with Spring Health, New York, where Mr. Chandra and Dr. Subramanyan are affiliated. Dr. Gueorguieva is also with the Department of Biostatistics, Yale University, New Haven, Connecticut. Mr. Foster is with Applied Data Science Partners, London. Dr. Zheutlin is with the Center for Genomic Medicine, Massachusetts General Hospital, Boston. Dr. Koutsouleris is with the Department of Psychiatry and Psychotherapy, Ludwig Maximilian University, Munich. Ms. Degli Esposti is with the Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom. Dr. Paulus is with the Laureate Institute for Brain Research, Tulsa, Oklahoma
| | - Nikolaos Koutsouleris
- Dr. Chekroud, Dr. Gerhard, Dr. Gueorguieva, and Dr. Krystal are with the Department of Psychiatry and Dr. Roy is with the Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut. Dr. Chekroud is also with Spring Health, New York, where Mr. Chandra and Dr. Subramanyan are affiliated. Dr. Gueorguieva is also with the Department of Biostatistics, Yale University, New Haven, Connecticut. Mr. Foster is with Applied Data Science Partners, London. Dr. Zheutlin is with the Center for Genomic Medicine, Massachusetts General Hospital, Boston. Dr. Koutsouleris is with the Department of Psychiatry and Psychotherapy, Ludwig Maximilian University, Munich. Ms. Degli Esposti is with the Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom. Dr. Paulus is with the Laureate Institute for Brain Research, Tulsa, Oklahoma
| | - Abhishek Chandra
- Dr. Chekroud, Dr. Gerhard, Dr. Gueorguieva, and Dr. Krystal are with the Department of Psychiatry and Dr. Roy is with the Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut. Dr. Chekroud is also with Spring Health, New York, where Mr. Chandra and Dr. Subramanyan are affiliated. Dr. Gueorguieva is also with the Department of Biostatistics, Yale University, New Haven, Connecticut. Mr. Foster is with Applied Data Science Partners, London. Dr. Zheutlin is with the Center for Genomic Medicine, Massachusetts General Hospital, Boston. Dr. Koutsouleris is with the Department of Psychiatry and Psychotherapy, Ludwig Maximilian University, Munich. Ms. Degli Esposti is with the Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom. Dr. Paulus is with the Laureate Institute for Brain Research, Tulsa, Oklahoma
| | - Michelle Degli Esposti
- Dr. Chekroud, Dr. Gerhard, Dr. Gueorguieva, and Dr. Krystal are with the Department of Psychiatry and Dr. Roy is with the Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut. Dr. Chekroud is also with Spring Health, New York, where Mr. Chandra and Dr. Subramanyan are affiliated. Dr. Gueorguieva is also with the Department of Biostatistics, Yale University, New Haven, Connecticut. Mr. Foster is with Applied Data Science Partners, London. Dr. Zheutlin is with the Center for Genomic Medicine, Massachusetts General Hospital, Boston. Dr. Koutsouleris is with the Department of Psychiatry and Psychotherapy, Ludwig Maximilian University, Munich. Ms. Degli Esposti is with the Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom. Dr. Paulus is with the Laureate Institute for Brain Research, Tulsa, Oklahoma
| | - Girish Subramanyan
- Dr. Chekroud, Dr. Gerhard, Dr. Gueorguieva, and Dr. Krystal are with the Department of Psychiatry and Dr. Roy is with the Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut. Dr. Chekroud is also with Spring Health, New York, where Mr. Chandra and Dr. Subramanyan are affiliated. Dr. Gueorguieva is also with the Department of Biostatistics, Yale University, New Haven, Connecticut. Mr. Foster is with Applied Data Science Partners, London. Dr. Zheutlin is with the Center for Genomic Medicine, Massachusetts General Hospital, Boston. Dr. Koutsouleris is with the Department of Psychiatry and Psychotherapy, Ludwig Maximilian University, Munich. Ms. Degli Esposti is with the Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom. Dr. Paulus is with the Laureate Institute for Brain Research, Tulsa, Oklahoma
| | - Ralitza Gueorguieva
- Dr. Chekroud, Dr. Gerhard, Dr. Gueorguieva, and Dr. Krystal are with the Department of Psychiatry and Dr. Roy is with the Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut. Dr. Chekroud is also with Spring Health, New York, where Mr. Chandra and Dr. Subramanyan are affiliated. Dr. Gueorguieva is also with the Department of Biostatistics, Yale University, New Haven, Connecticut. Mr. Foster is with Applied Data Science Partners, London. Dr. Zheutlin is with the Center for Genomic Medicine, Massachusetts General Hospital, Boston. Dr. Koutsouleris is with the Department of Psychiatry and Psychotherapy, Ludwig Maximilian University, Munich. Ms. Degli Esposti is with the Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom. Dr. Paulus is with the Laureate Institute for Brain Research, Tulsa, Oklahoma
| | - Martin Paulus
- Dr. Chekroud, Dr. Gerhard, Dr. Gueorguieva, and Dr. Krystal are with the Department of Psychiatry and Dr. Roy is with the Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut. Dr. Chekroud is also with Spring Health, New York, where Mr. Chandra and Dr. Subramanyan are affiliated. Dr. Gueorguieva is also with the Department of Biostatistics, Yale University, New Haven, Connecticut. Mr. Foster is with Applied Data Science Partners, London. Dr. Zheutlin is with the Center for Genomic Medicine, Massachusetts General Hospital, Boston. Dr. Koutsouleris is with the Department of Psychiatry and Psychotherapy, Ludwig Maximilian University, Munich. Ms. Degli Esposti is with the Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom. Dr. Paulus is with the Laureate Institute for Brain Research, Tulsa, Oklahoma
| | - John H Krystal
- Dr. Chekroud, Dr. Gerhard, Dr. Gueorguieva, and Dr. Krystal are with the Department of Psychiatry and Dr. Roy is with the Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut. Dr. Chekroud is also with Spring Health, New York, where Mr. Chandra and Dr. Subramanyan are affiliated. Dr. Gueorguieva is also with the Department of Biostatistics, Yale University, New Haven, Connecticut. Mr. Foster is with Applied Data Science Partners, London. Dr. Zheutlin is with the Center for Genomic Medicine, Massachusetts General Hospital, Boston. Dr. Koutsouleris is with the Department of Psychiatry and Psychotherapy, Ludwig Maximilian University, Munich. Ms. Degli Esposti is with the Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom. Dr. Paulus is with the Laureate Institute for Brain Research, Tulsa, Oklahoma
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Zheutlin AB, Chekroud AM, Polimanti R, Gelernter J, Sabb FW, Bilder RM, Freimer N, London ED, Hultman CM, Cannon TD. Multivariate Pattern Analysis of Genotype-Phenotype Relationships in Schizophrenia. Schizophr Bull 2018; 44. [PMID: 29534239 PMCID: PMC6101611 DOI: 10.1093/schbul/sby005] [Citation(s) in RCA: 11] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Genetic risk variants for schizophrenia have been linked to many related clinical and biological phenotypes with the hopes of delineating how individual variation across thousands of variants corresponds to the clinical and etiologic heterogeneity within schizophrenia. This has primarily been done using risk score profiling, which aggregates effects across all variants into a single predictor. While effective, this method lacks flexibility in certain domains: risk scores cannot capture nonlinear effects and do not employ any variable selection. We used random forest, an algorithm with this flexibility designed to maximize predictive power, to predict 6 cognitive endophenotypes in a combined sample of psychiatric patients and controls (N = 739) using 77 genetic variants strongly associated with schizophrenia. Tenfold cross-validation was applied to the discovery sample and models were externally validated in an independent sample of similar ancestry (N = 336). Linear approaches, including linear regression and task-specific polygenic risk scores, were employed for comparison. Random forest models for processing speed (P = .019) and visual memory (P = .036) and risk scores developed for verbal (P = .042) and working memory (P = .037) successfully generalized to an independent sample with similar predictive strength and error. As such, we suggest that both methods may be useful for mapping a limited set of predetermined, disease-associated SNPs to related phenotypes. Incorporating random forest and other more flexible algorithms into genotype-phenotype mapping inquiries could contribute to parsing heterogeneity within schizophrenia; such algorithms can perform as well as standard methods and can capture a more comprehensive set of potential relationships.
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Affiliation(s)
| | - Adam M Chekroud
- Department of Psychology, Yale University, New Haven, CT,Spring Health, New York, NY,Department of Psychiatry, Yale University School of Medicine, New Haven, CT
| | - Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT
| | - Fred W Sabb
- Lewis Center for Neuroimaging, University of Oregon, Eugene, OR
| | - Robert M Bilder
- Department of Psychology, University of California - Los Angeles, Los Angeles, CA
| | - Nelson Freimer
- Department of Psychiatry and Biobehavioral Sciences, University of California - Los Angeles, Los Angeles, CA
| | - Edythe D London
- Department of Psychiatry and Biobehavioral Sciences, University of California - Los Angeles, Los Angeles, CA
| | - Christina M Hultman
- Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Tyrone D Cannon
- Department of Psychology, Yale University, New Haven, CT,Department of Psychiatry, Yale University School of Medicine, New Haven, CT,To whom correspondence should be addressed; Department of Psychology, Yale University, PO Box 208205, New Haven, CT 06520; tel: 203-436-1545, e-mail:
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Abstract
Trauma-related symptoms among veterans of military engagement have been documented at
least since the time of the ancient Greeks.1 Since the third edition of the
Diagnostic and Statistical Manual in 1980, this condition has been known as posttraumatic
stress disorder, but the name has changed repeatedly over the past century, including
shell shock, war neurosis, and soldier’s heart. Using over 14 million articles in the
digital archives of the New York Times, Associated Press, and Reuters, we quantify
historical changes in trauma-related terminology over the past century. These data suggest
that posttraumatic stress disorder has historically peaked in public awareness after the
end of US military engagements, but denoted by a different name each time—a phenomenon
that could impede clinical and scientific progress.
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Affiliation(s)
- Adam M. Chekroud
- Department
of Psychiatry, Yale University, New Haven, CT,
USA
- Data Science Division, Spring Health, New York
City, NY, USA
- Adam M. Chekroud, Department of Psychiatry, Yale
University, 300 George St #901, New Haven, CT 06511, USA.
| | - Hieronimus Loho
- Department
of Psychiatry, Yale University, New Haven, CT,
USA
- Data Science Division, Spring Health, New York
City, NY, USA
| | - Martin Paulus
- Laureate Institute for Brain Research, Tulsa,
OK, USA
| | - John H. Krystal
- Department
of Psychiatry, Yale University, New Haven, CT,
USA
- US Department of Veterans Affairs National
Center for PTSD, VA Connecticut Healthcare System, Newington, CT, USA
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16
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Affiliation(s)
- Adam M Chekroud
- Spring Health, Brooklyn, New York.,Department of Psychology, Yale University, New Haven, Connecticut
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Abstract
Schizophrenia research is plagued by enormous challenges in integrating and analyzing large datasets and difficulties developing formal theories related to the etiology, pathophysiology, and treatment of this disorder. Computational psychiatry provides a path to enhance analyses of these large and complex datasets and to promote the development and refinement of formal models for features of this disorder. This presentation introduces the reader to the notion of computational psychiatry and describes discovery-oriented and theory-driven applications to schizophrenia involving machine learning, reinforcement learning theory, and biophysically-informed neural circuit models.
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Affiliation(s)
- John H. Krystal
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT;,Department of Neuroscience, Yale University School of Medicine, New Haven, CT;,Department of Neuroscience, Yale-New Hospital, New Haven, CT;,VA National Center for PTSD, VA Connecticut Healthcare System, West Haven, CT
| | - John D. Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT
| | - Adam M. Chekroud
- Department of Psychology, Yale University, New Haven, CT;,Department of Psychology, Spring Health, New York, NY
| | - Philip R. Corlett
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT;,Department of Psychology, Yale University, New Haven, CT
| | - Genevieve Yang
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT
| | | | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT
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18
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Affiliation(s)
- Adam M Chekroud
- Department of Psychology, New Haven, CT, USA; Yale University, New Haven, CT, USA; Spring Health, New York City, NY, USA.
| | - Hieronimus Loho
- Department of Psychology, New Haven, CT, USA; Yale University, New Haven, CT, USA
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Chekroud AM, Gueorguieva R, Krumholz HM, Trivedi MH, Krystal JH, McCarthy G. Reevaluating the Efficacy and Predictability of Antidepressant Treatments: A Symptom Clustering Approach. JAMA Psychiatry 2017; 74:370-378. [PMID: 28241180 PMCID: PMC5863470 DOI: 10.1001/jamapsychiatry.2017.0025] [Citation(s) in RCA: 165] [Impact Index Per Article: 23.6] [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] [Indexed: 12/28/2022]
Abstract
IMPORTANCE Depressive severity is typically measured according to total scores on questionnaires that include a diverse range of symptoms despite convincing evidence that depression is not a unitary construct. When evaluated according to aggregate measurements, treatment efficacy is generally modest and differences in efficacy between antidepressant therapies are small. OBJECTIVES To determine the efficacy of antidepressant treatments on empirically defined groups of symptoms and examine the replicability of these groups. DESIGN, SETTING, AND PARTICIPANTS Patient-reported data on patients with depression from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial (n = 4039) were used to identify clusters of symptoms in a depressive symptom checklist. The findings were then replicated using the Combining Medications to Enhance Depression Outcomes (CO-MED) trial (n = 640). Mixed-effects regression analysis was then performed to determine whether observed symptom clusters have differential response trajectories using intent-to-treat data from both trials (n = 4706) along with 7 additional placebo and active-comparator phase 3 trials of duloxetine (n = 2515). Finally, outcomes for each cluster were estimated separately using machine-learning approaches. The study was conducted from October 28, 2014, to May 19, 2016. MAIN OUTCOMES AND MEASURES Twelve items from the self-reported Quick Inventory of Depressive Symptomatology (QIDS-SR) scale and 14 items from the clinician-rated Hamilton Depression (HAM-D) rating scale. Higher scores on the measures indicate greater severity of the symptoms. RESULTS Of the 4706 patients included in the first analysis, 1722 (36.6%) were male; mean (SD) age was 41.2 (13.3) years. Of the 2515 patients included in the second analysis, 855 (34.0%) were male; mean age was 42.65 (12.17) years. Three symptom clusters in the QIDS-SR scale were identified at baseline in STAR*D. This 3-cluster solution was replicated in CO-MED and was similar for the HAM-D scale. Antidepressants in general (8 of 9 treatments) were more effective for core emotional symptoms than for sleep or atypical symptoms. Differences in efficacy between drugs were often greater than the difference in efficacy between treatments and placebo. For example, high-dose duloxetine outperformed escitalopram in treating core emotional symptoms (effect size, 2.3 HAM-D points during 8 weeks, 95% CI, 1.6 to 3.1; P < .001), but escitalopram was not significantly different from placebo (effect size, 0.03 HAM-D points; 95% CI, -0.7 to 0.8; P = .94). CONCLUSIONS AND RELEVANCE Two common checklists used to measure depressive severity can produce statistically reliable clusters of symptoms. These clusters differ in their responsiveness to treatment both within and across different antidepressant medications. Selecting the best drug for a given cluster may have a bigger benefit than that gained by use of an active compound vs a placebo.
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Affiliation(s)
- Adam M. Chekroud
- Department of Psychology, Yale University, New Haven, Connecticut,Spring Health, New York City, New York,Center for Outcomes Research and Evaluation, Yale–New Haven Hospital, New Haven, Connecticut
| | - Ralitza Gueorguieva
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut
| | - Harlan M. Krumholz
- Center for Outcomes Research and Evaluation, Yale–New Haven Hospital, New Haven, Connecticut,Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut,Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut
| | - Madhukar H. Trivedi
- Department of Psychiatry, University of Texas–Southwestern Medical School, Dallas
| | - John H. Krystal
- Department of Psychiatry, Yale University, New Haven, Connecticut
| | - Gregory McCarthy
- Department of Psychology, Yale University, New Haven, Connecticut
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Gueorguieva R, Chekroud AM, Krystal JH. Trajectories of relapse in randomised, placebo-controlled trials of treatment discontinuation in major depressive disorder: an individual patient-level data meta-analysis. Lancet Psychiatry 2017; 4:230-237. [PMID: 28189575 PMCID: PMC5340978 DOI: 10.1016/s2215-0366(17)30038-x] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Revised: 01/16/2017] [Accepted: 01/17/2017] [Indexed: 01/03/2023]
Abstract
BACKGROUND Understanding patterns of relapse in patients who respond to antidepressant treatment can inform strategies for prevention of relapse. We aimed to identify distinct trajectories of depression severity, assess whether similar or different trajectory classes exist for patients who continued or discontinued active treatment, and test whether clinical predictors of trajectory class membership exist using pooled data from clinical trials. METHODS We analysed individual patient data from four double-blind discontinuation clinical trials of duloxetine or fluoxetine versus placebo in major depression from before 2012 (n=1462). We modelled trajectories of relapse up to 26 weeks during double-blind treatment. Trajectories of depression severity, as measured by the Hamilton Depression Rating Scale score, were identified in the entire sample, and separately in groups in which antidepressants had been continued or discontinued, using growth mixture models. Predictors of trajectory class membership were assessed with weighted logistic regression. FINDINGS We identified similar relapse trajectories and two trajectories of stable depression scores in the normal range on active medication and on placebo. Active treatment significantly lowered the odds of membership in the relapse trajectory (odds ratio 0·47, 95% CI 0·37-0·61), whereas female sex (1·56, 1·23-2·06), shorter length of time with clinical response by 1 week (1·10, 1·06-1·15), and higher Clinical Global Impression score at baseline (1·28, 1·01-1·62) increased the odds. Overall, the protective effect of antidepressant medication relative to placebo on the risk of being classified as a relapser was about 13% (33% vs 46%). INTERPRETATION The existence of similar relapse trajectories on active medication and on placebo suggests that there is no specific relapse signature associated with antidepressant discontinuation. Furthermore, continued treatment offers only modest protection against relapse. These data highlight the need to incorporate treatment strategies that prevent relapse as part of the treatment of depression. FUNDING National Institutes of Health, the US Department of Veterans Affairs Alcohol Research Center, and National Center for Post-Traumatic Stress Disorder.
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Affiliation(s)
- Ralitza Gueorguieva
- Department of Biostatistics, School of Public Health, Yale University School of Medicine, New Haven, CT, USA.
| | - Adam M Chekroud
- Department of Psychology, Yale University, New Haven, CT, USA; Spring Health, New York City, NY, USA; Centre for Outcomes Research and Evaluation, Yale-New Haven Hospital, CT, USA
| | - John H Krystal
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; VA National Center for PTSD, VA Connecticut Healthcare System, West Haven, CT, USA
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21
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Chekroud AM, Anand G, Yong J, Pike M, Bridge H. Altered functional brain connectivity in children and young people with opsoclonus-myoclonus syndrome. Dev Med Child Neurol 2017; 59:98-104. [PMID: 27658927 DOI: 10.1111/dmcn.13262] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [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] [Accepted: 08/12/2016] [Indexed: 11/29/2022]
Abstract
AIM Opsoclonus-myoclonus syndrome (OMS) is a rare, poorly understood condition that can result in long-term cognitive, behavioural, and motor sequelae. Several studies have investigated structural brain changes associated with this condition, but little is known about changes in function. This study aimed to investigate changes in brain functional connectivity in patients with OMS. METHOD Seven patients with OMS and 10 age-matched comparison participants underwent 3T magnetic resonance imaging (MRI) to acquire resting-state functional MRI data (whole-brain echo-planar images; 2mm isotropic voxels; multiband factor ×2) for a cross-sectional study. A seed-based analysis identified brain regions in which signal changes over time correlated with the cerebellum. Model-free analysis was used to determine brain networks showing altered connectivity. RESULTS In patients with OMS, the motor cortex showed significantly reduced connectivity, and the occipito-parietal region significantly increased connectivity with the cerebellum relative to the comparison group. A model-free analysis also showed extensive connectivity within a visual network, including the cerebellum and basal ganglia, not present in the comparison group. No other networks showed any differences between groups. INTERPRETATION Patients with OMS showed reduced connectivity between the cerebellum and motor cortex, but increased connectivity with occipito-parietal regions. This pattern of change supports widespread brain involvement in OMS.
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Affiliation(s)
- Adam M Chekroud
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Geetha Anand
- Oxford Children's Hospital, John Radcliffe Hospital, Oxford, UK
| | - Jean Yong
- Oxford Children's Hospital, John Radcliffe Hospital, Oxford, UK
| | - Michael Pike
- Oxford Children's Hospital, John Radcliffe Hospital, Oxford, UK
| | - Holly Bridge
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
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22
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Koutsouleris N, Kahn RS, Chekroud AM, Leucht S, Falkai P, Wobrock T, Derks EM, Fleischhacker WW, Hasan A. Multisite prediction of 4-week and 52-week treatment outcomes in patients with first-episode psychosis: a machine learning approach. Lancet Psychiatry 2016; 3:935-946. [PMID: 27569526 DOI: 10.1016/s2215-0366(16)30171-7] [Citation(s) in RCA: 113] [Impact Index Per Article: 14.1] [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: 04/08/2016] [Revised: 06/24/2016] [Accepted: 06/28/2016] [Indexed: 01/19/2023]
Abstract
BACKGROUND At present, no tools exist to estimate objectively the risk of poor treatment outcomes in patients with first-episode psychosis. Such tools could improve treatment by informing clinical decision-making before the commencement of treatment. We tested whether such a tool could be successfully built and validated using routinely available, patient-reportable information. METHODS By applying machine learning to data from 334 patients in the European First Episode Schizophrenia Trial (EUFEST; International Clinical Trials Registry Platform number, ISRCTN68736636), we developed a tool to predict poor versus good treatment outcome (Global Assessment of Functioning [GAF] score ≥65 vs GAF <65, respectively) after 4 weeks and 52 weeks of treatment. To enable the unbiased estimation of the predictive system's generalisability to new patients, we used repeated nested cross-validation to prevent information leaking between patients used for training and validating the models. In pursuit of everyday clinical applicability, we retrained the 4-week outcome predictor with only the top ten predictors of the pooled prediction system and then tested this tool in 108 independent patients with 4-week outcome labels. Discontinuation and readmission to hospital events in patients with predicted poor versus good outcomes were assessed with Kaplan-Meier log-rank analyses, whereas generalised linear mixed-effects models were used to investigate the GAF-based predictions against several clinically meaningful outcome indicators, including treatment adherence, symptom remission, and quality of life. FINDINGS The generalisability of our outcome predictions were estimated with cross-validation (test-fold balanced accuracy [BAC] of 75·0% for 4-week outcomes and 73·8% for and 52-week outcomes), and leave-site-out validation across 44 European sites (BAC of 72·1% for 4-week outcomes and 71·1% for 52-week outcomes). We identified a smaller group of ten predictors still providing a BAC of 71·7% in 108 patients never used for model discovery. Unemployment, poor education, functional deficits, and unmet psychosocial needs predicted both endpoints, whereas previous depressive episodes, male sex, and suicidality additionally predicted poor 1-year outcomes. 52-week predictions identified patients at risk for symptom persistence, non-adherence to treatment, readmission to hospital and poor quality of life. Specifically among these patients, amisulpride and olanzapine showed superior efficacy versus haloperidol, quetiapine, and ziprasidone. INTERPRETATION Our results suggest that prognostic models operating on brief, patient-reportable pre-treatment data might provide useful insight into individualised outcome trajectories, optimising treatment selection, and targeted clinical trial designs. To embed these tools into real-world care, replication is needed in external first-episode samples with overlapping variables, which are not available in the field at present. FUNDING The European Group for Research in Schizophrenia.
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Affiliation(s)
- Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.
| | - René S Kahn
- Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, Utrecht, Netherlands
| | - Adam M Chekroud
- Department of Psychology, Yale University, New Haven, CT, USA; Centre for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
| | - Stefan Leucht
- Department of Psychiatry and Psychotherapy, Technical University, Munich, Germany
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Thomas Wobrock
- Centre of Mental Health, County Hospitals Darmstadt-Dieburg, Germany; Department of Psychiatry and Psychotherapy, Georg-August-University Göttingen, Göttingen, Germany
| | - Eske M Derks
- Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, Utrecht, Netherlands
| | | | - Alkomiet Hasan
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
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Affiliation(s)
- Adam M Chekroud
- Department of Psychology, Yale University, New Haven, CT 06511, USA
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24
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Anand G, Bridge H, Rackstraw P, Chekroud AM, Yong J, Stagg CJ, Pike M. Cerebellar and cortical abnormalities in paediatric opsoclonus-myoclonus syndrome. Dev Med Child Neurol 2015; 57:265-72. [PMID: 25290446 DOI: 10.1111/dmcn.12594] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [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] [Accepted: 08/04/2014] [Indexed: 11/28/2022]
Abstract
AIM Paediatric opsoclonus-myoclonus syndrome (OMS) is a poorly understood condition with long-term cognitive, behavioural, and motor sequelae. Neuroimaging has indicated cerebellar atrophy in the chronic phase, but this alone may not explain the cognitive sequelae seen in many children with OMS. This study aimed to determine the extent of structural change throughout the brain that may underpin the range of clinical outcomes. METHOD Nine participants with OMS (one male, eight females; mean age [SD] 14y, [6y 5mo], range 12-30y) and 10 comparison individuals (three males, seven females; mean age 12y 6mo, [4y 9mo], range 10-23y) underwent magnetic resonance imaging to acquire T1-weighted structural images, diffusion-weighted images, and magnetic resonance spectroscopy scans. Neuroblastoma had been present in four participants with OMS. Voxel-based morphometry was used to determine changes in grey matter volume, tract-based spatial statistics to analyze white matter integrity, and Freesurfer to analyze cortical thickness across visual and motor cortices. RESULTS Whole-brain analysis indicated that cerebellar grey matter was significantly reduced in the patients with OMS, particularly in the vermis and flocculonodular lobe. A region-of-interest analysis indicated significantly lower cerebellar grey matter volume, particularly in patients with the greatest OMS scores. Diffusion-weighted images did not show effects at a whole brain level, but all major cerebellar tracts showed increased mean diffusivity when analysis was restricted to the cerebellum. Cortical thickness was reduced across the motor and visual areas in the OMS group, indicating involvement beyond the cerebellum. INTERPRETATION Across individuals with OMS, there is considerable cerebellar atrophy, particularly in the vermis and flocculonodular lobes with atrophy severity associated with persistent symptomatology. Differences in cerebral cortical thickness indicate disease effects beyond the cerebellum.
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Affiliation(s)
- Geetha Anand
- Department of Paediatric Neurology, Children's Hospital, Oxford University Hospitals NHS Trust, Oxford, UK
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Abstract
Major Depressive Disorder is a debilitating and increasingly prevalent psychiatric condition (Compton et al., 2006; Andersen et al., 2011). At present, its primary treatments are antidepressant medications and psychotherapy. Curiously, although the pharmacological effects of antidepressants manifest within hours, remission of clinical symptoms takes a number of weeks—if at all. Independently, support has grown for an idea—proposed as early as Helmholtz (von Helmholtz, 1924)—that the brain is a prediction machine, holding generative models1 for the purpose of inferring causes of sensory information (Dayan et al., 1995; Rao and Ballard, 1999; Knill and Pouget, 2004; Friston et al., 2006; Friston, 2010). If the brain does indeed represent a collection of beliefs about the causal structure of the world, then the depressed phenotype may emerge from a collection of depressive beliefs. These beliefs are modified gradually through successive combinations of expectations with observations. As a result, phenotypic remission ought to take some time as the brain's relevant statistical structures become less pessimistic.
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Affiliation(s)
- Adam M Chekroud
- Department of Psychology, Yale University New Haven, CT, USA ; Department of Neuroscience, Oxford University Oxford, UK
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Chekroud AM, Everett JAC, Bridge H, Hewstone M. A review of neuroimaging studies of race-related prejudice: does amygdala response reflect threat? Front Hum Neurosci 2014; 8:179. [PMID: 24734016 PMCID: PMC3973920 DOI: 10.3389/fnhum.2014.00179] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2014] [Accepted: 03/10/2014] [Indexed: 11/13/2022] Open
Abstract
Prejudice is an enduring and pervasive aspect of human cognition. An emergent trend in modern psychology has focused on understanding how cognition is linked to neural function, leading researchers to investigate the neural correlates of prejudice. Research in this area using racial group memberships has quickly highlighted the amygdala as a neural structure of importance. In this article, we offer a critical review of social neuroscientific studies of the amygdala in race-related prejudice. Rather than the dominant interpretation that amygdala activity reflects a racial or outgroup bias per se, we argue that the observed pattern of sensitivity in this literature is best considered in terms of potential threat. More specifically, we argue that negative culturally-learned associations between black males and potential threat better explain the observed pattern of amygdala activity. Finally, we consider future directions for the field and offer specific experiments and predictions to directly address unanswered questions.
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Affiliation(s)
- Adam M Chekroud
- Department of Experimental Psychology, University of Oxford Oxford, UK ; Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), John Radcliffe Hospital, Oxford University Oxford, UK
| | - Jim A C Everett
- Department of Experimental Psychology, University of Oxford Oxford, UK
| | - Holly Bridge
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), John Radcliffe Hospital, Oxford University Oxford, UK
| | - Miles Hewstone
- Department of Experimental Psychology, University of Oxford Oxford, UK
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