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Poletti M, Pelizza L, Preti A, Raballo A. Clinical High-Risk for Psychosis (CHR-P) circa 2024: Synoptic analysis and synthesis of contemporary treatment guidelines. Asian J Psychiatr 2024; 100:104142. [PMID: 39083954 DOI: 10.1016/j.ajp.2024.104142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 06/05/2024] [Accepted: 06/26/2024] [Indexed: 08/02/2024]
Abstract
The construct of Clinical-High Risk for Psychosis (CHR-P) identifies young help-seeking subjects in putative prodromal stages of psychosis and is a central component of the Early Intervention (EI) paradigm in Mental Health, aimed at facilitating rapid entry into appropriate care pathways to prevent the onset of psychosis or mitigate is biopsychosocial consequences. This approach, which promotes an innovative culture of care for early, at risk situations, is inspired by a clinical staging concept as a guide to optimal treatment. The objective of this article is to map the existing guidelines in the field of CHR-P treatment recommendations, examine overlaps and differences, and critically evaluate blind spots to be addressed in future guideline updated. The search identified 9 guidelines focused on CHR-P or schizophrenia and other psychotic conditions but containing a specific section on CHR-P or prodromal psychosis. All guidelines acknowledge that psychosis is preceded by more or less pronounced prodromal stages, and most detail CHR-P criteria. Among guidelines, 8 out of 9 indicate cognitive-behavioural therapy as the best psychotherapeutic option and 7 out of 9 suggest that antipsychotics can be prescribed as second option in case psychosocial and/or other pharmacological interventions prove insufficient or inadequate in reducing clinical severity and subjective suffering. Antidepressants, mood stabilizers, and benzodiazepines were considered for the treatment of comorbid disorders. Only the European Psychiatric Association Guidance paper distinguished treatment recommendations for adults and minors. Agreements in treatment guidelines were discussed in light of recent meta-analytical evidences on pharmacological and non-pharmacological treatments for CHR-P, suggesting the need to provide an updated, age-sensitive consensus on how to manage CHR-P individuals.
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Affiliation(s)
- Michele Poletti
- Department of Mental Health and Pathological Addiction, Child and Adolescent Neuropsychiatry Service, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Lorenzo Pelizza
- Department of Biomedical and Neuromotor Sciences, Università di Bologna, Italy
| | - Antonio Preti
- Department of Neuroscience, University of Turin, Turin, Italy
| | - Andrea Raballo
- Chair of Psychiatry, Faculty of Biomedical Sciences, University of Southern Switzerland, Lugano, Switzerland; Cantonal Sociopsychiatric Organisation, Mendrisio, Switzerland.
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2
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Krakowski K, Oliver D, Arribas M, Stahl D, Fusar-Poli P. Dynamic and Transdiagnostic Risk Calculator Based on Natural Language Processing for the Prediction of Psychosis in Secondary Mental Health Care: Development and Internal-External Validation Cohort Study. Biol Psychiatry 2024:S0006-3223(24)01361-1. [PMID: 38852896 DOI: 10.1016/j.biopsych.2024.05.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 04/05/2024] [Accepted: 05/10/2024] [Indexed: 06/11/2024]
Abstract
BACKGROUND Automatic transdiagnostic risk calculators can improve the detection of individuals at risk of psychosis. However, they rely on assessment at a single point in time and can be refined with dynamic modeling techniques that account for changes in risk over time. METHODS We included 158,139 patients (5007 events) who received a first index diagnosis of a nonorganic and nonpsychotic mental disorder within electronic health records from the South London and Maudsley National Health Service Foundation Trust between January 1, 2008, and October 8, 2021. A dynamic Cox landmark model was developed to estimate the 2-year risk of developing psychosis according to the TRIPOD (Transparent Reporting of a multivariate prediction model for Individual Prognosis or Diagnosis) statement. The dynamic model included 24 predictors extracted at 9 landmark points (baseline, 0, 6, 12, 24, 30, 36, 42, and 48 months): 3 demographic, 1 clinical, and 20 natural language processing-based symptom and substance use predictors. Performance was compared with a static Cox regression model with all predictors assessed at baseline only and indexed via discrimination (C-index), calibration (calibration plots), and potential clinical utility (decision curves) in internal-external validation. RESULTS The dynamic model improved discrimination performance from baseline compared with the static model (dynamic: C-index = 0.9; static: C-index = 0.87) and the final landmark point (dynamic: C-index = 0.79; static: C-index = 0.76). The dynamic model was also significantly better calibrated (calibration slope = 0.97-1.1) than the static model at later landmark points (≥24 months). Net benefit was higher for the dynamic than for the static model at later landmark points (≥24 months). CONCLUSIONS These findings suggest that dynamic prediction models can improve the detection of individuals at risk for psychosis in secondary mental health care settings.
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Affiliation(s)
- Kamil Krakowski
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy; Early Psychosis: Interventions and Clinical-Detection Laboratory, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Dominic Oliver
- Early Psychosis: Interventions and Clinical-Detection Laboratory, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Department of Psychiatry, University of Oxford, Oxford, United Kingdom; National Institute for Health and Care Research Oxford Health Biomedical Research Centre, Oxford, United Kingdom; OPEN Early Detection Service, Oxford Health National Health Service Foundation Trust, Oxford, United Kingdom
| | - Maite Arribas
- Early Psychosis: Interventions and Clinical-Detection Laboratory, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Daniel Stahl
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
| | - Paolo Fusar-Poli
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy; Early Psychosis: Interventions and Clinical-Detection Laboratory, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; OASIS Service, South London and the Maudsley National Health Service Foundation Trust, London, United Kingdom; Department of Psychiatry and Psychotherapy, Ludwig Maximilian University Munich, Munich, Germany.
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3
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Reinen JM, Polosecki P, Castro E, Corcoran CM, Cecchi GA, Colibazzi T. Multimodal fusion of brain signals for robust prediction of psychosis transition. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:54. [PMID: 38773120 PMCID: PMC11109212 DOI: 10.1038/s41537-024-00464-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 03/15/2024] [Indexed: 05/23/2024]
Abstract
The prospective study of youths at clinical high risk (CHR) for psychosis, including neuroimaging, can identify neural signatures predictive of psychosis outcomes using algorithms that integrate complex information. Here, to identify risk and psychosis conversion, we implemented multiple kernel learning (MKL), a multimodal machine learning approach allowing patterns from each modality to inform each other. Baseline multimodal scans (n = 74, 11 converters) included structural, resting-state functional imaging, and diffusion-weighted data. Multimodal MKL outperformed unimodal models (AUC = 0.73 vs. 0.66 in predicting conversion). Moreover, patterns learned by MKL were robust to training set variations, suggesting it can identify cross-modality redundancies and synergies to stabilize the predictive pattern. We identified many predictors consistent with the literature, including frontal cortices, cingulate, thalamus, and striatum. This highlights the advantage of methods that leverage the complex pathophysiology of psychosis.
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Affiliation(s)
- Jenna M Reinen
- IBM T.J. Watson Research Center, Yorktown Heights, NY, USA.
| | | | - Eduardo Castro
- IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Cheryl M Corcoran
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Tiziano Colibazzi
- Department of Psychiatry, The New York State Psychiatric Institute, Columbia College of Physicians and Surgeons, New York, NY, USA
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4
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Wannan CMJ, Nelson B, Addington J, Allott K, Anticevic A, Arango C, Baker JT, Bearden CE, Billah T, Bouix S, Broome MR, Buccilli K, Cadenhead KS, Calkins ME, Cannon TD, Cecci G, Chen EYH, Cho KIK, Choi J, Clark SR, Coleman MJ, Conus P, Corcoran CM, Cornblatt BA, Diaz-Caneja CM, Dwyer D, Ebdrup BH, Ellman LM, Fusar-Poli P, Galindo L, Gaspar PA, Gerber C, Glenthøj LB, Glynn R, Harms MP, Horton LE, Kahn RS, Kambeitz J, Kambeitz-Ilankovic L, Kane JM, Kapur T, Keshavan MS, Kim SW, Koutsouleris N, Kubicki M, Kwon JS, Langbein K, Lewandowski KE, Light GA, Mamah D, Marcy PJ, Mathalon DH, McGorry PD, Mittal VA, Nordentoft M, Nunez A, Pasternak O, Pearlson GD, Perez J, Perkins DO, Powers AR, Roalf DR, Sabb FW, Schiffman J, Shah JL, Smesny S, Spark J, Stone WS, Strauss GP, Tamayo Z, Torous J, Upthegrove R, Vangel M, Verma S, Wang J, Rossum IWV, Wolf DH, Wolff P, Wood SJ, Yung AR, Agurto C, Alvarez-Jimenez M, Amminger P, Armando M, Asgari-Targhi A, Cahill J, Carrión RE, Castro E, Cetin-Karayumak S, Mallar Chakravarty M, Cho YT, Cotter D, D’Alfonso S, Ennis M, Fadnavis S, Fonteneau C, Gao C, Gupta T, Gur RE, Gur RC, Hamilton HK, Hoftman GD, Jacobs GR, Jarcho J, Ji JL, Kohler CG, Lalousis PA, Lavoie S, Lepage M, Liebenthal E, Mervis J, Murty V, Nicholas SC, Ning L, Penzel N, Poldrack R, Polosecki P, Pratt DN, Rabin R, Rahimi Eichi H, Rathi Y, Reichenberg A, Reinen J, Rogers J, Ruiz-Yu B, Scott I, Seitz-Holland J, Srihari VH, Srivastava A, Thompson A, Turetsky BI, Walsh BC, Whitford T, Wigman JTW, Yao B, Yuen HP, Ahmed U, Byun A(JS, Chung Y, Do K, Hendricks L, Huynh K, Jeffries C, Lane E, Langholm C, Lin E, Mantua V, Santorelli G, Ruparel K, Zoupou E, Adasme T, Addamo L, Adery L, Ali M, Auther A, Aversa S, Baek SH, Bates K, Bathery A, Bayer JMM, Beedham R, Bilgrami Z, Birch S, Bonoldi I, Borders O, Borgatti R, Brown L, Bruna A, Carrington H, Castillo-Passi RI, Chen J, Cheng N, Ching AE, Clifford C, Colton BL, Contreras P, Corral S, Damiani S, Done M, Estradé A, Etuka BA, Formica M, Furlan R, Geljic M, Germano C, Getachew R, Goncalves M, Haidar A, Hartmann J, Jo A, John O, Kerins S, Kerr M, Kesselring I, Kim H, Kim N, Kinney K, Krcmar M, Kotler E, Lafanechere M, Lee C, Llerena J, Markiewicz C, Matnejl P, Maturana A, Mavambu A, Mayol-Troncoso R, McDonnell A, McGowan A, McLaughlin D, McIlhenny R, McQueen B, Mebrahtu Y, Mensi M, Hui CLM, Suen YN, Wong SMY, Morrell N, Omar M, Partridge A, Phassouliotis C, Pichiecchio A, Politi P, Porter C, Provenzani U, Prunier N, Raj J, Ray S, Rayner V, Reyes M, Reynolds K, Rush S, Salinas C, Shetty J, Snowball C, Tod S, Turra-Fariña G, Valle D, Veale S, Whitson S, Wickham A, Youn S, Zamorano F, Zavaglia E, Zinberg J, Woods SW, Shenton ME. Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ): Rationale and Study Design of the Largest Global Prospective Cohort Study of Clinical High Risk for Psychosis. Schizophr Bull 2024; 50:496-512. [PMID: 38451304 PMCID: PMC11059785 DOI: 10.1093/schbul/sbae011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
This article describes the rationale, aims, and methodology of the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ). This is the largest international collaboration to date that will develop algorithms to predict trajectories and outcomes of individuals at clinical high risk (CHR) for psychosis and to advance the development and use of novel pharmacological interventions for CHR individuals. We present a description of the participating research networks and the data processing analysis and coordination center, their processes for data harmonization across 43 sites from 13 participating countries (recruitment across North America, Australia, Europe, Asia, and South America), data flow and quality assessment processes, data analyses, and the transfer of data to the National Institute of Mental Health (NIMH) Data Archive (NDA) for use by the research community. In an expected sample of approximately 2000 CHR individuals and 640 matched healthy controls, AMP SCZ will collect clinical, environmental, and cognitive data along with multimodal biomarkers, including neuroimaging, electrophysiology, fluid biospecimens, speech and facial expression samples, novel measures derived from digital health technologies including smartphone-based daily surveys, and passive sensing as well as actigraphy. The study will investigate a range of clinical outcomes over a 2-year period, including transition to psychosis, remission or persistence of CHR status, attenuated positive symptoms, persistent negative symptoms, mood and anxiety symptoms, and psychosocial functioning. The global reach of AMP SCZ and its harmonized innovative methods promise to catalyze the development of new treatments to address critical unmet clinical and public health needs in CHR individuals.
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Affiliation(s)
- Cassandra M J Wannan
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Barnaby Nelson
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Jean Addington
- Department of Psychiatry, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Kelly Allott
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, Instituto de Salud Carlos III, School of Medicine, Universidad Complutense, Madrid, Spain
| | - Justin T Baker
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Boston, MA, USA
| | - Carrie E Bearden
- Department of Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Tashrif Billah
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Sylvain Bouix
- Department of Software Engineering and Information Technology, École de technologie supérieure, Montréal, Canada
| | - Matthew R Broome
- School of Psychology, Institute for Mental Health, University of Birmingham, Birmingham, UK
- Early Intervention for Psychosis Services, Birmingham Women’s and Children’s NHS Foundation Trust, Birmingham, UK
| | - Kate Buccilli
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | | | - Monica E Calkins
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Tyrone D Cannon
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychology, Yale University, New Haven, CT, USA
| | | | - Eric Yu Hai Chen
- Department of Psychiatry, University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Kang Ik K Cho
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jimmy Choi
- Olin Neuropsychiatry Research Center, Hartford Hospital, Hartford, CT, USA
| | - Scott R Clark
- Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
- Basil Hetzel Institute, Woodville, SA, Australia
| | - Michael J Coleman
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Philippe Conus
- General Psychiatry Service, Treatment and Early Intervention in Psychosis Program (TIPP–Lausanne), Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Cheryl M Corcoran
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Barbara A Cornblatt
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Covadonga M Diaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, Instituto de Salud Carlos III, School of Medicine, Universidad Complutense, Madrid, Spain
| | - Dominic Dwyer
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Bjørn H Ebdrup
- Centre for Neuropsychiatric Schizophrenia Research, CNSR Mental Health Centre, Glostrup, Copenhagen, Denmark
| | - Lauren M Ellman
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
| | - Paolo Fusar-Poli
- Department of Psychosis Studies, King’s College London, London, UK
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Liliana Galindo
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Pablo A Gaspar
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
| | - Carla Gerber
- Behavioral Health Services, PeaceHealth Medical Group, Eugene, OR, USA
| | - Louise Birkedal Glenthøj
- Copenhagen Research Centre for Mental Health, Mental Health Copenhagen, University of Copenhagen, Denmark
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Robert Glynn
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health and Harvard Medical School, Boston, MA, USA
| | - Michael P Harms
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Leslie E Horton
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - René S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joseph Kambeitz
- Department of Psychiatry, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Lana Kambeitz-Ilankovic
- Department of Psychiatry, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - John M Kane
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Tina Kapur
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Sung-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
- Mindlink, Gwangju Bukgu Mental Health Center, Gwangju, Korea
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, Munich, Germany
| | - Marek Kubicki
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jun Soo Kwon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Korea
| | - Kerstin Langbein
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Kathryn E Lewandowski
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Boston, MA, USA
| | - Gregory A Light
- Department of Psychiatry, University of California, San Diego, CA, USA
- Veterans Affairs San Diego Health Care System, San Diego, CA, USA
| | - Daniel Mamah
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, USA
| | | | - Daniel H Mathalon
- Department of Psychiatry and Behavioral Sciences and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
- Mental Health Service 116D, Veterans Affairs San Francisco Health Care System, San Francisco, CA, USA
| | - Patrick D McGorry
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Merete Nordentoft
- Copenhagen Research Centre for Mental Health, Mental Health Copenhagen, University of Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Angela Nunez
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Ofer Pasternak
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Godfrey D Pearlson
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Olin Neuropsychiatry Research Center, Hartford Hospital, Hartford, CT, USA
| | - Jesus Perez
- CAMEO, Early Intervention in Psychosis Service, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
- Department of Medicine, Institute of Biomedical Research (IBSAL), Universidad de Salamanca, Salamanca, Spain
| | - Diana O Perkins
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Albert R Powers
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Fred W Sabb
- Prevention Science Institute, University of Oregon, Eugene, OR, USA
| | - Jason Schiffman
- Department of Psychological Science, University of California, Irvine, Irvine, CA, USA
| | - Jai L Shah
- PEPP-Montreal, Douglas Research Centre, Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Stefan Smesny
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Jessica Spark
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - William S Stone
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | | | - Zailyn Tamayo
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - John Torous
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Rachel Upthegrove
- Department of Software Engineering and Information Technology, École de technologie supérieure, Montréal, Canada
- Birmingham Womens and Childrens, NHS Foundation Trust, Birmingham, UK
| | - Mark Vangel
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Swapna Verma
- Department of Psychosis, Institute of Mental Health, Singapore
| | - Jijun Wang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Inge Winter-van Rossum
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Daniel H Wolf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Phillip Wolff
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Stephen J Wood
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
- School of Psychology, University of Birmingham, Edgbaston, UK
| | - Alison R Yung
- Institute of Mental and Physical Health and Clinical Translation (IMPACT), Deakin University, Geelong, VIC, Australia
- School of Health Sciences, University of Manchester, Manchester, UK
| | - Carla Agurto
- IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Mario Alvarez-Jimenez
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Paul Amminger
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Marco Armando
- Youth Early Detection/Intervention in Psychosis Platform (Plateforme ERA), Service of Child and Adolescent Psychiatry, Department of Psychiatry, Lausanne University Hospital and The University of Lausanne, Lausanne, Switzerland
| | | | - John Cahill
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Ricardo E Carrión
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Eduardo Castro
- IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Suheyla Cetin-Karayumak
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Youngsun T Cho
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - David Cotter
- Department Psychiatry, Beaumont Hospital, Dublin 9, Ireland
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin 2, Ireland
| | - Simon D’Alfonso
- School of Computing and Information Systems, The University of Melbourne, Parkville, VIC, Australia
| | - Michaela Ennis
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Boston, MA, USA
| | - Shreyas Fadnavis
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Clara Fonteneau
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Caroline Gao
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Tina Gupta
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Holly K Hamilton
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Gil D Hoftman
- Department of Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Grace R Jacobs
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Johanna Jarcho
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
| | - Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Christian G Kohler
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Paris Alexandros Lalousis
- School of Psychology, Institute for Mental Health, University of Birmingham, Birmingham, UK
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
| | - Suzie Lavoie
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Martin Lepage
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Einat Liebenthal
- Program for Specialized Treatment Early in Psychosis (STEP), CMHC, New Haven, CT, USA
| | - Josh Mervis
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
| | - Vishnu Murty
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
| | - Spero C Nicholas
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, USA
| | - Lipeng Ning
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Nora Penzel
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Russell Poldrack
- Department of Psychology, Stanford University, Stanford, CA, USA
| | | | - Danielle N Pratt
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Rachel Rabin
- PEPP-Montreal, Douglas Research Centre, Montreal, Quebec, Canada
| | | | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Avraham Reichenberg
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jenna Reinen
- IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Jack Rogers
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
| | - Bernalyn Ruiz-Yu
- Department of Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Isabelle Scott
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Johanna Seitz-Holland
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Vinod H Srihari
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Program for Specialized Treatment Early in Psychosis (STEP), CMHC, New Haven, CT, USA
| | - Agrima Srivastava
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Andrew Thompson
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Bruce I Turetsky
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Barbara C Walsh
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Thomas Whitford
- Orygen, Parkville, VIC, Australia
- School of Psychology, University of New South Wales (UNSW), Kensington, NSW, Australia
| | - Johanna T W Wigman
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center,Groningen, Netherlands
| | - Beier Yao
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Boston, MA, USA
| | - Hok Pan Yuen
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | | | - Andrew (Jin Soo) Byun
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Yoonho Chung
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Boston, MA, USA
| | - Kim Do
- Department of Psychiatry, Center for Psychiatric Neuroscience, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience King’s College London, London, UK
| | - Larry Hendricks
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Kevin Huynh
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Clark Jeffries
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC, USA
| | - Erlend Lane
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Carsten Langholm
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Eric Lin
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Boston, MA, USA
- Medical Informatics Fellowship, Veteran Affairs Boston Healthcare System, Boston, MA, USA
- Food and Drug Administration, Silver Spring, MD, USA
| | - Valentina Mantua
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Gennarina Santorelli
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Kosha Ruparel
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Eirini Zoupou
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Tatiana Adasme
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
| | - Lauren Addamo
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Laura Adery
- Department of Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Munaza Ali
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Andrea Auther
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Samantha Aversa
- PEPP-Montreal, Douglas Research Centre, Montreal, Quebec, Canada
| | - Seon-Hwa Baek
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
- Mindlink, Gwangju Bukgu Mental Health Center, Gwangju, Korea
| | - Kelly Bates
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Alyssa Bathery
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Johanna M M Bayer
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Rebecca Beedham
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Zarina Bilgrami
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Sonia Birch
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Ilaria Bonoldi
- Department of Psychosis Studies, King’s College London, London, UK
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Owen Borders
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Renato Borgatti
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, Italy
| | - Lisa Brown
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Alejandro Bruna
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
| | - Holly Carrington
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Rolando I Castillo-Passi
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
- Department of Neurology and Psychiatry, Clínica Alemana—Universidad del Desarrollo, Santiago, Chile
| | - Justine Chen
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Nicholas Cheng
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Ann Ee Ching
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Chloe Clifford
- School of Psychology, Institute for Mental Health, University of Birmingham, Birmingham, UK
| | - Beau-Luke Colton
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Pamela Contreras
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
| | - Sebastián Corral
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
| | - Stefano Damiani
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Monica Done
- Department of Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Andrés Estradé
- Early Psychosis Detection and Clinical Intervention (EPIC) Lab, Department of Psychosis Studies, King’s College London, London, UK
| | - Brandon Asika Etuka
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Melanie Formica
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Rachel Furlan
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
| | - Mia Geljic
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Carmela Germano
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Ruth Getachew
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | | | - Anastasia Haidar
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Jessica Hartmann
- Department of Public Mental Health, Central Institute of Mental Health, Heidelberg Univeristy, Mannheim, Germany
| | - Anna Jo
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Omar John
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Sarah Kerins
- Early Psychosis Detection and Clinical Intervention (EPIC) Lab, Department of Psychosis Studies, King’s College London, London, UK
| | - Melissa Kerr
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Irena Kesselring
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Honey Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Nicholas Kim
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Kyle Kinney
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
| | - Marija Krcmar
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Elana Kotler
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Melanie Lafanechere
- School of Psychology, University of Birmingham, Edgbaston, UK
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
| | - Clarice Lee
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Joshua Llerena
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | | | | | | | - Aissata Mavambu
- School of Psychology, Institute for Mental Health, University of Birmingham, Birmingham, UK
| | | | - Amelia McDonnell
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
| | - Alessia McGowan
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Rebecca McIlhenny
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Brittany McQueen
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Yohannes Mebrahtu
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Martina Mensi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, Italy
| | | | - Yi Nam Suen
- Department of Psychiatry, University of Hong Kong, Pok Fu Lam, Hong Kong
| | | | - Neal Morrell
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Mariam Omar
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Alice Partridge
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Christina Phassouliotis
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Anna Pichiecchio
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
| | - Pierluigi Politi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Christian Porter
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Umberto Provenzani
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Nicholas Prunier
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Jasmine Raj
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Susan Ray
- Northwell Health, Glen Oaks, NY, USA
| | - Victoria Rayner
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Manuel Reyes
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
- Department of Neurology and Psychiatry, Clínica Alemana—Universidad del Desarrollo, Santiago, Chile
| | - Kate Reynolds
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Sage Rush
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Cesar Salinas
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
| | - Jashmina Shetty
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Callum Snowball
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Sophie Tod
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | | | - Daniela Valle
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
| | - Simone Veale
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Sarah Whitson
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Alana Wickham
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Sarah Youn
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Francisco Zamorano
- Unidad de imágenes cuantitativas avanzadas, departamento de imágenes, clínica alemana, universidad del Desarrollo, Santiago, Chile
- Facultad de ciencias para el cuidado de la salud, Universidad San Sebastián, Campus Los Leones, Santiago, Chile
| | - Elissa Zavaglia
- PEPP-Montreal, Douglas Research Centre, Montreal, Quebec, Canada
| | - Jamie Zinberg
- Department of Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Scott W Woods
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Martha E Shenton
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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5
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Hao J, Tiles-Sar N, Habtewold TD, Liemburg EJ, Bruggeman R, van der Meer L, Alizadeh BZ. Shaping tomorrow's support: baseline clinical characteristics predict later social functioning and quality of life in schizophrenia spectrum disorder. Soc Psychiatry Psychiatr Epidemiol 2024:10.1007/s00127-024-02630-4. [PMID: 38456932 DOI: 10.1007/s00127-024-02630-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 01/28/2024] [Indexed: 03/09/2024]
Abstract
PURPOSE We aimed to explore the multidimensional nature of social inclusion (mSI) among patients diagnosed with schizophrenia spectrum disorder (SSD), and to identify the predictors of 3-year mSI and the mSI prediction using traditional and data-driven approaches. METHODS We used the baseline and 3-year follow-up data of 1119 patients from the Genetic Risk and Outcome in Psychosis (GROUP) cohort in the Netherlands. The outcome mSI was defined as clusters derived from combined analyses of thirteen subscales from the Social Functioning Scale and the brief version of World Health Organization Quality of Life questionnaires through K-means clustering. Prediction models were built through multinomial logistic regression (ModelMLR) and random forest (ModelRF), internally validated via bootstrapping and compared by accuracy and the discriminability of mSI subgroups. RESULTS We identified five mSI subgroups: "very low (social functioning)/very low (quality of life)" (8.58%), "low/low" (12.87%), "high/low" (49.24%), "medium/high" (18.05%), and "high/high" (11.26%). The mSI was robustly predicted by a genetic predisposition for SSD, premorbid adjustment, positive, negative, and depressive symptoms, number of met needs, and baseline satisfaction with the environment and social life. The ModelRF (61.61% [54.90%, 68.01%]; P =0.013) was cautiously considered outperform the ModelMLR (59.16% [55.75%, 62.58%]; P =0.994). CONCLUSION We introduced and distinguished meaningful subgroups of mSI, which were modestly predictable from baseline clinical characteristics. A possibility for early prediction of mSI at the clinical stage may unlock the potential for faster and more impactful social support that is specifically tailored to the unique characteristics of the mSI subgroup to which a given patient belongs.
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Affiliation(s)
- Jiasi Hao
- Department of Epidemiology, University Medical Centre Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.
| | - Natalia Tiles-Sar
- Department of Epidemiology, University Medical Centre Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
- Department of Psychiatry, University Medical Centre Groningen, University Centre for Psychiatry, Rob Giel Research Centre, University of Groningen, Groningen, The Netherlands
| | - Tesfa Dejenie Habtewold
- Department of Epidemiology, University Medical Centre Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Edith J Liemburg
- Department of Psychiatry, University Medical Centre Groningen, University Centre for Psychiatry, Rob Giel Research Centre, University of Groningen, Groningen, The Netherlands
| | - Richard Bruggeman
- Department of Psychiatry, University Medical Centre Groningen, University Centre for Psychiatry, Rob Giel Research Centre, University of Groningen, Groningen, The Netherlands
- Department of Clinical and Developmental Neuropsychology, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands
- Department of Rehabilitation, Lentis Psychiatric Institute, Zuidlaren, The Netherlands
| | - Lisette van der Meer
- Department of Clinical and Developmental Neuropsychology, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands
- Department of Rehabilitation, Lentis Psychiatric Institute, Zuidlaren, The Netherlands
| | - Behrooz Z Alizadeh
- Department of Epidemiology, University Medical Centre Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.
- Department of Psychiatry, University Medical Centre Groningen, University Centre for Psychiatry, Rob Giel Research Centre, University of Groningen, Groningen, The Netherlands.
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6
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Koehler JC, Dong MS, Song DY, Bong G, Koutsouleris N, Yoo H, Falter-Wagner CM. Classifying autism in a clinical population based on motion synchrony: a proof-of-concept study using real-life diagnostic interviews. Sci Rep 2024; 14:5663. [PMID: 38453972 PMCID: PMC10920641 DOI: 10.1038/s41598-024-56098-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 03/01/2024] [Indexed: 03/09/2024] Open
Abstract
Predictive modeling strategies are increasingly studied as a means to overcome clinical bottlenecks in the diagnostic classification of autism spectrum disorder. However, while some findings are promising in the light of diagnostic marker research, many of these approaches lack the scalability for adequate and effective translation to everyday clinical practice. In this study, our aim was to explore the use of objective computer vision video analysis of real-world autism diagnostic interviews in a clinical sample of children and young individuals in the transition to adulthood to predict diagnosis. Specifically, we trained a support vector machine learning model on interpersonal synchrony data recorded in Autism Diagnostic Observation Schedule (ADOS-2) interviews of patient-clinician dyads. Our model was able to classify dyads involving an autistic patient (n = 56) with a balanced accuracy of 63.4% against dyads including a patient with other psychiatric diagnoses (n = 38). Further analyses revealed no significant associations between our classification metrics with clinical ratings. We argue that, given the above-chance performance of our classifier in a highly heterogeneous sample both in age and diagnosis, with few adjustments this highly scalable approach presents a viable route for future diagnostic marker research in autism.
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Affiliation(s)
- Jana Christina Koehler
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany.
| | - Mark Sen Dong
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Max Planck Institute of Psychiatry, Munich, Germany
| | - Da-Yea Song
- Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
| | - Guiyoung Bong
- Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Max Planck Institute of Psychiatry, Munich, Germany
- Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Heejeong Yoo
- Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
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7
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Oliver D, Arribas M, Perry BI, Whiting D, Blackman G, Krakowski K, Seyedsalehi A, Osimo EF, Griffiths SL, Stahl D, Cipriani A, Fazel S, Fusar-Poli P, McGuire P. Using Electronic Health Records to Facilitate Precision Psychiatry. Biol Psychiatry 2024:S0006-3223(24)01107-7. [PMID: 38408535 DOI: 10.1016/j.biopsych.2024.02.1006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 01/30/2024] [Accepted: 02/21/2024] [Indexed: 02/28/2024]
Abstract
The use of clinical prediction models to produce individualized risk estimates can facilitate the implementation of precision psychiatry. As a source of data from large, clinically representative patient samples, electronic health records (EHRs) provide a platform to develop and validate clinical prediction models, as well as potentially implement them in routine clinical care. The current review describes promising use cases for the application of precision psychiatry to EHR data and considers their performance in terms of discrimination (ability to separate individuals with and without the outcome) and calibration (extent to which predicted risk estimates correspond to observed outcomes), as well as their potential clinical utility (weighing benefits and costs associated with the model compared to different approaches across different assumptions of the number needed to test). We review 4 externally validated clinical prediction models designed to predict psychosis onset, psychotic relapse, cardiometabolic morbidity, and suicide risk. We then discuss the prospects for clinically implementing these models and the potential added value of integrating data from evidence syntheses, standardized psychometric assessments, and biological data into EHRs. Clinical prediction models can utilize routinely collected EHR data in an innovative way, representing a unique opportunity to inform real-world clinical decision making. Combining data from other sources (e.g., meta-analyses) or enhancing EHR data with information from research studies (clinical and biomarker data) may enhance our abilities to improve the performance of clinical prediction models.
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Affiliation(s)
- Dominic Oliver
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom; OPEN Early Detection Service, Oxford Health NHS Foundation Trust, Oxford, United Kingdom; Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
| | - Maite Arribas
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Benjamin I Perry
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, United Kingdom
| | - Daniel Whiting
- Institute of Mental Health, University of Nottingham, Nottingham, United Kingdom
| | - Graham Blackman
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom
| | - Kamil Krakowski
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Aida Seyedsalehi
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Emanuele F Osimo
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, United Kingdom; Imperial College London Institute of Clinical Sciences and UK Research and Innovation MRC London Institute of Medical Sciences, Hammersmith Hospital Campus, London, United Kingdom; South London and the Maudsley National Health Service Foundation Trust, London, United Kingdom
| | - Siân Lowri Griffiths
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom; Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
| | - Daniel Stahl
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
| | - Andrea Cipriani
- NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom; Department of Psychiatry, University of Oxford, Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, United Kingdom
| | - Seena Fazel
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom
| | - Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy; South London and the Maudsley National Health Service Foundation Trust, London, United Kingdom; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Munich, Germany
| | - Philip McGuire
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom; OPEN Early Detection Service, Oxford Health NHS Foundation Trust, Oxford, United Kingdom
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8
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Worthington MA, Collins MA, Addington J, Bearden CE, Cadenhead KS, Cornblatt BA, Keshavan M, Mathalon DH, Perkins DO, Stone WS, Walker EF, Woods SW, Cannon TD. Improving prediction of psychosis in youth at clinical high-risk: pre-baseline symptom duration and cortical thinning as moderators of the NAPLS2 risk calculator. Psychol Med 2024; 54:611-619. [PMID: 37642172 DOI: 10.1017/s0033291723002301] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
BACKGROUND Clinical implementation of risk calculator models in the clinical high-risk for psychosis (CHR-P) population has been hindered by heterogeneous risk distributions across study cohorts which could be attributed to pre-ascertainment illness progression. To examine this, we tested whether the duration of attenuated psychotic symptom (APS) worsening prior to baseline moderated performance of the North American prodrome longitudinal study 2 (NAPLS2) risk calculator. We also examined whether rates of cortical thinning, another marker of illness progression, bolstered clinical prediction models. METHODS Participants from both the NAPLS2 and NAPLS3 samples were classified as either 'long' or 'short' symptom duration based on time since APS increase prior to baseline. The NAPLS2 risk calculator model was applied to each of these groups. In a subset of NAPLS3 participants who completed follow-up magnetic resonance imaging scans, change in cortical thickness was combined with the individual risk score to predict conversion to psychosis. RESULTS The risk calculator models achieved similar performance across the combined NAPLS2/NAPLS3 sample [area under the curve (AUC) = 0.69], the long duration group (AUC = 0.71), and the short duration group (AUC = 0.71). The shorter duration group was younger and had higher baseline APS than the longer duration group. The addition of cortical thinning improved the prediction of conversion significantly for the short duration group (AUC = 0.84), with a moderate improvement in prediction for the longer duration group (AUC = 0.78). CONCLUSIONS These results suggest that early illness progression differs among CHR-P patients, is detectable with both clinical and neuroimaging measures, and could play an essential role in the prediction of clinical outcomes.
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Affiliation(s)
| | | | - Jean Addington
- Department of Psychiatry, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Carrie E Bearden
- Department of Psychiatry and Biobehavioral Sciences, UCLA, Los Angeles, CA, USA
| | | | | | - Matcheri Keshavan
- Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center and Massachusetts General Hospital, Boston, MA, USA
| | - Daniel H Mathalon
- Department of Psychiatry, UCSF, and SFVA Medical Center, San Francisco, CA, USA
| | - Diana O Perkins
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - William S Stone
- Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center and Massachusetts General Hospital, Boston, MA, USA
| | - Elaine F Walker
- Departments of Psychology and Psychiatry, Emory University, Atlanta, GA, USA
| | - Scott W Woods
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Tyrone D Cannon
- Department of Psychology, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
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9
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Şahin D, Kambeitz-Ilankovic L, Wood S, Dwyer D, Upthegrove R, Salokangas R, Borgwardt S, Brambilla P, Meisenzahl E, Ruhrmann S, Schultze-Lutter F, Lencer R, Bertolino A, Pantelis C, Koutsouleris N, Kambeitz J. Algorithmic fairness in precision psychiatry: analysis of prediction models in individuals at clinical high risk for psychosis. Br J Psychiatry 2024; 224:55-65. [PMID: 37936347 DOI: 10.1192/bjp.2023.141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2023]
Abstract
BACKGROUND Computational models offer promising potential for personalised treatment of psychiatric diseases. For their clinical deployment, fairness must be evaluated alongside accuracy. Fairness requires predictive models to not unfairly disadvantage specific demographic groups. Failure to assess model fairness prior to use risks perpetuating healthcare inequalities. Despite its importance, empirical investigation of fairness in predictive models for psychiatry remains scarce. AIMS To evaluate fairness in prediction models for development of psychosis and functional outcome. METHOD Using data from the PRONIA study, we examined fairness in 13 published models for prediction of transition to psychosis (n = 11) and functional outcome (n = 2) in people at clinical high risk for psychosis or with recent-onset depression. Using accuracy equality, predictive parity, false-positive error rate balance and false-negative error rate balance, we evaluated relevant fairness aspects for the demographic attributes 'gender' and 'educational attainment' and compared them with the fairness of clinicians' judgements. RESULTS Our findings indicate systematic bias towards assigning less favourable outcomes to individuals with lower educational attainment in both prediction models and clinicians' judgements, resulting in higher false-positive rates in 7 of 11 models for transition to psychosis. Interestingly, the bias patterns observed in algorithmic predictions were not significantly more pronounced than those in clinicians' predictions. CONCLUSIONS Educational bias was present in algorithmic and clinicians' predictions, assuming more favourable outcomes for individuals with higher educational level (years of education). This bias might lead to increased stigma and psychosocial burden in patients with lower educational attainment and suboptimal psychosis prevention in those with higher educational attainment.
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Affiliation(s)
- Derya Şahin
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Lana Kambeitz-Ilankovic
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany; and Department of Psychology, Faculty of Psychology and Educational Sciences, Ludwig-Maximilian University, Munich, Germany
| | - Stephen Wood
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia; and Orygen, the National Centre of Excellence for Youth Mental Health, Melbourne, Victoria, Australia
| | - Dominic Dwyer
- Department of Psychology, Faculty of Psychology and Educational Sciences, Ludwig-Maximilian University, Munich, Germany; and Orygen, the National Centre of Excellence for Youth Mental Health, Melbourne, Victoria, Australia
| | - Rachel Upthegrove
- Institute for Mental Health and Centre for Brain Health, University of Birmingham, Birmingham, UK; and Early Intervention Service, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
| | | | - Stefan Borgwardt
- Department of Psychiatry (University Psychiatric Clinics, UPK), University of Basel, Basel, Switzerland; and Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; and Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Stephan Ruhrmann
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Frauke Schultze-Lutter
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany; Department of Psychology, Faculty of Psychology, Airlangga University, Surabaya, Indonesia; and University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Rebekka Lencer
- Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany; and Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, University of Melbourne & Melbourne Health, Melbourne, Victoria, Australia
| | - Nikolaos Koutsouleris
- Department of Psychology, Faculty of Psychology and Educational Sciences, Ludwig-Maximilian University, Munich, Germany; Max-Planck Institute of Psychiatry, Munich, Germany; and Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
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10
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Pelizza L, Leuci E, Quattrone E, Azzali S, Paulillo G, Pupo S, Poletti M, Raballo A, Pellegrini P, Menchetti M. Baseline antipsychotic prescription and short-term outcome indicators in individuals at clinical high-risk for psychosis: Findings from the Parma At-Risk Mental States (PARMS) program. Early Interv Psychiatry 2024; 18:71-81. [PMID: 37194411 DOI: 10.1111/eip.13434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 03/29/2023] [Accepted: 05/05/2023] [Indexed: 05/18/2023]
Abstract
AIM The prognostic prediction of outcomes in individuals at clinical high-risk for psychosis (CHR-P) is still a significant clinical challenge. Among multiple baseline variables of risk calculator models, the role of ongoing pharmacological medications has been partially neglected, despite meta-analytical evidence of higher risk of psychosis transition associated with baseline prescription exposure to antipsychotics (AP) in CHR-P individuals. The main aim of the current study was to test the hypothesis that ongoing AP need at baseline indexes a subgroup of CHR-P individuals with more severe psychopathology and worse prognostic trajectories along a 1-year follow-up period. METHODS This research was settled within the 'Parma At-Risk Mental States' program. Baseline and 1-year follow-up assessment included the Positive And Negative Syndrome Scale (PANSS) and the Global Assessment of Functioning (GAF). CHR-P individuals who were taking AP medications at entry were included in the CHR-P-AP+ subgroup. The remaining participants were grouped as CHR-P-AP-. RESULTS Hundred and seventy-eight CHR-P individuals (aged 12-25 years) were enrolled (91 CHR-P-AP+, 87 CHR-P-AP-). Compared to CHR-P AP-, CHR-P AP+ individuals had older age, greater baseline PANSS 'Positive Symptoms' and 'Negative Symptoms' factor subscores and a lower GAF score. At the end of our follow-up, CHR-P-AP+ subjects showed higher rates of psychosis transition, new hospitalizations and urgent/non-planned visits compared to CHRP- AP- individuals. CONCLUSIONS In agreement with increasing empirical evidence, also the results of the current study suggest that AP need is a significant prognostic variable in cohorts of CHR-P individuals and should be included in risk calculators.
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Affiliation(s)
- Lorenzo Pelizza
- Department of Mental Health and Pathological Addictions, Azienda USL di Parma, Parma, Italy
- Department of Biomedical and Neuromotor Sciences, "Alma Mater Studiorum" - University of Bologna, Bologna, Italy
| | - Emanuela Leuci
- Department of Mental Health and Pathological Addictions, Azienda USL di Parma, Parma, Italy
| | - Emanuela Quattrone
- Department of Mental Health and Pathological Addictions, Azienda USL di Parma, Parma, Italy
| | - Silvia Azzali
- Department of Mental Health and Pathological Addictions, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Giuseppina Paulillo
- Department of Mental Health and Pathological Addictions, Azienda USL di Parma, Parma, Italy
| | - Simona Pupo
- Pain Therapy Service, Department of Medicine and Surgery, Azienda Ospedaliero-Universitaria di Parma, Parma, Italy
| | - Michele Poletti
- Department of Mental Health and Pathological Addictions, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Andrea Raballo
- Department of Medicine, Section of Psychiatry, Clinical Psychology and Rehabilitation, University of Perugia, Perugia, Italy
- Center for Translational, Phenomenological and Developmental Psychopathology, Perugia, Italy
| | - Pietro Pellegrini
- Department of Mental Health and Pathological Addictions, Azienda USL di Parma, Parma, Italy
| | - Marco Menchetti
- Department of Biomedical and Neuromotor Sciences, "Alma Mater Studiorum" - University of Bologna, Bologna, Italy
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11
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Groening JM, Denton E, Parvaiz R, Brunet DL, Von Daniken A, Shi Y, Bhattacharyya S. A systematic evidence map of the association between cannabis use and psychosis-related outcomes across the psychosis continuum: An umbrella review of systematic reviews and meta-analyses. Psychiatry Res 2024; 331:115626. [PMID: 38096722 DOI: 10.1016/j.psychres.2023.115626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 11/18/2023] [Accepted: 11/23/2023] [Indexed: 01/02/2024]
Abstract
While the legal status and public perception of cannabis are currently changing in many countries, one of the important considerations from a public health viewpoint is its potential association with adverse health outcomes such as the development of psychosis. We conducted an umbrella review of systematic reviews and meta-analyses using the AMSTAR-2 to assess the quality of included reviews. We further created an evidence map to visualize and facilitate the overview of the published evidence synthesis on the association between cannabis use and all psychosis-related outcomes and risk moderators in healthy, high-risk, and clinical populations. Overall, we found 32 systematic reviews and meta-analyses. Based on a synthesis of current evidence, cannabis use is associated with subclinical psychosis states (psychotic-like experiences) and traits (schizotypal personality) in the healthy population, as well as earlier onset and development of psychosis. An association with the clinical-high-risk state for psychosis, attenuated psychosis symptoms and transition to psychosis in this population could not be confirmed. An association between cannabis use and psychosis outcomes in patients with psychotic disorder could solely be confirmed regarding relapse. Whether causal effects underlie those associations has not sufficiently been addressed in the evidence synthesis to date.
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Affiliation(s)
- Johanna Manja Groening
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - Emma Denton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - Rimsha Parvaiz
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - David Losada Brunet
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - Aisha Von Daniken
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - Yiling Shi
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - Sagnik Bhattacharyya
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom.
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12
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Caballero N, Machiraju S, Diomino A, Kennedy L, Kadivar A, Cadenhead KS. Recent Updates on Predicting Conversion in Youth at Clinical High Risk for Psychosis. Curr Psychiatry Rep 2023; 25:683-698. [PMID: 37755654 PMCID: PMC10654175 DOI: 10.1007/s11920-023-01456-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/05/2023] [Indexed: 09/28/2023]
Abstract
PURPOSE OF REVIEW This review highlights recent advances in the prediction and treatment of psychotic conversion. Over the past 25 years, research into the prodromal phase of psychotic illness has expanded with the promise of early identification of individuals at clinical high risk (CHR) for psychosis who are likely to convert to psychosis. RECENT FINDINGS Meta-analyses highlight conversion rates between 20 and 30% within 2-3 years using existing clinical criteria while research into more specific risk factors, biomarkers, and refinement of psychosis risk calculators has exploded, improving our ability to predict psychotic conversion with greater accuracy. Recent studies highlight risk factors and biomarkers likely to contribute to earlier identification and provide insight into neurodevelopmental abnormalities, CHR subtypes, and interventions that can target specific risk profiles linked to neural mechanisms. Ongoing initiatives that assess longer-term (> 5-10 years) outcome of CHR participants can provide valuable information about predictors of later conversion and diagnostic outcomes while large-scale international biomarker studies provide hope for precision intervention that will alter the course of early psychosis globally.
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Affiliation(s)
- Noe Caballero
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093-0810, USA
| | - Siddharth Machiraju
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093-0810, USA
| | - Anthony Diomino
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093-0810, USA
| | - Leda Kennedy
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093-0810, USA
| | - Armita Kadivar
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093-0810, USA
| | - Kristin S Cadenhead
- Department of Psychiatry, University of California San Diego, 9500 Gilman Dr., La Jolla, CA, 92093-0810, USA.
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13
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Byrne JF, Mongan D, Murphy J, Healy C, Fӧcking M, Cannon M, Cotter DR. Prognostic models predicting transition to psychotic disorder using blood-based biomarkers: a systematic review and critical appraisal. Transl Psychiatry 2023; 13:333. [PMID: 37898606 PMCID: PMC10613280 DOI: 10.1038/s41398-023-02623-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 09/15/2023] [Accepted: 10/06/2023] [Indexed: 10/30/2023] Open
Abstract
Accumulating evidence suggests individuals with psychotic disorder show abnormalities in metabolic and inflammatory processes. Recently, several studies have employed blood-based predictors in models predicting transition to psychotic disorder in risk-enriched populations. A systematic review of the performance and methodology of prognostic models using blood-based biomarkers in the prediction of psychotic disorder from risk-enriched populations is warranted. Databases (PubMed, EMBASE and PsycINFO) were searched for eligible texts from 1998 to 15/05/2023, which detailed model development or validation studies. The checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) was used to guide data extraction from eligible texts and the Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and applicability of the studies. A narrative synthesis of the included studies was performed. Seventeen eligible studies were identified: 16 eligible model development studies and one eligible model validation study. A wide range of biomarkers were assessed, including nucleic acids, proteins, metabolites, and lipids. The range of C-index (area under the curve) estimates reported for the models was 0.67-1.00. No studies assessed model calibration. According to PROBAST criteria, all studies were at high risk of bias in the analysis domain. While a wide range of potentially predictive biomarkers were identified in the included studies, most studies did not account for overfitting in model performance estimates, no studies assessed calibration, and all models were at high risk of bias according to PROBAST criteria. External validation of the models is needed to provide more accurate estimates of their performance. Future studies which follow the latest available methodological and reporting guidelines and adopt strategies to accommodate required sample sizes for model development or validation will clarify the value of including blood-based biomarkers in models predicting psychosis.
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Affiliation(s)
- Jonah F Byrne
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland.
- SFI FutureNeuro Research Centre, Royal College of Surgeons in Ireland, Dublin, Ireland.
| | - David Mongan
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
- Centre for Public Health, Queen's University Belfast, Belfast, United Kingdom
| | - Jennifer Murphy
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Colm Healy
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Melanie Fӧcking
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Mary Cannon
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
- SFI FutureNeuro Research Centre, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - David R Cotter
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
- SFI FutureNeuro Research Centre, Royal College of Surgeons in Ireland, Dublin, Ireland
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14
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Johansson Å, Andreassen OA, Brunak S, Franks PW, Hedman H, Loos RJ, Meder B, Melén E, Wheelock CE, Jacobsson B. Precision medicine in complex diseases-Molecular subgrouping for improved prediction and treatment stratification. J Intern Med 2023; 294:378-396. [PMID: 37093654 PMCID: PMC10523928 DOI: 10.1111/joim.13640] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease-both with regards to symptoms and underlying causal mechanisms-and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic-based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases-including psychiatric disorders and allergies-available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high-throughput molecular technologies, together with large collections of health data and novel data-driven approaches, offers promise toward improved individual health through precision medicine.
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Affiliation(s)
- Åsa Johansson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala university, Sweden
| | - Ole A. Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopment Research, University of Oslo, Oslo, Norway
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark
- Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, DK-2200 Copenhagen, Denmark
| | - Paul W. Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Sweden
- Novo Nordisk Foundation, Denmark
| | - Harald Hedman
- Department of Medical Biosciences, Umeå University, Umeå, Sweden
| | - Ruth J.F. Loos
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin Meder
- Precision Digital Health, Cardiogenetics Center Heidelberg, Department of Cardiology, University Of Heidelberg, Germany
| | - Erik Melén
- Department of Clinical Sciences and Education, Södersjukhuset, Karolinska Institutet, Stockholm
- Sachś Children and Youth Hospital, Södersjukhuset, Stockholm, Sweden
| | - Craig E Wheelock
- Unit of Integrative Metabolomics, Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden
- Department of Respiratory Medicine and Allergy, Karolinska University Hospital, 171 76 Stockholm, Sweden
| | - Bo Jacobsson
- Department of Obstetrics and Gynecology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Obstetrics and Gynaecology, Sahlgrenska University Hospital, Göteborg, Sweden
- Department of Genetics and Bioinformatics, Domain of Health Data and Digitalisation, Institute of Public Health, Oslo, Norway
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15
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Worthington MA, Addington J, Bearden CE, Cadenhead KS, Cornblatt BA, Keshavan M, Lympus CA, Mathalon DH, Perkins DO, Stone WS, Walker EF, Woods SW, Zhao Y, Cannon TD. Dynamic Prediction of Outcomes for Youth at Clinical High Risk for Psychosis: A Joint Modeling Approach. JAMA Psychiatry 2023; 80:1017-1025. [PMID: 37531131 PMCID: PMC10398543 DOI: 10.1001/jamapsychiatry.2023.2378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 05/03/2023] [Indexed: 08/03/2023]
Abstract
Importance Leveraging the dynamic nature of clinical variables in the clinical high risk for psychosis (CHR-P) population has the potential to significantly improve the performance of outcome prediction models. Objective To improve performance of prediction models and elucidate dynamic clinical profiles using joint modeling to predict conversion to psychosis and symptom remission. Design, Setting, and Participants Data were collected as part of the third wave of the North American Prodrome Longitudinal Study (NAPLS 3), which is a 9-site prospective longitudinal study. Participants were individuals aged 12 to 30 years who met criteria for a psychosis-risk syndrome. Clinical, neurocognitive, and demographic variables were collected at baseline and at multiple follow-up visits, beginning at 2 months and up to 24 months. An initial feature selection process identified longitudinal clinical variables that showed differential change for each outcome group across 2 months. With these variables, a joint modeling framework was used to estimate the likelihood of eventual outcomes. Models were developed and tested in a 10-fold cross-validation framework. Clinical data were collected between February 2015 and November 2018, and data were analyzed from February 2022 to December 2023. Main Outcomes and Measures Prediction models were built to predict conversion to psychosis and symptom remission. Participants met criteria for conversion if their positive symptoms reached the fully psychotic range and for symptom remission if they were subprodromal on the Scale of Psychosis-Risk Symptoms for a duration of 6 months or more. Results Of 488 included NAPLS 3 participants, 232 (47.5%) were female, and the mean (SD) age was 18.2 (3.4) years. Joint models achieved a high level of accuracy in predicting conversion (balanced accuracy [BAC], 0.91) and remission (BAC, 0.99) compared with baseline models (conversion: BAC, 0.65; remission: BAC, 0.60). Clinical variables that showed differential change between outcome groups across a 2-month span, including measures of symptom severity and aspects of functioning, were also identified. Further, intra-individual risks for each outcome were more negatively correlated when using joint models (r = -0.92; P < .001) compared with baseline models (r = -0.50; P < .001). Conclusions and Relevance In this study, joint models significantly outperformed baseline models in predicting both conversion and remission, demonstrating that monitoring short-term clinical change may help to parse heterogeneous dynamic clinical trajectories in a CHR-P population. These findings could inform additional study of targeted treatment selection and could move the field closer to clinical implementation of prediction models.
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Affiliation(s)
| | - Jean Addington
- Hotchkiss Brain Institute, Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
| | - Carrie E. Bearden
- Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, Department of Psychology, University of California, Los Angeles
| | | | | | - Matcheri Keshavan
- Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center and Massachusetts General Hospital, Boston
| | - Cole A. Lympus
- Department of Psychology, Rutgers University, New Brunswick, New Jersey
| | - Daniel H. Mathalon
- Department of Psychiatry, San Francisco VA Medical Center, University of California, San Francisco
| | - Diana O. Perkins
- Department of Psychiatry, University of North Carolina, Chapel Hill
| | - William S. Stone
- Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center and Massachusetts General Hospital, Boston
| | - Elaine F. Walker
- Department of Psychology, Emory University, Atlanta, Georgia
- Department of Psychiatry, Emory University, Atlanta, Georgia
| | - Scott W. Woods
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Yize Zhao
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut
| | - Tyrone D. Cannon
- Department of Psychology, Yale University, New Haven, Connecticut
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
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16
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Loch AA, Pinto MTC, Andrade JC, de Jesus LP, de Medeiros MW, Haddad NM, Bilt MTVD, Talib LL, Gattaz WF. Plasma levels of neurotrophin 4/5, NGF and pro-BDNF influence transition to mental disorders in a sample of individuals at ultra-high risk for psychosis. Psychiatry Res 2023; 327:115402. [PMID: 37544089 DOI: 10.1016/j.psychres.2023.115402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 06/19/2023] [Accepted: 07/31/2023] [Indexed: 08/08/2023]
Abstract
BACKGROUND Neurotrophins (NTs) and their precursors (pro-NTs) are polypeptides with important roles in neuronal development, differentiation, growth, survival and plasticity, as well as apoptosis and neuronal death. Imbalance in NT levels were observed in schizophrenia spectrum disorders, but evidence in ultra-high risk for psychosis (UHR) samples is scarce. METHODS A naturalistic sample of 87 non-help-seeking UHR subjects and 55 healthy controls was drawn from the general population. Blood samples were collected and NT-3, NT-4/5, BDNF, pro-BDNF, NGF, pro-NGF were analyzed through enzyme linked immunosorbent assay (ELISA). Information on cannabis and tobacco use was also collected. Logistic regression models and path analysis were used to control for confounders (tobacco, age, cannabis use). RESULTS NT-4/5 was significantly decreased, and pro-BDNF was significantly increased in UHR individuals compared to controls. Cannabis use and higher NGF levels were significantly related to transition to psychiatric disorders among UHR subjects. Increased pro-BDNF and decreased NT-4/5 influenced transition by the mediation of perceptual abnormalities. CONCLUSIONS Our study shows for the first time that NTs are altered in UHR compared to healthy control individuals, and that they can be a predictor of transition to psychiatric illnesses in this population. Future studies should employ larger naturalistic samples to confirm the findings.
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Affiliation(s)
- Alexandre Andrade Loch
- Laboratório de Neurociencias (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR; Instituto Nacional de Biomarcadores em Neuropsiquiatria (INBION), Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil.
| | - Marcel Tavares Camilo Pinto
- Laboratório de Neurociencias (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
| | - Julio Cesar Andrade
- Laboratório de Neurociencias (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
| | - Leonardo Peroni de Jesus
- Laboratório de Neurociencias (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
| | - Matheus Wanderley de Medeiros
- Laboratório de Neurociencias (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
| | - Natalia Mansur Haddad
- Laboratório de Neurociencias (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
| | - Martinus Theodorus van de Bilt
- Laboratório de Neurociencias (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR; Instituto Nacional de Biomarcadores em Neuropsiquiatria (INBION), Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil
| | - Leda Leme Talib
- Laboratório de Neurociencias (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR; Instituto Nacional de Biomarcadores em Neuropsiquiatria (INBION), Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil
| | - Wagner Farid Gattaz
- Laboratório de Neurociencias (LIM 27), Instituto de Psiquiatria, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR; Instituto Nacional de Biomarcadores em Neuropsiquiatria (INBION), Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil
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17
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Antonucci LA, Pergola G, Rampino A, Rocca P, Rossi A, Amore M, Aguglia E, Bellomo A, Bianchini V, Brasso C, Bucci P, Carpiniello B, Dell'Osso L, di Fabio F, di Giannantonio M, Fagiolini A, Giordano GM, Marcatilli M, Marchesi C, Meneguzzo P, Monteleone P, Pompili M, Rossi R, Siracusano A, Vita A, Zeppegno P, Galderisi S, Bertolino A, Maj M. Clinical and psychological factors associated with resilience in patients with schizophrenia: data from the Italian network for research on psychoses using machine learning. Psychol Med 2023; 53:5717-5728. [PMID: 36217912 DOI: 10.1017/s003329172200294x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
BACKGROUND Resilience is defined as the ability to modify thoughts to cope with stressful events. Patients with schizophrenia (SCZ) having higher resilience (HR) levels show less severe symptoms and better real-life functioning. However, the clinical factors contributing to determine resilience levels in patients remain unclear. Thus, based on psychological, historical, clinical and environmental variables, we built a supervised machine learning algorithm to classify patients with HR or lower resilience (LR). METHODS SCZ from the Italian Network for Research on Psychoses (N = 598 in the Discovery sample, N = 298 in the Validation sample) underwent historical, clinical, psychological, environmental and resilience assessments. A Support Vector Machine algorithm (based on 85 variables extracted from the above-mentioned assessments) was built in the Discovery sample, and replicated in the Validation sample, to classify between HR and LR patients, within a nested, Leave-Site-Out Cross-Validation framework. We then investigated whether algorithm decision scores were associated with the cognitive and clinical characteristics of patients. RESULTS The algorithm classified patients as HR or LR with a Balanced Accuracy of 74.5% (p < 0.0001) in the Discovery sample, and 80.2% in the Validation sample. Higher self-esteem, larger social network and use of adaptive coping strategies were the variables most frequently chosen by the algorithm to generate decisions. Correlations between algorithm decision scores, socio-cognitive abilities, and symptom severity were significant (pFDR < 0.05). CONCLUSIONS We identified an accurate, meaningful and generalizable clinical-psychological signature associated with resilience in SCZ. This study delivers relevant information regarding psychological and clinical factors that non-pharmacological interventions could target in schizophrenia.
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Affiliation(s)
- Linda A Antonucci
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Giulio Pergola
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Antonio Rampino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Paola Rocca
- Department of Neuroscience, Section of Psychiatry, University of Turin, Turin, Italy
| | - Alessandro Rossi
- Section of Psychiatry, Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Mario Amore
- Section of Psychiatry, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health, University of Genoa, Genoa, Italy
| | - Eugenio Aguglia
- Department of Clinical and Molecular Biomedicine, Psychiatry Unit, University of Catania, Catania, Italy
| | - Antonello Bellomo
- Psychiatry Unit, Department of Medical Sciences, University of Foggia, Foggia, Italy
| | - Valeria Bianchini
- Unit of Psychiatry, Department of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Italy
| | - Claudio Brasso
- Department of Neuroscience, Section of Psychiatry, University of Turin, Turin, Italy
| | - Paola Bucci
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Bernardo Carpiniello
- Section of Psychiatry, Department of Public Health, Clinical and Molecular Medicine, University of Cagliari, Cagliari, Italy
| | - Liliana Dell'Osso
- Section of Psychiatry, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Fabio di Fabio
- Department of Neurology and Psychiatry, "La Sapienza" University of Rome, Rome, Italy
| | | | - Andrea Fagiolini
- Department of Molecular Medicine and Clinical Department of Mental Health, University of Siena, Siena, Italy
| | | | | | - Carlo Marchesi
- Department of Neuroscience, Psychiatry Unit, University of Parma, Parma, Italy
| | - Paolo Meneguzzo
- Psychiatric Clinic, Department of Neurosciences, University of Padua, Padua, Italy
| | - Palmiero Monteleone
- Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana" Section of Neuroscience, University of Salerno, Salerno, Italy
| | - Maurizio Pompili
- Department of Neurosciences, Mental Health and Sensory Organs, S. Andrea Hospital, "La Sapienza" University of Rome, Rome, Italy
| | - Rodolfo Rossi
- Section of Psychiatry, Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Alberto Siracusano
- Department of Systems Medicine, Psychiatry and Clinical Psychology Unit, "Tor Vergata" University of Rome, Rome, Italy
| | - Antonio Vita
- Psychiatric Unit, School of Medicine, University of Brescia, Brescia, Italy
- Department of Mental Health, Spedali Civili Hospital, Brescia, Italy
| | - Patrizia Zeppegno
- Department of Translational Medicine, Psychiatric Unit, University of Eastern Piedmont, Novara, Italy
| | - Silvana Galderisi
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Mario Maj
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
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Andreou C, Eickhoff S, Heide M, de Bock R, Obleser J, Borgwardt S. Predictors of transition in patients with clinical high risk for psychosis: an umbrella review. Transl Psychiatry 2023; 13:286. [PMID: 37640731 PMCID: PMC10462748 DOI: 10.1038/s41398-023-02586-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 08/03/2023] [Accepted: 08/14/2023] [Indexed: 08/31/2023] Open
Abstract
Diagnosis of a clinical high-risk (CHR) state enables timely treatment of individuals at risk for a psychotic disorder, thereby contributing to improving illness outcomes. However, only a minority of patients diagnosed with CHR will make the transition to overt psychosis. To identify patients most likely to benefit from early intervention, several studies have investigated characteristics that distinguish CHR patients who will later develop a psychotic disorder from those who will not. We aimed to summarize evidence from systematic reviews and meta-analyses on predictors of transition to psychosis in CHR patients, among characteristics and biomarkers assessed at baseline. A systematic search was conducted in Pubmed, Scopus, PsychInfo and Cochrane databases to identify reviews and meta-analyses of studies that investigated specific baseline predictors or biomarkers for transition to psychosis in CHR patients using a cross-sectional or longitudinal design. Non-peer-reviewed publications, gray literature, narrative reviews and publications not written in English were excluded from analyses. We provide a narrative synthesis of results from all included reviews and meta-analyses. For each included publication, we indicate the number of studies cited in each domain and its quality rating. A total of 40 publications (21 systematic reviews and 19 meta-analyses) that reviewed a total of 272 original studies qualified for inclusion. Baseline predictors most consistently associated with later transition included clinical characteristics such as attenuated psychotic and negative symptoms and functioning, verbal memory deficits and the electrophysiological marker of mismatch negativity. Few predictors reached a level of evidence sufficient to inform clinical practice, reflecting generalizability issues in a field characterized by studies with small, heterogeneous samples and relatively few transition events. Sample pooling and harmonization of methods across sites and projects are necessary to overcome these limitations.
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Affiliation(s)
- Christina Andreou
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
- Center of Brain, Behavior, and Metabolism (CBBM), University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Sofia Eickhoff
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Marco Heide
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Renate de Bock
- University Psychiatric Clinics Basel, Wilhelm Klein-Strasse 27, 4002, Basel, Switzerland
| | - Jonas Obleser
- Center of Brain, Behavior, and Metabolism (CBBM), University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
- Department of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Stefan Borgwardt
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
- Center of Brain, Behavior, and Metabolism (CBBM), University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
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19
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Raballo A, Poletti M, Preti A. Do antidepressants prevent transition to psychosis in individuals at clinical high-risk (CHR-P)? Systematic review and meta-analysis. Psychol Med 2023; 53:4550-4560. [PMID: 35655405 DOI: 10.1017/s0033291722001428] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Emerging meta-analytical evidence indicates that baseline exposure to antipsychotics in individuals at clinical high-risk for psychosis (CHR-P) is associated with a higher risk of an imminent transition to psychosis. Despite their tolerability profile and potential beneficial effects, baseline exposure to antidepressants (AD) in CHR-P has surprisingly received far less attention as a potential risk modulator for transition to psychosis. The current systematic review and meta-analysis were performed to fix such a knowledge gap. METHODS Systematic scrutiny of Medline and Cochrane library, performed up to 1 August 2021, searching for English-language studies on CHR-P reporting numeric data about the sample, the transition outcome at a predefined follow-up time and raw data on AD baseline exposure in relation to such outcome. RESULTS Of 1942 identified records, 16 studies were included in the systematic review and meta-analysis. 26% of the participants were already exposed to AD at baseline; at the end of the follow-up 13.5% (95% CI 10.2-17.1%) of them (n = 448) transitioned to psychosis against 21.0% (18.9 to 23.3%) of non-AD exposed CHR-P (n = 1371). CHR-P participants who were already under AD treatment at baseline had a lower risk of transition than non-AD exposed CHR-P. The RR was 0.71 (95% CI 0.56-0.90) in the fixed-effects model (z = -2.79; p = 0.005), and 0.78 (0.58-1.05) in the random-effects model (z = -1.77; p = 0.096; tau-squared = 0.059). There was no relevant heterogeneity (Cochran's Q = 18.45; df = 15; p = 0.239; I2 = 18.7%). CONCLUSIONS Ongoing AD exposure at inception in CHR-P is associated to a reduced risk of transition to psychosis at follow up.
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Affiliation(s)
- Andrea Raballo
- Section of Psychiatry, Clinical Psychology and Rehabilitation, Department of Medicine, University of Perugia, Perugia, Italy
- Center for Translational, Phenomenological and Developmental Psychopathology (CTPDP), Perugia University Hospital, Perugia, Italy
| | - Michele Poletti
- Department of Mental Health and Pathological Addiction, Child and Adolescent Neuropsychiatry Service, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Antonio Preti
- Department of Neuroscience, University of Turin, Turin, Italy
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20
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Raballo A, Poletti M, Preti A. The temporal dynamics of transition to psychosis in individuals at clinical high-risk (CHR-P) shows negative prognostic effects of baseline antipsychotic exposure: a meta-analysis. Transl Psychiatry 2023; 13:112. [PMID: 37019886 PMCID: PMC10076303 DOI: 10.1038/s41398-023-02405-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/15/2023] [Accepted: 03/20/2023] [Indexed: 04/07/2023] Open
Abstract
Meta-analytic evidence indicates that baseline exposure to antipsychotics (AP) in individuals at clinical high-risk for psychosis (CHR-P) is associated with an even higher risk of transition to psychosis. However, the temporal dynamics of such prognostic effect have not been clarified yet. This study was therefore designed to address this knowledge gap. We performed a systematic review and meta-analysis of all longitudinal studies published up to 31 December 2021 on CHR-P individuals identified according to a validated diagnostic procedure and reporting numeric data of transition to psychosis according to baseline antipsychotic exposure. 28 studies covering a total of 2405 CHR-P were included. 554 (23.0%) were exposed to AP at baseline, whereas 1851 (77.0%) were not. At follow-up (12 to 72 months), 182 individuals among AP-exposed (32.9%; 95% CI: 29.4% to 37.8%) and 382 among AP-naive CHR-P (20.6%; 18.8% to 22.8%) developed psychosis. Transition rates increased over time, with the best-fit for an ascending curve peaking at 24 months and reaching then a plateau, with a further increase at 48 months. Baseline AP-exposed CHR-P had higher transition risk at 12 months and then again at 36 and 48 months, with an overall higher risk of transition (fixed-effect model: risk ratio = 1.56 [95% CI: 1.32-1.85]; z = 5.32; p < 0.0001; Random-effect model: risk ratio = 1.56 [95% CI: 1.07-2.26]; z = 2.54; p = 0.0196). In conclusion, the temporal dynamics of transition to psychosis differ in AP-exposed vs. AP-naive CHR-P. Baseline AP exposure in CHR-P is associated with a persistently higher risk of transition at follow up, supporting the rationale for more stringent clinical monitoring in AP-exposed CHR-P. The insufficiency of more granular information in available primary literature (e.g., temporal and quantitative details of AP exposure as well as psychopathological dimensions in CHR-P) did not allow the testing of causal hypotheses on this negative prognostic association.
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Affiliation(s)
- Andrea Raballo
- Chair of Psychiatry, Faculty of Biomedical Sciences, Università della Svizzera Italiana (USI), Lugano, Switzerland
- Cantonal Socio-psychiatric Organization (OSC), Public Health Division, Department of Health and Social Care, Repubblica e Cantone Ticino, Mendrisio, Switzerland
| | - Michele Poletti
- Department of Mental Health and Pathological Addiction, Child and Adolescent Neuropsychiatry Service, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy.
| | - Antonio Preti
- Department of Neuroscience, University of Turin, Turin, Italy
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21
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Paino M, González-Menéndez AM, Vallina-Fernández Ó, Rus-Calafell M. A novel algorithm to detect early risk of psychosis: Results from the Prevention Program for Psychosis (P3). Schizophr Res 2022; 248:196-197. [PMID: 36088749 DOI: 10.1016/j.schres.2022.08.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 07/12/2022] [Accepted: 08/20/2022] [Indexed: 10/14/2022]
Affiliation(s)
- Mercedes Paino
- Department of Psychology, University of Oviedo, Oviedo, Spain.
| | | | | | - Mar Rus-Calafell
- Mental Health Research and Treatment Center, Faculty of Psychology, Ruhr-University of Bochum, Bochum, Germany
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22
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Miley K, Michalowski M, Yu F, Leng E, McMorris BJ, Vinogradov S. Predictive models for social functioning in healthy young adults: A machine learning study integrating neuroanatomical, cognitive, and behavioral data. Soc Neurosci 2022; 17:414-427. [PMID: 36196662 PMCID: PMC9707316 DOI: 10.1080/17470919.2022.2132285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 09/14/2022] [Indexed: 10/10/2022]
Abstract
Poor social functioning is an emerging public health problem associated with physical and mental health consequences. Developing prognostic tools is critical to identify individuals at risk for poor social functioning and guide interventions. We aimed to inform prediction models of social functioning by evaluating models relying on bio-behavioral data using machine learning. With data from the Human Connectome Project Healthy Young Adult sample (age 22-35, N = 1,101), we built Support Vector Regression models to estimate social functioning from variable sets of brain morphology to behavior with increasing complexity: 1) brain-only model, 2) brain-cognition model, 3) cognition-behavioral model, and 4) combined brain-cognition-behavioral model. Predictive accuracy of each model was assessed and the importance of individual variables for model performance was determined. The combined and cognition-behavioral models significantly predicted social functioning, whereas the brain-only and brain-cognition models did not. Negative affect, psychological wellbeing, extraversion, withdrawal, and cortical thickness of the rostral middle-frontal and superior-temporal regions were the most important predictors in the combined model. Results demonstrate that social functioning can be accurately predicted using machine learning methods. Behavioral markers may be more significant predictors of social functioning than brain measures for healthy young adults and may represent important leverage points for preventative intervention.
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Affiliation(s)
- Kathleen Miley
- School of Nursing, University of Minnesota, Minneapolis MN, United States
| | - Martin Michalowski
- School of Nursing, University of Minnesota, Minneapolis MN, United States
| | - Fang Yu
- Edson College of Nursing and Health Innovation, Arizona State University, Phoenix, AZ, United States
| | - Ethan Leng
- Department of Biomedical Engineering, University of Minnesota, Minneapolis MN, United States
| | | | - Sophia Vinogradov
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis MN, United States
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23
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Fusar-Poli P, Manchia M, Koutsouleris N, Leslie D, Woopen C, Calkins ME, Dunn M, Tourneau CL, Mannikko M, Mollema T, Oliver D, Rietschel M, Reininghaus EZ, Squassina A, Valmaggia L, Kessing LV, Vieta E, Correll CU, Arango C, Andreassen OA. Ethical considerations for precision psychiatry: A roadmap for research and clinical practice. Eur Neuropsychopharmacol 2022; 63:17-34. [PMID: 36041245 DOI: 10.1016/j.euroneuro.2022.08.001] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 07/04/2022] [Accepted: 08/05/2022] [Indexed: 12/14/2022]
Abstract
Precision psychiatry is an emerging field with transformative opportunities for mental health. However, the use of clinical prediction models carries unprecedented ethical challenges, which must be addressed before accessing the potential benefits of precision psychiatry. This critical review covers multidisciplinary areas, including psychiatry, ethics, statistics and machine-learning, healthcare and academia, as well as input from people with lived experience of mental disorders, their family, and carers. We aimed to identify core ethical considerations for precision psychiatry and mitigate concerns by designing a roadmap for research and clinical practice. We identified priorities: learning from somatic medicine; identifying precision psychiatry use cases; enhancing transparency and generalizability; fostering implementation; promoting mental health literacy; communicating risk estimates; data protection and privacy; and fostering the equitable distribution of mental health care. We hope this blueprint will advance research and practice and enable people with mental health problems to benefit from precision psychiatry.
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Affiliation(s)
- Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.
| | - Mirko Manchia
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy; Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy; Department of Pharmacology, Dalhousie University, Halifax, Nova Scotia, Canada
| | | | | | | | - Monica E Calkins
- Neurodevelopment and Psychosis Section and Lifespan Brain Institute of Penn/CHOP, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, USA
| | - Michael Dunn
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore
| | - Christophe Le Tourneau
- Institut Curie, Department of Drug Development and Innovation (D3i), INSERM U900 Research unit, Paris-Saclay University, France
| | - Miia Mannikko
- European Federation of Associations of Families of People with Mental Illness (EUFAMI), Leuven, Belgium
| | - Tineke Mollema
- Global Alliance of Mental Illness Advocacy Networks-Europe (GAMIAN), Brussels, Belgium
| | - Dominic Oliver
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Eva Z Reininghaus
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria
| | - Alessio Squassina
- Section of Neuroscience and Clinical Pharmacology, Department of Biomedical Sciences, University of Cagliari, Italy
| | - Lucia Valmaggia
- South London and Maudsley NHS Foundation Trust, London, UK; Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Department of Psychiatry, KU Leuven, Belgium
| | - Lars Vedel Kessing
- Copenhagen Affective disorder Research Center (CADIC), Psychiatric Center Copenhagen, Denmark; Department of clinical Medicine, University of Copenhagen, Denmark
| | - Eduard Vieta
- Hospital Clinic, Institute of Neuroscience, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain
| | - Christoph U Correll
- The Zucker Hillside Hospital, Department of Psychiatry, Northwell Health, Glen Oaks, NY, USA; Department of Psychiatry and Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA; Center for Psychiatric Neuroscience; The Feinstein Institutes for Medical Research, Manhasset, NY, USA; Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Gregorio Marañón; Health Research Institute (IiGSM), School of Medicine, Universidad Complutense de Madrid; Biomedical Research Center for Mental Health (CIBERSAM), Madrid, Spain
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
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Moore TM, Calkins ME, Rosen AFG, Butler ER, Ruparel K, Fusar-Poli P, Koutsouleris N, McGuire P, Cannon TD, Gur RC, Gur RE. Development of a probability calculator for psychosis risk in children, adolescents, and young adults. Psychol Med 2022; 52:3159-3167. [PMID: 33431073 PMCID: PMC8273212 DOI: 10.1017/s0033291720005231] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
BACKGROUND Assessment of risks of illnesses has been an important part of medicine for decades. We now have hundreds of 'risk calculators' for illnesses, including brain disorders, and these calculators are continually improving as more diverse measures are collected on larger samples. METHODS We first replicated an existing psychosis risk calculator and then used our own sample to develop a similar calculator for use in recruiting 'psychosis risk' enriched community samples. We assessed 632 participants age 8-21 (52% female; 48% Black) from a community sample with longitudinal data on neurocognitive, clinical, medical, and environmental variables. We used this information to predict psychosis spectrum (PS) status in the future. We selected variables based on lasso, random forest, and statistical inference relief; and predicted future PS using ridge regression, random forest, and support vector machines. RESULTS Cross-validated prediction diagnostics were obtained by building and testing models in randomly selected sub-samples of the data, resulting in a distribution of the diagnostics; we report the mean. The strongest predictors of later PS status were the Children's Global Assessment Scale; delusions of predicting the future or having one's thoughts/actions controlled; and the percent married in one's neighborhood. Random forest followed by ridge regression was most accurate, with a cross-validated area under the curve (AUC) of 0.67. Adjustment of the model including only six variables reached an AUC of 0.70. CONCLUSIONS Results support the potential application of risk calculators for screening and identification of at-risk community youth in prospective investigations of developmental trajectories of the PS.
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Affiliation(s)
- Tyler M. Moore
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Penn Medicine and Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Monica E. Calkins
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Penn Medicine and Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Adon F. G. Rosen
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ellyn R. Butler
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kosha Ruparel
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Penn Medicine and Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Paolo Fusar-Poli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- OASIS service, South London and Maudsley NHS Foundation Trust, London, UK
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Germany
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Tyrone D. Cannon
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT 06520, USA
| | - Ruben C. Gur
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Penn Medicine and Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Raquel E. Gur
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Penn Medicine and Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
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25
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Chand GB, Singhal P, Dwyer DB, Wen J, Erus G, Doshi J, Srinivasan D, Mamourian E, Varol E, Sotiras A, Hwang G, Dazzan P, Kahn RS, Schnack HG, Zanetti MV, Meisenzahl E, Busatto GF, Crespo-Facorro B, Pantelis C, Wood SJ, Zhuo C, Shinohara RT, Shou H, Fan Y, Koutsouleris N, Kaczkurkin AN, Moore TM, Verma A, Calkins ME, Gur RE, Gur RC, Ritchie MD, Satterthwaite TD, Wolf DH, Davatzikos C. Schizophrenia Imaging Signatures and Their Associations With Cognition, Psychopathology, and Genetics in the General Population. Am J Psychiatry 2022; 179:650-660. [PMID: 35410495 PMCID: PMC9444886 DOI: 10.1176/appi.ajp.21070686] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
OBJECTIVE The prevalence and significance of schizophrenia-related phenotypes at the population level is debated in the literature. Here, the authors assessed whether two recently reported neuroanatomical signatures of schizophrenia-signature 1, with widespread reduction of gray matter volume, and signature 2, with increased striatal volume-could be replicated in an independent schizophrenia sample, and investigated whether expression of these signatures can be detected at the population level and how they relate to cognition, psychosis spectrum symptoms, and schizophrenia genetic risk. METHODS This cross-sectional study used an independent schizophrenia-control sample (N=347; ages 16-57 years) for replication of imaging signatures, and then examined two independent population-level data sets: typically developing youths and youths with psychosis spectrum symptoms in the Philadelphia Neurodevelopmental Cohort (N=359; ages 16-23 years) and adults in the UK Biobank study (N=836; ages 44-50 years). The authors quantified signature expression using support-vector machine learning and compared cognition, psychopathology, and polygenic risk between signatures. RESULTS Two neuroanatomical signatures of schizophrenia were replicated. Signature 1 but not signature 2 was significantly more common in youths with psychosis spectrum symptoms than in typically developing youths, whereas signature 2 frequency was similar in the two groups. In both youths and adults, signature 1 was associated with worse cognitive performance than signature 2. Compared with adults with neither signature, adults expressing signature 1 had elevated schizophrenia polygenic risk scores, but this was not seen for signature 2. CONCLUSIONS The authors successfully replicated two neuroanatomical signatures of schizophrenia and describe their prevalence in population-based samples of youths and adults. They further demonstrated distinct relationships of these signatures with psychosis symptoms, cognition, and genetic risk, potentially reflecting underlying neurobiological vulnerability.
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Affiliation(s)
- Ganesh B Chand
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Pankhuri Singhal
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Dominic B Dwyer
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Junhao Wen
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Dhivya Srinivasan
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Elizabeth Mamourian
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Erdem Varol
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Aristeidis Sotiras
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Gyujoon Hwang
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Paola Dazzan
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Rene S Kahn
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Hugo G Schnack
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Marcus V Zanetti
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Eva Meisenzahl
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Geraldo F Busatto
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Benedicto Crespo-Facorro
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Christos Pantelis
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Stephen J Wood
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Chuanjun Zhuo
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Russell T Shinohara
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Haochang Shou
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Nikolaos Koutsouleris
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Antonia N Kaczkurkin
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Tyler M Moore
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Anurag Verma
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Monica E Calkins
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Raquel E Gur
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Ruben C Gur
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Marylyn D Ritchie
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Theodore D Satterthwaite
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Daniel H Wolf
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, Satterthwaite, Wolf, Davatzikos, Shinohara, Shou), Department of Radiology (Chand, Wen, Erus, Doshi, Srinivasan, Mamourian, Varol, Sotiras, Hwang, Fan, R.E. Gur, R.C. Gur, Davatzikos), Department of Genetics (Singhal, Verma, Ritchie), Department of Psychiatry (Kaczkurkin, Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite, Wolf), and Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics (Shinohara, Shou), Perelman School of Medicine, University of Pennsylvania, Philadelphia; Department of Radiology, School of Medicine (Chand, Sotiras), and Institute of Informatics (Sotiras), Washington University in St. Louis; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich (Dwyer, Koutsouleris); Department of Statistics, Zuckerman Institute, Columbia University, New York (Varol); Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Dazzan); Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York (Kahn); Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands (Schnack); Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil (Zanetti, Busatto); Hospital Sírio-Libanês, São Paulo, Brazil (Zanetti); LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany (Meisenzahl); Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocio, IBiS-CIBERSAM, University of Sevilla, Spain (Crespo-Facorro); Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia (Pantelis); Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Australia, and Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia (Wood); School of Psychology, University of Birmingham, Edgbaston, U.K. (Wood); Department of Psychiatric Neuroimaging Genetics and Comorbidity Laboratory, Nankai University Affiliated Tianjin Anding Hospital, and Department of Psychiatry, Tianjin Medical University, Tianjin, China (Zhuo); Department of Psychology, Vanderbilt University, Nashville (Kaczkurkin); Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia (Moore, Calkins, R.E. Gur, R.C. Gur, Satterthwaite)
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Martinez-Millana A, Saez-Saez A, Tornero-Costa R, Azzopardi-Muscat N, Traver V, Novillo-Ortiz D. Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews. Int J Med Inform 2022; 166:104855. [PMID: 35998421 PMCID: PMC9551134 DOI: 10.1016/j.ijmedinf.2022.104855] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/01/2022] [Accepted: 08/11/2022] [Indexed: 12/04/2022]
Abstract
An overview of systematic reviews on the application of AI including 129 studies. AI use is prominent in Universal Health Coverage, featuring image analysis in neoplasms. Half of the reviews did not evaluate validation procedures nor reporting guidelines. Risk of bias was only included un a third of the reviews. There is not sufficient evidence to transfer AI to actual healthcare delivery.
Background Artificial intelligence is fueling a new revolution in medicine and in the healthcare sector. Despite the growing evidence on the benefits of artificial intelligence there are several aspects that limit the measure of its impact in people’s health. It is necessary to assess the current status on the application of AI towards the improvement of people’s health in the domains defined by WHO’s Thirteenth General Programme of Work (GPW13) and the European Programme of Work (EPW), to inform about trends, gaps, opportunities, and challenges. Objective To perform a systematic overview of systematic reviews on the application of artificial intelligence in the people’s health domains as defined in the GPW13 and provide a comprehensive and updated map on the application specialties of artificial intelligence in terms of methodologies, algorithms, data sources, outcomes, predictors, performance, and methodological quality. Methods A systematic search in MEDLINE, EMBASE, Cochrane and IEEEXplore was conducted between January 2015 and June 2021 to collect systematic reviews using a combination of keywords related to the domains of universal health coverage, health emergencies protection, and better health and wellbeing as defined by the WHO’s PGW13 and EPW. Eligibility criteria was based on methodological quality and the inclusion of practical implementation of artificial intelligence. Records were classified and labeled using ICD-11 categories into the domains of the GPW13. Descriptors related to the area of implementation, type of modeling, data entities, outcomes and implementation on care delivery were extracted using a structured form and methodological aspects of the included reviews studies was assessed using the AMSTAR checklist. Results The search strategy resulted in the screening of 815 systematic reviews from which 203 were assessed for eligibility and 129 were included in the review. The most predominant domain for artificial intelligence applications was Universal Health Coverage (N = 98) followed by Health Emergencies (N = 16) and Better Health and Wellbeing (N = 15). Neoplasms area on Universal Health Coverage was the disease area featuring most of the applications (21.7 %, N = 28). The reviews featured analytics primarily over both public and private data sources (67.44 %, N = 87). The most used type of data was medical imaging (31.8 %, N = 41) and predictors based on regions of interest and clinical data. The most prominent subdomain of Artificial Intelligence was Machine Learning (43.4 %, N = 56), in which Support Vector Machine method was predominant (20.9 %, N = 27). Regarding the purpose, the application of Artificial Intelligence I is focused on the prediction of the diseases (36.4 %, N = 47). With respect to the validation, more than a half of the reviews (54.3 %, N = 70) did not report a validation procedure and, whenever available, the main performance indicator was the accuracy (28.7 %, N = 37). According to the methodological quality assessment, a third of the reviews (34.9 %, N = 45) implemented methods for analysis the risk of bias and the overall AMSTAR score below was 5 (4.01 ± 1.93) on all the included systematic reviews. Conclusion Artificial intelligence is being used for disease modelling, diagnose, classification and prediction in the three domains of GPW13. However, the evidence is often limited to laboratory and the level of adoption is largely unbalanced between ICD-11 categoriesand diseases. Data availability is a determinant factor on the developmental stage of artificial intelligence applications. Most of the reviewed studies show a poor methodological quality and are at high risk of bias, which limits the reproducibility of the results and the reliability of translating these applications to real clinical scenarios. The analyzed papers show results only in laboratory and testing scenarios and not in clinical trials nor case studies, limiting the supporting evidence to transfer artificial intelligence to actual care delivery.
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Affiliation(s)
- Antonio Martinez-Millana
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Aida Saez-Saez
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Roberto Tornero-Costa
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Natasha Azzopardi-Muscat
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Vicente Traver
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - David Novillo-Ortiz
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark.
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The performance of artificial intelligence-driven technologies in diagnosing mental disorders: an umbrella review. NPJ Digit Med 2022; 5:87. [PMID: 35798934 PMCID: PMC9262920 DOI: 10.1038/s41746-022-00631-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 06/08/2022] [Indexed: 11/08/2022] Open
Abstract
Artificial intelligence (AI) has been successfully exploited in diagnosing many mental disorders. Numerous systematic reviews summarize the evidence on the accuracy of AI models in diagnosing different mental disorders. This umbrella review aims to synthesize results of previous systematic reviews on the performance of AI models in diagnosing mental disorders. To identify relevant systematic reviews, we searched 11 electronic databases, checked the reference list of the included reviews, and checked the reviews that cited the included reviews. Two reviewers independently selected the relevant reviews, extracted the data from them, and appraised their quality. We synthesized the extracted data using the narrative approach. We included 15 systematic reviews of 852 citations identified. The included reviews assessed the performance of AI models in diagnosing Alzheimer's disease (n = 7), mild cognitive impairment (n = 6), schizophrenia (n = 3), bipolar disease (n = 2), autism spectrum disorder (n = 1), obsessive-compulsive disorder (n = 1), post-traumatic stress disorder (n = 1), and psychotic disorders (n = 1). The performance of the AI models in diagnosing these mental disorders ranged between 21% and 100%. AI technologies offer great promise in diagnosing mental health disorders. The reported performance metrics paint a vivid picture of a bright future for AI in this field. Healthcare professionals in the field should cautiously and consciously begin to explore the opportunities of AI-based tools for their daily routine. It would also be encouraging to see a greater number of meta-analyses and further systematic reviews on performance of AI models in diagnosing other common mental disorders such as depression and anxiety.
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Turner RJ, Coenen F, Roelofs F, Hagoort K, Härmä A, Grünwald PD, Velders FP, Scheepers FE. Information extraction from free text for aiding transdiagnostic psychiatry: constructing NLP pipelines tailored to clinicians' needs. BMC Psychiatry 2022; 22:407. [PMID: 35715745 PMCID: PMC9206307 DOI: 10.1186/s12888-022-04058-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 06/10/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Developing predictive models for precision psychiatry is challenging because of unavailability of the necessary data: extracting useful information from existing electronic health record (EHR) data is not straightforward, and available clinical trial datasets are often not representative for heterogeneous patient groups. The aim of this study was constructing a natural language processing (NLP) pipeline that extracts variables for building predictive models from EHRs. We specifically tailor the pipeline for extracting information on outcomes of psychiatry treatment trajectories, applicable throughout the entire spectrum of mental health disorders ("transdiagnostic"). METHODS A qualitative study into beliefs of clinical staff on measuring treatment outcomes was conducted to construct a candidate list of variables to extract from the EHR. To investigate if the proposed variables are suitable for measuring treatment effects, resulting themes were compared to transdiagnostic outcome measures currently used in psychiatry research and compared to the HDRS (as a gold standard) through systematic review, resulting in an ideal set of variables. To extract these from EHR data, a semi-rule based NLP pipeline was constructed and tailored to the candidate variables using Prodigy. Classification accuracy and F1-scores were calculated and pipeline output was compared to HDRS scores using clinical notes from patients admitted in 2019 and 2020. RESULTS Analysis of 34 questionnaires answered by clinical staff resulted in four themes defining treatment outcomes: symptom reduction, general well-being, social functioning and personalization. Systematic review revealed 242 different transdiagnostic outcome measures, with the 36-item Short-Form Survey for quality of life (SF36) being used most consistently, showing substantial overlap with the themes from the qualitative study. Comparing SF36 to HDRS scores in 26 studies revealed moderate to good correlations (0.62-0.79) and good positive predictive values (0.75-0.88). The NLP pipeline developed with notes from 22,170 patients reached an accuracy of 95 to 99 percent (F1 scores: 0.38 - 0.86) on detecting these themes, evaluated on data from 361 patients. CONCLUSIONS The NLP pipeline developed in this study extracts outcome measures from the EHR that cater specifically to the needs of clinical staff and align with outcome measures used to detect treatment effects in clinical trials.
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Affiliation(s)
- Rosanne J. Turner
- grid.7692.a0000000090126352University Medical Center Utrecht, Brain Center, Amsterdam, Netherlands ,grid.6054.70000 0004 0369 4183Machine Learning Group, CWI, Amsterdam, Netherlands
| | - Femke Coenen
- grid.7692.a0000000090126352University Medical Center Utrecht, Brain Center, Amsterdam, Netherlands
| | - Femke Roelofs
- grid.7692.a0000000090126352University Medical Center Utrecht, Brain Center, Amsterdam, Netherlands
| | - Karin Hagoort
- grid.7692.a0000000090126352University Medical Center Utrecht, Brain Center, Amsterdam, Netherlands
| | - Aki Härmä
- grid.417284.c0000 0004 0398 9387Philips Research, Eindhoven, Netherlands
| | - Peter D. Grünwald
- grid.6054.70000 0004 0369 4183Machine Learning Group, CWI, Amsterdam, Netherlands ,grid.5132.50000 0001 2312 1970Department of Mathematics, Leiden University, Leiden, Netherlands
| | - Fleur P. Velders
- grid.7692.a0000000090126352University Medical Center Utrecht, Brain Center, Amsterdam, Netherlands
| | - Floortje E. Scheepers
- grid.7692.a0000000090126352University Medical Center Utrecht, Brain Center, Amsterdam, Netherlands
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Bjornestad J, Tjora T, Langeveld JH, Stain HJ, Joa I, Johannessen JO, Friedman-Yakoobian M, Ten Velden Hegelstad W. Exploring specific predictors of psychosis onset over a 2-year period: A decision-tree model. Early Interv Psychiatry 2022; 16:363-370. [PMID: 33991405 DOI: 10.1111/eip.13175] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 04/20/2021] [Accepted: 05/05/2021] [Indexed: 11/29/2022]
Abstract
AIM The fluctuating symptoms of clinical high risk for psychosis hamper conversion prediction models. Exploring specific symptoms using machine-learning has proven fruitful in accommodating this challenge. The aim of this study is to explore specific predictors and generate atheoretical hypotheses of onset using a close-monitoring, machine-learning approach. METHODS Study participants, N = 96, mean age 16.55 years, male to female ratio 46:54%, were recruited from the Prevention of Psychosis Study in Rogaland, Norway. Participants were assessed using the Structured Interview for Psychosis Risk Syndromes (SIPS) at 13 separate assessment time points across 2 years, yielding 247 specific scores. A machine-learning decision-tree analysis (i) examined potential SIPS predictors of psychosis conversion and (ii) hierarchically ranked predictors of psychosis conversion. RESULTS Four out of 247 specific SIPS symptom scores were significant: (i) reduced expression of emotion at baseline, (ii) experience of emotions and self at 5 months, (iii) perceptual abnormalities/hallucinations at 3 months and (iv) ideational richness at 6 months. No SIPS symptom scores obtained after 6 months of follow-up predicted psychosis. CONCLUSIONS Study findings suggest that early negative symptoms, particularly those observable by peers and arguably a risk factor for social exclusion, were predictive of psychosis. Self-expression and social behaviour might prove relevant entry points for early intervention in psychosis and psychosis risk. Testing study results in larger samples and at other sites is warranted.
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Affiliation(s)
- Jone Bjornestad
- Department of Social Studies, Faculty of Social Sciences, University of Stavanger, Stavanger, Norway.,TIPS - Network for Clinical Research in Psychosis, Stavanger University Hospital, Stavanger, Norway.,Department of Psychiatry, District General Hospital of Førde, Førde, Norway
| | - Tore Tjora
- Department of Social Studies, Faculty of Social Sciences, University of Stavanger, Stavanger, Norway
| | - Johannes H Langeveld
- TIPS - Network for Clinical Research in Psychosis, Stavanger University Hospital, Stavanger, Norway.,Faculty of Health, Network for Medical Sciences, University of Stavanger, Stavanger, Norway
| | - Helen J Stain
- TIPS - Network for Clinical Research in Psychosis, Stavanger University Hospital, Stavanger, Norway.,School of Arts and Humanities, Edith Cowan University, Joondalup, Western Australia, Australia
| | - Inge Joa
- TIPS - Network for Clinical Research in Psychosis, Stavanger University Hospital, Stavanger, Norway.,Faculty of Health, Network for Medical Sciences, University of Stavanger, Stavanger, Norway
| | - Jan Olav Johannessen
- TIPS - Network for Clinical Research in Psychosis, Stavanger University Hospital, Stavanger, Norway.,Faculty of Health, Network for Medical Sciences, University of Stavanger, Stavanger, Norway
| | - Michelle Friedman-Yakoobian
- Harvard Medical School, Department of Psychiatry, Boston, Massachusetts, USA.,Massachusetts Mental Health Center Division of Public Psychiatry, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Wenche Ten Velden Hegelstad
- Department of Social Studies, Faculty of Social Sciences, University of Stavanger, Stavanger, Norway.,TIPS - Network for Clinical Research in Psychosis, Stavanger University Hospital, Stavanger, Norway
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30
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Dwyer D, Koutsouleris N. Annual Research Review: Translational machine learning for child and adolescent psychiatry. J Child Psychol Psychiatry 2022; 63:421-443. [PMID: 35040130 DOI: 10.1111/jcpp.13545] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/06/2021] [Indexed: 12/14/2022]
Abstract
Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the limitations of the paradigm and the need to leverage large amounts of digital data. This review will suggest that a machine learning approach could address these challenges and is designed to introduce new readers to the background, methods, and results in the field. A rationale is first introduced followed by an outline of fundamental elements of machine learning approaches. To provide an overview of the use of the techniques in child and adolescent literature, a scoping review of broad trends is then presented. Selected studies are also highlighted in order to draw attention to research areas that are closest to translation and studies that exhibit a high degree of experimental innovation. Limitations to the research, and machine learning approaches generally, are outlined in the penultimate section highlighting issues related to sample sizes, validation, clinical utility, and ethical challenges. Finally, future directions are discussed that could enhance the possibility of clinical implementation and address specific questions relevant to the child and adolescent psychiatry. The review gives a broad overview of the machine learning paradigm in order to highlight the benefits of a shift in perspective towards practically oriented statistical solutions that aim to improve clinical care of children and adolescents.
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Affiliation(s)
- Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia.,Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Max-Planck Institute of Psychiatry, Munich, Germany.,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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31
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Penzel N, Sanfelici R, Antonucci LA, Betz LT, Dwyer D, Ruef A, Cho KIK, Cumming P, Pogarell O, Howes O, Falkai P, Upthegrove R, Borgwardt S, Brambilla P, Lencer R, Meisenzahl E, Schultze-Lutter F, Rosen M, Lichtenstein T, Kambeitz-Ilankovic L, Ruhrmann S, Salokangas RKR, Pantelis C, Wood SJ, Quednow BB, Pergola G, Bertolino A, Koutsouleris N, Kambeitz J, Dwyer D, Ruef A, Kambeitz-Ilankovic L, Sen Dong M, Erkens A, Gussmann E, Haas S, Hasan A, Hoff C, Khanyaree I, Melo A, Muckenhuber-Sternbauer S, Kohler J, Ozturk OF, Popovic D, Rangnick A, von Saldern S, Sanfelici R, Spangemacher M, Tupac A, Urquijo MF, Weiske J, Wosgien A, Kambeitz J, Ruhrmann S, Rosen M, Betz L, Lichtenstein T, Blume K, Seves M, Kaiser N, Penzel N, Pilgram T, Lichtenstein T, Wenzel J, Woopen C, Borgwardt S, Andreou C, Egloff L, Harrisberger F, Lenz C, Leanza L, Mackintosh A, Smieskova R, Studerus E, Walter A, Widmayer S, Upthegrove R, Wood SJ, Chisholm K, Day C, Griffiths SL, Lalousis PA, Iqbal M, Pelton M, Mallikarjun P, Stainton A, Lin A, Salokangas RKR, Denissoff A, Ellila A, From T, Heinimaa M, Ilonen T, Jalo P, Laurikainen H, Lehtinen M, Luutonen A, Makela A, Paju J, Pesonen H, Armio Säilä RL, Sormunen E, Toivonen A, Turtonen O, Solana AB, Abraham M, Hehn N, Schirmer T, Brambilla P, Altamura C, Belleri M, Bottinelli F, Ferro A, Re M, Monzani E, Percudani M, Sberna M, D’Agostino A, Del Fabro L, Perna G, Nobile M, Alciati A, Balestrieri M, Bonivento C, Cabras G, Fabbro F, Garzitto M, PiCCuin S, Bertolino A, Blasi G, Antonucci LA, Pergola G, Caforio G, Faio L, Quarto T, Gelao B, Romano R, Andriola I, Falsetti A, Barone M, Passatiore R, Sangiuliano M, Lencer R, Surman M, Bienek O, Romer G, Dannlowski U, Meisenzahl E, Schultze-Lutter F, Schmidt-Kraepelin C, Neufang S, Korda A, Rohner H. Pattern of predictive features of continued cannabis use in patients with recent-onset psychosis and clinical high-risk for psychosis. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2022; 8:19. [PMID: 35264631 PMCID: PMC8907166 DOI: 10.1038/s41537-022-00218-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 01/31/2022] [Indexed: 11/09/2022]
Abstract
Continued cannabis use (CCu) is an important predictor for poor long-term outcomes in psychosis and clinically high-risk patients, but no generalizable model has hitherto been tested for its ability to predict CCu in these vulnerable patient groups. In the current study, we investigated how structured clinical and cognitive assessments and structural magnetic resonance imaging (sMRI) contributed to the prediction of CCu in a group of 109 patients with recent-onset psychosis (ROP). We tested the generalizability of our predictors in 73 patients at clinical high-risk for psychosis (CHR). Here, CCu was defined as any cannabis consumption between baseline and 9-month follow-up, as assessed in structured interviews. All patients reported lifetime cannabis use at baseline. Data from clinical assessment alone correctly classified 73% (p < 0.001) of ROP and 59 % of CHR patients. The classifications of CCu based on sMRI and cognition were non-significant (ps > 0.093), and their addition to the interview-based predictor via stacking did not improve prediction significantly, either in the ROP or CHR groups (ps > 0.065). Lower functioning, specific substance use patterns, urbanicity and a lack of other coping strategies contributed reliably to the prediction of CCu and might thus represent important factors for guiding preventative efforts. Our results suggest that it may be possible to identify by clinical measures those psychosis-spectrum patients at high risk for CCu, potentially allowing to improve clinical care through targeted interventions. However, our model needs further testing in larger samples including more diverse clinical populations before being transferred into clinical practice.
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Affiliation(s)
- Nora Penzel
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Psychiatry and Psychotherapy, Cologne, Germany.,Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Group of Psychiatric Neuroscience, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari 'Aldo Moro', Bari, Italy
| | - Rachele Sanfelici
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Max-Planck Institute of Psychiatry, Munich, Germany
| | - Linda A Antonucci
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Department of Education, Psychology, Communication, University of Bari, Bari, Italy
| | - Linda T Betz
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Psychiatry and Psychotherapy, Cologne, Germany
| | - Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Anne Ruef
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Kang Ik K Cho
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Paul Cumming
- Department of Nuclear Medicine, Bern University Hospital, Bern, Switzerland.,School of Psychology and Counselling, Queensland University of Technology, Brisbane, QLD, Australia.,International Research Lab in Neuropsychiatry, Neuroscience Research Institute, Samara State Medical University, Samara, Russia
| | - Oliver Pogarell
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Oliver Howes
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, SE5 8AF, UK.,MRC London Institute of Medical Sciences, Hammersmith Hospital, London, W12 0NN, UK.,Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, W12 0NN, UK.,South London and Maudsley NHS Foundation Trust, London, SE5 8AF, UK
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Max-Planck Institute of Psychiatry, Munich, Germany
| | - Rachel Upthegrove
- Institute for Mental Health, University of Birmingham, Birmingham, UK.,Early Intervention Service, Birmingham Womens and Childrens NHS Foundation Trust, Birmingham, UK
| | - Stefan Borgwardt
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland.,Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCUS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Rebekka Lencer
- Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany.,Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany.,Otto Creutzfeldt Center for Behavioral and Cognitive Neuroscience, University of Münster, Münster, Germany
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - Frauke Schultze-Lutter
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany.,Department of Psychology, Faculty of Psychology, Airlangga University, Surabaya, Indonesia.,University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Marlene Rosen
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Psychiatry and Psychotherapy, Cologne, Germany
| | - Theresa Lichtenstein
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Psychiatry and Psychotherapy, Cologne, Germany
| | - Lana Kambeitz-Ilankovic
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Psychiatry and Psychotherapy, Cologne, Germany.,Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Stephan Ruhrmann
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Psychiatry and Psychotherapy, Cologne, Germany
| | | | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, University of Melbourne & Melbourne Health, Melbourne, VIC, Australia
| | - Stephen J Wood
- Institute for Mental Health, University of Birmingham, Birmingham, UK.,Orygen, Melbourne, VIC, Australia.,Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia
| | - Boris B Quednow
- Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric Hospital of the University of Zurich, Lenggstr. 31, 8032, Zurich, Switzerland
| | - Giulio Pergola
- Group of Psychiatric Neuroscience, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari 'Aldo Moro', Bari, Italy
| | - Alessandro Bertolino
- Group of Psychiatric Neuroscience, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari 'Aldo Moro', Bari, Italy
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Max-Planck Institute of Psychiatry, Munich, Germany.,Institute of Psychiatry, Psychology & Neuroscience, Department of Psychosis Studies, King's College London, London, UK
| | - Joseph Kambeitz
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Psychiatry and Psychotherapy, Cologne, Germany.
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Antonucci LA, Penzel N, Sanfelici R, Pigoni A, Kambeitz-Ilankovic L, Dwyer D, Ruef A, Sen Dong M, Öztürk ÖF, Chisholm K, Haidl T, Rosen M, Ferro A, Pergola G, Andriola I, Blasi G, Ruhrmann S, Schultze-Lutter F, Falkai P, Kambeitz J, Lencer R, Dannlowski U, Upthegrove R, Salokangas RKR, Pantelis C, Meisenzahl E, Wood SJ, Brambilla P, Borgwardt S, Bertolino A, Koutsouleris N. Using combined environmental-clinical classification models to predict role functioning outcome in clinical high-risk states for psychosis and recent-onset depression. Br J Psychiatry 2022; 220:1-17. [PMID: 35152923 DOI: 10.1192/bjp.2022.16] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
BACKGROUND Clinical high-risk states for psychosis (CHR) are associated with functional impairments and depressive disorders. A previous PRONIA study predicted social functioning in CHR and recent-onset depression (ROD) based on structural magnetic resonance imaging (sMRI) and clinical data. However, the combination of these domains did not lead to accurate role functioning prediction, calling for the investigation of additional risk dimensions. Role functioning may be more strongly associated with environmental adverse events than social functioning. AIMS We aimed to predict role functioning in CHR, ROD and transdiagnostically, by adding environmental adverse events-related variables to clinical and sMRI data domains within the PRONIA sample. METHOD Baseline clinical, environmental and sMRI data collected in 92 CHR and 95 ROD samples were trained to predict lower versus higher follow-up role functioning, using support vector classification and mixed k-fold/leave-site-out cross-validation. We built separate predictions for each domain, created multimodal predictions and validated them in independent cohorts (74 CHR, 66 ROD). RESULTS Models combining clinical and environmental data predicted role outcome in discovery and replication samples of CHR (balanced accuracies: 65.4% and 67.7%, respectively), ROD (balanced accuracies: 58.9% and 62.5%, respectively), and transdiagnostically (balanced accuracies: 62.4% and 68.2%, respectively). The most reliable environmental features for role outcome prediction were adult environmental adjustment, childhood trauma in CHR and childhood environmental adjustment in ROD. CONCLUSIONS Findings support the hypothesis that environmental variables inform role outcome prediction, highlight the existence of both transdiagnostic and syndrome-specific predictive environmental adverse events, and emphasise the importance of implementing real-world models by measuring multiple risk dimensions.
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Affiliation(s)
- Linda A Antonucci
- Department of Education Science, Psychology and Communication Science, University of Bari Aldo Moro, Italy; and Department of Psychiatry and Psychotherapy, Ludwig Maximilians University Munich, Germany
| | - Nora Penzel
- Department of Psychiatry and Psychotherapy, Ludwig Maximilians University Munich, Germany; and Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
| | - Rachele Sanfelici
- Department of Psychiatry and Psychotherapy, Ludwig Maximilians University Munich, Germany; and Institute for Psychiatry, Max Planck School of Cognition, Germany
| | - Alessandro Pigoni
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Italy; and Social and Affective Neuroscience Group, MoMiLab, IMT School for Advanced Studies Lucca, Italy
| | - Lana Kambeitz-Ilankovic
- Department of Education Science, Psychology and Communication Science, University of Bari Aldo Moro, Italy; and Department of Psychiatry and Psychotherapy, Ludwig Maximilians University Munich, Germany
| | - Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig Maximilians University Munich, Germany
| | - Anne Ruef
- Department of Psychiatry and Psychotherapy, Ludwig Maximilians University Munich, Germany
| | - Mark Sen Dong
- Department of Psychiatry and Psychotherapy, Ludwig Maximilians University Munich, Germany
| | - Ömer Faruk Öztürk
- Department of Psychiatry and Psychotherapy, Ludwig Maximilians University Munich, Germany; and Institute for Psychiatry, International Max Planck Research School for Translational Psychiatry, Germany
| | - Katharine Chisholm
- Institute for Mental Health, University of Birmingham, UK; and Department of Psychology, Aston University, UK
| | - Theresa Haidl
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
| | - Marlene Rosen
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
| | - Adele Ferro
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Italy
| | - Giulio Pergola
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Italy
| | - Ileana Andriola
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Italy
| | - Giuseppe Blasi
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Italy
| | - Stephan Ruhrmann
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
| | - Frauke Schultze-Lutter
- Department of Psychiatry and Psychotherapy, Heinrich-Heine University Düsseldorf, Germany; Department of Psychology and Mental Health, Faculty of Psychology, Airlangga University, Indonesia; and University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Ludwig Maximilians University Munich, Germany
| | - Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Münster, UK; and Department of Psychiatry and Psychotherapy, University of Lübeck, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, UK
| | - Rachel Upthegrove
- Institute for Mental Health, University of Birmingham, UK; and Early Intervention Service, Birmingham Women's and Children's NHS Foundation Trust, UK
| | | | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Australia
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Heinrich-Heine University Düsseldorf, Germany
| | - Stephen J Wood
- Department of Psychiatry and Psychotherapy, Ludwig Maximilians University Munich, Germany; Orygen, Australia; Centre for Youth Mental Health, University of Melbourne, Australia; and School of Psychology, University of Birmingham, UK
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Italy; and Department of Pathophysiology and Transplantation, University of Milan, Italy
| | - Stefan Borgwardt
- Institute for Translational Psychiatry, University of Münster, UK; and Department of Psychiatry (Psychiatric University Hospital, University Psychiatric Clinics Basel), University of Basel, Switzerland
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Italy
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig Maximilians University Munich, Germany
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Antonucci LA, Fazio L, Pergola G, Blasi G, Stolfa G, Di Palo P, Mucci A, Rocca P, Brasso C, di Giannantonio M, Maria Giordano G, Monteleone P, Pompili M, Siracusano A, Bertolino A, Galderisi S, Maj M. Joint structural-functional magnetic resonance imaging features are associated with diagnosis and real-world functioning in patients with schizophrenia. Schizophr Res 2022; 240:193-203. [PMID: 35032904 DOI: 10.1016/j.schres.2021.12.029] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 10/20/2021] [Accepted: 12/22/2021] [Indexed: 12/28/2022]
Abstract
OBJECTIVE Earlier evidence suggested that structural-functional covariation in schizophrenia patients (SCZ) is associated with cognition, a predictor of functioning. Moreover, studies suggested that functional brain abnormalities of schizophrenia may be related with structural network features. However, only few studies have investigated the relationship between structural-functional covariation and both diagnosis and functioning in SCZ. We hypothesized that structural-functional covariation networks associated with diagnosis are related to real-world functioning in SCZ. METHODS We performed joint Independent Component Analysis on T1 images and resting-state fMRI-based Degree Centrality (DC) maps from 89 SCZ and 285 controls. Structural-functional covariation networks in which we found a main effect of diagnosis underwent correlation analysis to investigate their relationship with functioning. Covariation networks showing a significant association with both diagnosis and functioning underwent univariate analysis to better characterize group-level differences at the spatial level. RESULTS A structural-functional covariation network characterized by frontal, temporal, parietal and thalamic structural estimates significantly covaried with temporo-parietal resting-state DC. Compared with controls, SCZ had reduced structural-functional covariation within this network (pFDR = 0.005). The same measure correlated positively with both social and occupational functioning (both pFDR = 0.042). Univariate analyses revealed grey matter deviations in SCZ compared with controls within this structural-functional network in hippocampus, cerebellum, thalamus, orbito-frontal cortex, and insula. No group differences were found in DC. CONCLUSIONS Findings support the existence of a phenotypical association between group-level differences and inter-individual heterogeneity of functional deficits in SCZ. Given that only the joint structural/functional analysis revealed this association, structural-functional covariation may be a potentially relevant schizophrenia phenotype.
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Affiliation(s)
- Linda A Antonucci
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Leonardo Fazio
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Giulio Pergola
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Giuseppe Blasi
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Giuseppe Stolfa
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Piergiuseppe Di Palo
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Armida Mucci
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Paola Rocca
- Department of Neuroscience, Section of Psychiatry, University of Turin, Turin, Italy
| | - Claudio Brasso
- Department of Neuroscience, Section of Psychiatry, University of Turin, Turin, Italy
| | | | | | - Palmiero Monteleone
- Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", Section of Neuroscience, University of Salerno, Salerno, Italy
| | - Maurizio Pompili
- Department of Neurosciences, Mental Health, and Sensory Organs, S. Andrea Hospital, Sapienza University of Rome, Rome, Italy
| | - Alberto Siracusano
- Department of Systems Medicine, Psychiatry and Clinical Psychology Unit, Tor Vergata University of Rome, Rome, Italy
| | - Alessandro Bertolino
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy.
| | - Silvana Galderisi
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Mario Maj
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
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Association between formal thought disorders, neurocognition and functioning in the early stages of psychosis: a systematic review of the last half-century studies. Eur Arch Psychiatry Clin Neurosci 2022; 272:381-393. [PMID: 34263359 PMCID: PMC8938342 DOI: 10.1007/s00406-021-01295-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Accepted: 07/04/2021] [Indexed: 12/18/2022]
Abstract
Recent review articles provided an extensive collection of studies covering many aspects of format thought disorders (FTD) among their epidemiology and phenomenology, their neurobiological underpinnings, genetics as well as their transdiagnostic prevalence. However, less attention has been paid to the association of FTD with neurocognitive and functioning deficits in the early stages of evolving psychosis. Therefore, this systematic review aims to investigate the state of the art regarding the association between FTD, neurocognition and functioning in the early stages of evolving psychotic disorders in adolescents and young adults, by following the PRISMA flowchart. A total of 106 studies were screened. We included 8 studies due to their reports of associations between FTD measures and functioning outcomes measured with different scales and 7 studies due to their reports of associations between FTD measures and neurocognition. In summary, the main findings of the included studies for functioning outcomes showed that FTD severity predicted poor social functioning, unemployment, relapses, re-hospitalisations, whereas the main findings of the included studies for neurocognition showed correlations between attentional deficits, executive functions and FTD, and highlighted the predictive potential of executive dysfunctions for sustained FTD. Further studies in upcoming years taking advantage of the acceleration in computational psychiatry would allow researchers to re-investigate the clinical importance of FTD and their role in the transition from at-risk to full-blown psychosis conditions. Employing automated computer-assisted diagnostic tools in the early stages of psychosis might open new avenues to develop targeted neuropsychotherapeutics specific to FTD.
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Koutsouleris N, Worthington M, Dwyer DB, Kambeitz-Ilankovic L, Sanfelici R, Fusar-Poli P, Rosen M, Ruhrmann S, Anticevic A, Addington J, Perkins DO, Bearden CE, Cornblatt BA, Cadenhead KS, Mathalon DH, McGlashan T, Seidman L, Tsuang M, Walker EF, Woods SW, Falkai P, Lencer R, Bertolino A, Kambeitz J, Schultze-Lutter F, Meisenzahl E, Salokangas RKR, Hietala J, Brambilla P, Upthegrove R, Borgwardt S, Wood S, Gur RE, McGuire P, Cannon TD. Toward Generalizable and Transdiagnostic Tools for Psychosis Prediction: An Independent Validation and Improvement of the NAPLS-2 Risk Calculator in the Multisite PRONIA Cohort. Biol Psychiatry 2021; 90:632-642. [PMID: 34482951 PMCID: PMC8500930 DOI: 10.1016/j.biopsych.2021.06.023] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 06/03/2021] [Accepted: 06/27/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND Transition to psychosis is among the most adverse outcomes of clinical high-risk (CHR) syndromes encompassing ultra-high risk (UHR) and basic symptom states. Clinical risk calculators may facilitate an early and individualized interception of psychosis, but their real-world implementation requires thorough validation across diverse risk populations, including young patients with depressive syndromes. METHODS We validated the previously described NAPLS-2 (North American Prodrome Longitudinal Study 2) calculator in 334 patients (26 with transition to psychosis) with CHR or recent-onset depression (ROD) drawn from the multisite European PRONIA (Personalised Prognostic Tools for Early Psychosis Management) study. Patients were categorized into three risk enrichment levels, ranging from UHR, over CHR, to a broad-risk population comprising patients with CHR or ROD (CHR|ROD). We assessed how risk enrichment and different predictive algorithms influenced prognostic performance using reciprocal external validation. RESULTS After calibration, the NAPLS-2 model predicted psychosis with a balanced accuracy (BAC) (sensitivity, specificity) of 68% (73%, 63%) in the PRONIA-UHR cohort, 67% (74%, 60%) in the CHR cohort, and 70% (73%, 66%) in patients with CHR|ROD. Multiple model derivation in PRONIA-CHR|ROD and validation in NAPLS-2-UHR patients confirmed that broader risk definitions produced more accurate risk calculators (CHR|ROD-based vs. UHR-based performance: 67% [68%, 66%] vs. 58% [61%, 56%]). Support vector machines were superior in CHR|ROD (BAC = 71%), while ridge logistic regression and support vector machines performed similarly in CHR (BAC = 67%) and UHR cohorts (BAC = 65%). Attenuated psychotic symptoms predicted psychosis across risk levels, while younger age and reduced processing speed became increasingly relevant for broader risk cohorts. CONCLUSIONS Clinical-neurocognitive machine learning models operating in young patients with affective and CHR syndromes facilitate a more precise and generalizable prediction of psychosis. Future studies should investigate their therapeutic utility in large-scale clinical trials.
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Affiliation(s)
- Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany; Max-Planck Institute of Psychiatry, Munich, Germany; Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom.
| | | | - Dominic B Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Lana Kambeitz-Ilankovic
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany; Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Rachele Sanfelici
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Paolo Fusar-Poli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy; Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom
| | - Marlene Rosen
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Stephan Ruhrmann
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Alan Anticevic
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Jean Addington
- Hotchkiss Brain Institute, Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
| | - Diana O Perkins
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina
| | - Carrie E Bearden
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California
| | | | | | - Daniel H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, California; San Francisco VA Medical Center, San Francisco, California
| | - Thomas McGlashan
- Department of Psychiatry, Yale University, New Haven, Connecticut
| | - Larry Seidman
- Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Ming Tsuang
- University of California San Diego, San Diego, California
| | - Elaine F Walker
- Department of Psychology and Psychiatry, Emory University, Atlanta, Georgia
| | - Scott W Woods
- Department of Psychiatry, Yale University, New Haven, Connecticut
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Rebekka Lencer
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany; Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Frauke Schultze-Lutter
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine-Universität Düsseldorf, Germany
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine-Universität Düsseldorf, Germany
| | | | - Jarmo Hietala
- Department of Psychiatry, University of Turku, Turku, Finland
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Rachel Upthegrove
- Institute of Mental Health, University of Birmingham, Birmingham, United Kingdom; School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Stefan Borgwardt
- Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany; Department of Psychiatry (Psychiatric University Hospital, UPK), University of Basel, Basel, Switzerland
| | - Stephen Wood
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia; Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Victoria, Australia
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Philip McGuire
- Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom
| | - Tyrone D Cannon
- Department of Psychology, Yale University, New Haven, Connecticut; Department of Psychiatry, Yale University, New Haven, Connecticut
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Raballo A, Poletti M, Preti A. Individualized Diagnostic and Prognostic Models for Psychosis Risk Syndromes: Do Not Underestimate Antipsychotic Exposure. Biol Psychiatry 2021; 90:e33-e35. [PMID: 34001370 DOI: 10.1016/j.biopsych.2021.01.020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 03/12/2021] [Indexed: 10/21/2022]
Affiliation(s)
- Andrea Raballo
- Section of Psychiatry, Clinical Psychology and Rehabilitation, Department of Medicine, University of Perugia, Italy; Center for Translational, Phenomenological and Developmental Psychopathology, Perugia University Hospital, Perugia, Italy.
| | - Michele Poletti
- Child and Adolescent Neuropsychiatry Service, Department of Mental Health and Pathological Addiction, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Antonio Preti
- Department of Neuroscience, University of Turin, Turin, Italy
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Sanfelici R, Antonucci LA, Dwyer DB, Koutsouleris N. Reply to: Individualized Diagnostic and Prognostic Models for Psychosis Risk Syndromes: Do Not Underestimate Antipsychotic Exposure. Biol Psychiatry 2021; 90:e37-e38. [PMID: 34001369 DOI: 10.1016/j.biopsych.2021.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 03/10/2021] [Indexed: 11/17/2022]
Affiliation(s)
- Rachele Sanfelici
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, Munich, Germany
| | - Linda A Antonucci
- Department of Education, Psychology and Communication, University of Bari Aldo Moro, Bari, Italy
| | - Dominic B Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, Munich, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, Munich, Germany; Max Planck Institute of Psychiatry, Munich, Germany; Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom.
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Salazar de Pablo G, Radua J, Pereira J, Bonoldi I, Arienti V, Besana F, Soardo L, Cabras A, Fortea L, Catalan A, Vaquerizo-Serrano J, Coronelli F, Kaur S, Da Silva J, Shin JI, Solmi M, Brondino N, Politi P, McGuire P, Fusar-Poli P. Probability of Transition to Psychosis in Individuals at Clinical High Risk: An Updated Meta-analysis. JAMA Psychiatry 2021; 78:970-978. [PMID: 34259821 PMCID: PMC8281006 DOI: 10.1001/jamapsychiatry.2021.0830] [Citation(s) in RCA: 137] [Impact Index Per Article: 45.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
IMPORTANCE Estimating the current likelihood of transitioning from a clinical high risk for psychosis (CHR-P) to psychosis holds paramount importance for preventive care and applied research. OBJECTIVE To quantitatively examine the consistency and magnitude of transition risk to psychosis in individuals at CHR-P. DATA SOURCES PubMed and Web of Science databases until November 1, 2020. Manual search of references from previous articles. STUDY SELECTION Longitudinal studies reporting transition risks in individuals at CHR-P. DATA EXTRACTION AND SYNTHESIS Meta-analysis compliant with Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and Meta-analysis of Observational Studies in Epidemiology (MOOSE) reporting guidelines; independent data extraction, manually and through digitalization of Kaplan-Meier curves. MAIN OUTCOME AND MEASURES Primary effect size was cumulative risk of transition to psychosis at 0.5, 1, 1.5, 2, 2.5, 3, 4, and more than 4 years' follow-up, estimated using the numbers of individuals at CHR-P transitioning to psychosis at each time point. These analyses were complemented by meta-analytical Kaplan-Meier curves and speed of transition to psychosis (hazard rate). Random-effects meta-analysis, between-study heterogeneity analysis, study quality assessment, and meta-regressions were conducted. RESULTS A total of 130 studies and 9222 individuals at CHR-P were included. The mean (SD) age was 20.3 (4.4) years, and 5100 individuals (55.3%) were male. The cumulative transition risk was 0.09 (95% CI, 0.07-0.10; k = 37; n = 6485) at 0.5 years, 0.15 (95% CI, 0.13-0.16; k = 53; n = 7907) at 1 year, 0.20 (95% CI, 0.17-0.22; k = 30; n = 5488) at 1.5 years, 0.19 (95% CI, 0.17-0.22; k = 44; n = 7351) at 2 years, 0.25 (95% CI, 0.21-0.29; k = 19; n = 3114) at 2.5 years, 0.25 (95% CI, 0.22-0.29; k = 29; n = 4029) at 3 years, 0.27 (95% CI, 0.23-0.30; k = 16; n = 2926) at 4 years, and 0.28 (95% CI, 0.20-0.37; k = 14; n = 2301) at more than 4 years. The cumulative Kaplan-Meier transition risk was 0.08 (95% CI, 0.08-0.09; n = 4860) at 0.5 years, 0.14 (95% CI, 0.13-0.15; n = 3408) at 1 year, 0.17 (95% CI, 0.16-0.19; n = 2892) at 1.5 years, 0.20 (95% CI, 0.19-0.21; n = 2357) at 2 years, 0.25 (95% CI, 0.23-0.26; n = 1444) at 2.5 years, 0.27 (95% CI, 0.25-0.28; n = 1029) at 3 years, 0.28 (95% CI, 0.26-0.29; n = 808) at 3.5 years, 0.29 (95% CI, 0.27-0.30; n = 737) at 4 years, and 0.35 (95% CI, 0.32-0.38; n = 114) at 10 years. The hazard rate only plateaued at 4 years' follow-up. Meta-regressions showed that a lower proportion of female individuals (β = -0.02; 95% CI, -0.04 to -0.01) and a higher proportion of brief limited intermittent psychotic symptoms (β = 0.02; 95% CI, 0.01-0.03) were associated with an increase in transition risk. Heterogeneity across the studies was high (I2 range, 77.91% to 95.73%). CONCLUSIONS AND RELEVANCE In this meta-analysis, 25% of individuals at CHR-P developed psychosis within 3 years. Transition risk continued increasing in the long term. Extended clinical monitoring and preventive care may be beneficial in this patient population.
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Affiliation(s)
- Gonzalo Salazar de Pablo
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Institute of Psychiatry, Psychology & Neuroscience, Department of Psychosis Studies, King’s College London, London, United Kingdom,Institute of Psychiatry and Mental Health, Department of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañón School of Medicine, Universidad Complutense, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain,Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Joaquim Radua
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Institute of Psychiatry, Psychology & Neuroscience, Department of Psychosis Studies, King’s College London, London, United Kingdom,Imaging of Mood- and Anxiety-Related Disorders (IMARD) Group, Institut d’Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Spain,Department of Clinical Neuroscience, Centre for Psychiatric Research and Education, Karolinska Institutet, Stockholm, Sweden
| | - Joana Pereira
- Centro Hospitalar Psiquiátrico de Lisboa, Lisbon, Portugal
| | - Ilaria Bonoldi
- Institute of Psychiatry, Psychology & Neuroscience, Department of Psychosis Studies, King’s College London, London, United Kingdom
| | - Vincenzo Arienti
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Filippo Besana
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Livia Soardo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Anna Cabras
- Department of Neurology and Psychiatry, University of Rome La Sapienza, Rome, Italy
| | - Lydia Fortea
- Imaging of Mood- and Anxiety-Related Disorders (IMARD) Group, Institut d’Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Spain,Institute of Neurosciences, University of Barcelona, Barcelona, Spain
| | - Ana Catalan
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Institute of Psychiatry, Psychology & Neuroscience, Department of Psychosis Studies, King’s College London, London, United Kingdom,Mental Health Department, Biocruces Bizkaia Health Research Institute, Basurto University Hospital, Facultad de Medicina y Odontología, Campus de Leioa, University of the Basque Country, UPV/EHU, Bizkaia, Spain
| | - Julio Vaquerizo-Serrano
- Institute of Psychiatry, Psychology & Neuroscience, Department of Psychosis Studies, King’s College London, London, United Kingdom,Institute of Psychiatry and Mental Health, Department of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañón School of Medicine, Universidad Complutense, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain,Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Francesco Coronelli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Simi Kaur
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Institute of Psychiatry, Psychology & Neuroscience, Department of Psychosis Studies, King’s College London, London, United Kingdom
| | - Josette Da Silva
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Institute of Psychiatry, Psychology & Neuroscience, Department of Psychosis Studies, King’s College London, London, United Kingdom
| | - Jae Il Shin
- Department of Paediatrics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Marco Solmi
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Institute of Psychiatry, Psychology & Neuroscience, Department of Psychosis Studies, King’s College London, London, United Kingdom,Neurosciences Department, University of Padova, Padova, Italy
| | - Natascia Brondino
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Pierluigi Politi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Philip McGuire
- Institute of Psychiatry, Psychology & Neuroscience, Department of Psychosis Studies, King’s College London, London, United Kingdom,OASIS service, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Institute of Psychiatry, Psychology & Neuroscience, Department of Psychosis Studies, King’s College London, London, United Kingdom,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy,OASIS service, South London and Maudsley NHS Foundation Trust, London, United Kingdom
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Roth CB, Papassotiropoulos A, Brühl AB, Lang UE, Huber CG. Psychiatry in the Digital Age: A Blessing or a Curse? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:8302. [PMID: 34444055 PMCID: PMC8391902 DOI: 10.3390/ijerph18168302] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/31/2021] [Accepted: 08/03/2021] [Indexed: 12/23/2022]
Abstract
Social distancing and the shortage of healthcare professionals during the COVID-19 pandemic, the impact of population aging on the healthcare system, as well as the rapid pace of digital innovation are catalyzing the development and implementation of new technologies and digital services in psychiatry. Is this transformation a blessing or a curse for psychiatry? To answer this question, we conducted a literature review covering a broad range of new technologies and eHealth services, including telepsychiatry; computer-, internet-, and app-based cognitive behavioral therapy; virtual reality; digital applied games; a digital medicine system; omics; neuroimaging; machine learning; precision psychiatry; clinical decision support; electronic health records; physician charting; digital language translators; and online mental health resources for patients. We found that eHealth services provide effective, scalable, and cost-efficient options for the treatment of people with limited or no access to mental health care. This review highlights innovative technologies spearheading the way to more effective and safer treatments. We identified artificially intelligent tools that relieve physicians from routine tasks, allowing them to focus on collaborative doctor-patient relationships. The transformation of traditional clinics into digital ones is outlined, and the challenges associated with the successful deployment of digitalization in psychiatry are highlighted.
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Affiliation(s)
- Carl B. Roth
- University Psychiatric Clinics Basel, Clinic for Adults, University of Basel, Wilhelm Klein-Strasse 27, CH-4002 Basel, Switzerland; (A.P.); (A.B.B.); (U.E.L.); (C.G.H.)
| | - Andreas Papassotiropoulos
- University Psychiatric Clinics Basel, Clinic for Adults, University of Basel, Wilhelm Klein-Strasse 27, CH-4002 Basel, Switzerland; (A.P.); (A.B.B.); (U.E.L.); (C.G.H.)
- Transfaculty Research Platform Molecular and Cognitive Neurosciences, University of Basel, Birmannsgasse 8, CH-4055 Basel, Switzerland
- Division of Molecular Neuroscience, Department of Psychology, University of Basel, Birmannsgasse 8, CH-4055 Basel, Switzerland
- Biozentrum, Life Sciences Training Facility, University of Basel, Klingelbergstrasse 50/70, CH-4056 Basel, Switzerland
| | - Annette B. Brühl
- University Psychiatric Clinics Basel, Clinic for Adults, University of Basel, Wilhelm Klein-Strasse 27, CH-4002 Basel, Switzerland; (A.P.); (A.B.B.); (U.E.L.); (C.G.H.)
| | - Undine E. Lang
- University Psychiatric Clinics Basel, Clinic for Adults, University of Basel, Wilhelm Klein-Strasse 27, CH-4002 Basel, Switzerland; (A.P.); (A.B.B.); (U.E.L.); (C.G.H.)
| | - Christian G. Huber
- University Psychiatric Clinics Basel, Clinic for Adults, University of Basel, Wilhelm Klein-Strasse 27, CH-4002 Basel, Switzerland; (A.P.); (A.B.B.); (U.E.L.); (C.G.H.)
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Fusar‐Poli P, Correll CU, Arango C, Berk M, Patel V, Ioannidis JP. Preventive psychiatry: a blueprint for improving the mental health of young people. World Psychiatry 2021; 20:200-221. [PMID: 34002494 PMCID: PMC8129854 DOI: 10.1002/wps.20869] [Citation(s) in RCA: 170] [Impact Index Per Article: 56.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Preventive approaches have latterly gained traction for improving mental health in young people. In this paper, we first appraise the conceptual foundations of preventive psychiatry, encompassing the public health, Gordon's, US Institute of Medicine, World Health Organization, and good mental health frameworks, and neurodevelopmentally-sensitive clinical staging models. We then review the evidence supporting primary prevention of psychotic, bipolar and common mental disorders and promotion of good mental health as potential transformative strategies to reduce the incidence of these disorders in young people. Within indicated approaches, the clinical high-risk for psychosis paradigm has received the most empirical validation, while clinical high-risk states for bipolar and common mental disorders are increasingly becoming a focus of attention. Selective approaches have mostly targeted familial vulnerability and non-genetic risk exposures. Selective screening and psychological/psychoeducational interventions in vulnerable subgroups may improve anxiety/depressive symptoms, but their efficacy in reducing the incidence of psychotic/bipolar/common mental disorders is unproven. Selective physical exercise may reduce the incidence of anxiety disorders. Universal psychological/psychoeducational interventions may improve anxiety symptoms but not prevent depressive/anxiety disorders, while universal physical exercise may reduce the incidence of anxiety disorders. Universal public health approaches targeting school climate or social determinants (demographic, economic, neighbourhood, environmental, social/cultural) of mental disorders hold the greatest potential for reducing the risk profile of the population as a whole. The approach to promotion of good mental health is currently fragmented. We leverage the knowledge gained from the review to develop a blueprint for future research and practice of preventive psychiatry in young people: integrating universal and targeted frameworks; advancing multivariable, transdiagnostic, multi-endpoint epidemiological knowledge; synergically preventing common and infrequent mental disorders; preventing physical and mental health burden together; implementing stratified/personalized prognosis; establishing evidence-based preventive interventions; developing an ethical framework, improving prevention through education/training; consolidating the cost-effectiveness of preventive psychiatry; and decreasing inequalities. These goals can only be achieved through an urgent individual, societal, and global level response, which promotes a vigorous collaboration across scientific, health care, societal and governmental sectors for implementing preventive psychiatry, as much is at stake for young people with or at risk for emerging mental disorders.
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Affiliation(s)
- Paolo Fusar‐Poli
- Early Psychosis: Interventions and Clinical‐detection (EPIC) Lab, Department of Psychosis StudiesInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonUK,OASIS Service, South London and Maudsley NHS Foundation TrustLondonUK,Department of Brain and Behavioral SciencesUniversity of PaviaPaviaItaly
| | - Christoph U. Correll
- Department of PsychiatryZucker Hillside Hospital, Northwell HealthGlen OaksNYUSA,Department of Psychiatry and Molecular MedicineZucker School of Medicine at Hofstra/NorthwellHempsteadNYUSA,Center for Psychiatric NeuroscienceFeinstein Institute for Medical ResearchManhassetNYUSA,Department of Child and Adolescent PsychiatryCharité Universitätsmedizin BerlinBerlinGermany
| | - Celso Arango
- Department of Child and Adolescent PsychiatryInstitute of Psychiatry and Mental Health, Hospital General Universitario Gregorio MarañónMadridSpain,Health Research Institute (IiGSM), School of MedicineUniversidad Complutense de MadridMadridSpain,Biomedical Research Center for Mental Health (CIBERSAM)MadridSpain
| | - Michael Berk
- Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin UniversityBarwon HealthGeelongVICAustralia,Department of PsychiatryUniversity of MelbourneMelbourneVICAustralia,Orygen Youth HealthUniversity of MelbourneMelbourneVICAustralia,Florey Institute for Neuroscience and Mental HealthUniversity of MelbourneMelbourneVICAustralia
| | - Vikram Patel
- Department of Global Health and Social MedicineHarvard University T.H. Chan School of Public HealthBostonMAUSA,Department of Global Health and PopulationHarvard T.H. Chan School of Public HealthBostonMAUSA
| | - John P.A. Ioannidis
- Stanford Prevention Research Center, Department of MedicineStanford UniversityStanfordCAUSA,Department of Biomedical Data ScienceStanford UniversityStanfordCAUSA,Department of Epidemiology and Population HealthStanford UniversityStanfordCAUSA
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Raballo A, Poletti M, Preti A. Negative Prognostic Effect of Baseline Antipsychotic Exposure in Clinical High Risk for Psychosis (CHR-P): Is Pre-Test Risk Enrichment the Hidden Culprit? Int J Neuropsychopharmacol 2021; 24:710-720. [PMID: 34036323 PMCID: PMC8453273 DOI: 10.1093/ijnp/pyab030] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 04/19/2021] [Accepted: 05/21/2021] [Indexed: 12/22/2022] Open
Abstract
INTRODUCTION Sample enrichment is a key factor in contemporary early-detection strategies aimed at the identification of help-seekers at increased risk of imminent transition to psychosis. We undertook a meta-analytic investigation to ascertain the role of sample enrichment in the recently highlighted negative prognostic effect of baseline antipsychotic (AP) exposure in clinical high-risk (CHR-P) of psychosis individuals. METHODS Systematic review and meta-analysis of all published studies on CHR-P were identified according to a validated diagnostic procedure. The outcome was the proportion of transition to psychosis, which was calculated according to the Freeman-Tukey double arcsine transformation. RESULTS Thirty-three eligible studies were identified, including 16 samples with details on AP exposure at baseline and 17 samples with baseline AP exposure as exclusion criterion for enrollment. Those with baseline exposure to AP (n = 395) had higher transition rates (29.9%; 95% CI: 25.1%-34.8%) than those without baseline exposure to AP in the same study (n = 1289; 17.2%; 15.1%-19.4%) and those coming from samples that did not include people who were exposed to AP at baseline (n = 2073; 16.2%; 14.6%-17.8%; P < .05 in both the fixed-effects and the random-effects models). Heterogeneity within studies was substantial, with values above 75% in all comparisons. CONCLUSIONS Sample enrichment is not a plausible explanation for the higher risk of transition to psychosis of CHR-P individuals who were already exposed to AP at the enrollment in specialized early-detection programs. Baseline exposure to AP at CHR-P assessment is a major index of enhanced, imminent risk of psychosis.
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Affiliation(s)
- Andrea Raballo
- Section of Psychiatry, Clinical Psychology and Rehabilitation, Department of Medicine, University of Perugia, Perugia, Italy,Center for Translational, Phenomenological and Developmental Psychopathology (CTPDP), Perugia University Hospital, Perugia, Italy,Correspondence: Andrea Raballo, MD, PhD, Section of Psychiatry, Clinical Psychology and Rehabilitation, Department of Medicine, University of Perugia Piazzale Lucio Severi 1, 06132, Perugia, Italy ()
| | - Michele Poletti
- Department of Mental Health and Pathological Addiction, Child and Adolescent Neuropsychiatry Service, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Antonio Preti
- Department of Neuroscience, University of Turin, Turin, Italy
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Abd-alrazaq A, Schneider J, Alhuwail D, Toro CT, Ahmed A, Alajlani M, Househ M. The performance of artificial intelligence-driven technologies in diagnosing mental disorders: An umbrella review (Preprint).. [DOI: 10.2196/preprints.29235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
BACKGROUND
Diagnosing mental disorders is usually not an easy task and requires a large amount of time and effort given the complex nature of mental disorders. Artificial intelligence (AI) has been successfully exploited in diagnosing many mental disorders. Numerous systematic reviews summarize the evidence on the accuracy of AI models in diagnosing different mental disorders.
OBJECTIVE
This umbrella review aims to synthesize results of previous systematic reviews on the performance of AI models in diagnosing mental disorders.
METHODS
To identify relevant systematic reviews, we searched 11 electronic databases, checked the reference list of the included reviews, and checked the reviews that cited the included reviews. Two reviewers independently selected the relevant reviews, extracted the data from them, and appraised their quality. We synthesized the extracted data using the narrative approach. Specifically, results of the included reviews were grouped based on the target mental disorders that the AI classifiers distinguish.
RESULTS
We included 15 systematic reviews of 852 citations identified by searching all databases. The included reviews assessed the performance of AI models in diagnosing Alzheimer’s disease (n=7), mild cognitive impairment (n=6), schizophrenia (n=3), bipolar disease (n=2), autism spectrum disorder (n=1), obsessive-compulsive disorder (n=1), post-traumatic stress disorder (n=1), and psychotic disorders (n=1). The performance of the AI models in diagnosing these mental disorders ranged between 21% and 100%.
CONCLUSIONS
AI technologies offer great promise in diagnosing mental health disorders. The reported performance metrics paint a vivid picture of a bright future for AI in this field. To expedite progress towards these technologies being incorporated into routine practice, we recommend that healthcare professionals in the field cautiously and consciously begin to explore the opportunities of AI-based tools for their daily routine. It would also be encouraging to see a greater number of meta-analyses and further systematic reviews on performance of AI models in diagnosing other common mental disorders such as depression and anxiety.
CLINICALTRIAL
CRD42021231558
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Antonucci LA, Raio A, Pergola G, Gelao B, Papalino M, Rampino A, Andriola I, Blasi G, Bertolino A. Machine learning-based ability to classify psychosis and early stages of disease through parenting and attachment-related variables is associated with social cognition. BMC Psychol 2021; 9:47. [PMID: 33757595 PMCID: PMC7989088 DOI: 10.1186/s40359-021-00552-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 03/16/2021] [Indexed: 12/21/2022] Open
Abstract
Background Recent views posited that negative parenting and attachment insecurity can be considered as general environmental factors of vulnerability for psychosis, specifically for individuals diagnosed with psychosis (PSY). Furthermore, evidence highlighted a tight relationship between attachment style and social cognition abilities, a key PSY behavioral phenotype. The aim of this study is to generate a machine learning algorithm based on the perceived quality of parenting and attachment style-related features to discriminate between PSY and healthy controls (HC) and to investigate its ability to track PSY early stages and risk conditions, as well as its association with social cognition performance. Methods Perceived maternal and paternal parenting, as well as attachment anxiety and avoidance scores, were trained to separate 71 HC from 34 PSY (20 individuals diagnosed with schizophrenia + 14 diagnosed with bipolar disorder with psychotic manifestations) using support vector classification and repeated nested cross-validation. We then validated this model on independent datasets including individuals at the early stages of disease (ESD, i.e. first episode of psychosis or depression, or at-risk mental state for psychosis) and with familial high risk for PSY (FHR, i.e. having a first-degree relative suffering from psychosis). Then, we performed factorial analyses to test the group x classification rate interaction on emotion perception, social inference and managing of emotions abilities. Results The perceived parenting and attachment-based machine learning model discriminated PSY from HC with a Balanced Accuracy (BAC) of 72.2%. Slightly lower classification performance was measured in the ESD sample (HC-ESD BAC = 63.5%), while the model could not discriminate between FHR and HC (BAC = 44.2%). We observed a significant group x classification interaction in PSY and HC from the discovery sample on emotion perception and on the ability to manage emotions (both p = 0.02). The interaction on managing of emotion abilities was replicated in the ESD and HC validation sample (p = 0.03). Conclusion Our results suggest that parenting and attachment-related variables bear significant classification power when applied to both PSY and its early stages and are associated with variability in emotion processing. These variables could therefore be useful in psychosis early recognition programs aimed at softening the psychosis-associated disability. Supplementary Information The online version contains supplementary material available at 10.1186/s40359-021-00552-3.
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Affiliation(s)
- Linda A Antonucci
- Department of Education, Psychology, Communication, University of Bari Aldo Moro, Via Scipione Crisanzio 42, 70122, Bari, Italy. .,Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy.
| | - Alessandra Raio
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Giulio Pergola
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy.,Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Barbara Gelao
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Marco Papalino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Antonio Rampino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | | | - Giuseppe Blasi
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
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Schnell K, Stein M. [Diagnostics and Therapy 24/7? Artificial Intelligence as a Challenge and Opportunity in Psychiatry and Psychotherapy]. PSYCHIATRISCHE PRAXIS 2021; 48:S5-S10. [PMID: 33652480 DOI: 10.1055/a-1364-5565] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
OBJECTIVE The aim of the article is to enable a fundamental understanding of the potentials and requirements of Artificial Intelligence (AI) for psychiatrists in the present and for the development of future working environments. Psychiatrists will need to understand the function of AI-systems and personalized AI-assistants in therapy systems and as part of their patients' daily life. METHOD The article provides an overview of basic categories and fields of application of AI and machine learning in the diagnosis, prevention and therapy of mental disorders. RESULTS AI-applications will shape the prevention, diagnosis and treatment as well as the basic etiological understanding of mental disorders. Notably, the treatment of mental disorders is significantly influenced by commercial product development and assistance systems outside the medical system, as the corresponding developments can exploit large data pools with significantly lower restrictions. CONCLUSION Psychiatrists should now seize the opportunity to actively shape the implementation of AI-systems as otherwise key competences could be transferred to a primary field outside the medical system to the detriment of the patient and the therapist.
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Affiliation(s)
- Knut Schnell
- AG Translationale Psychotherapieforschung, Klinik für Psychiatrie und Psychotherapie, Universitätsmedizin Göttingen, Asklepios Fachklinikum
| | - Miriam Stein
- AG Translationale Psychotherapieforschung, Klinik für Psychiatrie und Psychotherapie, Universitätsmedizin Göttingen, Asklepios Fachklinikum
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Rosen M, Betz LT, Schultze-Lutter F, Chisholm K, Haidl TK, Kambeitz-Ilankovic L, Bertolino A, Borgwardt S, Brambilla P, Lencer R, Meisenzahl E, Ruhrmann S, Salokangas RKR, Upthegrove R, Wood SJ, Koutsouleris N, Kambeitz J. Towards clinical application of prediction models for transition to psychosis: A systematic review and external validation study in the PRONIA sample. Neurosci Biobehav Rev 2021; 125:478-492. [PMID: 33636198 DOI: 10.1016/j.neubiorev.2021.02.032] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 01/14/2021] [Accepted: 02/20/2021] [Indexed: 01/13/2023]
Abstract
A multitude of prediction models for a first psychotic episode in individuals at clinical high-risk (CHR) for psychosis have been proposed, but only rarely validated. We identified transition models based on clinical and neuropsychological data through a registered systematic literature search and evaluated their external validity in 173 CHRs from the Personalised Prognostic Tools for Early Psychosis Management (PRONIA) study. Discrimination performance was assessed with the area under the receiver operating characteristic curve (AUC), and compared to the prediction of clinical raters. External discrimination performance varied considerably across the 22 identified models (AUC 0.40-0.76), with two models showing good discrimination performance. None of the tested models significantly outperformed clinical raters (AUC = 0.75). Combining predictions of clinical raters and the best model descriptively improved discrimination performance (AUC = 0.84). Results show that personalized prediction of transition in CHR is potentially feasible on a global scale. For implementation in clinical practice, further rounds of external validation, impact studies, and development of an ethical framework is necessary.
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Affiliation(s)
- Marlene Rosen
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital of Cologne, Cologne, Germany
| | - Linda T Betz
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital of Cologne, Cologne, Germany
| | - Frauke Schultze-Lutter
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany; Department of Psychology and Mental Health, Faculty of Psychology, Airlangga University, Surabaya, Indonesia; University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Katharine Chisholm
- Institute for Mental Health and Centre for Human Brain Health, University of Birmingham, Birmingham, UK; Department of Psychology, Aston University, Birmingham, UK
| | - Theresa K Haidl
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital of Cologne, Cologne, Germany
| | - Lana Kambeitz-Ilankovic
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital of Cologne, Cologne, Germany; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Alessandro Bertolino
- Department of Neurological and Psychiatric Sciences, University of Bari, Bari, Italy
| | - Stefan Borgwardt
- Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany; Department of Psychiatry, Psychiatric University Hospital, University of Basel, Switzerland
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Rebekka Lencer
- Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany; Department of Psychiatry, University of Münster, Münster, Germany
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - Stephan Ruhrmann
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital of Cologne, Cologne, Germany
| | | | - Rachel Upthegrove
- Institute for Mental Health and Centre for Human Brain Health, University of Birmingham, Birmingham, UK
| | - Stephen J Wood
- Institute for Mental Health and Centre for Human Brain Health, University of Birmingham, Birmingham, UK; Orygen, Melbourne, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany; Max-Planck Institute of Psychiatry, Munich, Germany; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital of Cologne, Cologne, Germany.
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Koutsouleris N, Dwyer DB, Degenhardt F, Maj C, Urquijo-Castro MF, Sanfelici R, Popovic D, Oeztuerk O, Haas SS, Weiske J, Ruef A, Kambeitz-Ilankovic L, Antonucci LA, Neufang S, Schmidt-Kraepelin C, Ruhrmann S, Penzel N, Kambeitz J, Haidl TK, Rosen M, Chisholm K, Riecher-Rössler A, Egloff L, Schmidt A, Andreou C, Hietala J, Schirmer T, Romer G, Walger P, Franscini M, Traber-Walker N, Schimmelmann BG, Flückiger R, Michel C, Rössler W, Borisov O, Krawitz PM, Heekeren K, Buechler R, Pantelis C, Falkai P, Salokangas RKR, Lencer R, Bertolino A, Borgwardt S, Noethen M, Brambilla P, Wood SJ, Upthegrove R, Schultze-Lutter F, Theodoridou A, Meisenzahl E. Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression. JAMA Psychiatry 2021; 78:195-209. [PMID: 33263726 PMCID: PMC7711566 DOI: 10.1001/jamapsychiatry.2020.3604] [Citation(s) in RCA: 113] [Impact Index Per Article: 37.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
IMPORTANCE Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear. OBJECTIVES To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models' geographic generalizability; to test and integrate clinicians' predictions; and to maximize clinical utility by building a sequential prognostic system. DESIGN, SETTING, AND PARTICIPANTS This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020. MAIN OUTCOMES AND MEASURES Accuracy and generalizability of prognostic systems. RESULTS A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results. CONCLUSIONS AND RELEVANCE These findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms' and clinicians' risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.
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Affiliation(s)
- Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany,Max-Planck Institute of Psychiatry, Munich, Germany,Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Dominic B. Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Franziska Degenhardt
- Institute of Human Genetics, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany,Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Carlo Maj
- Institute of Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany
| | | | - Rachele Sanfelici
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany,Max-Planck School of Cognition, Leipzig, Germany
| | - David Popovic
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany,International Max-Planck Research School for Translational Psychiatry, Munich, Germany
| | - Oemer Oeztuerk
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany,International Max-Planck Research School for Translational Psychiatry, Munich, Germany
| | - Shalaila S. Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Johanna Weiske
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Anne Ruef
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Lana Kambeitz-Ilankovic
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Linda A. Antonucci
- Department of Education, Psychology, and Communication, University of Bari Aldo Moro, Bari, Italy
| | - Susanne Neufang
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | | | - Stephan Ruhrmann
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Nora Penzel
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Theresa K. Haidl
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Marlene Rosen
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Katharine Chisholm
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom
| | - Anita Riecher-Rössler
- Department of Psychiatry, Psychiatric University Hospital, University of Basel, Switzerland
| | - Laura Egloff
- Department of Psychiatry, Psychiatric University Hospital, University of Basel, Switzerland
| | - André Schmidt
- Department of Psychiatry, Psychiatric University Hospital, University of Basel, Switzerland
| | - Christina Andreou
- Department of Psychiatry, Psychiatric University Hospital, University of Basel, Switzerland
| | - Jarmo Hietala
- Department of Psychiatry, University of Turku, Turku, Finland
| | - Timo Schirmer
- GE Healthcare GmbH (previously GE Global Research GmbH), Munich, Germany
| | - Georg Romer
- Department of Child and Adolescent Psychiatry, University of Münster, Münster, Germany
| | - Petra Walger
- Department of Child and Adolescent Psychiatry, Psychotherapy and Psychosomatics, LVR Clinic Düsseldorf, Düsseldorf, Germany
| | - Maurizia Franscini
- Department of Child and Adolescent Psychiatry and Psychotherapy, University of Zürich, Zürich, Switzerland
| | - Nina Traber-Walker
- Department of Child and Adolescent Psychiatry and Psychotherapy, University of Zürich, Zürich, Switzerland
| | - Benno G. Schimmelmann
- University Hospital of Child and Adolescent Psychiatry, University Hospital Hamburg-Eppendorf, Hamburg, Germany,University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Rahel Flückiger
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Chantal Michel
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Wulf Rössler
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry Zurich, Zurich, Switzerland
| | - Oleg Borisov
- Institute of Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany
| | - Peter M. Krawitz
- Institute of Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany
| | - Karsten Heekeren
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry Zurich, Zurich, Switzerland,Department of Psychiatry and Psychotherapy I, LVR Hospital Cologne, Cologne, Germany
| | - Roman Buechler
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry Zurich, Zurich, Switzerland,Department of Neuroradiology, University Hospital of Zurich, Zurich, Switzerland
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany,Max-Planck Institute of Psychiatry, Munich, Germany
| | | | - Rebekka Lencer
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany,Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Stefan Borgwardt
- Department of Psychiatry, Psychiatric University Hospital, University of Basel, Switzerland,Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Markus Noethen
- Institute of Human Genetics, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Stephen J. Wood
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia,Orygen, the National Centre of Excellence for Youth Mental Health, Melbourne, Australia
| | - Rachel Upthegrove
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom
| | - Frauke Schultze-Lutter
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany,Department of Psychology and Mental Health, Faculty of Psychology, Airlangga University, Surabaya, Indonesia
| | - Anastasia Theodoridou
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry Zurich, Zurich, Switzerland
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
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Puntis S, Oliver D, Fusar-Poli P. Third external replication of an individualised transdiagnostic prediction model for the automatic detection of individuals at risk of psychosis using electronic health records. Schizophr Res 2021; 228:403-409. [PMID: 33556673 DOI: 10.1016/j.schres.2021.01.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 01/03/2021] [Accepted: 01/18/2021] [Indexed: 12/16/2022]
Abstract
BACKGROUND Primary indicated prevention is a key target for reducing the incidence and burden of schizophrenia and related psychotic disorders. An individualised, clinically-based transdiagnostic model for the detection of individuals at risk of psychosis has been developed and validated in two large, urban healthcare providers. We tested its external validity in a geographically and demographically different non-urban population. METHOD Retrospective EHR cohort study. All individuals accessing secondary healthcare provided by Oxford Health NHS Foundation Trust between 1st January 2011 and 30th November 2019 and receiving a primary index diagnosis of a non-psychotic or non-organic mental disorder were considered eligible. The previously developed model was applied to this database and its external prognostic accuracy was measured with Harrell's C. FINDINGS The study included n = 33,710 eligible individuals, with an average age of 27.7 years (SD = 19.8), mostly white (92.0%) and female (57.3%). The mean follow-up was 1863.9 days (SD = 948.9), with 868 transitions to psychosis and a cumulative incidence of psychosis at 6 years of 2.9% (95%CI: 2.7-3.1). Compared to the urban development database, Oxford Health was characterised by a relevant case mix, lower incidence of psychosis, different distribution of baseline predictors, higher proportion of white females, and a lack of specialised clinical services for at risk individuals. Despite these differences the model retained an adequate prognostic performance (Harrell's C = 0.79, 95%CI: 0.78-0.81), with no major miscalibration. INTERPRETATION The transdiagnostic, individualised, clinically-based risk calculator is transportable outside urban healthcare providers. Further research should test transportability of this risk prediction model in an international setting.
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Affiliation(s)
- Stephen Puntis
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom.
| | - Dominic Oliver
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, London, United Kingdom; OASIS Service, South London and Maudsley National Health Service (NHS) Foundation Trust, London, United Kingdom; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
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Worthington MA, Cannon TD. Prediction and Prevention in the Clinical High-Risk for Psychosis Paradigm: A Review of the Current Status and Recommendations for Future Directions of Inquiry. Front Psychiatry 2021; 12:770774. [PMID: 34744845 PMCID: PMC8569129 DOI: 10.3389/fpsyt.2021.770774] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Accepted: 09/27/2021] [Indexed: 11/13/2022] Open
Abstract
Prediction and prevention of negative clinical and functional outcomes represent the two primary objectives of research conducted within the clinical high-risk for psychosis (CHR-P) paradigm. Several multivariable "risk calculator" models have been developed to predict the likelihood of developing psychosis, although these models have not been translated to clinical use. Overall, less progress has been made in developing effective interventions. In this paper, we review the existing literature on both prediction and prevention in the CHR-P paradigm and, primarily, outline ways in which expanding and combining these paths of inquiry could lead to a greater improvement in individual outcomes for those most at risk.
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Affiliation(s)
| | - Tyrone D Cannon
- Department of Psychology, Yale University, New Haven, CT, United States.,Department of Psychiatry, Yale University, New Haven, CT, United States
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Raballo A, Poletti M, Preti A. Meta-analyzing the prevalence and prognostic effect of antipsychotic exposure in clinical high-risk (CHR): when things are not what they seem. Psychol Med 2020; 50:2673-2681. [PMID: 33198845 DOI: 10.1017/s0033291720004237] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
BACKGROUND The clinical high-risk (CHR) for psychosis paradigm is changing psychiatric practice. However, a widespread confounder, i.e. baseline exposure to antipsychotics (AP) in CHR samples, is systematically overlooked. Such exposure might mitigate the initial clinical presentation, increase the heterogeneity within CHR populations, and confound the evaluation of transition to psychosis at follow-up. This is the first meta-analysis examining the prevalence and the prognostic impact on transition to psychosis of ongoing AP treatment at baseline in CHR cohorts. METHODS Major databases were searched for articles published until 20 April 2020. The variance-stabilizing Freeman-Tukey double arcsine transformation was used to estimate prevalence. The binary outcome of transition to psychosis by group was estimated with risk ratio (RR) and the inverse variance method was used for pooling. RESULTS Fourteen studies were eligible for qualitative synthesis, including 1588 CHR individuals. Out of the pooled CHR sample, 370 individuals (i.e. 23.3%) were already exposed to AP at the time of CHR status ascription. Transition toward full-blown psychosis at follow-up intervened in 112 (29%; 95% CI 24-34%) of the AP-exposed CHR as compared to 235 (16%; 14-19%) of the AP-naïve CHR participants. AP-exposed CHR had higher RR of transition to psychosis (RR = 1.47; 95% CI 1.18-1.83; z = 3.48; p = 0.0005), without influence by age, gender ratio, overall sample size, duration of the follow-up, or quality of the studies. CONCLUSIONS Baseline AP exposure in CHR samples is substantial and is associated with a higher imminent risk of transition to psychosis. Therefore, such exposure should be regarded as a non-negligible red flag for clinical risk management.
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Affiliation(s)
- Andrea Raballo
- Department of Medicine, Section of Psychiatry, Clinical Psychology and Rehabilitation, University of Perugia, Perugia, Italy
- Center for Translational, Phenomenological and Developmental Psychopathology (CTPDP), Perugia University Hospital, Perugia, Italy
| | - Michele Poletti
- Department of Mental Health and Pathological Addiction, Child and Adolescent Neuropsychiatry Service, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Antonio Preti
- Centro Medico 'Genneruxi', Cagliari, Italy
- Center of Liaison Psychiatry and Psychosomatics, University Hospital, University of Cagliari, Cagliari, Italy
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The Current State of the Clinical High Risk for Psychosis Research Paradigm. Biol Psychiatry 2020; 88:284-286. [PMID: 32731922 DOI: 10.1016/j.biopsych.2020.06.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 06/05/2020] [Accepted: 06/05/2020] [Indexed: 11/21/2022]
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