title: Multi-center MRI prediction models: Predicting sex and illness course in first episode psychosis patients.
abstract: Structural Magnetic Resonance Imaging (MRI) studies have attempted to use brain measures obtained at the first-episode of psychosis to predict subsequent outcome, with inconsistent results. Thus, there is a real need to validate the utility of brain measures in the prediction of outcome using large datasets, from independent samples, obtained with different protocols and from different MRI scanners. This study had three main aims: 1) to investigate whether structural MRI data from multiple centers can be combined to create a machine-learning model able to predict a strong biological variable like sex; 2) to replicate our previous finding that an MRI scan obtained at first episode significantly predicts subsequent illness course in other independent datasets; and finally, 3) to test whether these datasets can be combined to generate multicenter models with better accuracy in the prediction of illness course. The multi-center sample included brain structural MRI scans from 256 males and 133 females patients with first episode psychosis, acquired in five centers: University Medical Center Utrecht (The Netherlands) (n=67); Institute of Psychiatry, Psychology and Neuroscience, London (United Kingdom) (n=97); University of São Paulo (Brazil) (n=64); University of Cantabria, Santander (Spain) (n=107); and University of Melbourne (Australia) (n=54). All images were acquired on 1.5-Tesla scanners and all centers provided information on illness course during a follow-up period ranging 3 to 7years. We only included in the analyses of outcome prediction patients for whom illness course was categorized as either "continuous" (n=94) or "remitting" (n=118). Using structural brain scans from all centers, sex was predicted with significant accuracy (89%; p<0.001). In the single- or multi-center models, illness course could not be predicted with significant accuracy. However, when reducing heterogeneity by restricting the analyses to male patients only, classification accuracy improved in some samples. This study provides proof of concept that combining multi-center MRI data to create a well performing classification model is possible. However, to create complex multi-center models that perform accurately, each center should contribute a sample either large or homogeneous enough to first allow accurate classification within the single-center.
authors.affiliation: Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands. Electronic address: mireillen@gmail.com.
authors.forename: Mireille
authors.initials: M
authors.lastname: Nieuwenhuis
authors.affiliation: Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Hugo G
authors.initials: HG
authors.lastname: Schnack
authors.affiliation: Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Neeltje E
authors.initials: NE
authors.lastname: van Haren
authors.affiliation: Department of Psychosis Studies, Institute of Psychiatry, King's College London, UK.
authors.forename: Julia
authors.initials: J
authors.lastname: Lappin
authors.affiliation: Center for Epidemiology and Public Health, Health Service and Population Research Department, Institute of Psychiatry, King's College, London, UK.
authors.forename: Craig
authors.initials: C
authors.lastname: Morgan
authors.affiliation: Center for Epidemiology and Public Health, Health Service and Population Research Department, Institute of Psychiatry, King's College, London, UK.
authors.forename: Antje A
authors.initials: AA
authors.lastname: Reinders
authors.affiliation: Neuroimaging Unit, Technological Facilities, Valdecilla Biomedical Research Institute IDIVAL, Santander, Cantabria, Spain; CIBERSAM, Centro Investigación Biomédica en Red de Salud Mental, Department of Psychiatry, School of Medicine, University of Cantabria, Santander, Spain.
authors.forename: Diana
authors.initials: D
authors.lastname: Gutierrez-Tordesillas
authors.affiliation: CIBERSAM, Centro Investigación Biomédica en Red de Salud Mental, Department of Psychiatry, School of Medicine, University of Cantabria, Santander, Spain.
authors.forename: Roberto
authors.initials: R
authors.lastname: Roiz-Santiañez
authors.affiliation: Laboratory of Psychiatric Neuroimaging, Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Centro de Medicina Nuclear, São Paulo, SP, Brazil.
authors.forename: Maristela S
authors.initials: MS
authors.lastname: Schaufelberger
authors.affiliation: Laboratory of Psychiatric Neuroimaging, Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Centro de Medicina Nuclear, São Paulo, SP, Brazil.
authors.forename: Pedro G
authors.initials: PG
authors.lastname: Rosa
authors.affiliation: Laboratory of Psychiatric Neuroimaging, Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Centro de Medicina Nuclear, São Paulo, SP, Brazil.
authors.forename: Marcus V
authors.initials: MV
authors.lastname: Zanetti
authors.affiliation: Laboratory of Psychiatric Neuroimaging, Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Centro de Medicina Nuclear, São Paulo, SP, Brazil.
authors.forename: Geraldo F
authors.initials: GF
authors.lastname: Busatto
authors.affiliation: CIBERSAM, Centro Investigación Biomédica en Red de Salud Mental, Department of Psychiatry, School of Medicine, University of Cantabria, Santander, Spain; Department of Medicine and Psychiatry, University Hospital Marques de Valdecilla, School of Medicine, University of Cantabria-IDIVAL-CIBERSAM, Santander, Spain.
authors.forename: Benedicto
authors.initials: B
authors.lastname: Crespo-Facorro
authors.affiliation: Department of Psychiatry, Melbourne Neuropsychiatry Center, University of Melbourne, Melbourne, Australia.
authors.forename: Patrick D
authors.initials: PD
authors.lastname: McGorry
authors.affiliation: Department of Psychiatry, Melbourne Neuropsychiatry Center, University of Melbourne, Melbourne, Australia.
authors.forename: Dennis
authors.initials: D
authors.lastname: Velakoulis
authors.affiliation: Department of Psychiatry, Melbourne Neuropsychiatry Center, University of Melbourne, Melbourne, Australia.
authors.forename: Christos
authors.initials: C
authors.lastname: Pantelis
authors.affiliation: School of Psychology, University of Birmingham, UK.
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Wood
authors.affiliation: Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: René S
authors.initials: RS
authors.lastname: Kahn
authors.affiliation: Department of Computer Science, University College London, London, UK; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, UK.
authors.forename: Janaina
authors.initials: J
authors.lastname: Mourao-Miranda
authors.affiliation: Department of Psychosis Studies, Institute of Psychiatry, King's College London, UK; NIHR Mental Health Biomedical Research Center at South London and Maudsley NHS Foundation Trust and King's College, London, UK.
authors.forename: Paola
authors.initials: P
authors.lastname: Dazzan
mesh_terms: D000328:Adult; D066264:Datasets as Topic; D018450:Disease Progression; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D015337:Multicenter Studies as Topic; D000075082:Proof of Concept Study; D011618:Psychotic Disorders / Q000000981:diagnostic imaging*; D012737:Sex Factors; D055815:Young Adult