Towards precision medicine in psychosis: Benefits and challenges of multimodal multicenter studies - PSYSCAN: Translating neuroimaging findings from research into clinical practice

the PSYSCAN Consortium

doi:10.1093/schbul/sbz067

Towards precision medicine in psychosis: Benefits and challenges of multimodal multicenter studies - PSYSCAN: Translating neuroimaging findings from research into clinical practice

the PSYSCAN Consortium

Department of Medicine

King's College London
South London and Maudsley NHS Foundation Trust
Utrecht University
University of Pavia
University of Amsterdam
Hospital Universitario Virgen del Rocio
Hospital Universitario Marques de Valdecilla
Universidad de Cantabria
Imaging of Mood- and Anxiety-Related Disorders (IMARD)
University of Copenhagen
University of Edinburgh
University of Galway
Heidelberg University
Maastricht University
GGZE Mental Health Care
Complutense University
Hospital General Universitario Gregorio Marañon
University of Marburg
ORYGEN Youth Health
University of Melbourne
University of Campania Luigi Vanvitelli
Universidade Federal de São Paulo
Seoul National University
Sheba Medical Center at Tel Hashomer
Tel Aviv University
University of Toronto
Medical University of Vienna
University of Zurich
Icahn School of Medicine at Mount Sinai

Research output: Contribution to journal › Article › peer-review

66 Scopus citations

Abstract

In the last 2 decades, several neuroimaging studies investigated brain abnormalities associated with the early stages of psychosis in the hope that these could aid the prediction of onset and clinical outcome. Despite advancements in the field, neuroimaging has yet to deliver. This is in part explained by the use of univariate analytical techniques, small samples and lack of statistical power, lack of external validation of potential biomarkers, and lack of integration of nonimaging measures (eg, genetic, clinical, cognitive data). PSYSCAN is an international, longitudinal, multicenter study on the early stages of psychosis which uses machine learning techniques to analyze imaging, clinical, cognitive, and biological data with the aim of facilitating the prediction of psychosis onset and outcome. In this article, we provide an overview of the PSYSCAN protocol and we discuss benefits and methodological challenges of large multicenter studies that employ neuroimaging measures.

Original language	English
Pages (from-to)	432-441
Number of pages	10
Journal	Schizophrenia Bulletin
Volume	46
Issue number	2
DOIs	https://doi.org/10.1093/schbul/sbz067
State	Published - 1 Mar 2020

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

MRI
PSYSCAN
clinical high risk of psychosis
first episode of psychosis
machine learning
neuroimaging
prediction
psychosis

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10.1093/schbul/sbz067

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@article{01e8016391b749858543157242f02f33,

title = "Towards precision medicine in psychosis: Benefits and challenges of multimodal multicenter studies - PSYSCAN: Translating neuroimaging findings from research into clinical practice",

abstract = "In the last 2 decades, several neuroimaging studies investigated brain abnormalities associated with the early stages of psychosis in the hope that these could aid the prediction of onset and clinical outcome. Despite advancements in the field, neuroimaging has yet to deliver. This is in part explained by the use of univariate analytical techniques, small samples and lack of statistical power, lack of external validation of potential biomarkers, and lack of integration of nonimaging measures (eg, genetic, clinical, cognitive data). PSYSCAN is an international, longitudinal, multicenter study on the early stages of psychosis which uses machine learning techniques to analyze imaging, clinical, cognitive, and biological data with the aim of facilitating the prediction of psychosis onset and outcome. In this article, we provide an overview of the PSYSCAN protocol and we discuss benefits and methodological challenges of large multicenter studies that employ neuroimaging measures.",

keywords = "MRI, PSYSCAN, clinical high risk of psychosis, first episode of psychosis, machine learning, neuroimaging, prediction, psychosis",

author = "\{the PSYSCAN Consortium\} and Stefania Tognin and \{Van Hell\}, \{Hendrika H.\} and Kate Merritt and \{Winter-Van Rossum\}, Inge and Bossong, \{Matthijs G.\} and Kempton, \{Matthew J.\} and Gemma Modinos and Paolo Fusar-Poli and Andrea Mechelli and Paola Dazzan and Arija Maat and \{De Haan\}, Lieuwe and Benedicto Crespo-Facorro and Birte Glenth{\o}j and Lawrie, \{Stephen M.\} and Colm McDonald and Oliver Gruber and \{Van Amelsvoort\}, Therese and Celso Arango and Tilo Kircher and Barnaby Nelson and Silvana Galderisi and Rodrigo Bressan and Kwon, \{Jun Soo\} and Mark Weiser and Romina Mizrahi and Gabriele Sachs and Anke Maatz and Ren{\'e} Kahn and Phillip McGuire and George Gifford and Natalia Petros and Mathilde Antoniades and \{De Micheli\}, Andrea and Sandra Vieira and Spencer, \{Tom J.\} and Cristina Scarpazza and Emily Hird and \{Van Hell\}, Erika and Inge Winter and Wiepke Cahn and Hugo Schnack and DIeuwke Siegmann and Jana Barkhof and Lotte Hendriks and \{De Wit\}, Iris and DIana Tordesillas-Gutierrez and Esther Setien-Suero and Rosa Ayesa-Arriola and Lee, \{Tae Young\}",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2019.",

year = "2020",

month = mar,

day = "1",

doi = "10.1093/schbul/sbz067",

language = "English",

volume = "46",

pages = "432--441",

journal = "Schizophrenia Bulletin",

issn = "0586-7614",

publisher = "Oxford University Press",

number = "2",

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TY - JOUR

T1 - Towards precision medicine in psychosis

T2 - Benefits and challenges of multimodal multicenter studies - PSYSCAN: Translating neuroimaging findings from research into clinical practice

AU - the PSYSCAN Consortium

AU - Tognin, Stefania

AU - Van Hell, Hendrika H.

AU - Merritt, Kate

AU - Winter-Van Rossum, Inge

AU - Bossong, Matthijs G.

AU - Kempton, Matthew J.

AU - Modinos, Gemma

AU - Fusar-Poli, Paolo

AU - Mechelli, Andrea

AU - Dazzan, Paola

AU - Maat, Arija

AU - De Haan, Lieuwe

AU - Crespo-Facorro, Benedicto

AU - Glenthøj, Birte

AU - Lawrie, Stephen M.

AU - McDonald, Colm

AU - Gruber, Oliver

AU - Van Amelsvoort, Therese

AU - Arango, Celso

AU - Kircher, Tilo

AU - Nelson, Barnaby

AU - Galderisi, Silvana

AU - Bressan, Rodrigo

AU - Kwon, Jun Soo

AU - Weiser, Mark

AU - Mizrahi, Romina

AU - Sachs, Gabriele

AU - Maatz, Anke

AU - Kahn, René

AU - McGuire, Phillip

AU - Gifford, George

AU - Petros, Natalia

AU - Antoniades, Mathilde

AU - De Micheli, Andrea

AU - Vieira, Sandra

AU - Spencer, Tom J.

AU - Scarpazza, Cristina

AU - Hird, Emily

AU - Van Hell, Erika

AU - Winter, Inge

AU - Cahn, Wiepke

AU - Schnack, Hugo

AU - Siegmann, DIeuwke

AU - Barkhof, Jana

AU - Hendriks, Lotte

AU - De Wit, Iris

AU - Tordesillas-Gutierrez, DIana

AU - Setien-Suero, Esther

AU - Ayesa-Arriola, Rosa

AU - Lee, Tae Young

PY - 2020/3/1

Y1 - 2020/3/1

N2 - In the last 2 decades, several neuroimaging studies investigated brain abnormalities associated with the early stages of psychosis in the hope that these could aid the prediction of onset and clinical outcome. Despite advancements in the field, neuroimaging has yet to deliver. This is in part explained by the use of univariate analytical techniques, small samples and lack of statistical power, lack of external validation of potential biomarkers, and lack of integration of nonimaging measures (eg, genetic, clinical, cognitive data). PSYSCAN is an international, longitudinal, multicenter study on the early stages of psychosis which uses machine learning techniques to analyze imaging, clinical, cognitive, and biological data with the aim of facilitating the prediction of psychosis onset and outcome. In this article, we provide an overview of the PSYSCAN protocol and we discuss benefits and methodological challenges of large multicenter studies that employ neuroimaging measures.

AB - In the last 2 decades, several neuroimaging studies investigated brain abnormalities associated with the early stages of psychosis in the hope that these could aid the prediction of onset and clinical outcome. Despite advancements in the field, neuroimaging has yet to deliver. This is in part explained by the use of univariate analytical techniques, small samples and lack of statistical power, lack of external validation of potential biomarkers, and lack of integration of nonimaging measures (eg, genetic, clinical, cognitive data). PSYSCAN is an international, longitudinal, multicenter study on the early stages of psychosis which uses machine learning techniques to analyze imaging, clinical, cognitive, and biological data with the aim of facilitating the prediction of psychosis onset and outcome. In this article, we provide an overview of the PSYSCAN protocol and we discuss benefits and methodological challenges of large multicenter studies that employ neuroimaging measures.

KW - MRI

KW - PSYSCAN

KW - clinical high risk of psychosis

KW - first episode of psychosis

KW - machine learning

KW - neuroimaging

KW - prediction

KW - psychosis

UR - https://www.scopus.com/pages/publications/85081165552

U2 - 10.1093/schbul/sbz067

DO - 10.1093/schbul/sbz067

M3 - Article

C2 - 31424555

AN - SCOPUS:85081165552

SN - 0586-7614

VL - 46

SP - 432

EP - 441

JO - Schizophrenia Bulletin

JF - Schizophrenia Bulletin

IS - 2

ER -

Towards precision medicine in psychosis: Benefits and challenges of multimodal multicenter studies - PSYSCAN: Translating neuroimaging findings from research into clinical practice

Abstract

UN SDGs

Keywords

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