Format

Send to

Choose Destination
Neuroimage. 2017 Jan 15;145(Pt B):246-253. doi: 10.1016/j.neuroimage.2016.07.027. Epub 2016 Jul 12.

Multi-center MRI prediction models: Predicting sex and illness course in first episode psychosis patients.

Author information

1
Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands. Electronic address: mireillen@gmail.com.
2
Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands.
3
Department of Psychosis Studies, Institute of Psychiatry, King's College London, UK.
4
Center for Epidemiology and Public Health, Health Service and Population Research Department, Institute of Psychiatry, King's College, London, UK.
5
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.
6
CIBERSAM, Centro Investigación Biomédica en Red de Salud Mental, Department of Psychiatry, School of Medicine, University of Cantabria, Santander, Spain.
7
Laboratory of Psychiatric Neuroimaging, Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Centro de Medicina Nuclear, São Paulo, SP, Brazil.
8
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.
9
Department of Psychiatry, Melbourne Neuropsychiatry Center, University of Melbourne, Melbourne, Australia.
10
School of Psychology, University of Birmingham, UK.
11
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.
12
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.

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.

PMID:
27421184
PMCID:
PMC5193177
DOI:
10.1016/j.neuroimage.2016.07.027
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center