Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study.
Mourao-Miranda, J Reinders, A A T S Rocha-Rego, V Lappin, J Rondina, J Morgan, C Morgan, K D Fearon, P Jones, P B Doody, G A
Mourao-Miranda, J
Reinders, A A T S
Rocha-Rego, V
Lappin, J
Rondina, J
Morgan, C
Morgan, K D
Fearon, P
Jones, P B
Doody, G A
Citations
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Advisors
Editors
Other Contributors
Date
2012-05
Date Submitted
Keywords
Other Subjects
Subject Mesh
Adult
Brain
Brain Mapping
Cohort Studies
Disease Progression
Female
Follow-Up Studies
Humans
Image Processing, Computer-Assisted
Individuality
Magnetic Resonance Imaging
Male
Observer Variation
Predictive Value of Tests
Psychotic Disorders
Reproducibility of Results
Support Vector Machines
Brain
Brain Mapping
Cohort Studies
Disease Progression
Female
Follow-Up Studies
Humans
Image Processing, Computer-Assisted
Individuality
Magnetic Resonance Imaging
Male
Observer Variation
Predictive Value of Tests
Psychotic Disorders
Reproducibility of Results
Support Vector Machines
Planned Date
Start Date
Collaborators
Principal Investigators
Alternative Titles
Publisher
Abstract
To date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode.
Language
en
ISSN
1469-8978
eISSN
ISBN
DOI
10.1017/S0033291711002005
PMID
22059690