Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study
Web of Science
AuthorMourao-Miranda, J; Reinders, AATS; Rocha-Rego, V; Lappin, J; Rondina, J; Morgan, C; Morgan, KD; Fearon, P; Jones, PB; Doody, GA; ...
Source TitlePsychological Medicine
PublisherCAMBRIDGE UNIV PRESS
Document TypeJournal Article
CitationsMourao-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., Murray, R. M., Kapur, S. & Dazzan, P. (2012). Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study. PSYCHOLOGICAL MEDICINE, 42 (5), pp.1037-1047. https://doi.org/10.1017/S0033291711002005.
Access StatusOpen Access
BACKGROUND: 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. METHOD: One hundred patients at their first psychotic episode and 91 healthy controls had an MRI scan. Patients were re-evaluated 6.2 years (s.d.=2.3) later, and were classified as having a continuous, episodic or intermediate illness course. Twenty-eight subjects with a continuous course were compared with 28 patients with an episodic course and with 28 healthy controls. We trained each SVM classifier independently for the following contrasts: continuous versus episodic, continuous versus healthy controls, and episodic versus healthy controls. RESULTS: At baseline, patients with a continuous course were already distinguishable, with significance above chance level, from both patients with an episodic course (p=0.004, sensitivity=71, specificity=68) and healthy individuals (p=0.01, sensitivity=71, specificity=61). Patients with an episodic course could not be distinguished from healthy individuals. When patients with an intermediate outcome were classified according to the discriminating pattern episodic versus continuous, 74% of those who did not develop other episodes were classified as episodic, and 65% of those who did develop further episodes were classified as continuous (p=0.035). CONCLUSIONS: We provide preliminary evidence of MRI application in the individualized prediction of future illness course, using a simple and automated SVM pipeline. When replicated and validated in larger groups, this could enable targeted clinical decisions based on imaging data.
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