Machine learning classification of first-episode schizophrenia spectrum disorders and controls using whole brain white matter fractional anisotropy.

Journal: BMC psychiatry
PMID: 29636016

Abstract

BACKGROUND: Early diagnosis of schizophrenia could improve the outcome of the illness. Unlike classical between-group comparisons, machine learning can identify subtle disease patterns on a single subject level, which could help realize the potential of MRI in establishing a psychiatric diagnosis. Machine learning has previously been predominantly tested on gray-matter structural or functional MRI data. In this paper we used a machine learning classifier to differentiate patients with a first episode of schizophrenia-spectrum disorder (FES) from healthy controls using diffusion tensor imaging.

Authors

  • Pavol Mikolas
    Department of Psychiatry and Psychotherapy, Otto von Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany.
  • Jaroslav Hlinka
    National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic.
  • Antonin Skoch
    National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic.
  • Zbynek Pitra
    National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic.
  • Thomas Frodl
    Department of Psychiatry and Psychotherapy, Otto von Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany.
  • Filip Španiel
    National Institute of Mental Health, Klecany, Czech Republic.
  • Tomas Hajek
    Department of Psychiatry, Dalhousie University, Halifax, NS, Canada (Hajek, Cooke, Alda); Prague Psychiatric Centre/ National Institute of Mental Health, Prague, Czech Republic (Hajek, Kopecek, Novak, Hoschl, Alda); Charles University, 3rd Faculty of Medicine, Prague, Czech Republic (Kopecek, Novak, Hoschl, Alda).

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