Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI

Youngoh Bae, Kunaraj Kumarasamy, Issa M. Ali, Panagiotis Korfiatis, Zeynettin Akkus, Bradley J. Erickson

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Schizophrenia has been proposed to result from impairment of functional connectivity. We aimed to use machine learning to distinguish schizophrenic subjects from normal controls using a publicly available functional MRI (fMRI) data set. Global and local parameters of functional connectivity were extracted for classification. We found decreased global and local network connectivity in subjects with schizophrenia, particularly in the anterior right cingulate cortex, the superior right temporal region, and the inferior left parietal region as compared to healthy subjects. Using support vector machine and 10-fold cross-validation, nine features reached 92.1% prediction accuracy, respectively. Our results suggest that there are significant differences between control and schizophrenic subjects based on regional brain activity detected with fMRI.

Original languageEnglish (US)
Pages (from-to)252-261
Number of pages10
JournalJournal of Digital Imaging
Volume31
Issue number2
DOIs
StatePublished - Apr 1 2018

Keywords

  • Machine learning
  • Network properties
  • Schizophrenia
  • fMRI

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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