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Journal of Digital Imaging

, Volume 31, Issue 2, pp 252–261 | Cite as

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
Article

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.

Keywords

fMRI Machine learning Schizophrenia Network properties 

Notes

Acknowledgements

This work was funded in part by NCI CA160045.

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Copyright information

© Society for Imaging Informatics in Medicine 2017

Authors and Affiliations

  1. 1.School of MedicineCHA UniversitySeongnam-siSouth Korea
  2. 2.Department of RadiologyMayo Clinic College of MedicineRochesterUSA

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