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Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI

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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.

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Acknowledgements

This work was funded in part by NCI CA160045.

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Authors and Affiliations

  1. School of Medicine, CHA University, Seongnam-si, Gyeonggi-do, South Korea

    Youngoh Bae

  2. Department of Radiology, Mayo Clinic College of Medicine, Rochester, MN, USA

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

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Bae, Y., Kumarasamy, K., Ali, I.M. et al. Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI. J Digit Imaging 31, 252–261 (2018). https://doi.org/10.1007/s10278-017-0020-4

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