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Journal of Imaging Informatics in Medicine

Published in:

01-04-2018

Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI

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

Published in: Journal of Imaging Informatics in Medicine | Issue 2/2018

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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Title: Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI
Authors: Youngoh Bae
Kunaraj Kumarasamy
Issa M. Ali
Panagiotis Korfiatis
Zeynettin Akkus
Bradley J. Erickson
Publication date: 01-04-2018
Publisher: Springer International Publishing
Published in: Journal of Imaging Informatics in Medicine / Issue 2/2018
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI: https://doi.org/10.1007/s10278-017-0020-4

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI

Abstract

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

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