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Connectivity of the anterior insula differentiates participants with first-episode schizophrenia spectrum disorders from controls: a machine-learning study

Published online by Cambridge University Press:  25 July 2016

P. Mikolas ,
T. Melicher ,
A. Skoch ,
M. Matejka ,
A. Slovakova ,
E. Bakstein ,
T. Hajek  and
F. Spaniel
P. Mikolas
Affiliation:
Psychiatric Hospital Bohnice, Prague, Czech Republic 3rd Faculty of Medicine, Charles University, Prague, Czech Republic National Institute of Mental Health, Klecany, Czech Republic Institute of Neuropsychiatric Care (INEP), Prague, Czech Republic
T. Melicher
Affiliation:
3rd Faculty of Medicine, Charles University, Prague, Czech Republic National Institute of Mental Health, Klecany, Czech Republic Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
A. Skoch
Affiliation:
National Institute of Mental Health, Klecany, Czech Republic MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
M. Matejka
Affiliation:
Psychiatric Hospital Bohnice, Prague, Czech Republic 3rd Faculty of Medicine, Charles University, Prague, Czech Republic National Institute of Mental Health, Klecany, Czech Republic
A. Slovakova
Affiliation:
Psychiatric Hospital Bohnice, Prague, Czech Republic 3rd Faculty of Medicine, Charles University, Prague, Czech Republic National Institute of Mental Health, Klecany, Czech Republic
E. Bakstein
Affiliation:
National Institute of Mental Health, Klecany, Czech Republic
T. Hajek*
Affiliation:
3rd Faculty of Medicine, Charles University, Prague, Czech Republic National Institute of Mental Health, Klecany, Czech Republic Dalhousie University, Department of Psychiatry, Halifax, Nova Scotia, Canada
F. Spaniel
Affiliation:
3rd Faculty of Medicine, Charles University, Prague, Czech Republic National Institute of Mental Health, Klecany, Czech Republic
*
*Address for correspondence: T. Hajek, M.D., Ph.D., Dalhousie University, Department of Psychiatry, QEII HSC, A.J. Lane Bldg, Room 3093, 5909 Veteran's Memorial Lane, Halifax, NS B3H 2E2, Canada. (Email: [email protected])
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Abstract

Background

Early diagnosis of schizophrenia could improve the outcomes and limit the negative effects of untreated illness. Although participants with schizophrenia show aberrant functional connectivity in brain networks, these between-group differences have a limited diagnostic utility. Novel methods of magnetic resonance imaging (MRI) analyses, such as machine learning (ML), may help bring neuroimaging from the bench to the bedside. Here, we used ML to differentiate participants with a first episode of schizophrenia-spectrum disorder (FES) from healthy controls based on resting-state functional connectivity (rsFC).

Method

We acquired resting-state functional MRI data from 63 patients with FES who were individually matched by age and sex to 63 healthy controls. We applied linear kernel support vector machines (SVM) to rsFC within the default mode network, the salience network and the central executive network.

Results

The SVM applied to the rsFC within the salience network distinguished the FES from the control participants with an accuracy of 73.0% (p = 0.001), specificity of 71.4% and sensitivity of 74.6%. The classification accuracy was not significantly affected by medication dose, or by the presence of psychotic symptoms. The functional connectivity within the default mode or the central executive networks did not yield classification accuracies above chance level.

Conclusions

Seed-based functional connectivity maps can be utilized for diagnostic classification, even early in the course of schizophrenia. The classification was probably based on trait rather than state markers, as symptoms or medications were not significantly associated with classification accuracy. Our results support the role of the anterior insula/salience network in the pathophysiology of FES.

Keywords

First-episode schizophrenia spectrumfunctional connectivityfunctional magnetic resonance imagingmachine learningsalience network
Type
Original Articles
Information
Psychological Medicine , Volume 46 , Issue 13 , October 2016 , pp. 2695 - 2704
Copyright
Copyright © Cambridge University Press 2016 

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