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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023001%3A_____%2F16%3A00060077" target="_blank" >RIV/00023001:_____/16:00060077 - isvavai.cz</a>

Alternative codes found

RIV/00216208:11120/16:43912059 RIV/00023752:_____/16:43915263

Result on the web

<a href="https://doi.org/10.1017/S0033291716000878" target="_blank" >https://doi.org/10.1017/S0033291716000878</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1017/S0033291716000878" target="_blank" >10.1017/S0033291716000878</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

FH - Neurology, neuro-surgery, nuero-sciences

OECD FORD branch

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Project

<a href="/en/project/NV16-32696A" target="_blank" >NV16-32696A: Improving early diagnosis of schizophrenia and bipolar I disorder by combining magnetic resonance imaging and machine learning</a><br>

Continuities

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Psychological Medicine

ISSN

0033-2917

e-ISSN

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Volume of the periodical

46

Issue of the periodical within the volume

13

Country of publishing house

US - UNITED STATES

Number of pages

10

Pages from-to

2695-2704

UT code for WoS article

000385358000002

EID of the result in the Scopus database

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