Classification of low and high schizotypy levels via evaluation of brain connectivity

Zandbagleh, A., Mirzakuchaki, S., Daliri, M.R., Premkumar, P. ORCID: 0000-0003-1934-6741 and Sanei, S. ORCID: 0000-0002-3437-2801, 2022. Classification of low and high schizotypy levels via evaluation of brain connectivity. International Journal of Neural Systems. ISSN 0129-0657

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Abstract

Schizotypy is a latent cluster of personality traits that denote a vulnerability for schizophrenia or a type of spectrum disorder. The aim of the study is to investigate parametric effective brain connectivity features for classifying high versus low schizotypy (LS) status. Electroencephalography (EEG) signals are recorded from 13 high schizotypy (HS) and 11 LS participants during an emotional auditory odd-ball task. The brain connectivity signals for machine learning are taken after the settlement of event-related potentials. A multivariate autoregressive (MVAR)-based connectivity measure is estimated from the EEG signals using the directed transfer functions (DTFs) method. The values of DTF power in five standard frequency bands are used as features. The support vector machines (SVMs) revealed significant differences between HS and LS. The accuracy, specificity, and sensitivity of the results using SVM are as high as 89.21%, 90.3%, and 88.2%, respectively. Our results demonstrate that the effective brain connectivity in prefrontal/parietal and prefrontal/frontal brain regions considerably changes according to schizotypal status. These findings prove that the brain connectivity indices offer valuable biomarkers for detecting schizotypal personality. Further monitoring of the changes in DTF following the diagnosis of schizotypy may lead to the early identification of schizophrenia and other spectrum disorders.

Item Type: Journal article
Publication Title: International Journal of Neural Systems
Creators: Zandbagleh, A., Mirzakuchaki, S., Daliri, M.R., Premkumar, P. and Sanei, S.
Publisher: World Scientific Pub Co Pte Ltd
Date: 2 March 2022
ISSN: 0129-0657
Identifiers:
NumberType
10.1142/s0129065722500137DOI
1522956Other
Rights: Electronic version of an article published as Zandbagleh, A., Mirzakuchaki, S., Daliri, M. R., Premkumar, P., & Sanei, S. (in press). Classification of low and high schizotypy levels via evaluation of brain connectivity. International Journal of Neural Systems https://doi.org/10.1142/s0129065722500137. © World Scientific Publishing Company. https://www.worldscientific.com/doi/abs/10.1142/S0129065722500137.
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 03 Mar 2022 14:31
Last Modified: 03 Mar 2022 14:31
URI: https://irep.ntu.ac.uk/id/eprint/45787

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