Classification of Low and High Schizotypy Levels via Evaluation of Brain Connectivity

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.

Multiscale Fluctuation Dispersion Entropy of EEG as a Physiological Biomarker of Schizotypy

“…While spectral power analysis techniques have revealed alterations in brain EEG activity in schizotypy [12]- [15], research rarely combines comprehensive EEG analysis and machine learning techniques. For instance, we have observed this gap in the research in our previous work [17]. This gap was addressed by utilizing a directed transfer function, which was obtained from multivariate autoregressive coefficients, to assess effective brain connectivity within the same sample as the present study.…”

“…In this work, we employed the same dataset used in our previous study [17], [22]. A total of fifty participants were screened (aged 18-48 years) from the general population in Nottingham Trent University (NTU) based on their scores on the SPQ [39].…”

Tensor factorization approach for ERP-based assessment of schizotypy in a novel auditory oddball task on perceived family stress

“…On the other hand, any delay in diagnosis and treatment of psychosis-like signs contributes to poorer outcomes in psychosis [55]. Technological advances in signal processing exploit the multi-modal brain responses to accurately classify the people with schizotypy, which is a sub-clinical personality trait akin to psychosis [37]. In this study, tensor factorization, a state-of-the-art method, has been used for accurate detection of P300 subcomponents, namely P3a and P3b.…”

“…Then, this procedure is repeated for all the participants. Some performance measures namely, accuracy, specificity, sensitivity, and F1-score are derived as follows [44]: × 100 (16) where TP is the number of HS participants classified correctly in the HS class, FP is the number of LS participants classified incorrectly as HS class, TN is the number of LS participants recognized correctly in the LS class, and FN is the number of HS participants recognized incorrectly as LS class.…”

“…They examined a combination of features including weighted phase lag index (wPLI) as a measure of brain connectivity to distinguish between two schizotypy groups. Quite recently, the authors have used directed transfer function (DTF) derived from multivariate autoregressive (MVAR) coefficients and achieved an accuracy of 89.21% to effectively classify the subjects into HS and LS categories for the first time [37].…”