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

Abstract

Objective. Schizotypy, a potential phenotype for schizophrenia, is a personality trait that depicts psychosis-like signs in the normal range of psychosis continuum. Family communication may affect the social functioning of people with schizotypy. Greater family stress, such as irritability, criticism and less praise, is perceived at a higher level of schizotypy. This study aims to determine the differences between people with high and low levels of schizotypy using electroencephalography (EEG) during criticism, praise and neutral comments. EEGs were recorded from twenty-nine participants in the general community who varied from low schizotypy (LS) to high schizotypy (HS) during a novel emotional auditory oddball task. Approach. We consider the difference in event-related potential (ERP) parameters, namely the amplitude and latency of P300 subcomponents (P3a and P3b), between pairs of target words (standard, positive, negative and neutral). A model based on tensor factorization is then proposed to detect these components from the EEG using the CANDECOMP/PARAFAC (CP) decomposition technique. Finally, we employ the mutual information estimation method to select influential features for classification. Main results. The highest classification accuracy, sensitivity, and specificity of 93.1%, 94.73%, and 90% are obtained via leave-one-out cross validation. Significance. This is the first attempt to investigate the identification of individuals with psychometrically-defined HS from brain responses that are specifically associated with perceiving family stress and schizotypy. By measuring these brain responses to social stress, we achieve the goal of improving the accuracy in detection of early episodes of psychosis.

Auditory P300 in individuals with high schizotypy: associations of schizotypal traits with amplitude and latency under different oddball conditions

“…At present, P300 has been studied as a potential neural biomarker to achieve the goal of improving the accuracy in detection of early episodes of psychosis ( Zandbagleh et al, 2022b ), or as a predictor of treatment and prognosis ( Kim et al, 2020 , 2015 ), that are of significance in the research on psychopathology and early-intervention of schizophrenia-spectrum. Considering the various associated tendencies between auditory P300 effect and schizotypy dimensions in our result, elaborated characteristics and parameters should be used in the oddball paradigm to enhance the application of P300 in future studies of schizophrenia-spectrum.…”

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

“…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].…”

“…Prior to data collection, ethical approval was granted by the School of Social Sciences Research Ethics Committee at NTU (approval number 2017/232), and all participants provided informed consent before participating in the experiment. The data were collected during an emotional auditory odd-ball task [22], and post-ERP recordings were used for subsequent analysis. Specifically, we focused on the data obtained after the ERP waveform had stabilized.…”

“…Their classification algorithm attained a zero false positive rate between the schizotypy and healthy control groups, indicating high degree of accuracy in group classification. Quite recently, Zandbagleh et al [22] utilized machine learning approaches to differentiate between the HS and LS groups in the same sample as the present study during an auditory oddball task. They achieved an accuracy of 93.1% based on the P300 event-related potential (ERP) subcomponents derived from tensor factorization.…”