Predicting subclinical psychotic-like experiences on a continuum using machine learning

Examining the Most Important Risk Factors Predicting Persistent and Distressing Psychotic-like Experiences in Youth

BACKGROUND

Persistence and distress distinguish more clinically significant psychotic-like experiences (PLEs) from those that are less likely to be associated with impairment and/or need for care. Identifying risk factors that differentiate clinically relevant PLEs early in development is important for improving our understanding of the etiopathogenesis of these experiences. Machine learning analyses examined the most important baseline factors distinguishing persistent distressing PLEs.

METHODS

Using Adolescent Brain Cognitive Development Study PLEs data over three time points (ages 9-13), individuals with persistent distressing PLEs (n=303), transient distressing PLEs (n=374), and demographically matched low-level PLEs groups were created. Random forest classification models were trained to distinguish among persistent distressing vs. low-level PLEs, transient distressing vs. low-level PLEs, and persistent distressing vs. transient distressing PLEs. Models were trained using identified baseline predictors as input features (i.e., cognitive, neural [cortical thickness, resting state functional connectivity (RSFC)], developmental milestone delays, internalizing symptoms, adverse childhood events).

RESULTS

The model distinguishing persistent distressing vs. low-level PLEs showed the highest accuracy (test sample accuracy=69.33%; 95% CI:61.29%-76.59%). The most important predictors included internalizing symptoms, adverse childhood events, and cognitive functioning. Models distinguishing persistent vs. transient distressing PLEs generally performed poorly.

CONCLUSIONS

Model performance metrics indicated that while most important factors overlapped across models (e.g., internalizing symptoms), adverse childhood events were especially important for predicting persistent distressing PLEs. Machine learning analyses proved useful for distinguishing the most clinically relevant group from the least clinically relevant group but showed limited ability to distinguish among clinically relevant groups that differed in PLE persistence.

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

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.