Resting frontal EEG asymmetry and schizotypal traits: a test-retest study

Graph-based analysis of EEG for schizotypy classification applying flicker Ganzfeld stimulation

Ganzfeld conditions induce alterations in brain function and pseudo-hallucinatory experiences, particularly in people with high positive schizotypy. The current study uses graph-based parameters to investigate and classify brain networks under Ganzfeld conditions as a function of positive schizotypy. Participants from the general population (14 high schizotypy (HS), 29 low schizotypy (LS)) had an electroencephalography assessment during Ganzfeld conditions, with varying visual activation (8 frequencies of random light flicker) and soundscape-induced mood (neutral, serenity, and anxiety). Weighted functional networks were computed in six frequency sub-bands (delta, theta, alpha-low, alpha-high, beta, and gamma) as a function of light-flicker frequency and mood. The brain network was analyzed using graph theory parameters, including clustering coefficient (CC), strength, and global efficiency (GE). It was found that the LS groups had higher CC and strength than the HS groups, especially in bilateral temporal and frontotemporal brain regions. Moreover, some decreases in CC and strength measures were found in LS groups among occipital and parieto-occipital brain regions. LS groups also had significantly higher GE in all Ganzfeld conditions compared to the HS groups. The random under-sampling boosting (RUSBoost) algorithm achieved the best classification performance with an accuracy of 95.34%, specificity of 96.55%, and sensitivity of 92.85% during an anxiety-induction Ganzfeld condition. This is the first exploration of the relationship between brain functional state changes under Ganzfeld conditions in individuals who vary in positive schizotypy. The accuracy of graph-based parameters in classifying brain states as a function of schizotypy is shown, particularly for brain activity during anxiety induction, and should be investigated in psychosis.

Individual differences in frontal alpha asymmetry moderate the relationship between acute stress responsivity and state and trait anxiety in adolescents

Stress is a risk factor in the development and maintenance of psychopathology, particularly anxiety. Despite theory suggesting differences in stress responsivity may explain heterogeneity in anxiety, findings remain contradictory. This may be due to failure to account for individuals' neurobiological states and outdated methodologic analyses which confound conceptually and biologically distinct stress response pathways. In 145 adolescents, this study examined whether individual differences in neural activation underlying motivational states, indexed by resting frontal alpha asymmetry (FAA) before and after the Trier Social Stress Test (TSST), moderate the relationship between stress responsivity (measured by cortisol) and anxiety. Adolescents with rightward FAA activation (indexed by changes in resting FAA pre-to-post TSST) and high trait anxiety showed blunted cortisol reactivities while those with leftward FAA activation and high state anxiety showed prolonged cortisol recoveries. Our work reveals individual differences in vulnerability to psychosocial stressors and is the first study to show that FAA activation moderates the relationships between anxiety and distinct phases of the stress response in adolescents.

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.