Using EEG to Endophenotype Schizophrenia

Abstract

This dissertation investigates EEG-derived endophenotypes to elucidate the neurophysiological underpinnings of schizophrenia through three interconnected studies. Study 1: Meta-Analysis of Resting State Microstate Differences in Schizophrenia The first study is a meta-analysis of EEG-derived microstates in schizophrenia. Microstates are brief, stable patterns of synchronized brain activity. This meta-analysis consolidates data from numerous studies to identify consistent alterations in microstates in individuals with schizophrenia compared to healthy controls. The findings reveal significant differences in microstate classes, particularly an increase in the duration and occurrence of microstate class C and a reduction in class D. These alterations suggest disrupted neural dynamics in schizophrenia, highlighting microstate parameters as a potential endophenotype for the disorder. Study 2: Resting-State Microstate Differences in Early Psychosis as an Endophenotype Candidate. The second study explores resting-state EEG recordings to examine intrinsic brain activity in individuals with early-phase psychosis. Resting-state conditions reveal baseline brain functions, often associated with the default mode network. The study compares these microstates between individuals with early psychosis (n = 27) and healthy controls (n = 30) to determine if aberrant neural dynamics persist in the absence of external stimuli. The results indicate that individuals with early psychosis exhibit distinct microstate patterns, suggesting fundamental disruptions in brain function that could serve as reliable biomarkers for early diagnosis and monitoring of schizophrenia. Study 3: Mismatch Negativity as an Endophenotype of Schizophrenia. The third study focuses on auditory processing, a critical domain affected in schizophrenia. Using EEG, this research study investigated event-related potentials (ERPs), particularly the mismatch negativity (MMN) component, which reflects automatic auditory change detection. The study found no group differences. However, MMN measures were associated with clinical symptoms. General Conclusion: The collective findings of these studies advance our understanding of the neurophysiological abnormalities in schizophrenia. By identifying microstate alterations, this research provides evidence for EEG-derived markers as potential endophenotypes. These biomarkers offer promising avenues for early diagnosis, targeted interventions, and monitoring treatment efficacy, ultimately contributing to improved outcomes for individuals with schizophrenia.