Unveiling Hidden Dynamics: Neural Biomarkers for Psychiatric Disorders

depression, the thesis also explores the effects of ketamine, a rapid-acting antidepressant, in individuals with bipolar disorder (Chapter 4). As an NMDA receptor antagonist, ketamine has shown effectiveness in alleviating depressive symptoms, particularly in cases where traditional treatments have failed. This study examines the acute neurophysiological changes induced by ketamine, using EEG to monitor shifts in brain oscillatory patterns. The results suggest ketamine's impact on neural circuits involves distinct alterations in rhythmic and arrhythmic brain activity. The study identifies endophenotypic differences, as early and late responders exhibit different EEG patterns. This suggests that neurophysiological profiles could be used to tailor ketamine treatment, optimizing dosage and duration for individual patients. 4 The final study (illustrated in Chapter 5) focuses on the neurocircuitry of addiction, analyzing how disruptions in dopamine signaling contribute to reward processing deficits, impulsivity, and executive dysfunction. Addiction is marked by dysregulation in dopamine receptors, particularly the D2, D3, and D4 subtypes, which play crucial roles in reward, motivation, and cognitive control. This research uses event-related spectral perturbation (ERSP) and resting-state EEG to examine theta, beta, and gamma oscillations during the Iowa Gambling Task. The results reveal significant disruptions in gamma activity and a flatter power-law exponent (PLE) in individuals with addiction, pointing to dysregulation in dopamine-driven reward circuits. These oscillatory abnormalities correlate with risky decision-making, suggesting that EEG markers could serve as reliable biomarkers for addiction. Together, these studies illustrate the critical role of EEG in uncovering the neurophysiological underpinnings of psychiatric disorders. Across depression, bipolar disorder, and addiction, the consistent finding of disrupted neural oscillations emphasizes shared mechanisms of brain dysfunction. By identifying EEG biomarkers, this thesis offers potential diagnostic tools and paves the way for more personalized treatment strategies, ultimately advancing our understanding of brain disorders and improving clinical outcomes.