EEG Microstate Parameters and Neuroinflammatory Biomarkers in Patients With Treatment-Resistant Major Depressive Disorder Receiving ECT

This study aims to investigate the neurophysiological and inflammatory changes associated with electroconvulsive therapy (ECT) in patients diagnosed with Major Depressive Disorder who are resistant to at least two antidepressant treatments, using microstate analysis derived from resting-state electroencephalography (EEG) recordings. Within this scope, EEG recordings obtained before and after ECT will be compared to determine the relationships between changes in microstate parameters and inflammatory marker levels, clinical variables, and psychometric scale scores reflecting clinical improvement. Peripheral blood samples collected from the same patient group will be analyzed for complete blood count parameters as well as levels of interleukin-1 alpha (IL-1α), interleukin-1 beta (IL-1β), interleukin-2 (IL-2), interleukin-6 (IL-6), interleukin-8 (IL-8), interleukin-10 (IL-10), tumor necrosis factor-alpha (TNF-α), soluble glycoprotein 130 (sgp-130), soluble interleukin-6 receptor (sIL-6R), interferon gamma-induced protein 10 kDa (IP-10), and C-reactive protein (CRP). In addition, inflammatory indices, including the Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), and Monocyte-to-Lymphocyte Ratio (MLR), will be calculated. The association between baseline levels of these biomarkers and treatment response will be evaluated. Moreover, changes in biomarker levels following ECT will be statistically examined in relation to clinical scale scores and EEG microstate parameters. Although microstate analysis and inflammatory biomarkers have each been extensively investigated in psychiatric disorders, studies evaluating these two biomarkers together, particularly with the inclusion of healthy control participants, remain limited. In this regard, the present study aims to evaluate the effects of ECT on patients with treatment-resistant depression using objective neurophysiological indicators, to contribute to the understanding of the pathophysiology of depression at the level of brain networks, and to provide a scientific basis for the development of personalized treatment approaches in the future.