Clinical Research Directory

EEG Microstate and Neuroinflammatory Biomarkers in Older Age Patients With Major Depressive Disorder Receiving ECT

This study aims to investigate the neurophysiological and inflammatory changes associated with electroconvulsive therapy (ECT) in older age patients diagnosed with Major Depressive Episode, Major Depression, and Bipolar Disorder, 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, in the older age population remain limited. In this regard, the present study aims to evaluate the effects of ECT on older age patients using objective neurophysiological indicators, contribute to the understanding of the pathophysiology of depression at the level of brain networks, and provide a scientific basis for the development of personalised treatment approaches in the future.