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

Major Depressive Disorder (MDD) and Major Depressive Episodes are leading causes of disability worldwide because of the substantial functional impairment they cause in affected individuals as well as their considerable socioeconomic burden. Late-life depression is associated with physical disability, worsening of coexisting medical illnesses, institutionalization, and increased mortality. The primary goals of treatment are to achieve complete remission, restore psychosocial functioning and quality of life, and prevent relapse and recurrence. All patients diagnosed with depression should receive appropriate psychoeducation, followed by antidepressant pharmacotherapy and/or psychotherapy through a shared decision-making process involving both the clinician and the patient. Electroconvulsive therapy (ECT) is currently regarded as one of the most effective treatment modalities for Major Depressive Episode. Among elderly patients with severe major depressive episode, with or without psychotic features, particularly those presenting with active suicidal ideation, inadequate response to pharmacotherapy and/or psychotherapy, or intolerance to pharmacological treatment, ECT provides rapid clinical improvement with remission rates approaching 80%, making it one of the most effective biological treatment options currently available. Despite its well-established clinical efficacy, the mechanisms underlying ECT's therapeutic effects remain incompletely elucidated, and several biological, neurochemical, and structural hypotheses have been proposed. Electroencephalography (EEG) is a non-invasive neurophysiological technique that measures fluctuations in the electrical activity generated by neuronal populations with excellent temporal resolution. Brief periods of stable scalp topographies lasting approximately 80-120 milliseconds are referred to as EEG microstates. These microstates are considered the electrophysiological correlates of transient large-scale functional brain networks. Parameters such as microstate duration, frequency of occurrence, coverage, and transition probability provide valuable information about the brain's functional organization and dynamic stability. Although EEG-based studies investigating the effects of ECT on neuroplasticity, neurotransmitter systems, hippocampal volume, functional connectivity, and electrophysiological dynamics have attracted increasing attention in recent years, studies specifically focusing on EEG microstate analysis remain limited, especially in the older age population. Existing investigations are generally characterized by relatively small sample sizes, heterogeneity in treatment protocols, and methodological differences that may influence microstate measurements, emphasizing the need for larger and better-controlled studies. As the search for reliable predictors of treatment response has intensified, increasing evidence suggests that abnormalities in immune regulation contribute to the pathophysiology of both unipolar and bipolar depression, highlighting the immune system as a promising source of biological markers. In particular, accumulating evidence indicates that pro-inflammatory cytokines, including interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and C-reactive protein (CRP), as well as anti-inflammatory cytokines such as interleukin-2 (IL-2) and interleukin-10 (IL-10), are involved in the pathophysiology of depression and may influence response to antidepressant treatment and electroconvulsive therapy (8-10). Furthermore, a recent meta-analysis demonstrated significantly higher neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) values in patients with depression compared with healthy controls. However, studies evaluating NLR, PLR, and monocyte-to-lymphocyte ratio (MLR) as predictors of response to ECT remain scarce. Accordingly, the present study aims to investigate the neurophysiological changes associated with ECT in elderly patients diagnosed with Major Depressive Disorder through resting-state EEG microstate analysis. EEG recordings obtained before and after ECT will be compared to determine whether significant changes occur in microstate parameters, including duration, frequency, coverage, and transition probability. Furthermore, the relationships between changes in EEG microstate parameters and psychometric measures reflecting clinical improvement will be evaluated. In addition, peripheral blood samples obtained from the same patient cohort will be analyzed to determine complete blood count parameters together with concentrations 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 (sgp130), soluble interleukin-6 receptor (sIL-6R), C-reactive protein (CRP), and inflammatory indices including NLR, PLR, and MLR. Baseline inflammatory biomarker levels will be evaluated as potential predictors of treatment response, while post-treatment changes will be examined in relation to both clinical outcomes and EEG microstate parameters. By investigating the interactions between neurophysiological and inflammatory biomarkers, this study aims to identify objective predictors of ECT response and contribute to the development of more personalized treatment strategies for late-life depression. This prospective observational study will be conducted at the Department of Psychiatry, Cerrahpaşa Faculty of Medicine, Istanbul University-Cerrahpaşa. Patients aged 55 years or older who meet the DSM-5 diagnostic criteria for Major Depressive Disorder and Bipolar Disorder Major Depressive Episode, have been clinically indicated for electroconvulsive therapy (ECT), and voluntarily agree to participate in the study will be enrolled after providing written informed consent. Resting-state EEG recordings will be obtained at three time points: within 1 week before the initiation of ECT, 2 weeks after completion of the ECT course, and 2 months after completion of treatment. EEG recordings will be acquired using a computerized 19-channel EEG system equipped with Ag-AgCl disc electrodes positioned according to the International 10-20 System. The average of the A1 and A2 earlobe electrodes will serve as the reference. Signals will be sampled at 512 Hz with a high-pass filter of 0.30 Hz, a low-pass filter of 70 Hz, and an additional 50-Hz notch filter. Each recording session will last 10 minutes under resting-state conditions, consisting of 5 minutes with eyes open followed by 5 minutes with eyes closed. Participants will be instructed to remain awake, relaxed, motionless, and silent throughout the recording. Raw EEG data will undergo standard preprocessing procedures, including artifact rejection, band-pass filtering, and re-referencing before subsequent analyses. EEG microstate analysis will be performed using MICROSTATELAB v2.1, an EEGLAB extension implemented in MATLAB. Four canonical EEG microstate classes (A, B, C, and D) will be identified for each participant. The following microstate parameters will be calculated: Duration: the mean duration (milliseconds) during which an individual microstate remains stable after onset, reflecting the temporal stability of the underlying neural network. Frequency (Occurrence): the average number of occurrences of each microstate per second (Hz), representing the activation tendency of the corresponding functional network. Coverage: the percentage of the total recording time occupied by each microstate, reflecting its relative contribution to overall brain activity. Transition Probability (TP): the probability of transitions between different microstate classes. For example, the transition probability from microstate A to microstate B is calculated as the proportion of A-to-B transitions relative to all transitions originating from microstate A. This parameter provides information regarding the sequential dynamics of large-scale brain network activation. Clinical Assessments Before initiation of ECT, all patients will undergo a comprehensive clinical evaluation including a sociodemographic data form, ECT follow-up form, Hamilton Depression Rating Scale (HAM-D), Hamilton Anxiety Rating Scale (HAM-A), Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), Montgomery-Åsberg Depression Rating Scale (MADRS), Beck Scale for Suicide Ideation (BSSI), Clinical Global Impression Scale (CGI), Center for Epidemiologic Studies Depression Scale (CES-D), Cumulative Illness Rating Scale-Geriatric (CIRS-G), Maudsley Staging Scale (MSS), and Mini-Mental State Examination (MMSE). Rating scales will be administered at each EEG assessment time point, whereas clinical follow-up forms will be completed throughout the treatment period. Treatment response will be defined as a reduction of at least 50% in the baseline HAM-D-17 score, while remission will be defined as a HAM-D-17 score below 7. Peripheral venous blood samples will be collected from all patients at three time points: before the first ECT session (between 07:30 and 09:30 a.m. under fasting conditions), two weeks after completion of the ECT course, and eight weeks after treatment completion. Blood samples will be collected into 10-mL K2EDTA tubes (BD, UK), centrifuged at 4,000 rpm for 10 minutes, and plasma aliquots will be stored at -80°C until biochemical analyses are performed. Complete blood count parameters together with inflammatory biomarkers including pro-inflammatory and anti-inflammatory cytokines, C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR) will be analyzed. Age-matched healthy volunteers will also be recruited. Healthy controls will complete a sociodemographic questionnaire, MMSE, BDI, and BAI. A single resting-state EEG recording consisting of 5 minutes with eyes open and 5 minutes with eyes closed will be obtained. Peripheral blood samples will also be collected for inflammatory biomarker analyses using the same laboratory procedures as those applied to the patient group. Sample size estimation was performed using G\*Power version 3.1.9 (Faul et al., 2009). Based on an effect size of 0.70 and a statistical power (1-β) of 0.85, the required sample size was calculated to be 31 patients and 31 healthy controls. The power analysis was based on clinical treatment response as the primary outcome measure. The study population will consist of older-age patients diagnosed with Major Depressive Episode who are scheduled to receive electroconvulsive therapy at the Department of Psychiatry, Cerrahpaşa Faculty of Medicine, Istanbul University-Cerrahpaşa, and who voluntarily agree to participate in the study. EEG preprocessing and microstate analyses will be performed using EEGLAB implemented in MATLAB. Statistical analyses will be conducted using IBM SPSS Statistics version 27. Continuous variables will be presented as mean ± standard deviation or median (interquartile range), depending on data distribution, whereas categorical variables will be summarized as frequencies and percentages. Normality of continuous variables will be assessed using the Shapiro-Wilk test. For comparisons of repeated measurements obtained before ECT, two weeks after ECT, and two months after ECT, repeated-measures analysis of variance (repeated-measures ANOVA) will be used for normally distributed variables. When the assumptions for parametric testing are not met, the Friedman test will be performed. Pairwise comparisons will be adjusted using appropriate post hoc correction methods. Comparisons between patients and healthy controls will be performed using the independent-samples Student's t-test or the Mann-Whitney U test, depending on data distribution. Categorical variables will be analyzed using the Chi-square test or Fisher's exact test, as appropriate. Correlations between continuous variables, including EEG microstate parameters, inflammatory biomarkers, and psychometric scale scores, will be evaluated using Pearson's or Spearman's correlation coefficients, depending on the data distribution. To identify independent predictors of treatment response, multivariable logistic regression analysis will be performed. Statistical significance will be defined as a two-sided p-value \< 0.05.