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

Treatment-resistant depression (TRD) is defined as the failure to achieve an adequate clinical response to at least two antidepressant medications administered at appropriate doses and durations in individuals diagnosed with Major Depressive Disorder (MDD). TRD is associated with an increased risk of suicide, impaired social functioning, higher rates of hospitalization, and substantial economic burden. For this patient population, in whom conventional pharmacotherapy and psychotherapy often fail to provide sufficient benefit, electroconvulsive therapy (ECT) remains one of the most effective treatment options available. However, the mechanisms underlying the therapeutic effects of ECT have not been fully elucidated, and numerous biological, neurochemical, and structural hypotheses have been proposed.

Electroencephalography (EEG) is a non-invasive neurophysiological method capable of measuring the brain's electrical activity with high temporal resolution. Microstate analysis of EEG data focuses on brief periods of stable scalp topographies that are thought to represent transient states of large-scale neural networks. Each microstate is considered a temporary representation of a specific neural network, and parameters such as duration, occurrence, coverage, and transition probabilities 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 gained increasing attention in recent years, research specifically focusing on microstate analysis remains limited. Existing studies are often characterized by small sample sizes, heterogeneity in treatment parameters, and potential confounding effects on microstate measures, highlighting the need for more comprehensive and controlled investigations in this field.

Accordingly, the present study aims to investigate the neurophysiological changes induced by ECT in individuals diagnosed with treatment-resistant depression through resting-state EEG microstate analysis. EEG recordings obtained before and after ECT will be compared to evaluate whether significant changes occur in microstate parameters, including duration, occurrence, coverage, and transition probabilities. Furthermore, the relationships between changes in microstate parameters and psychometric scale scores reflecting clinical improvement, together with other clinical variables, will be examined. Thus, not only the neurophysiological effects of ECT but also their associations with clinical outcomes will be comprehensively evaluated.

Another objective of the study is to investigate whether baseline EEG microstate characteristics may serve as potential biomarkers capable of predicting clinical response to ECT. By identifying neurophysiological indicators associated with favorable treatment outcomes, the study aims to advance predictive models for individualized treatment planning.

Through these aspects, the study seeks 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 future personalized treatment approaches.

Materials and Methods

The study will include voluntary patients between 18 and 60 years of age diagnosed with Major Depressive Disorder according to DSM-5 criteria, who have failed to respond to at least two antidepressant treatments and have been referred for ECT at the Department of Psychiatry, Cerrahpaşa Faculty of Medicine. Written informed consent will be obtained from all participants before enrollment.

The healthy control group will consist of age- and sex-matched volunteers aged 18 to 60 years who do not use any medication that may significantly influence EEG activity or inflammatory biomarkers and who do not have a current neurological or psychiatric disorder. Healthy controls will also provide written informed consent before participation. EEG recordings will be obtained once and will serve as reference data for normal brain activity.

ECT will be administered using the Thymatron System IV Integrated ECT Device under general anesthesia according to standard clinical protocols. Treatments will be delivered using bilateral electrode placement with brief-pulse square-wave stimulation. The duration of treatment, number of sessions, and stimulation parameters will be determined according to each patient's clinical condition.

Resting-state EEG recordings will be obtained from all participants within one week before the initiation of ECT and again during the 14 days up to 6 days and 8 weeks up to 6 days following completion of the ECT course. EEG data will be recorded using a computerized 19-channel EEG system with Ag-AgCl disc electrodes placed according to the international 10-20 system. The average of the A1 and A2 earlobe electrodes will be used as the reference. Recordings 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.

Participants will be instructed to remain relaxed, quiet, and motionless under supervision during the recording procedure. EEG acquisition will consist of 5 minutes with eyes open, followed by 5 minutes with eyes closed. Raw EEG data will undergo preprocessing, including artifact removal, band-pass filtering, and re-referencing, before analysis.

Microstate analysis will be performed using MICROSTATELAB v2.1, an EEGLAB extension. Four canonical microstate classes (A, B, C, and D) will be identified. The following microstate parameters will be calculated for each participant and used in subsequent statistical analyses:

Duration: The average time (in milliseconds) during which a microstate remains stable once activated, reflecting the stability of the underlying neural network.

Occurrence: The average number of times a given microstate appears per second (Hz), reflecting the activation tendency of the associated neural network.

Coverage: The percentage of total recording time occupied by a specific microstate, indicating the relative contribution of that neural network over time.

Transition Probability (TP): The probability of transitioning from one microstate to another. For example, the transition probability from microstate A to microstate B is calculated as the number of A-to-B transitions divided by the total number of transitions from A to all other microstate classes. This parameter provides insight into the sequential activation dynamics of neural networks.

Before ECT initiation and 2 and 8 weeks after the cessation of ECT sessions, participants will complete a sociodemographic data form, the Hamilton Depression Rating Scale (HAM-D), Hamilton Anxiety Rating Scale (HAM-A), Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), Beck Scale for Suicide Ideation (BSSI), Montgomery-Asberg Depression Rating Scale (MADRS), Clinical Global Impression Scale (CGI), the Mini-Mental State Examination (MMSE), and the Center for Epidemiologic Studies-Depression (CES-D). These assessments will be repeated before the post-treatment EEG recording.

Treatment response on the BDI will be defined as a reduction of at least 50% from baseline scores, while remission will be defined as a total score of 10 or lower. Similarly, for the HAM-D-17, treatment response will be defined as a reduction of at least 50% from baseline scores, and remission will be defined as a total score below 7.

Peripheral venous blood samples will be collected from all patients at three time points: within one week before the initiation of ECT and during the second and eighth weeks following completion of the ECT treatment course. All blood samples will be obtained in the morning after an overnight fast.

A total of 10 mL of venous blood will be collected from each participant between 7.30-9.30 am in the fasting state. Blood samples will be divided into EDTA-containing tubes for complete blood count analyses and serum separator tubes for biochemical and inflammatory biomarker measurements.

Following collection, serum samples will be centrifuged and aliquoted according to standard laboratory procedures. All serum specimens will be stored at -80°C until biochemical analyses are performed.

Study Timeline

Ethics committee approval is planned during the first month of the study. Participant recruitment will take place between months 2 and 6, followed by data analysis and completion of the study between months 6 and 8.

Statistical Analysis

Sample size estimation will be performed using G*Power v3.1.9 (Faul et al., 2009), assuming an effect size of 0.70 and a statistical power (1-β) of 0.85. The power analysis indicated that a minimum of 31 patients and 31 healthy controls are required. The sample size calculation was based on clinical treatment response as the primary outcome measure.

EEG data will be analyzed using EEGLAB running on MATLAB. Statistical analyses will be conducted using SPSS version 27. Continuous variables will be analyzed using one-way analysis of variance (ANOVA) when comparing more than two groups. Appropriate post-hoc analyses will follow significant findings. Categorical variables will be analyzed using chi-square tests. Relationships between continuous variables will be evaluated using Pearson correlation coefficients. Independent group comparisons will be performed using the Paired Samples t-test. For within-subject comparisons of pre- and post-treatment measures, paired-samples t-tests or non-parametric equivalents will be employed as appropriate.