Cortical mechanisms of the symptomatology in major depressive disorder: a resting EEG study

Temporal stability and correlation of EEG markers and depression questionnaires scores in healthy people

Mental disorders, especially depression, have become a rising problem in modern society. The development of methods and markers for the early detection of mental disorders is an actual problem. Psychological questionnaires are the only tools for evaluating the symptoms of mental disorders in clinical practice today. The electroencephalography (EEG) based non-invasive and cost-effective method seems feasible for the early detection of depression in occupational and family medicine centers and personal monitoring. The reliability of the EEG markers in the early detection of depression assumes their high temporal stability and correlation with the scores of depression questionnaires. The study was been performed on 17 healthy people over three years. Two hypotheses have been evaluated in the current study: first, the temporal stability of EEG markers is close to the stability of the scores of depression questionnaires, and second, EEG markers and depression questionnaires' scores are not correlated in healthy people. The results of the performed study support both hypotheses: the temporal stability of EEG markers is high and close to the stability of depression questionnaires scores and the correlation between the EEG markers and depression questionnaires scores is not detected in healthy people. The results of the current study contribute to the interpretation of results in depression EEG studies and to the feasibility of EEG markers in the detection of depression.

The Resting State of Taiwan EEG Normative Database: Z-Scores of Patients with Major Depressive Disorder as the Cross-Validation

This study referred to the standard of electroencephalography (EEG) collection of normative databases and collected the Taiwan normative database to examine the reliability and validation of the Taiwan EEG normative database. We included 260 healthy participants and divided them into five groups in 10-year age-group segments and calculated the EEG means, standard deviation, and z-scores. Internal consistency reliability was verified at different frequencies between the three electrode locations in the Taiwan normative database. We recruited 221 major depressive disorder (MDD) patients for cross-validation between the Taiwan and NeuroGuide normative databases. There were high internal consistency reliabilities for delta, theta, alpha, beta, and high-beta at C3, Cz, and C4 in the HC group. There were high correlations between the two z-scores of the Taiwan and NeuroGuide normative databases in the frontal, central, parietal, temporal, and occipital lobes from MDD patients. The beta z-scores in the frontal lobe and central area, and the high-beta z-scores in the frontal, central, parietal, temporal, and occipital lobes were greater than one for MDD patients; in addition, the beta and high-beta absolute value z-scores in the whole brain were greater than the ones of MDD patients. The Taiwan EEG normative database has good psychometric characteristics of internal consistency reliability and cross-validation.

Efficacy of aripiprazole as adjunctive therapy in major depressive disorder with somatic symptoms: A randomized, double-blind, placebo-controlled trial with clinical and electroencephalography evidence

BACKGROUND

Somatic symptoms, which are common in major depressive disorder (MDD), are associated with a worse prognosis and increased health costs.

AIMS

This randomized, double-blind, placebo-controlled study evaluated the efficacy of aripiprazole augmentation in MDD patients with somatic symptoms.

METHODS

In all, 41 MDD patients with somatic symptoms completed the study. Participants who had been on a stable dose of antidepressants for at least 1 month were randomly assigned to receive an 8-week adjunctive treatment with either aripiprazole or placebo. The initiation dose of aripiprazole was 2 mg/day, which was later adjusted to 1-10 mg/day. The primary endpoint was the change in the Symptom Checklist-90-Revised-Somatization (SCL-90-R-SOM) score. We collected quantitative electroencephalogram data and performed spectral analyses to obtain the absolute power of frequency bands.

RESULTS/OUTCOMES

The aripiprazole group (n = 20; 2.98 ± 1.75 mg/day) showed a significant improvement in SCL-90-R-SOM scores compared to the placebo group (n = 21; F = 8.56, p = 0.006), without significant differences in changes in depression and anxiety symptoms. Compared to the control, the aripiprazole group showed a greater decrease in total alpha power (F = 7.03, p = 0.01). Changes in frontal alpha power were positively correlated with changes in SCL-90-R-SOM scores in the aripiprazole group (r = 0.53, p = 0.014).

CONCLUSIONS/INTERPRETATION

Aripiprazole adjunctive to antidepressants in patients with MDD and somatic symptoms improved somatic symptom severity without significant safety concerns, and this improvement correlated with a decrease in total and frontal alpha power.Trial Registration: https://cris.nih.go.kr; identifier: KCT0004607.

Coronavirus syndrome: COVID-19 psychotrauma

The authors propose term "coronavirus syndrome" for the mental disorder that is a psychical response to the global problem of COVID-19 pandemic. This syndrome will affect up to 10% of the population and we could already observe acute stress reactions to the spread of the infection and changes in people's ordinary lifestyle. However, the most severe response will be seen later, in this case the catastrophe is similar to the clinical picture of post-traumatic stress disorder. The problem is that coronavirus syndrome will affect the working capacity of population at the period, when economical recovery is essential. The risk groups are health caregivers who worked in COVID departments; patients recovered from a severe form of the disease; people who have lost their loved ones; and those who have suffered significant financial losses or lost their jobs. Adequate prophylaxis of coronavirus syndrome especially in high-risk groups are important for maintaining global mental health and economy.

Resting-state global EEG connectivity predicts depression and anxiety severity

There is a recent interest in finding neurophysiological biomarkers which will facilitate the diagnosis and understanding of the neural basis of different psychiatric disorders. In this paper, we evaluated the resting-state global EEG connectivity as a potential biomarker for depressive and anxiety symptoms. For this, we evaluated a population of 119 subjects, including 75 healthy subjects and 44 patients with major depressive disorder. We calculated the global connectivity (spectral coherence) in a setup of 60 EEG channels, for six different spectral bands: theta, alpha1, alpha2, beta1, beta2, and gamma. These global connectivity scores were used to train a Support Vector Regressor to predict symptoms measured by the Beck Depression Inventory (BDI) and the Spielberger Trait Anxiety Inventory (TAI). Experiments showed a significant prediction of both symptoms, with a mean absolute error (MAE) of 8.07±6.98 and 11.52±8.7 points, respectively. Among the most discriminating features, the global connectivity in the alpha2 band (10.0-12.0Hz) presented significantly positive Spearman's correlation with the depressive (rho = 0.32, p_FDR <0.01), and the anxiety symptoms (rho = 0.26, p_FDR<0.01).Clinical relevance-This study demonstrates that EEG global connectivity can be used to predict depression and anxiety symptoms measured by widely used questionnaires.

Features of Resting-State Electroencephalogram Theta Coherence in Somatic Symptom Disorder Compared With Major Depressive Disorder: A Pilot Study

OBJECTIVE

Somatic symptom disorder (SSD) often co-occurs with major depressive disorder (MDD). Both conditions share common psychobiological and biobehavioral characteristics, but little is known about differential patterns in brain function. In this study, we compared resting-state functional brain connectivity between SSD and MDD using quantitative electroencephalography.

METHODS

Fifteen patients with SSD (SSD group), 15 patients with MDD (MDD group), and 15 healthy volunteers (healthy control [HC] group) participated in this study. Participants were assessed with quantitative electroencephalography using a 21-channel electroencephalogram system. Electroencephalogram coherence in the theta frequency range (3.5-7.5 Hz) was assessed between the following seven electrode pairs: Fp1 and Fp2, F7 and T3, F8 and T4, T5 and P3, P4 and T6, P3 and Pz, and Pz and P4. Differences in coherence between groups were analyzed using analysis of variance.

RESULTS

Theta coherence between the F7 and T3 electrodes was lower in the SSD group than the MDD and HC groups (F(2,42) = 6.67, p = .0030). Theta coherence between the T5 and P3 electrodes was lower in the SSD and MDD groups than the HC group (F(2,42) = 5.65, p = .0067). Theta coherence between the Pz and P4 electrodes was lower in the SSD group than the MDD group (F(2,42) = 6.41, p = .0037).

CONCLUSIONS

Both SSD and MDD patients commonly showed decreased functional connectivity within the left temporoparietal junction, which has neurophysiological implications for cognitive-attentional processing and social interaction. Frontostriatal circuit dysfunction affects processes that control perception and emotion, as well as misperception of somatosensory data in the parietal somatosensory area, and is more likely to be a neuropathology of SSD than MDD.

Pre-treatment EEG signal variability is associated with treatment success in depression

Background

Previous work suggests that major depressive disorder (MDD) is associated with disturbances in global connectivity among brain regions, as well as local connectivity within regions. However, the relative importance of these global versus local changes for successful antidepressant treatment is unknown. We used multiscale entropy (MSE), a measure of brain signal variability, to examine how the propensity for local (fine scale MSE) versus global (coarse scale MSE) neural processing measured prior to antidepressant treatment is related to subsequent treatment response.

Methods

We collected resting-state EEG activity during eyes-open and closed conditions from unmedicated individuals with MDD prior to antidepressant pharmacotherapy (N = 36) as well as from non-depressed controls (N = 36). Treatment response was assessed after 12 weeks of treatment using the Montgomery-Åsberg Depression Rating Scale (MADRS), at which time participants with MDD were characterized as either responders (≥ 50% MADRS decrease) or non-responders. MSE was calculated from baseline EEG, and compared between controls, future treatment responders and non-responders. Putative interactions with the well-documented age effect on signal variability (increased reliance on local neural communication with increasing age, indexed by greater finer-scale variability) were assessed.

Results

Only in responders, we found that reduced MSE at fine temporal scales (especially fronto-centrally) and increased MSE diffusely at coarser temporal scales was related to the magnitude of the antidepressant response. In controls and MDD non-responders, but not MDD responders, there was an increase in MSE with age at fine temporal scales and a decrease in MSE with age at coarse temporal scales.

Conclusion

Our results suggest that an increased propensity toward global processing, indexed by greater MSE at coarser timescales, at baseline appears to facilitate eventual antidepressant treatment response.

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We measured resting-state EEG prior to antidepressant pharmacotherapy.

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We examined global vs. local processing in relation to antidepressant response.

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Greater response was linked with increased global processing.

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Age-related decreases in global communication were absent in future responders.

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Baseline brain dynamics in those who are/are not responsive to antidepressants differ.

Stress-Related Immune Markers in Depression: Implications for Treatment

Major depression is a serious psychiatric disorder; however, the precise biological basis of depression still remains elusive. A large body of evidence implicates a dysregulated endocrine and inflammatory response system in the pathogenesis of depression. Despite this, given the heterogeneity of depression, not all depressed patients exhibit dysregulation of the inflammatory and endocrine systems. Evidence suggests that inflammation is associated with depression in certain subgroups of patients and that those who have experienced stressful life events such as childhood trauma or bereavement may be at greater risk of developing depression. Consequently, prolonged exposure to stress is thought to be a key trigger for the onset of a depressive episode. This review assesses the relationship between stress and the immune system, with a particular interest in the mechanisms by which stress impacts immune function, and how altered immune functioning, in turn, may lead to a feed forward cascade of multiple systems dysregulation and the subsequent manifestation of depressive symptomology. The identification of stress-related immune markers and potential avenues for advances in therapeutic intervention is vital. Changes in specific biological markers may be used to characterize or differentiate depressive subtypes or specific symptoms and may predict treatment response, in turn facilitating a more effective, targeted, and fast-acting approach to treatment.

Neural complexity in patients with poststroke depression: A resting EEG study

BACKGROUND

Poststroke depression (PSD) is one of the most common emotional disorders affecting post-stroke patients. However, the neurophysiological mechanism remains elusive. This study was aimed to study the relationship between complexity of neural electrical activity and PSD.

METHODS

Resting state eye-closed electroencephalogram (EEG) signals of 16 electrodes were recorded in 21 ischemic poststroke depression (PSD) patients, 22 ischemic poststroke non-depression (PSND) patients and 15 healthy controls (CONT). Lempel-Ziv Complexity (LZC) was used to evaluate changes in EEG complexity in PSD patients. Statistical analysis was performed to explore difference among different groups and electrodes. Correlation between the severity of depression (HDRS) and EEG complexity was determined with pearson correlation coefficients. Receiver operating characteristic (ROC) and binary logistic regression analysis were conducted to estimate the discriminating ability of LZC for PSD in specificity, sensitivity and accuracy.

RESULTS

PSD patients showed lower neural complexity compared with PSND and CONT subjects in the whole brain regions. There was no significant difference among different brain regions, and no interactions between group and electrodes. None of the LZC significantly correlated with overall depression severity or differentiated symptom severity of 7 items in PSD patients, but in stroke patients, significant correlation was found between HDRS and LZC in the whole brain regions, especially in frontal and temporal. LZC parameters used for PSD recognition possessed more than 85% in specificity, sensitivity and accuracy, suggesting the feasibility of LZC to serve as screening indicators for PSD. Increased slow wave rhythms were found in PSD patients and clearly correlation was confirmed between neuronal complexity and spectral power of the four EEG rhythms.

LIMITATIONS

Lesion location of stroke patients in the study distributed in different brain regions, and most of the PSD patients were mild or moderate in depressive severity.

CONCLUSIONS

Compared with conventional spectral analysis, complexity of neural activity using LZC was more sensitive and stationary in the measurement of abnormal brain activity in PSD patients and may offer a potential approach to facilitate clinical screening of this disease.

EEG biomarkers in major depressive disorder: discriminative power and prediction of treatment response

Major depressive disorder (MDD) has high population prevalence and is associated with substantial impact on quality of life, not least due to an unsatisfactory time span of sometimes several weeks from initiation of treatment to clinical response. Therefore extensive research focused on the identification of cost-effective and widely available electroencephalogram (EEG)-based biomarkers that not only allow distinguishing between patients and healthy controls but also have predictive value for treatment response for a variety of treatments. In this comprehensive overview on EEG research on MDD, biomarkers that are either assessed at baseline or during the early course of treatment and are helpful in discriminating patients from healthy controls and assist in predicting treatment outcome are reviewed, covering recent decades up to now. Reviewed markers include quantitative EEG (QEEG) measures, connectivity measures, EEG vigilance-based measures, sleep-EEG-related measures and event-related potentials (ERPs). Further, the value and limitations of these different markers are discussed. Finally, the need for integrated models of brain function and the necessity for standardized procedures in EEG biomarker research are highlighted to enhance future research in this field.