Electroencephalogram alpha asymmetry in geriatric depression : Valid or vanished?

Neural correlates of emotional working memory predict depression and anxiety

Introduction

Emotional working memory (WM) plays a critical role in cognitive functions such as emotion regulation, decision-making, and learning. Understanding how emotional stimuli, particularly negative ones, affect WM performance is crucial for identifying cognitive markers of mental health issues like anxiety and depression. Our objective is to determine whether trait anxiety and depression levels are associated with specific performance outcomes in emotional WM and whether behavioral and neural indicators demonstrate statistically significant correlations with individual anxiety and depression levels in university students.

Methods

In our research: Experiment 1 (n = 25) tested WM performance with both positive and negative emotional stimuli under different cognitive loads (2 vs. 4 items), while Experiment 2 (n = 34) combined EEG recording to investigate the neural index of anxiety and depression during negative emotional WM.

Results

Results showed that negative emotional stimuli impaired WM performance, especially under higher cognitive loads, with anxiety level being linked to increased theta activity during encoding and depression level associated with decreased alpha activity during retrieval. Additionally, individuals with higher anxiety exhibited reduced sensitivity to cognitive load differences in WM tasks involving negative emotions.

Discussion

These results demonstrated that specific EEG patterns during negative emotional WM were significantly associated with individual anxiety and depression levels, suggesting the potential utility of EEG measures for identifying at-risk individuals of anxiety and depression in university student populations. By linking cognitive and neural indicators, the study contributes to the development of personalized interventions for mental health monitoring and treatment.

Psychophysiological indices of late-life depression: A systematic review

BACKGROUND

Major depression in the older population has a profound impact on patients' quality of life and is associated with an increased risk of developing several medical illnesses. Psychophysiological methods, such as electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and eye-tracking, have provided evidence of physiological changes associated with depression in adult life. However, these aspects have not been thoroughly investigated during late-life (over 60 years of age).

METHODS

A systematic review of the scientific literature covering the studies published between 1990 and 2022 was performed to describe the current evidence on easily attainable psychophysiological factors (detected by EEG, fNIRS and eye-tracking) associated with depression in late-life.

RESULTS

Twelve studies were included in the systematic review. The included studies showed some consistent physiological patterns associated with late-life depression, such as brain hypoactivation in frontal and temporal areas and attentional biases toward emotional stimuli. No reliable patterns in EEG asymmetry and power spectrum were found, in contrast to studies on early-life depression.

LIMITATIONS

The small number of available studies, together with the heterogeneity in the techniques and methods used, highlight the need for further research to reliably identify the psychophysiological aspects of depression in late-life.

CONCLUSIONS

Physiological indices of late-life depression, as assessed by EEG, fNIRS and eye-tracking, may differ from those of early-life. The study of these indices could better clarify the physiological mechanisms underlying late-life depression with possible clinical and research implications. Recommendations for future research are also discussed.

Meta analysis of resting frontal alpha asymmetry as a biomarker of depression

This meta-analysis investigated resting frontal alpha asymmetry (FAA) as a potential biomarker for major depressive disorder (MDD). Studies included articles utilizing FAA measure involving EEG electrodes (F3/F4, F7/F8, or Fp1/Fp2) and covering both MDD and controls. Hedges' d was calculated from FAA means and standard deviations (SDs). A systematic search of PubMed through July 2023 identified 23 studies involving 1928 MDD participants and 2604 controls. The analysis revealed a small but significant grand mean effect size (ES) for FAA (F4 - F3), suggesting limited diagnostic value of FAA in MDD. Despite the presence of high heterogeneity across studies, subgroup analyses did not identify significant differences based on calculation formula, reference montage, age, or depression severity. The findings indicate that FAA may have limited standalone diagnostic utility but could complement existing clinical assessments for MDD, highlighting the need for a multifaceted approach to depression diagnosis and prognosis.

A model for the diagnosis of anxiety in patients with epilepsy based on phase locking value and Lempel-Ziv complexity features of the electroencephalogram

OBJECTIVE

Anxiety disorders (AD) are critical factors that significantly (about one-fifth) impact the quality of life (QoL) in patients with epilepsy (PWE). Objective diagnostic methods have contributed to the identification of PWE susceptible to AD. This study aimed to identify AD in PWE by constructing a diagnostic model based on the phase locking value (PLV) and Lempel-Ziv Complexity (LZC) features of the electroencephalogram (EEG).

METHODS

EEG data from 131 patients with epilepsy (PWE) were enrolled in this study. Patients were divided into two groups, anxiety disorder (AD, n = 61) and non-anxiety disorder (NAD, n = 70), according to the Hamilton Rating Scale for Anxiety (HAM-A). Support vector machine (SVM) and K-Nearest-Neighbor(KNN) algorithms were used to construct three models - the PLVEEG, LZCEEG, and PLVEEG + LZCEEG feature models. Finally, the area under the receiver operating characteristic curve (AUC) and statistical analyses were performed to evaluate the model performance.

RESULTS

The efficiency of the KNN-based PLCEEG + LZCEEG feature model was the best, and the accuracy, precision, recall, F1-score, and AUC of the model after five-fold cross-validations scores were 87.89 %, 82.27 %, 98.33 %, 88.95 %, and 0.89, respectively. When the model efficiency was optimal, 29 EEG features were suggested. Further analysis of these features indicated 22 EEG features that were significantly different between the two groups, including 50 % features of the alpha (α)-band.

CONCLUSIONS

The PLVEEG + LZCEEG model features can identify AD in PWE. The PLVEEG and LZCEEG characteristics of the α-band may further be explored as potential biomarkers for AD in PWE.

Review of EEG-based neurofeedback as a therapeutic intervention to treat depression

Depression, or major depressive disorder, is a common mental disorder that affects individuals' behavior, mood, and physical health, and its prevalence has increased during the lockdowns implemented to curb the COVID-19 pandemic. There is an urgent need to update the treatment recommendations for mental disorders during such crises. Conventional interventions to treat depression include long-term pharmacotherapy and cognitive behavioral therapy. Electroencephalogram-neurofeedback (EEG-NF) training has been suggested as a non-invasive option to treat depression with minimal side effects. In this systematic review, we summarize the recent literature on EEG-NF training for treating depression. The 12 studies included in our final sample reported that despite several issues related to EEG-NF practices, patients with depression showed significant cognitive, clinical, and neural improvements following EEG-NF training. Given its low cost and the low risk of side effects due to its non-invasive nature, we suggest that EEG-NF is worth exploring as an augmented tool for patients who already receive standard medications but remain symptomatic, and that EEG-NF training may be an effective intervention tool that can be utilized as a supplementary treatment for depression. We conclude by providing some suggestions related to experimental designs and standards to improve current EEG-NF training practices for treating depression.

Identification of spatial patterns with maximum association between power of resting state neural oscillations and trait anxiety

Anxiety affects approximately 5-10% of the adult population worldwide, placing a large burden on the health systems. Despite its omnipresence and impact on mental and physical health, most of the individuals affected by anxiety do not receive appropriate treatment. Current research in the field of psychiatry emphasizes the need to identify and validate biological markers relevant to this condition. Neurophysiological preclinical studies are a prominent approach to determine brain rhythms that can be reliable markers of key features of anxiety. However, while neuroimaging research consistently implicated prefrontal cortex and subcortical structures, such as amygdala and hippocampus, in anxiety, there is still a lack of consensus on the underlying neurophysiological processes contributing to this condition. Methods allowing non-invasive recording and assessment of cortical processing may provide an opportunity to help identify anxiety signatures that could be used as intervention targets. In this study, we apply Source-Power Comodulation (SPoC) to electroencephalography (EEG) recordings in a sample of participants with different levels of trait anxiety. SPoC was developed to find spatial filters and patterns whose power comodulates with an external variable in individual participants. The obtained patterns can be interpreted neurophysiologically. Here, we extend the use of SPoC to a multi-subject setting and test its validity using simulated data with a realistic head model. Next, we apply our SPoC framework to resting state EEG of 43 human participants for whom trait anxiety scores were available. SPoC inter-subject analysis of narrow frequency band data reveals neurophysiologically meaningful spatial patterns in the theta band (4-7 Hz) that are negatively correlated with anxiety. The outcome is specific to the theta band and not observed in the alpha (8-12 Hz) or beta (13-30 Hz) frequency range. The theta-band spatial pattern is primarily localised to the superior frontal gyrus. We discuss the relevance of our spatial pattern results for the search of biomarkers for anxiety and their application in neurofeedback studies.

Resting-state electroencephalography of neural oscillation and functional connectivity patterns in late-life depression

BACKGROUND

The clinical manifestations of late-life depression (LLD) are highly heterogeneous. Currently, abnormal characteristics of resting-state electroencephalography (EEG) power and functional connectivity are considered trait markers of depressive symptoms in major depression. However, the relationship between EEG spectral features and functional connectivity in LLD remains unknown.

METHODS

Forty-one patients with LLD and 44 participants without depression underwent an eyes-closed resting-state EEG. EEG power spectra, alpha asymmetry, and functional connectivity were calculated and analyzed.

RESULTS

Although alpha frontal asymmetry and cortical functional connectivity between the two groups showed no significant differences, the LLD group exhibited abnormal neural oscillation patterns of higher beta frequency activity in the parietal, central, and occipital lobes while alpha activity was increased in the parietal central electrodes.

LIMITATIONS

The number of EEG electrodes used in this study was low, and the sample size was limited.

CONCLUSIONS

Increased alpha and beta frequency band powers were observed in patients with LLD. These abnormal patterns may be associated with a disturbed balance of cortical excitation, inhibition, and hyperactivity. In the future, a neurofeedback protocol based on the findings of neural oscillation patterns in certain types of LLD should be explored.

The novel frontal alpha asymmetry factor and its association with depression, anxiety, and personality traits

Frontal alpha asymmetry (FAA) is widely examined in EEG research, yet a procedural consensus on its assessment is lacking. In this study, we tested a latent factorial approach to measure FAA. We assessed resting-state FAA at broad, low, and high alpha bands (8-13; 8-10.5; and 11-13 Hz) using mastoids as reference electrodes and Current Source Density (CSD) transformation (N = 139 non-clinical participants). From mastoid-referenced data, we extracted a frontal alpha asymmetry factor (FAAf) and a parietal factor (PAAf) subjecting all asymmetry indices to a varimax-rotated, principal component analysis. We explored split-half reliability and discriminant validity of the mastoid factors and the mastoid and CSD raw asymmetry indices (F3/4, F7/8, P3/4, and P7/8). Both factor and raw scores reached an excellent split-half reliability (>.99), but only the FAAf reached the maximum discriminant validity from parietal scores. Next, we explored the correlations of latent factor and raw FAA scores with symptoms of depression, anxiety, and personality traits to determine which associations were driven by FAA after variance from parietal activity was removed. After correcting for false discovery rate, only FAAf at the low alpha band was negatively associated with depression symptoms (a latent CES-D factor) and significantly diverged from PAAf's association with depression symptoms. With respect to personality traits, only CSD-transformed F7/8 was positively correlated with Conscientiousness and significantly diverged from the correlations between Conscientiousness and P3/4 and P7/8. Overall, the latent factor approach shows promise for isolating functionally distinct resting-state EEG signatures, although further research is needed to examine construct validity.

Frontal Alpha Asymmetry and Negative Mood: A Cross-Sectional Study in Older and Younger Adults

Frontal alpha asymmetry (FAA) has been associated with emotional processing and affective psychopathology. Negative and withdrawal stimuli and behaviors have been related to high levels of right cortical activity, while positive and approach stimuli and behaviors have been related to increased left cortical activity. Inconsistent results in terms of depressive and anxious symptoms and their relationship to FAA have been previously observed, especially at older ages. Additionally, no studies to date have evaluated whether loneliness, a negative feeling, is related to FAA. Therefore, this study aimed (i) to compare FAA between younger and older adults and (ii) to investigate the possible relationships between loneliness, depressive and anxious symptomatology with FAA in young and older adults. Resting electroencephalogram recordings of 39 older (Mage = 70.51, SD = 7.12) and 57 younger adults (Mage = 22.54, SD = 3.72) were analyzed. Results showed greater left than right cortical activity for both groups and higher FAA for older than younger adults. FAA was not predicted by loneliness, depressive or anxious symptomatology as shown by regression analyses. Findings bring clarity about FAA patterns at different ages and open the discussion about the relationship between negative emotional processing and frontal cortical imbalances, especially at older ages.

The relationship between emotional regulation and hemispheric lateralization in depression: a systematic review and a meta-analysis

From a neurobiological perspective, diverse studies have associated emotional regulation with cognitive deficits. Structural and/or metabolic changes in the frontal cortex are often inferred from dysfunction in cognitive-emotional processing. In addition, electroencephalographic findings support the idea that alpha band oscillations are responses to these same processes. Thus, the objective of this meta-analytical literature review is to verify whether the possible hemispheric lateralization attributed to frontal alpha asymmetry (FAA) correlates with emotional regulation and the cognitive deficits underlying depression. The data included in our meta-analysis are from articles published from 2009 to July 2020, which utilized DSM or ICD criteria to diagnose depression or anxiety disorders and included a control group. For statistical analysis, the measurements obtained through the 10-20 electroencephalography system were used. The frontal alpha asymmetry index was calculated from the difference between the logarithm of the absolute spectral values in the alpha rhythm observed from the F4 and F3 electrodes that were fixed to the scalp of the frontal region of the right and left hemispheres (ln µV² RH-ln µV² LH) = (F4-F3). Eighteen articles were included in the systematic review. Of these, 9 were homogeneous enough for statistical analyses (total N: 1061; N_Dep: 326; N_cont: 735). Nine others could not be statistically analyzed due to the absence of FAA measurements from the F4 and F3 electrodes. A random effects meta-analysis revealed low heterogeneity (Qt = 11,00, df = 8, p = 0.20, I² = 27%) and an average effect size of the studies equal to -0.03 (CI = [-0.07 to 0.01]). The results, although not significant, suggested a slight tendency toward left lateralization in the depression group. Although the effects shown in these data did not confirm hemispherical lateralization in depressed patients, it was found that emotional regulation and cognitive processes share similar neural circuits. Therefore, future research on this complex relationship is encouraged, especially studies that are focused on the search for quantitative biological markers in depression.

No relationship between frontal alpha asymmetry and depressive disorders in a multiverse analysis of five studies

For decades, the frontal alpha asymmetry (FAA) - a disproportion in EEG alpha oscillations power between right and left frontal channels - has been one of the most popular measures of depressive disorders (DD) in electrophysiology studies. Patients with DD often manifest a left-sided FAA: relatively higher alpha power in the left versus right frontal lobe. Recently, however, multiple studies failed to confirm this effect, questioning its reproducibility. Our purpose is to thoroughly test the validity of FAA in depression by conducting a multiverse analysis - running many related analyses and testing the sensitivity of the effect to changes in the analytical approach - on data from five independent studies. Only 13 of the 270 analyses revealed significant results. We conclude the paper by discussing theoretical assumptions underlying the FAA and suggest a list of guidelines for improving and expanding the EEG data analysis in future FAA studies.

Predicting Dementia With Prefrontal Electroencephalography and Event-Related Potential

Objective: To examine whether prefrontal electroencephalography (EEG) can be used for screening dementia.

Methods: We estimated the global cognitive decline using the results of Mini-Mental Status Examination (MMSE), measurements of brain activity from resting-state EEG, responses elicited by auditory stimulation [sensory event-related potential (ERP)], and selective attention tasks (selective-attention ERP) from 122 elderly participants (dementia, 35; control, 87). We investigated that the association between MMSE and each EEG/ERP variable by using Pearson’s correlation coefficient and performing univariate linear regression analysis. Kernel density estimation was used to examine the distribution of each EEG/ERP variable in the dementia and non-dementia groups. Both Univariate and multiple logistic regression analyses with the estimated odds ratios were conducted to assess the associations between the EEG/ERP variables and dementia prevalence. To develop the predictive models, five-fold cross-validation was applied to multiple classification algorithms.

Results: Most prefrontal EEG/ERP variables, previously known to be associated with cognitive decline, show correlations with the MMSE score (strongest correlation has |r| = 0.68). Although variables such as the frontal asymmetry of the resting-state EEG are not well correlated with the MMSE score, they indicate risk factors for dementia. The selective-attention ERP and resting-state EEG variables outperform the MMSE scores in dementia prediction (areas under the receiver operating characteristic curve of 0.891, 0.824, and 0.803, respectively). In addition, combining EEG/ERP variables and MMSE scores improves the model predictive performance, whereas adding demographic risk factors do not improve the prediction accuracy.

Conclusion: Prefrontal EEG markers outperform MMSE scores in predicting dementia, and additional prediction accuracy is expected when combining them with MMSE scores.

Significance: Prefrontal EEG is effective for screening dementia when used independently or in combination with MMSE.

FUNCTIONAL NEUROMARKERS IN PARKINSON’S DISEASE (PD)

New neurotechnologies which help to study not only the structure but also brain work, especially in milliseconds, allow for a more accurate diagnosis of a given disease entity. The aim of our study was to characterize the functional neuromarkers, including a new neuromarker, that is high rolandic beta, in Parkinson’s disease (PD). A 76-year-old male patient, a university professor, a widower, in an intimate relationship with a beloved partner, was tested in the Reintegration and Training Center of the Polish Society of Neuropsychology. Five years earlier (when he was 71 years old), following long-term stress, he had had a transient ischemic attack (TIA). In the following years he experienced two neurological episodes, and was diagnosed, on the basis of MRI findings and clinical symptoms, with vascular (multi-infarct) Parkinsonism. A sudden deterioration in his functioning, including hand tremors at rest, bradykinesia (motor slowdown), asymmetrical gait difficulties, postural instability, and falls typical for PD, as well as MRI finding (the appearance of ‘a swallow tail ’ on the left side, and the lack on the right of the substantia nigra within the midbrain) was the cause of further differential diagnosis. He was assessed using the HBI methodology (Kropotov 2016; Pąchalska, Kaczmarek, Kropotov 2014). EEG was recorded from 19 scalp sites, in resting state conditions, with eyes open and eyes closed, and during the cued GO/NOGO tasks with animal/plants as GO/NOGO stimuli. The electrodes were applied according to the International 10-20 system. The EEG was recorded referentially to linked ears, allowing for a computational re-referencing of the data (remontaging). Event related potentials (ERPs) were used to assess the functional changes manifested by the patient. To compare our patient with healthy controls we used the normative Human Brain Index (HBI), a database obtained through joint research by Swiss, Norwegian, Polish and Russian neuroscientists (Kropotov 2018). This database included behavioral parameters and ERP measures in 6 different neuropsychological tasks for 1000 healthy subjects. What is striking, no signs of cognitive dysfunction was found; however observed were an asymmetrical frontal lobe alpha (a neuromarker of depression) and excessive Rolandic beta (a neuromarker of Parkinson’s disease). We will discuss the results on the basis of recent subject literature findings, including the personal factors that might influenced the process of the diagnosis and treatment of this patient, ones which should be also taken into account in any differential diagnosis. The obtained results show the importance of using HBI methodology in clinical practice. Physicians involved in the diagnosis and treatment of those with progressive ambulatory impairment and an abnormal white matter (WM) signal on neuroimaging, should when formulating any differential diagnosis consider the use of this approach. ------------------------------------------------------------------------------------------------------------------------------------

Relationship Between Abnormalities in Resting-State Quantitative Electroencephalogram Patterns and Poststroke Depression

PURPOSE

Spectral power analysis of quantitative EEG has gained popularity in the assessment of depression, but findings across studies concerning poststroke depression (PSD) have been inconsistent. The goal of this study was to determine the extent to which abnormalities in quantitative EEG differentiate patients with PSD from poststroke nondepressed (PSND) subjects.

METHODS

Resting-state EEG signals of 34 participants (11 patients with PSD and 23 PSND subjects) were recorded, and then the spectral power analysis for six frequency bands (alpha1, alpha2, beta1, beta2, delta, and theta) was conducted at 16 electrodes. Pearson linear correlation analysis was used to investigate the association between depression severity measured with the Hamilton Depression Rating Scale (HDRS) total score and absolute power values. In addition, receiver operating characteristic curves were used to assess the sensitivity and specificity of quantitative EEG in discriminating PSD.

RESULTS

In comparison with PSND patients, PSD patients showed significantly higher alpha1 power in left temporal region and alpha2 power at left frontal pole. Higher theta power in central, temporal, and occipital regions was observed in patients with PSD. The results of Pearson linear correlation analysis showed significant association between HDRS total score and the absolute alpha1 power in frontal, temporal, and parietal regions.

CONCLUSIONS

Absolute powers of alpha and theta bands significantly distinguish between PSD patients and PSND subjects. Besides, absolute alpha1 power is positively associated with the severity of depression.

EEG as a Functional Marker of Nicotine Activity: Evidence From a Pilot Study of Adults With Late-Life Depression

Late-life depression (LLD) is a debilitating condition that is associated with poor response to antidepressant medications and deficits in cognitive performance. Nicotinic cholinergic stimulation has emerged as a potentially effective candidate to improve cognitive performance in patients with cognitive impairment. Previous studies of nicotinic stimulation in animal models and human populations with cognitive impairment led to examining potential cognitive and mood effects of nicotinic stimulation in older adults with LLD. We report results from a pilot study of transdermal nicotine in LLD testing whether nicotine treatment would enhance cognitive performance and mood. The study used electroencephalography (EEG) recordings as a tool to test for potential mechanisms underlying the effect of nicotine. Eight non-smoking participants with LLD completed EEG recordings at baseline and after 12 weeks of transdermal nicotine treatment (NCT02816138). Nicotine augmentation treatment was associated with improved performance on an auditory oddball task. Analysis of event-related oscillations showed that nicotine treatment was associated with reduced beta desynchronization at week 12 for both standard and target trials. The change in beta power on standard trials was also correlated with improvement in mood symptoms. This pilot study provides preliminary evidence for the impact of nicotine in modulating cortical activity and improving mood in depressed older adults and shows the utility of using EEG as a marker of functional engagement in nicotinic interventions in clinical geriatric patients.

A study on BOLD fMRI of the brain basic activities of MDD and the first-degree relatives

Introduction: The present study aims to explore the characteristics and differences of the ReHo, ALFF and fALFF of brain in the resting state of depression and first-degree relatives, in order to identify candidate central prodromal biomarkers of depression.Method: Three groups of medication-free patients (39-59 years old) was involved in this study, including the patients with major depression disorder (MDD group, n = 15), healthy volunteers with first-degree relatives with MDD (first-degree relatives group, n = 15), healthy volunteers with no personal or family history of MDD (the control group [HC], n = 15). Participants underwent functional MRI while staying in a resting state after a conventional MRI scanning on a clinical 3 T system(Siemens Skyra, Germany).Results: The ReHo, ALFF and fALFF values are different in brain of MDD, first-degree relatives, and HC (p<.05). MDD patients exhibited abnormal spontaneous activity in multiple brain regions which are closely related to emotion regulation and perception. The present findings provide further insight into the pathological mechanisms underlying MDD.Conclusion: With the widespread abnormal values of brain in MDD and first-degree relatives measured, we can get a hypothesis that these abnormalities may be associated with cognitive network disorders and emotional distress in MDD.Key pointsThe fMRI could increase the early validity of MDD as a new diagnostic and disease-monitoring tool.Monitoring ReHo, ALFF, fALFF values using fMRI can provide insight into the presence and evolution of MDD disease and permit objective evaluation of brain abnormalities.It appears that ReHo, ALFF, fALFF could be used as markers for monitoring disease progression and treatment effects in MDD patients in the future.

EEG based Classification of Long-term Stress Using Psychological Labeling

Stress research is a rapidly emerging area in the field of electroencephalography (EEG) signal processing. The use of EEG as an objective measure for cost effective and personalized stress management becomes important in situations like the nonavailability of mental health facilities. In this study, long-term stress was classified with machine learning algorithms using resting state EEG signal recordings. The labeling for the stress and control groups was performed using two currently accepted clinical practices: (i) the perceived stress scale score and (ii) expert evaluation. The frequency domain features were extracted from five-channel EEG recordings in addition to the frontal and temporal alpha and beta asymmetries. The alpha asymmetry was computed from four channels and used as a feature. Feature selection was also performed to identify statistically significant features for both stress and control groups (via t-test). We found that support vector machine was best suited to classify long-term human stress when used with alpha asymmetry as a feature. It was observed that the expert evaluation-based labeling method had improved the classification accuracy by up to 85.20%. Based on these results, it is concluded that alpha asymmetry may be used as a potential bio-marker for stress classification, when labels are assigned using expert evaluation.

The effects of alpha asymmetry and high-beta down-training neurofeedback for patients with the major depressive disorder and anxiety symptoms

BACKGROUND

Alpha-asymmetry neurofeedback (ALAY) was applied to patients with major depressive disorder (MDD) based on the theory of frontal alpha asymmetry. Neurophysiological studies have found a higher high-beta activity of electroencephalography (EEG) at the posterior cortex among patients with comorbid MDD and anxiety symptoms. The present study examined the effects of ALAY and high-beta down-training (Beta) neurofeedback in symptoms of depression and anxiety and EEG parameters.

METHOD

Eighty-seven patients with comorbid MDD and anxiety symptoms were allocated to the ALAY, Beta, or control groups. Both neurofeedback groups received ten-session neurofeedback. All participants completed the Beck Depression Inventory II (BDI-II), Beck Anxiety Inventory (BAI), and five minutes resting EEG recording at pre-test and post-test. EEG raw signals were transformed into an A1 score [log (F4 alpha) - log (F3 alpha)], P3 and P4 high-beta power.

RESULTS

BDI-II and BAI scores decreased at post-test in both ALAY and Beta groups, but no significant difference between the two groups. No significant interaction effect in A1 score at pre-test and post-test between the ALAY, Beta, and control groups. The P3 high-beta was significantly decreased in the Beta group, an increase in the control group, and no change in the ALAY group at post-test compared to the pre-test.

CONCLUSIONS

Both neurofeedback groups decreased symptoms of depression and anxiety. The Beta group was more effective in decreasing high-beta power at the parietal cortex compared to other groups. This non-invasive psychological intervention can be used in the future for patients with comorbid MDD and anxiety symptoms.

Electroencephalogram alpha asymmetry in patients with depressive disorders: current perspectives

PURPOSE

Electroencephalogram (EEG) alpha asymmetry (AA) in depressive disorders has been of interest over the last few decades, but it continues to remain unclear whether EEG AA can discriminate between healthy and depressive individuals.

MATERIALS AND METHODS

A systematic literature search for papers addressing EEG AA using the keywords alpha asymmetry, depression, and EEG was performed in PubMed. All studies were checked for sample size, gender, handedness, reference, recording protocol, EEG band range, impedance, type of analysis, drugs, and comorbidity.

RESULTS

A total of 61 articles were found, of which 44 met our inclusion criteria. They have been consecutively analyzed in respect of methodology and results. Approximately 25% (11/44) of the studies did not mention or ignored handedness, 41% (18/44) of the studies used data with only self-reported handedness, and only 34.1% (15/44) of all studies tested handedness. Only 35% (15/44) of the studies reported pharmacological treatment, and only 35% (15/44) of the studies controlled for medication. A total of 52% (23/44) of the studies reported comorbidity, and only 30% (13/44) of the studies controlled for comorbidity. Only 29.6% (13/44) of the studies reported education. In all, 30.5% (13/44) of the studies analyzed group differences and correlations, while 15.9 (7/44) of the studies used only correlational analyses. A total of 52.3% (23/44) of the studies analyzed only group differences. Alpha range was fixed (8-13 Hz) in 59.1% (26/44) of all studies. Reference to common average was used in seven of 44 studies (15.9%). In all, nine of 44 (20.5%) studies used the midline central position as reference, 22 of 44 (50%) studies used the ear or the mastoid as reference, and four of 44 (9.1%) studies used the nose as reference.

CONCLUSION

Discriminative power of EEG AA for depressed and healthy controls remains unclear. A systematic analysis of 44 studies revealed that differences in methodology and disregarding proper sampling are problematic. Ignoring handedness, gender, age, medication, and comorbidity could explain inconsistent findings. Hence, we formulated a guideline for requirements for future studies on EEG AA in order to allow for better comparisons.

Frontal alpha asymmetry as a diagnostic marker in depression: Fact or fiction? A meta-analysis

BACKGROUND

Frontal alpha asymmetry (FAA) has frequently been reported as potential discriminator between depressed and healthy individuals, although contradicting results have been published. The aim of the current study was to provide an up to date meta-analysis on the diagnostic value of FAA in major depressive disorder (MDD) and to further investigate discrepancies in a large cross-sectional dataset.

METHODS

SCOPUS database was searched through February 2017. Studies were included if the article reported on both MDD and controls, provided an FAA measure involving EEG electrodes F3/F4, and provided data regarding potential covariates. Hedges' d was calculated from FAA means and standard deviations (SDs). Potential covariates, such as age and gender, were explored. Post hoc analysis was performed to elucidate interindividual differences that could explain interstudy discrepancies.

RESULTS

16 studies were included (MDD: n = 1883, controls: n = 2161). After resolving significant heterogeneity by excluding studies, a non-significant Grand Mean effect size (ES) was obtained (d = - 0.007;CI = [- 0.090]-[0.075]). Crosssectional analyses showed a significant three-way interaction for Gender × Age × Depression severity in the depressed group, which was prospectively replicated in an independent sample.

CONCLUSIONS

The main result was a non-significant, negligible ES, demonstrating limited diagnostic value of FAA in MDD. The high degree of heterogeneity across studies indicates covariate influence, as was confirmed by crosssectional analyses, suggesting future studies should address this Gender × Age × Depression severity interaction. Upcoming studies should focus more on prognostic and research domain usages of FAA rather than a pure diagnostic tool.