EEG Power Spectra at Early Stages of Depressive Disorders

Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives

Electroencephalography (EEG) is useful for studying brain activity in major depressive disorder (MDD), particularly focusing on theta and alpha frequency bands via power spectral density (PSD). However, PSD-based analysis has often produced inconsistent results due to difficulties in distinguishing between periodic and aperiodic components of EEG signals. We analyzed EEG data from 114 young adults, including 74 healthy controls (HCs) and 40 MDD patients, assessing periodic and aperiodic components alongside conventional PSD at both source and electrode levels. Machine learning algorithms classified MDD versus HC based on these features. Sensor-level analysis showed stronger Hedge's g effect sizes for parietal theta and frontal alpha activity than source-level analysis. MDD individuals exhibited reduced theta and alpha activity relative to HC. Logistic regression-based classifications showed that periodic components slightly outperformed PSD, with the best results achieved by combining periodic and aperiodic features (AUC = 0.82). Strong negative correlations were found between reduced periodic parietal theta and frontal alpha activities and higher scores on the Beck Depression Inventory, particularly for the anhedonia subscale. This study emphasizes the superiority of sensor-level over source-level analysis for detecting MDD-related changes and highlights the value of incorporating both periodic and aperiodic components for a more refined understanding of depressive disorders.

Resting-state EEG features modulated by depressive state in healthy individuals: insights from theta PSD, theta-beta ratio, frontal-parietal PLV, and sLORETA

Depressive states in both healthy individuals and those with major depressive disorder exhibit differences primarily in symptom severity rather than symptom type, suggesting that there is a spectrum of depressive symptoms. The increasing prevalence of mild depression carries lifelong implications, emphasizing its clinical and social significance, which parallels that of moderate depression. Early intervention and psychotherapy have shown effective outcomes in subthreshold depression. Electroencephalography serves as a non-invasive, powerful tool in depression research, with many studies employing it to discover biomarkers and explore underlying mechanisms for the identification and diagnosis of depression. However, the efficacy of these biomarkers in distinguishing various depressive states in healthy individuals and in understanding the associated mechanisms remains uncertain. In our study, we examined the power spectrum density and the region-based phase-locking value in healthy individuals with various depressive states during their resting state. We found significant differences in neural activity, even among healthy individuals. Participants were categorized into high, middle, and low depressive state groups based on their response to a questionnaire, and eyes-open resting-state electroencephalography was conducted. We observed significant differences among the different depressive state groups in theta- and beta-band power, as well as correlations in the theta-beta ratio in the frontal lobe and phase-locking connections in the frontal, parietal, and temporal lobes. Standardized low-resolution electromagnetic tomography analysis for source localization comparing the differences in resting-state networks among the three depressive state groups showed significant differences in the frontal and temporal lobes. We anticipate that our study will contribute to the development of effective biomarkers for the early detection and prevention of depression.

Vulnerable at rest? A resting-state EEG study and psychosocial factors of young adult offspring of alcohol-dependent parents

BACKGROUND

Offspring of parents with alcohol use disorder (AUD) are more susceptible to developing AUD, with an estimated heritability of around 50%. Vulnerability to AUD in first-degree relatives is influenced by biological factors, such as spontaneous brain activity, and high-risk psychosocial characteristics. However, existing resting-state EEG studies in AUD offspring have shown inconsistent findings regarding theta, alpha, and beta band frequencies. Additionally, research consistently demonstrates an increased risk of internalizing and externalizing disorders, self-regulation difficulties, and interpersonal issues among AUD offspring.

METHODS

This study aimed to investigate the absolute power of theta, alpha, and beta frequencies in young adult offspring with a family history of AUD compared to individuals without family history. The psychosocial profiles of the offspring were also examined in relation to individuals without a family history of AUD. Furthermore, the study sought to explore the potential association between differences in frequency bands and psychosocial variables. Resting-state EEG recordings were obtained from 31 young adult healthy offspring of alcohol-dependent individuals and 43 participants with no family history of AUD (age range: 16-25 years). Participants also completed self-report questionnaires assessing anxiety and depressive symptoms, impulsivity, emotion regulation, and social involvement.

RESULTS

The results revealed no significant differences in spontaneous brain activity between the offspring and participants without a family history of AUD. However, in terms of psychosocial factors, the offspring exhibited significantly lower social involvement than the control group.

CONCLUSIONS

This study does not provide evidence suggesting vulnerability in offspring based on differences in spontaneous brain activity. Moreover, this investigation highlights the importance of interventions aimed at enhancing social connections in offspring. Such interventions can not only reduce the risk of developing AUD, given its strong association with increased feelings of loneliness but also improve the overall well-being of the offspring.

Excessive daytime sleepiness is associated with relative delta frequency power among patients with mild OSA

Background

Excessive daytime sleepiness (EDS) is a cause of low quality of life among obstructive sleep apnoea (OSA) patients. Current methods of assessing and predicting EDS are limited due to time constraints or differences in subjective experience and scoring. Electroencephalogram (EEG) power spectral densities (PSDs) have shown differences between OSA and non-OSA patients, and fatigued and non-fatigued patients. Therefore, polysomnographic EEG PSDs may be useful to assess the extent of EDS among patients with OSA.

Methods

Patients presenting to Israel Loewenstein hospital reporting daytime sleepiness who recorded mild OSA on polysomnography and undertook a multiple sleep latency test. Alpha, beta, and delta relative powers were assessed between patients categorized as non-sleepy (mean sleep latency (MSL) ≥10 min) and sleepy (MSL <10 min).

Results

139 patients (74% male) were included for analysis. 73 (53%) were categorized as sleepy (median MSL 6.5 min). There were no significant differences in demographics or polysomnographic parameters between sleepy and non-sleepy groups. In multivariate analysis, increasing relative delta frequency power was associated with increased odds of sleepiness (OR 1.025 (95% CI 1.024-1.026)), while relative alpha and beta powers were associated with decreased odds. The effect size of delta PSD on sleepiness was significantly greater than that of either alpha or beta frequencies.

Conclusion

Delta PSD during polysomnography is significantly associated with a greater degree of objective daytime sleepiness among patients with mild OSA. Further research is needed to corroborate our findings and identify the direction of potential causal correlation between delta PSD and EDS.

Altered resting-state brain oscillation and the associated cognitive impairments in late-life depression with different depressive severity: An EEG power spectrum and functional connectivity study

OBJECTIVE

Cognitive impairments are prevalent in late-life depression (LLD). However, it remains unclear whether there are concurrent brain oscillation alterations in resting condition across varying level of depression severity. This cross-sectional study aims to investigate the characteristics of altered resting-state oscillations, including power spectrum and functional connectivity, and their association with the cognitive impairments in LLD with different depression severity.

METHODS

A total of 65 patients with LLD and 40 elder participants without depression were recruited. Global cognition and subtle cognitive domains were evaluated. A five-minute resting-state electroencephalography (EEG) was conducted under eyes-closed conditions. Measurements included the ln-transformed absolute power for power spectrum analysis and the weighted phase lag index (wPLI) for functional connectivity analysis.

RESULTS

Attentional and executive dysfunction were exhibited in Moderate-Severe LLD group. Enhanced posterior upper gamma power was observed in both LLD groups. Additionally, enhanced parietal and fronto-parietal/occipital theta connectivity were observed in Moderate-Severe LLD group, which were associated with the attentional impairment.

LIMITATIONS

Limitations include a small sample size, concomitant medication use, and a relatively higher proportion of females.

CONCLUSIONS

Current study observed aberrant brain activity patterns in LLD across different levels of depression severity, which were linked to cognitive impairments. The altered posterior brain oscillations may be trait marker of LLD. Moreover, cognitive impairments and associated connectivity alterations were exhibited in moderate-severe group, which may be a state-like marker of moderate-to severe LLD. The study deepens understanding of cognitive impairments with the associated oscillation changes, carrying implications for neuromodulation targets in LLD.

[Electroencephalography: features of the obtained data and its applicability in psychiatry]

Presently, there is an increased interest in expanding the range of diagnostic and scientific applications of electroencephalography (EEG). The method is attractive due to non-invasiveness, availability of equipment with a wide range of modifications for various purposes, and the ability to track the dynamics of brain electrical activity directly and with high temporal resolution. Spectral, coherency and other types of analysis provide volumetric information about its power, frequency distribution, spatial organization of signal and its self-similarity in dynamics or in different sections at a time. The development of computing technologies provides processing of volumetric data obtained using EEG and a qualitatively new level of their analysis using various mathematical models. This review discusses benefits and limitations of using the EEG in scientific research, currently known interpretation of the obtained data and its physiological and pathological correlates. It is expected to determine the complex relationship between the parameters of brain electrical activity and various functional and pathological conditions. The possibility of using EEG characteristics as biomarkers of various physiological and pathological conditions is being considered. Electronic databases, including MEDLINE (on PubMed), Google Scholar and Russian Scientific Citation Index (RSCI, on elibrary.ru), scientific journals and books were searched to find relevant studies.

The Three-Lead EEG Sensor: Introducing an EEG-Assisted Depression Diagnosis System Based on Ant Lion Optimization

For depression diagnosis, traditional methods such as interviews and clinical scales have been widely leveraged in the past few decades, but they are subjective, time-consuming, and labor-consuming. With the development of affective computing and Artificial Intelligence (AI) technologies, Electroencephalogram (EEG)-based depression detection methods have emerged. However, previous research has virtually neglected practical application scenarios, as most studies have focused on analyzing and modeling EEG data. Furthermore, EEG data is typically obtained from specialized devices that are large, complex to operate, and poorly ubiquitous. To address these challenges, a wearable three-lead EEG sensor with flexible electrodes was developed to obtain prefrontal-lobe EEG data. Experimental measurements show that the EEG sensor achieves promising performance (background noise of no more than 0.91 μVpp, Signal-to-Noise Ratio (SNR) of 26--48 dB, and electrode-skin contact impedance of less than 1 K Ω). In addition, EEG data from 70 depressed patients and 108 healthy controls were collected using the EEG sensor, and the linear and nonlinear features were extracted. The features were then weighted and selected using the Ant Lion Optimization (ALO) algorithm to improve classification performance. The experimental results show that the k-NN classifier achieves a classification accuracy of 90.70%, specificity of 96.53%, and sensitivity of 81.79%, indicating the promising potential of the three-lead EEG sensor combined with the ALO algorithm and the k-NN classifier for EEG-assisted depression diagnosis.

Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning

Depressive disorder (DD) has become one of the most common mental diseases, seriously endangering both the affected person's psychological and physical health. Nowadays, a DD diagnosis mainly relies on the experience of clinical psychiatrists and subjective scales, lacking objective, accurate, practical, and automatic diagnosis technologies. Recently, electroencephalogram (EEG) signals have been widely applied for DD diagnosis, but mainly with high-density EEG, which can severely limit the efficiency of the EEG data acquisition and reduce the practicability of diagnostic techniques. The current study attempts to achieve accurate and practical DD diagnoses based on combining frontal six-channel electroencephalogram (EEG) signals and deep learning models. To this end, 10 min clinical resting-state EEG signals were collected from 41 DD patients and 34 healthy controls (HCs). Two deep learning models, multi-resolution convolutional neural network (MRCNN) combined with long short-term memory (LSTM) (named MRCNN-LSTM) and MRCNN combined with residual squeeze and excitation (RSE) (named MRCNN-RSE), were proposed for DD recognition. The results of this study showed that the higher EEG frequency band obtained the better classification performance for DD diagnosis. The MRCNN-RSE model achieved the highest classification accuracy of 98.48 ± 0.22% with 8-30 Hz EEG signals. These findings indicated that the proposed analytical framework can provide an accurate and practical strategy for DD diagnosis, as well as essential theoretical and technical support for the treatment and efficacy evaluation of DD.

Comparing resting state and task-based EEG using machine learning to predict vulnerability to depression in a non-clinical population

Major Depressive Disorder (MDD) affects a large portion of the population and levies a huge societal burden. It has serious consequences like decreased productivity and reduced quality of life, hence there is considerable interest in understanding and predicting it. As it is a mental disorder, neural measures like EEG are used to study and understand its underlying mechanisms. However most of these studies have either explored resting state EEG (rs-EEG) data or task-based EEG data but not both, we seek to compare their respective efficacy. We work with data from non-clinically depressed individuals who score higher and lower on the depression scale and hence are more and less vulnerable to depression, respectively. Forty participants volunteered for the study. Questionnaires and EEG data were collected from participants. We found that people who are more vulnerable to depression had on average increased EEG amplitude in the left frontal channel, and decreased amplitude in the right frontal and occipital channels for raw data (rs-EEG). Task-based EEG data from a sustained attention to response task used to measure spontaneous thinking, an increased EEG amplitude in the central part of the brain for individuals with low vulnerability and an increased EEG amplitude in right temporal, occipital and parietal regions in individuals more vulnerable to depression were found. In an attempt to predict vulnerability (high/low) to depression, we found that a Long Short Term Memory model gave the maximum accuracy of 91.42% in delta wave for task-based data whereas 1D-Convolution neural network gave the maximum accuracy of 98.06% corresponding to raw rs-EEG data. Hence if one has to look at the primary question of which data will be good for predicting vulnerability to depression, rs-EEG seems to be better than task-based EEG data. However, if mechanisms driving depression like rumination or stickiness are to be understood, task-based data may be more effective. Furthermore, as there is no consensus as to which biomarker of rs-EEG is more effective in the detection of MDD, we also experimented with evolutionary algorithms to find the most informative subset of these biomarkers. Higuchi fractal dimension, phase lag index, correlation and coherence features were also found to be the most important features for predicting vulnerability to depression using rs-EEG. These findings bring up new possibilities for EEG-based machine/deep learning diagnostics in the future.

Altered electroencephalography resting state network coherence in remitted MDD

Individuals with remitted depression are at greater risk for subsequent depression and therefore may provide a unique opportunity to understand the neurophysiological correlates underlying the risk of depression. Research has identified abnormal resting-state electroencephalography (EEG) power metrics and functional connectivity patterns associated with major depression, however little is known about these neural signatures in individuals with remitted depression. We investigate the spectral dynamics of 64-channel EEG surface power and source-estimated network connectivity during resting states in 37 individuals with depression, 56 with remitted depression, and 49 healthy adults that did not differ on age, education, and cognitive ability across theta, alpha, and beta frequencies. Average reference spectral EEG surface power analyses identified greater left and midfrontal theta in remitted depression compared to healthy adults. Using Network Based Statistics, we also demonstrate within and between network alterations in LORETA transformed EEG source-space coherence across the default mode, fronto-parietal, and salience networks where individuals with remitted depression exhibited enhanced coherence compared to those with depression, and healthy adults. This work builds upon our currently limited understanding of resting EEG connectivity in depression, and helps bridge the gap between aberrant EEG power and brain network connectivity dynamics in this disorder. Further, our unique examination of remitted depression relative to both healthy and depressed adults may be key to identifying brain-based biomarkers for those at high risk for future, or subsequent 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.

QEEG characteristics associated with malnutrition-inflammation complex syndrome

End-stage renal disease (ESRD) has been linked to cerebral complications due to the comorbidity of malnutrition and inflammation, which is referred to as malnutrition-inflammation complex syndrome (MICS). The severity of this condition is clinically assessed with the malnutrition-inflammation score (MIS), and a cutoff of five is used to optimally distinguish patients with and without MICS. However, this tool is still invasive and inconvenient, because it combines medical records, physical examination, and laboratory results. These steps require clinicians and limit MIS usage on a regular basis. Cerebral diseases in ESRD patients can be evaluated reliably and conveniently by using quantitative electroencephalogram (QEEG), which possibly reflects the severity of MICS likewise. Given the links between kidney and brain abnormalities, we hypothesized that some QEEG patterns might be associated with the severity of MICS and could be used to distinguish ESRD patients with and without MICS. Hence, we recruited 62 ESRD participants and divided them into two subgroups: ESRD with MICS (17 women (59%), age 60.31 ± 7.79 years, MIS < 5) and ESRD without MICS (20 women (61%), age 62.03 ± 9.29 years, MIS ≥ 5). These participants willingly participated in MIS and QEEG assessments. We found that MICS-related factors may alter QEEG characteristics, including the absolute power of the delta, theta, and beta 1 bands, the relative power of the theta and beta 3 subbands, the coherence of the delta and theta bands, and the amplitude asymmetry of the beta 1 band, in certain brain regions. Although most of these QEEG patterns are significantly correlated with MIS, the delta absolute power, beta 1 amplitude asymmetry, and theta coherence are the optimal inputs for the logistic regression model, which can accurately classify ESRD patients with and without MICS (90.0 ± 5.7% area under the receiver operating characteristic curve). We suggest that these QEEG features can be used not only to evaluate the severity of cerebral disorders in ESRD patients but also to noninvasively monitor MICS in clinical practice.

Difference analysis of multidimensional electroencephalogram characteristics between young and old patients with generalized anxiety disorder

Growing evidences indicate that age plays an important role in the development of mental disorders, but few studies focus on the neuro mechanisms of generalized anxiety disorder (GAD) in different age groups. Therefore, this study attempts to reveal the neurodynamics of Young_GAD (patients with GAD under the age of 50) and Old_GAD (patients with GAD over 50 years old) through statistical analysis of multidimensional electroencephalogram (EEG) features and machine learning models. In this study, 10-min resting-state EEG data were collected from 45 Old_GAD and 33 Young_GAD. And multidimensional EEG features were extracted, including absolute power (AP), fuzzy entropy (FE), and phase-lag-index (PLI), on which comparison and analyses were performed later. The results showed that Old_GAD exhibited higher power spectral density (PSD) value and FE value in beta rhythm compared to theta, alpha1, and alpha2 rhythms, and functional connectivity (FC) also demonstrated significant reorganization of brain function in beta rhythm. In addition, the accuracy of machine learning classification between Old_GAD and Young_GAD was 99.67%, further proving the feasibility of classifying GAD patients by age. The above findings provide an objective basis in the field of EEG for the age-specific diagnosis and treatment of GAD.

Electrophysiological correlates and predictors of the antidepressant response to repeated ketamine infusions in treatment-resistant depression

BACKGROUND

Sub-anesthetic ketamine doses rapidly reduce depressive symptoms, although additional investigations of the underlying neural mechanisms and the prediction of response outcomes are needed. Electroencephalographic (EEG)-derived measures have shown promise in predicting antidepressant response to a variety of treatments, and are sensitive to ketamine administration. This study examined their utility in characterizing changes in depressive symptoms following single and repeated ketamine infusions.

METHODS

Recordings were obtained from patients with treatment-resistant major depressive disorder (MDD) (N = 24) enrolled in a multi-phase clinical ketamine trial. During the randomized, double-blind, crossover phase (Phase 1), patients received intravenous ketamine (0.5 mg/kg) and midazolam (30 μg/kg), at least 1 week apart. For each medication, three resting, eyes-closed recordings were obtained per session (pre-infusion, immediately post-infusion, 2 h post-infusion), and changes in power (delta, theta1/2/total, alpha1/2/total, beta, gamma), alpha asymmetry, theta cordance, and theta source-localized anterior cingulate cortex activity were quantified. The relationships between ketamine-induced changes with early (Phase 1) and sustained (Phases 2,3: open-label repeated infusions) decreases in depressive symptoms (Montgomery-Åsberg Depression Rating Score, MADRS) and suicidal ideation (MADRS item 10) were examined.

RESULTS

Both medications decreased alpha and theta immediately post-infusion, however, only midazolam increased delta (post-infusion), and only ketamine increased gamma (immediately post- and 2 h post-infusion). Regional- and frequency-specific ketamine-induced EEG changes were related to and predictive of decreases in depressive symptoms (theta, gamma) and suicidal ideation (alpha). Early and sustained treatment responders differed at baseline in surface-level and source-localized theta.

CONCLUSIONS

Ketamine exerts frequency-specific changes on EEG-derived measures, which are related to depressive symptom decreases in treatment-resistant MDD and provide information regarding early and sustained individual response to ketamine.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov: Action of Ketamine in Treatment-Resistant Depression, NCT01945047.

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.

Characteristics of single-channel electroencephalogram in depression during conversation with noise reduction technology

Background

Previous studies have attempted to characterize depression using electroencephalography (EEG), but results have been inconsistent. New noise reduction technology allows EEG acquisition during conversation.

Methods

We recorded EEG from 40 patients with depression as they engaged in conversation using a single-channel EEG device while conducting real-time noise reduction and compared them to those of 40 healthy subjects. Differences in EEG between patients and controls, as well as differences in patients’ depression severity, were examined using the ratio of the power spectrum at each frequency. In addition, the effects of medications were examined in a similar way.

Results

In comparing healthy controls and depression patients, significant power spectrum differences were observed at 3 Hz, 4 Hz, and 10 Hz and higher frequencies. In the patient group, differences in the power spectrum were observed between asymptomatic patients and healthy individuals, and between patients of each respective severity level and healthy individuals. In addition, significant differences were observed at multiple frequencies when comparing patients who did and did not take antidepressants, antipsychotics, and/or benzodiazepines. However, the power spectra still remained significantly different between non-medicated patients and healthy individuals.

Limitations

The small sample size may have caused Type II error. Patients’ demographic characteristics varied. Moreover, most patients were taking various medications, and cannot be compared to the non-medicated control group.

Conclusion

A study with a larger sample size should be conducted to gauge reproducibility, but the methods used in this study could be useful in clinical practice as a biomarker of depression.

Disruption of large-scale electrophysiological networks in stroke patients with visuospatial neglect

Stroke frequently produces attentional dysfunctions including symptoms of hemispatial neglect, which is characterized by a breakdown of awareness for the contralesional hemispace. Recent studies with functional MRI (fMRI) suggest that hemineglect patients display abnormal intra- and interhemispheric functional connectivity. However, since stroke is a vascular disorder and fMRI signals remain sensitive to nonneuronal (i.e., vascular) coupling, more direct demonstrations of neural network dysfunction in hemispatial neglect are warranted. Here, we utilize electroencephalogram (EEG) source imaging to uncover differences in resting-state network organization between patients with right hemispheric stroke (N = 15) and age-matched, healthy controls (N = 27), and determine the relationship between hemineglect symptoms and brain network organization. We estimated intra- and interregional differences in cortical communication by calculating the spectral power and amplitude envelope correlations of narrow-band EEG oscillations. We first observed focal frequency-slowing within the right posterior cortical regions, reflected in relative delta/theta power increases and alpha/beta/gamma decreases. Secondly, nodes within the right temporal and parietal cortex consistently displayed anomalous intra- and interhemispheric coupling, stronger in delta and gamma bands, and weaker in theta, alpha, and beta bands. Finally, a significant association was observed between the severity of left-hemispace search deficits (e.g., cancellation test omissions) and reduced functional connectivity within the alpha and beta bands. In sum, our novel results validate the hypothesis of large-scale cortical network disruption following stroke and reinforce the proposal that abnormal brain oscillations may be intimately involved in the pathophysiology of visuospatial neglect.

Stroke patients often exhibit a disabling deficit of visual awareness in the hemifield opposite to their brain lesion, known as hemineglect. Recent studies with functional MRI (fMRI) suggest that hemineglect patients display abnormal functional coupling (i.e., connectivity) within and between brain hemispheres. However, since stroke is a vascular disorder and fMRI measures nonneuronal (i.e., vascular) coupling, we here provide direct evidence of neural network dysfunction in hemineglect by using electroencephalogram (EEG) source imaging, which measures the electrical fluctuations of large neuronal populations. Overall, we observed a breakdown of interhemispheric network connectivity within alpha/beta rhythms, which specifically correlated with the degree of patients’ hemispatial errors. The high temporal resolution and frequency content of EEG signals could lead to more sensitive markers and targeted rehabilitation approaches of hemineglect.

Reduced inhibition in depression impairs stimulus processing in human cortical microcircuits

Cortical processing depends on finely tuned excitatory and inhibitory connections in neuronal microcircuits. Reduced inhibition by somatostatin-expressing interneurons is a key component of altered inhibition associated with treatment-resistant major depressive disorder (depression), which is implicated in cognitive deficits and rumination, but the link remains to be better established mechanistically in humans. Here we test the effect of reduced somatostatin interneuron-mediated inhibition on cortical processing in human neuronal microcircuits using a data-driven computational approach. We integrate human cellular, circuit, and gene expression data to generate detailed models of human cortical microcircuits in health and depression. We simulate microcircuit baseline and response activity and find a reduced signal-to-noise ratio and increased false/failed detection of stimuli due to a higher baseline activity in depression. We thus apply models of human cortical microcircuits to demonstrate mechanistically how reduced inhibition impairs cortical processing in depression, providing quantitative links between altered inhibition and cognitive deficits.

Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021

BACKGROUND

With the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis.

METHODS

QEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform (bibliometric.com) were employed to perform bibliographic and visualized analysis.

RESULTS

A total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends.

CONCLUSION

The present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.

Classifying subclinical depression using EEG spectral and connectivity measures

Detecting depression on its early stages helps preventing the onset of severe depressive episodes. In this study, we propose an automatic classification pipeline to detect subclinical depression (i.e., dysphoria) through the electroencephalography (EEG) signal. To this aim, we recorded the EEG signals in resting condition from 26 female participants with dysphoria and 38 female controls. The EEG signals were processed to extract several spectral and functional connectivity features to feed a nonlinear Support Vector Machine (SVM) classifier embedded with a Recursive Feature Elimination (RFE) algorithm. Our recognition pipeline obtained a maximum classification accuracy of 83.91% in recognizing dysphoria patients with a combination of connectivity and spectral measures. Moreover, an accuracy of 76.11% was achieved with only the 4 most informative functional connections, suggesting a central role of cortical connectivity in the theta band for early depression recognition. The present study can facilitate the diagnosis of subclinical conditions of depression and may provide reliable indicators of depression for the clinical community.

Electroencephalogram patterns in patients comorbid with major depressive disorder and anxiety symptoms: Proposing a hypothesis based on hypercortical arousal and not frontal or parietal alpha asymmetry

BACKGROUND

Major depressive disorder (MDD) is often comorbid with anxiety disorders or symptoms. Brain hyperactivity, frontal alpha asymmetry (FAA), and parietal alpha asymmetry (PAA) have been considered as trait markers in patients with MDD. This study investigated the electroencephalogram (EEG) patterns among patients with MDD comorbid with anxiety symptoms.

METHODS

One hundred and thirty-five patients with MDD comorbid with anxiety (MDD group) and 135 healthy controls (HC group) were analyzed. The Beck Depression Inventory-II (BDI-II) and Beck Anxiety Inventory (BAI) were completed, and 19 EEG channels were measured during the resting state, depressive recall and recovery tasks, and happiness recall and recovery tasks. FAA and PAA were computed by log (F4 alpha)-log (F3 alpha) and log (P4 alpha)-log (P3 alpha).

RESULTS

The FAA and PAA indices between the two groups showed no significant differences; however, compared with the HC group, the MDD group had lower total delta and theta values, and higher total beta, low beta, and high beta values in the resting state. The total beta value positively correlated with the BDI-II and BAI scores in the MDD group.

LIMITATIONS

Most patients had anxious MDD and taking prescriptions, antidepressants or benzodiazepine may affect EEG patterns.

CONCLUSION

Compared with HCs, patients with MDD comorbid with anxiety had a higher beta activity in the entire brain region, supporting the role of brain hyperactivity, instead of FAA or PAA, as a trait marker in these patients. A neurofeedback protocol could be developed in future based on the brain hyperactivity findings.

Increased functional connectivity within alpha and theta frequency bands in dysphoria: A resting-state EEG study

BACKGROUND

The understanding of neurophysiological correlates underlying the risk of developing depression may have a significant impact on its early and objective identification. Research has identified abnormal resting-state electroencephalography (EEG) power and functional connectivity patterns in major depression. However, the entity of dysfunctional EEG dynamics in dysphoria is yet unknown.

METHODS

32-channel EEG was recorded in 26 female individuals with dysphoria and in 38 age-matched, female healthy controls. EEG power spectra and alpha asymmetry in frontal and posterior channels were calculated in a 4-minute resting condition. An EEG functional connectivity analysis was conducted through phase locking values, particularly mean phase coherence.

RESULTS

While individuals with dysphoria did not differ from controls in EEG spectra and asymmetry, they exhibited dysfunctional brain connectivity. Particularly, in the theta band (4-8 Hz), participants with dysphoria showed increased connectivity between right frontal and central areas and right temporal and left occipital areas. Moreover, in the alpha band (8-12 Hz), dysphoria was associated with increased connectivity between right and left prefrontal cortex and between frontal and central-occipital areas bilaterally.

LIMITATIONS

All participants belonged to the female gender and were relatively young. Mean phase coherence did not allow to compute the causal and directional relation between brain areas.

CONCLUSIONS

An increased EEG functional connectivity in the theta and alpha bands characterizes dysphoria. These patterns may be associated with the excessive self-focus and ruminative thinking that typifies depressive symptoms. EEG connectivity patterns may represent a promising measure to identify individuals with a higher risk of developing depression.

Resting-state EEG beta band power predicts quality of life outcomes in patients with depressive disorders: A longitudinal investigation

BACKGROUND

Quality of life is severely impaired in patients with depressive disorders. Previous studies have focused on biomarkers predicting depressive symptomatology; however, studies investigating biomarkers predicting quality of life outcomes are limited. Improving quality of life is important because it is related not only to mental health but also to physical health. We need to develop a biomarker related to quality of life as a therapeutic target for patients with depressive disorders. Resting-state electroencephalography (EEG) is easy to record in clinical settings. The index of bandwidth spectral power predicts treatment response in depressive disorders and thus may be a candidate biomarker. However, no longitudinal studies have investigated whether EEG-recorded power could predict quality of life outcomes in patients with depressive disorders.

METHODS

The resting-state EEG-recorded bandwidth spectral power at baseline and the World Health Organization Quality of Life (QOL)-26 scores at 3-year follow-up were measured in 44 patients with depressive disorders.

RESULTS

The high beta band power (20-30 Hz) at baseline significantly predicted QOL at the 3-year follow-up after considering depressive symptoms and medication effects in a longitudinal investigation in patients with depressive disorders (β = 0.38, p = 0.01).

LIMITATIONS

We did not have healthy subjects as a comparison group in this study.

CONCLUSIONS

Our findings suggest that resting-state beta activity has the potential to be a useful biomarker for predicting future quality of life outcomes in patients with depressive disorders.

Altered directed functional connectivity of the right amygdala in depression: high-density EEG study

The cortico-striatal-pallidal-thalamic and limbic circuits are suggested to play a crucial role in the pathophysiology of depression. Stimulation of deep brain targets might improve symptoms in treatment-resistant depression. However, a better understanding of connectivity properties of deep brain structures potentially implicated in deep brain stimulation (DBS) treatment is needed. Using high-density EEG, we explored the directed functional connectivity at rest in 25 healthy subjects and 26 patients with moderate to severe depression within the bipolar affective disorder, depressive episode, and recurrent depressive disorder. We computed the Partial Directed Coherence on the source EEG signals focusing on the amygdala, anterior cingulate, putamen, pallidum, caudate, and thalamus. The global efficiency for the whole brain and the local efficiency, clustering coefficient, outflow, and strength for the selected structures were calculated. In the right amygdala, all the network metrics were significantly higher (p < 0.001) in patients than in controls. The global efficiency was significantly higher (p < 0.05) in patients than in controls, showed no correlation with status of depression, but decreased with increasing medication intake (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\bf{R}}}^{{\bf{2}}}{\boldsymbol{=}}{\bf{0.59}}\,{\bf{and}}\,{\bf{p}}{\boldsymbol{=}}{\bf{1.52}}{\bf{e}}{\boldsymbol{ \mbox{-} }}{\bf{05}}$$\end{document}R2=0.59andp=1.52e‐05). The amygdala seems to play an important role in neurobiology of depression. Practical treatment studies would be necessary to assess the amygdala as a potential future DBS target for treating depression.

Resting State Quantitative Electroencephalogram Power Spectra in Patients with Depressive Disorder as Compared to Normal Controls: An Observational Study

INTRODUCTION

A significant number of quantitative electroencephalogram (qEEG) studies indicate that increased spectral activities distinguish patients with depressive disorder from control subjects. But they did not yield consistent findings in the delta, theta, alpha, or beta bands.

METHODS

A total of 30 drug-naïve or drug-free subjects with a depressive episode or recurrent depressive disorder were compared with 30 age, sex, education, and handedness-matched healthy controls using qEEG power spectra in six frequency bands (delta, theta, alpha, beta, slow beta, and fast beta) and total activities separately. Spectral analysis was performed on a section of 180 s of qEEG digitized at the rate of 512 samples/s/channel, and absolute powers were log-transformed before statistical analysis.

RESULTS

Statistically significant differences between the patients and normal controls were found in the delta and the total bands, while Structured Interview Guide for the Hamilton Depression Rating Scale ( SIGH-D) score predicted the fast beta spectral power at the left temporal region. In the entire region of the brain, in the theta band, lesser absolute spectral power was found in patients than normal controls, whereas in the fast beta band, it was greater. In other bands, greater powers of spectral activities were found in patients than normal controls consistently in the parietal and occipital regions.

CONCLUSION

Various findings of qEEG absolute power spectra could demonstrate a difference between the patients with depressive disorder and the normal controls independently and efficiently. However, all the differences collectively showed stronger evidence. The findings may steer future studies to differentiate the patients with depressive disorder from controls.

Sympathetic arousal in children with oppositional defiant disorder and its relation to emotional dysregulation

BACKGROUND

Emotional dysregulation (ED) is a trans-nosographical condition characterized by mood instability, severe irritability, aggression, temper outburst, and hyper-arousal. Pathophysiology of emotional dysregulation and its potential biomarkers are an emerging field of interest. A Child Behaviour Checklist (CBCL) profile, defined as Dysregulation Profile (DP), has been correlated to ED in youth. We examined the association between the CBCL-DP and indices of sympathetic arousal in children with Oppositional Defiant Disorder (ODD) and healthy controls.

METHOD

The current study sought to compare the arousal level measured via electrodermal activity in response to emotional stimuli in three non-overlapping groups of children: (1) ODD+CBCL-DP (n = 28), (2) ODD without CBCL-DP (n = 35), and (3) typically developing controls (n = 25).

RESULTS

Analyses revealed a distinct electrodermal activity profile in the three groups. Specifically, children with ODD+CBCL-DP presented higher levels of sympathetic arousal for anger and sadness stimuli compared to the other two groups.

LIMITATIONS

The relatively small sample and the lack of assessing causality limit the generalizability of this study which results need to be replicated in larger, different samples.

CONCLUSION

The CBCL-DP was associated to higher levels of arousal for negative emotions, consistently with previous reports in individuals with depression and anxiety. Further work may identify potential longitudinal relationships between this profile and clinical outcomes.

Multimodal Depression Detection: Fusion of Electroencephalography and Paralinguistic Behaviors Using a Novel Strategy for Classifier Ensemble

Currently, depression has become a common mental disorder and one of the main causes of disability worldwide. Due to the difference in depressive symptoms evoked by individual differences, how to design comprehensive and effective depression detection methods has become an urgent demand. This study explored from physiological and behavioral perspectives simultaneously and fused pervasive electroencephalography (EEG) and vocal signals to make the detection of depression more objective, effective and convenient. After extraction of several effective features for these two types of signals, we trained six representational classifiers on each modality, then denoted diversity and correlation of decisions from different classifiers using co-decision tensor and combined these decisions into the ultimate classification result with multi-agent strategy. Experimental results on 170 (81 depressed patients and 89 normal controls) subjects showed that the proposed multi-modal depression detection strategy is superior to the single-modal classifiers or other typical late fusion strategies in accuracy, f1-score and sensitivity. This work indicates that late fusion of pervasive physiological and behavioral signals is promising for depression detection and the multi-agent strategy can take advantage of diversity and correlation of different classifiers effectively to gain a better final decision.

EEG Frequency Bands in Psychiatric Disorders: A Review of Resting State Studies

A significant proportion of the electroencephalography (EEG) literature focuses on differences in historically pre-defined frequency bands in the power spectrum that are typically referred to as alpha, beta, gamma, theta and delta waves. Here, we review 184 EEG studies that report differences in frequency bands in the resting state condition (eyes open and closed) across a spectrum of psychiatric disorders including depression, attention deficit-hyperactivity disorder (ADHD), autism, addiction, bipolar disorder, anxiety, panic disorder, post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD) and schizophrenia to determine patterns across disorders. Aggregating across all reported results we demonstrate that characteristic patterns of power change within specific frequency bands are not necessarily unique to any one disorder but show substantial overlap across disorders as well as variability within disorders. In particular, we show that the most dominant pattern of change, across several disorder types including ADHD, schizophrenia and OCD, is power increases across lower frequencies (delta and theta) and decreases across higher frequencies (alpha, beta and gamma). However, a considerable number of disorders, such as PTSD, addiction and autism show no dominant trend for spectral change in any direction. We report consistency and validation scores across the disorders and conditions showing that the dominant result across all disorders is typically only 2.2 times as likely to occur in the literature as alternate results, and typically with less than 250 study participants when summed across all studies reporting this result. Furthermore, the magnitudes of the results were infrequently reported and were typically small at between 20% and 30% and correlated weakly with symptom severity scores. Finally, we discuss the many methodological challenges and limitations relating to such frequency band analysis across the literature. These results caution any interpretation of results from studies that consider only one disorder in isolation, and for the overall potential of this approach for delivering valuable insights in the field of mental health.

Resting EEG Measures of Brain Arousal in a Multisite Study of Major Depression

Several studies have found upregulated brain arousal during 15-minute EEG recordings at rest in depressed patients. However, studies based on shorter EEG recording intervals are lacking. Here we aimed to compare measures of brain arousal obtained from 2-minute EEGs at rest under eyes-closed condition in depressed patients and healthy controls in a multisite project-Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC). We expected that depressed patients would show stable and elevated brain arousal relative to controls. Eighty-seven depressed patients and 36 healthy controls from four research sites in the United States were included in the analyses. The Vigilance Algorithm Leipzig (VIGALL) was used for the fully automatic classification of EEG-vigilance stages (indicating arousal states) of 1-second EEG segments; VIGALL-derived measures of brain arousal were calculated. We found that depressed patients scored higher on arousal stability ( Z = -2.163, P = .015) and A stages (dominant alpha activity; P = .027) but lower on B1 stages (low-voltage non-alpha activity, P = .008) compared with healthy controls. No significant group differences were observed in Stage B2/3. In summary, we were able to demonstrate stable and elevated brain arousal during brief 2-minute recordings at rest in depressed patients. Results set the stage for examining the value of these measures for predicting clinical response to antidepressants in the entire EMBARC sample and evaluating whether an upregulated brain arousal is particularly characteristic for responders to antidepressants.

Mutual Information-Based Brain Network Analysis in Post-stroke Patients With Different Levels of Depression

Post-stroke depression (PSD) is the most common stroke-related emotional disorder, and it severely affects the recovery process. However, more than half cases are not correctly diagnosed. This study was designed to develop a new method to assess PSD using EEG signal to analyze the specificity of PSD patients' brain network. We have 107 subjects attended in this study (72 stabilized stroke survivors and 35 non-depressed healthy subjects). A Hamilton Depression Rating Scale (HDRS) score was determined for all subjects before EEG data collection. According to HDRS score, the 72 patients were divided into 3 groups: post-stroke non-depression (PSND), post-stroke mild depression (PSMD) and post-stroke depression (PSD). Mutual information (MI)-based graph theory was used to analyze brain network connectivity. Statistical analysis of brain network characteristics was made with a threshold of 10-30% of the strongest MIs. The results showed significant weakened interhemispheric connections and lower clustering coefficient in post-stroke depressed patients compared to those in healthy controls. Stroke patients showed a decreasing trend in the connection between the parietal-occipital and the frontal area as the severity of the depression increased. PSD subjects showed abnormal brain network connectivity and network features based on EEG, suggesting that MI-based brain network may have the potential to assess the severity of depression post stroke.

Electrophysiological changes in poststroke subjects with depressed mood: A quantitative EEG study

BACKGROUND

We aimed to explore the electrophysiological changes in poststroke subjects with depressed mood.

METHODS

Resting-state electroencephalogram (EEG) signals of 16 electrodes in 35 poststroke depressed, 24 poststroke nondepressed, and 35 age-matched healthy control subjects were analyzed by means of spectral power analysis, a quantitative EEG measurement of different frequency bands. The relationship among depressed mood, functional status, lesion side, and poststroke time was assessed by using variance and Spearman correlation analysis. Multiple analysis of variance was used to compare the differences among the 3 groups. Binary logistic regression analysis was used to establish a regression model to predict depressed mood in stroke subjects and to explore the association between depression and EEG band power. Receiver operating characteristic curves were used to estimate the ability of spectral power selected by binary logistic regression to indicate depressed mood in stroke subjects.

RESULTS

We found that the hemisphere in which the lesion was located and the time since stroke onset had no effect on depressed mood. Only the patient's functional status was related to emotional symptoms. Quantitative EEG analysis revealed increased delta, theta, and beta2 power in stroke subjects with depressed mood, particularly in temporal regions. The theta and beta2 power in the right temporal area were shown to be highly sensitive to depressed mood, and these parameters showed good discriminatory ability for depressed subjects following stroke.

CONCLUSION

Depressed mood after stroke is associated with functional status. Quantitative EEG parameters may be a useful tool in timely screening for depressed mood after stroke.

Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns

Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP). The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs) are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA) to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total). Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM) classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be achieved by the KEFP-CSP feature and the SVM classifier with only several trials, and this level of accuracy seems to become stable as more trials (i.e., <7 trials) are used. These findings therefore suggest that the proposed method has a great potential for developing an efficient (required only a few 6-s EEG signals from the 8 electrodes over the temporal) and effective (~80% classification accuracy) EEG-based brain-computer interface (BCI) system which may, in the future, help psychiatrists provide individualized and effective treatments for MDD patients.

Increased Alpha-Rhythm Dynamic Range Promotes Recovery from Visuospatial Neglect: A Neurofeedback Study

Despite recent attempts to use electroencephalogram (EEG) neurofeedback (NFB) as a tool for rehabilitation of motor stroke, its potential for improving neurological impairments of attention—such as visuospatial neglect—remains underexplored. It is also unclear to what extent changes in cortical oscillations contribute to the pathophysiology of neglect, or its recovery. Utilizing EEG-NFB, we sought to causally manipulate alpha oscillations in 5 right-hemisphere stroke patients in order to explore their role in visuospatial neglect. Patients trained to reduce alpha oscillations from their right posterior parietal cortex (rPPC) for 20 minutes daily, over 6 days. Patients demonstrated successful NFB learning between training sessions, denoted by improved regulation of alpha oscillations from rPPC. We observed a significant negative correlation between visuospatial search deficits (i.e., cancellation test) and reestablishment of spontaneous alpha-rhythm dynamic range (i.e., its amplitude variability). Our findings support the use of NFB as a tool for investigating neuroplastic recovery after stroke and suggest reinstatement of intact parietal alpha oscillations as a promising target for reversing attentional deficits. Specifically, we demonstrate for the first time the feasibility of EEG-NFB in neglect patients and provide evidence that targeting alpha amplitude variability might constitute a valuable marker for clinical symptoms and self-regulation.

Different Resting State EEG Features in Children from Switzerland and Saudi Arabia

Background: Cultural neuroscience is an emerging research field concerned with studying the influences of different cultures on brain anatomy and function. In this study, we examined whether different cultural or genetic influences might influence the resting state electroencephalogram (EEG) in young children (mean age 10 years) from Switzerland and Saudi Arabia.

Methods: Resting state EEG recordings were obtained from relatively large groups of healthy children (95 healthy Swiss children and 102 Saudi Arabian children). These EEG data were analyzed using group independent components analyses (gICA) and conventional analyses of spectral data, together with estimations of the underlying intracortical sources, using LORETA software.

Results: We identified many similarities, but also some substantial differences with respect to the resting state EEG data. For Swiss children, we found stronger delta band power values in mesial frontal areas and stronger power values in three out of four frequency bands in occipital areas. For Saudi Arabian children, we uncovered stronger alpha band power over the sensorimotor cortex. The additionally measured theta/beta ratio (TBR) was similar for Swiss and Saudi Arabian children.

Conclusions: The different EEG resting state features identified, are discussed in the context of different cultural experiences and possible genetic influences. In addition, we emphasize the importance of using appropriate EEG databases when comparing resting state EEG features between groups.

Personalized Medicine: Review and Perspectives of Promising Baseline EEG Biomarkers in Major Depressive Disorder and Attention Deficit Hyperactivity Disorder

Personalized medicine in psychiatry is in need of biomarkers that resemble central nervous system function at the level of neuronal activity. Electroencephalography (EEG) during sleep or resting-state conditions and event-related potentials (ERPs) have not only been used to discriminate patients from healthy subjects, but also for the prediction of treatment outcome in various psychiatric diseases, yielding information about tailored therapy approaches for an individual. This review focuses on baseline EEG markers for two psychiatric conditions, namely major depressive disorder and attention deficit hyperactivity disorder. It covers potential biomarkers from EEG sleep research and vigilance regulation, paroxysmal EEG patterns and epileptiform discharges, quantitative EEG features within the EEG main frequency bands, connectivity markers and ERP components that might help to identify favourable treatment outcome. Further, the various markers are discussed in the context of their potential clinical value and as research domain criteria, before giving an outline for future studies that are needed to pave the way to an electrophysiological biomarker-based personalized medicine.

Lempel-Ziv and multiscale Lempel-Ziv complexity in depression

There is a high demand for objective indicators in diagnosis of depression as diagnosis of depression is still based on psychiatrist's subjective judgment. A nonlinear method Lempel Ziv Complexity (LZC) has been previously successfully used for detection of neuronal or mental disorders based on electroencephalographic (EEG) signals. However, the method overlooks the high frequency content of EEG signals. Therefore, this study is aimed to find out whether the use of Multiscale Lempel Ziv Complexity (MLZC), considering also high frequencies, could overcome the limitations of LZC and better differentiate depression. In current study the EEG recordings were carried out on the groups of depressive and healthy subjects of 11 volunteers each. The LZC and MLZC were calculated on resting EEG signals in eyes open condition from 30 channels at a length of 2 minutes. The results revealed the incapability of traditional LZC to differentiate depressive subjects from healthy controls in eyes open condition, while MLZC differentiated two groups in numerous channels at different frequencies, giving the highest classification accuracy in channel F3 (86 %) at frequencies 9 and 15.5 Hz. The results indicate that the high frequency information, which is lost in calculation of traditional LZC, has a great value in differentiating between depressive and control groups.

EEG neurofeedback effects in the treatment of adolescent anorexia nervosa

A pre-post design including 22 females was used to evaluate the effectiveness of neurofeedback in the treatment of adolescent anorexia nervosa. Resting EEG measures and a psychological test-battery assessing eating behavior traits, clinical symptoms, emotionality, and mood were obtained. While both the experimental (n = 10) and control group (n = 12) received their usual maintenance treatment, the experimental group received 10 sessions of individual alpha frequency training over a period of 5 weeks as additional treatment. Significant training effects were shown in eating behavior traits, emotion regulation, and in relative theta power in the eyes closed condition. Although the results are limited due to the small sample size, these are the first empirical data demonstrating the benefits of neurofeedback as a treatment adjunct in individuals with anorexia nervosa.

Electrophysiological correlates of bilateral and unilateral repetitive transcranial magnetic stimulation in patients with bipolar depression

Repetitive transcranial magnetic stimulation (rTMS) has been demonstrated to have efficacy in the treatment of unipolar depression but limited research has explored the efficacy of rTMS in bipolar depression. Therefore, we conducted a comparative clinical trial evaluating clinical responses to prefrontal bilateral and unilateral rTMS in patients suffering from bipolar depression. We hypothesized that, 1) the response to the treatment would be associated with a decrease in the frequency of beta waves, 2) bilateral stimulation of the cortex would bring about more extensive changes in brain activity than unilateral stimulation, and 3) bilateral stimulation is more effective than unilateral. Thirty patients with bipolar depression were divided into two groups. Bilateral Group (n=15) who received rTMS in the left DLPFC (10Hz) and right DLPFC (1-Hz), and unilateral group (n=15) who received the stimulation only in the right DLPFC (1-Hz) during 20 treatment sessions. The proportion of responders in the bilateral stimulation group was significantly higher than that in the unilateral group [80% versus 47%]. The remission rate was 40% in the bilateral group and 40% in the unilateral group (not significant). In the responders to bilateral rTMS treatment, a significant reduction of alpha1-2, beta 1-3, and gamma frequencies were observed in medial and superior frontal and cingulate gyrus . Responders to the unilateral treatment showed decrease of gamma frequency in postcentral gyrus, precuneus, superior and inferior parietal lobule, Cuneus and angular gyrus. In conclusion, we found that bilateral stimulation was more effective than the unilateral stimulation and evidence that beta frequency activity could possibly be used as a marker for response to rTMS.

Mild Depression Detection of College Students: an EEG-Based Solution with Free Viewing Tasks

Depression is a common mental disorder with growing prevalence; however current diagnoses of depression face the problem of patient denial, clinical experience and subjective biases from self-report. By using a combination of linear and nonlinear EEG features in our research, we aim to develop a more accurate and objective approach to depression detection that supports the process of diagnosis and assists the monitoring of risk factors. By classifying EEG features during free viewing task, an accuracy of 99.1%, which is the highest to our knowledge by far, was achieved using kNN classifier to discriminate depressed and non-depressed subjects. Furthermore, through correlation analysis, comparisons of performance on each electrode were discussed on the availability of single channel EEG recording depression detection system. Combined with wearable EEG collecting devices, our method offers the possibility of cost effective wearable ubiquitous system for doctors to monitor their patients with depression, and for normal people to understand their mental states in time.

EEG alpha asymmetry as a gender-specific predictor of outcome to acute treatment with different antidepressant medications in the randomized iSPOT-D study

OBJECTIVE

To determine whether EEG occipital alpha and frontal alpha asymmetry (FAA) distinguishes outpatients with major depression (MDD) from controls, predicts antidepressant treatment outcome, and to explore the role of gender.

METHODS

In the international Study to Predict Optimized Treatment in Depression (iSPOT-D), a multi-center, randomized, prospective open-label trial, 1008 MDD participants were randomized to escitalopram, sertraline or venlafaxine-extended release. The study also recruited 336 healthy controls. Treatment response was established after eight weeks and resting EEG was measured at baseline (two minutes eyes open and eyes closed).

RESULTS

No differences in EEG alpha for occipital and frontal cortex, or for FAA, were found in MDD participants compared to controls. Alpha in the occipital and frontal cortex was not associated with treatment outcome. However, a gender and drug-class interaction effect was found for FAA. Relatively greater right frontal alpha (less cortical activity) in women only was associated with a favorable response to the Selective Serotonin Reuptake Inhibitors escitalopram and sertraline. No such effect was found for venlafaxine-extended release.

CONCLUSIONS

FAA does not differentiate between MDD and controls, but is associated with antidepressant treatment response and remission in a gender and drug-class specific manner.

SIGNIFICANCE

Future studies investigating EEG alpha measures in depression should a-priori stratify by gender.

Discriminative analysis with a limited number of MEG trials in depression

BACKGROUND

In studies when exploring distinct patterns of functional abnormalities inherent in depression, experiments are generally repeated over many trials, and then the data are averaged across those trials in order to improve the signal to noise ratio. Repeated stimuli will lead to unpredictable impairment on signals, due to material familiarity or subjects׳ fatigue. In this consideration, signal processing tools powerful on small numbers of trials are expected to alleviate the work load on subjects, especially for mental disease studies.

METHODS

Forty-four subjects, half-depressed patients and half-healthy subjects, were recruited for MEG scanning in response to sad facial stimuli. Multichannel matching pursuit (MMP) was implemented to manage the limited number of trials. The post-MMP MEG signals were utilized to calculate the power topography over the whole brain, as inputs for a Support Vector Machine (SVM) classifier. Standard ICA and conventional ensemble averaging plus Butterworth filtering were employed as well as benchmark studies for performance comparison.

RESULTS

A limited number of trials were required via MMP to discriminate the depressive. Post-MMP discriminative analysis revealed a deficit theta pattern and an excessive alpha/beta pattern.

LIMITATIONS

The small sample size may impair the stability of the reported findings. The transient tiny variance of the signal was excluded from exploration.

CONCLUSIONS

The deficit theta pattern together with the excessive alpha/beta pattern in depression may indicate the dysfunction of the limbic-cortical circuit in a 'top-down' process. The post-MMP discrimination helps alleviate the scanning burden, facilitating the possibility for neuroimaging supporting the affective disorder clinical diagnosis.

Recurrence analysis of the EEG during sleep accurately identifies subjects with mental health symptoms

Analysis of brain recurrence (ABR) is a novel computational method that uses two variables for sleep depth and two for sleep fragmentation to quantify temporal changes in non-random brain electrical activity. We postulated that ABR of the sleep-staged EEG could identify an EEG signature specific for the presence of mental health symptoms. Using the Mental Health Inventory Questionnaire (MHI-5) as ground truth, psychological distress was assessed in a study cohort obtained from the Sleep Heart Health Study. Subjects with MHI-5 <50 (N=34) were matched for sex, BMI, age, and race with 34 subjects who had MHI-5 scores >50. Sixteen ABR markers derived from the EEG were analyzed using linear discriminant analysis to identify marker combinations that reliably classified individual subjects. A biomarker function computed from 12 of the markers accurately classified the subjects based on their MHI-5 scores (AUROC=82%). Use of additional markers did not improve classification accuracy. Subgroup analysis (20 highest and 20 lowest MHI-5 scores) improved classification accuracy (AUROC=89%). Biomarker values for individual subjects were significantly correlated with MHI-5 score (r=0.36, 0.54 for N=68, 40, respectively). ABR of EEGs obtained during sleep successfully classified subjects with regard to the severity of mental health symptoms, indicating that mood systems were reflected in brain electrical activity.

A potential electroencephalography and cognitive biosignature for the child behavior checklist-dysregulation profile

OBJECTIVE

The Child Behavior Checklist-Dysregulation Profile (CBCL/DP) identifies youth at increased risk for significant psychopathology. Although the genetic architecture and several biological correlates of the CBCL/DP have been described, little work has elucidated its underlying neurobiology. We examined the potential utility of electroencephalography (EEG), along with behavioral and cognitive assessments, in differentiating individuals based on the CBCL/DP.

METHOD

Participants aged 7 to 14 years of age were categorized into 3 age- and sex-matched groups based on clinical assessment and CBCL/DP: typically developing controls without attention-deficit/hyperactivity disorder (ADHD) (n = 38), individuals with ADHD without the CBCL/DP (ADHD/DP-) (n = 38), and individuals with the CBCL/DP (CBCL/DP+) (n = 38). Groups were compared with EEG and measures of clinical phenomenology and cognition.

RESULTS

ADHD/DP- and CBCL/DP+ groups had increased inattention, but the CBCL/DP+ group had increased hyperactive/impulsive symptoms, disruptive behavior, mood, and anxiety comorbidities compared with the group with ADHD alone. Cognitive profiles suggested that ADHD/DP-participants had fast impulsive responses, whereas CBCL/DP+ participants were slow and inattentive. On EEG, CBCL/DP+ had a distinct profile of attenuated δ-band and elevated α-band spectral power in the central and parietal regions compared to ADHD/DP- and controls. The low-δ/high-α profile was correlated with measures of emotion and behavior problems and not with inattentive symptomatology or cognitive measures. There were no EEG differences between the ADHD/DP- and control groups.

CONCLUSIONS

An EEG/cognitive profile suggests a distinct pattern of underlying neural dysfunction with the CBCL/DP that might ultimately serve as a biosignature. Further work is required to identify potential relationships with clinically defined psychiatric disorders, particularly those of dysregulated mood.

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.

SYNTAX score effect on electroencephalography power dynamics in patients undergoing on-pump coronary artery bypass grafting

Background

The severity of angiographically assessed coronary artery disease may be the factor that influences the degree of brain damage during on-pump surgery. Modern technology such as computed electroencephalography (EEG) that is used to detect signs of brain damage could also be used to determine the advantages and disadvantages of various surgical myocardial revascularization methods in certain categories of patients. The present study investigated EEG power dynamics for 1 postoperative month in patients undergoing on-pump coronary artery bypass grafting (CABG) who were divided into two groups: those with moderate coronary lesions (SYNTAX score ≤ 22, n = 12) and those with severe coronary lesions (SYNTAX score ≥ 23, n = 18).

Results

At 7–10 days after CABG, all patients showed theta type 1 rhythm power higher than that seen preoperatively, possibly indicating that brain damage occurred during bypass. At 1 month after CABG, the theta type 1 rhythm power had decreased to the baseline level in patients with SYNTAX scores of ≤22, whereas it had increased in patients with SYNTAX scores ≥23.

Conclusions

SYNTAX scores ≥ 23 are associated with EEG markers of perioperative brain damage during CABG. Careful preoperative assessment, preparation, and more effective intraoperative brain protection are essential for this category of coronary heart disease (CHD) patients.