Altered cortical functional network in major depressive disorder: A resting-state electroencephalogram study

Predicting depression severity using effective and functional brain connectivity of the electroencephalography signals

Depression, also known as major depressive disorder (MDD), is a mental health condition that can lead to self-injury and suicide with significant effects on individuals and communities. Recent studies suggest that analysing functional connectivity (FC) from electroencephalography (EEG) signals provides insights into brain network integration in depressive states. Effective connectivity (EC) assesses the directional influence between brain regions, offering deeper insights into neural circuit dynamics. This study aimed to capture the subtle changes in brain dynamics, identify predictive biomarkers of MDD, and elucidate its neurophysiological basis. Resting-state EEG signals from 44 subjects with MDD were used to extract connectivity features. Graph-theoretical-based EC features from the phase slope index (PSI) and FC features from the weighted phase lag index (WPLI) were analysed. Correlation analysis showed significant associations between EC features (diameter) and depression severity, as well as between FC features (global efficiency) and severity, both in the alpha frequency band. These significant features were fed into several machine learning regression models, which demonstrated comparable performance in predicting depression scores. FC features performed slightly better (4.71 root mean square error and 3.93 mean absolute error) than EC features (5.01 root mean square error and 4.29 mean absolute error). These findings indicate that alterations in functional and effective connectivity are linked to depression severity and could improve diagnostic accuracy and therapeutic strategies, while offering new avenues for research into brain connectivity in MDD.

Abnormal resting-state brain networks and their relationship with cognitive reappraisal preferences in depressive tendencies

OBJECTIVE

Currently, the neural mechanisms underlying the topological changes in the brains of individuals with depressive tendencies and the decline in their emotion regulation abilities remain largely unknown. Therefore, this study investigates resting-state brain network characteristics in college students with depressive tendencies (DT) and their preference to cognitive reappraisal strategies.

METHOD

A group of 38 DT students and 41 healthy controls (HCs) were assessed using questionnaires on cognitive reappraisal sub-strategies, followed by alpha and beta frequency band EEG feature extraction.

RESULTS

Through complex network analysis, DT participants showed significantly reduced preferences for positive reappraisal and detached reappraisal compared to HCs, while exhibiting higher preferences for involved reappraisal and negative reappraisal. Additionally, abnormalities in brain network centrality were observed, particularly in the frontal and limbic lobes across various frequency bands. A significant correlation was found between the preference for cognitive reappraisal sub-strategies in DT participants and significant changes in graph indices.

CONCLUSIONS

The findings highlight substantial alterations in the resting-state brain networks of DT individuals, closely associated with cognitive reappraisal strategy preferences. These alterations may affect emotion regulation strategy choices, offering insights into the neural mechanisms of emotional regulation difficulties in DT.

Infraslow Closed-Loop Brain Training for Anxiety and Depression (ISAD): A pilot randomised, sham-controlled trial in adult females with internalizing disorders

INTRODUCTION

The core resting-state networks (RSNs) have been shown to be dysfunctional in individuals with internalizing disorders (IDs; e.g., anxiety, depression). Source-localised, closed-loop brain training of infraslow (≤ 0.1 Hz) EEG signals may have the potential to reduce symptoms associated with IDs and restore normal core RSN function.

METHODS

We conducted a pilot randomized, double-blind, sham-controlled, parallel-group (3-arm) trial of infraslow neurofeedback (ISF-NFB) in adult females (n = 60) with IDs. Primary endpoints, which included the Hospital Anxiety and Depression Scale (HADS) and resting-state EEG activity and connectivity, were measured at baseline and post 6 sessions.

RESULTS

This study found credible evidence of strong nonspecific effects as evidenced by clinically important HADS score improvements (i.e., reductions) across groups. An absence of HADS score change differences between the sham and active groups indicated a lack of specific effects. Although there were credible slow (0.2-1.5 Hz) and delta (2-3.5 Hz) band activity reductions in the 1-region ISF-NFB group relative to sham within the targeted region of interest (i.e., posterior cingulate), differences in activity and connectivity modulation in the targeted frequency band of interest (i.e., ISFs = 0.01-0.1 Hz) were lacking between sham and active groups. Credible positive associations between changes in HADS depression scores and anterior cingulate cortex slow and delta activity also were found.

CONCLUSIONS

Short-term sham and genuine ISF-NFB resulted in rapid, clinically important improvements that were nonspecific in nature and possibly driven by placebo-related mechanisms. Future ISF-NFB trials should consider implementing design modifications that may better induce differential modulation of ISFs between sham and treatment groups, thereby enhancing the potential for specific clinical effects in ID populations.

TRIAL REGISTRATION

The trial was prospectively registered with the Australia New Zealand Clinical Trials Registry (ANZCTR; Trial ID: ACTRN12619001428156).

Time-frequency and functional connectivity analysis in drug-naive adolescents with depression based on electroencephalography using a visual cognitive task: A comparative study

BACKGROUND

Previous research studies have demonstrated cognitive deficits in adolescents with depression; however, the neuroelectrophysiological mechanisms underlying these deficits remain poorly understood. Utilizing electroencephalography (EEG) data collected during cognitive tasks, this study applies time-frequency analysis and functional connectivity (FC) techniques to explore the neuroelectrophysiological alterations associated with cognitive deficits in adolescents with depression.

METHODS

A total of 173 adolescents with depression and 126 healthy controls (HC) participated in the study, undergoing EEG while performing a visual oddball task. Delta, theta, and alpha power spectra, along with FC, were calculated and analyzed.

RESULTS

Adolescents with depression exhibited significantly reduced delta, theta, and alpha power at the Fz, Cz, C5, C6, Pz, P5, and P6 electrodes compared to the HC group. Notably, theta power at the F5 electrode and alpha power at the F5 and F6 electrodes were significantly lower in the depression group than in the HC group. Additionally, cortical FC in the frontal and central regions was markedly decreased in adolescents with depression compared to HC.

CONCLUSIONS

During cognitive tasks, adolescents with depression display distinct abnormalities in both high- and low-frequency brain oscillations, as well as reduced functional connectivity in the frontal, central, and parietal regions compared to HC. These findings offer valuable neuroelectrophysiological insights into the cognitive deficits associated with adolescent depression.

Altered electroencephalography-based source functional connectivity in drug-free patients with major depressive disorder

BACKGROUND

Compared to functional magnetic resonance imaging (fMRI), source localization of a scalp-recorded electroencephalogram (EEG) provides higher temporal resolution and frequency synchronization to better understand the potential neurophysiological origins of disrupted functional connectivity (FC) in major depressive disorder (MDD). The present study aimed to investigate EEG-sourced measures to examine the FC in drug-free patients with MDD.

METHOD

Resting-state 32-channel EEG were recorded in 84 drug-free patients with MDD and 143 healthy controls, and the cortical source signals were estimated. Exact low-resolution brain electromagnetic tomography (eLORETA) was used to compute the intracortical activity from regions within the default mode network (DMN) and frontoparietal network (PFN). Lagged phase synchronization was used as a measure of functional connectivity.

RESULTS

Compared with control subjects, the MDD group showed greater within-DMN alpha 1 and 2 bands and within-FPN alpha 1, 2, and beta 3 bands. Furthermore, the MDD group showed hyperconnectivity between the DMN and the FPN in the alpha 1 and 2 bands. Finally, higher levels of anhedonia were associated with higher between-network DMN and FPN connectivity in the alpha-1 band.

LIMITATIONS

Due to the inherent limitations of eLORETA with predefined seeds, we could not exclude connectivity between regions of interest (ROIs), which may be related to the activity from regions adjacent to the ROIs.

CONCLUSIONS

The present findings support the importance of phase-lagged functional dysconnectivity in the neurophysiological mechanisms underlying MDD. Exploring the potential of these patterns as surrogates for treatment responses may advance targeted interventions for depression.

Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning

Major depressive disorder (MDD) is associated with substantial morbidity and mortality, yet its diagnosis and treatment rates remain low due to its diverse and often overlapping clinical manifestations. In this context, electroencephalography (EEG) has gained attention as a potential objective tool for diagnosing depression. This study aimed to evaluate the effectiveness of EEG in identifying MDD by analyzing 140 EEG recordings from patients diagnosed with depression and healthy volunteers. Using various machine learning (ML) classification models, we achieved up to 80% accuracy in distinguishing individuals with MDD from healthy controls. Despite its promise, this approach has limitations. The variability in the clinical and biological presentations of depression, as well as patient-specific confounding factors, must be carefully considered when integrating ML technologies into clinical practice. Nevertheless, our findings suggest that an EEG-based ML model holds potential as a diagnostic aid for MDD, paving the way for further refinement and clinical application.

Temporal Effects of Hypoxia Exposure at High Altitudes on Compensatory Brain Function: Evidence from Functional Connectivity of Resting-State EEG Brain Networks

Ren, Hong, Xi-Yue Yang, Rui Su, HaiLin Ma, and Hao Li. Temporal Effects of Hypoxia Exposure at High Altitudes on Compensatory Brain Function: Evidence from Functional Connectivity of Resting-State EEG Brain Networks. High Alt Med Biol. 00:00-00, 2024. Background: The aim of this study was to investigate the effects of prolonged exposure to hypobaric hypoxia at high altitude on changes in brain function measured by electroencephalography (EEG), focusing specifically on the resting-state brain network functional connectivity and compensatory adaptations in brain function among individuals with varying durations of high altitude residency. Methods: In study I, 64 participants were divided into high-altitude group (HG) and low-altitude group (LG). Ninety-six long-term migrants residing at an altitude of 3,650 m were recruited for studyII and categorized into three groups based on their duration of stay at high altitude: group A (1-2 years), group B (8-10 years), and group C (18-20 years). Resting-state EEG data were collected from each participant, and functional connectivity analysis was conducted using Phase Locking Value. Results: Study I showed that participants with HG had stronger functional connectivity in the occipital lobe than those with LG (p < 0.05). The study II findings indicate that there were significant differences in functional connectivity strength among the frontal and occipital lobes in groups A, B, and C across the α, β, δ, and θ frequency bands. Specifically, the functional connectivity strength of the frontal lobe was significantly higher in group A compared with group B, and in group B compared with group C (p < 0.05). Additionally, the functional connectivity of the occipital lobe was significantly higher in group C compared with group B, and in group B compared with group A (p < 0.05). Conclusions: The consistent results of the whole frequency band suggest that the individual's occipital lobe function is enhanced to compensate for the damage of frontal lobe function, so as to better adapt to the extreme environment at high altitude.

Machine-learning-based classification of obstructive sleep apnea using 19-channel sleep EEG data

OBJECTIVE

This study aimed to investigate the neurophysiological effects of obstructive sleep apnea (OSA) using multi-channel sleep electroencephalography (EEG) through machine learning methods encompassing various analysis methodologies including power spectral analysis, network analysis, and microstate analysis.

METHODS

Twenty participants with apnea-hypopnea index (AHI) ≥ 15 and 18 participants with AHI <15 were recruited. Overnight polysomnography was conducted concurrently with 19-channel EEG. Preprocessed EEG data underwent computation of relative spectral power. A weighted network based on graph theory was generated; and indices of strength, path length, eigenvector centrality, and clustering coefficient were calculated. Microstate analysis was conducted to derive four topographic maps. Machine learning techniques were employed to assess EEG features capable of differentiating two groups.

RESULTS

Among 71 features that showed significant differences between the two groups, seven exhibited good classification performance, achieving 88.3 % accuracy, 92 % sensitivity, and 84 % specificity. These features were power at C4 theta, P3 theta, P4 theta, and F8 gamma during NREM1 sleep and at Pz gamma during REM sleep from power spectral analysis; eigenvector centrality at F7 gamma during REM sleep from network analysis; and duration of microstate 4 during NREM2 sleep from microstate analysis. These seven EEG features were significantly correlated with polysomnographic parameters reflecting the severity of OSA.

CONCLUSIONS

The application of machine learning techniques and various EEG analytical methods resulted in a model that showed good performance in classifying moderate to severe OSA and highlights the potential of EEG to serve as a biomarker of functional changes in OSA.

Using deep learning and pretreatment EEG to predict response to sertraline, bupropion, and placebo

OBJECTIVE

Predicting an individual's response to antidepressant medication remains one of the most challenging tasks in the treatment of major depressive disorder (MDD). Our objective was to use the large EMBARC study database to develop an electroencephalography (EEG)-based method to predict response to antidepressant treatment.

METHODS

Pre-treatment EEG data were collected from study participants treated with either sertraline (N = 105), placebo (N = 119), or bupropion (N = 35). After preprocessing, the robust exact low-resolution electromagnetic tomography (ReLORETA) brain source localization method was used to reconstruct the source signals in 54 brain regions. Connectivity between regions was determined using symbolic transfer entropy (STE). A convolutional neural network (CNN) classified participants as responders or non-responders to each treatment.

RESULTS

Classification accuracy was 91.0%, 95.4%, and 86.8% for sertraline, placebo, and bupropion, respectively. The most highly predictive features were connectivity between i) the anterior cingulate cortex and superior parietal lobule (alpha frequency), ii) the anterior cingulate cortex and orbitofrontal area (beta frequency), and iii) the orbitofrontal area and anterior cingulate cortex (gamma frequency).

CONCLUSION

CNN analysis of EEG connectivity may accurately predict response to sertraline, bupropion, and placebo.

SIGNIFICANCE

The suggested method may offer clinicians an accessible and cost-effective tool for speedy treatment and helps pharmaceutical firms to test new antidepressants efficiently.

Unveiling perinatal depression: A dual-network EEG analysis for diagnosis and severity assessment

Perinatal depression (PD), which affects about 10-20 percent of women, often goes unnoticed because related symptoms frequently overlap with those commonly experienced during pregnancy. Moreover, identifying PD currently depends heavily on the use of questionnaires, and objective biological indicators for diagnosis has yet to be identified. This research proposes a safe and non-invasive method for diagnosing PD and aims to delve deeper into its underlying mechanism. Considering the non-invasiveness and clinical convenience of electroencephalogram (EEG) for mothers-to-be and fetuses, we collected the resting-state scalp EEG of pregnant women (with PD/healthy) at the 38th week of gestation. To compensate for the low spatial resolution of scalp EEG, source analysis was first applied to project the scalp EEG to the cortical-space. Afterwards, cortical-space networks and large-scale networks were constructed to investigate the mechanism of PD from two different level. Herein, differences in the two distinct types of networks between PD patients and healthy mothers-to-be were explored, respectively. We found that the PD patients illustrated decreased network connectivity in the cortical-space, while the large-scale networks revealed weaker connections at cerebellar area. Further, related spatial topological features derived from the two different networks were combined to promote the recognition of pregnant women with PD from those healthy ones. Meanwhile, the depression severity at patient level was effectively predicted based on the combined spatial topological features as well. These findings consistently validated that the two kinds of networks indeed played off each other, which thus helped explore the underlying mechanism of PD; and further verified the superiority of the combination strategy, revealing its reliability and potential in diagnosis and depression severity evaluation.

Support vector machine classification of patients with depression based on resting-state electroencephalography

Background

Depression is one of the most common mental disorders. Although depression is typically diagnosed by identifying specific symptoms and through history, no recognized standard for depression diagnosis exists. This assures the development of objective diagnostic tools for depression.

Objectives

We investigated the differences in the resting-state electroencephalograms (EEGs) of patients with depression and healthy controls (HCs) to distinguish patients from HCs by using a support vector machine (SVM) classifier with the following two feature selection approaches: t test and receiver operating characteristic analysis.

Methods

We used the EEG data from the Patient Repository of EEG Data + Computational Tools; this study included 21 patients with depressive disorder (MDD) and 21 HCs. The relative frequency power, alpha interhemispheric asymmetry, left-right coherence, strength, clustering coefficient (CC), shortest path length, sample entropy (SampEn), multiscale entropy (MSE), and detrended fluctuation analysis (DFA) data were extracted to determine candidate EEG features associated with depression.

Results

With the t-test selection, the SVM classifier demonstrated the highest performance with the accuracy, sensitivity, and specificity of 96.66%, 95.93%, and 97.550% for the eye-open condition and 91.33%, 90.59%, and 91.81% for the eye-closed condition, respectively. For comparisons of features in the 2 selection approaches, the most influential features were relative frequency power and left-right coherence.

Conclusion

Using this information to distinguish patients with MDD from HC subjects with the SVM classifier resulted in a mean accuracy over 90%. Although this result may not be robust enough for clinical applications, further exploration is necessary given the simplicity, objectivity, and efficiency of the classifier.

MDD brain network analysis based on EEG functional connectivity and graph theory

Background

Existing studies have shown that the brain network of major depression disorder (MDD) has abnormal topologies. However, constructing reliable MDD brain networks is still an open problem.

New method

This paper proposed a reliable MDD brain network construction method. First, seven connectivity methods are used to calculate the correlation between channels and obtain the functional connectivity matrix. Then, the matrix is binarized using four binarization methods to obtain the EEG brain network. Besides, we proposed an improved binarization method based on the criterion of maximizing differences between groups: the adaptive threshold (AT) method. The AT can automatically set the optimal binarization threshold and overcome the artificial influence of traditional methods. After that, several network metrics are extracted from the brain network to analyze inter-group differences. Finally, we used statistical analysis and Fscore values to compare the performance of different methods and establish the most reliable method for brain network construction.

Results

In theta, alpha, and total frequency bands, the clustering coefficient, global efficiency, local efficiency, and degree of the MDD brain network decrease, and the path length of the MDD brain network increases.

Comparison with existing methods

The results show that AT outperforms the existing binarization methods. Compared with other methods, the brain network construction method based on phase-locked value (PLV) and AT has better reliability.

Conclusions

MDD has brain dysfunction, particularly in the frontal and temporal lobes.

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.

Resting-state EEG dynamic functional connectivity distinguishes non-psychotic major depression, psychotic major depression and schizophrenia

This study aims to identify dynamic patterns within the spatiotemporal feature space that are specific to nonpsychotic major depression (NPMD), psychotic major depression (PMD), and schizophrenia (SCZ). The study also evaluates the effectiveness of machine learning algorithms based on these network manifestations in differentiating individuals with NPMD, PMD, and SCZ. A total of 579 participants were recruited, including 152 patients with NPMD, 45 patients with PMD, 185 patients with SCZ, and 197 healthy controls (HCs). A dynamic functional connectivity (DFC) approach was employed to estimate the principal FC states within each diagnostic group. Incremental proportions of data (ranging from 10% to 100%) within each diagnostic group were used for variability testing. DFC metrics, such as proportion, mean duration, and transition number, were examined among the four diagnostic groups to identify disease-related neural activity patterns. These patterns were then used to train a two-layer classifier for the four groups (HC, NPMD, PMD, and SCZ). The four principal brain states (i.e., states 1,2,3, and 4) identified by the DFC approach were highly representative within and across diagnostic groups. Between-group comparisons revealed significant differences in network metrics of state 2 and state 3, within delta, theta, and gamma frequency bands, between healthy individuals and patients in each diagnostic group (p < 0.01, FDR corrected). Moreover, the identified key dynamic network metrics achieved an accuracy of 73.1 ± 2.8% in the four-way classification of HC, NPMD, PMD, and SCZ, outperforming the static functional connectivity (SFC) approach (p < 0.001). These findings suggest that the proposed DFC approach can identify dynamic network biomarkers at the single-subject level. These biomarkers have the potential to accurately differentiate individual subjects among various diagnostic groups of psychiatric disorders or healthy controls. This work may contribute to the development of a valuable EEG-based diagnostic tool with enhanced accuracy and assistive capabilities.

COVID-19 pandemic impacts on the elderly: the relationship between PPPD and prefrontal alpha rhythm

BACKGROUND

In the COVID-19 epidemic more patients presented with persistent postural-perceptual dizziness (PPPD), but it has received little attention by the doctors in China and many patients reject psychological measurements or scales. Therefore, there is an urgent need for an objective method to diagnose and evaluate PPPD.

OBJECTIVE

To investigate the effect of the COVID-19 epidemic on elderly PPPD patients and define the relationship between prefrontal alpha rhythm asymmetry (FAA) by Electroencephalography (EEG) and PPPD.

METHODS

This case-control study was conducted to discuss the differences of elderly outpatients (>60 years) with PPPD during the peak period of COVID-19 in 2020 and the corresponding period in 2019, and collect the prefrontal FAA value in PPPD during COVID-19 outbreak, which were compared to its FAA in healthy control.

RESULTS

Compared with the same period in 2019, the number of elderly PPPD patients during the epidemic period in 2020 increased significantly (16.4%, p = 0.000, x2 =31.802) . The left alpha wave signal power (F3) was significantly higher than the right alpha wave signal power (F4) (Z= -3.073, p = 0.002). In PPPD patients FAA were significantly lower in patients compared to control group (Z = -11.535, p = 0.000). There was a negative correlation between FAA and HAMA scores (R2 =0.906, p < 0.05) and a negative correlation between FAA and HAMD scores (R2 =0.859, p < 0.05), too.

CONCLUSIONS

The increase in cases of elderly PPPD patients is most likely attributed to the mental health in older adults during the COVID-19 pandemic. Less left frontal brain activity in EEG may be related to elderly PPPD.

The effects of electroencephalography functional connectivity during emotional recognition among patients with major depressive disorder and healthy controls

BACKGROUND

The brain of major depressive disorder (MDD) is associated with altered functional connectivity (FC) compared to that of healthy individuals when processing positive and negative visual stimuli. Building upon alterations in brain connectivity, some researchers have employed electroencephalography (EEG) to study FC in MDD, aiming to enhance both diagnosis and treatment; however, the results have been inconsistent and the studies involving FC during emotional recognition are limited. This study aims to 1) investigate the effects of MDD on EEG patterns during visual emotional processing, 2) explore the therapeutic effects of antidepressant treatment on brain FC within the first week, and assess whether these effects can be predictive of treatment outcomes four weeks later, and 3) study baseline FC parameter biomarkers that can be used to predict treatment responsiveness in MDD patients.

METHODS

This clinical observational study recruited 38 healthy controls (HC) and 48 MDD patients. Patients underwent an EEG exam while looking at validated images of happy and sad faces at week 0 and 1. MDD patients were categorized into treatment responders and non-responders after 4 weeks of treatment. We conducted the FC analysis (node strength (NS), global efficiency (GE), and cluster coefficient (CC)) on HC and MDD patients using graph theoretical analysis. Multivariable linear regression was used to evaluate the influence of MDD on FC compared to HC, while controlling for confounding variables including age, gender, and academic degrees.

RESULTS

At week 0 and week 1, MDD patients revealed to have significant reductions in FC parameters (NS, GE and CC) compared to HC. When comparing MDD patients at week 1 post-antidepressant treatment and pre-treatment, no significant differences in FC changes were observed. Multivariable regression revealed a significant negative effect on FC of MDD. Compared to the treatment non-responsive group, the responsive group revealed a significantly higher FC in delta band frequency at baseline.

CONCLUSIONS

MDD patient group showed impaired FC during visual emotion-processing and we observed baseline FC parameters to be associated with treatment response at week 4. While signs of FC changes were observed in the brain after a week of treatment, it is possible that one week may still be insufficient to demonstrate significant alterations in the brain. Our results suggest the potential utilization of EEG-based FC as an indicative measure for predicting treatment response and monitoring treatment progress in MDD patients.

Decreased Resting-State Alpha Self-Synchronization in Depressive Disorder

Background. Depression disorder has been associated with altered oscillatory brain activity. The common methods to quantify oscillatory activity are Fourier and wavelet transforms. Both methods have difficulties distinguishing synchronized oscillatory activity from nonrhythmic and large-amplitude artifacts. Here we proposed a method called self-synchronization index (SSI) to quantify synchronized oscillatory activities in neural data. The method considers temporal characteristics of neural oscillations, amplitude, and cycles, to estimate the synchronization value for a specific frequency band. Method. The recorded electroencephalography (EEG) data of 45 depressed and 55 healthy individuals were used. The SSI method was applied to each EEG electrode filtered in the alpha frequency band (8-13 Hz). The multiple linear regression model was used to predict depression severity (Beck Depression Inventory-II scores) using alpha SSI values. Results. Patients with severe depression showed a lower alpha SSI than those with moderate depression and healthy controls in all brain regions. Moreover, the alpha SSI values negatively correlated with depression severity in all brain regions. The regression model showed a significant performance of depression severity prediction using alpha SSI. Conclusion. The findings support the SSI measure as a powerful tool for quantifying synchronous oscillatory activity. The data examined in this article support the idea that there is a strong link between the synchronization of alpha oscillatory neural activities and the level of depression. These findings yielded an objective and quantitative depression severity prediction.

Disrupted properties of functional brain networks in major depressive disorder during emotional face recognition: an EEG study via graph theory analysis

Previous neuroimaging studies have revealed abnormal brain networks in patients with major depressive disorder (MDD) in emotional processing. While any cognitive task consists of a series of stages, little is yet known about the topology of functional brain networks in MDD for these stages during emotional face recognition. To address this problem, electroencephalography (EEG)-based functional brain networks of MDD patients at different stages of facial information processing were investigated in this study. First, EEG signals were collected from 16 patients with MDD and 18 age-, gender-, and education-matched normal subjects when performing an emotional face recognition task. Second, the global field power (GFP) method was employed to divide group-averaged event-related potentials into different stages. Third, using the phase transfer entropy (PTE) approach, the brain networks of MDD patients and normal individuals were constructed for each stage in negative and positive face processing, respectively. Finally, we compared the topological properties of brain networks of each stage between the two groups using graph theory approaches. The results showed that the analyzed three stages of emotional face processing corresponded to specific neurophysiological phases, namely, visual perception, face recognition, and emotional decision-making. It was also demonstrated that depressed patients showed abnormally decreased characteristic path length at the visual perception stage of negative face recognition and normalized characteristic path length in the stage of emotional decision-making during positive face processing compared to healthy subjects. Furthermore, while both the MDD and normal groups' brain networks were found to exhibit small-world network characteristics, the brain network of patients with depression tended to be randomized. Moreover, for patients with MDD, the centro-parietal region may lose its status as a hub in the process of facial expression identification. Together, our findings suggested that altered emotional function in MDD patients might be associated with disruptions in the topological organization of functional brain networks during emotional face recognition, which further deepened our understanding of the emotion processing dysfunction underlying MDD.

Evaluating the depression state during perinatal period by non-invasive scalp EEG

Perinatal depression, with a prevalence of 10 to 20% in United States, is usually missed as multiple symptoms of perinatal depression are common in pregnant women. Worse, the diagnosis of perinatal depression still largely relies on questionnaires, leaving the objective biomarker being unveiled yet. This study suggested a safe and non-invasive technique to diagnose perinatal depression and further explore its underlying mechanism. Considering the non-invasiveness and clinical convenience of electroencephalogram for mothers-to-be and fetuses, we collected the resting-state electroencephalogram of pregnant women at the 38th week of gestation. Subsequently, the difference in network topology between perinatal depression patients and healthy mothers-to-be was explored, with related spatial patterns being adopted to achieve the classification of pregnant women with perinatal depression from those healthy ones. We found that the perinatal depression patients had decreased brain network connectivity, which indexed impaired efficiency of information processing. By adopting the spatial patterns, the perinatal depression could be accurately recognized with an accuracy of 87.88%; meanwhile, the depression severity at the individual level was effectively predicted, as well. These findings consistently illustrated that the resting-state electroencephalogram network could be a reliable tool for investigating the depression state across pregnant women, and will further facilitate the clinical diagnosis of perinatal depression.

Analysis of EEG features and study of automatic classification in first-episode and drug-naïve patients with major depressive disorder

BACKGROUND

Major depressive disorder (MDD) has a high incidence and an unknown mechanism. There are no objective and sensitive indicators for clinical diagnosis.

OBJECTIVE

This study explored specific electrophysiological indicators and their role in the clinical diagnosis of MDD using machine learning.

METHODS

Forty first-episode and drug-naïve patients with MDD and forty healthy controls (HCs) were recruited. EEG data were collected from all subjects in the resting state with eyes closed for 10 min. The severity of MDD was assessed by the Hamilton Depression Rating Scale (HAMD-17). Machine learning analysis was used to identify the patients with MDD.

RESULTS

Compared to the HC group, the relative power of the low delta and theta bands was significantly higher in the right occipital region, and the relative power of the alpha band in the entire posterior occipital region was significantly lower in the MDD group. In the MDD group, the alpha band scalp functional connectivity was overall lower, while the scalp functional connectivity in the gamma band was significantly higher than that in the HC group. In the feature set of the relative power of the ROI in each band, the highest accuracy of 88.2% was achieved using the KNN classifier while using PCA feature selection. In the explanatory model using SHAP values, the top-ranking influence feature is the relative power of the alpha band in the left parietal region.

CONCLUSIONS

Our findings reveal that the abnormal EEG neural oscillations may reflect an imbalance of excitation, inhibition and hyperactivity in the cerebral cortex in first-episode and drug-naïve patients with MDD. The relative power of the alpha band in the left parietal region is expected to be an objective electrophysiological indicator of MDD.

Toward practical machine-learning-based diagnosis for drug-naïve women with major depressive disorder using EEG channel reduction approach

BACKGROUND

A machine-learning-based computer-aided diagnosis (CAD) system can complement the traditional diagnostic error for major depressive disorder (MDD) using trait-like neurophysiological biomarkers. Previous studies have shown that the CAD system has the potential to differentiate between female MDD patients and healthy controls. The aim of this study was to develop a practically useful resting-state electroencephalography (EEG)-based CAD system to assist in the diagnosis of drug-naïve female MDD patients by considering both the drug and gender effects. In addition, the feasibility of the practical use of the resting-state EEG-based CAD system was evaluated using a channel reduction approach.

METHODS

Eyes-closed, resting-state EEG data were recorded from 49 drug-naïve female MDD patients and 49 sex-matched healthy controls. Six different EEG feature sets were extracted: power spectrum densities (PSDs), phase-locking values (PLVs), and network indices for both sensor- and source-level, and four different EEG channel montages (62, 30, 19, and 10-channels) were designed to investigate the channel reduction effects in terms of classification performance.

RESULTS

The classification performances for each feature set were evaluated using a support vector machine with leave-one-out cross-validation. The optimum classification performance was achieved when using sensor-level PLVs (accuracy: 83.67 % and area under curve: 0.92). Moreover, the classification performance was maintained until the number of EEG channels was reduced to 19 (over 80 % accuracy).

CONCLUSION

We demonstrated the promising potential of sensor-level PLVs as diagnostic features when developing a resting-state EEG-based CAD system for the diagnosis of drug-naïve female MDD patients and verified the feasibility of the practical use of the developed resting-state EEG-based CAD system using the channel reduction approach.

Functional brain network features specify DBS outcome for patients with treatment resistant depression

Deep brain stimulation (DBS) has shown therapeutic benefits for treatment resistant depression (TRD). Stimulation of the subcallosal cingulate gyrus (SCG) aims to alter dysregulation between subcortical and cortex. However, the 50% response rates for SCG-DBS indicates that selection of appropriate patients is challenging. Since stimulation influences large-scale network function, we hypothesized that network features can be used as biomarkers to inform outcome. In this pilot project, we used resting-state EEG recorded longitudinally from 10 TRD patients with SCG-DBS (11 at baseline). EEGs were recorded before DBS-surgery, 1-3 months, and 6 months post surgery. We used graph theoretical analysis to calculate clustering coefficient, global efficiency, eigenvector centrality, energy, and entropy of source-localized EEG networks to determine their topological/dynamical features. Patients were classified as responders based on achieving a 50% or greater reduction in Hamilton Depression (HAM-D) scores from baseline to 12 months post surgery. In the delta band, false discovery rate analysis revealed that global brain network features (segregation, integration, synchronization, and complexity) were significantly lower and centrality of subgenual anterior cingulate cortex (ACC) was higher in responders than in non-responders. Accordingly, longitudinal analysis showed SCG-DBS increased global network features and decreased centrality of subgenual ACC. Similarly, a clustering method separated two groups by network features and significant correlations were identified longitudinally between network changes and depression symptoms. Despite recent speculation that certain subtypes of TRD are more likely to respond to DBS, in the SCG it seems that underlying brain network features are associated with ability to respond to DBS. SCG-DBS increased segregation, integration, and synchronizability of brain networks, suggesting that information processing became faster and more efficient, in those patients in whom it was lower at baseline. Centrality results suggest these changes may occur via altered connectivity in specific brain regions especially ACC. We highlight potential mechanisms of therapeutic effect for SCG-DBS.

Brain mechanisms underlying the emotion processing bias in treatment-resistant depression

Depression is associated with a cognitive bias towards negative information and away from positive information. This biased emotion processing may underlie core depression symptoms, including persistent feelings of sadness or low mood and a reduced capacity to experience pleasure. The neural mechanisms responsible for this biased emotion processing remain unknown. Here, we had a unique opportunity to record stereotactic electroencephalography (sEEG) signals in the amygdala and prefrontal cortex (PFC) from 5 treatment-resistant depression (TRD) patients and 12 epilepsy patients (as control) while they participated in an affective bias task in which happy and sad faces were rated. First, compared with the control group, patients with TRD showed increased amygdala responses to sad faces in the early stage (around 300 ms) and decreased amygdala responses to happy faces in the late stage (around 600 ms) following the onset of faces. Further, during the late stage of happy face processing, alpha-band activity in PFC as well as alpha-phase locking between the amygdala and PFC were significantly greater in TRD patients compared to the controls. Second, after deep brain stimulation (DBS) delivered to bilateral subcallosal cingulate (SCC) and ventral capsule/ventral striatum (VC/VS), atypical amygdala and PFC processing of happy faces in TRD patients remitted toward the normative pattern. The increased amygdala activation during the early stage of sad face processing suggests an overactive bottom-up processing system in TRD. Meanwhile, the reduced amygdala response during the late stage of happy face processing could be attributed to inhibition by PFC through alpha-band oscillation, which can be released by DBS in SCC and VC/VS.

Disrupted resting-state functional connectivity of frontal network in opium use disorder

Opioid use disorder (OUD) as a chronic relapsing disorder is initially driven by dysfunction of brain reward networks and associated with several psychiatric disorders. Resting-state EEG was recorded in 24 healthy participants as well as 31 patients with OUD. Healthy participants do not meet OUD criteria. After pre-processing of the raw EEG, functional connectivity in the frontal network using eLORETA and all networks using graph analysis method were calculated. Patients with OUD had higher electrical neuronal activity compared to healthy participants in higher frequency bands. The statistical analysis revealed that patients with OUD had significantly decreased phase synchronization in β1 and β2 frequency bands compared with the healthy group in the frontal network. Regarding global network topology, we found a significant decrease in the characteristic path length and an increase in global efficiency, clustering coefficient, and transitivity in patients compared with the healthy group. These changes indicated that local specialization and global integration of the brain were disrupted in OUD and it suggests a tendency toward random network configuration of functional brain networks in patients with OUD. Disturbances in EEG-based brain network indices might reflect an altered cortical functional network in OUD. These findings might provide useful biomarkers to understand cortical brain pathology in opium use disorder.

Alterations in EEG functional connectivity in individuals with depression: A systematic review

The brain works as an organised, network-like structure of functionally interconnected regions. Disruptions to interconnectivity in certain networks have been linked to symptoms of depression and impairments in cognition. Electroencephalography (EEG) is a low-burden tool by which differences in functional connectivity (FC) can be assessed. This systematic review aims to provide a synthesis of evidence relating to EEG FC in depression. A comprehensive electronic literature search for terms relating to depression, EEG, and FC was conducted on studies published before the end of November 2021, according to PRISMA guidelines. Studies comparing EEG measures of FC of individuals with depression to that of healthy control groups were included. Data was extracted by two independent reviewers, and the quality of EEG FC methods was assessed. Fifty-two studies assessing EEG FC in depression were identified: 36 assessed resting-state FC, and 16 assessed task-related or other (i.e., sleep) FC. Somewhat consistent findings in resting-state studies suggest for no differences between depression and control groups in EEG FC in the delta and gamma frequencies. However, while most resting-state studies noted a difference in alpha, theta, and beta, no clear conclusions could be drawn about the direction of the difference, due to considerable inconsistencies between study design and methodology. This was also true for task-related and other EEG FC. More robust research is needed to understand the true differences in EEG FC in depression. Given that the FC between brain regions drives behaviour, cognition, and emotion, characterising how FC differs in depression is essential for understanding the aetiology of depression.

Do we really measure what we think we are measuring?

Tests used in the empirical sciences are often (implicitly) assumed to be representative of a given research question in the sense that similar tests should lead to similar results. Here, we show that this assumption is not always valid. We illustrate our argument with the example of resting-state electroencephalogram (EEG). We used multiple analysis methods, contrary to typical EEG studies where one analysis method is used. We found, first, that many EEG features correlated significantly with cognitive tasks. However, these EEG features correlated weakly with each other. Similarly, in a second analysis, we found that many EEG features were significantly different in older compared to younger participants. When we compared these EEG features pairwise, we did not find strong correlations. In addition, EEG features predicted cognitive tasks poorly as shown by cross-validated regression analysis. We discuss several explanations of these results.

The Role of Alpha Oscillations among the Main Neuropsychiatric Disorders in the Adult and Developing Human Brain: Evidence from the Last 10 Years of Research

Alpha oscillations (7-13 Hz) are the dominant rhythm in both the resting and active brain. Accordingly, translational research has provided evidence for the involvement of aberrant alpha activity in the onset of symptomatological features underlying syndromes such as autism, schizophrenia, major depression, and Attention Deficit and Hyperactivity Disorder (ADHD). However, findings on the matter are difficult to reconcile due to the variety of paradigms, analyses, and clinical phenotypes at play, not to mention recent technical and methodological advances in this domain. Herein, we seek to address this issue by reviewing the literature gathered on this topic over the last ten years. For each neuropsychiatric disorder, a dedicated section will be provided, containing a concise account of the current models proposing characteristic alterations of alpha rhythms as a core mechanism to trigger the associated symptomatology, as well as a summary of the most relevant studies and scientific contributions issued throughout the last decade. We conclude with some advice and recommendations that might improve future inquiries within this field.

Abnormal Properties of Cortical Functional Brain Network in Major Depressive Disorder: Graph Theory Analysis Based on Electroencephalography-Source Estimates

Studies of scalp electroencephalography (EEG) had shown altered topological organization of functional brain networks in patients with major depressive disorder (MDD). However, most previous EEG-based network analyses were performed at sensor level, while the interpretation of obtained results was not straightforward due to volume conduction effect. To reduce the impact of this defect, the whole cortical functional brain networks of MDD patients were studied during resting state based on EEG-source estimates in this paper. First, scalp EEG signals were recorded from 19 patients with MDD and 20 normal controls under resting eyes-closed state, and cortical neural signals were estimated by using sLORETA method. Then, the correntropy coefficient of wavelet packet coefficients was performed to calculate functional connectivity (FC) matrices in four different frequency bands: δ, θ, α, β, respectively. Afterwards, topological properties of brain networks were analyzed by graph theory approaches. The results showed that the global FC strength of MDD patients was significantly higher than that of healthy subjects in α band. Also, it was found that MDD patients have abnormally increased clustering coefficient and local efficiency in both α and β bands compared to normal people. Furthermore, patients with MDD exhibited increased nodal clustering coefficients in the left lingual gryus and left precuneus in α band. In addition, β band global clustering coefficient was positively correlated with the scores of depression severity. Therefore, the findings indicated the cortical functional brain networks in MDD patients were disruptions, which suggested it would be one of potential causes of depression.

Electroencephalographic Network Topologies Predict Antidepressant Responses in Patients with Major Depressive Disorder

Medication therapy seems to be an effective treatment for major depressive disorder (MDD). However, although the efficacies of various medicines are equal or similar on average, they vary widely among individuals. Therefore, an understanding of methods for the timely evaluation of short-term therapeutic response and prediction of symptom improvement after a specific course of medication at the individual level at the initial stage of treatment is very important. In our present study, we sought to identify a neurobiological signature of the response to short-term antidepressant treatment. Related brain network analysis was applied in resting-state electroencephalogram (EEG) datasets from patients with MDD. The corresponding EEG networks were constructed accordingly and then quantitatively measured to predict the efficacy after eight weeks of medication, as well as to distinguish the therapeutic responders from non-responders. The results of our present study revealed that the corresponding resting-state EEG networks became significantly weaker after one week of treatment, and the eventual medication efficacy was reliably predicted using the changes in those network properties within the one-week medication regimen. Moreover, the corresponding resting-state networks at baseline were also proven to precisely distinguish those responders from other individuals with an accuracy of 96.67% when using the spatial network topologies as the discriminative features. These findings consistently provide a deeper neurobiological understanding of antidepressant treatment and a reliable and quantitative approach for personalized treatment of MDD.

An electroencephalography connectome predictive model of major depressive disorder severity

Emerging evidence showed that major depressive disorder (MDD) is associated with disruptions of brain structural and functional networks, rather than impairment of isolated brain region. Thus, connectome-based models capable of predicting the depression severity at the individual level can be clinically useful. Here, we applied a machine-learning approach to predict the severity of depression using resting-state networks derived from source-reconstructed Electroencephalography (EEG) signals. Using regression models and three independent EEG datasets (N = 328), we tested whether resting state functional connectivity could predict individual depression score. On the first dataset, results showed that individuals scores could be reasonably predicted (r = 0.6, p = 4 × 10-18) using intrinsic functional connectivity in the EEG alpha band (8-13 Hz). In particular, the brain regions which contributed the most to the predictive network belong to the default mode network. We further tested the predictive potential of the established model by conducting two external validations on (N1 = 53, N2 = 154). Results showed statistically significant correlations between the predicted and the measured depression scale scores (r1 = 0.52, r2 = 0.44, p < 0.001). These findings lay the foundation for developing a generalizable and scientifically interpretable EEG network-based markers that can ultimately support clinicians in a biologically-based characterization of MDD.

Enhanced Performance by Interpretable Low-Frequency Electroencephalogram Oscillations in the Machine Learning-Based Diagnosis of Post-traumatic Stress Disorder

Electroencephalography (EEG)-based diagnosis of psychiatric diseases using machine-learning approaches has made possible the objective diagnosis of various psychiatric diseases. The objective of this study was to improve the performance of a resting-state EEG-based computer-aided diagnosis (CAD) system to diagnose post-traumatic stress disorder (PTSD), by optimizing the frequency bands used to extract EEG features. We used eyes-closed resting-state EEG data recorded from 77 PTSD patients and 58 healthy controls (HC). Source-level power spectrum densities (PSDs) of the resting-state EEG data were extracted from 6 frequency bands (delta, theta, alpha, low-beta, high-beta, and gamma), and the PSD features of each frequency band and their combinations were independently used to discriminate PTSD and HC. The classification performance was evaluated using support vector machine with leave-one-out cross validation. The PSD features extracted from slower-frequency bands (delta and theta) showed significantly higher classification performance than those of relatively higher-frequency bands. The best classification performance was achieved when using delta PSD features (86.61%), which was significantly higher than that reported in a recent study by about 13%. The PSD features selected to obtain better classification performances could be explained from a neurophysiological point of view, demonstrating the promising potential to develop a clinically reliable EEG-based CAD system for PTSD diagnosis.

Alteration of cortical functional networks in mood disorders with resting-state electroencephalography

Studies comparing bipolar disorder (BD) and major depressive disorder (MDD) are scarce, and the neuropathology of these disorders is poorly understood. This study investigated source-level cortical functional networks using resting-state electroencephalography (EEG) in patients with BD and MDD. EEG was recorded in 35 patients with BD, 39 patients with MDD, and 42 healthy controls (HCs). Graph theory-based source-level weighted functional networks were assessed via strength, clustering coefficient (CC), and path length (PL) in six frequency bands. At the global level, patients with BD and MDD showed higher strength and CC, and lower PL in the high beta band, compared to HCs. At the nodal level, compared to HCs, patients with BD showed higher high beta band nodal CCs in the right precuneus, left isthmus cingulate, bilateral paracentral, and left superior frontal; however, patients with MDD showed higher nodal CC only in the right precuneus compared to HCs. Although both MDD and BD patients had similar global level network changes, they had different nodal level network changes compared to HCs. Our findings might suggest more altered cortical functional network in patients with BD than in those with MDD.

Brain Functional Network and Amino Acid Metabolism Association in Females with Subclinical Depression

This study aimed to investigate the association between complex brain functional networks and the metabolites in urine in subclinical depression. Electroencephalography (EEG) signals were recorded from 78 female college students, including 40 with subclinical depression (ScD) and 38 healthy controls (HC). The phase delay index was utilized to construct functional connectivity networks and quantify the topological properties of brain networks using graph theory. Meanwhile, the urine of all participants was collected for non-targeted LC-MS metabolic analysis to screen differential metabolites. The global efficiency was significantly increased in the α-2, β-1, and β-2 bands, while the characteristic path length of β-1 and β-2 and the clustering coefficient of β-2 were decreased in the ScD group. The severity of depression was negatively correlated with the level of cortisone (p = 0.016, r = −0.40). The metabolic pathways, including phenylalanine metabolism, phenylalanine tyrosine tryptophan biosynthesis, and nitrogen metabolism, were disturbed in the ScD group. The three metabolic pathways were negatively correlated (p = 0.014, r = −0.493) with the global efficiency of the brain network of the β-2 band, whereas they were positively correlated (p = 0.014, r = 0.493) with the characteristic path length of the β-2 band. They were mainly associated with low levels of L-phenylalanine, and the highest correlation sparsity was 0.11. The disturbance of phenylalanine metabolism and the phenylalanine, tryptophan, tyrosine biosynthesis pathways cause depressive symptoms and changes in functional brain networks. The decrease in the L-phenylalanine level may be related to the randomization trend of the β-1 frequency brain functional network.

Automated detection of clinical depression based on convolution neural network model

As a common mental disorder, depression is placing an increasing burden on families and society. However, the current methods of depression detection have some limitations, and it is essential to find an objective and efficient method. With the development of automation and artificial intelligence, computer-aided diagnosis has attracted more and more attention. Therefore, exploring the use of deep learning (DL) to detect depression has valuable potential. In this paper, convolutional neural network (CNN) is applied to build a diagnostic model for depression based on electroencephalogram (EEG). EEG recordings are analyzed by three different CNN structures, namely EEGNet, DeepConvNet and ShallowConvNet, to dichotomize depression patients and healthy controls. EEG data were collected in the resting state from three electrodes (Fp1, Fz, Fp2) among 80 subjects (40 depressive patients and 40 normal subjects). After the preprocessing step, the DL structures are employed to classify the data, and their recognition performance is evaluated by comparing the classification results. The classification performance shows that depression was effectively detected using EEGNet with 93.74% accuracy, 94.85% sensitivity and 92.61% specificity. In the process of optimizing the parameters of EEGNet structure, the highest accuracy can reach 94.27%. Compared with traditional diagnostic methods, EEGNet is highly worthy for the future depression detection and valuable in terms of accuracy and speed.

A survey of brain network analysis by electroencephalographic signals

Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.

Abnormal core functional connectivity on the pathology of MDD and antidepressant treatment: A systematic review

RATIONALE/IMPORTANCE

Researches have highlighted communication deficits between resting-state brain networks in major depressive disorder (MDD), as reflected in abnormal functional connectivity (FC). However, it is unclear whether impaired FC is associated with MDD pathology or is simply incidental to MDD symptoms. Moreover, there is no generalized theory to analyze the impact of treatment modalities on MDD.

OBJECTIVES

To address the issues, we conducted a systematic review of 49 eligible papers to provide insight into the pathological mechanisms of MDD patients by summarizing resting-state FC alterations involving mood and cognitive abnormalities and the effects of medications on them.

RESULTS

Mood disorders in MDD were characterized by abnormal FC between the amygdala, insula, anterior cingulate cortex (ACC), and prefrontal cortex (PFC). Cognitive impairment manifests as deficits in executive function, attention, memory, and rumination, primarily modulated by dysfunction between the fronto-parietal network and default mode network. Especially, we proposed the set of core abnormal FC (CA-FC) contributing to mood and cognitive impairment in MDD, currently including ACC-left precuneus/amygdala, rostral ACC-left dorsolateral PFC, left subgenual ACC-left cerebellar, left PFC- anterior subcallosal, and left precuneus-left pulvinar. After treatment, patients with normalized CA-FC showed remission of depressive symptoms.

CONCLUSIONS

We propose a CA-FC set for possible causative principle of MDD, which unifies the FC results from specific, difficult-to-analyze conditions into one outcome set for screening. Furthermore, CA-FC varies from person to person, and the low success rate of a single treatment may be due to the inability to cover too many CA-FC.

Prediction model for potential depression using sex and age-reflected quantitative EEG biomarkers

Depression is a prevalent mental disorder in modern society, causing many people to suffer or even commit suicide. Psychiatrists and psychologists typically diagnose depression using representative tests, such as the Beck's Depression Inventory (BDI) and the Hamilton Depression Rating Scale (HDRS), in conjunction with patient consultations. Traditional tests, however, are time-consuming, can be trained on patients, and entailed a lot of clinician subjectivity. In the present study, we trained the machine learning models using sex and age-reflected z-score values of quantitative EEG (QEEG) indicators based on data from the National Standard Reference Data Center for Korean EEG, with 116 potential depression subjects and 80 healthy controls. The classification model has distinguished potential depression groups and normal groups, with a test accuracy of up to 92.31% and a 10-cross-validation loss of 0.13. This performance proposes a model with z-score QEEG metrics, considering sex and age as objective and reliable biomarkers for early screening for the potential depression.

Mindfulness-Based Cognitive Therapy in Recurrent MDD Patients With Residual Symptoms: Alterations in Resting-State Theta Oscillation Dynamics Associated With Changes in Depression and Rumination

Many patients with major depressive disorder (MDD) suffer from residual symptoms. Rumination is a specific known risk factor for the onset, severity, prolongation, and relapse of MDD. This study aimed to examine the efficacy and EEG substrates of mindfulness-based cognitive therapy (MBCT) in alleviating depression and rumination in an MDD population with residual symptoms. We recruited 26 recurrent MDD individuals who had residual symptoms with their current antidepressants to participate in the 8-week MBCT intervention. We evaluated the efficacy and changes in the dynamics of resting-state theta rhythm after the intervention, as well as the associations between theta alterations and improvements in depression and rumination. The participants showed reduced depression, enhanced adaptive reflective rumination, and increased theta power and phase synchronization after MBCT. The increased theta-band phase synchronizations between the right occipital regions and the right prefrontal, central, and parietal regions were associated with reduced depression, while the increase in theta power in the left parietal region was associated with improvements in reflective rumination. MBCT could alleviate depression and enhance adaptive, reflective rumination in recurrent MDD individuals with residual symptoms through the modulation of theta dynamics in specific brain regions.

Emotion dysregulation and integration of emotion-related brain networks affect intraindividual change in ADHD severity throughout late adolescence

The course of attention deficit hyperactivity disorder (ADHD) from adolescence into adulthood shows large variations between individuals; nonetheless determinants of interindividual differences in the course are not well understood. A frequent problem in ADHD, associated with worse outcomes, is emotion dysregulation. We investigated whether emotion dysregulation and integration of emotion-related functional brain networks affect interindividual differences in ADHD severity change. ADHD severity and resting state neuroimaging data were measured in ADHD and unaffected individuals at two points during adolescence and young adulthood. Bivariate latent change score models were applied to investigate whether emotion dysregulation and network integration affect ADHD severity changes. Emotion dysregulation was gauged from questionnaire subscales for conduct problems, emotional problems and emotional lability. Better emotion regulation was associated with a better course of ADHD (104 participants, 44 females, age range: 12-27). Using graph analysis, we determined network integration of emotion-related functional brain networks. Network integration was measured by nodal efficiency, i.e., the average inverse path distance from one node to all other nodes. A pattern of low nodal efficiency of cortical regions associated with emotion processing and high nodal efficiency in subcortical areas and cortical areas involved in implicit emotion regulation predicted a better ADHD course. Larger nodal efficiency of the right orbitofrontal cortex was related to a better course of ADHD (99 participants, 42 females, age range: 10-29). We demonstrated that neural and behavioral covariates associated with emotion regulation affect the course of ADHD severity throughout adolescence and early adulthood beyond baseline effects of ADHD severity.

DelIrium VULnerability in GEriatrics (DIVULGE) study: a protocol for a prospective observational study of electroencephalogram associations with incident postoperative delirium

INTRODUCTION

Delirium is a neurocognitive disorder common in older adults in acute care settings. Those who develop delirium are at an increased risk of dementia, cognitive decline and death. Electroencephalography (EEG) during delirium in older adults is characterised by slowing and reduced functional connectivity, but markers of vulnerability are poorly described. We aim to identify EEG spectral power and event-related potential (ERP) markers of incident delirium in older adults to understand neural mechanisms of delirium vulnerability. Characterising delirium vulnerability will provide substantial theoretical advances and outcomes have the potential to be translated into delirium risk assessment tools.

METHODS AND ANALYSIS

We will record EEG in 90 participants over 65 years of age prior to elective coronary artery bypass grafting (CABG) or transcatheter aortic valve implantation (TAVI). We will record 4-minutes of resting state (eyes open and eyes closed) and a 5-minute frequency auditory oddball paradigm. Outcome measures will include frequency band power, 1/f offset and slope, and ERP amplitude measures. Participants will undergo cognitive and EEG testing before their elective procedures and daily postoperative delirium assessments. Group allocation will be done retrospectively by linking preoperative EEG data according to postoperative delirium status (presence, severity, duration and subtype).

ETHICS AND DISSEMINATION

This study is approved by the Human Research Ethics Committee of the Royal Adelaide Hospital, Central Adelaide Local Health Network and the University of South Australia Human Ethics Committee. Findings will be disseminated through peer-reviewed journal articles and presentations at national and international conferences.

TRIAL REGISTRATION NUMBER

ACTRN12618001114235 and ACTRN12618000799257.

Phase-Dependent Deep Brain Stimulation: A Review

Neural oscillations are repetitive patterns of neural activity in the central nervous systems. Oscillations of the neurons in different frequency bands are evident in electroencephalograms and local field potential measurements. These oscillations are understood to be one of the key mechanisms for carrying out normal functioning of the brain. Abnormality in any of these frequency bands of oscillations can lead to impairments in different cognitive and memory functions leading to different pathological conditions of the nervous system. However, the exact role of these neural oscillations in establishing various brain functions is still under investigation. Closed loop deep brain stimulation paradigms with neural oscillations as biomarkers could be used as a mechanism to understand the function of these oscillations. For making use of the neural oscillations as biomarkers to manipulate the frequency band of the oscillation, phase of the oscillation, and stimulation signal are of importance. This paper reviews recent trends in deep brain stimulation systems and their non-invasive counterparts, in the use of phase specific stimulation to manipulate individual neural oscillations. In particular, the paper reviews the methods adopted in different brain stimulation systems and devices for stimulating at a definite phase to further optimize closed loop brain stimulation strategies.

Altered cortical functional network in drug-naive adult male patients with attention-deficit hyperactivity disorder: A resting-state electroencephalographic study

Relatively little is known about the neurophysiology of adult Attention-deficit/hyperactivity disorder (ADHD). Brain network analysis can yield important insights into the neuropathology in adult ADHD. The objective of this study was to investigate source-level cortical functional network using resting-state electroencephalography (EEG) in drug-naive adult patients with ADHD. Resting-state EEG was performed for 30 adult male patients with ADHD and 27 male healthy controls. Source-level weighted functional networks based on graph theory were evaluated, including strength, clustering coefficient (CC) and path length (PL) in six frequency bands. At the global level, strength (η² = 0.167) and CC (η² = 0.156) were lower while PL (η² = 0.159) was higher for the high beta band in the ADHD patient group compared to healthy controls. At the nodal level, CCs of the high beta band were lower in the left middle temporal gyrus (η² = 0.244), right inferior occipital cortex (η² = 0.214), right posterior transverse collateral sulcus (η² = 0.237), and right anterior occipital sulcus (η² = 0.251) for the adult ADHD group. Furthermore, the nodal-level high beta band CCs of the left middle temporal gyrus and right anterior occipital sulcus were significantly negatively correlated with ADHD symptoms. The altered cortical functional network showed inefficient connectivity in the left middle temporal gyrus, belonging to the default mode network, the right inferior occipital cortex, belonging to the extrastriate visual resting state network, the right posterior transverse collateral sulcus, belonging to the visual network, and the anterior occipital sulcus, reflecting visual attention, which might affect the pathophysiology of ADHD. Taken together, these attenuated network inefficiencies in adult patients with ADHD may lead to suboptimal information processing and affect symptoms of ADHD, such as inattention and hyperactivity. Our findings should be further replicated using longitudinal study designs.

Sex differences in innate and adaptive neural oscillatory patterns link resilience and susceptibility to chronic stress in rats

BACKGROUND

Major depressive disorder is a chronic illness with a higher incidence in women. Dysregulated neural oscillatory activity is an emerging mechanism thought to underlie major depressive disorder, but whether sex differences in these rhythms contribute to the development of symptoms is unknown.

METHODS

We exposed male and female rats to chronic unpredictable stress and characterized them as stress-resilient or stress-susceptible based on behavioural output in the forced swim test and the sucrose preference test. To identify sex-specific neural oscillatory patterns associated with stress response, we recorded local field potentials from the prefrontal cortex, cingulate cortex, nucleus accumbens and dorsal hippocampus throughout stress exposure.

RESULTS

At baseline, female stress-resilient rats innately exhibited higher theta coherence in hippocampal connections compared with stress-susceptible female rats. Following stress exposure, additional oscillatory changes manifested: stress-resilient females were characterized by increased dorsal hippocampal theta power and cortical gamma power, and stress-resilient males were characterized by a widespread increase in high gamma coherence. In stress-susceptible animals, we observed a pattern of increased delta and reduced theta power; the changes were restricted to the cingulate cortex and dorsal hippocampus in males but occurred globally in females. Finally, stress exposure was accompanied by the time-dependent recruitment of specific neural pathways, which culminated in system-wide changes that temporally coincided with the onset of depression-like behaviour.

LIMITATIONS

We could not establish causality between the electrophysiological changes and behaviours with the methodology we employed.

CONCLUSION

Sex-specific neurophysiological patterns can function as early markers for stress vulnerability and the onset of depression-like behaviours in rats.

Predicting hallucination proneness based on mindfulness in university students: the mediating role of mental distress

As a risk factor of hallucination proneness, the level of mindfulness has not yet been investigated in non-clinical participants. Other potential mediators, such as mental distress (depression, anxiety, and stress) which contribute to hallucination proneness also need to be assessed. This study investigated the mediating effect of mental distress in predicting hallucination proneness based on mindfulness. A number of 168 Iranian university students completed three questionnaires: (1) the five-facet mindfulness questionnaire, (2) the depression, anxiety and stress scale; and (3) the revised hallucination scale. The results showed that there was a significant association between levels of mindfulness and hallucination proneness. Mental distress has a significant effect on four facets of mindfulness questionnaire and an insignificant effect on one facet (awareness) in predicting hallucination. These effects were both direct and indirect. The indirect effect was developed by the mediating role of mental distress.

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.

Prediction of Depression Severity Scores Based on Functional Connectivity and Complexity of the EEG Signal

BACKGROUND

Depression is one of the most common mental disorders and the leading cause of functional disabilities. This study aims to specify whether functional connectivity and complexity of brain activity can predict the severity of depression (Beck Depression Inventory-II scores).

METHODS

Resting-state, eyes-closed EEG data were recorded from 60 depressed patients. A phase synchronization measure was used to estimate functional connectivity between all pairs of the EEG channels in the delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequency bands. To quantify the local value of functional connectivity, 2 graph theory metrics, degree, and clustering coefficient (CC), were measured. Moreover, Lempel-Ziv complexity (LZC) and fuzzy entropy (FuzzyEn) were used to measure the complexity of the EEG signal.

RESULTS

Through correlation analysis, a significant negative relationship was found between graph metrics and depression severity in the alpha band. This association was strongly positive for the complexity measures in alpha and delta bands. Also, the linear regression model represented a substantial performance of depression severity prediction based on EEG features of the alpha band (r = 0.839; P < .0001, root mean square error score of 7.69).

CONCLUSION

We found that the brain activity of patients with depression was related to depression severity. Abnormal brain activity reflects an increase in the severity of depression. The presented regression model provides a quantitative depression severity prediction, which can inform the development of EEG state and exhibit potential desirable application for the medical treatment of the depressive disorder.