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

Functional Network Connectivity for Components of Depression-Related Psychological Fragility

Psychological resilience (PR) is known to be inversely associated with depression. While there is a growing body of research examining how depression alters activity across multiple functional neural networks, how differences in PR affect these networks is largely unexplored. This study examines the relationship between PR and functional connectivity in the alpha and beta bands within (and between) eighteen established cortical nodes in the default mode network, the central executive network, and the salience network. Resting-state EEG data from 99 adult participants (32 depressed, 67 non-depressed) were used to measure the correlation between the five factors of PR sourced from the Connor-Davidson Resilience Scale and eLORETA-based measures of coherence and phase synchronisation. Distinct functional connectivity patterns were seen across each resilience factor, with a notable absence of overlapping positive results across the depressed and non-depressed samples. These results indicate that depression may modulate how resilience is expressed in terms of fundamental neural activity.

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.

EEG based functional connectivity in resting and emotional states may identify major depressive disorder using machine learning

OBJECTIVE

Disrupted brain network connectivity underlies major depressive disorder (MDD). Altered EEG based Functional connectivity (FC) with Emotional stimuli in major depressive disorder (MDD) in addition to resting state FC may help in improving the diagnostic accuracy of machine learning classification models. We explored the potential of EEG-based FC during resting state and emotional processing, for diagnosing MDD using machine learning approach.

METHODS

EEG was recorded during resting state and while watching emotionally contagious happy and sad videos in 24 drug-naïve MDD patients and 25 healthy controls. FC was quantified using the Phase Lag Index. Three Random Forest classifier models were constructed to classify MDD patients and healthy controls, Model-I incorporating FC features from the resting state and Model-II and Model-III incorporating FC features while watching happy and sad videos respectively.

RESULTS

Important features distinguishing MDD and healthy controls were from all frequency bands and represent functional connectivity between fronto-temporal, fronto-parietal and fronto occipital regions. The cross-validation accuracies for Model-I, Model-II and Model-III were 92.3%, 94.9% and 89.7% and test accuracies were 60%, 80% and 70% respectively. Incorporating emotionally contagious videos improved the classification accuracies.

CONCLUSION

Findings support EEG FC patterns during resting state and emotional processing along with machine learning can be used to diagnose MDD. Future research should focus on replicating and validating these results.

SIGNIFICANCE

EEG FC pattern combined with machine learning may be used for assisting in diagnosing MDD.

Alterations in electroencephalographic functional connectivity in individuals with major depressive disorder: a resting-state electroencephalogram study

Background

Major depressive disorder (MDD) is the leading cause of disability among all mental illnesses with increasing prevalence. The diagnosis of MDD is susceptible to interference by several factors, which has led to a trend of exploring objective biomarkers. Electroencephalography (EEG) is a non-invasive procedure that is being gradually applied to detect and diagnose MDD through some features such as functional connectivity (FC).

Methods

In this research, we analyzed the resting-state EEG of patients with MDD and healthy controls (HCs) in both eyes-open (EO) and eyes-closed (EC) conditions. The phase locking value (PLV) method was utilized to explore the connection and synchronization of neuronal activities spatiotemporally between different brain regions. We compared the PLV between participants with MDD and HCs in five frequency bands (theta, 4-8 Hz; alpha, 8-12 Hz; beta1, 12-16 Hz; beta2, 16-24 Hz; and beta3, 24-40 Hz) and further analyzed the correlation between the PLV of connections with significant differences and the severity of depression (via the scores of 17-item Hamilton Depression Rating Scale, HDRS-17).

Results

During the EO period, lower PLVs were found in the right temporal-left midline occipital cortex (RT-LMOC; theta, alpha, beta1, and beta2) and posterior parietal-right temporal cortex (PP-RT; beta1 and beta2) in the MDD group compared with the HC group, while PLVs were higher in the MDD group in LT-LMOC (beta2). During the EC period, for the MDD group, lower theta and beta (beta1, beta2, and beta3) PLVs were found in PP-RT, as well as lower theta, alpha, and beta (beta1, beta2, and beta3) PLVs in RT-LMOC. Additionally, in the left midline frontal cortex-right temporal cortex (LMFC-RT) and posterior parietal cortex-right temporal cortex (PP-RMOC), higher PLVs were observed in beta2. There were no significant correlations between PLVs and HDRS-17 scores when connections with significantly different PLVs (all p > 0.05) were checked.

Conclusion

Our study confirmed the presence of differences in FC between patients with MDD and healthy individuals. Lower PLVs in the connection of the right temporal-left occipital cortex were mostly observed, whereas an increase in PLVs was observed in patients with MDD in the connections of the left temporal with occipital lobe (EO), the circuits of the frontal-temporal lobe, and the parietal-occipital lobe. The trends in FC involved in this study were not correlated with the level of depression.

Limitations

The study was limited due to the lack of further analysis of confounding factors and follow-up data. Future studies with large-sampled and long-term designs are needed to further explore the distinguishable features of EEG FC in individuals with MDD.

Prediction of brain sex from EEG: using large-scale heterogeneous dataset for developing a highly accurate and interpretable ML model

This study presents a comprehensive examination of sex-related differences in resting-state electroencephalogram (EEG) data, leveraging two different types of machine learning models to predict an individual's sex. We utilized data from the Two Decades-Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) EEG study, affirming that gender prediction can be attained with noteworthy accuracy. The best performing model achieved an accuracy of 85% and an ROC AUC of 89%, surpassing all prior benchmarks set using EEG data and rivaling the top-tier results derived from fMRI studies. A comparative analysis of LightGBM and Deep Convolutional Neural Network (DCNN) models revealed DCNN's superior performance, attributed to its ability to learn complex spatial-temporal patterns in the EEG data and handle large volumes of data effectively. Despite this, interpretability remained a challenge for the DCNN model. The LightGBM interpretability analysis revealed that the most important EEG features for accurate sex prediction were related to left fronto-central and parietal EEG connectivity. We also showed the role of both low (delta and theta) and high (beta and gamma) activity in the accurate sex prediction. These results, however, have to be approached with caution, because it was obtained from a dataset comprised largely of participants with various mental health conditions, which limits the generalizability of the results and necessitates further validation in future studies. . Overall, the study illuminates the potential of interpretable machine learning for sex prediction, alongside highlighting the importance of considering individual differences in prediction sex from brain activity.

EEG Network Analysis of Depressive Emotion Interference Spatial Cognition Based on a Simulated Robotic Arm Docking Task

Depressive emotion (DE) refers to clinically relevant depressive symptoms without meeting the diagnostic criteria for depression. Studies have demonstrated that DE can cause spatial cognition impairment. However, the brain network mechanisms underlying DE interference spatial cognition remain unclear. This study aimed to reveal the differences in brain network connections between DE and healthy control (HC) groups during resting state and a spatial cognition task. The longer operation time of the DE group during spatial cognition task indicated DE interference spatial cognition. In the resting state stage, the DE group had weaker network connections in theta and alpha bands than the HC group had. Specifically, the electrodes in parietal regions were hubs of the differential networks, which are related to spatial attention. Moreover, in docking task stages, the left frontoparietal network connections in delta, beta, and gamma bands were stronger in the DE group than those of the HC group. The enhanced left frontoparietal connections in the DE group may be related to brain resource reorganization to compensate for spatial cognition decline and ensure the completion of spatial cognition tasks. Thus, these findings might provide new insights into the neural mechanisms of depressive emotion interference spatial cognition.

Decreased beta 1 (12-15 Hertz) power modulates the transfer of suicidal ideation to suicide in major depressive disorder

BACKGROUND

Suicide prevention for major depressive disorder (MDD) is a worldwide challenge, especially for suicide attempt (SA). Viewing suicide as a state rather than a lifetime event provided new perspectives on suicide research.

OBJECTIVE

This study aimed to verify and complement SAs biomarkers of MDD with a recent SA sample.

METHODS

This study included 189 participants (60 healthy controls; 47 MDD patients with non-suicide (MDD-NSs), 40 MDD patients with suicide ideation (MDD-SIs) and 42 MDD patients with SA (MDD-SAs)). MDD patients with an acute SA time was determined to be within 1 week since the last SA. SUICIDALITY Part in MINI was applied to evaluate suicidality. Absolute powers in 14 frequency bands were extracted from subject's resting-state electroencephalography data and compared within four groups. The relationship among suicidality, the number of SA and powers in significant frequency bands were investigated.

RESULTS

MDD-SIs had increased powers in delta, theta, alpha and beta band on the right frontocentral channels compared to MDD-NSs, while MDD-SAs had decreased powers in delta, beta and gamma bands on widely the right frontocentral and parietooccipital channels compared to MDD-SIs. Beta 1 power was the lowest in MDD-SAs and was modulated by the number of SA. The correlation between suicidality and beta 1 power was negative in MDD-SAs and positive in MDD-SIs.

CONCLUSION

Reduced beta 1 (12-15 Hz) power could be essential in promoting suicidal behaviour in MDD. Research on recent SA samples contributes to a better understanding of suicide mechanisms and preventing suicidal behaviour in MDD.

The Relationship Between Theta Power, Theta Asymmetry and the Effect of Escitalopram in the Treatment of Depression

Objective

Only about one-third of depressed patients respond to initial antidepressant treatment. Therefore, it is crucial to find effective predictors of antidepressants. The purpose of our study was to learn the relationship between EEG theta power, theta asymmetry, and the efficacy of escitalopram.

Methods

The study included 34 patients with depression. Before and after each patient's course of treatment, EEG data was gathered. Both the Hamilton Anxiety Scale (HAMA) and the 17-item Hamilton Depression Scale (HAMD-17) were evaluated simultaneously. The natural logarithm of right frontal theta power minus left frontal theta power was used to calculate inter-electrode theta asymmetry (AT).

Results

First, our study found no statistically significant difference between intra-electrode theta power and inter-electrode AT before and after treatment (P ≥ 0.05). When we later looked at the data regarding treatment effects, the findings revealed that patients (n = 9) who did not respond to treatment had lower baseline theta power at C4 [6.190 (2.000, 12.990) vs 15.800 (7.255, 22.330), z = -2.166, P = 0.030]. The two groups had no difference in other electrodes (P ≥ 0.05). The AT of C3/C4 in non-responders (n = 9) was lower [0.012 (0.795) vs 0.733 (0.539), t = -3.224, P = 0.005]. However, there was no difference in inter-electrode AT between the two groups in F3/F4 and F7/F8 (P ≥ 0.05). We finally show that the theta power at C4 was negatively correlated with HAMD scores before treatment (r = -0.346, P = 0.045).

Conclusion

Our findings determined that increased theta power and positive asymmetry in the right frontal-central area correlate with favourable escitalopram treatment, providing a basis for finding predictive markers for antidepressants.

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.

Cross-subject classification of depression by using multiparadigm EEG feature fusion

BACKGROUND AND OBJECTIVE

The aim of this study is to solve the non-stationarity and complexity characteristics and huge individual differences in the electroencephalogram (EEG) signals for depression classification.

METHODS

To address those problems, the Lempel-Ziv complexity feature matrices were extracted from the EEG signals under the two paradigms of eyes open and eyes closed in the resting state. Topographical map of brain and statistical analysis were introduced to investigate the significance of eyes open and eyes closed EEG for depression classification. To promote the classification accuracy, feature matrices from the two paradigms were fused. And linear combination and concatenation fusion methods were proposed to further reveal the underlying mechanism of improving classification accuracy. Support vector machine (SVM), K-nearest neighbor, and decision tree classifiers were employed and compared to classify depression under the eyes open, eyes closed and fused paradigm.

RESULTS

The classification results of 10-fold cross-validation showed that the highest average accuracy (86.58%) under a single paradigm was achieved in the eyes-open state. The multiparadigm fusion method of concatenation was better than the linear combination. The best classification result was obtained using multiparadigm feature concatenation under the SVM classifier, yielding an accuracy of 94.03%.

CONCLUSION

The multiparadigm feature fusion method proposed in this paper can effectively improve the accuracy of depression classification. It was proved that eyes open and eyes closed EEG have complementary information, which was benefit for the cross-subject classification of depression. It provides new ideas for depression classification in clinics.

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.

The potential of electroencephalography coherence to predict the outcome of repetitive transcranial magnetic stimulation in insomnia disorder

BACKGROUND

It is unknown whether repetitive Transcranial Magnetic Stimulation (rTMS) could improve sleep quality by modulating electroencephalography (EEG) connectivity of insomnia disorder (ID) patients. Great heterogeneity had been found in the clinical outcomes of rTMS for ID. The study aimed to investigate the potential mechanisms of rTMS therapy for ID and develop models to predict clinical outcomes.

METHODS

In Study 1, 50 ID patients were randomly divided into active and sham groups, and subjected to 20 sessions of treatment with 1 Hz rTMS over the left dorsolateral prefrontal cortex. EEG during awake, Polysomnography, and clinical assessment were collected and analyzed before and after rTMS. In Study 2, 120 ID patients were subjected to active rTMS stimulation and were then separated into optimal and sub-optimal groups due to the median of Pittsburgh Sleep Quality Index reduction rate. Machine learning models were developed based on baseline EEG coherence to predict rTMS treatment effects.

RESULTS

In Study 1, decreased EEG coherence in theta and alpha bands were observed after rTMS treatment, and changes in theta band (F7-O1) coherence were correlated with changes in sleep efficiency. In Study 2, baseline EEG coherence in theta, alpha, and beta bands showed the potential to predict the treatment effects of rTMS for ID.

CONCLUSION

rTMS improved sleep quality of ID patients by modulating the abnormal EEG coherence. Baseline EEG coherence between certain channels in theta, alpha, and beta bands could act as potential biomarkers to predict the therapeutic effects.

Alpha transcranial alternating current stimulation reduces depressive symptoms in people with schizophrenia and auditory hallucinations: a double-blind, randomized pilot clinical trial

People with schizophrenia exhibit reduced alpha oscillations and frontotemporal coordination of brain activity. Alpha oscillations are associated with top-down inhibition. Reduced alpha oscillations may fail to censor spurious endogenous activity, leading to auditory hallucinations. Transcranial alternating current stimulation (tACS) at the alpha frequency was shown to enhance alpha oscillations in people with schizophrenia and may thus be a network-based treatment for auditory hallucinations. We conducted a double-blind, randomized, placebo-controlled pilot clinical trial to examine the efficacy of 10-Hz tACS in treating auditory hallucinations in people with schizophrenia. 10-Hz tACS was administered in phase at the dorsolateral prefrontal cortex and the temporoparietal junction with a return current at Cz. Patients were randomized to receive tACS or sham for five consecutive days during the treatment week (40 min/day), followed by a maintenance period, during which participants received weekly tACS (40 min/visit) or sham. tACS treatment reduced general psychopathology (p < 0.05, Cohen's d = -0.690), especially depression (p < 0.005, Cohen's d = -0.806), but not auditory hallucinations. tACS treatment increased alpha power in the target region (p < 0.05), increased the frequency of peak global functional connectivity towards 10 Hz (p < 0.05), and reduced left-right frontal functional connectivity (p < 0.005). Importantly, changes in brain functional connectivity significantly correlated with symptom improvement (p < 0.05). Daily 10 Hz-tACS increased alpha power and altered alpha-band functional connectivity. Successful target engagement reduced depression and other general psychopathology symptoms, but not auditory hallucinations. Considering existing research of 10Hz tACS as a treatment for major depressive disorder, our study demonstrates its transdiagnostic potential for treating depression.

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

BACKGROUND

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

METHODS

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

RESULTS

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

LIMITATIONS

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

CONCLUSIONS

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

Identifying depression disorder using multi-view high-order brain function network derived from electroencephalography signal

Brain function networks (BFN) are widely used in the diagnosis of electroencephalography (EEG)-based major depressive disorder (MDD). Typically, a BFN is constructed by calculating the functional connectivity (FC) between each pair of channels. However, it ignores high-order relationships (e.g., relationships among multiple channels), making it a low-order network. To address this issue, a novel classification framework, based on matrix variate normal distribution (MVND), is proposed in this study. The framework can simultaneously generate high-and low-order BFN and has a distinct mathematical interpretation. Specifically, the entire time series is first divided into multiple epochs. For each epoch, a BFN is constructed by calculating the phase lag index (PLI) between different EEG channels. The BFNs are then used as samples, maximizing the likelihood of MVND to simultaneously estimate its low-order BFN (Lo-BFN) and high-order BFN (Ho-BFN). In addition, to solve the problem of the excessively high dimensionality of Ho-BFN, Kronecker product decomposition is used for dimensionality reduction while retaining the original high-order information. The experimental results verified the effectiveness of Ho-BFN for MDD diagnosis in 24 patients and 24 normal controls. We further investigated the selected discriminative Lo-BFN and Ho-BFN features and revealed that those extracted from different networks can provide complementary information, which is beneficial for MDD diagnosis.

Mental Activity as the Bridge between Neural Biomarkers and Symptoms of Psychiatric Illness

The Research Domain Criteria (RDoC) initiative challenges researchers to build neurobehavioral models of psychiatric illness with the hope that such models identify better targets that will yield more effective treatment. However, a guide for building such models was not provided and symptom heterogeneity within Diagnostic Statistical Manual categories has hampered progress in identifying endophenotypes that underlie mental illness. We propose that the best chance to discover viable biomarkers and treatment targets for psychiatric illness is to investigate a triangle of relationships: severity of a specific psychiatric symptom that correlates to mental activity that correlates to a neural activity signature. We propose that this is the minimal model complexity required to advance the field of psychiatry. With an understanding of how neural activity relates to the experience of the patient, a genuine understanding for how treatment imparts its therapeutic effect is possible. After the discovery of this three-fold relationship, causal testing is required in which the neural activity pattern is directly enhanced or suppressed to provide causal, instead of just correlational, evidence for the biomarker. We suggest using non-invasive brain stimulation (NIBS) as these techniques provide tools to precisely manipulate spatial and temporal activity patterns. We detail how this approach enabled the discovery of two orthogonal electroencephalography (EEG) activity patterns associated with anhedonia and anxiosomatic symptoms in depression that can serve as future treatment targets. Altogether, we propose a systematic approach for building neurobehavioral models for dimensional psychiatry.

Cross-validated Adaboost Classification of Emotion Regulation Strategies Identified by Spectral Coherence in Resting-State

In the present study, quantitative relations between Cognitive Emotion Regulation strategies (CERs) and EEG synchronization levels have been investigated for the first time. For this purpose, spectral coherence (COH), phase locking value and mutual information have been applied to short segments of 62-channel resting state eyes-opened EEG data collected from healthy adults who use contrasting emotion regulation strategies (frequently and rarely use of rumination&distraction, frequently and rarely use of suppression&reappraisal). In tests, the individuals are grouped depending on their self-responses to both emotion regulation questionnaire (ERQ) and cognitive ERQ. Experimental data are downloaded from publicly available data-base, LEMON. Regarding EEG electrode pairs that placed on right and left cortical regions, inter-hemispheric dependency measures are computed for non-overlapped short segments of 2 sec at 2 min duration trials. In addition to full-band EEG analysis, dependency metrics are also obtained for both alpha and beta sub-bands. The contrasting groups are discriminated from each other with respect to the corresponding features using cross-validated adaboost classifiers. High classification accuracies (CA) of 99.44% and 98.33% have been obtained through instant classification driven by full-band COH estimations. Considering regional features that provide the high CA, CERs are found to be highly relevant with associative memory functions and cognition. The new findings may indicate the close relation between neuroplasticity and cognitive skills.

Global and localized network characteristics of the resting brain predict and adapt to foreign language learning in older adults

Resting brain (rs) activity has been shown to be a reliable predictor of the level of foreign language (L2) proficiency younger adults can achieve in a given time-period. Since rs properties change over the lifespan, we investigated whether L2 attainment in older adults (aged 64-74 years) is also predicted by individual differences in rs activity, and to what extent rs activity itself changes as a function of L2 proficiency. To assess how neuronal assemblies communicate at specific frequencies to facilitate L2 development, we examined localized and global measures (Minimum Spanning Trees) of connectivity. Results showed that central organization within the beta band (~ 13-29.5 Hz) predicted measures of L2 complexity, fluency and accuracy, with the latter additionally predicted by a left-lateralized centro-parietal beta network. In contrast, reduced connectivity in a right-lateralized alpha (~ 7.5-12.5 Hz) network predicted development of L2 complexity. As accuracy improved, so did central organization in beta, whereas fluency improvements were reflected in localized changes within an interhemispheric beta network. Our findings highlight the importance of global and localized network efficiency and the role of beta oscillations for L2 learning and suggest plasticity even in the ageing brain. We interpret the findings against the background of networks identified in socio-cognitive processes.

Reduction in Left Frontal Alpha Oscillations by Transcranial Alternating Current Stimulation in Major Depressive Disorder Is Context Dependent in a Randomized Clinical Trial

BACKGROUND

Left frontal alpha oscillations are associated with decreased approach motivation and have been proposed as a target for noninvasive brain stimulation for the treatment of depression and anhedonia. Indeed, transcranial alternating current stimulation (tACS) at the alpha frequency reduced left frontal alpha power and was associated with a higher response rate than placebo stimulation in patients with major depressive disorder (MDD) in a recent double-blind, placebo-controlled clinical trial.

METHODS

In this current study, we aimed to replicate successful target engagement by delineating the effects of a single session of bifrontal tACS at the individualized alpha frequency (IAF-tACS) on alpha oscillations in patients with MDD. Eighty-four participants were randomized to receive verum or sham IAF-tACS. Electrical brain activity was recorded during rest and while viewing emotionally salient images before and after stimulation to investigate whether the modulation of alpha oscillation by tACS exhibited specificity with regard to valence.

RESULTS

In agreement with the previous study of tACS in MDD, we found that a single session of bifrontal IAF-tACS reduced left frontal alpha power during the resting state when compared with placebo. Furthermore, the reduction of left frontal alpha oscillation by tACS was specific for stimuli with positive valence. In contrast, these effects on left frontal alpha power were not found in healthy control participants.

CONCLUSIONS

Together, these results support an important role of tACS in reducing left frontal alpha oscillations as a future treatment for MDD.

Theta oscillations: A rhythm difference comparison between major depressive disorder and anxiety disorder

BACKGROUND

Due to substantial comorbidities of major depressive disorder (MDD) and anxiety disorder (AN), these two disorders must be distinguished. Accurate identification and diagnosis facilitate effective and prompt treatment. EEG biomarkers are a potential research hotspot for neuropsychiatric diseases. The purpose of this study was to investigate the differences in EEG power spectrum at theta oscillations between patients with MDD and patients with AN.

METHODS

Spectral analysis was used to study 66 patients with MDD and 43 patients with AN. Participants wore 16-lead EEG caps to measure resting EEG signals. The EEG power spectrum was measured using the fast Fourier transform. Independent samples t-test was used to analyze the EEG power values of the two groups, and p < 0.05 was statistically significant.

RESULTS

EEG power spectrum of the MDD group significantly differed from the AN group in the theta oscillation on 4-7 Hz at eight electrode points at F3, O2, T3, P3, P4, FP1, FP2, and F8.

CONCLUSION

Participants with anxiety demonstrated reduced power in the prefrontal cortex, left temporal lobe, and right occipital regions. Confirmed by further studies, theta oscillations could be another biomarker that distinguishes MDD from AN.

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.

Intensification of functional neural control on heartbeat dynamics in subclinical depression

Subclinical depression (dysphoria) is a common condition that may increase the risk of major depression and leads to impaired quality of life and severe comorbid somatic diseases. Despite its prevalence, specific biological markers are unknown; consequently, the identification of dysphoria currently relies exclusively on subjective clinical scores and structured interviews. Based on recent neurocardiology studies that link brain and cardiovascular disorders, it was hypothesized that multi-system biomarkers of brain-body interplay may effectively characterize dysphoria. Thus, an ad hoc computational technique was developed to quantify the functional bidirectional brain-heart interplay. Accordingly, 32-channel electroencephalographic and heart rate variability series were obtained from 24 young dysphoric adults and 36 healthy controls. All participants were females of a similar age, and results were obtained during a 5-min resting state. The experimental results suggest that a specific feature of dysphoria is linked to an augmented functional central-autonomic control to the heart, which originates from central, frontopolar, and occipital oscillations and acts through cardiovascular sympathovagal activity. These results enable further development of a large set of novel biomarkers for mood disorders based on comprehensive brain-body measurements.