Emotion Regulating Attentional Control Abnormalities In Major Depressive Disorder: An Event-Related Potential Study

Impaired cognitive flexibility in major depressive disorder: Evidences from spatial-temporal ERPs analysis

BACKGROUND

Major Depressive Disorder (MDD) may exhibit impairments in cognitive flexibility. This study investigated whether the cognitive flexibility deficits in MDD are evident across general stimuli or specific to emotional stimuli, while exploring the underlying neuropsychological mechanism.

METHODS

A total of 41 MDD patients and 42 healthy controls (HCs) were recruited. Event-related potentials (ERPs) were recorded when participants performed a non-emotional and an emotional task switching paradigm (N-ETSP and ETSP), both of which assessed cognitive flexibility. Microstate and source localization analysis were applied to reflect brain activity among different brain areas during task switching.

RESULTS

In the N-ETSP, MDD group showed larger P3 difference wave (Pd3) amplitudes and longer P2 difference wave (Pd2) latencies. In the ETSP, MDD group displayed smaller N2 difference wave (Nd2) amplitudes and larger Pd3 amplitudes. The comparison of sLORETA images of emotional switch task and emotional repeat task showed that MDD group had increased activation in the precentral gyrus in microstate2 of the P2 time window and had reduced activation in the middle occipital gyrus in microstate3 of the N2 time window.

LIMITATIONS

The cross-sectional design failed to capture dynamic changes in cognitive flexibility in MDD.

CONCLUSIONS

MDD demonstrated impaired cognitive flexibility respond to both non-emotional and emotional stimuli, with greater impairment for negative emotional stimuli. These deficits are evident in abnormal ERPs component during the early attention stage and the later task preparation stage. Furthermore, abnormal emotional switching cost in MDD appears to be related to early abnormal perceptual control in the parietal-occipital cortex.

Emotional contagion and cognitive empathy regulate the effect of depressive symptoms on empathy-related brain functional connectivity in patients with chronic back pain

BACKGROUND

Chronic pain and depression share common neural mechanisms, but their impacts on empathy are different. It is unclear how comorbid depressive symptoms affect empathy-related brain function in patients with chronic pain.

METHODS

A total of 29 healthy participants and 107 patients with chronic back pain (CBP) were included in this study. All subjects underwent a functional MRI scan with concurrent empathic stimulation. Multiple linear regression, moderation analysis, and mediation analysis were used to explore the impacts of chronic pain and comorbid depression on empathy.

RESULTS

The interaction between the pain intensity and the depressive symptoms affected the functional connectivity (FC) of the insula-middle frontal gyrus (MFG), and the severity of the self-rating depression scale (SDS) scores moderated the effect of the pain on the left insula-left MFG FC. Within the CBP group, the emotional contagion (EC) scores served as a mediator in the association between the SDS scores and the FC of the left middle cingulate cortex (MCC)-inferior temporal gyrus (ITG), and the level of cognitive empathy (CE) moderated the effect of the SDS scores on the left MCC-ITG FC.

LIMITATIONS

There is a lack of research on the effects of depressive symptoms on empathy in individuals with different types of chronic pain.

CONCLUSION

Depressive symptoms were strongly associated with the emotional contagion in patients with chronic back pain. Furthermore, the emotional contagion and the cognitive empathy regulated the effect of the comorbid depressive symptoms on the MCC-ITG connectivity in patients with chronic back pain.

Neural substrates of predicting anhedonia symptoms in major depressive disorder via connectome-based modeling

MAIN PROBLEM

Anhedonia is a critical diagnostic symptom of major depressive disorder (MDD), being associated with poor prognosis. Understanding the neural mechanisms underlying anhedonia is of great significance for individuals with MDD, and it encourages the search for objective indicators that can reliably identify anhedonia.

METHODS

A predictive model used connectome-based predictive modeling (CPM) for anhedonia symptoms was developed by utilizing pre-treatment functional connectivity (FC) data from 59 patients with MDD. Node-based FC analysis was employed to compare differences in FC patterns between melancholic and non-melancholic MDD patients. The support vector machines (SVM) method was then applied for classifying these two subtypes of MDD patients.

RESULTS

CPM could successfully predict anhedonia symptoms in MDD patients (positive network: r = 0.4719, p < 0.0020, mean squared error = 23.5125, 5000 iterations). Compared to non-melancholic MDD patients, melancholic MDD patients showed decreased FC between the left cingulate gyrus and the right parahippocampus gyrus (p_bonferroni = 0.0303). This distinct FC pattern effectively discriminated between melancholic and non-melancholic MDD patients, achieving a sensitivity of 93.54%, specificity of 67.86%, and an overall accuracy of 81.36% using the SVM method.

CONCLUSIONS

This study successfully established a network model for predicting anhedonia symptoms in MDD based on FC, as well as a classification model to differentiate between melancholic and non-melancholic MDD patients. These findings provide guidance for clinical treatment.

Depressive states in healthy subjects lead to biased processing in frontal-parietal ERPs during emotional stimuli

Subthreshold depressive (sD) states and major depression are considered to occur on a continuum, and there are only quantitative and not qualitative differences between depressive states in healthy individuals and patients with depression. sD is showing a progressively increasing prevalence and has a lifelong impact, and the social and clinical impacts of sD are no less than those of major depressive disorder (MDD). Because depression leads to biased cognition, patients with depression and healthy individuals show different visual processing properties. However, it remains unclear whether there are significant differences in visual information recognition among healthy individuals with various depressive states. In this study, we investigated the event-related potentials (ERPs) and event-related spectrum perturbation (ERSP) of healthy individuals with various depressive states during the perception of emotional visual stimulation. We show that different neural activities can be detected even among healthy individuals. We divided healthy participants into high, middle, and low depressive state groups and found that participants in a high depressive state had a lower P300 amplitude and significant differences in fast and slow neural responses in the frontal and parietal lobes. We anticipate our study to provide useful parameters for assessing the evaluation of depressive states in healthy individuals.

Event-Related Potential Markers of Suicidality in Adolescents

BACKGROUND

Implicit cognitive markers may assist with the prediction of suicidality beyond clinical risk factors. The aim of this study was to investigate neural correlates associated with the Death/Suicide Implicit Association Test (DS-IAT) via event-related potentials (ERP) in suicidal adolescents.

METHODS

Thirty inpatient adolescents with suicidal ideations and behaviors (SIBS) and 30 healthy controls from the community were recruited. All participants underwent 64-channel electroencephalography, DS-IAT, and clinical assessments. Hierarchical generalized linear models with spatiotemporal clustering were used to identify significant ERPs associated with the behavioral outcome of DS-IAT (D scores) and group differences.

RESULTS

Behavioral results (D scores) showed that the adolescents with SIBS had stronger implicit associations between "death" and "self" than the healthy group (P = .02). Within adolescents with SIBS, participants with stronger implicit associations between "death" and "self" reported more difficulty in controllability of suicidal ideation in the past 2 weeks based on the Columbia-Suicide Severity Rating Scale (P = .03). For the ERP data, the D scores and N100 component over the left parieto-occipital cortex had significant correlations. Significant group differences without behavioral correlation were observed for a second N100 cluster (P = .01), P200 (P = .02), and late positive potential (5 clusters, all P ≤ .02). Exploratory predictive models combining both neurophysiological and clinical measures distinguished adolescents with SIBS from healthy adolescents.

CONCLUSIONS

Our results suggest that N100 may be a marker of attentional resources involved in the distinction of stimuli that are congruent or incongruent to associations between death and self. Combined clinical and ERP measures may have utility in future refinements of assessment and treatment approaches for adolescents with suicidality.

Discovering the neuronal dynamics in major depressive disorder using Hidden Markov Model

Introduction

Major Depressive Disorder (MDD) is a leading cause of worldwide disability, and standard clinical treatments have limitations due to the absence of neurological evidence. Electroencephalography (EEG) monitoring is an effective method for recording neural activities and can provide electroneurophysiological evidence of MDD.

Methods

In this work, we proposed a probabilistic graphical model for neural dynamics decoding on MDD patients and healthy controls (HC), utilizing the Hidden Markov Model with Multivariate Autoregressive observation (HMM-MAR). We testified the model on the MODMA dataset, which contains resting-state and task-state EEG data from 53 participants, including 24 individuals with MDD and 29 HC.

Results

The experimental results suggest that the state time courses generated by the proposed model could regress the Patient Health Questionnaire-9 (PHQ-9) score of the participants and reveal differences between the MDD and HC groups. Meanwhile, the Markov property was observed in the neuronal dynamics of participants presented with sad face stimuli. Coherence analysis and power spectrum estimation demonstrate consistent results with the previous studies on MDD.

Discussion

In conclusion, the proposed HMM-MAR model has revealed its potential capability to capture the neuronal dynamics from EEG signals and interpret brain disease pathogenesis from the perspective of state transition. Compared with the previous machine-learning or deep-learning-based studies, which regarded the decoding model as a black box, this work has its superiority in the spatiotemporal pattern interpretability by utilizing the Hidden Markov Model.

Brain function changes reveal rapid antidepressant effects of nitrous oxide for treatment-resistant depression:Evidence from task-state EEG

Nitrous oxide has rapid antidepressant effects in patients with treatment-resistant depression (TRD), but its underlying mechanisms of therapeutic actions are not well understood. Moreover, most of the current studies lack objective biological indicators to evaluate the changes of nitrous oxide-induced brain function for TRD. Therefore, this study assessed the effect of nitrous oxide on brain function for TRD based on event-related potential (ERP) components and functional connectivity networks (FCNs) methods. In this randomized, longitudinal, placebo-controlled trial, all TRD participants were divided into two groups to receive either a 1-hour inhalation of nitrous oxide or a placebo treatment, and they took part in the same task-state electroencephalogram (EEG) experiment before and after treatment. The experimental results showed that nitrous oxide improved depressive symptoms better than placebo in terms of 17-Hamilton Depression Rating Scale score (HAMD-17). Statistical analysis based on ERP components showed that nitrous oxide-induced significant differences in amplitude and latency of N1, P1, N2, P2. In addition, increased brain functional connectivity was found after nitrous oxide treatment. And the change of network metrics has a significant correlation with decreased depressive symptoms. These findings may suggest that nitrous oxide improves depression symptoms for TRD by modifying brain function.

The Neural Correlates of the Recognition of Emotional Intensity Deficits in Major Depression: An ERP Study

PURPOSE

Deficits in facial emotional intensity recognition have been associated with social cognition in patients with major depression. The study examined multiple event-related potential (ERP) components in patients with major depression and investigated the relationships between ERPs, social cognition, and clinical features.

PARTICIPANTS AND METHODS

Thirty-one patients met DSM-IV diagnosis of depression and 31 healthy participants completed the emotion intensity recognition task (EIRT), while ERPs were recorded. Data on ERP components (P100, N170, P200, and P300) were analyzed.

RESULTS

The behavioral results showed that patients with major depression performed worse on EIRT, including all six categories of emotions (sadness, disgust, happiness, surprise, anger, and fear), compared to healthy participants. The ERP results showed that patients with major depression exhibited higher P100 amplitudes for sad and happy faces than healthy participants; P300 amplitudes induced by sad and surprise faces were also higher than in healthy participants, mainly in the central and temporal lobes. A positive correlation was found between sadness intensity scores and P100 amplitudes in patients with major depression.

CONCLUSION

Patients with major depression are biased in their identification of facial expressions indicating emotional intensity. Specifically, they have emotional biases in the early and late stages of cognitive processing, mainly in the form of sensitivity to sad stimuli. It may lead to a persistent rumination of sadness that is detrimental to the remission of depression. Additionally, patients with major depression devote different amounts of cognitive resources for different intensities of sad faces during the preconscious stage of cognitive processing. The more intense their perception of sadness, the more cognitive resources they devote. Therefore, the assessment of the intensity of facial expressions is an important research topic, with clinical implications on social cognitive function in patients with major depression.

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.

Neural Activity Associated with Symptoms Change in Depressed Adolescents following Self-Processing Neurofeedback

Adolescent depression is prevalent, debilitating, and associated with chronic lifetime mental health disorders. Understanding the neurobiology of depression is critical to developing novel treatments. We tested a neurofeedback protocol targeting emotional regulation and self-processing circuitry and examined brain activity associated with reduced symptom severity, as measured through self-report questionnaires, four hours after neurofeedback. Depressed (n = 34) and healthy (n = 19) adolescents participated in (i) a brief neurofeedback task that involves simultaneously viewing their own happy face, recalling a positive autobiographical memory, and increasing amygdala-hippocampal activity; (ii) a self- vs. other- face recognition task with happy, neutral, and sad facial expressions before and after the neurofeedback. In depressed youth, reduced depression after neurofeedback was associated with increased self-referential and visual areas' activity during neurofeedback, specifically, increased activity in the cuneus, precuneus and parietal lobe. Reduced depression was also associated with increased activation of emotional regulation and cross-modal areas during a self-recognition task. These areas included the cerebellum, middle temporal gyrus, superior temporal gyrus, and supramarginal gyrus. However, decreased rumination was linked to decreased precuneus, angular and temporal gyri activity during neurofeedback. These results tentatively suggest that neurofeedback may induce short-term neurobiological changes in the self-referential and emotional regulation networks associated with reduced symptom severity among depressed adolescents.

Diminished frontal pole size and functional connectivity in young adults with high suicidality

BACKGROUND

Suicide rates among young people have been increasing in recent years, yet no validated methods are available for identifying those who are at greatest risk for suicide. Abnormalities in the medial prefrontal cortex have been previously observed in suicidal individuals, but confounding factors such as treatment and chronic illness may have contributed to these findings. Thus, in this study we tested whether the size of the medial prefrontal cortex is altered in suicidal young adults who have received no treatment with psychotropic medications.

METHODS

Suicidality was evaluated using the Suicide Behaviors Questionnaire-Revised (SBQ-R) and surface areas of four regions-of-interest (ROIs) within the medial prefrontal cortex were measured using magnetic resonance imaging (MRI) in a cohort of college students (n = 102). In addition, a secondary seed-based functional connectivity analysis was conducted using resting-state functional MRI data. Areas and functional connectivity of the medial prefrontal cortex of young adults with high suicidality (HS; SBQ-R score > 7; n = 20) were compared to those with low suicidality (LS; SBQ-R score = 3, n = 37).

RESULTS

Compared to the LS group, the HS group had a significantly lower surface area of the right frontal pole (p < 0.05, Bonferroni-corrected) and significantly lower functional connectivity of the right frontal pole with the bilateral inferior frontal cortex (p < 0.001, Monte-Carlo corrected).

LIMITATION

These findings require replication in a larger sample and extension in younger (adolescent) populations.

CONCLUSION

Diminished frontal pole surface area and functional connectivity may be linked to elevated levels of suicidality in young people.

Altered Brain Function in First-Episode and Recurrent Depression: A Resting-State Functional Magnetic Resonance Imaging Study

Background

Studies on differences in brain function activity between the first depressive episode (FDE) and recurrent depressive episodes (RDE) are scarce. In this study, we used regional homogeneity (ReHo) and amplitude of low-frequency fluctuations (ALFF) as indices of abnormal brain function activity. We aimed to determine the differences in these indices between patients with FDE and those with RDE, and to investigate the correlation between areas of abnormal brain function and clinical symptoms.

Methods

A total of 29 patients with RDE, 28 patients with FDE, and 29 healthy controls (HCs) who underwent resting-state functional magnetic resonance imaging were included in this study. The ReHo and ALFF measurements were used for image analysis and further analysis of the correlation between different brain regions and clinical symptoms.

Results

Analysis of variance showed significant differences among the three groups in ReHo and ALFF in the frontal, parietal, temporal, and occipital lobes. ReHo was higher in the right inferior frontal triangular gyrus and lower in the left inferior temporal gyrus in the RDE group than in the FDE group. Meanwhile, ALFF was higher in the right inferior frontal triangular gyrus, left anterior cingulate gyrus, orbital part of the left middle frontal gyrus, orbital part of the left superior frontal gyrus, and right angular gyrus, but was lower in the right lingual gyrus in the RDE group than in the FDE group. ReHo and ALFF were lower in the left angular gyrus in the RDE and FDE groups than in the HC group. Pearson correlation analysis showed a positive correlation between the ReHo and ALFF values in these abnormal areas in the frontal lobe and the severity of depressive symptoms (P < 0.05). Abnormal areas in the temporal and occipital lobes were negatively correlated with the severity of depressive symptoms (P < 0.05).

Conclusion

The RDE and FDE groups had abnormal neural function activity in some of the same brain regions. ReHo and ALFF were more widely distributed in different brain regions and had more complex neuropathological mechanisms in the RDE group than in the FDE group, especially in the right inferior frontal triangular gyrus of the frontal lobe.

Differential effects of stress-related and stress-unrelated humor in remitted depression

Enhancing emotion regulation among previously depressed people is crucial for improving their resilience and reducing relapse. Therefore, emphasis is placed on determining effective regulation strategies, particularly those that, besides down-regulating negative emotions, also up-regulate positive emotions. One promising strategy, with great potential in both these respects, is humor. It is unclear, however, what type of humor is most adaptive in remitted depression. This study compared two distinct humor-based strategies: stress-related humor and stress-unrelated humor. Outpatients with remitted depression (N = 94) participated in a randomized experiment evoking personal stress and the subsequent application of stress-related humor, stress-unrelated humor, or a non-humorous regulation. They repeatedly reported positive and negative emotions (at four time points) and experienced distress (at three time points). There were also assessments of selective attention, subsequent performance, effort, and intrusive thoughts. Unlike non-humorous regulation, humor-based strategies had adaptive consequences, both immediately and after a delay; however, stress-unrelated humor was most beneficial and was the only effective strategy when attention deficits were present. Humor, especially if unrelated to stressors, might broaden the repertoire of powerful emotion regulation strategies in remitted depression. Humorous focusing on distress can be detrimental for patients with attention impairment.Clinical trial registration: The study was registered under the number ISRCTN86314628 (20/09/2021).

A multi-modal open dataset for mental-disorder analysis

According to the WHO, the number of mental disorder patients, especially depression patients, has overgrown and become a leading contributor to the global burden of disease. With the rising of tools such as artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applications for mental disorder diagnosis has become a new research hot topic. We present a multi-modal open dataset for mental-disorder analysis. The dataset includes EEG and recordings of spoken language data from clinically depressed patients and matching normal controls, who were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes data collected using a traditional 128-electrodes mounted elastic cap and a wearable 3-electrode EEG collector for pervasive computing applications. The 128-electrodes EEG signals of 53 participants were recorded as both in resting state and while doing the Dot probe tasks; the 3-electrode EEG signals of 55 participants were recorded in resting-state; the audio data of 52 participants were recorded during interviewing, reading, and picture description.

Measurement(s)	Human Brainwave • spoken language
Technology Type(s)	EEG collector • audio recorder
Sample Characteristic - Organism	Homo Sapiens
Sample Characteristic - Location	China

Depression Assessment Method: An EEG Emotion Recognition Framework Based on Spatiotemporal Neural Network

The main characteristic of depression is emotional dysfunction, manifested by increased levels of negative emotions and decreased levels of positive emotions. Therefore, accurate emotion recognition is an effective way to assess depression. Among the various signals used for emotion recognition, electroencephalogram (EEG) signal has attracted widespread attention due to its multiple advantages, such as rich spatiotemporal information in multi-channel EEG signals. First, we use filtering and Euclidean alignment for data preprocessing. In the feature extraction, we use short-time Fourier transform and Hilbert-Huang transform to extract time-frequency features, and convolutional neural networks to extract spatial features. Finally, bi-directional long short-term memory explored the timing relationship. Before performing the convolution operation, according to the unique topology of the EEG channel, the EEG features are converted into 3D tensors. This study has achieved good results on two emotion databases: SEED and Emotional BCI of 2020 WORLD ROBOT COMPETITION. We applied this method to the recognition of depression based on EEG and achieved a recognition rate of more than 70% under the five-fold cross-validation. In addition, the subject-independent protocol on SEED data has achieved a state-of-the-art recognition rate, which exceeds the existing research methods. We propose a novel EEG emotion recognition framework for depression detection, which provides a robust algorithm for real-time clinical depression detection based on EEG.

Efficacy of acupuncture on cognitive function in poststroke depression: study protocol for a randomized, placebo-controlled trial

Introduction

Poststroke depression (PSD) is the most common mental complication after stroke and has a serious impact on functional outcomes and quality of life. Antidepressants are the first-line treatment for PSD, but many reported side effects remain. Clinical research has shown that acupuncture has a positive effect on PSD. This trial aims to study the efficacy and safety of acupuncture for PSD and to explore its effect on cognitive function. It is hypothesized that acupuncture treatment improves depressive symptoms, cognitive behavior, and negative emotion processing bias in PSD.

Methods

In this randomized, placebo-controlled, single-blinded trial, fifty-six people with PSD will be randomly allocated into the intervention (n=28) or control (n=28) groups. The intervention group will receive acupuncture treatment, and the control group will receive sham acupuncture treatment, in 20 sessions over 4 weeks. The primary outcome is the change from baseline in the Hamilton Depression Scale-17 (HAMD-17) scores at week 4. Secondary outcomes include the Wisconsin Card Sorting Test (WCST) and latency and amplitude of P1, N170, and P3 of the event-related potentials (ERPs) components to assess the changes in cognitive function and electroencephalography. Outcomes are assessed at baseline and post intervention.

Discussion

Acupuncture therapy could become an alternative treatment for PSD, and it is expected that this trial will provide reliable clinical evidence for the future use of acupuncture for the treatment of PSD.

Trial registration

Chinese Clinical Trial Registry ChiCTR1900026948. Registered on 27 October 2019.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-022-06011-7.

An End-to-End Depression Recognition Method Based on EEGNet

Major depressive disorder (MDD) is a common and highly debilitating condition that threatens the health of millions of people. However, current diagnosis of depression relies on questionnaires that are highly correlated with physician experience and hence not completely objective. Electroencephalography (EEG) signals combined with deep learning techniques may be an objective approach to effective diagnosis of MDD. This study proposes an end-to-end deep learning framework for MDD diagnosis based on EEG signals. We used EEG signals from 29 healthy subjects and 24 patients with severe depression to calculate Accuracy, Precision, Recall, F1-Score, and Kappa coefficient, which were 90.98%, 91.27%, 90.59%, and 81.68%, respectively. In addition, we found that these values were highest when happy-neutral face pairs were used as stimuli for detecting depression. Compared with exiting methods for EEG-based MDD classification, ours can maintain stable model performance without re-calibration. The present results suggest that the method is highly accurate for diagnosis of MDD and can be used to develop an automatic plug-and-play EEG-based system for diagnosing depression.

Emotional face processing correlates with depression/anxiety symptoms but not wellbeing in non-clinical adults: An event-related potential study

Whilst alterations in emotional face processing, as indicated by event-related potentials (ERPs), are associated with depression and anxiety symptoms in clinical and non-clinical samples, it has remained unclear whether they are related to mental wellbeing. The current study aimed to address this question in a non-clinical sample. The analysis included 402 adult twins from the TWIN-E study. The COMPAS-W and the Depression Anxiety Stress Scale (DASS-42) were used to measure mental wellbeing and depression/anxiety symptoms, respectively. Participants viewed facial expressions under Unmasked (conscious) and Masked (subliminal) conditions while ERPs were recorded. The associations of emotion processing with mental wellbeing and depression/anxiety symptoms were assessed using multivariate linear mixed models. There was a strong association between depression/anxiety symptoms and the N170 amplitude difference for the Fear - Happy contrast in the Masked condition after controlling for wellbeing scores (B = 0.34, p < .001). Specifically, higher depression/anxiety symptoms were associated with a lack of differentiation between fearful and happy faces. No associations were found between emotional face processing and mental wellbeing scores. These results indicate that even within a non-clinical sample, alterations in emotional ERPs, namely the N170, reflect differences in depression/anxiety symptoms rather than differences in wellbeing. Furthermore, this effect was limited to automatic processing, rather than conscious processing of emotional stimuli, suggesting the observed differences apply only to the subconscious pathway.

fNIRS Evidence for Distinguishing Patients With Major Depression and Healthy Controls

In recent years, major depressive disorder (MDD) has been shown to negatively impact physical recovery in a variety of patients. Functional near-infrared spectroscopy (fNIRS) is a tool that can potentially supplement clinical interviews and mental state examinations to establish a psychiatric diagnosis and monitor treatment progress. Thirty-two subjects, including 16 patients clinically diagnosed with MDD and 16 healthy controls (HCs), participated in the study. Brain oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) responses were recorded using a 22-channel continuous-wave fNIRS device while the subjects performed the emotional sound test. This study evaluated the difference between MDD patients and HCs using a variety of methods. In a comparison of the Pearson correlation coefficients between the HbO/HbR responses of each fNIRS channel and four scores, MDD patients and HCs had significantly different Athens Insomnia Scale (AIS) scores. By quantitative evaluation of the functional association, we found that MDD patients had aberrant functional connectivity compared with HCs. Furthermore, we concluded that compared with HCs, there were marked abnormalities in blood oxygen in the bilateral ventrolateral prefrontal cortex (VLPFC) and bilateral dorsolateral prefrontal cortex (DLPFC). Four statistical-based features extracted from HbO signals and four vector-based features from both HbO and HbR served as inputs to four simple neural networks (multilayer neural network (MNN), feedforward neural network (FNN), cascade forward neural network (CFNN) and recurrent neural network (RNN)). Through an analysis of combinations of different features, the combination of 4 common features (mean, STD, area under the receiver operating characteristic curve (AUC) and slope) yielded the highest classification accuracy of 89.74% for fear emotion. The combination of four novel feature (CBV, COE, |L | and K) resulted in a classification accuracy of 99.94% for fear emotion. The top 10 common and novel features were selected by the ReliefF feature selection algorithm, resulting in classification accuracies of 83.52% and 91.99%, respectively. This study identified the AUC and angle K as specific neuromarkers for predicting MDD across specific depression-related regions of the prefrontal cortex (PFC). These findings suggest that the fNIRS measurement of the PFC may serve as a supplementary test in routine clinical practice to further support a diagnosis of MDD.

Ketamine and Attentional Bias Toward Emotional Faces: Dynamic Causal Modeling of Magnetoencephalographic Connectivity in Treatment-Resistant Depression

The glutamatergic modulator ketamine rapidly reduces depressive symptoms in individuals with treatment-resistant major depressive disorder (TRD) and bipolar disorder. While its underlying mechanism of antidepressant action is not fully understood, modulating glutamatergically-mediated connectivity appears to be a critical component moderating antidepressant response. This double-blind, crossover, placebo-controlled study analyzed data from 19 drug-free individuals with TRD and 15 healthy volunteers who received a single intravenous infusion of ketamine hydrochloride (0.5 mg/kg) as well as an intravenous infusion of saline placebo. Magnetoencephalographic recordings were collected prior to the first infusion and 6-9 h after both drug and placebo infusions. During scanning, participants completed an attentional dot probe task that included emotional faces. Antidepressant response was measured across time points using the Montgomery-Asberg Depression Rating Scale (MADRS). Dynamic causal modeling (DCM) was used to measure changes in parameter estimates of connectivity via a biophysical model that included realistic local neuronal architecture and receptor channel signaling, modeling connectivity between the early visual cortex, fusiform cortex, amygdala, and inferior frontal gyrus. Clinically, ketamine administration significantly reduced depressive symptoms in TRD participants. Within the model, ketamine administration led to faster gamma aminobutyric acid (GABA) and N-methyl-D-aspartate (NMDA) transmission in the early visual cortex, faster NMDA transmission in the fusiform cortex, and slower NMDA transmission in the amygdala. Ketamine administration also led to direct and indirect changes in local inhibition in the early visual cortex and inferior frontal gyrus and to indirect increases in cortical excitability within the amygdala. Finally, reductions in depressive symptoms in TRD participants post-ketamine were associated with faster α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) transmission and increases in gain control of spiny stellate cells in the early visual cortex. These findings provide additional support for the GABA and NMDA inhibition and disinhibition hypotheses of depression and support the role of AMPA throughput in ketamine's antidepressant effects. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT00088699?term=NCT00088699&draw=2&rank=1, identifier NCT00088699.

Attention or Distraction? The Impact of Mobile Phone on Users' Psychological Well-Being

Cumulative evidence has demonstrated that mobile phone distraction, in particular among emerging adults, is a growing problem. Considerable efforts have been made to contribute to the literature by proposing cognitive emotion pre-occupation which acts as an underlying mechanism through which mobile phone distraction results in a reduction in psychological well-being. The proposed model is supported by distraction-conflict theory which reveals that users, with high attention control, are better at coping with the negative consequences of mobile phone distraction. The data, consisting of 914 University students in China, was analyzed using statistical tools. The results support that mobile phone distraction has a significant positive relationship with cognitive emotional pre-occupation which negatively affects users' psychological well-being. Our findings also reveal that attention control moderated the mediation effect of cognitive emotional pre-occupation in association with mobile phone distraction and psychological well-being. The theoretical and practical implications are also discussed along with limitations and future research.

Multi-Scale Frequency Bands Ensemble Learning for EEG-Based Emotion Recognition

Emotion recognition has a wide range of potential applications in the real world. Among the emotion recognition data sources, electroencephalography (EEG) signals can record the neural activities across the human brain, providing us a reliable way to recognize the emotional states. Most of existing EEG-based emotion recognition studies directly concatenated features extracted from all EEG frequency bands for emotion classification. This way assumes that all frequency bands share the same importance by default; however, it cannot always obtain the optimal performance. In this paper, we present a novel multi-scale frequency bands ensemble learning (MSFBEL) method to perform emotion recognition from EEG signals. Concretely, we first re-organize all frequency bands into several local scales and one global scale. Then we train a base classifier on each scale. Finally we fuse the results of all scales by designing an adaptive weight learning method which automatically assigns larger weights to more important scales to further improve the performance. The proposed method is validated on two public data sets. For the “SEED IV” data set, MSFBEL achieves average accuracies of 82.75%, 87.87%, and 78.27% on the three sessions under the within-session experimental paradigm. For the “DEAP” data set, it obtains average accuracy of 74.22% for four-category classification under 5-fold cross validation. The experimental results demonstrate that the scale of frequency bands influences the emotion recognition rate, while the global scale that directly concatenating all frequency bands cannot always guarantee to obtain the best emotion recognition performance. Different scales provide complementary information to each other, and the proposed adaptive weight learning method can effectively fuse them to further enhance the performance.

An Exploratory Study on Cross-Cultural Differences in Facial Emotion Recognition Between Adults From Malaysia and Australia

While culture and depression influence the way in which humans process emotion, these two areas of investigation are rarely combined. Therefore, the aim of this study was to investigate the difference in facial emotion recognition among Malaysian Malays and Australians with a European heritage with and without depression. A total of 88 participants took part in this study (Malays n = 47, Australians n = 41). All participants were screened using The Structured Clinical Interview for DSM-5 Clinician Version (SCID-5-CV) to assess the Major Depressive Disorder (MDD) diagnosis and they also completed the Beck Depression Inventory (BDI). This study consisted of the facial emotion recognition (FER) task whereby the participants were asked to look at facial images and determine the emotion depicted by each of the facial expressions. It was found that depression status and cultural group did not significantly influence overall FER accuracy. Malaysian participants without MDD and Australian participants with MDD performed quicker as compared to Australian participants without MDD on the FER task. Also, Malaysian participants more accurately recognized fear as compared to Australian participants. Future studies can focus on the extent of the influence and other aspects of culture and participant condition on facial emotion recognition.

Negative Bias During Early Attentional Engagement in Major Depressive Disorder as Examined Using a Two-Stage Model: High Sensitivity to Sad but Bluntness to Happy Cues

Negative attentional bias has been well established in depression. However, there is very limited knowledge about whether this depression-relevant negative bias exits during initial attentional allocation, as compared with the converging evidence for the negative bias during sustained attention engagement. This study used both behavioral and electrophysiological measures to examine the initial attention engagement in depressed patients influenced by mood-congruent and mood-incongruent emotions. The dot-probe task was performed with a 100-ms exposure time of the emotional cues (emotional and neutral face pairs). The behavioral results showed that the patients responded faster following valid compared with invalid sad facial cues. Electrophysiological indexes in the frame of the two-stage model of attentional modulation by emotions provided cognitive mechanisms in distinct attention engagement stages: (1) the patients exhibited reduced P1 amplitudes following validly than invalidly happy cues than did the healthy controls, indicating a positive attenuation at an early stage of automatic attention orientation; and (2) the patients exhibited enhanced whereas the controls showed reduced P3 amplitudes following validly than invalidly sad cues, suggesting a mood-congruent negative potentiation in depression at the later stage of top-down voluntary control of attention. Depressed patients show a negative bias in early attentional allocation, reflected by preferred engagement with mood-congruent and diminished engagement with positive emotional cues/stimuli.

The Impact of Anodal tDCS on the Attentional Networks as a Function of Trait Anxiety and Depressive Symptoms: A Preregistered Double-Blind Sham-Controlled Experiment

OBJECTIVE

Attention is a multifaceted construct, including three distinct attentional networks: the alerting, orienting, and executive conflict networks. Recently, researchers have started to envision strategies to enhance the attentional networks, and transcranial Direct Current Stimulation (tDCS) has emerged as a promising tool to do so, especially regarding the executive conflict network. On the other hand, other research lines have suggested that anodal tDCS might yield more substantial impacts among depressive and anxious participants.

METHOD

In this preregistered study, we thus examined two questions. First, we wanted to replicate previous observations and tested whether anodal tDCS does improve the executive conflict network's efficiency. Second, we set out to clarify the impact of anxiety and depressive symptoms on this effect. To do so, we adopted a double-blind within-subject protocol in an unselected sample (n = 50) and delivered a single session of anodal- applied over the dorsolateral part of the left prefrontal cortex-versus sham tDCS during the completion of a task assessing the attentional networks. We assessed anxiety and depressive symptoms at baseline.

RESULTS AND CONCLUSIONS

Although there were no significant direct effects of tDCS on the attentional networks, we found that the higher the levels of depression and trait anxiety, the larger the executive conflict network's enhancement during tDCS. By highlighting the importance of trait anxiety and depression when considering the impact of tDCS on the attentional networks, this study fulfills a valuable niche in clinical neuroscience, wherein preclinical data provide critical clues for larger, more definitive future translational efforts.

State and trait neural correlates of the balance between work and nonwork roles

Difficulty managing the demands of work and nonwork roles (often referred to in terms of managing balance) can be detrimental to psychological wellbeing and contribute to occupational burnout. The current study investigated the neural correlates of perceived satisfaction with this balance using both trait and state EEG alpha measures. EEG was recorded from 14 participants in full time employment (12 females, aged 35.1 ± 10.1 years) during a resting state and performance of an auditory oddball task; e-mail and messaging alert sounds were used as target stimuli. It was predicted that dissatisfaction with the balance between work and nonwork roles would be associated with increased resting alpha power, consistent with studies of burnout, and diminished alpha response to oddball distractors, consistent with difficulty suppressing automatic responses to work-related stimuli. Significant correlations between self-reported measures of work/nonwork balance and both resting, and task-related alpha responses, supported our predictions. Furthermore, an exploratory partial correlation between work and nonwork balance and resting EEG, controlling for task-related alpha response, suggested that the three variables were interrelated. We propose that dissatisfaction with work/nonwork balance is associated with a state hypervigilance to work-related cues, and a trait neural marker of fatigue, both symptomatic of lowered cognitive capacity.

Depression symptomatology correlates with event-related potentials in Parkinson's disease: An affective priming study

BACKGROUND

Depression is a predominant non-motor symptom of Parkinson's disease (PD), which is often under recognised and undertreated. To improve identification of depression in PD it is imperative to examine objective brain-related markers. The present study addresses this gap by using electroencephalography (EEG) to evaluate the processing of emotionally valanced words in PD.

METHODS

Fifty non-demented PD patients, unmedicated for depression or anxiety, completed an affective priming task while EEG was simultaneously recorded. Prime and target word pairs of negative or neutral valence were presented at a short 250 ms stimulus onset asynchrony. Participants were asked to evaluate the valence of the target word by button press. Depression was measured using an established rating scale. Repeated measures analysis of covariance and correlational analyses were performed to examine whether event-related potentials (ERP) varied as a function of depression scores.

RESULTS

Key ERP findings reveal reduced responses in parietal midline P300, N400 and Late Positive Potential (LPP) difference waves between congruent and incongruent neutral targets in patients with higher depression scores.

LIMITATIONS

Comparisons of ERPs were limited by insufficient classification of participants with and without clinical depression. A majority of PD patients who had high depression scores were excluded from the analysis as they were receiving antidepressant and/or anxiolytic medications which could interfere with ERP sensitivity.

CONCLUSIONS

The present study suggests that the Pz-P300, N400 and LPP are ERP markers relates to emotional dysfunction in PD. These findings thus advance current knowledge regarding the neurophysiological markers of a common neuropsychiatric deficit in PD.

Sympathovagal imbalance and neurophysiologic cognitive assessment using evoked potentials in polycystic ovary syndrome in young adolescents - a cross-sectional study

Background Altered lifestyle and urbanization have potentially increased the prevalence of polycystic ovary syndrome (PCOS) among the women in India. The aim of the present study was to evaluate the autonomic function and subclinical cognition impairment using evoked potentials in PCOS-affected young adolescents. Methods This was a cross-sectional study, approved by Indian Medical of Council Research as a short-term student project. The study was performed with adolescent girls (age group, 10-18 years) diagnosed as having PCOS, attending the Department of Obstetrics and Gynecology at Sri Manakula Vinayagar Medical College and Hospital, Puducherry. Autonomic function was evaluated using heart rate variability and cognition employing auditory evoked potentials (P300 latency and amplitude) among the control group (n=30) and the PCOS group (n=30). Results Our study reports indicated that autonomic functions were significantly affected among the PCOS group compared to the control group (p=0.03), with sympathetic dominance and decreased vagal tone. P300 latency was prolonged and amplitude was decreased among the PCOS group, but the results were not statistically significant when compared to the control group. Body mass index showed significant correlation with sympathovagal imbalance. Conclusion The study indicates that autonomic functions are significantly altered in the PCOS group. Subclinical cognition impairment is seen among the PCOS group but is not pronounced enough to be proven statistically. This study informs adolescent girls to make early lifestyle changes as soon as possible before any significant clinical impairment occurs.