EEG beta and low gamma power correlates with inattention in patients with major depressive disorder

Neurophysiological Signatures of Major Depressive Disorder and Frontocentral Gamma Auditory Response Deficits

Background: Aberrant gamma oscillations in major depressive disorder (MDD) have attracted extensive attention, but evidence delineating such neural signatures is lacking. The auditory steady-state response (ASSR) elicited by periodic auditory stimuli is a robust probe of gamma oscillations. Here, we sought to characterize early transient auditory evoked responses (AEPs) and sustained gamma ASSRs in MDD, thereby identifying reliable neurophysiological signatures and providing preliminary interpretations of gamma auditory response deficits in MDD. Methods: Electroencephalographic data were obtained from 40 first-episode drug-naïve patients with MDD and 41 demographically matched healthy controls (HCs) during a 40-Hz ASSR paradigm, encompassing two periodic stimuli-chirp and click stimuli. Source analysis of transient AEPs was performed to identify generators involved in early information processing dysfunction. In addition, spectrotemporal and spatial characteristics of 40-Hz ASSRs were analyzed using event-related spectral perturbation, inter-trial phase coherence, and functional connectivity index. Results: Compared to HCs, patients showed a reduced P200 amplitude that was source-localized to the middle temporal gyrus, possibly reflecting an underlying impairment in the processes of early allocation or auditory information perception within the auditory pathways. Meanwhile, attenuated 40-Hz power and phase coherence, in conjunction with suppressed right frontotemporal and frontocentral connectivity, were observed in MDD, highlighting the multidimensional entrained gamma inhibition. Correlation analyses revealed that the decreased right frontocentral connectivity was strongly related to increased anxiety severity. Importantly, these abnormalities correlated with the patient's symptoms were only found with the chirp stimulus, suggesting that the chirp stimulus has tremendous potential to reveal specific neurophysiological signatures of MDD. Conclusions: Our data reveal impaired gamma auditory responses in first-episode drug-naïve patients with MDD and suggest that right frontocentral connectivity elicited by the chirp stimulus may represent a promising signature for predicting clinical symptoms.

Electroencephalographic markers in Major Depressive Disorder: insights from absolute, relative power, and asymmetry analyses

Introduction

Major Depressive Disorder (MDD) leads to dysfunction and impairment in neurological structures and cognitive functions. Despite extensive research, the pathophysiological mechanisms and effects of MDD on the brain remain unclear. This study aims to assess the impact of MDD on brain activity using EEG power spectral analysis and asymmetry metrics.

Methods

EEG recordings were obtained from 48 patients with MDD and 78 healthy controls. The data were segmented into 2-second windows (1024 data points) and analyzed using the Welch method, an advanced variant of the Fast Fourier Transform (FFT). A Hanning time window with 50% overlap was applied to compute the modified periodogram. Absolute and relative power, along with asymmetry values in the theta, alpha, and beta frequency bands, were calculated.

Results

Patients with MDD exhibited significantly higher absolute and relative power in the theta and beta bands and decreased power in the alpha band compared to healthy controls. Asymmetry analysis revealed significant differences between symmetric channels in the theta band (F7-F8, C3-C4, T3-T4, T5-T6), alpha band (F7-F8, C3-C4, T3-T4, T5-T6, O1-O2), and beta band (C3-C4, T3-T4, T5-T6, P3-P4).

Discussion

The findings suggest that MDD affects brain mechanisms and cognitive functions, as evidenced by altered power values in the theta and alpha bands. Additionally, asymmetry values in theta, alpha, and beta bands may serve as potential biomarkers for MDD. This study highlights that beyond the commonly used alpha asymmetry, theta and beta asymmetry can also provide valuable insights into the neurophysiological effects of MDD, aligning with previous neuroimaging studies that indicate impairments in memory, attention, and neuroanatomical connectivity in MDD.

Therapeutic dose prediction of α5-GABA receptor modulation from simulated EEG of depression severity

Treatment for major depressive disorder (depression) often has partial efficacy and a large portion of patients are treatment resistant. Recent studies implicate reduced somatostatin (SST) interneuron inhibition in depression, and new pharmacology boosting this inhibition via positive allosteric modulators of α5-GABAA receptors (α5-PAM) offers a promising effective treatment. However, testing the effect of α5-PAM on human brain activity is limited, meriting the use of detailed simulations. We utilized our previous detailed computational models of human depression microcircuits with reduced SST interneuron inhibition and α5-PAM effects, to simulate EEG of individual microcircuits across depression severity and α5-PAM doses. We developed machine learning models that predicted optimal dose from EEG with high accuracy and recovered microcircuit activity and EEG. This study provides dose prediction models for α5-PAM administration based on EEG biomarkers of depression severity. Given limitations in doing the above in the living human brain, the results and tools we developed will facilitate translation of α5-PAM treatment to clinical use.

Quantitative EEG and its relationship with attentional control in patients with anxiety disorders

Introduction

Attentional control is crucial in the development and maintenance of anxiety disorders. Understanding the underlying mechanisms of attentional control can help to shed light on the neuropathological processes in anxiety disorders (ANX). Quantitative electroencephalography (QEEG) offers a cost-effective, noninvasive method for examining the neuropathological mechanisms of mental disorders.

Methods

In this study, 67 patients with ANX and 45 healthy controls (HC) were recruited. EEG recordings were obtained for 5 minutes in an eyes-closed condition. QEEG was employed to evaluate the mechanisms of attentional control in ANX.

Results

Neurophysiological measures indicated that anxiety patients exhibited a more frontal topographic pattern of theta/beta ratio (TBR) compared to HC. Additionally, a significant decrease in temporal beta power was observed in the ANX group. Correlation analysis revealed that decreased beta power and increased TBR were significant association between attentional control deficits in ANX.

Discussion

These findings provide electrophysiological evidence of impaired attentional control processing in anxiety patients, characterized by decreased temporal beta power and increased frontal TBR. Temporal beta power and frontal TBR may serve as promising biomarkers for attentional control in ANX.

Standardized weighted low-resolution electromagnetic tomography study of the amygdala activity in patients with comorbid major depressive disorder and anxiety symptoms

Major depressive disorder (MDD) often coexists with anxiety disorders or symptoms, as identified by previous functional magnetic resonance imaging (fMRI) studies. These studies have found abnormal amplitudes of low-frequency fluctuations (ALFF) in the amygdala, which serve as traits and state markers of MDD. This study used standardized weighted low-resolution electromagnetic tomography (swLORETA) technology to explore amygdala markers in patients with comorbid MDD and anxiety. Participants included patients with MDD comorbid with anxiety symptoms (MDD group) and healthy controls (HC group) who completed the Beck Depression Inventory-II (BDI-II) and the Beck Anxiety Inventory (BAI). EEG data collected under resting state, happiness recall, and depressive recall tasks were converted into current-source density (CSD) values using swLORETA to assess amygdala activation. The results indicated higher beta2, beta3, and high beta levels in both the left and right amygdalae during the resting state in the MDD group than in the HC group. Similarly, elevated levels of beta2, beta3, and high beta were observed in the left and right amygdalae of the MDD group during happiness and depressive recall tasks. These findings support the presence of hyperactivity in the amygdala under resting state and emotional tasks in patients with comorbid MDD and anxiety symptoms.

Resting-State EEG Alterations of Practice-Related Spectral Activity and Connectivity Patterns in Depression

BACKGROUND

Depression presents with altered energy regulation and neural plasticity. Previous electroencephalography (EEG) studies showed that practice in learning tasks increases power in beta range (13-30 Hz) in healthy subjects but not in those with impaired plasticity. Here, we ascertain whether depression presents with alterations of spectral activity and connectivity before and after a learning task.

METHODS

We used publicly available resting-state EEG recordings (64 electrodes) from 122 subjects. Based on Beck Depression Inventory (BDI) scores, they were assigned to either a high BDI (hBDI, BDI > 13, N = 46) or a control (CTL, BDI < 7, N = 75) group. We analyzed spectral activity, theta-beta, and theta-gamma phase-amplitude coupling (PAC) of EEG recorded at rest before and after a learning task.

RESULTS

At baseline, compared to CTL, hBDI exhibited greater power in beta over fronto-parietal regions and in gamma over the right parieto-occipital area. At post task, power increased in all frequency ranges only in CTL. Theta-beta and theta-gamma PAC were greater in hBDI at baseline but not after the task.

CONCLUSIONS

The lack of substantial post-task growth of beta power in depressed subjects likely represents power saturation due to greater baseline values. We speculate that inhibitory/excitatory imbalance, altered plasticity mechanisms, and energy dysregulation present in depression may contribute to this phenomenon.

Non-suicidal Self-injury and Quantified Electroencephalogram in Adolescents and Young Adults with Depression

Objective

: This study investigated the association between non-suicidal self-injury (NSSI) and quantified electroencephalogram (QEEG) in patients with depression. We aimed to identify clinical features of NSSI and differences in QEEG findings.

Methods

: This retrospective study used the medical records of 52 inpatients with major depressive episodes, aged from 15 to 30. The patients were categorized according to their history of NSSI. Their main diagnosis and sex were also considered. To evaluate clinical symptoms, self-reported scales were used. The absolute power and the Z-scores of various waves were included.

Results

: NSSI was associated with suicidal ideations (p = 0.001) and trauma history (p = 0.014). In the binary logistic regression analysis, the Z-score of absolute alpha power was higher on the FP2 node (p = 0.029), lower on the F4 node (p = 0.029) in the NSSI group. The absolute high beta power in the NSSI group was higher on the FP2 and the F3 node, but lower on the F7 and F8 node. Patients with NSSI showed higher Z-score of the absolute delta power at the FP2 node (p = 0.044). The absolute gamma power was higher on the FP2 (p = 0.012) and the F3 node (0.043), lower on the FP1 (p = 0.019) and the F7 node (0.018) in the NSSI group. The absolute high gamma power at the FP2 (p = 0.017) and F8 nodes (p = 0.045) were higher in the NSSI group.

Conclusion

: Patients with NSSI may have clinical features distinct from those of patients without NSSI. QEEG results have shown some differences, although it is less applicable due to some limitations.

Local and large-scale resting-state oscillatory dysfunctions for early antidepressant response prediction in major depressive disorder

BACKGROUND

Magnetoencephalography (MEG) could explore and resolve brain signals with realistic temporal resolution to investigate the underlying electrophysiology of major depressive disorder (MDD) and the treatment efficacy. Here, we explore whether neuro-electrophysiological features of MDD at baseline can be used as a neural marker to predict their early antidepressant response.

METHODS

Sixty-six medication-free patients with MDD and 48 healthy controls were enrolled and underwent resting-state MEG scans. Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after two-week pharmacotherapy. We measured local and large-scale resting-state oscillatory dysfunctions with a data-driven model, the Fitting Oscillations & One-Over F algorithm. Then, we quantified band-limited regional power and functional connectivity between brain regions.

RESULTS

After two-week follow-up, 52 patients completed the re-interviews. Thirty-one patients showed early response (ER) to pharmacotherapy and 21 patients did not. Treatment response was defined as at least 50 % reduction of severity reflected by HAMD-17. We observed decreased regional periodic power in patients with MDD comparing to controls. However, patients with ER exhibited that functional couplings across brain regions in both alpha and beta band were increased and significantly correlated with severity of depressive symptoms after treatment. Receiver operating characteristic curves (ROC) further confirmed the predictive ability of baseline large-scale functional connectivity for early antidepressant efficacy (AUC = 0.9969).

LIMITATIONS

Relatively small sample size and not a double-blind design.

CONCLUSIONS

The current study demonstrated the electrophysiological dysfunctions of local neural oscillatory related with depression and highlighted the identification ability of large-scale couplings biomarkers in early antidepressant response prediction.

A resting state EEG study on depressed persons with suicidal ideation

Background

Major Depressive Disorder (MDD) is a psychiatric illness that is often associated with potentially life-threatening physiological changes and increased risk for suicidal behavior. Electroencephalography (EEG) research suggests an association between depression and specific frequency imbalances in the frontal brain region. Further, while recently developed technology has been proposed to simplify EEG data acquisition, more research is still needed to support its use in patients with MDD.

Methods

Using the 14-channel EMOTIV EPOC cap, we recorded resting state EEG from 15 MDD patients with and MDD persons with suicidal ideation (SI) vs. 12 healthy controls (HC) to investigate putative power spectral density (PSD) between-group differences at the F3 and F4 electrode sites. Specifically, we explored 1) between-group alpha power asymmetries (AA), 2) between-group differences in delta, theta, alpha and beta power, 3) between PSD data and the scores in the Beck's Depression Inventory-II (BDI-II), Beck's Anxiety Inventory (BAI), Reasons for Living Inventory (RFL), and Self-Disgust Questionnaire (SDS).

Results

When compared to HC, patients had higher scores on the BAI (p = 0.0018), BDI-II (p = 0.0001) or SDS (p = 0.0142) scale and lower scores in the RFL (p = 0.0006) scale. The PSD analysis revealed no between-group difference or correlation with questionnaire scores for any of the measures considered.

Conclusions

The present study could not confirm previous research suggesting frequency-specific anomalies in depressed persons with SI but might suggest that frontal EEG imbalances reflect greater anxiety and negative self-referencing. Future studies should confirm these findings in a larger population sample.

Fronto-central resting-state 15-29 Hz transient beta events change with therapeutic transcranial magnetic stimulation for posttraumatic stress disorder and major depressive disorder

Repetitive transcranial magnetic stimulation (rTMS) is an established treatment for major depressive disorder (MDD) and shows promise for posttraumatic stress disorder (PTSD), yet effectiveness varies. Electroencephalography (EEG) can identify rTMS-associated brain changes. EEG oscillations are often examined using averaging approaches that mask finer time-scale dynamics. Recent advances show some brain oscillations emerge as transient increases in power, a phenomenon termed "Spectral Events," and that event characteristics correspond with cognitive functions. We applied Spectral Event analyses to identify potential EEG biomarkers of effective rTMS treatment. Resting 8-electrode EEG was collected from 23 patients with MDD and PTSD before and after 5 Hz rTMS targeting the left dorsolateral prefrontal cortex. Using an open-source toolbox ( https://github.com/jonescompneurolab/SpectralEvents ), we quantified event features and tested for treatment associated changes. Spectral Events in delta/theta (1-6 Hz), alpha (7-14 Hz), and beta (15-29 Hz) bands occurred in all patients. rTMS-induced improvement in comorbid MDD PTSD were associated with pre- to post-treatment changes in fronto-central electrode beta event features, including frontal beta event frequency spans and durations, and central beta event maxima power. Furthermore, frontal pre-treatment beta event duration correlated negatively with MDD symptom improvement. Beta events may provide new biomarkers of clinical response and advance the understanding of rTMS.

Fronto-central resting-state 15-29Hz transient beta events change with therapeutic transcranial magnetic stimulation for posttraumatic stress disorder and major depressive disorder

Repetitive transcranial magnetic stimulation (rTMS) is an established treatment for major depressive disorder (MDD) and shows promise for posttraumatic stress disorder (PTSD), yet effectiveness varies. Electroencephalography (EEG) can identify rTMS-associated brain changes. EEG oscillations are often examined using averaging approaches that mask finer time-scale dynamics. Recent advances show some brain oscillations emerge as transient increases in power, a phenomenon termed "Spectral Events," and that event characteristics correspond with cognitive functions. We applied Spectral Event analyses to identify potential EEG biomarkers of effective rTMS treatment. Resting 8-electrode EEG was collected from 23 patients with MDD and PTSD before and after 5Hz rTMS targeting the left dorsolateral prefrontal cortex. Using an open-source toolbox ( https://github.com/jonescompneurolab/SpectralEvents ), we quantified event features and tested for treatment associated changes. Spectral Events in delta/theta (1-6 Hz), alpha (7-14 Hz), and beta (15-29 Hz) bands occurred in all patients. rTMS-induced improvement in comorbid MDD PTSD were associated with pre-to post-treatment changes in fronto-central electrode beta event features, including frontal beta event frequency spans and durations, and central beta event maxima power. Furthermore, frontal pre-treatment beta event duration correlated negatively with MDD symptom improvement. Beta events may provide new biomarkers of clinical response and advance the understanding of rTMS.

Convergent and divergent cognitive impairment of unipolar and bipolar depression: A magnetoencephalography resting-state study

BACKGROUND

Unipolar depression (UD) and bipolar depression (BD) showed convergent and divergent cognitive impairments. Neural oscillations are linked to the foundational cognitive processes. We aimed to investigate the underpinning spectral neuronal power patterns by magnetoencephalography (MEG), which combinates high spatial and temporal resolution. We hypothesized that patients with UD and BD exhibit common and distinct patterns, which may contribute to their cognitive impairments.

METHODS

Group cognitive tests were performed. Eyes closed resting-state MEG data were collected from 61 UD, 55 BD, and 52 healthy controls (HC). Nonparametric cluster-based permutation tests were performed to deal with the multiple comparison problem on channel-frequency MEG data. Correlation analysis of cognitive dysfunction scores and MEG oscillation were conducted by Spearman or partial correlation analysis.

RESULTS

Wisconsin Card Sorting Test showed similar cognitive impairment in patients with UD and BD. Moreover, patients with BD exhibited extensive cognitive deficits in verbal executive functions and visuospatial processing. Compare to HC, both patients with UD and BD showed increased frontal-central beta power while high gamma power was decreased in UD groups during the resting-state. The significant correlations between cognitive function and average beta power were observed.

CONCLUSIONS

Patients with BD had more cognitive impairments on different dimensions than those with UD, involving disrupted beta power modulations. Our investigation provides a better understanding of the neuroelectrophysiological process underlying cognitive impairments in patients with UD and BD.

Predicting treatment response using EEG in major depressive disorder: A machine-learning meta-analysis

Selecting a course of treatment in psychiatry remains a trial-and-error process, and this long-standing clinical challenge has prompted an increased focus on predictive models of treatment response using machine learning techniques. Electroencephalography (EEG) represents a cost-effective and scalable potential measure to predict treatment response to major depressive disorder. We performed separate meta-analyses to determine the ability of models to distinguish between responders and non-responders using EEG across treatments, as well as a performed subgroup analysis of response to transcranial magnetic stimulation (rTMS), and antidepressants (Registration Number: CRD42021257477) in Major Depressive Disorder by searching PubMed, Scopus, and Web of Science for articles published between January 1960 and February 2022. We included 15 studies that predicted treatment responses among patients with major depressive disorder using machine-learning techniques. Within a random-effects model with a restricted maximum likelihood estimator comprising 758 patients, the pooled accuracy across studies was 83.93% (95% CI: 78.90-89.29), with an Area-Under-the-Curve (AUC) of 0.850 (95% CI: 0.747-0.890), and partial AUC of 0.779. The average sensitivity and specificity across models were 77.96% (95% CI: 60.05-88.70), and 84.60% (95% CI: 67.89-92.39), respectively. In a subgroup analysis, greater performance was observed in predicting response to rTMS (Pooled accuracy: 85.70% (95% CI: 77.45-94.83), Area-Under-the-Curve (AUC): 0.928, partial AUC: 0.844), relative to antidepressants (Pooled accuracy: 81.41% (95% CI: 77.45-94.83, AUC: 0.895, pAUC: 0.821). Furthermore, across all meta-analyses, the specificity (true negatives) of EEG models was greater than the sensitivity (true positives), suggesting that EEG models thus far better identify non-responders than responders to treatment in MDD. Studies varied widely in important features across models, although relevant features included absolute and relative power in frontal and temporal electrodes, measures of connectivity, and asymmetry across hemispheres. Predictive models of treatment response using EEG hold promise in major depressive disorder, although there is a need for prospective model validation in independent datasets, and a greater emphasis on replicating physiological markers. Crucially, standardization in cut-off values and clinical scales for defining clinical response and non-response will aid in the reproducibility of findings and the clinical utility of predictive models. Furthermore, several models thus far have used data from open-label trials with small sample sizes and evaluated performance in the absence of training and testing sets, which increases the risk of statistical overfitting. Large consortium studies are required to establish predictive signatures of treatment response using EEG, and better elucidate the replicability of specific markers. Additionally, it is speculated that greater performance was observed in rTMS models, since EEG is assessing neural networks more likely to be directly targeted by rTMS, comprising electrical activity primarily near the surface of the cortex. Prospectively, there is a need for models that examine the comparative effectiveness of multiple treatments across the same patients. However, this will require a thoughtful consideration towards cumulative treatment effects, and whether washout periods between treatments should be utilised. Regardless, longitudinal cross-over trials comparing multiple treatments across the same group of patients will be an important prerequisite step to both facilitate precision psychiatry and identify generalizable physiological predictors of response between and across treatment options.

80 Hz but not 40 Hz, transcranial alternating current stimulation of 80 Hz over right intraparietal sulcus increases visuospatial working memory capacity

Working memory (WM) is a complex cognitive function involved in the temporary storage and manipulation of information, which has been one of the target cognitive functions to be restored in neurorehabilitation. WM capacity is known to be proportional to the number of gamma cycles nested in a single theta cycle. Therefore, gamma-band transcranial alternating current stimulation (tACS) should be dependent of the stimulation frequency; however, the results of previous studies that employed 40 Hz tACS have not been consistent. The optimal locations and injection currents of multiple scalp electrodes were determined based on numerical simulations of electric field. Experiments were conducted with 20 healthy participants. The order of three stimulation conditions (40 Hz tACS, 80 Hz tACS, and sham stimulation) were randomized but counterbalanced. Visual hemifield-specific visual WM capacity was assessed using a delayed visual match to the sample task. High gamma tACS significantly increased WM capacity, while low gamma tACS had no significant effect. Notably, 80 Hz tACS increased WM capacity on both the left and right visual hemifields, while previous tACS studies only reported the effects of tACS on contralateral hemifields. This is the first study to investigate the frequency-dependent effect of gamma-band tACS on WM capacity. Our findings also suggest that high gamma tACS might influence not only WM capacity but also communication between interhemispheric cortical regions. It is expected that high gamma tACS could be a promising neurorehabilitation method to enhance higher-order cognitive functions with similar mechanisms.

State and Trait Anxiety Related Gamma Oscillations in Patients With Anxiety Within the Research Domain Criteria Framework

OBJECTIVE

Diagnosis of anxiety has relied primarily on self-report. This study aimed to investigate the neural correlates of anxiety with quantitative electroencephalography (qEEG) focusing on the state and trait anxiety defined according to the Research Domain Criteria framework existing across the differential diagnosis, rather than focusing on the diagnosis.

METHODS

A total of 41 participants who visited a psychiatric clinic underwent resting state EEG and completed the State-Trait Anxiety Inventory. The absolute power of six frequency bands were analyzed: delta (1-4 Hz), theta (4-8 Hz), alpha (8-10 Hz), fast alpha (10-13.5 Hz), beta (13.5-30 Hz), and gamma (30-80 Hz).

RESULTS

State anxiety scores were significantly negatively correlated with absolute gamma power in frontal (Fz, r=-0.484) and central (Cz, r=-0.523) regions, while trait anxiety scores were significantly negatively correlated with absolute gamma power in frontal (Fz, r= -0.523), central (Cz, r=-0.568), parietal (P7, r=-0.500; P8, r=-0.541), and occipital (O1, r=-0.510; O2, r=-0.480) regions.

CONCLUSION

The present study identified the significantly negative correlations between the anxiety level and gamma band power in fronto-central and posterior regions assessed at resting status. Further studies to confirm our findings and identify the neural correlates of anxiety are needed.

Longitudinal qEEG changes correlate with clinical outcomes in patients with somatic symptom disorder

OBJECTIVE

The quantitative electroencephalography (qEEG) of patients with somatic symptom disorder (SSD) was not yet thoroughly studied. This study aimed to investigate qEEG of SSD patients compared with those of normal controls (NCs), and changes therein after treatment.

METHODS

SSD patients currently without treatment and age- and sex-matched NCs were recruited. Spectral analysis of 64-channel EEG recording was performed and somatization, anxiety, and depression were evaluated via self-rating scales at baseline. After six months of treatment as usual, SSD patients were longitudinally followed up for assessments.

RESULTS

At baseline, the SSD group (n = 44) had higher alpha (p = 0.047) and lower beta 2 (p = 0.027) and gamma power (p = 0.001) compared with NCs (n = 29). After 6-month treatment, SSD patients showed improvement in symptoms, as well as increased beta 1 (p = 0.032), beta 2 (p = 0.012), and gamma power (p = 0.009) compared with baseline. A significant correlation was observed between the change in somatization score and temporal gamma power (r = -0.424, p = 0.031), and between the change in anxiety score and beta 2 power in the frontal (r = -0.420, p = 0.033) and central (r = -0.484, p = 0.012) regions.

CONCLUSIONS

EEG findings in this study may provide neurophysiological features of SSD. The alpha enhancement and reduced fast wave activity may reflect attentional dysfunction in patients with SSD. Decreased fast wave activity is reversible and may serve as a state marker of SSD.

Intraoperative neural signals predict rapid antidepressant effects of deep brain stimulation

Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) is a promising intervention for treatment-resistant depression (TRD). Despite the failure of a clinical trial, multiple case series have described encouraging results, especially with the introduction of improved surgical protocols. Recent evidence further suggests that tractography targeting and intraoperative exposure to stimulation enhances early antidepressant effects that further evolve with ongoing chronic DBS. Accelerating treatment gains is critical to the care of this at-risk population, and identification of intraoperative electrophysiological biomarkers of early antidepressant effects will help guide future treatment protocols. Eight patients underwent intraoperative electrophysiological recording when bilateral DBS leads were implanted in the SCC using a connectomic approach at the site previously shown to optimize 6-month treatment outcomes. A machine learning classification method was used to discriminate between intracranial local field potentials (LFPs) recorded at baseline (stimulation-naïve) and after the first exposure to SCC DBS during surgical procedures. Spectral inputs (theta, 4-8 Hz; alpha, 9-12 Hz; beta, 13-30 Hz) to the model were then evaluated for importance to classifier success and tested as predictors of the antidepressant response. A decline in depression scores by 45.6% was observed after 1 week and this early antidepressant response correlated with a decrease in SCC LFP beta power, which most contributed to classifier success. Intraoperative exposure to therapeutic stimulation may result in an acute decrease in symptoms of depression following SCC DBS surgery. The correlation of symptom improvement with an intraoperative reduction in SCC beta power suggests this electrophysiological finding as a biomarker for treatment optimization.

EEG-based Major Depressive Disorder Detection Using Data Mining Techniques

Major depressive disorder (MDD) is a common mental illness characterized by a persistent feeling of low mood, sadness, fatigue, despair, etc.. In a serious case, patients with MDD may have suicidal thoughts or even suicidal behaviors. In clinical practice, a widely used method of MDD detection is based on a professional rating scale. However, the scale-based diagnostic method is highly subjective, and requires a professional assessment from a trained staff. In this work, 92 participants were recruited to collect EEG signals in the Shenzhen Traditional Chinese Medicine Hospital, assessing MDD severity with the HAMD-17 rating scale by a trained physician. Two data mining methods of logistic regression (LR) and support vector machine (SVM) with derived EEG-based beta-alpha-ratio features, namely LR-DF and SVM-DF, are employed to screen out patients with MDD. Experimental results show that the presented the LR-DF and SVM-DF achieved F 1 scores of 0:76 0:30 and 0:92 0:18, respectively, which have obvious superiority to the LR and SVM without derived EEG-based beta-alpha-ratio features.

Classification of Gamers Using Multiple Physiological Signals: Distinguishing Features of Internet Gaming Disorder

The proliferating and excessive use of internet games has caused various comorbid diseases, such as game addiction, which is now a major social problem. Recently, the American Psychiatry Association classified “Internet gaming disorder (IGD)” as an addiction/mental disorder. Although many studies have been conducted on the diagnosis, treatment, and prevention of IGD, screening studies for IGD are still scarce. In this study, we classified gamers using multiple physiological signals to contribute to the treatment and prevention of IGD. Participating gamers were divided into three groups based on Young’s Internet Addiction Test score and average game time as follows: Group A, those who rarely play games; Group B, those who enjoy and play games regularly; and Group C, those classified as having IGD. In our game-related cue-based experiment, we obtained self-reported craving scores and multiple physiological data such as electrooculogram (EOG), photoplethysmogram (PPG), and electroencephalogram (EEG) from the users while they watched neutral (natural scenery) or stimulating (gameplay) videos. By analysis of covariance (ANCOVA), 13 physiological features (vertical saccadic movement from EOG, standard deviation of N-N intervals, and PNN50 from PPG, and many EEG spectral power indicators) were determined to be significant to classify the three groups. The classification was performed using a 2-layers feedforward neural network. The fusion of three physiological signals showed the best result compared to other cases (combination of EOG and PPG or EEG only). The accuracy was 0.90 and F-1 scores were 0.93 (Group A), 0.89 (Group B), and 0.88 (Group C). However, the subjective self-reported scores did not show a significant difference among the three groups by ANCOVA analysis. The results indicate that the fusion of physiological signals can be an effective method to objectively classify gamers.

Relative Power Correlates With the Decoding Performance of Motor Imagery Both Across Time and Subjects

One of the most significant challenges in the application of brain-computer interfaces (BCI) is the large performance variation, which often occurs over time or across users. Recent evidence suggests that the physiological states may explain this performance variation in BCI, however, the underlying neurophysiological mechanism is unclear. In this study, we conducted a seven-session motor-imagery (MI) experiment on 20 healthy subjects to investigate the neurophysiological mechanism on the performance variation. The classification accuracy was calculated offline by common spatial pattern (CSP) and support vector machine (SVM) algorithms to measure the MI performance of each subject and session. Relative Power (RP) values from different rhythms and task stages were used to reflect the physiological states and their correlation with the BCI performance was investigated. Results showed that the alpha band RP from the supplementary motor area (SMA) within a few seconds before MI was positively correlated with performance. Besides, the changes of RP between task and pre-task stage from theta, alpha, and gamma band were also found to be correlated with performance both across time and subjects. These findings reveal a neurophysiological manifestation of the performance variations, and would further provide a way to improve the BCI performance.

EEG response to game-craving according to personal preference for games

Recently, the World Health Organization included ‘gaming disorder’ in its latest revision of the international classification of diseases (ICD-11). Despite extensive research on internet gaming disorder (IGD), few studies have addressed game-related stimuli eliciting craving, which plays an important role in addiction. Particularly, most previous studies did not consider personal preferences in games presented to subjects as stimuli. In this study, we compared neurophysiological responses elicited for favorite game (FG) videos and non-favorite game (NFG) videos. We aimed to demonstrate neurophysiological characteristics according to the game preference in the IGD group. We measured participants’ electroencephalogram (EEG) while they watched FG, NFG and neutral videos. For FG videos, the parieto-occipital theta power (TP_PO) were significantly increased compared with those for NFG videos (P < 0.05, paired t-test). TP_PO also differed significantly between the healthy control and IGD groups only on FG videos controlling covariate (TP_PO on neutral videos) (P < 0.05, analysis of covariance [ANCOVA]). And TP_PO was significantly correlated to self-reported craving score only on FG videos (r = 0.334, P < 0.05). In the present study, we demonstrate that FG videos induce higher TP_PO than that induced by NFG videos in the IGD group and TP_PO is a reliable EEG feature associated with craving for gaming.

A major depressive disorder classification framework based on EEG signals using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis

BACKGROUND

Major depressive disorder (MDD) is a prevalent mental illness that is diagnosed through questionnaire-based approaches; however, these methods may not lead to an accurate diagnosis. In this regard, many studies have focused on using electroencephalogram (EEG) signals and machine learning techniques to diagnose MDD.

NEW METHOD

This paper proposes a machine learning framework for MDD diagnosis, which uses different types of EEG-derived features. The features are extracted using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis methods. The sequential backward feature selection (SBFS) algorithm is also employed to perform feature selection. Various classifier models are utilized to select the best one for the proposed framework.

RESULTS

The proposed method is validated with a public EEG dataset, including the EEG data of 34 MDD patients and 30 healthy subjects. The evaluation of the proposed framework is conducted using 10-fold cross-validation, providing the metrics such as accuracy (AC), sensitivity (SE), specificity (SP), F1-score (F1), and false discovery rate (FDR). The best performance of the proposed method has provided an average AC of 99%, SE of 98.4%, SP of 99.6%, F1 of 98.9%, and FDR of 0.4% using the support vector machine with RBF kernel (RBFSVM) classifier.

COMPARISON WITH EXISTING METHODS

The obtained results demonstrate that the proposed method outperforms other approaches for MDD classification based on EEG signals.

CONCLUSIONS

According to the obtained results, a highly accurate MDD diagnosis would be provided using the proposed method, while it can be utilized to develop a computer-aided diagnosis (CAD) tool for clinical purposes.

Towards a Pragmatic Approach to a Psychophysiological Unit of Analysis for Mental and Brain Disorders: An EEG-Copeia for Neurofeedback

This article proposes what we call an "EEG-Copeia" for neurofeedback, like the "Pharmacopeia" for psychopharmacology. This paper proposes to define an "EEG-Copeia" as an organized list of scientifically validated EEG markers, characterized by a specific association with an identified cognitive process, that define a psychophysiological unit of analysis useful for mental or brain disorder evaluation and treatment. A characteristic of EEG neurofeedback for mental and brain disorders is that it targets a EEG markers related to a supposed cognitive process, whereas conventional treatments target clinical manifestations. This could explain why EEG neurofeedback studies encounter difficulty in achieving reproducibility and validation. The present paper suggests that a first step to optimize EEG neurofeedback protocols and future research is to target a valid EEG marker. The specificity of the cognitive skills trained and learned during real time feedback of the EEG marker could be enhanced and both the reliability of neurofeedback training and the therapeutic impact optimized. However, several of the most well-known EEG markers have seldom been applied for neurofeedback. Moreover, we lack a reliable and valid EEG targets library for further RCT to evaluate the efficacy of neurofeedback in mental and brain disorders. With the present manuscript, our aim is to foster dialogues between cognitive neuroscience and EEG neurofeedback according to a psychophysiological perspective. The primary objective of this review was to identify the most robust EEG target. EEG markers linked with one or several clearly identified cognitive-related processes will be identified. The secondary objective was to organize these EEG markers and related cognitive process in a psychophysiological unit of analysis matrix inspired by the Research Domain Criteria (RDoC) project.

Unified protocol for anxiety disorders in two cities of Mexico measuring gamma activity: Study protocol for a randomized controlled trial

BACKGROUND

The Unified Protocol for Emotional Disorders (UP) for emotional regulation manifests effective results in a broad range of mental disorders. The UP efficacy was tested in several countries, but it has not been tested within Mexican population. It is crucial to do more research and implement effective protocols to intervene Mexican population with Anxiety Disorders (AD).

OBJECTIVE

This study aims to examine and describe the research procedures and treatment interventions of the UP in a Randomized Controlled Trial (RCT), to approach and treat AD in patients in 2 Mexican borderland cities, by applying the UP and an Electroencephalogram (EGG) neuro screening.

METHODS

The enrolled patients will be randomized in a two-arm control trial with repeated measures, comprising between 18 and 60 years, that were diagnosed with an AD, and low scored in depression symptoms and suicidal ideation. The study will comprise of two conditions: an intervention group clinical trial with the UP or a waiting list control. The primary outcome measures will be applied on AD quantitative self-reports and a gamma activity by EGG before and after the intervention and in follow-ups of 3 and 6 months. The participants in the waiting list group, will receive the treatment after the trial first group completes the treatment.

CONCLUSIONS

Processes and outcomes of this project, will provide evidence in order to apply the UP in a broader population with AD and other mental disorders also covered by this protocol, such as depression and borderline personality disorder in a broader Mexican population, a country that suffers with a major health issue with an increasing rate of mental disorders and scarce psychological and health coverage.

The successful discrimination of depression from EEG could be attributed to proper feature extraction and not to a particular classification method

Reliable diagnosis of depressive disorder is essential for both optimal treatment and prevention of fatal outcomes. This study aimed to elucidate the effectiveness of two non-linear measures, Higuchi's Fractal Dimension (HFD) and Sample Entropy (SampEn), in detecting depressive disorders when applied on EEG. HFD and SampEn of EEG signals were used as features for seven machine learning algorithms including Multilayer Perceptron, Logistic Regression, Support Vector Machines with the linear and polynomial kernel, Decision Tree, Random Forest, and Naïve Bayes classifier, discriminating EEG between healthy control subjects and patients diagnosed with depression. This study confirmed earlier observations that both non-linear measures can discriminate EEG signals of patients from healthy control subjects. The results suggest that good classification is possible even with a small number of principal components. Average accuracy among classifiers ranged from 90.24 to 97.56%. Among the two measures, SampEn had better performance. Using HFD and SampEn and a variety of machine learning techniques we can accurately discriminate patients diagnosed with depression vs controls which can serve as a highly sensitive, clinically relevant marker for the diagnosis of depressive disorders.

Striatal Beta Oscillation and Neuronal Activity in the Primate Caudate Nucleus Differentially Represent Valence and Arousal Under Approach-Avoidance Conflict

An approach-avoidance (Ap–Av) conflict arises when an individual has to decide whether to accept or reject a compound offer that has features indicating both reward and punishment. During value judgments of likes and dislikes, arousal responses simultaneously emerge and influence reaction times and the frequency of behavioral errors. In Ap–Av decision-making, reward and punishment differentially influence valence and arousal, allowing us to dissociate their neural processing. The primate caudate nucleus (CN) has been implicated in affective judgment, but it is still unclear how neural responses in the CN represent decision-related variables underlying choice. To address this issue, we recorded spikes and local field potentials (LFPs) from the CN while macaque monkeys performed an Ap–Av decision-making task. We analyzed 450 neuronal units and 667 beta oscillatory activities recorded during the performance of the task. To examine how these activities represented valence, we focused on beta-band responses and unit activities that encoded the chosen value (ChV) of the compound offer as derived from an econometric model. Unit activities exhibited either positive (65.0% = 26/40) or negative (35.0% = 14/40) correlations with the ChV, whereas beta responses exhibited almost exclusively positive correlations with the ChV (98.4% = 62/63). We examined arousal representation by focusing on beta responses and unit activities that encoded the frequency of omission errors (FOE), which were negatively correlated with arousal. The unit activities were either positively (65.3% = 17/26) or negatively (34.6% = 9/26) correlated with the FOE, whereas the beta responses were almost entirely positively correlated with the FOE (95.8% = 23/24). We found that the temporal onset of the beta-band responses occurred sequentially across conditions: first, the negative-value, then low-arousal, and finally, high-value conditions. These findings suggest the distinctive roles of CN beta oscillations that were sequentially activated for the valence and arousal conditions. By identifying dissociable groups of CN beta-band activity responding in relation to valence and arousal, we demonstrate that the beta responses mainly exhibited selective activation for the high-valence and low-arousal conditions, whereas the unit activities simultaneously recorded in the same experiments responded to chosen value and other features of decision-making under approach-avoidance conflict.

Connectivity of the Frontal Cortical Oscillatory Dynamics Underlying Inhibitory Control During a Go/No-Go Task as a Predictive Biomarker in Major Depression

BACKGROUND

Major depressive disorder (MDD) is characterized by core functional deficits in cognitive inhibition, which is crucial for emotion regulation. To assess the response to ruminative and negative mood states, it was hypothesized that MDD patients have prolonged disparities in the oscillatory dynamics of the frontal cortical regions across the life course of the disease.

METHOD

A "go/no-go" response inhibition paradigm was tested in 31 MDD patients and 19 age-matched healthy controls after magnetoencephalography (MEG) scanning. The use of minimum norm estimates (MNE) examined the changes of inhibitory control network which included the right inferior frontal gyrus (rIFG), pre-supplementary motor area (preSMA), and left primary motor cortex (lM1). The power spectrum (PS) within each node and the functional connectivity (FC) between nodes were compared between two groups. Furthermore, Pearson correlation was calculated to estimate the relationship between altered FC and clinical features.

RESULT

PS was significantly reduced in left motor and preSMA of MDD patients in both beta (13-30 Hz) and low gamma (30-50 Hz) bands. Compared to the HC group, the MDD group demonstrated higher connectivity between lM1 and preSMA in the beta band (t = 3.214, p = 0.002, FDR corrected) and showed reduced connectivity between preSMA and rIFG in the low gamma band (t = -2.612, p = 0.012, FDR corrected). The FC between lM1 and preSMA in the beta band was positively correlated with illness duration (r = 0.475, p = 0.005, FDR corrected), while the FC between preSMA and rIFG in the low gamma band was negatively correlated with illness duration (r = -0.509, p = 0.002, FDR corrected) and retardation factor scores (r = -0.288, p = 0.022, uncorrected).

CONCLUSION

In this study, a clinical neurophysiological signature of cognitive inhibition leading to sustained negative affect as well as functional non-recovery in MDD patients is highlighted. Duration of illness (DI) plays a key role in negative emotional processing, heighten rumination, impulsivity, and disinhibition.

Exploratory Analysis of Behavioral Impulsivity, Pro-inflammatory Cytokines, and Resting-State Frontal EEG Activity Associated With Non-suicidal Self-Injury in Patients With Mood Disorder

Introduction: Non-suicidal self-injury (NSSI) is a rapidly increasing mental health problem that requires more clinical attention. In this study, we aimed to explore the biobehavioral markers of NSSI in participants with mood disorders. Methods: A total of 45 participants with mood disorders (bipolar I, II, and major depressive disorder) were included in the study. Behavioral impulsivity was measured using the immediate memory task (IMT)/delayed memory task (DMT) and the go-no-go (GNG) tests. Plasma levels of tumor necrosis factor-α (TNF-α), interleukin 1 beta (IL-1 β), and interleukin 6 (IL-6) and resting-state quantitative electroencephalography (qEEG) were measured. Results: The NSSI group had shorter GNG reaction time (GNG-RT) and higher TNF-α levels compared to the non-NSSI group. TNF-α was positively correlated with frontal theta power. In addition, GNG-RT showed a significant positive association with frontal alpha activity. Conclusion: NSSI in mood disorders was associated with increased behavioral impulsivity and greater inflammation. Increased pro-inflammatory cytokines were associated with frontal theta power. Increased inflammation might change major neurotransmitter metabolism, which eventually affects frontal function and decreases response inhibition. Further studies to explore their causal relationship are warranted.

Resting-state alpha and gamma activity in affective disorder with ADHD symptoms: Comparison between bipolar disorder and major depressive disorder

Although comorbid attention deficit/hyperactivity disorder (ADHD) symptoms are very common in mood disorder, its neurophysiological correlates have not been explored. This study aimed to examine clinical and neurophysiological correlates of ADHD symptoms in major depressive disorder (MDD) and bipolar disorder (BP). A total of 67 subjects with mood disorder, current depressive episode (38 subjects with MDD and 29 subjects with BP depression) were included in the analysis. Resting quantitative electroencephalography (qEEG) recordings were collected under eyes closed condition. ADHD symptoms, depression, anxiety, and lifetime hypomania were evaluated using self-report questionnaires. In MDD, ADHD symptoms did not show significant associations with anxiety and depression. In BP, ADHD symptoms showed significant associations with depression, anxiety and lifetime hypomania. Significant correlations with Adult ADHD self-report scales (ASRS) inattention score and total score were detected in left and right frontal alpha powers in MDD while significant correlation with ASRS hyperactivity score and ASRS total score were detected in right frontal gamma power in BP. Linear regression analyses revealed that left and right frontal alpha powers, depression and lifetime hypomania showed significant association with ASRS inattention score and ASRS total score in MDD. In BP, linear regression analysis showed ASRS hyperactivity score was associated with lifetime hypomania and the right frontal gamma power. MDD and BP showed different correlation patterns between frontal qEEG measures and ADHD symptoms. This might be associated with distinct neurobiological underpinnings of co-occurring ADHD symptoms in MDD and BP.

Electroencephalogram Theta/Beta Ratio and Spectral Power Correlates of Executive Functions in Children and Adolescents With AD/HD

OBJECTIVE

The electroencephalogram (EEG) has been widely used in AD/HD research. The current study firstly aimed to replicate a recent trend related to EEG theta/beta ratio (TBR) in children and adolescents. Also, the study aimed to examine the value of resting EEG activity as biomarkers for executive function (EF) in participants with AD/HD.

METHOD

Fifty-three participants with AD/HD and 37 healthy controls were recruited. Resting EEG was recorded with eyes closed. Participants with AD/HD additionally completed EF tasks via the Cambridge Neuropsychological Test Automated Battery.

RESULTS

TBR did not differ between groups; however, TBR was positively correlated with inattentive symptoms in AD/HD. Other correlations were found between EEG activity and neuropsychological functions including spatial planning and decision making in the AD/HD group.

CONCLUSION

The results do not support the diagnostic value of TBR. Instead, given the heterogeneous features, the results support the prognostic value of EEG in AD/HD.

Unsuccessful reduction of high-frequency alpha activity during cognitive activation in schizophrenia

AIMS

Electroencephalogram (EEG) alpha activity during resting state reflects the 'readiness' of an individual to respond to the environment; this includes the performance of cognitive processes. Alpha activity is reported to be attenuated in schizophrenia (SCZ). Understanding the interaction between alpha activity during rest and when cognitively engaged may provide insights into the neural circuitry, which is dysfunctional in SCZ. This study investigated the changes of alpha activity between resting state and cognitive engagement in SCZ patients.

METHODS

Thirty-four SCZ patients and 29 healthy controls (HC) were recruited. EEG was performed in the resting state and during an auditory P300 task. All experimental procedures followed the relevant institutional guidelines and regulations.

RESULTS

In SCZ, high-frequency alpha activity was reduced in the resting state. High-frequency alpha source density was decreased in both the resting-state and a P300 task condition in patients, compared to HC. HC, but not SCZ patients, showed a reduction in high-frequency alpha source density during the P300 task compared to the resting state. The negative correlation between high-frequency alpha source density in the resting state and positive symptoms was significant.

CONCLUSIONS

High-frequency alpha activity in SCZ patients and its unsuccessful reduction during cognitive processing may be biological markers of SCZ.

Auditory steady state response deficits are associated with symptom severity and poor functioning in patients with psychotic disorder

OBJECTIVES

Gamma oscillation is important for cortico-cortical coordination and the integration of information across neural networks. The 40 Hz auditory steady-state response (ASSR), which reflects neural synchrony in the gamma band (30-100 Hz), is abnormal in patients with schizophrenia (SZ). The present study used the ASSR at multiple frequencies to examine (1) gamma dysfunction in patients with SZ, schizoaffective (SA), and bipolar disorder (BD) compared with controls, (2) the relationship between ASSR measures and clinical symptom severity, and (3) the relationship between ASSR measures and real-life community functioning.

METHODS

EEG was recorded from 75 controls, 52 SZ, 55 SA, and 89 BD patients during 20-30-40-Hz binaural click trains. ANCOVA was used to compare ASSR measures between groups controlling for age, sex, and education. Associations between ASSR measures, symptom severity, and community functioning were examined using linear regression and Pearson partial correlations.

RESULTS

ASSR deficits at gamma frequency were observed in all patient groups. SA patients showed additional specific deficit in the 20 Hz ASSR. Severity of manic, depressive, and anxiety symptoms mediated ASSR deficits. Severity of hallucinatory symptom and community functioning, particularly independent living/meaningful activity, were significantly and independently associated with the 40 Hz ASSR.

CONCLUSIONS

SZ, SA and BD patients are likely to share the same abnormalities in neural processes that generate gamma oscillations. 40 Hz ASSR are associated with community functioning across patients and may serve as a biomarker for predicting functional outcome.

Light Affects Mood and Learning through Distinct Retina-Brain Pathways

Light exerts a range of powerful biological effects beyond image vision, including mood and learning regulation. While the source of photic information affecting mood and cognitive functions is well established, viz. intrinsically photosensitive retinal ganglion cells (ipRGCs), the central mediators are unknown. Here, we reveal that the direct effects of light on learning and mood utilize distinct ipRGC output streams. ipRGCs that project to the suprachiasmatic nucleus (SCN) mediate the effects of light on learning, independently of the SCN's pacemaker function. Mood regulation by light, on the other hand, requires an SCN-independent pathway linking ipRGCs to a previously unrecognized thalamic region, termed perihabenular nucleus (PHb). The PHb is integrated in a distinctive circuitry with mood-regulating centers and is both necessary and sufficient for driving the effects of light on affective behavior. Together, these results provide new insights into the neural basis required for light to influence mood and learning.

Oophorectomy Reduces Estradiol Levels and Long-Term Spontaneous Neurovascular Recovery in a Female Rat Model of Focal Ischemic Stroke

Although epidemiological evidence suggests significant sex and gender-based differences in stroke risk and recovery, females have been widely under-represented in preclinical stroke research. The neurovascular sequelae of brain ischemia in females, in particular, are largely uncertain. We set out to address this gap by a multimodal in vivo study of neurovascular recovery from endothelin-1 model of cortical focal-stroke in sham vs. ovariectomized female rats. Three weeks post ischemic insult, sham operated females recapitulated the phenotype previously reported in male rats in this model, of normalized resting perfusion but sustained peri-lesional cerebrovascular hyperreactivity. In contrast, ovariectomized (Ovx) females showed reduced peri-lesional resting blood flow, and elevated cerebrovascular responsivity to hypercapnia in the peri-lesional and contra-lateral cortices. Electrophysiological recordings showed an attenuation of theta to low-gamma phase-amplitude coupling in the peri-lesional tissue of Ovx animals, despite relative preservation of neuronal power. Further, this chronic stage neuronal network dysfunction was inversely correlated with serum estradiol concentration. Our pioneering data demonstrate dramatic differences in spontaneous recovery in the neurovascular unit between Ovx and Sham females in the chronic stage of stroke, underscoring the importance of considering hormonal-dependent aspects of the ischemic sequelae in the development of novel therapeutic approaches and patient recruitment in clinical trials.

EEG gamma synchronization is associated with response to paroxetine treatment

BACKGROUND

Resistance to medication is a significant problem in psychiatric practice, and effective methods for predicting response are needed to optimize treatment efficacy and limit morbidity. Gamma oscillations are considered as an index of the brain's general cognitive activity; however, the role of gamma oscillations in disease has not been studied sufficiently.

AIM

This study aimed to determine if gamma power during rest can be used to predict response to anti-depressant medication treatment.

METHOD

Hamilton Depression Rating Scale (HDRS) score and resting state gamma power was measured in 18 medication-free patients during an episode of major depression. After 6 weeks of paroxetine monotherapy HDRS was administered again.

RESULTS

Baseline gamma power at frontal, central and temporal electrodes before treatment was significantly related to post-treatment change in HDRS scores.

CONCLUSION

The results indicate that gamma oscillations could be considered a marker of response to paroxetine treatment in patients with major depression.

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

Background

Electroencephalogram (EEG)-based brain network analysis is a useful biological correlate reflecting brain function. Sensor-level network analysis might be contaminated by volume conduction and does not explain regional brain characteristics. Source-level network analysis could be a useful alternative. We analyzed EEG-based source-level network in major depressive disorder (MDD).

Method

Resting-state EEG was recorded in 87 MDD and 58 healthy controls, and cortical source signals were estimated. Network measures were calculated: global indices (strength, clustering coefficient (CC), path length (PL), and efficiency) and nodal indices (eigenvector centrality and nodal CC) in six frequency. Correlation analyses were performed between network indices and symptom scales.

Results

At the global level, MDD showed decreased strength, CC in theta and alpha bands, and efficiency in alpha band, while enhanced PL in alpha band. At nodal level, eigenvector centrality of alpha band showed region dependent changes in MDD. Nodal CCs of alpha band were reduced in MDD and were negatively correlated with depression and anxiety scales.

Conclusion

Disturbances in EEG-based brain network indices might reflect altered emotional processing in MDD. These source-level network indices might provide useful biomarkers to understand regional brain pathology in MDD.

•

We investigated the altered function of electroencephalogram-based cortical network for major depressive disorder (MDD).

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MDD showed altered cortical brain network at both global and nodal level network in alpha frequency band.

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Abnormal network indices in MDD were significantly correlated with depression and anxiety symptom scale scores.

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Disrupted cortical networks band might reflect altered neural pathway during emotional processing in patients with MDD.

Task Performance Changes the Amplitude and Timing of the BOLD Signal

Translational studies comparing imaging data of animals and humans have gained increasing scientific interests. With this upcoming translational approach, however, identifying harmonized statistical analysis as well as shared data acquisition protocols and/or combined statistical approaches is necessary. Following this idea, we applied Bayesian Adaptive Regression Splines (BARS), which have until now mainly been used to model neural responses of electrophysiological recordings from rodent data, on human hemodynamic responses as measured via fMRI. Forty-seven healthy subjects were investigated while performing the Attention Network Task in the MRI scanner. Fluctuations in the amplitude and timing of the BOLD response were determined and validated externally with brain activation using GLM and also ecologically with the influence of task performance (i.e. good vs. bad performers). In terms of brain activation, bad performers presented reduced activation bilaterally in the parietal lobules, right prefrontal cortex (PFC) and striatum. This was accompanied by an enhanced left PFC recruitment. With regard to the amplitude of the BOLD-signal, bad performers showed enhanced values in the left PFC. In addition, in the regions of reduced activation such as the parietal and striatal regions, the temporal dynamics were higher in bad performers. Based on the relation between BOLD response and neural firing with the amplitude of the BOLD signal reflecting gamma power and timing dynamics beta power, we argue that in bad performers, an enhanced left PFC recruitment hints towards an enhanced functioning of gamma-band activity in a compensatory manner. This was accompanied by reduced parieto-striatal activity, associated with increased and potentially conflicting beta-band activity.

Increased beta power in the bereaved families of the Sewol ferry disaster: A paradoxical compensatory phenomenon? A two-channel electroencephalography study

AIM

The Sewol ferry capsizing accident on South Korea's southern coast resulted in the death of 304 people, and serious bereavement problems for their families. Electroencephalography (EEG) beta frequency is associated with psychiatric symptoms, such as insomnia. The aim of this study was to investigate the relation between frontal beta power, psychological symptoms, and insomnia in the bereaved families.

METHODS

Eighty-four family members of the Sewol ferry victims (32 men and 52 women) were recruited and their EEG was compared with that of 25 (13 men and 12 women) healthy controls. A two-channel EEG device was used to measure cortical activity in the frontal lobe. Symptom severity of insomnia, post-traumatic stress disorder, complicated grief, and anxiety were evaluated.

RESULTS

The bereaved families showed a higher frontal beta power than healthy controls. Subgroup analysis showed that frontal beta power was lower in the individuals with severe insomnia than in those with normal sleep. There was a significant inverse correlation between frontal beta power and insomnia symptom in the bereaved families.

CONCLUSION

This study suggests that increased beta power, reflecting the psychopathology in the bereaved families of the Sewol ferry disaster, may be a compensatory mechanism that follows complex trauma. Frontal beta power could be a potential marker indicating the severity of sleep disturbances. Our results suggest that sleep disturbance is an important symptom in family members of the Sewol ferry disaster's victims, which may be screened by EEG beta power.

Disrupted cortical brain network in post-traumatic stress disorder patients: a resting-state electroencephalographic study

This study aimed to examine the source-level cortical brain networks of post-traumatic stress disorder (PTSD) based on the graph theory using electroencephalography (EEG). Sixty-six cortical source signals were estimated from 78 PTSD and 58 healthy controls (HCs) of resting-state EEG. Four global indices (strength, clustering coefficient (CC), path length (PL) and efficiency) and one nodal index (CC) were evaluated in six frequency bands (delta, theta, alpha, low beta, high beta and gamma). PTSD showed decreased global strength, CC and efficiency, in delta, theta, and low beta band and enhanced PL in theta and low beta band. In low beta band, the strength and CC correlated positively with the anxiety scores, while PL had a negative correlation. In addition, nodal CCs were reduced in PTSD in delta, theta and low beta band. Nodal CCs of theta band correlated negatively with rumination and re-experience symptom scores; while, nodal CCs in low beta band correlated positively with anxiety and pain severity. Inefficiently altered and symptom-dependent changes in cortical networks were seen in PTSD. Our source-level cortical network indices might be promising biomarkers for evaluating PTSD.

Childhood Trauma Associated with Enhanced High Frequency Band Powers and Induced Subjective Inattention of Adults

Childhood trauma can lead to various psychological and cognitive symptoms. It has been demonstrated that high frequency electroencephalogram (EEG) powers could be closely correlated with inattention. In this study, we explored the relationship between high frequency EEG powers, inattention, symptoms of adult attention deficit hyperactivity disorder (ADHD), and childhood traumatic experiences. A total of 157 healthy Korean adult volunteers were included and divided into two groups using the Childhood Trauma Questionnaire (CTQ) score. The subjective inattention scores, ADHD scale, and anxiety and depression symptom were evaluated. EEG was recorded and quantitative band powers were analyzed. The results were as follows: (1) the high CTQ group showed significantly increased delta, beta1, beta2, beta3 and gamma, and significantly decreased low alpha power compared to the low CTQ group; (2) the high CTQ group had higher inattention score compared to the low CTQ group; (3) the high CTQ group had higher adult ADHD scores; (4) CTQ scores showed significant positive correlations with inattention scores, and adult ADHD scores; (5) unexpectedly, the inattention scores showed significant positive correlations with beta powers and a negative correlation with low alpha power; and (6) the moderated mediation model was confirmed: the depression fully mediated the path from state anxiety to inattention, and the CTQ significantly moderated the pathway between anxiety and depression. Our results show the possibility that childhood adversity may cause subjective inattention and adult ADHD symptoms. Depressive symptoms fully mediated the path from anxiety to inattention, especially in those who report severe childhood traumatic experiences.