Hyperstable regulation of vigilance in patients with major depressive disorder

Frequency-dependent alterations of global signal topography in patients with major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is associated not only with disorders in multiple brain networks but also with frequency-specific brain activities. The abnormality of spatiotemporal networks in patients with MDD remains largely unclear.

METHODS

We investigated the alterations of the global spatiotemporal network in MDD patients using a large-sample multicenter resting-state functional magnetic resonance imaging dataset. The spatiotemporal characteristics were measured by the variability of global signal (GS) and its correlation with local signals (GSCORR) at multiple frequency bands. The association between these indicators and clinical scores was further assessed.

RESULTS

The GS fluctuations were reduced in patients with MDD across the full frequency range (0-0.1852 Hz). The GSCORR was also reduced in the MDD group, especially in the relatively higher frequency range (0.0728-0.1852 Hz). Interestingly, these indicators showed positive correlations with depressive scores in the MDD group and relative negative correlations in the control group.

CONCLUSION

The GS and its spatiotemporal effects on local signals were weakened in patients with MDD, which may impair inter-regional synchronization and related functions. Patients with severe depression may use the compensatory mechanism to make up for the functional impairments.

A posterior-alpha ageing network is differentially associated with antidepressant effects of venlafaxine and rTMS

Major depressive disorder (MDD) is a highly prevalent psychiatric disorder, but chances for remission largely decrease with each failed treatment attempt. It is therefore desirable to assign a given patient to the most promising individual treatment option as early as possible. We used a polygenic score (PGS) informed electroencephalography (EEG) data-driven approach to identify potential predictors for MDD treatment outcome. Post-hoc we conducted exploratory analyses in order to understand the results in depth. First, an EEG independent component analysis produced 54 functional brain networks in a large heterogeneous cohort of psychiatric patients (n = 4,045; 5-84 yrs.). Next, the network that was associated to PGS for antidepressant-response (PRS-AR) in an independent sample (n = 722) was selected: an age-related posterior alpha network that explained >60 % of EEG variance, and was highly stable over recording time. Translational analyses were performed in two other independent datasets to examine if the network was predictive of psychopharmacotherapy (n = 535) and/or repetitive transcranial magnetic stimulation (rTMS) and concomitant psychotherapy (PT; n = 186) outcome. The network predicted remission to venlafaxine (p = 0.015), resulting in a normalized positive predicted value (nPPV) of 138 %, and rTMS + PT - but in opposite direction for women (p = 0.002) relative to men (p = 0.018) - yielding a nPPV of 131 %. Blinded out-of-sample validations for venlafaxine (n = 29) and rTMS + PT (n = 36) confirmed the findings for venlafaxine, while results for rTMS + PT could not be replicated. These data suggest the existence of a relatively stable EEG posterior alpha aging network related to PGS-AR that has potential as MDD treatment predictor.

EEG-vigilance regulation is associated with and predicts ketamine response in major depressive disorder

Ketamine offers promising new therapeutic options for difficult-to-treat depression. The efficacy of treatment response, including ketamine, has been intricately linked to EEG measures of vigilance. This research investigated the interplay between intravenous ketamine and alterations in brain arousal, quantified through EEG vigilance assessments in two distinct cohorts of depressed patients (original dataset: n = 24; testing dataset: n = 24). Clinical response was defined as a decrease from baseline of >33% on the Montgomery-Åsberg Depression Rating Scale (MADRS) 24 h after infusion. EEG recordings were obtained pre-, start-, end- and 24 h post- infusion, and the resting EEG was automatically scored using the Vigilance Algorithm Leipzig (VIGALL). Relative to placebo (sodium chloride 0.9%), ketamine increased the amount of low-vigilance stage B1 at end-infusion. This increase in B1 was positively related to serum concentrations of ketamine, but not to norketamine, and was independent of clinical response. In contrast, treatment responders showed a distinct EEG pattern characterized by a decrease in high-vigilance stage A1 and an increase in low-vigilance B2/3, regardless of whether placebo or ketamine had been given. Furthermore, pretreatment EEG differed between responders and non-responders with responders showing a higher percentage of stage A1 (53% vs. 21%). The logistic regression fitted on the percent of A1 stages was able to predict treatment outcomes in the testing dataset with an area under the ROC curve of 0.7. Ketamine affects EEG vigilance in a distinct pattern observed only in responders. Consequently, the percentage of pretreatment stage A1 shows significant potential as a predictive biomarker of treatment response.Clinical Trials Registration: https://www.clinicaltrialsregister.eu/ctr-search/trial/2013-000952-17/CZ Registration number: EudraCT Number: 2013-000952-17.

The impact of emotional dysregulation and comorbid depressive symptoms on clinical features, brain arousal, and treatment response in adults with ADHD

Introduction

The role of emotional dysregulation (ED) in attention-deficit/hyperactivity disorder (ADHD) has become an important issue. This study, in which we analyzed data from a predictive pharmaco-EEG-trial, aimed to examine whether symptoms of ED in adult ADHD affect ADHD symptom severity, brain arousal regulation as measured by resting EEG, and the response to stimulant medication.

Methods

ED is defined as having a sex- and age-corrected T-score of >70 on the emotional lability subscale of the German version of Conners' Adult ADHD Rating Scale. A total of 115 participants were included in the study, 56 of whom had ED. Participants with ED were more impaired in terms of the severity of core ADHD symptoms, especially inattentive symptoms, comorbid depressive symptoms, interpersonal relationships, and quality of life. In addition, participants with ED were more likely to report a total score above 13 on the Beck Depression Inventory-II, which was considered to be the cutoff for mild depression.

Results

No differences were found between the ED and non-ED groups in response to stimulant medication or in brain arousal regulation. In addition, there was no significant effect of ED with comorbid depressive symptoms on treatment response. There was a trend for subgroups that showed a change in brain arousal regulation associated with symptom improvement.

Discussion

Our findings may support the assumption that ED may be an important feature of ADHD. The use of EEG-based brain arousal regulation as a diagnostic and predictive tool in ADHD in the presence of ED and comorbid depressive symptoms should be further investigated.

Neurobiological Underpinnings of Hyperarousal in Depression: A Comprehensive Review

Patients with major depressive disorder (MDD) exhibit an abnormal physiological arousal pattern known as hyperarousal, which may contribute to their depressive symptoms. However, the neurobiological mechanisms linking this abnormal arousal to depressive symptoms are not yet fully understood. In this review, we summarize the physiological and neural features of arousal, and review the literature indicating abnormal arousal in depressed patients. Evidence suggests that a hyperarousal state in depression is characterized by abnormalities in sleep behavior, physiological (e.g., heart rate, skin conductance, pupil diameter) and electroencephalography (EEG) features, and altered activity in subcortical (e.g., hypothalamus and locus coeruleus) and cortical regions. While recent studies highlight the importance of subcortical-cortical interactions in arousal, few have explored the relationship between subcortical-cortical interactions and hyperarousal in depressed patients. This gap limits our understanding of the neural mechanism through which hyperarousal affects depressive symptoms, which involves various cognitive processes and the cerebral cortex. Based on the current literature, we propose that the hyperconnectivity in the thalamocortical circuit may contribute to both the hyperarousal pattern and depressive symptoms. Future research should investigate the relationship between thalamocortical connections and abnormal arousal in depression, and explore its implications for non-invasive treatments for depression.

EEG wakefulness regulation in transdiagnostic patients after a recent suicide attempt

OBJECTIVE

Decades of research have not yet produced statistically reliable predictors of preparatory behavior eventually leading to suicide attempts or deaths by suicide. As the nature of suicidal behavior is complex, it is best investigated in a transdiagnostic approach, while assessing objective markers, as proposed by the Research Domain Criteria (Cuthbert, 2013).

METHODS

A 15-min resting-state EEG was recorded in 45 healthy controls, and 49 transdiagnostic in-patients with a recent (<6 months) suicide attempt. Brain arousal regulation in eyes-closed condition was assessed with the Vigilance Algorithm Leipzig (VIGALL) (Sander et al., 2015).

RESULTS

A significant incline of median vigilance and vigilance slope was observed in patients within the first 3-min of the EEG recording. Additionally, a significant positive correlation of self-reported suicidal ideation with the vigilance slope over 15-min recording time, as well as a significant negative correlation with EEG vigilance stage A1 during the first 3-min was found.

CONCLUSIONS

Transdiagnostic patients with a recent suicide attempt show a distinct vigilance regulation pattern. Further studies including a control group consisting of patients without life-time suicide attempts are needed to increase the clinical utility of the findings.

SIGNIFICANCE

These findings might serve as potential objective markers of suicidal behavior.

EEG source functional connectivity in patients after a recent suicide attempt

OBJECTIVE

Electroencephalogram (EEG) based frequency measures within the alpha frequency range (AFR), including functional connectivity, show potential in assessing the underlying pathophysiology of depression and suicide-related outcomes. We investigated the association between AFR connectivity, suicidal thoughts and behaviors, and depression in a transdiagnostic sample of patients after a recent suicide attempt (SA).

METHODS

Lagged source-based measures of linear and nonlinear whole-brain connectivity within the standard AFR ([sAFR], 8-12 Hz) and the individually referenced AFR (iAFR) were applied to 70 15-minute resting-state EEGs from patients after a SA and 70 age- and gender-matched healthy controls (HC). Hypotheses were tested using network-based statistics and multiple regression models.

RESULTS

Results showed no significant differences between patients after a SA and HC in any of the assessed connectivity modalities. However, a subgroup analysis revealed significantly increased nonlinear connectivity within the sAFR for patients after a SA with a depressive disorder or episode ([DD], n = 53) compared to matched HC. Furthermore, a multiple regression model, including significant main effects for group and global nonlinear connectivity within the sAFR outperformed all other models in explaining variance in depressive symptom severity.

CONCLUSIONS

Our study further supports the importance of the AFR in pathomechanisms of suicidality and depression. The iAFR does not seem to improve validity of phase-based connectivity.

SIGNIFICANCE

Our results implicate distinct neurophysiological patterns in suicidal subgroups. Exploring the potential of these patterns for treatment stratification might advance targeted interventions for suicidal thoughts and behaviors.

Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.

A review of critical brain oscillations in depression and the efficacy of transcranial magnetic stimulation treatment

Repetitive transcranial magnetic stimulation (rTMS) and intermittent theta burst stimulation (iTBS) have been proven effective non-invasive treatments for patients with drug-resistant major depressive disorder (MDD). However, some depressed patients do not respond to these treatments. Therefore, the investigation of reliable and valid brain oscillations as potential indices for facilitating the precision of diagnosis and treatment protocols has become a critical issue. The current review focuses on brain oscillations that, mostly based on EEG power analysis and connectivity, distinguish between MDD and controls, responders and non-responders, and potential depression severity indices, prognostic indicators, and potential biomarkers for rTMS or iTBS treatment. The possible roles of each biomarker and the potential reasons for heterogeneous results are discussed, and the directions of future studies are proposed.

Heart and brain traumatic stress biomarker analysis with and without machine learning: A scoping review

The enigma of post-traumatic stress disorder (PTSD) is embedded in a complex array of physiological responses to stressful situations that result in disruptions in arousal and cognitions that characterise the psychological disorder. Deciphering these physiological patterns is complex, which has seen the use of machine learning (ML) grow in popularity. However, it is unclear to what extent ML has been used with physiological data, specifically, the electroencephalogram (EEG) and electrocardiogram (ECG) to further understand the physiological responses associated with PTSD. To better understand the use of EEG and ECG biomarkers, with and without ML, a scoping review was undertaken. A total of 124 papers based on adult samples were identified comprising 19 ML studies involving EEG and ECG. A further 21 studies using EEG data, and 84 studies employing ECG meeting all other criteria but not employing ML were included for comparison. Identified studies indicate classical ML methodologies currently dominate EEG and ECG biomarkers research, with derived biomarkers holding clinically relevant diagnostic implications for PTSD. Discussion of the emerging trends, algorithms used and their success is provided, along with areas for future research.

Effect of Acoustic fMRI-Scanner Noise on the Human Resting State

Our knowledge about the human resting state is predominantly based on either electroencephalographic (EEG) or functional magnetic resonance imaging (fMRI) methods. While EEG recordings can be performed in seated posture in quiet conditions, the fMRI environment presents a substantial contrast with supine and restricted posture in a narrow tube that is filled with acoustic scanner noise (ASN) at a chainsaw-like volume level. However, the influence of these diverging conditions on resting-state brain activation is neither well studied nor broadly discussed. In order to promote data as a source of sharper hypotheses for future studies, we investigated alterations in EEG-frequency-band power (delta, theta, alpha, beta, gamma) and spatial power distribution as well as cortical vigilance measures in different postures and ASN surroundings over the course of time. Participants (N = 18) underwent three consecutive resting-state EEG recordings with a fixed posture and ASN setting sequence; seated, supine, and supine with ASN (supnoise) using an MRI simulator. The results showed that compared to seated, supnoise, the last instance within the posture sequence, was characterized by lower power and altered spatial power distribution in all assessed frequency bands. This might also have been an effect of time alone. In delta, theta, alpha, and beta, the power of supnoise was also reduced compared to supine, as well as the corresponding distribution maps. The vigilance analysis revealed that in supine and supnoise, the highest and lowest vigilance stages were more dominant compared to the seated and earliest posture condition within the sequence. Hence, our results demonstrate that the differences in recording settings and progress of time are related to changes in cortical arousal and vigilance regulation, findings that should be taken into account more profoundly for hypothesis generation as well as analytic strategies in future resting-state studies.

Resting and TMS-EEG markers of treatment response in major depressive disorder: A systematic review

Electroencephalography (EEG) is a non-invasive method to identify markers of treatment response in major depressive disorder (MDD). In this review, existing literature was assessed to determine how EEG markers change with different modalities of MDD treatments, and to synthesize the breadth of EEG markers used in conjunction with MDD treatments. PubMed and EMBASE were searched from 2000 to 2021 for studies reporting resting EEG (rEEG) and transcranial magnetic stimulation combined with EEG (TMS-EEG) measures in patients undergoing MDD treatments. The search yielded 966 articles, 204 underwent full-text screening, and 51 studies were included for a narrative synthesis of findings along with confidence in the evidence. In rEEG studies, non-linear quantitative algorithms such as theta cordance and theta current density show higher predictive value than traditional linear metrics. Although less abundant, TMS-EEG measures show promise for predictive markers of brain stimulation treatment response. Future focus on TMS-EEG measures may prove fruitful, given its ability to target cortical regions of interest related to MDD.

A reusable benchmark of brain-age prediction from M/EEG resting-state signals

Population-level modeling can define quantitative measures of individual aging by applying machine learning to large volumes of brain images. These measures of brain age, obtained from the general population, helped characterize disease severity in neurological populations, improving estimates of diagnosis or prognosis. Magnetoencephalography (MEG) and Electroencephalography (EEG) have the potential to further generalize this approach towards prevention and public health by enabling assessments of brain health at large scales in socioeconomically diverse environments. However, more research is needed to define methods that can handle the complexity and diversity of M/EEG signals across diverse real-world contexts. To catalyse this effort, here we propose reusable benchmarks of competing machine learning approaches for brain age modeling. We benchmarked popular classical machine learning pipelines and deep learning architectures previously used for pathology decoding or brain age estimation in 4 international M/EEG cohorts from diverse countries and cultural contexts, including recordings from more than 2500 participants. Our benchmarks were built on top of the M/EEG adaptations of the BIDS standard, providing tools that can be applied with minimal modification on any M/EEG dataset provided in the BIDS format. Our results suggest that, regardless of whether classical machine learning or deep learning was used, the highest performance was reached by pipelines and architectures involving spatially aware representations of the M/EEG signals, leading to R^2 scores between 0.60-0.71. Hand-crafted features paired with random forest regression provided robust benchmarks even in situations in which other approaches failed. Taken together, this set of benchmarks, accompanied by open-source software and high-level Python scripts, can serve as a starting point and quantitative reference for future efforts at developing M/EEG-based measures of brain aging. The generality of the approach renders this benchmark reusable for other related objectives such as modeling specific cognitive variables or clinical endpoints.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

CLINICAL TRIAL REGISTRATION

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

Possible Effects of Bright Light Therapy on Electroencephalogram-Vigilance in the Treatment of Depression in Adolescents: A Pilot Study

BACKGROUND

Chronotherapeutic treatments for depression, such as bright light therapy (BLT), are non-invasive and produce almost no side effects. However, study evidence for reliable neurobiological changes associated with treatment response is still rare. Several studies using EEG-vigilance indicate higher arousal and a later decline during resting state in adult depressive patients compared to healthy controls. To our knowledge, there are no study reports on EEG-vigilance in depressive youth to date.

METHODS

A total of 11 adolescents with depression receiving BLT were compared to 11 age and gender-matched patients with depression receiving treatment as usual (TAU). The BLT was administered in the morning for 2 weeks on five consecutive days per week. The depressive symptomatology was assessed using the Beck Depression Inventory (BDI-II) and the resting state electroencephalogram (EEG) of 20 min was recorded. EEG and BDI-II were assessed before and after 10 days of treatment. Vigilance level and vigilance decline were estimated using the VIGALL toolbox.

RESULTS

Brain arousal increased after 10 days of bright light therapy in adolescents with depression. Severe depressive symptoms were associated with higher brain arousal levels; the BDI-II sum score correlated negatively with the amount of drowsiness.

LIMITATIONS

The sample size was small and participants' brain arousal at baseline was not matched and differed between BLT and TAU groups.

CONCLUSION

The BLT might have an additional effect on brain arousal. EEG-vigilance seems to be a reliable and valid marker for neurobiological changes that are probably associated with depression and its treatment and, therefore, might be of clinical relevance.

Factors associated with different levels of daytime sleepiness among Korean construction drivers: a cross-sectional study

Background

Commercial vehicle accidents are the leading cause of occupational fatalities and an increased risk of traffic accidents is associated with excessive fatigue, other health problems as well as poor sleep during work. This study explores individual and occupational factors associated with different levels of daytime sleepiness and identifies their association with driving risk among occupational drivers working at construction sites.

Methods

This cross-sectional and correlational study adopted a self-reported questionnaire of Korean construction drivers (N = 492). The data were collected from October 2018 to February 2019 using a battery of six validated instruments about participants’ sociodemographic, health-related, and occupational characteristics. One-way ANOVA and multinomial logistic regression were conducted using IBM SPSS WIN/VER 25.0, with a two-tailed alpha of .05.

Results

Based on the Epworth Sleepiness Scale, “moderate” (31.7%) and “severe” (10.2%) daytime sleepiness groups were identified. There were significant differences in break time, driving fatigue, depressive symptom, subjective sleep quality, physical and mental health, and driving risk among the three groups (all p-values < .001). Driving fatigue (Adjusted Odds Ratio [aOR] = 1.08, 1.17), depressive symptoms (aOR = 0.91, 0.98), subjective sleep quality (aOR = 1.18 in moderate only), and driving over the speed limit (aOR = 1.43, 2.25) were significant factors for determining “moderate” and “severe” daytime sleepiness groups, respectively.

Conclusion

A significant number of construction drivers experience excessive daytime sleepiness; thus it is important to reduce the negative impact of driving fatigue and other factors on daytime sleepiness. Our study findings suggest that occupational health care providers should pay attention to development and implementation of health management interventions to reduce driving fatigue that incorporate the drivers’ physical, mental, and occupational factors. Professional organizations need to establish internal regulations and public policies to promote health and safety among occupational drivers who specifically work at construction sites.

EEG revealed improved vigilance regulation after stress exposure under Nx4 - A randomized, placebo-controlled, double-blind, cross-over trial

Objectives

Vigilance is characterized by alertness and sustained attention. The hyper-vigilance states are indicators of stress experience in the resting brain. Neurexan (Nx4) has been shown to modulate the neuroendocrine stress response. Here, we hypothesized that the intake of Nx4 would alter brain vigilance states at rest.

Method

In this post-hoc analysis of the NEURIM study, EEG recordings of three, 12 min resting-state conditions in 39 healthy male volunteers were examined in a randomized, placebo-controlled, double-blind, cross-over clinical trial. EEG was recorded at three resting-state sessions: at baseline (RS0), after single-dose treatment with Nx4 or placebo (RS1), and subsequently after a psychosocial stress task (RS2). During each resting-state session, each 2-s segment of the consecutive EEG epochs was classified into one of seven different brain states along a wake-sleep continuum using the VIGALL 2.1 algorithm.

Results

In the post-stress resting-state, subjects exhibited a hyper-stable vigilance regulation characterized by an increase in the mean vigilance level and by more rigidity in the higher vigilance states for a longer period of time. Importantly, Nx4-treated participants exhibited significantly lower mean vigilance level compared to placebo-treated ones. Also, Nx4- compared to placebo-treated participants spent comparably less time in higher vigilance states and more time in lower vigilance states in the post-stress resting-state.

Conclusion

Study participants showed a significantly lower mean vigilance level in the post-stress resting-state condition and tended to stay longer in lower vigilance states after treatment with Nx4. These findings support the known stress attenuation effect of Nx4.

The Big Five Personality Traits and Brain Arousal in the Resting State

Based on Eysenck's biopsychological trait theory, brain arousal has long been considered to explain individual differences in human personality. Yet, results from empirical studies remained inconclusive. However, most published results have been derived from small samples and, despite inherent limitations, EEG alpha power has usually served as an exclusive indicator for brain arousal. To overcome these problems, we here selected N = 468 individuals of the LIFE-Adult cohort and investigated the associations between the Big Five personality traits and brain arousal by using the validated EEG- and EOG-based analysis tool VIGALL. Our analyses revealed that participants who reported higher levels of extraversion and openness to experience, respectively, exhibited lower levels of brain arousal in the resting state. Bayesian and frequentist analysis results were especially convincing for openness to experience. Among the lower-order personality traits, we obtained the strongest evidence for neuroticism facet 'impulsivity' and reduced brain arousal. In line with this, both impulsivity and openness have previously been conceptualized as aspects of extraversion. We regard our findings as well in line with the postulations of Eysenck and consistent with the recently proposed 'arousal regulation model'. Our results also agree with meta-analytically derived effect sizes in the field of individual differences research, highlighting the need for large (collaborative) studies.

The Big Five Personality Traits and Brain Arousal in the Resting State

Based on Eysenck's biopsychological trait theory, brain arousal has long been considered to explain individual differences in human personality. Yet, results from empirical studies remained inconclusive. However, most published results have been derived from small samples and, despite inherent limitations, EEG alpha power has usually served as an exclusive indicator for brain arousal. To overcome these problems, we here selected N = 468 individuals of the LIFE-Adult cohort and investigated the associations between the Big Five personality traits and brain arousal by using the validated EEG- and EOG-based analysis tool VIGALL. Our analyses revealed that participants who reported higher levels of extraversion and openness to experience, respectively, exhibited lower levels of brain arousal in the resting state. Bayesian and frequentist analysis results were especially convincing for openness to experience. Among the lower-order personality traits, we obtained the strongest evidence for neuroticism facet 'impulsivity' and reduced brain arousal. In line with this, both impulsivity and openness have previously been conceptualized as aspects of extraversion. We regard our findings as well in line with the postulations of Eysenck and consistent with the recently proposed 'arousal regulation model'. Our results also agree with meta-analytically derived effect sizes in the field of individual differences research, highlighting the need for large (collaborative) studies.

The Acute Effects of Aerobic Exercise on Nocturnal and Pre-Sleep Arousal in Patients with Unipolar Depression: Preplanned Secondary Analysis of a Randomized Controlled Trial

Unipolar depression is associated with insomnia and autonomic arousal. The aim of this study was to quantify the effect of a single bout of aerobic exercise on nocturnal heart rate variability and pre-sleep arousal in patients with depression. This study was designed as a two-arm, parallel-group, randomized, outcome assessor-blinded, controlled, superiority trial. Patients with a primary diagnosis of unipolar depression aged 18-65 years were included. The intervention consisted of a single 30 min moderate-intensity aerobic exercise bout. The control group sat and read for 30 min. The primary outcome of interest was RMSSD during the sleep period assessed with polysomnography. Secondary outcomes were additional heart rate variability outcomes during the sleep and pre-sleep period as well as subjective pre-sleep arousal. A total of 92 patients were randomized to either the exercise (N = 46) or the control (N = 46) group. Intent-to-treat analysis ANCOVA of follow-up sleep period RMSSD, adjusted for baseline levels and minimization factors, did not detect a significant effect of the allocation (β = 0.12, p = 0.94). There was no evidence for significant differences between both groups in any other heart rate variability measure nor in measures of cognitive or somatic pre-sleep arousal. As this is the first trial of its kind in this population, the findings need to be confirmed in further studies. Patients with depression should be encouraged to exercise regularly in order to profit from the known benefits on sleep and depressive symptoms, which are supported by extensive literature.

NeuroPharm study: EEG wakefulness regulation as a biomarker in MDD

While several electroencephalogram (EEG)-based biomarkers have been proposed as diagnostic or predictive tools in major depressive disorder (MDD), there is a clear lack of replication studies in this field. Markers that link clinical features such as disturbed wakefulness regulation in MDD with neurophysiological patterns are particularly promising candidates for e.g., EEG-informed choices of antidepressive treatment. We investigate if we in an independent MDD sample can replicate abnormal findings of EEG-vigilance regulation during rest and as a predictor for antidepressive treatment response. EEG-resting state was recorded in 91 patients and 35 healthy controls from the NeuroPharm trial. EEG-vigilance was assessed using the Vigilance Algorithm Leipzig (VIGALL). We compared the vigilance regulation during rest between patients and healthy controls and between remitters/responders and non-remitters/non-responders after eight weeks of SSRI/SNRI treatment using two different sets of response criteria (NeuroPharm and iSPOT-D). We replicated previous findings showing hyperstable EEG-wakefulness regulation in patients in comparison to healthy subjects. Responders defined by the iSPOT-D criteria showed a higher propensity toward low vigilance stages in comparison to patients with no response at pretreatment, however, this did not apply when using the NeuroPharm criteria. EEG-wakefulness regulation patterns normalized toward patterns of healthy controls after 8 weeks of treatment. This replication study supports the diagnostic value of EEG-vigilance regulation and its usefulness as a biomarker for the choice of treatment in MDD.

Increased occurrence of PTSD symptoms in adolescents with major depressive disorder soon after the start of the COVID-19 outbreak in China: a cross-sectional survey

BACKGROUND

The Coronavirus Disease 2019 (COVID-19) pandemic continues to threaten the physical and mental health of people across the world. This study aimed to understand the psychological impact of this disease on adolescents with major depressive disorder (MDD) at 1 month after the start of the outbreak in China.

METHODS

Using the Children's Impact of Event Scale (CRIES-13) questionnaire, we investigated the occurrence of posttraumatic stress disorder (PTSD) in two groups of adolescents: MDD patients who were in continuous antidepressant therapy and healthy controls. Total scores and factor subscores were compared between the two groups and subgroups stratified by sex and school grade. Logistic regression was used to identify variables associated with high total CRIES-13 scores.

RESULTS

Compared to controls (n = 107), the MDD group (n = 90) had higher total CRIES-13 scores and a higher proportion with a total score ≥ 30. They also had a lower intrusion subscore and a higher arousal subscore. In the MDD group, males and females did not differ significantly in total CRIES-13 scores or factor subscores, but junior high school students had higher avoidance subscores than senior high school students. Logistic regression showed high total CRIES-13 scores to be associated with MDD and the experience of "flashbacks" or avoidance of traumatic memories associated with COVID-19.

CONCLUSIONS

It is crucial to understand the psychological impact of COVID-19 on adolescents with MDD in China, especially females and junior high school students. Long-term monitoring of adolescents with a history of mental illness is required to further understand these impacts.

TRIAL REGISTRATION

ChiCTR, ChiCTR2000033402 , Registered 31 May 2020.

The "VIP-ADHD trial": Does brain arousal have prognostic value for predicting response to psychostimulants in adult ADHD patients?

EEG studies have shown that adult ADHD patients have less stable brain arousal regulation than age and gender matched controls. Psychostimulants have brain arousal stabilising properties evident in EEG patterns. The aim of this study was to investigate whether the stability of brain arousal regulation has prognostic value in predicting response to methylphenidate therapy in adult ADHD patients. In an open-label, single-arm, multi-centre, confirmatory trial, 121 adult ADHD patients were recruited and 112 qualified for the full analysis set. All participants received an initial dose of 20 mg extended release methylphenidate at baseline. After a titration phase of up to 4 weeks, patients remained on a weight-based target dose of extended release methylphenidate for 4 weeks. Using the Vigilance Algorithm Leipzig (VIGALL 2.1), we assessed brain arousal regulation before the treatment with methylphenidate, based on a 15-min EEG at quiet rest recorded at baseline. Using automatic stage-classification of 1 s segments, we computed the mean EEG-vigilance (indexing arousal level) and an arousal stability score (indexing arousal regulation). The primary endpoint was the association between successful therapy, defined by a 30% reduction in CAARS, and stable/unstable brain arousal. 52 patients (46%) showed an unstable brain arousal regulation of which 23% had therapy success. In the stable group, 35% had therapy success, implying an absolute difference of 12 percentage points (95% CI -5 to 29, p = 0.17) in the direction opposite to the hypothesized one. There were no new findings regarding the tolerability and safety of extended release methylphenidate therapy.

Fatigue and brain arousal in patients with major depressive disorder

Fatigue is considered a key symptom of major depressive disorder (MDD), yet the term lacks specificity. It can denote a state of increased sleepiness and lack of drive (i.e., downregulated arousal) as well as a state of high inner tension and inhibition of drive with long sleep onset latencies (i.e., upregulated arousal), the latter typically found in depression. It has been proposed to differentiate fatigue along the dimension of brain arousal. We investigated whether such stratification within a group of MDD patients would reveal a subgroup with distinct clinical features. Using an automatic classification of EEG vigilance stages, an arousal stability score was calculated for 15-min resting EEGs of 102 MDD patients with fatigue. 23.5% of the patients showed signs of hypoarousal with EEG patterns indicating drowsiness or sleep; this hypoaroused subgroup was compared with remaining patients (non-hypoaroused subgroup) concerning self-rated measures of depressive symptoms, sleepiness, and sleep. The hypoaroused subgroup scored higher on the Beck Depression Inventory items "loss of energy" (Z = - 2.13, p = 0.033; ɳ² = 0.044, 90% CI 0.003-0.128) and "concentration difficulty" (Z = - 2.40, p = 0.017; ɳ² = 0.056, 90% CI 0.009-0.139), and reported higher trait and state sleepiness (p < 0.05) as compared to the non-hypoaroused group. The non-hypoaroused subgroup, in contrast, reported more frequently the presence of suicidal ideation (Chi² = 3.81, p = 0.051; ɳ² = 0.037, 90% CI 0.0008-0.126). In this study, we found some evidence that stratifying fatigued MDD patients by arousal may lead to subgroups that are pathophysiologically and clinically more homogeneous. Brain arousal may be a worth while target in clinical research for better understanding the mechanisms underlying suicidal tendencies and to improve treatment response.

Brain Arousal as Measured by EEG-Assessment Differs Between Children and Adolescents With Attention-Deficit/Hyperactivity Disorder (ADHD) and Depression

Objective: Disturbed regulation of vigilance in the wake state seems to play a key role in the development of mental disorders. It is assumed that hyperactivity in adult ADHD is an attempt to increase a general low vigilance level via external stimulation in order to avoid drowsiness. For depression, the avoidance of stimulation is interpreted as a reaction to a tonic increased vigilance state. Although ADHD is assumed to start during childhood, this vigilance model has been barely tested with children diagnosed for ADHD so far. Methods: Resting-state EEG (8 min) measures from two groups of children diagnosed with either ADHD [N = 76 (16 female, 60 male), age: (mean/SD) 118/33 months] or depression [N = 94 (73 female, 21 male), age: 184/23 months] were analyzed. Using the VIGALL toolbox, EEG patterns of vigilance level, and regulation were derived and compared between both groups. In correlation analysis, the relations between vigilance measures, attentional test performance (alertness and inhibition), and mental health symptoms were analyzed. Results: Children with ADHD differed from children with most prominent depressive symptoms in brain arousal regulation and level, but EEG vigilance was not related to behavior problems and not related to the attentional test performance. Brain arousal was dependent on the age of the participant in the whole sample; younger children showed lower vigilance stages than teenagers; this effect was not present when analyzed separately for each diagnostic group. EEG assessment time and received medication had no effect on the EEG vigilance. Discussion: Although based on a small sample, this explorative research revealed that EEG vigilance level is different between children with ADHD and with depression. Moreover, even the standard procedure of the clinical routine EEG (resting state) can be used to differentiate brain arousal states between participants with ADHD and depression. Because routine EEG is not specialized to vigilance assessment, it may not be sufficiently sensitive to find vigilance-symptomatology associations. Further research should address developmental changes in EEG measurements in children and use bigger samples of participants within the same age range.

Enhanced Vigilance Stability during Daytime in Insomnia Disorder

Central nervous hyperarousal is as a key component of current pathophysiological concepts of chronic insomnia disorder. However, there are still open questions regarding its exact nature and the mechanisms linking hyperarousal to sleep disturbance. Here, we aimed at studying waking state hyperarousal in insomnia by the perspective of resting-state vigilance dynamics. The VIGALL (Vigilance Algorithm Leipzig) algorithm has been developed to investigate resting-state vigilance dynamics, and it revealed, for example, enhanced vigilance stability in depressive patients. We hypothesized that patients with insomnia also show a more stable vigilance regulation. Thirty-four unmedicated patients with chronic insomnia and 25 healthy controls participated in a twenty-minute resting-state electroencephalography (EEG) measurement following a night of polysomnography. Insomnia patients showed enhanced EEG vigilance stability as compared to controls. The pattern of vigilance hyperstability differed from that reported previously in depressive patients. Vigilance hyperstability was also present in insomnia patients showing only mildly reduced sleep efficiency. In this subgroup, vigilance hyperstability correlated with measures of disturbed sleep continuity and arousal. Our data indicate that insomnia disorder is characterized by hyperarousal at night as well as during daytime.

Using prefrontal and midline right frontal EEG-derived theta cordance and depressive symptoms to predict the differential response or remission to antidepressant treatment in major depressive disorder

There is a growing need for optimizing treatment selection and response prediction in individuals with major depressive disorder (MDD). Prior investigations have shown that changes in electroencephalographic (EEG)-based measures precede symptom improvement and could serve as biomarkers of treatment outcome. One such method is cordance, a computation of regional brain activity based on a combination of absolute and relative resting EEG activity. Specifically, early reduction in prefrontal (PF) and midline right frontal (MRF) theta (4-8Hz) cordance has been shown to predict response to various antidepressants, though replication is required. Thus, this study examined early changes (baseline to week 1) in PF and MRF cordance in 47 MDD patients undergoing antidepressant treatment. Early changes in cordance and in Montgomery Åsberg Depression Rating Scale (MADRS) scores were assessed alone, and in combination, to predict eventual (by week 12) treatment response and remission. Models combining early changes in theta cordance (PF and MRF) and depressive symptoms were most predictive of response to treatment at week 12; remission models (cordance, MADRS, and their combination) were weaker, though provided modest prediction values. These results suggest that antidepressant response may be optimally predicted by combining both EEG and symptom-based measures after one week of treatment.

Fatigue in Cancer and Neuroinflammatory and Autoimmune Disease: CNS Arousal Matters

The term fatigue is not only used to describe a sleepy state with a lack of drive, as observed in patients with chronic physical illnesses, but also a state with an inhibition of drive and central nervous system (CNS) hyperarousal, as frequently observed in patients with major depression. An electroencephalogram (EEG)-based algorithm has been developed to objectively assess CNS arousal and to disentangle these pathophysiologically heterogeneous forms of fatigue. The aim of this study was to test the hypothesis that fatigued patients with CNS hyperarousal score higher on depressive symptoms than those without this neurophysiological pattern.

METHODS

Subjects with fatigue (Multidimensional Fatigue Inventory sum-score > 40) in the context of cancer, neuroinflammatory, or autoimmune diseases were drawn from the 60+ cohort of the Leipzig Research Center for Civilization Diseases. CNS arousal was assessed by automatic EEG-vigilance stage classification using the Vigilance Algorithm Leipzig (VIGALL 2.1) based on 20 min EEG recordings at rest with eyes closed. Depression was assessed by the Inventory of Depressive Symptomatology (IDS-SR).

RESULTS

Sixty participants (33 female; median age: 67.5 years) were included in the analysis. As hypothesized, fatigued patients with CNS hyperarousal had higher IDS-SR scores than those without hyperarousal (F_1,58 = 18.34; p < 0.0001, η² = 0.240).

CONCLUSION

hyperaroused fatigue in patients with chronic physical illness may be a sign of comorbid depression.

Electroencephalogram Source Connectivity in the Prediction of Electroconvulsive Therapy Outcome in Major Depressive Disorder

Objectives. Major depressive disorder (MDD) is a common and potentially lethal disorder affecting up to 14% of all persons worldwide. However, one-third to thwo-thirds of patients are nonresponders to first-line therapy. Even the electroconvulsive therapy (ECT) as the option of choice in therapy-resistant MDD still shows a high proportion of nonresponders. In case of a predicted nonresponse to ECT, for example, by means of electrophysiological electroencephalogram (EEG) parameters, other therapies of MDD (eg, augmentation, polypharmacy etc) could be chosen. Methods. In this study, we retrospectively analyzed 2-minute resting state EEGs from patients with MDD who underwent ECT (6-12 sessions with 3 sessions per week) between 2006 and 2015 at the University Hospital of Zurich. Following several lines of evidence, we hypothesized altered linear EEG connectivity within the alpha band being predictive for treatment outcome. We used a network-based statistics (NBS) approach to compare connectivity measures between responders and nonresponders. Source estimates and connectivity measures were mapped using low-resolution brain tomography (LORETA). As the main outcome parameter served the retrospectively assessed efficacy index (CGI-E) from the Clinical Global Impression (CGI) rating scale. Results. Responders in comparison with non-responders showed a significant lower linear lagged connectivity in widespread cortical areas within the EEG alpha 2 band. Additionally, there were strong correlations between CGI ratings and connectivity strength mainly within frontal cortices. Conclusions. Pretreatment EEG-connectivity within the alpha 2 band has a predictive value for the efficacy of ECT treatment.

Reported and Recorded Sleepiness in Obesity and Depression

BACKGROUND

Obesity and depression are both associated with changes in sleep/wake regulation, with potential implications for individualized treatment especially in comorbid individuals suffering from both. However, the associations between obesity, depression, and subjective, questionnaire-based and objective, EEG-based measurements of sleepiness used to assess disturbed sleep/wake regulation in clinical practice are not well known.

OBJECTIVES

The study investigates associations between sleep/wake regulation measures based on self-reported subjective questionnaires and EEG-derived measurements of sleep/wake regulation patterns with depression and obesity and how/whether depression and/or obesity affect associations between such self-reported subjective questionnaires and EEG-derived measurements.

METHODS

Healthy controls (HC, N_HC = 66), normal-weighted depressed (DEP, N_DEP = 16), non-depressed obese (OB, N_OB = 68), and obese depressed patients (OBDEP, N_OBDEP = 43) were included from the OBDEP (Obesity and Depression, University Leipzig, Germany) study. All subjects completed standardized questionnaires related to daytime sleepiness (ESS), sleep quality and sleep duration once as well as questionnaires related to situational sleepiness (KSS, SSS, VAS) before and after a 20 min resting state EEG in eyes-closed condition. EEG-based measurements of objective sleepiness were extracted by the VIGALL algorithm. Associations of subjective sleepiness with objective sleepiness and moderating effects of obesity, depression, and additional confounders were investigated by correlation analyses and regression analyses.

RESULTS

Depressed and non-depressed subgroups differed significantly in most subjective sleepiness measures, while obese and non-obese subgroups only differed significantly in few. Objective sleepiness measures did not differ significantly between the subgroups. Moderating effects of obesity and/or depression on the associations between subjective and objective measures of sleepiness were rarely significant, but associations between subjective and objective measures of sleepiness in the depressed subgroup were systematically weaker when patients comorbidly suffered from obesity than when they did not.

CONCLUSION

This study provides some evidence that both depression and obesity can affect the association between objective and subjective sleepiness. If confirmed, this insight may have implications for individualized diagnosis and treatment approaches in comorbid depression and obesity.

Hyperstable arousal regulation in multiple sclerosis

BACKGROUND

Fatigue is common in multiple sclerosis (MS) patients. Exhaustion of physiological reserves and mental stress are postulated causes, the latter supported by more pronounced hypothalamic-pituitary-adrenal (HPA) axis activation in fatigued patients. Divergent dysregulation of arousal appears to play important roles in depression- (hyperstable arousal) and in cancer-related (unstable arousal) fatigue, where HPA axis is hyperactive or hypoactive, respectively.

OBJECTIVE

This study assessed arousal regulation in multiple sclerosis patients, explored if fatigue can be physiologically described by altered arousal regulation, and if HPA axis activity corresponds to the type(s) of arousal regulation.

METHODS

51 mildly-affected patients with relapsing-remitting MS (86% on disease-modifying treatment) and 20 healthy controls were analysed via Vigilance Algorithm Leipzig and combined dexamethasone/corticotropin releasing hormone test.

RESULTS

Hyperstable arousal pattern was significantly more frequent in patients than in controls (62.7% vs. 45.0%, p = 0.011). Patients scored higher on all fatigue, but not on sleepiness scales. All patients combined showed mild activation of the hypothalamic-pituitary-adrenal axis (p < 0.05 for post-CRH ACTH and AUC ACTH; cortisol n.s.). While fatigue was numerically more pronounced in both hyperstable and unstable arousal, HPA axis activity was highest in hyperstable and lowest in unstable arousal (p = 0.013 for post-CRH ACTH; p = 0.087 for AUC ACTH; cortisol n.s.).

CONCLUSION

Frequency of arousal patterns are altered in MS. An association with HPA axis activity was weak, possibly because the present sample was stable on immunotherapy.

Spatiotemporal brain activation pattern following acute citalopram challenge is dose dependent and associated with neuroticism: A human phMRI study

BACKGROUND

The initial effects of selective serotonin reuptake inhibitors (SSRIs) in the human living brain are poorly understood. We carried out a 3T resting state fMRI study with pharmacological challenge to determine the brain activation changes over time following different dosages of citalopram.

METHODS

During the study, 7.5 mg i.v. citalopram was administered to 32 healthy subjects. In addition, 11.25 mg citalopram was administered to a subset of 9 subjects to investigate the dose-response. Associations with neuroticism (assessed by the NEO PI-R) of the emerging brain activation to citalopram was also investigated.

RESULTS

Citalopram challenge evoked significant activation in brain regions that are part of the default mode network, the visual network and the sensorimotor network, extending to the thalamus, and midbrain. Most effects appeared to be dose-dependent and this was statistically significant in the middle cingulate gyrus. Individual citalopram-induced brain responses were positively correlated with neuroticism scores and its subscales in specific brain areas; anxiety subscale scores in thalamus and midbrain and self-consciousness scores in middle cingulate gyrus. There were no sex differences.

LIMITATIONS

We investigated only healthy subjects and we used a relatively low sample size in the 11.25 mg citalopram analysis.

DISCUSSION

Our results suggest that SSRIs acutely induce an increased arousal-like state of distributed cortical and subcortical systems that is mediated by enhanced serotonin neurotransmission according to levels of neuroticism and underpins trait sensitivity to environmental stimuli and stressors. Studies in depression are needed to determine how therapeutic effects eventually emerge. This article is part of the special issue entitled 'Serotonin Research: Crossing Scales and Boundaries'.

Combined cognitive, psychomotor and electrophysiological biomarkers in major depressive disorder

The diagnosis of major depressive disorder (MDD) should be based on multimodal evidence, because MDD not only affects mood, but also psychomotor and cognitive functions. Clinical markers such as executive dysfunctions and a reduction in daily motor activity have been observed in MDD. Neurophysiological biomarkers have also been described. In this study, we investigate the utility of combining biomarkers related to executive dysfunctions, motor activity, neurophysiological patterns (i.e. alpha power asymmetry and EEG-vigilance as indicators of brain arousal), and the interaction of these parameters in the diagnosis of MDD. Twenty (female: 11) patients with MDD (age: 51.05 ± 10.50) and 20 (female: 13) healthy controls (HC; age: 47.15 ± 12.57) underwent a 10-min resting EEG. Executive dysfunctions were assessed using the Trail Making Test B (TMT B). Motor activity was analysed by actigraphy measurements. MDD patients displayed significant impairments in executive functions and reduced daily motor activity. In the EEG, MDD patients showed more right than left frontal activity and lower brain arousal relative to HC. TMT B and asymmetrical frontal alpha power alone discriminated between MDD patients and HC with an accuracy of 78%. The interaction of motor activity and the EEG-vigilance stage alongside TMT B increased the accuracy of the discrimination test to 81%. This improved accuracy suggests that the combination of these biomarkers in a discriminant analysis resulted in a more reliable identification of MDD patients.

Brain arousal regulation and depressive symptomatology in adults with attention-deficit/hyperactivity disorder (ADHD)

BACKGROUND

The aim of the present study was to evaluate the stability of brain arousal in adult attention-deficit/hyperactivity disorder (ADHD) outpatients with and without depressive symptomatology, and its association with depressive symptom severity and absolute electroencephalogram (EEG) power in different frequency bands.

METHODS

We included 31 outpatient adults (45.16% females), who were diagnosed according to DSM-IV and received no medication. Their arousal stability score (index of the steepness of arousal decline during a 15-min EEG under resting conditions), the absolute EEG power and self-reports, including depressive and ADHD-related symptoms, were analyzed. Participants were split into an unstable and stable arousal group based on the median (= 6) of the arousal stability score.

RESULTS

ADHD patients in the stable group reported more severe depressive symptoms (p = 0.018) and showed reduced absolute EEG power in the delta (0.002 ≤ p ≤ 0.025) and theta (0.011 ≤ p ≤ 0.034) bands compared to those in the unstable group. There was no correlation between the arousal stability score and self-report-scales concerning ADHD-related symptoms (0.214 ≤ p ≤ 0.989), but a positive association with self-reported depressive severity (p = 0.018) and negative association with powers in the EEG delta and theta bands (0.001 ≤ p ≤ 0.033).

CONCLUSIONS

In view of high comorbidity of depression and ADHD in adult patients, these findings support the assumption that brain arousal regulation could be considered as a helpful marker for the clinical differentiation between ADHD and depression.

EEG-vigilance regulation in Borderline Personality Disorder

BACKGROUND

Borderline Personality Disorder (BPD) is characterized by emotional instability, impulsivity, disturbed cognition, sleeplessness and states of high inner tension. Altered arousal regulation which is regarded as a higher domain of functioning according to the research domain criteria of the NIMH and which has previously been reported in several psychiatric disorders, such as mania or major depression could be involved in these features of BPD.

METHODS

40 unmedicated patients with BPD and 42 matched healthy volunteers participated in a twenty minute resting-state EEG measurement with closed eyes. EEG-vigilance regulation was assessed with VIGALL2.0 (Vigilance Algorithm Leipzig), which allows a classification of consecutive 1-s segments in different vigilance stages ranging from high alertness/relaxed wakefulness (stages 0, A1, A2, A3) to drowsiness (B1, B2/3) and sleep onset (C).

RESULTS

Across 20 min, both groups showed a similar decline from higher to lower vigilance stages, but patients with BPD remained in higher stages of vigilance compared to healthy volunteers across the whole measurement (p = .013). Contrary to this, pre-experimental ratings indicated enhanced subjective sleepiness but no differences in self-reported sleep quantity in the previous night in patients with BPD compared to healthy volunteers.

CONCLUSIONS

The results of an elevated arousal regulation (in combination with increased subjective sleepiness) might reflect several symptoms, such as aversive inner tension and impoverished sense of self in patients with BPD. As arousal is linked to the noradrenergic system, further investigations in this field seem to be promising.

Brain arousal regulation in SSRI-medicated patients with major depression

EEG measures of arousal have been suggested as diagnostic and predictive biomarkers for major depression. The aim of the present study was to examine whether self-rated depression severity in SSRI-medicated patients with major depression (MD) is associated with EEG measures of brain arousal. Based on previous studies, we expected that a higher level of brain arousal and a slower arousal decline during a 15-min EEG recording are associated with higher symptom severity as assessed with the Beck Depression Inventory (BDI) at the time of the EEG recording. EEGs of 78 MD patients and 46 healthy controls were analyzed. Brain arousal was assessed using the Vigilance Algorithm Leipzig (VIGALL 2.1). Based on automatically classified 1-s segments (EEG-vigilance Stages 0, A1, A2, A3, B1, B2/3 or C) we computed indices to assess the level (mean EEG-vigilance) and the decline of arousal (slope index) during the 15-min resting state EEG under eyes-closed condition. We found that a higher arousal level and a slower arousal decline corresponded to higher severity of depressive symptoms (rho = 0.238, p = .018; and rho = 0.236; p = .019). Self-rated non-remitters (BDI>12) had a higher arousal level (mean EEG-vigilance: t₇₆ = -2.19, p = .016) and slower arousal decline (slope index: Z = -2.08, p = .019) during the 15-min recording as compared to remitters. Similar results were obtained between non-remitters and healthy controls (mean EEG-vigilance: t₁₀₂ = -2.75, p = .004; slope index: Z = -1.92, p = .028), but not between remitters and controls (p > .260). The findings support the model that brain arousal regulation plays an important role in the pathophysiology and treatment of MD.

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

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

Changes in brain arousal (EEG-vigilance) after therapeutic sleep deprivation in depressive patients and healthy controls

Depressed patients frequently exhibit a hyperstable brain arousal regulation. According to the arousal regulation model of affective disorders, the antidepressant effect of therapeutic sleep deprivation could be achieved by counter-acting this dysregulation. We investigated the impact of partial sleep deprivation (PSD) on EEG-vigilance (an indicator of brain arousal regulation) in depressed patients (n = 27) and healthy controls (n = 16). PSD was hypothesized to cause a more prominent destabilisation of brain arousal regulation in depressed patients (reflected by increased occurrence of lower EEG-vigilance stages). Furthermore, it was studied whether responders (n = 17) exhibit a more stable baseline brain arousal regulation and would show a more prominent arousal destabilisation after PSD than non-responders (n = 10). Before PSD, patients showed a more stable EEG-vigilance with less declines to lower vigilance stages compared to controls. Contrary to the hypothesis, a greater destabilisation of brain arousal after PSD was seen in controls. Within the patient sample, responders generally showed a less stable EEG-vigilance, especially after PSD when we found significant differences compared to non-responders. EEG-vigilance in non-responders showed only little change from baseline to after PSD. In summary, PSD had a destabilizing impact on brain arousal regulation in healthy controls whereas depressed patients reacted heterogeneously depending on the outcome of treatment.

Ketamine influences the locus coeruleus norepinephrine network, with a dependency on norepinephrine transporter genotype - a placebo controlled fMRI study

Background

Ketamine is receiving increasing attention as a rapid-onset antidepressant in patients suffering from major depressive disorder (MDD) with treatment resistance or severe suicidal ideation. Ketamine modulates several neurotransmitter systems, including norepinephrine via the norepinephrine transporter (NET), both peripherally and centrally. The locus coeruleus (LC), which has high NET concentration, has been attributed to brain networks involved in depression. Thus we investigated the effects of single-dose of racemic ketamine on the LC using resting state functional MRI.

Methods

Fifty-nine healthy participants (mean age 25.57 ± 4.72) were examined in a double-blind, randomized, placebo-controlled study with 7 Tesla MRI. We investigated the resting state functional connectivity (rs-fc) of the LC before and one hour after subanesthetic ketamine injection (0.5 mg/kg), as well as associations between its rs-fc and a common polymorphism in the NET gene (rs28386840).

Results

A significant interaction of drug and time was revealed, and post hoc testing showed decreased rs-fc between LC and the thalamus after ketamine administration compared with baseline levels, including the mediodorsal, ventral anterior, ventral lateral, ventral posterolateral and centromedian nuclei. The rs-fc reduction was more pronounced in NET rs28386840 [AA] homozygous subjects than in [T] carriers.

Conclusions

We demonstrated acute rs-fc changes after ketamine administration in the central node of the norepinephrine pathway. These findings may contribute to understanding the antidepressant effect of ketamine at the system level, supporting modes of action on networks subserving aberrant arousal regulation in depression.

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Ketamine decreased connectivity between locus coeruleus and bilateral thalamus in resting state fMRI.

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This reduction of rs-fc between LC and thalamus was dependent on norepinephrine transporter genotype.

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The central effects of ketamine involve norepinephrine and attention networks.

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Antidepressive effects of ketamine may involve LC attention system.

Electrophysiological activity is associated with vulnerability of Internet addiction in non-clinical population

This study investigated the electrophysiological activity associated with vulnerability of problematic Internet use in non-clinical population. The resting EEG spectrum of alpha (8-13 Hz) rhythm was measured in 22 healthy subjects who have used the Internet for recreational purpose. The vulnerability of Internet addiction was assessed using Young's Internet Addiction Test (IAT) and Assessment for Computer and Internet Addiction-Screener (AICA-S) respectively. Depression and impulsivity were also measured with Beck Depression Inventory (BDI) and Barratt Impulsiveness Scale 11(BIS-11) respectively. The IAT was positively correlated with alpha power obtained during eyes closed (EC, r = 0.50, p = 0.02) but not during eyes open (EO). This was further supported by a negative correlation (r = -0.48, p = 0.02) between IAT scores and alpha desynchronization (EO-EC). These relationships remained significant following correction for multiple comparisons. Furthermore, The BDI score showed positive correlation with alpha asymmetry at mid-lateral (r = 0.54, p = 0.01) and mid-frontal (r = 0.46, p = 0.03) regions during EC, and at mid-frontal (r = 0.53, p = 0.01) region during EO. The current findings suggest that there are associations between neural activity and the vulnerability of problematic Internet use. Understanding of the neurobiological mechanisms underlying problematic Internet use would contribute to improved early intervention and treatment.

Exposure to attachment narratives dynamically modulates cortical arousal during the resting state in the listener

BACKGROUND

Affective stimulation entails changes in brain network patterns at rest, but it is unknown whether exogenous emotional stimulation has a prolonged effect on the temporal dynamics of endogenous cortical arousal. We therefore investigated differences in cortical arousal in the listener following stimulation with different attachment-related narratives.

METHODS

Resting-state EEG was recorded from sixteen healthy subjects for ten minutes each with eyes closed: first at baseline and then after passively listening to three affective narratives from strangers about their early childhood experiences (prototypical for insecure-dismissing, insecure-preoccupied, and secure attachment). Using the VIGALL 2.1 algorithm, low or high vigilance stages in consecutive EEG segments were classified, and their dynamic profile was analyzed. Questionnaires assessed the listeners' emotional response to the content of the narrative.

RESULTS

As a general effect of preceding affective stimulation, vigilance following the stimulation was significantly elevated compared to baseline rest, and carryover effects in dynamic vigilance profiles were observed. A difference between narrative conditions was revealed for the insecure-dismissing condition, in which the decrease in duration of high vigilance stages was fastest compared to the other two conditions. The behavioral data supported the observation that especially the insecure narratives induced a tendency in the listener to affectively disengage from the narrative content.

DISCUSSION

This study revealed carryover effects in endogenous cortical arousal evoked by preceding affective stimulation and provides evidence for attachment-specific dynamic alterations of brain states and individual differences in emotional reactivity.

Impact of brain arousal and time-on-task on autonomic nervous system activity in the wake-sleep transition

BACKGROUND

Autonomic nervous system (ANS) activity has been shown to vary with the state of brain arousal. In a previous study, this association of ANS activity with distinct states of brain arousal was demonstrated using 15-min EEG data, but without directly controlling for possible time-on-task effects. In the current study we examine ANS-activity in fine-graded EEG-vigilance stages (indicating states of brain arousal) during two conditions of a 2-h oddball task while controlling for time-on-task. In addition, we analyze the effect of time-on-task on ANS-activity while holding the level of brain arousal constant.

METHODS

Heart rate and skin conductance level of healthy participants were recorded during a 2-h EEG with eyes closed under simultaneous presentation of stimuli in an ignored (N = 39) and attended (N = 39) oddball condition. EEG-vigilance stages were classified using the Vigilance Algorithm Leipzig (VIGALL 2.1). The time-on-task effect was tested by dividing the EEG into four 30-min consecutive time blocks. ANS-activity was compared between EEG-vigilance stages across the entire 2 h and within each time block.

RESULTS

We found a coherent decline of ANS-activity with declining brain arousal states, over the 2-h recording and in most cases within each 30-min block in both conditions. Furthermore, we found a significant time-on-task effect on heart rate, even when arousal was kept constant. It was most pronounced between the first and all subsequent blocks and could have been a consequence of postural change at the beginning of the experiment.

CONCLUSION

Our findings contribute to the validation of VIGALL 2.1 using ANS parameters in 2-h EEG recording under oddball conditions.

Brain arousal regulation in adults with attention-deficit/hyperactivity disorder (ADHD)

The main aim of the current study was to test the hypothesis that adult patients with attention-deficit/hyperactivity disorder (ADHD) have less stable brain arousal regulation than healthy controls. We objectively assessed brain arousal regulation using the Vigilance Algorithm Leipzig (VIGALL 2.1) to analyze 15-min resting EEG data of thirty-three ADHD patients and thirty-five matched controls. Based on automatically classified 1-s segments we computed mean EEG-vigilance (indexing arousal level) and arousal stability score (indexing arousal regulation). Adult ADHD patients showed significantly lower arousal levels and significantly less stable brain arousal regulation than controls. Multiple regression analysis indicated that arousal regulation (i.e., arousal stability score) predicted the retrospectively-assessed severity of childhood ADHD symptoms, supporting the trait aspect of brain arousal regulation. Our findings support the arousal regulation model of ADHD, which interprets hyperactivity and sensation seeking as an autoregulatory reaction to an unstable regulation of brain arousal. EEG-based arousal parameters may be candidate biomarkers for adult ADHD.

The effect of chronotherapy on depressive symptoms. Evidence-based practice

The global prevalence of depression is increasing, along with rates of depression-associated disability and mortality, rendering depressive disorders a major public health issue. Chronotherapy involves a variety of strategies that control exposure to environmental stimuli that influence the biological clock, such as sleep deprivation (SD) or wake therapy (WT), sleep phase advance (SPA), and light and dark therapy (LT, DT). The purpose of this Evidence-Based Practice (EBP) paper is to answer PICOT questions by a review the effectiveness of chronotherapeutics interventions on depressive symptoms with depression and the depressive episode in the course of bipolar disorder. Early studies suggested the effectiveness of LT, SD, and SPA, more recent research has revealed differences in the efficacy of single and combined interventions involving both chronotherapeutic and pharmacological components. This therapy reduced dura­tion of hospitalization, improvement recovery, and reduced for changes in drug prescriptions.

Sleep disturbances and upregulation of brain arousal during daytime in depressed versus non-depressed elderly subjects

OBJECTIVES

Although patients with depression often suffer from sleep disturbances, most of them are not sleepy. Upregulation of brain arousal has been proposed as pathophysiological mechanism explaining sleep disturbances, inner tension, autonomic hyperarousal and anhedonia in depression. The aim of the current study was to examine the association between night-time sleep disturbances and brain arousal regulation the next day in depressed versus non-depressed subjects.

METHODS

Twenty-eight elderly subjects (21 female; age = 70.5 ± 4.4 years) with depressive syndromes without psychotropic medication, and 28 controls (22 female; age = 70.9 ± 4.5 years), underwent a 15-min resting electroencephalogram; the Vigilance Algorithm Leipzig (VIGALL 2.1) provided an objective measure of brain arousal regulation. Sleep disturbances were assessed by a validated and self-rated sleep questionnaire.

RESULTS

In the depressive group, but not in controls, more sleep disturbances were associated with a higher brain arousal stability score (high score corresponds to upregulation) the next day (sleep onset latency: r_s = 0.69, P < .0001; sleep quality: r_s = -0.59, P < .001).

CONCLUSIONS

The data confirm the hypothesis that in persons with depressive syndromes sleep disturbances are related to upregulation of brain arousal the next day. This finding is in line with the concept that dysregulation of brain arousal is a central pathophysiological aspect in depression.

Coupling and dynamics of cortical and autonomic signals are linked to central inhibition during the wake-sleep transition

Maintaining temporal coordination across physiological systems is crucial at the wake-sleep transition. As shown in recent studies, the degree of coordination between brain and autonomic arousal influences attention, which highlights a previously unrecognised point of potential failure in the attention system. To investigate how cortical and autonomic dynamics are linked to the attentive process we analysed electroencephalogram, electrocardiogram and skin conductance data of 39 healthy adults recorded during a 2-h resting-state oddball experiment. We related cross-correlations to fluctuation periods of cortical and autonomic signals and correlated obtained measures to event-related potentials N1 and P2, reflecting excitatory and inhibitory processes. Increasing alignment of cortical and autonomic signals and longer periods of vigilance fluctuations corresponded to a larger and earlier P2; no such relations were found for N1. We compared two groups, with (I) and without measurable (II) delay in cortico-autonomic correlations. Individuals in Group II had more stable vigilance fluctuations, larger and earlier P2 and fell asleep more frequently than individuals in Group I. Our results support the hypothesis of a link between cortico-autonomic coupling and dynamics and central inhibition. Quantifying this link could help refine classification in psychiatric disorders with attention and sleep-related symptoms, particularly in ADHD, depression, and insomnia.

Demonstrating test-retest reliability of electrophysiological measures for healthy adults in a multisite study of biomarkers of antidepressant treatment response

Growing evidence suggests that loudness dependency of auditory evoked potentials (LDAEP) and resting EEG alpha and theta may be biological markers for predicting response to antidepressants. In spite of this promise, little is known about the joint reliability of these markers, and thus their clinical applicability. New standardized procedures were developed to improve the compatibility of data acquired with different EEG platforms, and used to examine test-retest reliability for the three electrophysiological measures selected for a multisite project-Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC). Thirty-nine healthy controls across four clinical research sites were tested in two sessions separated by about 1 week. Resting EEG (eyes-open and eyes-closed conditions) was recorded and LDAEP measured using binaural tones (1000 Hz, 40 ms) at five intensities (60-100 dB SPL). Principal components analysis of current source density waveforms reduced volume conduction and provided reference-free measures of resting EEG alpha and N1 dipole activity to tones from auditory cortex. Low-resolution electromagnetic tomography (LORETA) extracted resting theta current density measures corresponding to rostral anterior cingulate (rACC), which has been implicated in treatment response. There were no significant differences in posterior alpha, N1 dipole, or rACC theta across sessions. Test-retest reliability was .84 for alpha, .87 for N1 dipole, and .70 for theta rACC current density. The demonstration of good-to-excellent reliability for these measures provides a template for future EEG/ERP studies from multiple testing sites, and an important step for evaluating them as biomarkers for predicting treatment response.