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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 (F1,58 = 18.34; p < 0.0001, η2 = 0.240). Conclusion: hyperaroused fatigue in patients with chronic physical illness may be a sign of comorbid depression.
Background and purpose: The ENIGMA-EEG working group was established to enable large-scale international collaborations among cohorts that investigate the genetics of brain function measured with electroencephalography (EEG). In this perspective, we will discuss why analyzing the genetics of functional brain activity may be crucial for understanding how neurological and psychiatric liability genes affect the brain. Methods: We summarize how we have performed our currently largest genome-wide association study of oscillatory brain activity in EEG recordings by meta-analyzing the results across five participating cohorts, resulting in the first genome-wide significant hits for oscillatory brain function located in/near genes that were previously associated with psychiatric disorders. We describe how we have tackled methodological issues surrounding genetic meta-analysis of EEG features. We discuss the importance of harmonizing EEG signal processing, cleaning, and feature extraction. Finally, we explain our selection of EEG features currently being investigated, including the temporal dynamics of oscillations and the connectivity network based on synchronization of oscillations. Results: We present data that show how to perform systematic quality control and evaluate how choices in reference electrode and montage affect individual differences in EEG parameters. Conclusion: The long list of potential challenges to our large-scale meta-analytic approach requires extensive effort and organization between participating cohorts; however, our perspective shows that these challenges are surmountable. Our perspective argues that elucidating the genetic of EEG oscillatory activity is a worthwhile effort in order to elucidate the pathway from gene to disease liability.
Background: Cytokines are mediators of inflammation that contribute to a low-grade inflammation in different disorders like major depression and obesity. It still remains unclear which psychological and medical factors interact with cytokine regulation. In the current investigation, the association between levels of pro-and anti-inflammatory cytokines and anthropometrics, mood state (depressiveness), physical activity and sleep were investigated in a sample of community-dwelled adults.
Methods: Forty-nine subjects met the inclusion criteria for analyses and were assessed at two time-points (baseline (T1) and follow-up (T2), average T1-T2-interval = 215 days). Serum cytokine measures included the pro-inflammatory cytokines interleukin (IL)-2, IL-12, IFN-γ and TNF-α, the anti-inflammatory cytokines IL-4, IL-5, IL-10 and IL-13 and the granulocyte-macrophage colony-stimulating factor (GM-CSF); anthropometrics were assessed via physical examination, depressiveness was assessed via Beck Depression Inventory (BDI)2, parameters of physical activity (steps, METs) and sleep (night/total sleep duration) were measured via a 1-week actigraphy.
Results: Correlation analyses showed low-to moderate significant relationships between the majority of cytokines and the BDI2 at T1, positive correlation with weight and BMI at T1 and T2, and negative correlations with the number of steps and METs at T2 and T2. Regression analyses for T1 revealed that the BDI2 score was the best positive predictor for the concentrations of all nine cytokines, followed by the number of steps and the nightsleep duration as negative predictors. At T2, the amount of steps was found to be negatively associated with IL-4, IL5, IL-10, GM-CSF, IFN-γ, and TNF-α, whereas the BMI could significantly predict IL-12 and IL-13. The BDI2-score was not significantly associated with any of the cytokines. No associations could be found between dynamics in cytokines from T1 and T2 and changes in any of the variables.
Discussion: The present results indicate an influence of physical activity, subjective well-being and body composition on inflammatory mediators. Since there was no standardized intervention targeting the independent variables between T1 and T2, no assumptions on causality can be drawn from the association results.
Background: Meta-analysis of observational studies concluded that soft drinks may increase the risk of depression, while high consumption of coffee and tea may reduce the risk. Objectives were to explore the associations between the consumption of soft drinks, coffee or tea and: (1) a history of major depressive disorder (MDD) and (2) the severity of depressive symptoms clusters (mood, cognitive and somatic/vegetative symptoms). Methods: Cross-sectional and longitudinal analysis based on baseline and 12-month-follow-up data collected from four countries participating in the European MooDFOOD prevention trial. In total, 941 overweight adults with subsyndromal depressive symptoms aged 18 to 75 years were analyzed. History of MDD, depressive symptoms and beverages intake were assessed. Results: Sugar-sweetened soft drinks were positively related to MDD history rates whereas soft drinks with non-nutritive sweeteners were inversely related for the high vs. low categories of intake. Longitudinal analysis showed no significant associations between beverages and mood, cognitive and somatic/vegetative clusters. Conclusion: Our findings point toward a relationship between soft drinks and past MDD diagnoses depending on how they are sweetened while we found no association with coffee and tea. No significant effects were found between any studied beverages and the depressive symptoms clusters in a sample of overweight adults.
Background: Internet- and mobile-based interventions are most efficacious in the treatment of depression when they involve some form of guidance, but providing guidance requires resources such as trained personnel, who might not always be available (eg, during lockdowns to contain the COVID-19 pandemic).
Objective: The current analysis focuses on changes in symptoms of depression in a guided sample of patients with depression who registered for an internet-based intervention, the iFightDepression tool, as well as the extent of intervention use, compared to an unguided sample. The objective is to further understand the effects of guidance and adherence on the intervention’s potential to induce symptom change.
Methods: Log data from two convenience samples in German routine care were used to assess symptom change after 6-9 weeks of intervention as well as minimal dose (finishing at least two workshops). A linear regression model with changes in Patient Health Questionnaire (PHQ-9) score as a dependent variable and guidance and minimal dose as well as their interaction as independent variables was specified.
Results: Data from 1423 people with symptoms of depression (n=940 unguided, 66.1%) were included in the current analysis. In the linear regression model predicting symptom change, a significant interaction of guidance and minimal dose revealed a specifically greater improvement for patients who received guidance and also worked with the intervention content (β=–1.75, t=–2.37, P=.02), while there was little difference in symptom change due to guidance in the group that did not use the intervention. In this model, the main effect of guidance was only marginally significant (β=–.53, t=–1.78, P=.08).
Conclusions: Guidance in internet-based interventions for depression is not only an important factor to facilitate adherence, but also seems to further improve results for patients adhering to the intervention compared to those who do the same but without guidance.
Background: While the antidepressant efficacy of guided digital interventions has been proven in randomized controlled trials, findings from routine care are less clear. Low adherence rates are common and limit the potential effectiveness. Adherence has been linked to sociodemographic variables and the amount of guidance, but the role of the guide's profession and their work setting has not yet been studied for routine care.
Methods: Routinely collected log data from a digital intervention for depressed patients (iFightDepression tool) were analyzed in an exploratory manner. The sample is a convenience sample from routine care, where guidance is provided by general practitioners (GP), certified psychotherapists (PT) or medical doctors specialized in mental health. Log data from 2184 patients were analyzed and five usage parameters were extracted to measure adherence (first-to-last login, time on tool, number of sessions, workshops completed and minimal dose). Multiple logistic regression was used to analyze relations between the guide's profession and clinical context as well as other covariates and adherence and symptom change on a brief depression questionnaire (PHQ-9).
Results: The analyses showed a significant relation of guide profession and adherence. Guidance by PT was associated to the highest adherence scores (reference category). The odds ratios (ORs) of scoring above the median in each usage parameter for patients guided by GPs were 0.50–0.63 (all ps < 0.002) and 0.61–0.80 (p = .002–0.197) for MH. Higher age, initial PHQ-9 score and self-reported diagnosis of depression were also significantly associated with higher adherence scores. In a subsample providing enough data on the PHQ-9 (n = 347), no association of guide profession with symptom reduction was found. Instead, a greater reduction was observed for patients with a higher baseline PHQ-9 (β = −0. 39, t(341.75) = −8.814, p < .001) and for those who had achieved minimal dose (β = −2.42, t(340.34) = −4.174, P < .001) and those who had achieved minimal dose and scored high on time on tool (β = 0.22, t(341.75) = 1.965, P = .050).
Conclusion: Being guided by PT was associated with the highest adherence. The lowest adherence was observed in patients who were guided by GP. While no association of guide profession and symptom reduction was found in a subsample, greater adherence was associated with symptom reduction.
Suicide represents a major challenge to public mental health. In order to provide empirical evidence for prevention strategies, we hypothesized current levels of low socioeconomic status (SES) and high social isolation (SI) to be linked to increased suicide rates in N = 390 administrative districts since SES and SI are associated with mental illness. Effects of SES on suicide rates were further expected to be especially pronounced in districts with individuals showing high SI levels as SI reduces the reception of social support and moderates the impact of low SES on poor mental health. We linked German Microcensus data to register data on all 149,033 German suicides between 1997 and 2010 and estimated Prentice and Sheppard’s model for aggregate data to test the hypotheses, accounting for spatial effect correlations. The findings reveal increases in district suicide rates by 1.20% (p < 0.035) for 1% increases of district unemployment, suicide rate decreases of −0.39% (p < 0.028) for 1% increases in incomes, increases of 1.65% (p < 0.033) in suicides for 1% increases in one-person-households and increases in suicide rates of 0.54% (p < 0.036) for 1% decreases in single persons’ incomes as well as suicide rate increases of 3.52% (p < 0.000) for 1% increases in CASMIN scores of individuals who moved throughout the year preceding suicide. The results represent appropriate starting points for the development of suicide prevention strategies. For the definition of more precise measures, future work should focus on the causal mechanisms resulting in suicidality incorporating individual level data.
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, NHC = 66), normal-weighted depressed (DEP, NDEP = 16), non-depressed obese (OB, NOB = 68), and obese depressed patients (OBDEP, NOBDEP = 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.
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.