Molecular signatures of attention networks.

Attention network theory proposes three distinct types of attention-alerting, orienting, and control-that are supported by separate brain networks and modulated by different neurotransmitters, that is, norepinephrine, acetylcholine, and dopamine. Here, we explore the extent of cortical, genetic, and molecular dissociation of these three attention systems using multimodal neuroimaging. We evaluated the spatial overlap between fMRI activation maps from the attention network test (ANT) and cortex-wide gene expression data from the Allen Human Brain Atlas. The goal was to identify genes associated with each of the attention networks in order to determine whether specific groups of genes were co-expressed with the corresponding attention networks. Furthermore, we analyzed publicly available PET-maps of neurotransmitter receptors and transporters to investigate their spatial overlap with the attention networks. Our analyses revealed a substantial number of genes (3871 for alerting, 6905 for orienting, 2556 for control) whose cortex-wide expression co-varied with the activation maps, prioritizing several molecular functions such as the regulation of protein biosynthesis, phosphorylation, and receptor binding. Contrary to the hypothesized associations, the ANT activation maps neither aligned with the distribution of norepinephrine, acetylcholine, and dopamine receptor and transporter molecules, nor with transcriptomic profiles that would suggest clearly separable networks. Independence of the attention networks appeared additionally constrained by a high level of spatial dependency between the network maps. Future work may need to reconceptualize the attention networks in terms of their segregation and reevaluate the presumed independence at the neural and neurochemical level.

White Matter Tract Integrity Is Reduced in Depression and in Individuals With Genetic Liability to Depression.

BACKGROUND: While major depression has been linked to changes in white matter architecture, it remains unclear whether risk factors for depression are directly associated with these alterations. We reexamined white matter fiber tracts in individuals with depressive symptoms and investigated the connection between genetic and environmental risk for depression and structural changes in the brain. METHODS: We included 19,183 participants from the UK Biobank imaging cohort, with depression status and adverse life experience based on questionnaire data and genetic liability for depression quantified by polygenic scores. The integrity of 27 white matter tracts was assessed using mean fractional anisotropy derived from diffusion magnetic resonance imaging. RESULTS: White matter integrity was reduced, particularly in thalamic and intracortical fiber tracts, in individuals with depressive symptoms, independent of current symptom status. In a group of healthy individuals without depression, increasing genetic risk and increasing environmental risk were associated with reduced integrity in relevant fiber tracts, particularly in thalamic radiations. This association was stronger than expected based on statistical dependencies between samples, as confirmed by subsequent in silico simulations and permutation tests. CONCLUSIONS: White matter alterations in thalamic and association tracts are associated with depressive symptoms and genetic risk for depression in unaffected individuals, suggesting an intermediate phenotype at the brain level.

Enhancing precision in human neuroscience.

Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability - in science in general, but also specifically in human neuroscience - have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience.

Individual differences in inhibitory control are not related to downregulation of negative emotion via distancing.

Evidence suggests that cognitive control and emotional control share partly the same cognitive processes. For example, downregulation of negative emotions requires inhibiting or limiting the expression of a prepotent appraisal of a situation in favor of selecting an alternative appraisal. Although inhibitory control seems to be a particularly relevant process in emotion regulation (ER), previous studies reported inconsistent findings on their relationship, likely because of the application of single task measures in relatively small samples. Therefore, this study implemented a battery of six commonly used inhibitory control tasks in a large sample of young healthy adults (N = 190) and investigated whether inhibitory control is associated with the downregulation of negative emotion. ER was measured via self-reported reappraisal and suppression use and via a laboratory ER task where participants had to distance themselves from emotions in response to negative and neutral pictures. The ER task was accompanied by concurrent physiological measurements of corrugator electromyography (EMG), skin conductance response (SCR), and heart period (HP). Frequentist and Bayesian analyses indicated that inhibitory control was neither associated with self-reported reappraisal and suppression use, nor with successful downregulation of negative emotion via distancing. Compared with HP and SCR, corrugator EMG was the only peripheral physiological measure that was indicative of regulatory success. The findings question the view that inhibitory control represents an underlying process in emotion regulation via distancing, at least at the behavioral level. Further studies should investigate the generalizability of these findings to other ER strategies, tactics, paradigms, and participant groups. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Linking Brain Age Gap to Mental and Physical Health in the Berlin Aging Study II.

From a biological perspective, humans differ in the speed they age, and this may manifest in both mental and physical health disparities. The discrepancy between an individual's biological and chronological age of the brain ("brain age gap") can be assessed by applying machine learning techniques to Magnetic Resonance Imaging (MRI) data. Here, we examined the links between brain age gap and a broad range of cognitive, affective, socioeconomic, lifestyle, and physical health variables in up to 335 adults of the Berlin Aging Study II. Brain age gap was assessed using a validated prediction model that we previously trained on MRI scans of 32,634 UK Biobank individuals. Our statistical analyses revealed overall stronger evidence for a link between higher brain age gap and less favorable health characteristics than expected under the null hypothesis of no effect, with 80% of the tested associations showing hypothesis-consistent effect directions and 23% reaching nominal significance. The most compelling support was observed for a cluster covering both cognitive performance variables (episodic memory, working memory, fluid intelligence, digit symbol substitution test) and socioeconomic variables (years of education and household income). Furthermore, we observed higher brain age gap to be associated with heavy episodic drinking, higher blood pressure, and higher blood glucose. In sum, our results point toward multifaceted links between brain age gap and human health. Understanding differences in biological brain aging may therefore have broad implications for future informed interventions to preserve mental and physical health in old age.

Attention networks and the intrinsic network structure of the human brain.

Attention network theory distinguishes three independent systems, each supported by its own distributed network: an alerting network to deploy attentional resources in anticipation, an orienting network to direct attention to a cued location, and a control network to select relevant information at the expense of concurrently available information. Ample behavioral and neuroimaging evidence supports the dissociation of the three attention domains. The strong assumption that each attentional system is realized through a separable network, however, raises the question how these networks relate to the intrinsic network structure of the brain. Our understanding of brain networks has advanced majorly in the past years due to the increasing focus on brain connectivity. The brain is intrinsically organized into several large-scale networks whose modular structure persists across task states. Existing proposals on how the presumed attention networks relate to intrinsic networks rely mostly on anecdotal and partly contradictory arguments. We addressed this issue by mapping different attention networks at the level of cifti-grayordinates. Resulting group maps were compared to the group-level topology of 23 intrinsic networks, which we reconstructed from the same participants' resting state fMRI data. We found that all attention domains recruited multiple and partly overlapping intrinsic networks and converged in the dorsal fronto-parietal and midcingulo-insular network. While we observed a preference of each attentional domain for its own set of intrinsic networks, implicated networks did not match well to those proposed in the literature. Our results indicate a necessary refinement of the attention network theory.

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.

Large-scale collaboration in ENIGMA-EEG: A perspective on the meta-analytic approach to link neurological and psychiatric liability genes to electrophysiological brain activity.

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.

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 (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.

Molecular genetics in psychology and personality neuroscience: On candidate genes, genome wide scans, and new research strategies.

Despite the substantial heritability estimates for psychological traits, their precise genetic foundation from a molecular perspective remains elusive. We summarize findings and advances from more than twenty years of research into the molecular genetics of personality and other psychological traits. We describe how the candidate gene approach has - despite its appealing theoretical foundations - often (but not always) failed to point towards replicable associations between genetic polymorphisms and behavioral traits. The genome wide analysis approach on the contrary has become more fruitful in recent years and pointed towards reliable genetic associations. Results from genome wide scan studies (GWAS) are currently leveraged to explore gene-behavior associations through genetic correlation and polygenic score prediction which are important steps towards a precision medicine where treatment options are tailored to a patient's individual biology. But it is also true that future work needs to take a closer look at GWAS findings to link the growing list of statistical associations to biopsychological theory. We argue that research strategies from the candidate gene approach can be used to address these issues - given that necessary precautions are taken to avoid the problem of false-positive associations.

Specific and segregated changes to the functional connectome evoked by the processing of emotional faces: A task-based connectome study.

The functional connectome is organized into several separable intrinsic connectivity networks (ICNs) that are thought to be the building blocks of the mind. However, it is currently not well understood how these networks are engaged by emotionally salient information, and how such engagement fits into emotion theories. The current study assessed how ICNs respond during the processing of angry and fearful faces in a large sample (N = 843) and examined how connectivity changes relate to the ICNs. All ICNs were modulated by emotional faces and showed functional interactions, a finding which is in line with the "theory of constructed emotions" that assumes that basic emotion do not arise from separable ICNs but from their interplay. We further identified a set of brain regions whose connectivity changes during the tasks suggest a special role as "affective hubs" in the brain. While hubs were located in all ICNs, we observed high selectivity for the amygdala within the subcortical network, a finding which also fits into "primary emotion" theory. The topology of hubs corresponded closely to a set of brain regions that has been implicated in anxiety disorders, pointing towards a clinical relevance of the present findings. The present data are the most comprehensive mapping of connectome-wide changes in functionally connectivity evoked by an affective processing task thus far and support two competing views on how emotions are represented in the brain, suggesting that the connectome paradigm might help with unifying the two ideas.

Vulnerability to bipolar disorder is linked to sleep and sleepiness.

Sleep impairments are a hallmark of acute bipolar disorder (BD) episodes and are present even in the euthymic state. Studying healthy subjects who are vulnerable to BD can improve our understanding of whether sleep impairment is a predisposing factor. Therefore, we investigated whether vulnerability to BD, dimensionally assessed by the hypomanic personality scale (HPS), is associated with sleep disturbances in healthy subjects. We analyzed participants from a population-based cohort who had completed the HPS and had either a 7-day actigraphy recording or a Pittsburgh sleep quality index (PSQI) assessment. In addition, subjects had to be free of confounding diseases or medications. This resulted in 771 subjects for actigraphy and 1766 for PSQI analyses. We found strong evidence that higher HPS scores are associated with greater intraindividual sleep variability, more disturbed sleep and more daytime sleepiness. In addition, factor analyses revealed that core hypomanic features were especially associated with self-reported sleep impairments. Results support the assumption of disturbed sleep as a possibly predisposing factor for BD and suggest sleep improvement as a potential early prevention target.

Human brain arousal in the resting state: a genome-wide association study.

Arousal affects cognition, emotion, and behavior and has been implicated in the etiology of psychiatric disorders. Although environmental conditions substantially contribute to the level of arousal, stable interindividual characteristics are well-established and a genetic basis has been suggested. Here we investigated the molecular genetics of brain arousal in the resting state by conducting a genome-wide association study (GWAS). We selected N = 1877 participants from the population-based LIFE-Adult cohort. Participants underwent a 20-min eyes-closed resting state EEG, which was analyzed using the computerized VIGALL 2.1 (Vigilance Algorithm Leipzig). At the SNP-level, GWAS analyses revealed no genome-wide significant locus (p < 5E-8), although seven loci were suggestive (p < 1E-6). The strongest hit was an expression quantitative trait locus (eQTL) of TMEM159 (lead-SNP: rs79472635, p = 5.49E-8). Importantly, at the gene-level, GWAS analyses revealed significant evidence for TMEM159 (p = 0.013, Bonferroni-corrected). By mapping our SNPs to the GWAS results from the Psychiatric Genomics Consortium, we found that all corresponding markers of TMEM159 showed nominally significant associations with Major Depressive Disorder (MDD; 0.006 ≤ p ≤ 0.011). More specifically, variants associated with high arousal levels have previously been linked to an increased risk for MDD. In line with this, the MetaXcan database suggests increased expression levels of TMEM159 in MDD, as well as Autism Spectrum Disorder, and Alzheimer's Disease. Furthermore, our pathway analyses provided evidence for a role of sodium/calcium exchangers in resting state arousal. In conclusion, the present GWAS identifies TMEM159 as a novel candidate gene which may modulate the risk for psychiatric disorders through arousal mechanisms. Our results also encourage the elaboration of the previously reported interrelations between ion-channel modulators, sleep-wake behavior, and psychiatric disorders.

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.

Inhibitory attentional control in anxiety: Manipulating cognitive load in an antisaccade task.

Theorists have proposed that heightened anxiety vulnerability is characterised by reduced attentional control performance and have made the prediction in turn that elevating cognitive load will adversely impact attentional control performance for high anxious individuals to a greater degree than low anxious individuals. Critically however, existing attempts to test this prediction have been limited in their methodology and have presented inconsistent findings. Using a methodology capable of overcoming the limitations of previous research, the present study sought to investigate the effect of manipulating cognitive load on inhibitory attentional control performance of high anxious and low anxious individuals. High and low trait anxious participants completed an antisaccade task, requiring the execution of prosaccades towards, or antisaccades away from, emotionally toned stimuli while eye movements were recorded. Participants completed the antisaccade task under conditions that concurrently imposed a lesser cognitive load, or greater cognitive load. Analysis of participants' saccade latencies revealed high trait anxious participants demonstrated generally poorer inhibitory attentional control performance as compared to low trait anxious participants. Furthermore, conditions imposing greater cognitive load, as compared to lesser cognitive load, resulted in enhanced inhibitory attentional control performance across participants generally. Crucially however, analyses did not reveal an effect of cognitive load condition on anxiety-linked differences in inhibitory attentional control performance, indicating that elevating cognitive load did not adversely impact attentional control performance for high anxious individuals to a greater degree than low anxious individuals. Hence, the present findings are inconsistent with predictions made by some theorists and are in contrast to the findings of earlier investigations. These findings further highlight the need for research into the relationship between anxiety, attentional control, and cognitive load.

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.

Recorded and Reported Sleepiness: The Association Between Brain Arousal in Resting State and Subjective Daytime Sleepiness.

Objectives: Daytime sleepiness is a significant public health concern. Early evidence points toward the computerized VIGALL (Vigilance Algorithm Leipzig) as time-efficient tool to assess sleepiness objectively. In the present study, we investigated the association between VIGALL variables of EEG vigilance (indicating brain arousal in resting state) and subjective daytime sleepiness in the LIFE cohort study. Additionally, we validated VIGALL against the self-rated likelihood of having fallen asleep during the conducted resting EEG and against heart periods. Methods: Participants of the primary sample LIFE 60+ (N = 1927, 60-79 years) and replication sample LIFE 40+ (N = 293, 40-56 years) completed the Epworth Sleepiness Scale (ESS). After an average interval of 3 weeks (LIFE 60+) and 65 weeks (LIFE 40+), respectively, participants underwent a single 20-minute resting EEG, analyzed using VIGALL 2.1. Results: Analyses revealed significant associations between ESS and EEG vigilance in LIFE 60+ (rho = -0.17, p = 1E-14) and LIFE 40+ (rho = -0.24, p = 2E-5). Correlations between EEG vigilance and self-rated sleep likelihood reached rho = -0.43 (p = 2E-91) in LIFE 60+ and rho = -0.50 (p = 5E-20) in LIFE 40+. Overall, strongest correlations were obtained for EEG vigilance variable "slope index." Furthermore, lower EEG vigilance was consistently associated with longer heart periods. Conclusions: The present study contributes to the validation of VIGALL. Despite the considerable interval between ESS and EEG assessment dates, the strength of ESS-VIGALL association approximates prior ESS-Multiple Sleep Latency Test results. In this light, VIGALL might constitute an economical choice for the objective assessment of daytime sleepiness in large cohort studies. The discriminative power to identify disorders of hypersomnolence, however, remains to be addressed.

Evoked potentials and behavioral performance during different states of brain arousal.

BACKGROUND: Previous studies compared evoked potentials (EPs) between several sleep stages but only one uniform wake state. However, using electroencephalography (EEG), several arousal states can be distinguished before sleep onset. Recently, the Vigilance Algorithm Leipzig (VIGALL 2.0) has been developed, which automatically attributes one out of seven EEG-vigilance stages to each 1-s EEG segment, ranging from stage 0 (associated with cognitively active wakefulness), to stages A1, A2 and A3 (associated with relaxed wakefulness), to stages B1 and B2/3 (associated with drowsiness) up to stage C (indicating sleep onset). Applying VIGALL, we specified the effects of these finely differentiated EEG-vigilance stages (indicating arousal states) on EPs (P1, N1, P2, N300, MMN and P3) and behavioral performance. Subjects underwent an ignored and attended condition of a 2-h eyes-closed oddball-task. Final analysis included 43 subjects in the ignored and 51 subjects in the attended condition. First, the effect of brain arousal states on EPs and performance parameters were analyzed between EEG-vigilance stages A (i.e. A1, A2 and A3 combined), B1 and B2/3&C (i.e. B2/3 and C combined). Then, in a second step, the effects of the finely differentiated EEG-vigilance stages were further specified. RESULTS: Comparing stages A versus B1 versus B2/3&C, a significant effect of EEG-vigilance stages on all behavioral parameters and all EPs, with exception of MMN and P3, was found. By applying VIGALL, a more detailed view of arousal effects on EP and performance was possible, such as the finding that the P2 showed no further significant increase in stages deeper than B1. Stage 0 did not differ from any of the A-stages. Within more fine-graded stages, such as the A-substages, EPs and performance only partially differed. However, these analyses were partly based on small sample sizes and future studies should take effort to get enough epochs of rare stages (such as A3 and C). CONCLUSIONS: A clear impact of arousal on EPs and behavioral performance was obtained, which emphasize the necessity to consider arousal effects when interpreting EPs.

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: rs = 0.69, P < .0001; sleep quality: rs = -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.