Search

Objective To investigate whether regional white matter hyperintensities (WMHs) relate to alpha oscillations (AO) in a large population-based sample of elderly individuals. Methods We associated voxel-wise WMHs from high-resolution 3-Tesla MRI with neuronal alpha oscillations (AO) from resting-state multichannel EEG at sensor (N=907) and source space (N=855) in older participants of the LIFE-Adult study (60–80 years). In EEG, we computed relative alpha power (AP), individual alpha peak frequency (IAPF), as well as long-range temporal correlations (LRTC) that represent dynamic properties of the signal. We implemented whole-brain voxel-wise regression models to identify regions where parameters of AO were linked to probability of WMH occurrence. We further used mediation analyses to examine whether WMH volume mediated the relationship between age and AO. Results Higher prevalence of WMHs in the superior and posterior corona radiata was related to elevated relative AP, with strongest correlations in the bilateral occipital cortex, even after controlling for potential confounding factors. The age-related increase of relative AP in the right temporal brain region was shown to be mediated by total WMH volume. Conclusion A high relative AP corresponding to increased regional WMHs was not associated with age per se, in fact, this relationship was mediated by WMHs. We argue that the WMH-associated increase of AP reflects a generalized and likely compensatory spread of AO leading to a larger number of synchronously recruited neurons. Our findings thus suggest that longitudinal EEG recordings might be sensitive to detect functional changes due to WMHs.

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 2 = 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.

While many structural and biochemical changes in the brain have previously been associated with older age, findings concerning functional properties of neuronal networks, as reflected in their electrophysiological signatures, remain rather controversial. These discrepancies might arise due to several reasons, including diverse factors determining general spectral slowing in the alpha frequency range as well as amplitude mixing between the rhythmic and non-rhythmic parameters. We used a large dataset (N = 1703, mean age 70) to comprehensively investigate age-related alterations in multiple EEG biomarkers taking into account rhythmic and non-rhythmic activity and their individual contributions to cognitive performance. While we found strong evidence for an individual alpha peak frequency (IAF) decline in older age, we did not observe a significant relationship between theta power and age while controlling for IAF. Not only did IAF decline with age, but it was also positively associated with interference resolution in a working memory task primarily in the right and left temporal lobes suggesting its functional role in information sampling. Critically, we did not detect a significant relationship between alpha power and age when controlling for the 1/f spectral slope, while the latter one showed age-related alterations. These findings thus suggest that the entanglement of IAF slowing and power in the theta frequency range, as well as 1/f slope and alpha power measures, might explain inconsistencies reported previously in the literature. Finally, despite the absence of age-related alterations, alpha power was negatively associated with the speed of processing in the right frontal lobe while 1/f slope showed no consistent relationship to cognitive performance. Our results thus demonstrate that multiple electrophysiological features, as well as their interplay, should be considered for the comprehensive assessment of association between age, neuronal activity, and cognitive performance.

BackgroundThe heartbeat-evoked potential (HEP) is a brain response to each heartbeat, which is thought to reflect cardiac signaling to central autonomic areas and suggested to be a marker of internal body awareness (e.g., interoception).ObjectivesBecause cardiac communication with central autonomic circuits has been shown to be impaired in patients with atrial fibrillation (AF), we hypothesized that HEPs are attenuated in these patients.MethodsBy simultaneous electroencephalography and electrocardiography recordings, HEP was investigated in 56 individuals with persistent AF and 56 control subjects matched for age, sex, and body mass index.ResultsHEP in control subjects was characterized by right frontotemporal negativity peaking around 300 to 550 ms after the R-peak, consistent with previous studies. In comparison with control subjects, HEP amplitudes were attenuated, and HEP amplitude differences remained significant when matching the samples for heart frequency, stroke volume (assessed by echocardiography), systolic blood pressure, and the amplitude of the T-wave. Effect sizes for the group differences were medium to large (Cohen’s d between 0.6 and 0.9). EEG source analysis on HEP amplitude differences pointed to a neural representation within the right insular cortex, an area known as a hub for central autonomic control.ConclusionsThe heartbeat-evoked potential is reduced in AF, particularly in the right insula. We speculate that the attenuated HEP in AF may be a marker of impaired heart–brain interactions. Attenuated interoception might furthermore underlie the frequent occurrence of silent AF.

Unemployed people could be at risk of developing inefficient sleep habits by spending excessive time in bed, as they lack a structuring activity. This could impact their mental health and reintegration into labour. This study aims to analyse possible associations between employment status and sleep parameters using actigraphy. Subjects (148 employed and 50 unemployed) were drawn from a German population-based cohort. Sleep parameters were measured with the SenseWear Bodymedia Pro 3 armband. Comparison of means concerning sleep duration, sleep efficiency, time of sleep and sleep fragmentation was performed separately for week days and weekends. Multiple linear regression analysis was performed to analyse group differences controlling for covariates. Finally, we defined cut-off scores for each sleep variable, and analysed the distribution of subjects above and below these values. Unemployed people did not sleep significantly longer than employed people. However, on week days, they displayed night sleep efficiency reduced by on average > 5% points, they lay down for 28 min longer, had later mid sleep time (38 min) and sleep offset (55 min), as well as more frequent awakenings after sleep onset accounting for being awake 28 min longer (all p ≤ 0.005). Sleep in unemployed subjects compared with employed subjects aged 41-64 years was less efficient, more fragmented and shifted to a later point of the night. Results support prior findings that unemployment has a negative influence on sleep quality. Unemployed individuals could benefit from intervention programmes aiming at the adoption of healthier sleep habits.

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.

Brain vascular damage accumulate in aging and often manifest as white matter hyperintensities (WMHs) on MRI. Despite increased interest in automated methods to segment WMHs, a gold standard has not been achieved and their longitudinal reproducibility has been poorly investigated. The aim of present work is to evaluate accuracy and reproducibility of two freely available segmentation algorithms. A harmonized MRI protocol was implemented in 3T-scanners across 13 European sites, each scanning five volunteers twice (test-retest) using 2D-FLAIR. Automated segmentation was performed using Lesion segmentation tool algorithms (LST): the Lesion growth algorithm (LGA) in SPM8 and 12 and the Lesion prediction algorithm (LPA). To assess reproducibility, we applied the LST longitudinal pipeline to the LGA and LPA outputs for both the test and retest scans. We evaluated volumetric and spatial accuracy comparing LGA and LPA with manual tracing, and for reproducibility the test versus retest. Median volume difference between automated WMH and manual segmentations (mL) was −0.22[IQR = 0.50] for LGA-SPM8, −0.12[0.57] for LGA-SPM12, −0.09[0.53] for LPA, while the spatial accuracy (Dice Coefficient) was 0.29[0.31], 0.33[0.26] and 0.41[0.23], respectively. The reproducibility analysis showed a median reproducibility error of 20%[IQR = 41] for LGA-SPM8, 14% [31] for LGA-SPM12 and 10% [27] with the LPA cross-sectional pipeline. Applying the LST longitudinal pipeline, the reproducibility errors were considerably reduced (LGA: 0%[IQR = 0], p < 0.001; LPA: 0% [3], p < 0.001) compared to those derived using the cross-sectional algorithms. The DC using the longitudinal pipeline was excellent (median = 1) for LGA [IQR = 0] and LPA [0.02]. LST algorithms showed moderate accuracy and good reproducibility. Therefore, it can be used as a reliable cross-sectional and longitudinal tool in multi-site studies.

Based on Eysenck’s pioneering work, CNS arousal has long been considered an encouraging biological candidate that may explain individual differences in human personality. Yet, results from empirical studies remained inconclusive. Notably, the vast majority of published results have been derived from small samples, and EEG alpha power has usually served as exclusive indicator for CNS arousal. In this study, we selected N = 468 individuals of the LIFE-Adult cohort and investigated the associations between the Big Five personality traits and CNS arousal by using the low-resolution electromagnetic tomography-based analysis tool VIGALL. Our analyses revealed that subjects who reported higher levels of extraversion and openness to experience, respectively, exhibited lower levels of CNS 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 strongest evidence for neuroticism facet ‘impulsivity’ and reduced CNS arousal. We regard these findings as well in line with the postulations of Eysenck and Zuckerman and consistent with the assumptions of the ‘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 studies with at least several hundreds of subjects.

Background: The amygdala and the hippocampus are two limbic structures that play a critical role in cognition and behavior, however their manual segmentation and that of their smaller nuclei/subfields in multicenter datasets is time consuming and difficult due to the low contrast of standard MRI. Here, we assessed the reliability of the automated segmentation of amygdalar nuclei and hippocampal subfields across sites and vendors using FreeSurfer in two independent cohorts of older and younger healthy adults.Methods: Sixty-five healthy older (cohort 1) and 68 younger subjects (cohort 2), from the PharmaCog and CoRR consortia, underwent repeated 3D-T1 MRI (interval 1-90 days). Segmentation was performed using FreeSurfer v6.0. Reliability was assessed using volume reproducibility error (epsilon) and spatial overlapping coefficient (DICE) between test and retest session.Results: Significant MRI site and vendor effects (p 0.43, p 0.80).Conclusion: Our results support the use of volumetric measures of larger amygdalar nuclei and hippocampal subfields in multisite MRI studies. These measures could be useful for disease tracking and assessment of efficacy in drug trials.

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

The ENIGMA-EEG working group was established to enable large scale international collaborations among cohorts who investigate the genetics of brain function measured with electroencephalography (EEG). The collaboration resulted in the currently largest genome-wide association study of oscillatory brain activity in EEG recordings by meta-analyzing the results across five participating cohorts’ results. Our endeavor has resulted in the first genome-wide significant hits for oscillatory brain function, and significant genes that were previously associated with psychiatric disorders. Our results have provided insight into the influence that psychitaric liability genes have on the functioning brain. In this overview, we also highlight how we have tackled methodological issues surrounding genetic meta-analysis of EEG features, and identify possible sources of heterogeneity across cohorts, which could affect the results of our meta-analysis. We discuss the importance of harmonizing EEG signal processing, cleaning, and feature extraction. Finally, we explain our selection of EEG features to be investigated in our future studies, e.g. temporal dynamics of oscillations and the connectivity network based on synchronization of oscillations. We argue that these represent some of the most important characteristics of the functioning brain. We conclude that disentangling the genetics of EEG will elucidate effects that genes have on brain function, as well as pathways from genes to neurological and psychiatric disorders.

Amyloid and tau pathological accumulation should be considered for Alzheimer's disease (AD) definition and before subjects' enrollment in disease-modifying trials. Although age, APOEε4, and sex influence cerebrospinal fluid (CSF) biomarker levels, none of these variables are considered by current normality/abnormality cutoffs. Using baseline CSF data from 2 independent cohorts (PharmaCOG/European Alzheimer's Disease Neuroimaging Initiative and Alzheimer's Disease Neuroimaging Initiative), we investigated the effect of age, APOEε4 status, and sex on CSF Aβ42/P-tau distribution and cutoff extraction by applying mixture models with covariates. The Aβ42/P-tau distribution revealed the presence of 3 subgroups (AD-like, intermediate, control-like) and 2 cutoffs. The identification of the intermediate subgroup and of the higher cutoff was APOEε4 dependent in both cohorts. APOE-specific classification (higher cutoff for APOEε4+, lower cutoff for APOEε4-) showed higher diagnostic accuracy in identifying MCI due to AD compared to single Aβ42 and Aβ42/P-tau cutoffs. APOEε4 influences amyloid and tau CSF markers and AD progression in MCI patients supporting i) the use of APOE-specific cutoffs to identify MCI due to AD and ii) the utility of considering APOE genotype for early AD diagnosis.

In diesem Kapitel soll das grose Potenzial der differentiellen Perspektive in der Klinischen Psychologie aufgezeigt werden. Dabei werden zunachst grundlegende Konzepte dargestellt, die fur den Zusammenhang differentieller und klinischer Fragestellungen zentral sind. Anschliesend werden klinische Personlichkeitskonzepte besprochen, die sich differentiellpsychologischer Herangehensweisen bedienen. Dies sind vor allem Vulnerabilitats- und Protektivfaktoren. In den letzten drei Abschnitten werden die originar differentiellpsychologischen Theorien besprochen, die sich als klinisch relevant herausgestellt haben, die stark mit den Vulnerabilitats- und Protektivfaktoren uberlappen und vor allem durch das Beschreiben fundamentaler Verhaltensbereiche (Approach und Avoidance) fur das Verstandnis klinischer Phanomene nutzlich sind.

Auditory "oddball" event-related potentials (aoERPs), resting state functional magnetic resonance imaging (rsfMRI) connectivity, and electroencephalographic (rsEEG) rhythms were tested as longitudinal functional biomarkers of prodromal Alzheimer's disease (AD). Data were collected at baseline and four follow-ups at 6, 12, 18, and 24 months in amnesic mild cognitive impairment (aMCI) patients classified in two groups: "positive" (i.e., "prodromal AD" n=81) or "negative" (n=63) based on a diagnostic marker of AD derived from cerebrospinal samples (Aβ 42 /P-tau ratio). A linear mixed model design was used to test functional biomarkers for Group, Time, and Group×Time effects adjusted by nuisance covariates (only data until conversion to dementia was used). Functional biomarkers that showed significant Group effects ("positive" versus "negative", p

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.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.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: rThe 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.

Dopamine has been implicated in the regulation of sleep–wake states and the circadian rhythm. However, there is no consensus on the impact of two established dopaminergic gene variants: the catechol-O-methyltransferase Val158Met (COMT Val158Met; rs4680) and the dopamine D4 receptor Exon III variable-number-of-tandem-repeat polymorphism (DRD4 VNTR). Pursuing a multi-method approach, we examined their potential effects on circadian preferences, arousal regulation and sleep. Subjects underwent a 7-day actigraphy assessment (SenseWear Pro3), a 20-minute resting EEG (analyzed using VIGALL 2.0) and a body mass index (BMI) assessment. Further, they completed the Morningness–Eveningness Questionnaire (MEQ), the Epworth Sleepiness Scale (ESS) and the Pittsburgh Sleep Quality Index (PSQI). The sample comprised 4625 subjects (19–82 years) genotyped for COMT Val158Met, and 689 elderly subjects (64–82 years) genotyped for DRD4 VNTR. The number of subjects varied across phenotypes. Power calculations revealed a minimum required phenotypic variance explained by genotype ranging between 0.5% and 1.5% for COMT Val158Met and between 3.3% and 6.0% for DRD4 VNTR. Analyses did not reveal significant genotype effects on MEQ, ESS, PSQI, BMI, actigraphy and EEG variables. Additionally, we found no compelling evidence in sex- and age-stratified subsamples. Few associations surpassed the threshold of nominal significance (p < .05), providing some indication for a link between DRD4 VNTR and daytime sleepiness. Taken together, in light of the statistical power obtained in the present study, our data particularly suggest no impact of the COMT Val158Met polymorphism on circadian preferences, arousal regulation and sleep. The suggestive link between DRD4 VNTR and daytime sleepiness, on the other hand, might be worth investigation in a sample enriched with younger adults.

The genetic basis of sleep is still poorly understood. Despite the moderate to high heritability of sleep-related phenotypes, known genetic variants explain only a small proportion of the phenotypical variance. However, most previous studies were based solely upon self-report measures. The present study aimed to conduct the first genome-wide association (GWA) of actigraphic sleep phenotypes. The analyses included 956 middle- to older-aged subjects (40-79 years) from the LIFE Adult Study. The SenseWear Pro 3 Armband was used to collect 11 actigraphic parameters of night- and daytime sleep and three parameters of rest (lying down). The parameters comprised measures of sleep timing, quantity and quality. A total of 7 141 204 single nucleotide polymorphisms (SNPs) were analysed after imputation and quality control. We identified several variants below the significance threshold of P ≤ 5× 10-8 (not corrected for analysis of multiple traits). The most significant was a hit near UFL1 associated with sleep efficiency on weekdays (P = 1.39 × 10-8 ). Further SNPs were close to significance, including an association between sleep latency and a variant in CSNK2A1 (P = 8.20 × 10-8 ), a gene known to be involved in the regulation of circadian rhythm. In summary, our GWAS identified novel candidate genes with biological plausibility being promising candidates for replication and further follow-up studies.

Understanding how to reduce the influence of physiological noise in resting state fMRI data is important for the interpretation of functional brain connectivity. Limited data is currently available to assess the performance of physiological noise correction techniques, in particular when evaluating longitudinal changes in the default mode network (DMN) of healthy elderly participants. In this 3T harmonized multisite fMRI study, we investigated how different retrospective physiological noise correction (rPNC) methods influence the within-site test-retest reliability and the across-site reproducibility consistency of DMN-derived measurements across 13 MRI sites. Elderly participants were scanned twice at least a week apart (five participants per site). The rPNC methods were: none (NPC), Tissue-based regression, PESTICA and FSL-FIX. The DMN at the single subject level was robustly identified using ICA methods in all rPNC conditions. The methods significantly affected the mean z-scores and, albeit less markedly, the cluster-size in the DMN; in particular, FSL-FIX tended to increase the DMN z-scores compared to others. Within-site test-retest reliability was consistent across sites, with no differences across rPNC methods. The absolute percent errors were in the range of 5-11% for DMN z-scores and cluster-size reliability. DMN pattern overlap was in the range 60-65%. In particular, no rPNC method showed a significant reliability improvement relative to NPC. However, FSL-FIX and Tissue-based physiological correction methods showed both similar and significant improvements of reproducibility consistency across the consortium (ICC = 0.67) for the DMN z-scores relative to NPC. Overall these findings support the use of rPNC methods like tissue-based or FSL-FIX to characterize multisite longitudinal changes of intrinsic functional connectivity. Hum Brain Mapp 37:2114-2132, 2016. © 2016 Wiley Periodicals, Inc.

Background: Assessment of human brain atrophy in temporal regions using magnetic resonance imaging (MRI), resting state functional MRI connectivity in the left parietal cortex, and limbic electroencephalographic (rsEEG) rhythms as well as plasma amyloid peptide 42 (Aβ 42 ) has shown that each is a promising biomarker of disease progression in amnestic mild cognitive impairment (aMCI) patients with prodromal Alzheimer's disease (AD). However, the value of their combined use is unknown. Objective: To evaluate the association with cognitive decline and the effect on sample size calculation when using a biomarker composite matrix in prodromal AD clinical trials. Methods: Multicenter longitudinal study with follow-up of 2 years or until development of incident dementia. APOE ϵ4-specific cerebrospinal fluid (CSF) Aβ 42 /P-tau cut-offs were used to identify aMCI with prodromal AD. Linear mixed models were performed 1) with repeated matrix values and time as factors to explain the longitudinal changes in ADAS-cog13, 2) with CSF Aβ 42 /P-tau status, time, and CSF Aβ 42 /P-tau status×time interaction as factors to explain the longitudinal changes in matrix measures, and 3) to compute sample size estimation for a trial implemented with the selected matrices. Results: The best composite matrix included the MRI volumes of hippocampal dentate gyrus and lateral ventricle. This matrix showed the best sensitivity to track disease progression and required a sample size 31% lower than that of the best individual biomarker (i.e., volume of hippocampal dentate gyrus). Conclusion: Optimal matrices improved the statistical power to track disease development and to measure clinical progression in the short-term period. This might contribute to optimize the design of future clinical trials in MCI.

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.

Recently, there has been an increased interest in the use of automatically segmented subfields of the human hippocampal formation derived from magnetic resonance imaging (MRI). However, little is known about the test-retest reproducibility of such measures, particularly in the context of multisite studies. Here, we report the reproducibility of automated Freesurfer hippocampal subfields segmentations in 65 healthy elderly enrolled in a consortium of 13 3T MRI sites (five subjects per site). Participants were scanned in two sessions (test and retest) at least one week apart. Each session included two anatomical 3D T1 MRI acquisitions harmonized in the consortium. We evaluated the test-retest reproducibility of subfields segmentation (i) to assess the effects of averaging two within-session T1 images and (ii) to compare subfields with whole hippocampus volume and spatial reliability. We found that within-session averaging of two T1 images significantly improved the reproducibility of all hippocampal subfields but not that of the whole hippocampus. Volumetric and spatial reproducibility across MRI sites were very good for the whole hippocampus, CA2-3, CA4-dentate gyrus (DG), subiculum (reproducibility error∼2% and DICE > 0.90), good for CA1 and presubiculum (reproducibility error ∼ 5% and DICE ∼ 0.90), and poorer for fimbria and hippocampal fissure (reproducibility error ∼ 15% and DICE

Objectives: Recent genome-wide association studies identified a number of chromosomal risk loci for bipolar disorder (BD, ‘manic-depressive illness'). According to the vigilance regulation model, the regulation of brain arousal (referred to as ‘vigilance') when assessed via EEG is an emerging biomarker linked to the pathogenesis of manic and depressive episodes. On this basis, the present study aimed to assess whether carriers of BD risk alleles differ in brain arousal regulation. Methods: Healthy participants of the population-based Leipzig Health Care Study (LIFE) underwent a 20-min eyes-closed resting EEG paradigm. Brain arousal was assessed applying the computer-based Vigilance Algorithm Leipzig (VIGALL). The primary sample (n = 540) was genotyped for ten of the most reliable BD risk variants, of which two qualified for replication (n = 509). Results: Primary sample analyses revealed Bonferroni-adjusted significance for rs1006737 in CACNA1C (encoding a calcium channel subunit), with risk allele carriers exhibiting relatively steep brain arousal declines. Further, carriers of two risk alleles of rs472913 at 1p32.1 showed generally lower brain arousal levels for the duration of the resting paradigm. However, both associations failed replication. Conclusion: Although our initial findings are in line with the vigilance regulation model and convincing in view of the previously reported notable role of ion channelopathies in BD, our results do not provide consistent evidence for a link between BD risk variants and brain arousal regulation. Several between-sample differences may account for this inconsistency. The molecular genetics of brain arousal regulation remain to be clarified.

During the last few decades, much knowledge has been gained about sleep being a heterogeneous condition with several distinct sleep stages that represent fundamentally different physiological states. The same applies for the wake state which also comprises distinct global functional states (called vigilance stages). However, various terms and concepts have been introduced describing different aspects of wakefulness, and accordingly several methods of assessment exist, e.g. sleep laboratory assessments (Multiple Sleep Latency Test, Maintenance of Wakefulness Test), questionnaires (Epworth Sleepiness Scale, Karolinska Sleepiness Scale), behavioural tasks (Psychomotor Vigilance Test) or electroencephalography (EEG)-based assessments (Alpha Attenuation Test, Karolinska Drowsiness Test). Furthermore, several theoretical concepts about the regulation of sleep and wakefulness have been put forward, and physiological correlates have been identified. Most relevant for healthy functioning is the regulation of brain arousal and the adaption of wakefulness to the environmental and situational needs so that the optimal balance between energy conservation and responsiveness can be obtained. Since one approach to the assessment of brain arousal regulation is the classification of EEG vigilance stages, a computer-based algorithm (Vigilance Algorithm Leipzig) has been introduced, allowing classification of EEG vigilance stages in EEG recordings under resting conditions. The time course of EEG vigilance stages in EEGs of 15-20 min duration allows estimation of the individual arousal regulation (hyperstable, adaptive, or unstable vigilance pattern). The vigilance model of affective disorders and attention-deficit/hyperactivity disorder links a disturbed arousal regulation to the pathogenesis of psychiatric disorders and accordingly helps to explain and possibly also predict treatment effects of pharmacological and non-pharmacological interventions for these conditions.

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. Electronic supplementary material The online version of this article (10.1186/s12868-018-0419-y) contains supplementary material, which is available to authorized users.

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.

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.

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.

Hypoarousal as indicated by skin conductance and electroencephalography (EEG) has been discussed as a pathogenetic factor in attention-deficit/hyperactivity disorder (ADHD). The aim of this paper was to review these arousal-related pathogenetic concepts and to present the more recently proposed vigilance regulation model of affective disorders and ADHD. The latter builds on methodological advances in classifying short EEG segments into vigilance stages (Vigilance Algorithm Leipzig, VIGALL), indicating different states of global brain function ("brain arousal"). VIGALL allows the objective assessment of vigilance regulation under defined conditions, e.g. how fast vigilance declines to lower vigilance stages associated with drowsiness during 15-20-min EEG recordings under resting conditions with eyes closed. According to the vigilance regulation model, the hyperactivity and sensation seeking observed in overtired children, ADHD and mania may be interpreted as an autoregulatory attempt to create a stimulating environment in order to stabilize vigilance. The unstable regulation of vigilance observed in both mania and ADHD may thus explain the attention deficits, which become especially prominent in monotonous sustained attention tasks. Among the arguments supporting the vigilance regulation model are the facts that destabilizing vigilance (e.g., via sleep deprivation) can trigger or exacerbate symptoms of ADHD or mania, whereas stabilizing vigilance (e.g., via psychostimulants, reducing sleep deficits) alleviates these symptoms. The potential antimanic effects of methylphenidate are presently being studied in an international randomized controlled trial. We propose vigilance regulation as a converging biomarker, which could be useful for identifying treatment responders to psychostimulants and forming pathophysiologically more homogeneous ADHD subgroups for research purposes.

Few studies have examined the relationship between CSF and structural biomarkers, and cognitive function in MCI. We examined the relationship between cognitive function, hippocampal volume and cerebrospinal fluid (CSF) Aβ42 and tau in 145 patients with MCI. Patients were assessed on cognitive tasks from the Cambridge Neuropsychological Test Automated Battery (CANTAB), the Geriatric Depression Scale and the Functional Activities Questionnaire. Hippocampal volume was measured using magnetic resonance imaging (MRI), and CSF markers of Aβ42, tau and p-tau181 were also measured. Worse performance on a wide range of memory and sustained attention tasks were associated with reduced hippocampal volume, higher CSF tau and p-tau181 and increased tau/Aβ42 ratio. Memory tasks were also associated with lower ability to conduct functional activities of daily living, providing a link between AD biomarkers, memory performance and functional outcome. These results suggest that biomarkers of Aβ and tau are strongly related to cognitive performance as assessed by the CANTAB, and have implications for the early detection and characterization of incipient AD.