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.

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.

Summary: 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 theLIFEAdult 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 ofP ≤ 5× 10⁻⁸(not corrected for analysis of multiple traits). The most significant was a hit nearUFL1associated with sleep efficiency on weekdays (P = 1.39 × 10⁻⁸). FurtherSNPs were close to significance, including an association between sleep latency and a variant inCSNK2A1(P = 8.20 × 10⁻⁸), a gene known to be involved in the regulation of circadian rhythm. In summary, ourGWASidentified novel candidate genes with biological plausibility being promising candidates for replication and further follow‐up studies.

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.

Different levels of brain arousal can be delineated not only during sleep but also during wakefulness. Electroencephalography (EEG) is the gold standard to assess different levels of brain arousal. A novel EEG- and electrooculography (EOG)-based tool, the Vigilance Algorithm Leipzig (VIGALL 2.0), allows determining the level of EEG-vigilance (indicating brain arousal). Considering the frequency patterns and LORETA-based cortical distribution of electroencephalic activity, VIGALL 2.0 automatically attributes one out of seven vigilance stages to each EEG segment (1-sec EEG segments by default), ranging from high alertness (stage 0), to relaxed wakefulness (stage A1 to A3), to drowsiness (stage B1 to B2/3) up to sleep onset (stage C). Building on the time series of these seven vigilance stages across 20 min, two parameterizations of the temporal dynamic (brain arousal regulation) are calculated: the lability score and the slope index. 27 healthy participants (age = 22.93 ± 3.44 years, 18 females) underwent two sessions (7 days apart) of a twenty-minute eyes-closed resting EEG paradigm. The test-retest reliability coefficients for the EEG-vigilance stages were between rho = .53 and .86 (all p < .01). For the temporal dynamic of the stages across 20 min, the test-retest reliability coefficients were rho = .70 (lability score, p < .001) and .71 (slope index, p < .001). This study demonstrated some trait aspects of brain arousal regulation by confirming the stability of temporal dynamic of EEG-vigilance stages as assessed with VIGALL 2.0. Considering the “first day in lab” effect identified in the present study, more adaptation to the lab surrounding and a stricter control of other state factors should be taken into account, which might improve reliability. Additionally, in a clinical context, a broader range of brain arousal regulation patterns might be found, possibly leading to higher test-retest reliability than was found in this homogenous healthy sample. This would be desirable, as parameters of brain arousal regulation are promising diagnostic and prognostic biomarkers for diseases with arousal disturbances, such as affective disorders, ADHD and fatigue.

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. 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. 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. 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.