No abstract

Emerging efforts toward prevention of stress-related mental disorders have created a need for unobtrusive real-life monitoring of stress-related symptoms. We used ecological momentary assessments (EMA) combined with wearable biosensors to investigate whether these can be used to detect periods of prolonged stress. During stressful high-stake exam (versus control) weeks, participants reported increased negative affect and decreased positive affect. Intriguingly, physiological arousal was decreased on average during the exam week. Time-resolved analyses revealed peaks in physiological arousal associated with both self-reported stress and self-reported positive affect, while the overall decrease in physiological arousal was mediated by lower positive affect during the stress period. We then used machine learning to show that a combination of EMA and physiology yields optimal classification of week types. Our findings highlight the potential of wearable biosensors in stress-related mental-health monitoring, but critically show that psychological context is essential for interpreting physiological arousal detected using these devices.

It has recently been shown that acute stress affects the allocation of neural resources between large-scale brain networks, and the balance between the executive control network and the salience network in particular. Maladaptation of this dynamic resource reallocation process is thought to play a major role in stress-related psychopathology, suggesting that stress resilience may be determined by the retained ability to adaptively reallocate neural resources between these two networks. Actively training this ability could hence be a potentially promising way to increase resilience in individuals at risk for developing stress-related symptomatology. Using real-time functional Magnetic Resonance Imaging, the current study investigated whether individuals can learn to self-regulate stress-related large-scale network balance. Participants were engaged in a bidirectional and implicit real-time fMRI neurofeedback paradigm in which they were intermittently provided with a visual representation of the difference signal between the average activation of the salience and executive control networks, and tasked with attempting to self-regulate this signal. Our results show that, given feedback about their performance over three training sessions, participants were able to (1) learn strategies to differentially control the balance between SN and ECN activation on demand, as well as (2) successfully transfer this newly learned skill to a situation where they (a) did not receive any feedback anymore, and (b) were exposed to an acute stressor in form of the prospect of a mild electric stimulation. The current study hence constitutes an important first successful demonstration of neurofeedback training based on stress-related large-scale network balance - a novel approach that has the potential to train control over the central response to stressors in real-life and could build the foundation for future clinical interventions that aim at increasing resilience.

Etwa 35 Jahre nach der ersten Anwendung auf einer Baustelle in Deutschland ist das Düsenstrahlverfahren heute im Spezialtiefbau etabliert und gehört zum Portfolio vieler Unternehmen. Für dessen Anwendung gelten DIN EN 12716 als zentrales technisches Regelwerk sowie DIN 18321 für die Regelung technisch/vertraglicher Belange. Erfahrungen aus Streit‐ und Schadensfällen machen die Fortschreibung und Ergänzung der Regelwerke erforderlich. Der vorliegende Bericht zeigt anhand wesentlicher verfahrensbedingter Zusammenhänge in Beispielen, dass bei der Befolgung der vorliegenden und zukünftigen Regelwerke auch verfahrensspezifische handwerkliche Gesichtspunkte zu beachten sind.On the skilled application of jet grouting. Some 35 years ago jet grouting was introduced on a construction site in Germany for the first time. Ever since then it has become an approved method in special ground engineering and is now being offered and applied by many companies. As to its application DIN EN 12716 refers to the central technical guidelines whereas DIN 18321 deals with the technical and/or contractual issues. Experiences with events of damage and disputes show that the regulations need further updates and supplements. This report explains in examples which process‐specific craft aspects have to be observed when applying present and future rules.

Physiological noise has been shown to have a large impact on the quality of functional MRI data, especially in areas close to fluid-filled cavities and arteries, such as the brainstem. Commonly, physiological recordings during scanning are transformed with methods such as RETROICOR and used as nuisance regressors in general linear models to remove variance associated with cardiac and respiratory cycles from the data. In contrast, modern pre-processing pipelines such as fMRIPrep, have created easy access to streamlined data-driven noise reduction methods such as aCompCor and ICA-AROMA. In combination, these methods have shown efficacy in correcting for motion, scanner as well as physiological artifacts. Given the ease of usability, it has to be questioned, whether there is any added benefit to applying logistically demanding methods such as RETROICOR. To answer this question, we applied RETROICOR, ICA-AROMA and aCompCor to a resting-state data set and compared variance explained by the respective methods and improvements in temporal signal-to-noise ratio throughout different regions of interest in the brain. In line with previous literature, RETROICOR significantly explains variance throughout the brain with peaks around areas of strong cardiac pulsations. ICA-AROMA and aCompCor largely account for the same variance. Nonetheless, RETROICOR retains unique explanatory power in individual participants. Further analysis points towards a pattern of unreliability of ICA-AROMA and aCompCor to consistently remove physiological noise across recordings, which is compensated by RETROICOR. While some of this inconsistency could be attributed to misclassifications in the noise selection models of ICA-AROMA, most is likely the consequence of secondary factors such as fMRI sequence parameters (e.g. long TR) limiting the efficiency of aCompCor and ICA-AROMA. Thus, it is advisable to additionally apply RETROICOR, especially when assuming regionally high levels of physiological noise.