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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.

Background Stress-related disorders are a growing public health concern. While stress is a natural and adaptive process, chronic exposure to stressors can lead to dysregulation and take a cumulative toll on physical and mental well-being. One approach to coping with stress and building resilience is through Mindfulness-Based Stress Reduction (MBSR). By understanding the neural mechanisms of MBSR, we can gain insight into how it reduces stress and what drives individual differences in treatment outcomes. This study aims to establish the clinical effects of MBSR on stress regulation in a population that is susceptible to develop stress-related disorders (i.e., university students with mild to high self-reported stress), to assess the role of large-scale brain networks in stress regulation changes induced by MBSR, and to identify who may benefit most from MBSR. Methods This study is a longitudinal two-arm randomised, wait-list controlled trial to investigate the effects of MBSR on a preselected, Dutch university student population with elevated stress levels. Clinical symptoms are measured at baseline, post-treatment, and three months after training. Our primary clinical symptom is perceived stress, with additional measures of depressive and anxiety symptoms, alcohol use, stress resilience, positive mental health, and stress reactivity in daily life. We investigate the effects of MBSR on stress regulation in terms of behaviour, self-report measures, physiology, and brain activity. Repetitive negative thinking, cognitive reactivity, emotional allowance, mindfulness skills, and self-compassion will be tested as potential mediating factors for the clinical effects of MBSR. Childhood trauma, personality traits and baseline brain activity patterns will be tested as potential moderators of the clinical outcomes. Discussion This study aims to provide valuable insights into the effectiveness of MBSR in reducing stress-related symptoms in a susceptible student population and crucially, to investigate its effects on stress regulation, and to identify who may benefit most from the intervention. Trial registration Registered on September 15, 2022, at clinicaltrials.gov, NCT05541263.

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.