Mediating roles of sleep quality and resilience in the relationships between chronotypes and mental health symptoms.

Sleep and mental health are intrinsically intertwined, but not every individual with problems sleeping develops a mental health disorder. This study examined the association among chronotypes, resilience, sleep quality and mental health symptoms amongst otherwise healthy individuals. Two hundred adults (Mage = 27.75 ± 5.11, 68% female) with no previous diagnosis of mental illness were recruited and filled in a set of questionnaires measuring chronotypes, sleep quality, depression and anxiety symptoms. The findings from the path analysis showed that the morning type had a statistically significant direct effect on a range of sleep quality indices. These included better subjective sleep quality, shortened sleep latency, and fewer daytime dysfunctions, as well as a higher level of resilience. However, it did not significantly affect depression and anxiety symptoms. In addition, the morning type had statistically significant indirect effects on a higher level of resilience and fewer depression and anxiety symptoms through the mediating effect of sleep quality indices. Findings from this study support that morning type is associated with better resilience and psychological health, which is mediated through better sleep quality.

Conceptualizing psychological resilience through resting-state functional MRI in a mentally healthy population: a systematic review.

Conceptualizations and operational definitions of psychological resilience vary across resilience neuroimaging studies. Data on the neural features of resilience among healthy individuals has been scarce. Furthermore, findings from resting-state functional magnetic resonance imaging (fMRI) studies were inconsistent across studies. This systematic review summarized resting-state fMRI findings in different modalities from various operationally defined resilience in a mentally healthy population. The PubMed and MEDLINE databases were searched. Articles that focused on resting-state fMRI in relation to resilience, and published before 2022, were targeted. Orbitofrontal cortex, anterior cingulate cortex, insula and amygdala, were reported the most from the 19 included studies. Regions in emotional network was reported the most from the included studies. The involvement of regions like amygdala and orbitofrontal cortex indicated the relationships between emotional processing and resilience. No common brain regions or neural pathways were identified across studies. The emotional network appears to be studied the most in association with resilience. Matching fMRI modalities and operational definitions of resilience across studies are essential for meta-analysis.

Integrative psycho-biophysiological markers in predicting psychological resilience.

The hypothalamic-pituitary-adrenal (HPA) and parasympathetic nervous systems have been reported to play important roles in emotion regulation and stress coping. Yet, their direct relationship with psychological resilience remains unclear. These biophysiological features should be considered together with the traditional psychometric properties in studying resilience more comprehensively. The current study aimed to examine the role of these systems during a laboratory stress task and to determine the prediction power of resilience by combining psychological and biophysiological features. One hundred and seven (52 females) university students without psychiatric disorders underwent the Trier Social Stress Task (TSST). Psychometric properties of resilience were measured at rest; vagal heart rate variability (HRV), salivary cortisol, and dehydroepiandrosterone (DHEA) levels were captured at baseline, during, and after TSST. Multivariate linear regression as well as support vector regression machine-learning analyses were performed to investigate significant predictors and the prediction power of resilience. Results showed that positive and negative affects, HRV during the anticipatory phase of stress, and the ratio of cortisol/DHEA at the first recovery time point were significant predictors of resilience. The addition of biophysiological features increased the prediction power of resilience by 1.2-fold compared to psychological features alone. Results from machine learning analyses further demonstrated that the increased prediction power of resilience by adding the ratio of cortisol/DHEA was significant in "cortisol responders"; whereas a trend level was observed in "cortisol non-responders". Our findings extend the knowledge from the literature that high vagal activity during the anticipating phase of stress and the ability to restore the balance between cortisol and DHEA after a stress event could be an important feature in predicting resilience. Our findings also further support the need of combining psychological and biophysiological features in studying/predicting resilience.