Revisiting the stress recovery hypothesis: Differential associations of cortisol stress reactivity and recovery after acute psychosocial stress with markers of long-term stress and health

Exposure to excessive and long-term stress may result in dysregulation of the stress system, including the acute stress response. In particular, failure to downregulate stress-related reactivity may lead to prolonged stress responses and the accumulation of allostatic load. However, the contribution of altered acute cortisol recovery to chronic stress and associated health impairments has often been neglected. Addressing this lack of research, we explored whether recovery from - more so than reactivity to - acute stress captures the basal stress load of an individual. Using Piecewise Growth Curve Models with Landmark Registration, we analyzed cortisol reactivity and recovery slopes of 130 healthy participants exposed to a standardized psychosocial laboratory stressor. Reactivity and recovery were predicted by measures indicative of long-term stress and its downstream effects, including self-report questionnaires, diurnal cortisol indices [cortisol awakening response (CAR); diurnal cortisol slope], markers of pro-inflammatory activity (interleukin-6; high-sensitive C-reactive protein), and hippocampal volume (HCV). Among these measures, only an increased CAR was specifically and consistently associated with relatively impaired recovery. Since the CAR represents the physiological enhancement needed to meet the anticipated demands of the forthcoming day, this finding may highlight the contribution of cognitive processes in determining both CAR and acute stress recovery. Furthermore, greater cortisol reactivity covaried with smaller HCV, showing that increased acute reactivity translates to health-relevant downstream effects. The lack of further associations between long-term and acute stress measures may arise from biases in self-reported chronic stress and the rigorously health-screened study sample. Overall, our findings suggest that while cortisol stress recovery might not supersede reactivity as an indicator of the long-term stress load or associated health effects, recovery and reactivity have differential utility in describing individuals' allostatic states

Deep learning and differential equations for modeling changes in individual‐level latent dynamics between observation periods

When modeling longitudinal biomedical data, often dimensionality reduction as well as dynamic modeling in the resulting latent representation is needed. This can be achieved by artificial neural networks for dimension reduction and differential equations for dynamic modeling of individual-level trajectories. However, such approaches so far assume that parameters of individual-level dynamics are constant throughout the observation period. Motivated by an application from psychological resilience research, we propose an extension where different sets of differential equation parameters are allowed for observation subperiods. Still, estimation for intra-individual subperiods is coupled for being able to fit the model also with a relatively small dataset. We subsequently derive prediction targets from individual dynamic models of resilience in the application. These serve as outcomes for predicting resilience from characteristics of individuals, measured at baseline and a follow-up time point, and selecting a small set of important predictors. Our approach is seen to successfully identify individual-level parameters of dynamic models that allow to stably select predictors, that is, resilience factors. Furthermore, we can identify those characteristics of individuals that are the most promising for updates at follow-up, which might inform future study design. This underlines the usefulness of our proposed deep dynamic modeling approach with changes in parameters between observation subperiods

Open and reproducible science practices in psychoneuroendocrinology: Opportunities to foster scientific progress

This perspective article was written by invitation of the editors in chief as a summary and extension of the symposium entitled Psychoneuroendocrine Research in the Era of the Replication Crisis which was held at the virtual meeting of the International Society of Psychoneuroendocrinology 2021. It highlights the opportunities presented by the application of open and reproducible scientific practices in psychoneuroendocrinology (PNE), an interdisciplinary field at the intersection of psychology, endocrinology, immunology, neurology, and psychiatry. It conveys an introduction to the topics preregistration, registered reports, quantifying the impact of equally-well justifiable analysis decisions, and open data and scripts, while emphasizing ‘selfish’ reasons to adopt such practices as individual researcher. Complementary to the call for adoption of open science practices, we highlight the need for methodological best practice guidelines in the field of PNE, which could further contribute to enhancing replicability of results. We propose concrete steps for future actions and provide links to additional resources for those interested in adopting open and reproducible science practices in future studies