Aversive events can evoke strong emotions that trigger cerebral neuroactivity to facilitate behavioral and cognitive shifts to secure physiological stability. However, upon intense and/or chronic exposure to such events, the neural coping processes can be maladaptive and disrupt mental well-being. This maladaptation denotes a pivotal point when psychological stress occurs, which can trigger subconscious, “automatic” neuroreactivity as a defence mechanism to protect the individual from potential danger including overwhelming unpleasant feelings and disturbing or threatening thoughts.The outcomes of maladaptive neural activity are cognitive dysfunctions such as altered memory, decision making, and behavior that impose a risk for mental disorders. Although the neurocognitive phenomena associated with psychological stress are well documented, the complex neural activity and pathways related to stressor detection and stress coping have not been outlined in detail. Accordingly, we define acute and chronic stress-induced pathways, phases, and stages in relation to novel/unpredicted, uncontrollable, and ambiguous stressors. We offer a comprehensive model of the stress-induced alterations associated with multifaceted pathophysiology related to cognitive appraisal and executive functioning in stress.

Palamarchuk IS, Baker J, Kimpinski K. The utility of Valsalva maneuver in the diagnoses of orthostatic disorders.

Background Abnormalities in heart rate during sleep linked to impaired neuro-cardiac modulation may provide new information about physiological sleep signatures of depression. This study assessed the validity of an algorithm using patterns of heart rate changes during sleep to discriminate between individuals with depression and healthy controls. Methods A heart rate profiling algorithm was modeled using machine-learning based on 1203 polysomnograms from individuals with depression referred to a sleep clinic for the assessment of sleep abnormalities, including insomnia, excessive daytime fatigue, and sleep-related breathing disturbances ( n = 664) and mentally healthy controls ( n = 529). The final algorithm was tested on a distinct sample ( n = 174) to categorize each individual as depressed or not depressed. The resulting categorizations were compared to medical record diagnoses. Results The algorithm had an overall classification accuracy of 79.9% [sensitivity: 82.8, 95% CI (0.73–0.89), specificity: 77.0, 95% CI (0.67–0.85)]. The algorithm remained highly sensitive across subgroups stratified by age, sex, depression severity, comorbid psychiatric illness, cardiovascular disease, and smoking status. Conclusions Sleep-derived heart rate patterns could act as an objective biomarker of depression, at least when it co-occurs with sleep disturbances, and may serve as a complimentary objective diagnostic tool. These findings highlight the extent to which some autonomic functions are impaired in individuals with depression, which warrants further investigation about potential underlying mechanisms. Electronic supplementary material The online version of this article (10.1186/s12888-019-2152-1) contains supplementary material, which is available to authorized users.