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.

Posttraumatic stress disorder (PTSD) and depression frequently co-occur following a traumatic event. Differences in the processing of autobiographical memory have been observed in both disorders in the form of overgeneralised memories and negative intrusive memories. The current study examined how symptoms of PTSD and depression influence the phenomenological characteristics of trauma memories. Undergraduate students who had experienced a traumatic event (n = 696) completed questionnaires online including measures of PTSD and depressive symptom severity. They rated their trauma memory on several phenomenological characteristics using the Memory Experiences Questionnaire [Sutin, A. R., & Robins, R. W. (2007). Phenomenology of autobiographical memories: The memory experiences questionnaire. Memory.]. Moderated multiple regression was used to examine how PTSD and depressive symptom severity related to each phenomenological characteristic. Symptoms of PTSD and depression were related separately and uniquely to the phenomenological characteristics of the trauma memory. PTSD severity predicted trauma memories that were more negative, contained higher sensory detail, and were more vivid. In contrast, depressive symptom severity predicted trauma memories that were less accessible and less coherent. These findings suggest that depressive and PTSD symptomatology affect traumatic memory differently and support a distinction between these two disorders.

Study Objectives: The effects of serotonergic agents on respiration neuromodulation may vary according to differences in the serotonin system, such as those linked to depression. This study investigated how sleep-related respiratory disturbances relate to depression and the use of medications commonly prescribed for depression. Methods: Retrospective polysomnography was collated for all 363 individuals who met selection criteria out of 2,528 consecutive individuals referred to a specialized sleep clinic (Ottawa, Canada) between 2006 and 2016. The apnea-hypopnea index (AHI), oxygen saturation nadir, and oxygen desaturation index during REM and NREM sleep were analyzed using mixed analyses of covariance comparing 3 main groups: (1) medicated individuals with depressive disorders (antidepressant group; subdivided into the selective serotonin reuptake inhibitor and norepinephrine-dopamine reuptake inhibitor subgroups), (2) non-medicated individuals with depressive disorders (non-medicated group), and (3) mentally healthy control patients (control group). Results: Individuals with depressive disorders (on antidepressants or not) had significantly higher AHIs compared to control patients (both P ≤ .007). The antidepressant group had a lower NREM sleep oxygen saturation nadir and a higher NREM sleep oxygen desaturation index than the control and non-medicated groups (all P ≤.009). Within individuals with depressive disorders, independent of depression severity, the selective serotonin reuptake inhibitor group had a lower oxygen saturation nadir and a higher oxygen desaturation index during NREM sleep than the norepinephrine-dopamine reuptake inhibitor (both P ≤ .045) and non-medicated groups (both P < .001) and a higher NREM sleep AHI than the non-medicated group (P = .014). Conclusions: These findings suggest that the use of selective serotonin reuptake inhibitors may be associated with impaired breathing and worse nocturnal oxygen saturation in individuals with depressive disorders and sleep complaints, but this needs to be confirmed by prospective studies.

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