Rapid eye movement sleep parasympathetic activity predicts wake hyperarousal symptoms following a traumatic event.

Heart rate variability (HRV) can be used to assess changes in output of the parasympathetic nervous system (PNS). Considering that patients with post-traumatic stress disorder (PTSD) often experience disturbances in sleep, arousal, and autonomic functioning, we sought to explore the association of PNS activity during sleep with hyperarousal symptoms of PTSD. Because a broad literature supports the importance of rapid eye movement (REM) sleep in PTSD, REM-sleep features were specifically examined as predictors of PTSD symptom severity. A total of 90 participants, primarily civilian and female, aged 18-40 years who had experienced a traumatic event in the last 2 years, underwent an ambulatory polysomnography (PSG) acclimation night followed by a second PSG night from which sleep physiological measures were computed. Participants underwent an ambulatory polysomnography (PSG) acclimation night followed by a second PSG night from which sleep physiological measures were computed. PTSD severity was measured using the PTSD Checklist for the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (PCL-5). Dependent variables were total PCL-5 score as well as its hyperarousal symptom subscore. Predictors included REM latency, percentage, density, segment length, and an index of parasympathetic tone (root mean square of the successive differences in the R-R interval or RMSSD). Hierarchical regression models were conducted to analyse the association of REM features with PCL-5 total and hyperarousal subscales. Using hierarchical regression, REM-sleep RMSSD accounted for a significant proportion of the variation in outcome variables, even when accounting for other REM-sleep features. The present findings support hypothesised relationships between PTSD symptomatology and REM-sleep physiology and, specifically, that lowered parasympathetic tone in REM may be an important associate of the hyperarousal symptom cluster in PTSD.

Sleep Power Spectral Density and Spindles in PTSD and Their Relationship to Symptom Severity.

Sleep disturbances are common in post-traumatic stress disorder (PTSD), although which sleep microarchitectural characteristics reliably classify those with and without PTSD remains equivocal. Here, we investigated sleep microarchitectural differences (i.e., spectral power, spindle activity) in trauma-exposed individuals that met (n = 45) or did not meet (n = 52) criteria for PTSD and how these differences relate to post-traumatic and related psychopathological symptoms. Using ecologically-relevant home sleep polysomnography recordings, we show that individuals with PTSD exhibit decreased beta spectral power during NREM sleep and increased fast sleep spindle peak frequencies. Contrary to prior reports, spectral power in the beta frequency range (20.31-29.88 Hz) was associated with reduced PTSD symptoms, reduced depression, anxiety and stress and greater subjective ability to regulate emotions. Increased fast frequency spindle activity was not associated with individual differences in psychopathology. Our findings may suggest an adaptive role for beta power during sleep in individuals exposed to a trauma, potentially conferring resilience. Further, we add to a growing body of evidence that spindle activity may be an important biomarker for studying PTSD pathophysiology.

Fear extinction memory is negatively associated with REM sleep in insomnia disorder.

STUDY OBJECTIVES: Formation and maintenance of fear-extinction memories are disrupted in post-traumatic stress disorder (PTSD) and anxiety disorders. Sleep contributes to emotional memory consolidation and emotion regulation. Insomnia disorder (ID) is characterized by persistent sleep disturbance as well as rapid eye movement (REM) sleep abnormalities and often precedes or develops in parallel with PTSD and anxiety disorders. Here, we explore the impact of chronic poor sleep and sleep immediately following fear conditioning and extinction learning on preservation of extinction memories. METHODS: Twenty-four ID age- and sex-matched to 24 healthy, good sleeper controls (GS) completed up to 2 weeks of habitual sleep monitoring with daily sleep-wake diaries and actigraphy, and then participated in a two-session fear conditioning, extinction learning and extinction recall procedure. Fear Conditioning and Extinction Learning occurred during session 1, followed by Extinction Recall approximately 24 hours later. Skin-conductance responses (SCR) and shock expectancies were recorded throughout all experimental phases to evaluate associative learning and memory. Overnight sleep between sessions 1 and 2 was recorded using ambulatory polysomnography. RESULTS: ID showed greater physiological reactivity during Fear Conditioning. REM sleep physiology was associated with poorer extinction memory in ID but better extinction memory in GS. CONCLUSION: REM sleep physiology may differentially support emotional memory retention and expression in ID and GS. In the former, REM may enhance retention of fear memories, while in the later, REM may enhance the expression of extinction memories.

Effects of post-exposure naps on exposure therapy for social anxiety.

Exposure therapy for social anxiety disorder (SAD) utilizes fear extinction, a memory process enhanced by sleep. We investigated whether naps following exposure sessions might improve symptoms and biomarkers in response to social stress in adults undergoing 5-week exposure-based group SAD therapy. Thirty-two participants aged 18-39 (18 females) with SAD were randomized. Before and after treatment, participants completed the Liebowitz Social Anxiety Scale (LSAS) and underwent a Trier Social Stress Test with psychophysiological monitoring (mpTSST) that included skin conductance (SCL), electromyographic (EMG) and electrocardiographic recording, and an auditory startle procedure while anticipating the social stressor. At sessions 3 and 4, exposure was followed by either a 120-min polysomnographically monitored sleep opportunity (Nap, N = 17) or wakefulness (Wake, N = 15). Primary hypotheses about SAD symptom change (LSAS) and EMG blink-startle response failed to differ with naps, despite significant symptom improvement (LSAS) with therapy. Some secondary biomarkers, however, provided preliminary support for enhanced extinction learning with naps, with trend-level Time (pre-, post-treatment) × Arm interactions and significant reduction from pre- to post treatment in the Nap arm alone for mpTSST SCL and salivary cortisol rise. Because of the small sample size and limited sleep duration, additional well-powered studies with more robust sleep interventions are indicated.

Clinical predictors of central sleep apnea evoked by positive airway pressure titration.

PURPOSE: Treatment-emergent central sleep apnea (TECSA), also called complex apnea, occurs in 5%-15% of sleep apnea patients during positive airway pressure (PAP) therapy, but the clinical predictors are not well understood. The goal of this study was to explore possible predictors in a clinical sleep laboratory cohort, which may highlight those at risk during clinical management. METHODS: We retrospectively analyzed 728 patients who underwent PAP titration (n=422 split-night; n=306 two-night). Demographics and self-reported medical comorbidities, medications, and behaviors as well as standard physiological parameters from the polysomnography (PSG) data were analyzed. We used regression analysis to assess predictors of binary presence or absence of central apnea index (CAI) ≥5 during split-night PSG (SN-PSG) versus full-night PSG (FN-PSG) titrations. RESULTS: CAI ≥5 was present in 24.2% of SN-PSG and 11.4% of FN-PSG patients during titration. Male sex, maximum continuous positive airway pressure, and use of bilevel positive airway pressure were predictors of TECSA, and rapid eye movement dominance was a negative predictor, for both SN-PSG and FN-PSG patients. Self-reported narcotics were a positive predictor of TECSA, and the time spent in stage N2 sleep was a negative predictor only for SN-PSG patients. Self-reported history of stroke and the CAI during the diagnostic recording predicted TECSA only for FN-PSG patients. CONCLUSION: Clinical predictors of treatment-evoked central apnea spanned demographic, medical history, sleep physiology, and titration factors. Improved predictive models may be increasingly important as diagnostic and therapeutic modalities move away from the laboratory setting, even as PSG remains the gold standard for characterizing primary central apnea and TECSA.

Tracking the sleep onset process: an empirical model of behavioral and physiological dynamics.

The sleep onset process (SOP) is a dynamic process correlated with a multitude of behavioral and physiological markers. A principled analysis of the SOP can serve as a foundation for answering questions of fundamental importance in basic neuroscience and sleep medicine. Unfortunately, current methods for analyzing the SOP fail to account for the overwhelming evidence that the wake/sleep transition is governed by continuous, dynamic physiological processes. Instead, current practices coarsely discretize sleep both in terms of state, where it is viewed as a binary (wake or sleep) process, and in time, where it is viewed as a single time point derived from subjectively scored stages in 30-second epochs, effectively eliminating SOP dynamics from the analysis. These methods also fail to integrate information from both behavioral and physiological data. It is thus imperative to resolve the mismatch between the physiological evidence and analysis methodologies. In this paper, we develop a statistically and physiologically principled dynamic framework and empirical SOP model, combining simultaneously-recorded physiological measurements with behavioral data from a novel breathing task requiring no arousing external sensory stimuli. We fit the model using data from healthy subjects, and estimate the instantaneous probability that a subject is awake during the SOP. The model successfully tracked physiological and behavioral dynamics for individual nights, and significantly outperformed the instantaneous transition models implicit in clinical definitions of sleep onset. Our framework also provides a principled means for cross-subject data alignment as a function of wake probability, allowing us to characterize and compare SOP dynamics across different populations. This analysis enabled us to quantitatively compare the EEG of subjects showing reduced alpha power with the remaining subjects at identical response probabilities. Thus, by incorporating both physiological and behavioral dynamics into our model framework, the dynamics of our analyses can finally match those observed during the SOP.