Quantifying sleep architecture dynamics and individual differences using big data and Bayesian networks

The pattern of sleep stages across a night (sleep architecture) is influenced by biological, behavioral, and clinical variables. However, traditional measures of sleep architecture such as stage proportions, fail to capture sleep dynamics. Here we quantify the impact of individual differences on the dynamics of sleep architecture and determine which factors or set of factors best predict the next sleep stage from current stage information. We investigated the influence of age, sex, body mass index, time of day, and sleep time on static (e.g. minutes in stage, sleep efficiency) and dynamic measures of sleep architecture (e.g. transition probabilities and stage duration distributions) using a large dataset of 3202 nights from a non-clinical population. Multi-level regressions show that sex effects duration of all Non-Rapid Eye Movement (NREM) stages, and age has a curvilinear relationship for Wake After Sleep Onset (WASO) and slow wave sleep (SWS) minutes. Bayesian network modeling reveals sleep architecture depends on time of day, total sleep time, age and sex, but not BMI. Older adults, and particularly males, have shorter bouts (more fragmentation) of Stage 2, SWS, and they transition less frequently to these stages. Additionally, we showed that the next sleep stage and its duration can be optimally predicted by the prior 2 stages and age. Our results demonstrate the potential benefit of big data and Bayesian network approaches in quantifying static and dynamic architecture of normal sleep

Network Physiology reveals relations between network topology and physiological function

The human organism is an integrated network where complex physiologic systems, each with its own regulatory mechanisms, continuously interact, and where failure of one system can trigger a breakdown of the entire network. Identifying and quantifying dynamical networks of diverse systems with different types of interactions is a challenge. Here, we develop a framework to probe interactions among diverse systems, and we identify a physiologic network. We find that each physiologic state is characterized by a specific network structure, demonstrating a robust interplay between network topology and function. Across physiologic states the network undergoes topological transitions associated with fast reorganization of physiologic interactions on time scales of a few minutes, indicating high network flexibility in response to perturbations. The proposed system-wide integrative approach may facilitate the development of a new field, Network Physiology.Comment: 12 pages, 9 figure

Time-Restricted Feeding Improves Circadian Dysfunction as well as Motor Symptoms in the Q175 Mouse Model of Huntington's Disease.

Huntington's disease (HD) patients suffer from a progressive neurodegeneration that results in cognitive, psychiatric, cardiovascular, and motor dysfunction. Disturbances in sleep/wake cycles are common among HD patients with reports of delayed sleep onset, frequent bedtime awakenings, and fatigue during the day. The heterozygous Q175 mouse model of HD has been shown to phenocopy many HD core symptoms including circadian dysfunctions. Because circadian dysfunction manifests early in the disease in both patients and mouse models, we sought to determine if early intervention that improve circadian rhythmicity can benefit HD and delay disease progression. We determined the effects of time-restricted feeding (TRF) on the Q175 mouse model. At six months of age, the animals were divided into two groups: ad libitum (ad lib) and TRF. The TRF-treated Q175 mice were exposed to a 6-h feeding/18-h fasting regimen that was designed to be aligned with the middle of the time when mice are normally active. After three months of treatment (when mice reached the early disease stage), the TRF-treated Q175 mice showed improvements in their locomotor activity rhythm and sleep awakening time. Furthermore, we found improved heart rate variability (HRV), suggesting that their autonomic nervous system dysfunction was improved. Importantly, treated Q175 mice exhibited improved motor performance compared to untreated Q175 controls, and the motor improvements were correlated with improved circadian output. Finally, we found that the expression of several HD-relevant markers was restored to WT levels in the striatum of the treated mice using NanoString gene expression assays

Light as a chronobiologic countermeasure for long-duration space operations

Long-duration space missions require adaptation to work-rest schedules which are substantially shifted with respect to earth. Astronauts are expected to work in two-shift operations and the environmental synchronizers (zeitgebers) in a spacecraft differ significantly from those on earth. A study on circadian rhythms, sleep, and performance was conducted by exposing four subjects to 6 deg head-down tilt bedrest (to simulate the effects of the weightless condition) and imposing a 12-h shift (6 h delay per day for two days). Bright light was tested in a cross-over design as a countermeasure for achieving faster resynchronization and regaining stable conditions for sleep and circadian rhythmicity. Data collection included objective sleep recording, temperature, heart rate, and excretion of hormones and electrolytes as well as performance and responses to questionnaires. Even without a shift in the sleep-wake cycle, the sleep quantity, circadian amplitudes and 24 h means decreased in many functions under bedrest conditions. During the shift days, sleepiness and fatigue increased, and alertness decreased. However, sleep quantity was regained, and resynchronization was completed within seven days after the shift for almost all functions, irrespective of whether light was administered during day-time or night-time hours. The time of day of light exposure surprisingly appeared not to have a discriminatory effect on the resynchronization speed under shift and bedrest conditions. The results indicate that simulated weightlessness alters circadian rhythms and sleep, and that schedule changes induce additional physiological disruption with decreased subjective alertness and increased fatigue. Because of their operational implications, these phenomena deserve additional investigation

Autonomic Nervous System and Rem Behavior Sleep Disorder: a new tool to identify Idiopathic or Parkinsonians patients through Heart Rate Variability Polysomnography Analysis

Objective: RBD is a sleep disorder known to be associated in a very high percentage of cases with alfa-synucleopathies. During rapid eye movement (REM) sleep, the cardiovascular system is unstable and greatly influenced by sympathetic activity. Heart Rate Variability (HRV) indirectly tests functions and activities of the ANS during sleep. We evaluate whether HRV and Hypnogram Indices are polysomnographic valid biomarkers to distinguish subjects with idiopathic RBD from those with Parkinson's Disease. METHODS: Our study examines HRV linear and non-linear indices of 37 patients aged 53 years and older (median 72.7; mean 72.3 ± 7.4; range 53-84), 7 women (18.9%) and 30 men (81.1%). 22 pts were idiopathic REM sleep behavior disorder (59.5%; age: median 72.5; mean 74.5 ± 5.2; range 68 83), of which 3 women (13.6%) and 19 men (86.4%); 15 pts had REM Sleep Behavior Disorder secondary to Parkinson's disease (40.5%; a ge: median 73; mean 69.5 ± 9 ; range 53 84), including 4 women (26.7%) and 11 men (73.3%). A parallel Analysis was made on Hypnogram Parameters. RESULTS: The REM sleep phase allowed to record the greatest number of significant differences in HRV Index between the two groups of patients. Among the Frequency HRV Linear Indices, VLF signal band recorded the highest number of significant results suggesting that the sympathetic component may be the one most compromised in the autonomic neurodegeneration process of RBD. HRV Complexity Non-Linear Indices (LZC and KC) have the highest number of statistically significant results so that could be the right parameter to use to distinguish our two RBD populations. Hypnogram Indices Analysis showed no significant value for not even a parameter. CONCLUSIONS: HRV can be a valid biomarker to distinguish the two populations of patients affected by RBD, both idiopathic and affected by Parkinson's disease. It could represent a new and easy tool to identify, among the REM Behavior Sleep Disorder, patients affected or not by Parkinson’s desease or could be even useful when an early diagnosis is needed or it is necessary monitoring a probable conversion from idiopathic form to PD or evaluate the effectiveness of RBD therapies

Heart rate variability in normal and pathological sleep

Sleep is a physiological process involving different biological systems, from molecular to organ level; its integrity is essential for maintaining health and homeostasis in human beings. Although in the past sleep has been considered a state of quiet, experimental and clinical evidences suggest a noteworthy activation of different biological systems during sleep. A key role is played by the autonomic nervous system (ANS), whose modulation regulates cardiovascular functions during sleep onset and different sleep stages. Therefore, an interest on the evaluation of autonomic cardiovascular control in health and disease is growing by means of linear and non-linear heart rate variability (HRV) analyses. the application of classical tools for ANS analysis, such as HRV during physiological sleep, showed that the rapid eye movement (REM) stage is characterized by a likely sympathetic predominance associated with a vagal withdrawal, while the opposite trend is observed during non-REM sleep. More recently, the use of non-linear tools, such as entropy-derived indices, have provided new insight on the cardiac autonomic regulation, revealing for instance changes in the cardiovascular complexity during REM sleep, supporting the hypothesis of a reduced capability of the cardiovascular system to deal with stress challenges. Interestingly, different HRV tools have been applied to characterize autonomic cardiac control in different pathological conditions, from neurological sleep disorders to sleep disordered breathing (SDB). in summary, linear and non-linear analysis of HRV are reliable approaches to assess changes of autonomic cardiac modulation during sleep both in health and diseases. the use of these tools could provide important information of clinical and prognostic relevance.European Regional Development Fund-Project FNUSA-ICRCUniv Milan, L Sacco Hosp, Dept Biomed & Clin Sci L Sacco, Div Med & Pathophysiol, I-20157 Milan, ItalyOsped Niguarda Ca Granda, Ctr Epilepsy Surg C Munari, Ctr Sleep Med, Dept Neurosci, Milan, ItalyUniversidade Federal de São Paulo, Inst Sci & Technol, Dept Sci & Technol, São Paulo, BrazilUniv Fdn Cardiol, Inst Cardiol Rio Grande do Sul, Porto Alegre, RS, BrazilFdn S Maugeri, Sleep Med Unit, Veruno, ItalySt Annes Univ Hosp, Int Clin Res Ctr, Brno, Czech RepublicUniversidade Federal de São Paulo, Inst Sci & Technol, Dept Sci & Technol, São Paulo, BrazilEuropean Regional Development Fund-Project FNUSA-ICRC: CZ.1.05/1.1.00/02.0123Web of Scienc