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Representations of temporal sleep dynamics: review and synthesis of the literature. Sleep Med Rev 2022; 63:101611. [DOI: 10.1016/j.smrv.2022.101611] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 01/25/2022] [Accepted: 02/07/2022] [Indexed: 12/13/2022]
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Choi JW, Jeong WS, Kang MK, Lee JY, Chung YS. Counterclockwise Rotational Orthognathic Surgery for the Obstructive Sleep Apnea in Class II Dentofacial Deformity: Polysomnography and 3D Computed Tomographic Analysis. Ann Plast Surg 2021; 86:640-646. [PMID: 33346553 DOI: 10.1097/sap.0000000000002580] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
ABSTRACT Traditionally, maxillomandibular advancement is an orthognathic surgical procedure that has been used to manage obstructive sleep apnea in patients not able or willing to maintain adherence to continuous positive airway pressure therapy or for patient who are not able to adhere to treatment. However, maxillomandibular advancement often leads to unsatisfactory cosmetic results.This prospective study investigated functional and esthetic outcomes using polysomnography and 3-dimensional computed tomography, after counterclockwise rotational orthognathic surgery. We enrolled 17 patients with obstructive sleep apnea, who underwent orthognathic surgery at single institution between March 2013 and December 2018.After 12 months, the patients' mean self-rated score for facial appearance, using a 10-step visual analog scale, was 7.36. The preoperative apnea-hypopnea index and respiratory disturbance index were 34.70 and 37.45, respectively; postoperative indices were 11.60 and to 12.69, respectively (P = 0.003, 0.003). The mean posterior pharyngeal airway space increased from 5357.88 mm3 preoperatively to 8481.94 mm3 after 6 postoperative months.Counterclockwise rotational orthognathic surgery for the correction of obstructive sleep apnea turned out to be the ideal solution not only in the correction of the sleep apnea, but also in the facial esthetics.
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Affiliation(s)
- Jong Woo Choi
- From the Department of Plastic and Reconstructive Surgery, University of Ulsan College of Medicine, Seoul Asan Medical Center
| | - Woo Shik Jeong
- From the Department of Plastic and Reconstructive Surgery, University of Ulsan College of Medicine, Seoul Asan Medical Center
| | - Min Kyu Kang
- From the Department of Plastic and Reconstructive Surgery, University of Ulsan College of Medicine, Seoul Asan Medical Center
| | | | - Yoo Sam Chung
- Department of Otolaryngology, University of Ulsan College of Medicine, Seoul Asan Medical Center, Seoul, South Korea
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Hermans LWA, Regis M, Fonseca P, Hoondert B, Leufkens TRM, Overeem S, van Gilst MM. Sleep-Wake Survival Dynamics in People with Insomnia. Nat Sci Sleep 2021; 13:349-360. [PMID: 33737849 PMCID: PMC7966352 DOI: 10.2147/nss.s295699] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 02/04/2021] [Indexed: 01/28/2023] Open
Abstract
INTRODUCTION Assessing objective measures of sleep fragmentation could yield important features reflecting impaired sleep quality in people with insomnia. Survival analysis allows the specific examination of the stability of NREM sleep, REM sleep and wake. The objective of this study was to assess the differences between survival dynamics of NREM sleep, REM sleep and wake between people with insomnia and healthy controls. METHODS We analyzed retrospective polysomnography recordings from 86 people with insomnia and 94 healthy controls. For each participant, survival dynamics of REM sleep, NREM sleep and wake were represented using Weibull distributions. We used lasso penalized parameter selection in combination with linear regression to analyze the difference between participant groups with respect to the Weibull scale and shape parameters, while correcting for age, sex, total sleep time and relevant interaction effects. RESULTS Significant effects of group were found for the NREM scale parameter, and for the wake scale and shape parameters. Results indicated that people with insomnia had less stable NREM sleep and more stable wake after sleep onset compared to healthy controls. Additionally, the altered distribution of wake segment lengths indicated an increased difficulty to fall asleep after longer awakenings in the insomnia group. However, these differences were mainly observed in younger participants. Significant effects of group for the survival parameters of REM sleep were not found. CONCLUSION As illustrated by our results, survival analysis can be very useful for disentangling different types of sleep fragmentation in people with insomnia. For instance, the current findings suggest that people with insomnia have an increased fragmentation of NREM sleep, but not necessarily of REM sleep. Additional research into the underlying mechanisms of NREM sleep fragmentation could possibly lead to a better understanding of impaired sleep quality in people with insomnia, and consequently to improved treatment.
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Affiliation(s)
- Lieke W A Hermans
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Marta Regis
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Pedro Fonseca
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.,Philips Research, Eindhoven, the Netherlands
| | | | | | - Sebastiaan Overeem
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.,Sleep Medicine Center Kempenhaeghe, Heeze, the Netherlands
| | - Merel M van Gilst
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.,Sleep Medicine Center Kempenhaeghe, Heeze, the Netherlands
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A Focal Inactivation and Computational Study of Ventrolateral Periaqueductal Gray and Deep Mesencephalic Reticular Nucleus Involvement in Sleep State Switching and Bistability. eNeuro 2020; 7:ENEURO.0451-19.2020. [PMID: 33055199 PMCID: PMC7768273 DOI: 10.1523/eneuro.0451-19.2020] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 10/02/2020] [Accepted: 10/06/2020] [Indexed: 11/21/2022] Open
Abstract
Neurons of the ventrolateral periaqueductal gray (vlPAG) and adjacent deep mesencephalic reticular nucleus (DpMe) are implicated in the control of sleep-wake state and are hypothesized components of a flip-flop circuit that maintains sleep bistability by preventing the overexpression of non-rapid eye movement (NREM)/REM sleep intermediary states (NRt). To determine the contribution of vlPAG/DpMe neurons in maintaining sleep bistability we combined computer simulations of flip-flop circuitry with focal inactivation of vlPAG/DpMe neurons by microdialysis delivery of the GABAA receptor agonist muscimol in freely behaving male rats (n = 25) instrumented for electroencephalographic and electromyographic recording. REM sleep was enhanced by muscimol at the vlPAG/DpMe, consistent with previous studies; however, our analyses of NRt dynamics in vivo and those produced by flop-flop circuit simulations show that current thinking is too narrowly focused on the contribution of REM sleep-inactive populations toward vlPAG/DpMe involvement in REM sleep control. We found that much of the muscimol-mediated increase in REM sleep was more appropriately classified as NRt. This loss of sleep bistability was accompanied by fragmentation of REM sleep, as evidenced by an increased number of short REM sleep bouts. REM sleep fragmentation stemmed from an increased number and duration of NRt bouts originating in REM sleep. By contrast, NREM sleep bouts were not likewise fragmented by vlPAG/DpMe inactivation. In flip-flop circuit simulations, these changes could not be replicated through inhibition of the REM sleep-inactive population alone. Instead, combined suppression of REM sleep active and inactive vlPAG/DpMe subpopulations was required to replicate the changes in NRt dynamics.
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5
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Covert sleep-related biological processes are revealed by probabilistic analysis in Drosophila. Proc Natl Acad Sci U S A 2020; 117:10024-10034. [PMID: 32303656 PMCID: PMC7211995 DOI: 10.1073/pnas.1917573117] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Reduced sleep duration and disrupted sleep quality are correlated with adverse mental and physical health outcomes. Better tools for measuring the internal drives for sleep and wake in model organisms would facilitate understanding the role of sleep quality in health. We defined two conditional probabilities, P(Wake) and P(Doze), that can be calculated from recordings of Drosophila activity without disturbing the animal. We demonstrated that P(Wake) is a measure of sleep depth and that P(Doze) is a measure of sleep pressure. In parallel, we developed an automatic classifier for state-based analysis of Drosophila behavior. These analysis tools will improve our understanding of the pharmacology and neuronal regulation of behavioral drives in the Drosophila brain. Sleep pressure and sleep depth are key regulators of wake and sleep. Current methods of measuring these parameters in Drosophila melanogaster have low temporal resolution and/or require disrupting sleep. Here we report analysis tools for high-resolution, noninvasive measurement of sleep pressure and depth from movement data. Probability of initiating activity, P(Wake), measures sleep depth while probability of ceasing activity, P(Doze), measures sleep pressure. In vivo and computational analyses show that P(Wake) and P(Doze) are largely independent and control the amount of total sleep. We also develop a Hidden Markov Model that allows visualization of distinct sleep/wake substates. These hidden states have a predictable relationship with P(Doze) and P(Wake), suggesting that the methods capture the same behaviors. Importantly, we demonstrate that both the Doze/Wake probabilities and the sleep/wake substates are tied to specific biological processes. These metrics provide greater mechanistic insight into behavior than measuring the amount of sleep alone.
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Abstract
Over the past decade, basic sleep research investigating the circuitry controlling sleep and wakefulness has been boosted by pharmacosynthetic approaches, including chemogenetic techniques using designed receptors exclusively activated by designer drugs (DREADD). DREADD offers a series of tools that selectively control neuronal activity as a way to probe causal relationship between neuronal sub-populations and the regulation of the sleep-wake cycle. Following the path opened by optogenetics, DREADD tools applied to discrete neuronal sub-populations in numerous brain areas quickly made their contribution to the discovery and the expansion of our understanding of critical brain structures involved in a wide variety of behaviors and in the control of vigilance state architecture.
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Wächter M, Kantelhardt JW, Bonsignore MR, Bouloukaki I, Escourrou P, Fietze I, Grote L, Korzybski D, Lombardi C, Marrone O, Paranicova I, Pataka A, Ryan S, Schiza SE, Sliwinski P, Steiropoulos P, Verbraecken J, Penzel T. Unique sleep-stage transitions determined by obstructive sleep apnea severity, age and gender. J Sleep Res 2019; 29:e12895. [PMID: 31347213 DOI: 10.1111/jsr.12895] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Revised: 06/06/2019] [Accepted: 06/19/2019] [Indexed: 01/21/2023]
Abstract
In obstructive sleep apnea, patients' sleep is fragmented leading to excessive daytime sleepiness and co-morbidities like arterial hypertension. However, traditional metrics are not always directly correlated with daytime sleepiness, and the association between traditional sleep quality metrics like sleep duration and arterial hypertension is still ambiguous. In a development cohort, we analysed hypnograms from mild (n = 209), moderate (n = 222) and severe (n = 272) obstructive sleep apnea patients as well as healthy controls (n = 105) from the European Sleep Apnea Database. We assessed sleep by the analysis of two-step transitions depending on obstructive sleep apnea severity and anthropometric factors. Two-step transition patterns were examined for an association to arterial hypertension or daytime sleepiness. We also tested cumulative distributions of wake as well as sleep-states for power-laws (exponent α) and exponential distributions (decay time τ) in dependency on obstructive sleep apnea severity and potential confounders. Independent of obstructive sleep apnea severity and potential confounders, wake-state durations followed a power-law distribution, while sleep-state durations were characterized by an exponential distribution. Sleep-stage transitions are influenced by obstructive sleep apnea severity, age and gender. N2 → N3 → wake transitions were associated with high diastolic blood pressure. We observed higher frequencies of alternating (symmetric) patterns (e.g. N2 → N1 → N2, N2 → wake → N2) in sleepy patients both in the development cohort and in a validation cohort (n = 425). In conclusion, effects of obstructive sleep apnea severity and potential confounders on sleep architecture are small, but transition patterns still link sleep fragmentation directly to obstructive sleep apnea-related clinical outcomes like arterial hypertension and daytime sleepiness.
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Affiliation(s)
- Marcel Wächter
- Schlafmedizinisches Zentrum, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Jan W Kantelhardt
- Institut für Physik, Martin-Luther-Universität Halle-Wittenberg, Halle, Germany
| | - Maria R Bonsignore
- PROMISE Department, University of Palermo, and National Research Council, IBIM, Palermo, Palermo, Italy
| | | | | | - Ingo Fietze
- Schlafmedizinisches Zentrum, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Ludger Grote
- Sleep Medicine Center, Sahlgrenska University Hospital, Gothenborg, Sweden
| | - Damian Korzybski
- 2nd Department of Respiratory Medicine, Institute of Tuberculosis and Lung Diseases, Warsaw, Poland
| | - Carolina Lombardi
- Istituto Auxologico Italiano, IRCCS-Milano Bicocca University, Milano, Italy
| | - Oreste Marrone
- PROMISE Department, University of Palermo, and National Research Council, IBIM, Palermo, Palermo, Italy
| | | | | | - Silke Ryan
- University College Dublin, Dublin, Ireland
| | | | - Pawel Sliwinski
- 2nd Department of Respiratory Medicine, Institute of Tuberculosis and Lung Diseases, Warsaw, Poland
| | - Paschalis Steiropoulos
- Medical School, Democritus University of Thrace, University Hospital of Alexandroupolis, Alexandroupolis, Greece
| | - Johan Verbraecken
- Antwerp University Hospital and University of Antwerp, Antwerp, Belgium
| | - Thomas Penzel
- Schlafmedizinisches Zentrum, Charité-Universitätsmedizin Berlin, Berlin, Germany
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Kishi A, Yamaguchi I, Togo F, Yamamoto Y. Markov modeling of sleep stage transitions and ultradian REM sleep rhythm. Physiol Meas 2018; 39:084005. [PMID: 30089099 DOI: 10.1088/1361-6579/aad900] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE One of the highly characteristic features of sleep is the cyclic occurrence of non-rapid eye movement (NREM) and REM sleep, which is referred to as the ultradian rhythm of sleep. Even though REM sleep was discovered over half a century ago, surprisingly, the mechanism of the ultradian REM sleep rhythm has not yet been fully elucidated. In the present study, we aim to provide a mechanistic insight into the generation of the ultradian REM sleep rhythm. Approach and Main results: By simulating hypnograms with the dynamic features of sleep stage transitions, i.e. stage transition probabilities and stage-specific survival time functions, we show that the second-order Markov transition probabilities and the stage-specific survival time functions can reproduce the central position (∼90 min) of the REM-onset intervals (ROIs), but with a larger variance in distribution. In addition, we demonstrate the direct effect of the increased probability of the transitions from light to deep sleep within NREM sleep on the prolongation of the ROIs in a dose-response manner. SIGNIFICANCE These results suggest that dynamic sleep stage transitions constitute the basis of the formation of the ultradian rhythm of sleep; however, further elaboration of the model would be required to reduce the variability in rhythmicity.
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Affiliation(s)
- Akifumi Kishi
- Educational Physiology Laboratory, Graduate School of Education, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
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Rempe MJ, Grønli J, Pedersen TT, Mrdalj J, Marti A, Meerlo P, Wisor JP. Mathematical modeling of sleep state dynamics in a rodent model of shift work. Neurobiol Sleep Circadian Rhythms 2018; 5:37-51. [PMID: 31236510 PMCID: PMC6584688 DOI: 10.1016/j.nbscr.2018.04.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Revised: 04/19/2018] [Accepted: 04/20/2018] [Indexed: 01/12/2023] Open
Abstract
Millions of people worldwide are required to work when their physiology is tuned for sleep. By forcing wakefulness out of the body’s normal schedule, shift workers face numerous adverse health consequences, including gastrointestinal problems, sleep problems, and higher rates of some diseases, including cancers. Recent studies have developed protocols to simulate shift work in rodents with the intention of assessing the effects of night-shift work on subsequent sleep (Grønli et al., 2017). These studies have already provided important contributions to the understanding of the metabolic consequences of shift work (Arble et al., 2015; Marti et al., 2016; Opperhuizen et al., 2015) and sleep-wake-specific impacts of night-shift work (Grønli et al., 2017). However, our understanding of the causal mechanisms underlying night-shift-related sleep disturbances is limited. In order to advance toward a mechanistic understanding of sleep disruption in shift work, we model these data with two different approaches. First we apply a simple homeostatic model to quantify differences in the rates at which sleep need, as measured by slow wave activity during slow wave sleep (SWS) rises and falls. Second, we develop a simple and novel mathematical model of rodent sleep and use it to investigate the timing of sleep in a simulated shift work protocol (Grønli et al., 2017). This mathematical framework includes the circadian and homeostatic processes of the two-process model, but additionally incorporates a stochastic process to model the polyphasic nature of rodent sleep. By changing only the time at which the rodents are forced to be awake, the model reproduces some key experimental results from the previous study, including correct proportions of time spent in each stage of sleep as a function of circadian time and the differences in total wake time and SWS bout durations in the rodents representing night-shift workers and those representing day-shift workers. Importantly, the model allows for deeper insight into circadian and homeostatic influences on sleep timing, as it demonstrates that the differences in SWS bout duration between rodents in the two shifts is largely a circadian effect. Our study shows the importance of mathematical modeling in uncovering mechanisms behind shift work sleep disturbances and it begins to lay a foundation for future mathematical modeling of sleep in rodents. Millions of people worldwide are required to work when their physiology is tuned for sleep. Enforcing wakefulness during this time leads to numerous adverse health consequences including sleep problems and higher rates of some diseases. Rodent models of shift work have illuminated some of the effects of night shift work on subsequent sleep. This study uses mathematical modeling to accurately simulate rodent sleep during baseline and shift work conditions. A simple mathematical framework can help us understand possible mechanisms underlying the sleep disturbances seen in shift work.
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Affiliation(s)
- Michael J Rempe
- Sleep and Performance Research Center, Washington State University, Spokane, WA, USA.,Dept. of Mathematics and Computer Science, Whitworth University, Spokane, WA, USA
| | - Janne Grønli
- Sleep and Performance Research Center, Washington State University, Spokane, WA, USA.,Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
| | - Torhild Thue Pedersen
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
| | - Jelena Mrdalj
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
| | - Andrea Marti
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
| | - Peter Meerlo
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands
| | - Jonathan P Wisor
- Sleep and Performance Research Center, Washington State University, Spokane, WA, USA.,Elson S. Floyd College of Medicine, Washington State University, Spokane, WA, USA
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Yetton BD, McDevitt EA, Cellini N, Shelton C, Mednick SC. Quantifying sleep architecture dynamics and individual differences using big data and Bayesian networks. PLoS One 2018; 13:e0194604. [PMID: 29641599 PMCID: PMC5894981 DOI: 10.1371/journal.pone.0194604] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Accepted: 03/06/2018] [Indexed: 01/19/2023] Open
Abstract
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.
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Affiliation(s)
- Benjamin D. Yetton
- Department of Psychology, University of California, Irvine, Irvine, California, United States of America
| | - Elizabeth A. McDevitt
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America
| | - Nicola Cellini
- Department of Psychology, University of California, Irvine, Irvine, California, United States of America
- Department of General Psychology, University of Padova, Padova, Italy
| | - Christian Shelton
- Department of Computer Science, University of California, Riverside, Riverside, California, United States of America
| | - Sara C. Mednick
- Department of Psychology, University of California, Irvine, Irvine, California, United States of America
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Bizzotto R, Zamuner S. Analysis of variability in length of sleep state bouts reveals memory-free sleep subcomponents consistent among primary insomnia patients. J Neurophysiol 2018; 119:1836-1851. [PMID: 29384456 DOI: 10.1152/jn.00649.2017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
The statistical distributions of bout lengths for the different (macro) sleep states (wake, N1, N2, N3, and REM sleep) are essential to understanding whether any memory-free subcomponent ("micro state") is involved in the organization of sleep. Micro state detection can be prevented by the fusion of data including various sources of variability, in particular by the differences in sleep architecture between individuals, along sleep time (or nighttime), or between different nights. In this analysis, a mathematical model of sleep was adopted to disentangle these features and advance the understanding of the dynamics and mechanisms of sleep and its states. The analysis involved 116 primary insomnia patients taking placebo before going to bed and undergoing polysomnography for one night. The individual sequences of macro sleep states had been previously modeled with a mixed-effect nonhomogeneous modified Markov chain model, from which individual conditional probability distributions for the bout durations were derived in this analysis as functions of sleep time. The probability distributions, affected by neither subject, night-time, nor multiple-night pooling, substantially changed at ¼ and ¾ sleep time, had modified exponential shape, and were best described as the sum of one to four exponentials, depending on the sleep state. The time constants and proportions of bouts contributing to each exponential were similar in the different subjects, changing over sleep time. Variability in bout durations thus indicated the presence of multiple memory-free sleep subcomponents whose mean residence times and access probabilities could be identified and shown to be consistent among the studied subjects. NEW & NOTEWORTHY We present a new methodology for deriving, from polysomnography data, the individual conditional probability for the duration of the bouts of wake, N1, N2, N3, and REM sleep. We evaluated the variability of this probability within and between primary insomnia patients and along sleep time. The multiexponential shapes of the probability distributions within the individuals revealed memory-free mechanisms and sleep subcomponents with consistent features in the studied population.
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Affiliation(s)
- Roberto Bizzotto
- Neuroscience Institute, National Research Council , Padua , Italy
| | - Stefano Zamuner
- Clinical Pharmacology Modeling and Simulation, GlaxoSmithKline, Stevenage , United Kingdom
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12
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Perez-Atencio L, Garcia-Aracil N, Fernandez E, Barrio LC, Barios JA. A four-state Markov model of sleep-wakefulness dynamics along light/dark cycle in mice. PLoS One 2018; 13:e0189931. [PMID: 29304108 PMCID: PMC5755762 DOI: 10.1371/journal.pone.0189931] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Accepted: 12/05/2017] [Indexed: 11/18/2022] Open
Abstract
Behavioral states alternate between wakefulness (wk), rapid eye movement (rem) and non-rem (nrem) sleep at time scale of hours i.e., light and dark cycle rhythms and from several tens of minutes to seconds (i.e., brief awakenings during sleep). Using statistical analysis of bout duration, Markov chains of sleep-wk dynamics and quantitative EEG analysis, we evaluated the influence of light/dark (ld) changes on brain function along the sleep-wk cycle. Bout duration (bd) histograms and Kaplan-Meier (km) survival curves of wk showed a bimodal statistical distribution, suggesting that two types of wk do exist: brief-wk (wkb) and long-wk (wkl). Light changes modulated specifically wkl bouts, increasing its duration during active/dark period. In contrast, wkb, nrem and rembd histograms and km curves did not change significantly along ld cycle. Hippocampal eeg of both types of wk were different: in comparison wkb showed a lower spectral power in fast gamma and fast theta bands and less emg tone. After fitting a four-states Markov chain to mice hypnograms, moreover in states transition probabilities matrix was found that: in dark/active period, state-maintenance probability of wkl increased, and probability of wkl to nrem transition decreased; the opposite was found in light period, favoring the hypothesis of the participation of brief wk into nrem-rem intrinsic sleep cycle, and the role of wkl in SWS homeostasis. In conclusion, we propose an extended Markov model of sleep using four stages (wkl, nrem, rem, wkb) as a fully adequate model accounting for both modulation of sleep-wake dynamics based on the differential regulation of long-wk (high gamma/theta) epochs during dark and light phases.
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Affiliation(s)
- Leonel Perez-Atencio
- Unit of Experimental Neurology, “Ramón y Cajal” Hospital-IRYCIS, Carretera de Colmenar km 9, 28034 Madrid, Spain
- Biomedical Engineering program, National Experimental University “Francisco de Miranda”, Calle Norte, 4101 Falcon, Venezuela
| | - Nicolas Garcia-Aracil
- Biomedical Neuroengineering research group (nBio), Systems Engineering and Automation Department of Miguel Hernandez University, Avda. de la Universidad s/n, 03202 Elche, Spain
| | - Eduardo Fernandez
- Biomedical Neuroengineering research group (nBio), Systems Engineering and Automation Department of Miguel Hernandez University, Avda. de la Universidad s/n, 03202 Elche, Spain
| | - Luis C. Barrio
- Unit of Experimental Neurology, “Ramón y Cajal” Hospital-IRYCIS, Carretera de Colmenar km 9, 28034 Madrid, Spain
| | - Juan A. Barios
- Biomedical Neuroengineering research group (nBio), Systems Engineering and Automation Department of Miguel Hernandez University, Avda. de la Universidad s/n, 03202 Elche, Spain
- * E-mail:
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13
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Kang DY, DeYoung PN, Malhotra A, Owens RL, Coleman TP. A State Space and Density Estimation Framework for Sleep Staging in Obstructive Sleep Apnea. IEEE Trans Biomed Eng 2017; 65:1201-1212. [PMID: 28499990 DOI: 10.1109/tbme.2017.2702123] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Although the importance of sleep is increasingly recognized, the lack of robust and efficient algorithms hinders scalable sleep assessment in healthy persons and those with sleep disorders. Polysomnography (PSG) and visual/manual scoring remain the gold standard in sleep evaluation, but more efficient/automated systems are needed. Most previous works have demonstrated algorithms in high agreement with the gold standard in healthy/normal (HN) individuals-not those with sleep disorders. METHODS This paper presents a statistical framework that automatically estimates whole-night sleep architecture in patients with obstructive sleep apnea (OSA)-the most common sleep disorder. Single-channel frontal electroencephalography was extracted from 65 HN/OSA sleep studies, and decomposed into 11 spectral features in 60 903 30 s sleep epochs. The algorithm leveraged kernel density estimation to generate stage-specific likelihoods, and a 5-state hidden Markov model to estimate per-night sleep architecture. RESULTS Comparisons to full PSG expert scoring revealed the algorithm was in fair agreement with the gold standard (median Cohen's kappa = 0.53). Further, analysis revealed modest decreases in median scoring agreement as OSA severity increased from HN (kappa = 0.63) to severe (kappa = 0.47). A separate implementation on HN data from the Physionet Sleep-EDF Database resulted in a median kappa = 0.65, further indicating the algorithm's broad applicability. CONCLUSION Results of this work indicate the proposed single-channel framework can emulate expert-level scoring of sleep architecture in OSA. SIGNIFICANCE Algorithms constructed to more accurately model physiological variability during sleep may help advance automated sleep assessment, for practical and general use in sleep medicine.
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Bianchi MT, Russo K, Gabbidon H, Smith T, Goparaju B, Westover MB. Big data in sleep medicine: prospects and pitfalls in phenotyping. Nat Sci Sleep 2017; 9:11-29. [PMID: 28243157 PMCID: PMC5317347 DOI: 10.2147/nss.s130141] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Clinical polysomnography (PSG) databases are a rich resource in the era of "big data" analytics. We explore the uses and potential pitfalls of clinical data mining of PSG using statistical principles and analysis of clinical data from our sleep center. We performed retrospective analysis of self-reported and objective PSG data from adults who underwent overnight PSG (diagnostic tests, n=1835). Self-reported symptoms overlapped markedly between the two most common categories, insomnia and sleep apnea, with the majority reporting symptoms of both disorders. Standard clinical metrics routinely reported on objective data were analyzed for basic properties (missing values, distributions), pairwise correlations, and descriptive phenotyping. Of 41 continuous variables, including clinical and PSG derived, none passed testing for normality. Objective findings of sleep apnea and periodic limb movements were common, with 51% having an apnea-hypopnea index (AHI) >5 per hour and 25% having a leg movement index >15 per hour. Different visualization methods are shown for common variables to explore population distributions. Phenotyping methods based on clinical databases are discussed for sleep architecture, sleep apnea, and insomnia. Inferential pitfalls are discussed using the current dataset and case examples from the literature. The increasing availability of clinical databases for large-scale analytics holds important promise in sleep medicine, especially as it becomes increasingly important to demonstrate the utility of clinical testing methods in management of sleep disorders. Awareness of the strengths, as well as caution regarding the limitations, will maximize the productive use of big data analytics in sleep medicine.
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Affiliation(s)
- Matt T Bianchi
- Neurology Department, Massachusetts General Hospital
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Kathryn Russo
- Neurology Department, Massachusetts General Hospital
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Stephenson R, Caron AM, Famina S. Significance of the zero sum principle for circadian, homeostatic and allostatic regulation of sleep-wake state in the rat. Physiol Behav 2016; 167:35-48. [PMID: 27594095 DOI: 10.1016/j.physbeh.2016.08.034] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2016] [Revised: 08/08/2016] [Accepted: 08/31/2016] [Indexed: 11/17/2022]
Abstract
Sleep-wake behavior exhibits diurnal rhythmicity, rebound responses to acute total sleep deprivation (TSD), and attenuated rebounds following chronic sleep restriction (CSR). We investigated how these long-term patterns of behavior emerge from stochastic short-term dynamics of state transition. Male Sprague-Dawley rats were subjected to TSD (1day×24h, N=9), or CSR (10days×18h TSD, N=7) using a rodent walking-wheel apparatus. One baseline day and one recovery day following TSD and CSR were analyzed. The implications of the zero sum principle were evaluated using a Markov model of sleep-wake state transition. Wake bout duration (a combined function of the probability of wake maintenance and proportional representations of brief and long wake) was a key variable mediating the baseline diurnal rhythms and post-TSD responses of all three states, and the attenuation of the post-CSR rebounds. Post-NREM state transition trajectory was an important factor in REM rebounds. The zero sum constraint ensures that a change in any transition probability always affects bout frequency and cumulative time of at least two, and usually all three, of wakefulness, NREM and REM. Neural mechanisms controlling wake maintenance may play a pivotal role in regulation and dysregulation of all three states.
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Affiliation(s)
- Richard Stephenson
- University of Toronto, Department of Cell and Systems Biology, 25 Harbord Street, Toronto, Ontario M5S 3G5, Canada.
| | - Aimee M Caron
- University of Toronto, Department of Cell and Systems Biology, 25 Harbord Street, Toronto, Ontario M5S 3G5, Canada
| | - Svetlana Famina
- University of Toronto, Department of Cell and Systems Biology, 25 Harbord Street, Toronto, Ontario M5S 3G5, Canada
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Garcia-Molina G, Vissapragada S, Mahadevan A, Goodpaster R, Riedner B, Bellesi M, Tononi G. Probabilistic characterization of sleep architecture: home based study on healthy volunteers. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2016:2834-2838. [PMID: 28268907 DOI: 10.1109/embc.2016.7591320] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The quantification of sleep architecture has high clinical value for diagnostic purposes. While the clinical standard to assess sleep architecture is in-lab based polysomnography, higher ecological validity can be obtained with multiple sleep recordings at home. In this paper, we use a dataset composed of fifty sleep EEG recordings at home (10 per study participant for five participants) to analyze the sleep stage transition dynamics using Markov chain based modeling. The statistical analysis of the duration of continuous sleep stage bouts is also analyzed to identify the speed of transition between sleep stages. This analysis identified two types of NREM states characterized by fast and slow exit rates which from the EEG analysis appear to correspond to shallow and deep sleep respectively.
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Soliński M, Gierałtowski J, Żebrowski J. Modeling heart rate variability including the effect of sleep stages. CHAOS (WOODBURY, N.Y.) 2016; 26:023101. [PMID: 26931582 DOI: 10.1063/1.4940762] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
We propose a model for heart rate variability (HRV) of a healthy individual during sleep with the assumption that the heart rate variability is predominantly a random process. Autonomic nervous system activity has different properties during different sleep stages, and this affects many physiological systems including the cardiovascular system. Different properties of HRV can be observed during each particular sleep stage. We believe that taking into account the sleep architecture is crucial for modeling the human nighttime HRV. The stochastic model of HRV introduced by Kantelhardt et al. was used as the initial starting point. We studied the statistical properties of sleep in healthy adults, analyzing 30 polysomnographic recordings, which provided realistic information about sleep architecture. Next, we generated synthetic hypnograms and included them in the modeling of nighttime RR interval series. The results of standard HRV linear analysis and of nonlinear analysis (Shannon entropy, Poincaré plots, and multiscale multifractal analysis) show that-in comparison with real data-the HRV signals obtained from our model have very similar properties, in particular including the multifractal characteristics at different time scales. The model described in this paper is discussed in the context of normal sleep. However, its construction is such that it should allow to model heart rate variability in sleep disorders. This possibility is briefly discussed.
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Affiliation(s)
- Mateusz Soliński
- Faculty of Physics, Warsaw University of Technology, Warsaw 00-662, Poland
| | - Jan Gierałtowski
- Faculty of Physics, Warsaw University of Technology, Warsaw 00-662, Poland
| | - Jan Żebrowski
- Faculty of Physics, Warsaw University of Technology, Warsaw 00-662, Poland
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Sleep architecture and obstructive sleep apnea in obese children with and without metabolic syndrome: a case control study. Sleep Breath 2015; 20:845-51. [PMID: 26711131 DOI: 10.1007/s11325-015-1291-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2015] [Revised: 10/29/2015] [Accepted: 11/23/2015] [Indexed: 12/18/2022]
Abstract
PURPOSE Obesity and biochemical parameters of metabolic disorders are both closely related to obstructive sleep apnea (OSA). The aim of this study was to compare sleep architecture and OSA in obese children with and without metabolic syndrome. METHODS Forty-two children with metabolic syndrome were selected as case group and 38 children without metabolic syndrome were matched for age, sex, and BMI as control group. The standardized Persian version of bedtime problems, excessive daytime sleepiness, awakenings during the night, regularity and duration of sleep, snoring (BEARS) and Children's Sleep Habits Questionnaires were completed, and polysomnography (PSG) was performed for all study subjects. Scoring was performed using the manual of American Academy of Sleep Medicine for children. Data were analyzed using chi-square test, T test, Mann-Whitney U test, and logistic regression analysis. RESULTS Non-rapid eye movement (NREM) sleep and N1 stage in the case group were significantly longer than the control group, while REM sleep was significantly shorter. Waking after sleep onset (WASO) was significantly different between two groups. Severe OSA was more frequent in the control group. Multivariate logistic regression analysis showed that severe OSA (OR 21.478, 95 % CI 2.160-213.600; P = 0.009) and REM sleep (OR 0.856, 95 % CI 0.737-0.994; P = 0.041) had independent association with metabolic syndrome. CONCLUSIONS Obese children with metabolic syndrome had increased WASO, N1 sleep stage, and severe OSA. But the results regarding sleep architecture are most likely a direct result of OSA severity. More longitudinal studies are needed to confirm the association of metabolic syndrome and OSA.
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Morgenthaler TI, Croft JB, Dort LC, Loeding LD, Mullington JM, Thomas SM. Development of the National Healthy Sleep Awareness Project Sleep Health Surveillance Questions. J Clin Sleep Med 2015; 11:1057-62. [PMID: 26235156 DOI: 10.5664/jcsm.5026] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Accepted: 07/20/2015] [Indexed: 11/13/2022]
Abstract
OBJECTIVES For the first time ever, as emphasized by inclusion in the Healthy People 2020 goals, sleep health is an emphasis of national health aims. The National Healthy Sleep Awareness Project (NHSAP) was tasked to propose questions for inclusion in the next Behavioral Risk Factor Surveillance System (BRFSS), a survey that includes a number of questions that target behaviors thought to impact health, as a means to measure community sleep health. The total number of questions could not exceed five, and had to include an assessment of the risk for obstructive sleep apnea (OSA). METHODS An appointed workgroup met via teleconference and face-to-face venues to develop an inventory of published survey questions being used to identify sleep health, to develop a framework on which to analyze the strengths and weaknesses of current survey questions concerning sleep, and to develop recommendations for sleep health and disease surveillance questions going forward. RESULTS The recommendation was to focus on certain existing BRFSS questions pertaining to sleep duration, quality, satisfaction, daytime alertness, and to add to these other BRFSS existing questions to make a modified STOP-BANG questionnaire (minus the N for neck circumference) to assess for risk of OSA. CONCLUSIONS Sleep health is an important dimension of health that has previously received less attention in national health surveys. We believe that 5 questions recommended for the upcoming BRFSS question banks will assist as important measures of sleep health, and may help to evaluate the effectiveness of interventions to improve sleep health in our nation.
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Affiliation(s)
| | - Janet B Croft
- Centers for Disease Control and Prevention, Atlanta, GA
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Stephenson R, Caron AM, Famina S. Behavioral sleep-wake homeostasis and EEG delta power are decoupled by chronic sleep restriction in the rat. Sleep 2015; 38:685-97. [PMID: 25669184 DOI: 10.5665/sleep.4656] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2014] [Accepted: 09/30/2014] [Indexed: 11/03/2022] Open
Abstract
STUDY OBJECTIVES Chronic sleep restriction (CSR) is prevalent in society and is linked to adverse consequences that might be ameliorated by acclimation of homeostatic drive. This study was designed to test the hypothesis that the sleep-wake homeostat will acclimatize to CSR. DESIGN A four-parameter model of proportional control was used to quantify sleep homeostasis with and without recourse to a sleep intensity function. SETTING Animal laboratory, rodent walking-wheel apparatus. SUBJECTS Male Sprague-Dawley rats. INTERVENTIONS Acute total sleep deprivation (TSD, 1 day × 18 or 24 h, N = 12), CSR (10 days × 18 h TSD, N = 5, or 5 days × 20 h TSD, N = 6). MEASUREMENTS AND RESULTS Behavioral rebounds were consistent with model predictions for proportional control of cumulative times in wake, nonrapid eye movement (NREM) and rapid eye movement (REM). Delta (D) energy homeostasis was secondary to behavioral homeostasis; a biphasic NREM D power rebound contributed to the dynamics (rapid response) but not to the magnitude of the rebound in D energy. REM behavioral homeostasis was little affected by CSR. NREM behavioral homeostasis was attenuated in proportion to cumulative NREM deficit, whereas the biphasic NREM D power rebound was only slightly suppressed, indicating decoupled regulatory mechanisms following CSR. CONCLUSIONS We conclude that sleep homeostasis is achieved through behavioral regulation, that the NREM behavioral homeostat is susceptible to attenuation during CSR and that the concept of sleep intensity is not essential in a model of sleep-wake regulation. STUDY OBJECTIVES Chronic sleep restriction (CSR) is prevalent in society and is linked to adverse consequences that might be ameliorated by acclimation of homeostatic drive. This study was designed to test the hypothesis that the sleep-wake homeostat will acclimatize to CSR. DESIGN A four-parameter model of proportional control was used to quantify sleep homeostasis with and without recourse to a sleep intensity function. SETTING Animal laboratory, rodent walking-wheel apparatus. SUBJECTS Male Sprague-Dawley rats. INTERVENTIONS Acute total sleep deprivation (TSD, 1 day × 18 or 24 h, N = 12), CSR (10 days × 18 h TSD, N = 5, or 5 days × 20 h TSD, N = 6). MEASUREMENTS AND RESULTS Behavioral rebounds were consistent with model predictions for proportional control of cumulative times in wake, nonrapid eye movement (NREM) and rapid eye movement (REM). Delta (D) energy homeostasis was secondary to behavioral homeostasis; a biphasic NREM D power rebound contributed to the dynamics (rapid response) but not to the magnitude of the rebound in D energy. REM behavioral homeostasis was little affected by CSR. NREM behavioral homeostasis was attenuated in proportion to cumulative NREM deficit, whereas the biphasic NREM D power rebound was only slightly suppressed, indicating decoupled regulatory mechanisms following CSR. CONCLUSIONS We conclude that sleep homeostasis is achieved through behavioral regulation, that the NREM behavioral homeostat is susceptible to attenuation during CSR and that the concept of sleep intensity is not essential in a model of sleep-wake regulation.
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Affiliation(s)
- Richard Stephenson
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
| | - Aimee M Caron
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
| | - Svetlana Famina
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
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Abstract
There is increasing awareness of the role of sleep disturbance as an important factor in health and disease. Although sub-clinical sleep disturbances (insufficient sleep duration or inadequate sleep quality) may be difficult to assess with conceptual and/or methodological clarity, this review attempts to summarize and synthesize these findings. First, the concept of sleep disturbance in a public health context is introduced, to provide context and rationale. Second, operational definitions of 'cardiometabolic disease' and 'sleep disturbance' are offered, to address many unclear operationalizations. Third, the extant literature is summarized regarding short or long sleep duration and/or insufficient sleep, insomnia and insomnia symptoms, general (non-specific sleep disturbances), circadian rhythm abnormalities that result in sleep disturbances, and, briefly, sleep-disordered breathing. Fourth, the review highlights the social/behavioural context of sleep, including discussions of sleep and race/ethnicity, socio-economic position, and other social/environmental factors, in order to place these findings in a social-environmental context relevant to public health. Fifth, the review highlights the issue of sleep as a domain of health behaviour and addresses issues regarding development of healthy sleep interventions. Finally, a research agenda of future directions is proposed.
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Affiliation(s)
- Michael A Grandner
- Behavioral Sleep Medicine Program, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania , USA
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Physiologically-based modeling of sleep-wake regulatory networks. Math Biosci 2014; 250:54-68. [PMID: 24530893 DOI: 10.1016/j.mbs.2014.01.012] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2013] [Revised: 01/23/2014] [Accepted: 01/31/2014] [Indexed: 12/27/2022]
Abstract
Mathematical modeling has played a significant role in building our understanding of sleep-wake and circadian behavior. Over the past 40 years, phenomenological models, including the two-process model and oscillator models, helped frame experimental results and guide progress in understanding the interaction of homeostatic and circadian influences on sleep and understanding the generation of rapid eye movement sleep cycling. Recent advances in the clarification of the neural anatomy and physiology involved in the regulation of sleep and circadian rhythms have motivated the development of more detailed and physiologically-based mathematical models that extend the approach introduced by the classical reciprocal-interaction model. Using mathematical formalisms developed in the field of computational neuroscience to model neuronal population activity, these models investigate the dynamics of proposed conceptual models of sleep-wake regulatory networks with a focus on generating appropriate sleep and wake state transition patterns as well as simulating disease states and experimental protocols. In this review, we discuss several recent physiologically-based mathematical models of sleep-wake regulatory networks. We identify common features among these models in their network structures, model dynamics and approaches for model validation. We describe how the model analysis technique of fast-slow decomposition, which exploits the naturally occurring multiple timescales of sleep-wake behavior, can be applied to understand model dynamics in these networks. Our purpose in identifying commonalities among these models is to propel understanding of both the mathematical models and their underlying conceptual models, and focus directions for future experimental and theoretical work.
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Farabi SS, Prasad B, Quinn L, Carley DW. Impact of dronabinol on quantitative electroencephalogram (qEEG) measures of sleep in obstructive sleep apnea syndrome. J Clin Sleep Med 2014; 10:49-56. [PMID: 24426820 DOI: 10.5664/jcsm.3358] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
STUDY OBJECTIVES To determine the effects of dronabinol on quantitative electroencephalogram (EEG) markers of the sleep process, including power distribution and ultradian cycling in 15 patients with obstructive sleep apnea (OSA). METHODS EEG (C4-A1) relative power (% total) in the delta, theta, alpha, and sigma bands was quantified by fast Fourier transformation (FFT) over 28-second intervals. An activation ratio (AR = [alpha + sigma] / [delta + theta]) also was computed for each interval. To assess ultradian rhythms, the best-fitting cosine wave was determined for AR and each frequency band in each polysomnogram (PSG). RESULTS Fifteen subjects were included in the analysis. Dronabinol was associated with significantly increased theta power (p = 0.002). During the first half of the night, dronabinol decreased sigma power (p = 0.03) and AR (p = 0.03), and increased theta power (p = 0.0006). At increasing dronabinol doses, ultradian rhythms accounted for a greater fraction of EEG power variance in the delta band (p = 0.04) and AR (p = 0.03). Females had higher amplitude ultradian rhythms than males (theta: p = 0.01; sigma: p = 0.01). Decreasing AHI was associated with increasing ultradian rhythm amplitudes (sigma: p < 0.001; AR: p = 0.02). At the end of treatment, lower relative power in the theta band (p = 0.02) and lower AHI (p = 0.05) correlated with a greater decrease in sleepiness from baseline. CONCLUSIONS This exploratory study demonstrates that in individuals with OSA, dronabinol treatment may yield a shift in EEG power toward delta and theta frequencies and a strengthening of ultradian rhythms in the sleep EEG.
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Affiliation(s)
| | - Bharati Prasad
- Center for Narcolepsy, Sleep and Health Research ; Department of Medicine
| | | | - David W Carley
- Center for Narcolepsy, Sleep and Health Research ; Department of Medicine ; Department of Biobehavioral Health Science ; Department of Bioengineering, University of Illinois at Chicago, Chicago, IL
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Stephenson R, Famina S, Caron AM, Lim J. Statistical properties of sleep-wake behavior in the rat and their relation to circadian and ultradian phases. Sleep 2013; 36:1377-90. [PMID: 23997372 DOI: 10.5665/sleep.2970] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
STUDY OBJECTIVES To examine the statistical characteristics of short-term sleep-wake architecture and to evaluate their dependence on ultradian and circadian phase. DESIGN Observational, time series. SETTING Laboratory. PARTICIPANTS Ten male adult Sprague-Dawley rats. INTERVENTIONS N/A. MEASUREMENTS AND RESULTS States of wakefulness (WAKE), rapid eye movement sleep (REM) and nonrapid eye movement sleep (NREM) were recorded in 5-sec epochs over 7 consecutive days. State bout durations were analyzed using parametric regression of survival curves, comparing exponential, biexponential, and power law models. WAKE survival curves were best fit by biexponential models, suggesting that there are two statistically distinct stochastic mechanisms generating two types of WAKE--"brief" WAKE and "long" WAKE. Exponential time constants varied as a function of circadian and ultradian phase, with "long" WAKE showing the largest effect. NREM survival curves exhibited biexponential and monoexponential distributions in light and dark, respectively, with weak effects of ultradian phase. REM survival curves approximated a monoexponential distribution that varied with circadian but not ultradian phase. χ(2) analysis was used in a three-state Markov model to evaluate whether conditional state transition probabilities exhibit the property of first-order dependence. This was partially confirmed, but only after accounting for heterogeneity associated with circadian and ultradian phase. However, there was evidence of residual second-order dependence indicating that additional sources of statistical heterogeneity may remain to be identified. CONCLUSIONS Sleep-wake state is regulated over short timescales by stochastic mechanisms. When the major sources of heterogeneity are taken into account, including two-component WAKE and NREM states, the sleep-wake system of the rat behaves, to a reasonable approximation, as a Markovian system that is modulated over ultradian and circadian timescales.
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Affiliation(s)
- Richard Stephenson
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada.
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Bianchi MT, Thomas RJ. Technical advances in the characterization of the complexity of sleep and sleep disorders. Prog Neuropsychopharmacol Biol Psychiatry 2013; 45:277-86. [PMID: 23174482 PMCID: PMC3631575 DOI: 10.1016/j.pnpbp.2012.09.017] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2012] [Revised: 08/16/2012] [Accepted: 09/27/2012] [Indexed: 01/18/2023]
Abstract
The current clinical standard for quantifying sleep physiology is the laboratory polysomnogram, from which basic sleep-wake stages are determined. However, the complexity of sleep physiology has inspired alternative metrics that are providing additional insights into the rich dynamics of sleep. Electro-encephalography, magneto-encephalography, and functional magnetic resonance imaging represent advanced imaging modalities for understanding brain dynamics. These methods are complemented by autonomic measurements that provide additional important insights. We review here the spectrum of approaches that have been leveraged towards improved understanding of the complexity of sleep.
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Affiliation(s)
- Matt T. Bianchi
- Department of Neurology, Sleep Division, Massachusetts General Hospital, 55 Fruit Street, Wang 720 Neurology, Boston, MA 02114, Phone: 617-724-7426, Fax: 617-724-6513
| | - Robert J. Thomas
- Beth Israel Deaconess Medical Center & Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, Phone: 617-667-5864, Fax: 617-667-4849
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