Literature DB >> 28934523

Sleep Stage Transition Dynamics Reveal Specific Stage 2 Vulnerability in Insomnia.

Yishul Wei1, Michele A Colombo1,2,3, Jennifer R Ramautar1, Tessa F Blanken1,4, Ysbrand D van der Werf5, Kai Spiegelhalder6, Bernd Feige6, Dieter Riemann6, Eus J W Van Someren1,4.   

Abstract

Study
Objectives: Objective sleep impairments in insomnia disorder (ID) are insufficiently understood. The present study evaluated whether whole-night sleep stage dynamics derived from polysomnography (PSG) differ between people with ID and matched controls and whether sleep stage dynamic features discriminate them better than conventional sleep parameters.
Methods: Eighty-eight participants aged 21-70 years, including 46 with ID and 42 age- and sex-matched controls without sleep complaints, were recruited through www.sleepregistry.nl and completed two nights of laboratory PSG. Data of 100 people with ID and 100 age- and sex-matched controls from a previously reported study were used to validate the generalizability of findings. The second night was used to obtain, in addition to conventional sleep parameters, probabilities of transitions between stages and bout duration distributions of each stage. Group differences were evaluated with nonparametric tests.
Results: People with ID showed higher empirical probabilities to transition from stage N2 to the lighter sleep stage N1 or wakefulness and a faster decaying stage N2 bout survival function. The increased transition probability from stage N2 to stage N1 discriminated people with ID better than any of their deviations in conventional sleep parameters, including less total sleep time, less sleep efficiency, more stage N1, and more wake after sleep onset. Moreover, adding this transition probability significantly improved the discriminating power of a multiple logistic regression model based on conventional sleep parameters. Conclusions: Quantification of sleep stage dynamics revealed a particular vulnerability of stage N2 in insomnia. The feature characterizes insomnia better than-and independently of-any conventional sleep parameter. © Sleep Research Society 2017. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

Entities:  

Keywords:  Markov chain; binary classification; feature selection; hypnogram; insomnia disorder; non-REM sleep; polysomnography; sleep architecture; sleep fragmentation; sleep stage

Mesh:

Year:  2017        PMID: 28934523     DOI: 10.1093/sleep/zsx117

Source DB:  PubMed          Journal:  Sleep        ISSN: 0161-8105            Impact factor:   5.849


  10 in total

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Authors:  James T McKenna; Stephen Thankachan; David S Uygun; Charu Shukla; James M McNally; Felipe L Schiffino; Joshua Cordeira; Fumi Katsuki; Janneke C Zant; Mackenzie C Gamble; Karl Deisseroth; Robert W McCarley; Ritchie E Brown; Robert E Strecker; Radhika Basheer
Journal:  Curr Biol       Date:  2020-05-14       Impact factor: 10.834

2.  A four-state Markov model of sleep-wakefulness dynamics along light/dark cycle in mice.

Authors:  Leonel Perez-Atencio; Nicolas Garcia-Aracil; Eduardo Fernandez; Luis C Barrio; Juan A Barios
Journal:  PLoS One       Date:  2018-01-05       Impact factor: 3.240

3.  Lying Awake at Night: Cardiac Autonomic Activity in Relation to Sleep Onset and Maintenance.

Authors:  Marina Nano; Pedro Fonseca; Sebastiaan Overeem; Rik Vullings; Ronald M Aarts
Journal:  Front Neurosci       Date:  2020-01-15       Impact factor: 4.677

4.  Dysfunction of the NAc-mPFC circuit in insomnia disorder.

Authors:  Ziqiang Shao; Yan Xu; Longmao Chen; Shicong Wang; Min Zhang; Shuang Liu; Xinwen Wen; Dahua Yu; Kai Yuan
Journal:  Neuroimage Clin       Date:  2020-10-22       Impact factor: 4.881

5.  Sleep-Wake Survival Dynamics in People with Insomnia.

Authors:  Lieke W A Hermans; Marta Regis; Pedro Fonseca; Bertram Hoondert; Tim R M Leufkens; Sebastiaan Overeem; Merel M van Gilst
Journal:  Nat Sci Sleep       Date:  2021-03-12

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

Authors:  Benjamin D Yetton; Elizabeth A McDevitt; Nicola Cellini; Christian Shelton; Sara C Mednick
Journal:  PLoS One       Date:  2018-04-11       Impact factor: 3.240

7.  Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep.

Authors:  A B A Stevner; D Vidaurre; J Cabral; K Rapuano; S F V Nielsen; E Tagliazucchi; H Laufs; P Vuust; G Deco; M W Woolrich; E Van Someren; M L Kringelbach
Journal:  Nat Commun       Date:  2019-03-04       Impact factor: 14.919

8.  Actigraphic multi-night home-recorded sleep estimates reveal three types of sleep misperception in Insomnia Disorder and good sleepers.

Authors:  Bart H W Te Lindert; Tessa F Blanken; Wisse P van der Meijden; Kim Dekker; Rick Wassing; Ysbrand D van der Werf; Jennifer R Ramautar; Eus J W Van Someren
Journal:  J Sleep Res       Date:  2019-10-31       Impact factor: 3.981

9.  Network outcome analysis identifies difficulty initiating sleep as a primary target for prevention of depression: a 6-year prospective study.

Authors:  Tessa F Blanken; Denny Borsboom; Brenda Wjh Penninx; Eus Jw Van Someren
Journal:  Sleep       Date:  2020-05-12       Impact factor: 5.849

10.  Enhanced Vigilance Stability during Daytime in Insomnia Disorder.

Authors:  Ariane Losert; Christian Sander; Michael Schredl; Ivonne Heilmann-Etzbach; Michael Deuschle; Ulrich Hegerl; Claudia Schilling
Journal:  Brain Sci       Date:  2020-11-07
  10 in total

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